Energy dispersive x-ray spectral phase spectrum synthesis

Abstract

And synthesizing an energy dispersion X-ray spectrum phase spectrum. Systems, computer program products, and / or computer-implemented methods provided herein involve generating a synthetic spectrum of a target component in a synthetic manner. A system may include a memory storing computer executable components and a processor executing the computer executable components stored in the memory. The computer executable components may include a determination component that determines a known component having the same composition as the target component, and a generation component that generates a second synthetic spectrum of the target component based on the synthetic spectrum of the known component and the first synthetic spectrum of the target component.

Description

Background Art

[0001] Scientific instruments used for materials analysis can help determine the composition and properties of unknown components. In one or more examples, scientific instruments that provide energy dispersive X-ray spectroscopy (EDS) or analyze the results of an EDS device can help identify unknown components by helping to provide one or more spectra that compare the incident energy and peak intensity readings of the electrons employed. BRIEF DESCRIPTION OF THE DRAWINGS

[0002] Each embodiment will be easily understood by the following detailed description in conjunction with the accompanying drawings. For ease of description, the same reference numerals indicate the same structural elements. Each embodiment is illustrated in each figure of the accompanying drawings by way of example and not limitation.

[0003] Figure 1 A block diagram of an exemplary scientific instrument for performing operations according to one or more embodiments described herein is shown.

[0004] Figure 2 The use of one or more embodiments described herein is shown Figure 1 A flow chart of an exemplary method of performing operations of a scientific instrument.

[0005] Figure 3 A graphical user interface (GUI) that can be used to perform one or more methods described herein is shown according to one or more embodiments described herein.

[0006] Figure 4 A block diagram is shown of an exemplary computing device that can perform one or more of the methods disclosed herein, according to one or more embodiments described herein.

[0007] Figure 5 A block diagram is shown of an exemplary non-limiting system that can provide a process for synthetically generating a synthetic spectrum of a target component according to one or more embodiments described herein.

[0008] Figure 6 A block diagram is shown of another exemplary non-limiting system that can provide a process for synthetically generating a synthetic spectrum of a target component according to one or more embodiments described herein.

[0009] Figure 7 According to one or more embodiments described herein, Figure 5 The process by which a materials analysis system determines known components that are the nearest neighbors of a target component.

[0010] Figure 8 A method for performing a Figure 5A flow chart of a set of processes for synthesizing and determining a synthetic spectrum of a component using a material analysis system.

[0011] Fig. 9 According to one or more embodiments described herein, Figure 5 The general process by which a material analysis system generates a synthetic spectrum of a target component.

[0012] Fig. 10A A method for performing a Figure 5 A flow chart of a set of processes for performing spectral determination in a material analysis system.

[0013] Fig. 10B According to one or more embodiments described herein, Figure 5 The material analysis system employs a collection of arrays that index the number of photons within a collection of spectral energy ranges.

[0014] Fig. 10C shows an initial synthetic spectrum of a target component according to one or more embodiments described herein, from which a Fig. 10B An array of this collection.

[0015] Fig. 10D The resulting synthetic spectrum of the target component according to one or more embodiments described herein is shown, from which the synthetic spectrum is obtained Fig. 10B An array of this collection.

[0016] Fig.11 A comparison of visual results provided by existing material analysis methods and visual results that may be provided by one or more embodiments described herein is shown.

[0017] Fig.12 The invention shows that a Figure 5 A flow chart of one or more processes performed by a materials analysis system.

[0018] Fig.13 The invention shows that a Figure 5 The material analysis system performs one or more processes Fig.12 Continuation of the flowchart.

[0019] Fig.14 The invention shows that a Figure 5 A flow chart of one or more processes performed by a materials analysis system.

[0020] Fig.15 The invention shows that a Figure 5 The material analysis system performs one or more processes Fig.14 Continuation of the flowchart.

[0021] Fig.16 The invention shows that a Figure 5 A flow chart of one or more processes performed by a materials analysis system.

[0022] Fig.17 The invention shows that a Figure 5 The material analysis system performs one or more processes Fig.16 Continuation of the flowchart.

[0023] Fig.18 A block diagram of an exemplary scientific instrument system is shown in which one or more of the methods described herein may be performed, according to one or more embodiments described herein.

[0024] Fig.19 A block diagram is shown of an exemplary operating environment in which implementations of the subject matter described herein may be incorporated.

[0025] Fig. 20 An example schematic block diagram of a computing environment is shown with which the subject matter described herein may interact and / or be at least partially implemented. Summary of the invention

[0026] An overview is presented below to provide a basic understanding of one or more embodiments described herein. This overview is not intended to identify key or important elements and / or to delineate the scope of a particular embodiment or the scope of the claims. Its sole purpose is to present the concepts in a simplified form as a preface to a more detailed description presented later. In one or more embodiments, the systems, computer-implemented methods, devices, and / or computer program products described herein can provide a process for synthesizing a spectrum of a target component using known components and their corresponding measured and synthesized spectra.

[0027] According to one embodiment, the system may include a memory storing computer executable components and a processor executing the computer executable components stored in the memory. The computer executable components include a determination component and a generation component, wherein the determination component determines a known component having the same composition as a target component, and the generation component generates a second synthetic spectrum of the target component based on a synthetic spectrum of the known component and a first synthetic spectrum of the target component.

[0028] According to another embodiment, a computer-implemented method may include: synthesizing a corrected spectrum of a target component by a system operatively coupled to a processor, including generating by the system an intermediate synthetic relationship that is a relationship that defines increments of a synthetic spectrum of a known component and increments of a first synthetic spectrum of the target component; aggregating by the system the intermediate synthetic relationship and a measured spectrum of the known component; and generating by the system an aggregated synthetic spectrum based on the aggregation and as a corrected spectrum of the target component.

[0029] According to another embodiment, a computer program product that facilitates a process for synthesizing a spectrum of a target component, the program instructions being executable by a processor to cause the processor to synthesize the spectrum of the target component through the processor, wherein the synthesis includes causing the processor to determine, through the processor, an increment of a first number of photons of a portion of a synthesized spectrum of a known component and an increment of a second number of photons of a portion of the first synthesized spectrum of the target component, thereby generating an intermediate synthetic relationship, the processor aggregating, by the processor, a third number of photons of a portion of the intermediate synthetic relationship and a fourth number of photons of a portion of a measured spectrum of the known component, and the processor generating, based on the result of the aggregation, a second synthetic spectrum of the target component. DETAILED DESCRIPTION

[0030] The following detailed description is illustrative only and is not intended to limit the application or utilization of the embodiments and / or embodiments. In addition, there is no intention to be constrained by any express or implied information presented in the previous summary of the invention or the specific implementation part. One or more embodiments are now described with reference to the accompanying drawings, wherein the same reference numerals are used throughout to refer to the same elements. In the following description, for the purpose of explanation, many specific details are set forth to provide a more thorough understanding of one or more embodiments. However, it is apparent that in various cases, one or more embodiments can be practiced without these specific details.

[0031] Various operations may be described as multiple discrete actions or operations in a manner that is most helpful for understanding the subject matter disclosed herein. However, the described order should not be interpreted as implying that these operations must rely on the order. In particular, these operations may be performed in an order different from the order of presentation. The described operations may be performed in an order different from the described embodiments. Various additional operations may be performed, and / or the described operations may be omitted in additional embodiments.

[0032] Turning now to the subject of materials analysis and one or more embodiments described herein, when an unknown material is identified using a measured spectrum, such as an energy dispersive X-ray spectrum (EDS) from a known material or a known database spectrum (e.g., obtained by accessing a known database of known materials), the material phases (e.g., components) can be distinguished by using one or more spectra of known components. The known components can not only be used as a comparison, but can actually be employed by one or more embodiments described herein to generate a synthetic spectrum of the unknown component.

[0033] As used herein, a "synthetic spectrum" or "synthetically generated spectrum" refers to a spectrum that is not measured from a sample of an intended source, but rather is generated by a computing device without contact with a sample of an intended source of unknown material.

[0034] As used herein, a sample of "established origin" refers to a sample whose material composition and other information (such as component weight percentages) are known with a high degree of accuracy.

[0035] Even if the database used is not exhaustive and the known material information is limited, the synthetic spectrum generated by one or more embodiments described herein can be generated. In fact, material databases are usually not exhaustive and inherently cannot be exhaustive. For example, many materials can be defined by a series of chemical formulas, in which one cation can be changed to another cation. As another example, with respect to minerals alone, there are about five thousand known minerals, most of which are rare or uncommon and therefore often missing from available databases.

[0036] Furthermore, a synthetic spectrum generated by one or more embodiments described herein may be generated even if such a synthetic spectrum has two or more (e.g., multiple) distinct peaks (e.g., one or more peaks are larger than other peaks of the spectrum). In one or more embodiments, these distinct peaks may be from the same component (e.g., atomic elements, molecular elements, phases of atomic or molecular elements, or combinations thereof). This is particularly noteworthy when one of the materials in the multi-component composition is, for example, copper, iron, or zinc, and / or when the unknown material contains a high proportion of these elements, and therefore, the synthetic spectrum generated may have a recognizable portion of the spectrum produced by these elements.

[0037] Since distinct peaks can be used to define unknown substances, when two or more peaks have similar output values, it may be very difficult to distinguish them by basic comparison alone. This may be caused at least in part by inherent errors in the values ​​being compared and / or such as when statistical comparison techniques are used. In contrast, such multiple distinct peaks will be distinguished with high accuracy.

[0038] In order to address one or more unachievable capabilities or deficiencies of existing frameworks (e.g., existing material analysis frameworks using spectral comparison), one or more embodiments are described herein that can use one or more known components that have a known material composition similar to an unknown material composition (e.g., a target component) to accurately identify the target component and determine a synthetically generated spectrum that defines the target component. Even in the case where the measured spectrum of the target component has multiple obvious peaks, one or more embodiments described herein can accurately identify the target component and enable the spectrum of the target component to be synthetically generated.

[0039] Furthermore, the resulting synthetic spectra of various target components can be used as future known components to generate synthetic spectra of other target components. In other words, a library of synthetic spectra can be built and used for further material analysis purposes, thereby enabling the system to improve itself and perform target component analysis and identification more accurately.

[0040] As used herein, an "atomic element" is a chemical substance that exists as individual atoms.

[0041] As used herein, the phrase "based on" should be understood to mean "based, at least in part, on" unless otherwise specified.

[0042] As used herein, the term "component" may refer to an atomic element, a molecular element, a phase of atomic or molecular elements, or a combination thereof.

[0043] As used herein, the term "data" may include metadata.

[0044] As used herein, the terms "entity," "requesting entity," and "user entity" may refer to machines, devices, components, hardware, software, intelligent devices, parties, organizations, individuals, and / or humans.

[0045] As used herein, a "measured spectrum" is a spectrum that has been generated based on measurements of a sample of a given source of a component.

[0046] As used herein, a "molecular element" is a molecular substance comprising two or more atoms of a single element.

[0047] As used herein, the term "phase" may refer to a change in atomic elements or molecular elements.

[0048] As used herein, the term "spectrum" may refer to an X-ray spectrum.

[0049] Scientific instrument systems and related methods, computing devices, and computer-readable media are disclosed herein. For example, in one or more embodiments, the system may include a memory storing computer executable components and a processor executing the computer executable components stored in the memory. The computer executable components include a determination component and a generation component, the determination component determining a known component having the same component as a target component, and the generation component generating a second synthetic spectrum of the target component based on a synthetic spectrum of the known component and a first synthetic spectrum of the target component.

[0050] As mentioned above, the term "component" may refer to an atomic element, a molecular element, a phase of an atomic or molecular element, or a combination thereof. Thus, a component may refer to a multi-element material, such as NaOH.

[0051] One or more embodiments disclosed herein can achieve improved performance relative to existing methods. For example, as described above, even when the measured spectrum of the target component has multiple obvious peaks, one or more embodiments described herein can accurately identify the target component and enable the spectrum of the target component to be generated in a synthetic manner. Thus, the embodiments disclosed herein provide improvements to scientific instrument technology (e.g., improvements in computer technology to support such scientific instruments, as well as other improvements).

[0052] Various embodiments disclosed herein can improve existing methods to achieve the technical advantage of identifying material components with high accuracy by using known components, particularly their measured and synthetically generated spectra, as a baseline for generating a high-precision spectrum that defines the target component. Then, by using this synthetically generated spectrum (i.e., a non-measured spectrum), the target component can be accurately identified. As described above, a "synthetically generated spectrum" refers to a spectrum that is not measured from a sample of a given source but is generated by a computing device without contacting a sample of a given source of an unknown material. That is, a synthetic spectrum can be generated without generating a measured spectrum of the target component from a sample of a given source, because the source of the target component itself has not yet been determined.

[0053] Such technical advantages are not achievable through conventional and existing methods, and all user entities of systems including such embodiments may benefit from these advantages (e.g., by assisting the user entities in performing technical tasks, such as generating synthetic spectra as part of and / or separate from target component identification through a guided human-computer interaction process).

[0054] Thus, the technical features of the embodiments disclosed herein (e.g., the ability to generate spectra to identify unknown components without samples from established sources and using spectral data from known components) are clearly unconventional in the field of materials analysis, as are the combinations of features of the embodiments disclosed herein. For example, such combinations may include determining the nearest neighbor known components and using spectral data of known components to generate a synthetic spectrum of an unknown component (e.g., a target component).

[0055] As further discussed herein, various aspects of the embodiments disclosed herein can improve the functionality of the computer itself; in addition, a synthetically generated spectrum can be employed for the next known component to subsequently identify a subsequent target component. The computational and user interface features disclosed herein not only involve the collection and comparison of information, but also the application of new analytical and specialized techniques to alter the computer analysis operations of the target component. That is, a library of synthetically generated spectra can be established and used for further material analysis purposes, thereby enabling the system to self-improve and more accurately perform target component analysis and identification.

[0056] Thus, the present disclosure introduces functionality that neither existing computing devices nor humans can perform. Instead, such existing computing devices require material samples of a known origin, as opposed to the identification of an unknown target component (e.g., in the case of a mining operation, when rapidly evaluating multiple samples, even hundreds or thousands of samples of unknown composition, it is impractical to provide each sample with a material sample of a known origin).

[0057] Thus, embodiments of the present disclosure may serve any of a number of technical purposes, such as controlling a particular technical system or method; determining how to control a machine based on a measurement result; digital audio, image, or video enhancement or analysis; separation of material sources in a mixed signal; generating data for reliable and / or efficient transmission or storage; providing estimates and confidence intervals for material samples; or providing faster processing of sensor data. In particular, the present disclosure provides a technical solution to a technical problem, including, but not limited to: determining the nearest neighbor known component of a target component, initially generating a baseline synthetic spectrum defining the nearest neighbor known component, generating a resulting synthetic spectrum defining the target component, and identifying the target component based on the resulting synthetic spectrum.

[0058] Thus, embodiments disclosed herein provide improvements to materials analysis technology (eg, improvements in computer technology to support materials analysis, among other improvements).

[0059] One or more embodiments are now described with reference to the accompanying drawings, wherein the same reference numerals are used throughout to refer to the same drawing elements. In the following description, for the purpose of explanation, numerous specific details are set forth in order to provide a more thorough understanding of one or more embodiments. However, it is apparent that in various circumstances, one or more embodiments may be practiced without these specific details.

[0060] In addition, it should be understood that the embodiments shown in one or more of the figures described herein are for illustrative purposes only, and therefore the architecture of the embodiments is not limited to the systems, devices and / or components shown therein, nor is it limited to any specific order, connection and / or coupling of the systems, devices and / or components shown therein.

[0061] Turning now in particular to one or more of the accompanying drawings, first to Figure 1 , shows a block diagram of a scientific instrument module 100 for performing material analysis operations using composition spectra synthetically generated and measured by one or more spectroscopic techniques, according to various embodiments described herein. The scientific instrument module 100 may be implemented by circuitry (e.g., including electrical and / or optical components) such as a programmed computing device. The logic of the scientific instrument module 100 may be contained in a single computing device or may be distributed across multiple computing devices that communicate with each other, as appropriate. Reference is made herein to Figure 4 Examples of computing devices that may implement the scientific instrument support module 100, either alone or in combination, are discussed in conjunction with computing device 400 and are referred to herein. Fig.18 An example of a system of interconnected computing devices is discussed in scientific instrument system 1800 , where scientific instrument module 100 may be implemented across one or more computing devices.

[0062] The scientific instrument module 100 may include a first logic component 102, a second logic component 104, and a third logic component 106. As used herein, the term "logic component" may include a device that performs a set of operations associated with logic. For example, any of the logic elements included in the module 100 is implemented by one or more computing devices, and the one or more computing devices are programmed with instructions to make one or more processing devices of the computing device perform an associated set of operations. In a specific embodiment, the logic element may include one or more non-transient computer-readable media, and the one or more non-transient computer-readable media have instructions thereon, which, when executed by one or more processing devices in one or more computing devices, make the one or more computing devices perform an associated set of operations. As used herein, the term "module" may refer to a collection of one or more logic elements, which together perform functions associated with the module. Different logic elements in the logic elements in the module may take the same form or may take different forms. For example, some logic components in the module may be implemented by a programmed general processing device, while other logic components in the module may be implemented by an application-specific integrated circuit (ASIC). In another example, different logic elements in the logic elements in the module may be associated with different instruction sets executed by one or more processing devices. A module may omit one or more logic elements shown in the associated figures; for example, when the module is to perform a subset of the operations discussed herein with reference to the module, the module may include a subset of the logic elements depicted in the associated figures.

[0063] The first logic component 102 may determine a nearest neighbor to the target component in the set of known components. That is, the first logic component 102 may determine a known component in the set of components that has a material composition, such as in terms of atomic element weight percentage (also referred to herein as element weight percentage) or other component weight percentage, that is closest to the target component, such as in terms of component weight percentage.

[0064] The second logic component 104 may generate a baseline synthetic spectrum for the identified known components (ie, nearest neighbors) and the target component, where the term "baseline" refers to the synthetic spectrum as an initial spectrum for further spectrum generation.

[0065] The third logic component 106 may use the initial synthetic spectrum of the target component, the baseline spectrum of the known component, and the measured spectrum of the known component (eg, based on a sample of the known component from a known source) to generate a final or resulting synthetic spectrum defining the target component.

[0066] Figure 2A flow chart of a method 200 of performing operations of the scientific instrument module 100 according to various embodiments is shown. Although reference may be made to specific embodiments disclosed herein (e.g., Figure 1 The scientific instrument module 100 discussed in this article is referenced Figure 3 GUI 300 discussed herein, reference Figure 4 The computing device 400 discussed and / or referenced herein Fig.18 The operation of method 200 is described with reference to the scientific instrument system 1800 discussed above, but method 200 can be used in any suitable setting to perform any suitable operation. Figure 2 The operations are each shown once in a particular order, but the operations may be reordered and / or repeated as needed and appropriate (eg, different operations may be performed in parallel where appropriate).

[0067] At 202, a first operation may be performed. For example, the first logic component 102 of the module 100 may perform the first operation 202. The first operation 202 may include identifying a set of known components based on the target component. The first operation 202 may also include determining the nearest neighbor of the target component in the set of known components.

[0068] At 204, a second operation may be performed. For example, the second logic component 104 of the module 100 may perform the second operation 204. The second operation 204 may include generating a baseline synthetic spectrum for the identified known components (i.e., nearest neighbors) and generating an initial synthetic spectrum for the target component. These synthetic spectra may be based on a scaled component spectrum of the component, by running a matrix correction procedure to obtain a calculated peak ratio for each component. That is, the synthetic spectrum may also be based on a matrix correction procedure for one or more of the sum of the component weight percentages of the components of the component. The synthetic spectrum may also be based on the scaling of the components based on the matrix correction procedure.

[0069] As used herein, a matrix correction procedure refers to a procedure for compensating for one or more deficiencies of a traditional quantitative method, such as ZAF analysis, inverse ZAF analysis, PAP analysis, Armstrong analysis, or XPHI analysis, but is not limited thereto.

[0070] At 206, a third operation may be performed. For example, the third logic component 106 of the module 100 may perform the third operation 206. The third operation 206 may include generating an intermediate synthetic relationship based on the baseline synthetic spectrum of the known component and the initial synthetic spectrum of the target component, and more specifically based on the number of photons of the portion of the spectrum. The third operation 206 may also include generating an aggregated synthetic spectrum based on the intermediate synthetic relationship and the measured spectrum of the known component. The third operation 206 may also include generating a resulting synthetic spectrum of the target component as the aggregated synthetic spectrum or based on the aggregated synthetic spectrum.

[0071] The scientific instrument methods disclosed herein may include interaction with a user entity (e.g., via the reference Fig.18 The user local computing device 1820 discussed above. These interactions may include providing information to the user entity (e.g., about scientific instruments such as Fig.18 information about the operation of a scientific instrument such as a scientific instrument 1810, information about a sample being analyzed or other test or measurement performed by the scientific instrument, information retrieved from a local or remote database, or other information) or provide a user entity with the option of inputting commands (e.g., controlling a scientific instrument such as Fig.18 In some embodiments, these interactions may be performed through a graphical user interface (GUI) that includes a display device (e.g., a graphical user interface such as a display device such as a display device such as a computer program product such as a computer program product, or a display device such as a computer program product, ... such as a computer program product, or a display device such as a computer program product, such as a computer program product, such as a computer program product, and a display device such as a computer program product, such as a computer program product, and a display device such as Figure 4 410) that provides output to a user entity and / or prompts the user entity to provide input (e.g., via a display device 410 discussed herein). Figure 4 Other I / O devices 412 discussed include one or more input devices such as a keyboard, mouse, trackpad, or touch screen). The scientific instrument system 1800 disclosed herein may include any GUI suitable for interacting with a user entity.

[0072] Next turn Figure 3 , which depicts an exemplary GUI 300 that can be used to perform some or all of the methods described herein according to various embodiments described herein. As described above, the GUI 300 can be provided on a scientific instrument system (e.g., Fig.18 A computing device (e.g., a scientific instrument system 1800 discussed herein) Figure 4 The computing device 400 discussed herein may include a display device (eg, Figure 4 The user entity may use any suitable input device (e.g., the display device 410 discussed herein) to input the content of the input to the display device 410. Figure 4 Any input devices (including those discussed in other I / O devices 412 ) and input techniques (e.g., cursor movement, motion capture, facial recognition, gesture detection, voice recognition, button activation, etc.) interact with GUI 300 .

[0073] GUI 300 may include a data display area 302 , a data analysis area 304 , a scientific instrument control area 306 , and a settings area 308 . Figure 3 The specific number and arrangement of regions shown in GUI 300 is illustrative only, and any number and arrangement of regions including any desired features may be included in GUI 300 .

[0074] The data display area 302 may display data generated by a scientific instrument (e.g., Fig.18 For example, the data display area 302 may display the resulting synthetic spectrum or data representing such a spectrum, such as in Fig.11 The spectrum is shown at 1160. Fig.11 The spectrum graph 1160 in FIG. 1 has a horizontal x-axis depicting the energy of the incident photons in kiloelectronvolts and is divided into 5 electronvolt ranges. The vertical y-axis depicts the number of photons acquired in a single range, where the sum of the spectra is normalized to 1 photon to display the measured and synthesized spectra in the same vertical range to highlight the relative heights of the element peaks in that range.

[0075] The data analysis area 304 may display the results of the data analysis (e.g., the results of analyzing the data shown in the data display area 302 and / or other data). For example, the data analysis area 304 may display target components or identifiers, such as Fig.11 The visual identification shown at 1150 is displayed. In one or more embodiments, the data display area 302 and the data analysis area 304 can be combined in the GUI 300 (eg, including the data output from the scientific instrument and some analysis of the data in a common graphic or area).

[0076] The scientific instrument control area 306 may include options that allow a user entity to control scientific instruments (e.g., Fig.18 For example, the scientific instrument control area 306 may include one or more controls for selecting and / or verifying the selection of a known component for use in identifying a target component.

[0077] The settings area 308 may include options that allow the user to physically control the features and functions of the GUI 300 (and / or other GUIs), and / or to perform common computing operations with respect to the data display area 302 and the data analysis area 304 (e.g., saving data to a storage device (such as a storage device referred to herein)). Figure 4 storage device 404 discussed above), sending data to another user entity, marking data, etc.). For example, the settings area 308 may include one or more options to change the graphic representation (such as Fig.11 The color, fill, or format of icon 1150 and icon 1160).

[0078] As described above, the scientific instrument module 100 may be implemented by one or more computing devices. Therefore, the following discussion turns to Figure 4, which illustrates a block diagram of a computing device 400 that can perform some or all of the scientific instrument methods disclosed herein, according to various embodiments. In one or more embodiments, the scientific instrument module 100 can be implemented by a single computing device 400 or multiple computing devices 400. In addition, as discussed below, the computing device 400 (or multiple computing devices 400) implementing the scientific instrument module 100 can be Fig.18 Part of one or more of a scientific instrument 1810, a user local computing device 1820, a service local computing device 1830, or a remote computing device 1840.

[0079] Figure 4 The computing device 400 is shown as having multiple components, but any one or more of these components may be omitted or duplicated depending on the application and settings. As shown, these components may include one or more of a processor 402, a storage device 404, an interface device 406, a battery / power circuit 408, a display device 410, and other input / output (I / O) devices 412, as described below.

[0080] In one or more embodiments, one or more of the components included in computing device 400 may be attached to one or more motherboards and enclosed in a housing (e.g., including plastic, metal, and / or other materials). In one or more embodiments, some of these components may be fabricated onto a single system on a chip (SoC) (e.g., an SoC may include one or more processors 402 and one or more storage devices 404). Additionally, in one or more embodiments, computing device 400 may omit Figure 4 . In one or more embodiments, computing device 400 may include interface circuitry (not shown) for coupling to one or more components using any suitable interface (e.g., a universal serial bus (USB) interface, a high-definition multimedia interface (HDMI) interface, a controller area network (CAN) interface, a serial peripheral interface (SPI) interface, an Ethernet interface, a wireless interface, or any other suitable interface). For example, computing device 400 may omit display device 410, but may include display device interface circuitry (e.g., a connector and driver circuitry) to which display device 410 may be coupled.

[0081] Computing device 400 may include processor 402 (e.g., one or more processing devices). As used herein, the term "processing device" may refer to any device or portion of a device that processes electronic data from registers and / or memory to convert the electronic data into other electronic data that can be stored in registers and / or memory. Processor 402 may include one or more digital signal processors (DSPs), application specific integrated circuits (ASICs), central processing units (CPUs), graphics processing units (GPUs), cryptographic processors (specialized processors that execute cryptographic algorithms in hardware), server processors, or any other suitable processing devices.

[0082] The computing device 400 may include a storage device 404 (e.g., one or more storage devices). The storage device 404 may include one or more memory devices, such as a random access memory (RAM) (e.g., a static RAM (SRAM) device, a magnetic RAM (MRAM) device, a dynamic RAM (DRAM) device, a resistive RAM (RRAM) device, or a conductive bridge RAM (CBRAM) device), a hard drive-based memory device, a solid-state memory device, a network drive, a cloud drive, or any combination of memory devices. In one or more embodiments, the storage device 404 may include a memory that shares a die with the processor 402. In such embodiments, the memory may be used as a cache memory and may include, for example, an embedded dynamic random access memory (eDRAM) or a spin transfer torque magnetic random access memory (STT-MRAM). In one or more embodiments, the storage device 404 may include a non-transitory computer-readable medium having instructions thereon that, when executed by one or more processing devices (e.g., the processor 402), cause the computing device 400 to perform any appropriate method or portion of the methods disclosed herein.

[0083] The computing device 400 may include an interface device 406 (e.g., one or more interface devices 406). The interface device 406 may include one or more communication chips, connectors, and / or other hardware and software to manage communications between the computing device 400 and other computing devices. For example, the interface device 406 may include circuitry for managing wireless communications for transmitting data to and from the computing device 400. The term "wireless" and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communication channels, etc. that can transmit data through a non-solid medium using modulated electromagnetic radiation. The term does not mean that the associated device does not contain any wires, but in one or more embodiments, the associated device may not contain any wires. The circuitry for managing wireless communications included in the interface device 406 may implement any of a variety of wireless standards or protocols, including but not limited to Institute of Electrical and Electronics Engineers (IEEE) standards, including Wi-Fi (IEEE 802.11 series), IEEE 802.16 standards (e.g., IEEE 802.16-2005 amendment), Long Term Evolution (LTE) project and any amendments, updates and / or revisions (e.g., Advanced LTE project, Ultra Mobile Broadband (UMB) project (also known as "3GPP2"), etc.). In one or more embodiments, the circuitry included in the interface device 406 for managing wireless communications may operate according to a Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), High Speed ​​Packet Access (HSPA), Evolved HSPA (E-HSPA), or LTE network. In one or more embodiments, the circuitry included in the interface device 406 for managing wireless communications may operate according to an Enhanced Data GSM Evolved In one or more embodiments, the interface device 406 may include one or more antennas (e.g., one or more antenna arrays) to receive and / or transmit wireless communications.

[0084] In one or more embodiments, the interface device 406 may include a circuit for managing wired communication, such as an electrical communication protocol, an optical communication protocol, or any other suitable communication protocol. For example, the interface device 406 may include a circuit supporting communication according to Ethernet technology. In one or more embodiments, the interface device 406 may support wireless and wired communication, and / or may support multiple wired communication protocols and / or multiple wireless communication protocols. For example, a first group of circuits of the interface device 406 may be dedicated to short-range wireless communication such as Wi-Fi or Bluetooth, and a second group of circuits of the interface device 406 may be dedicated to long-range wireless communication such as global positioning system (GPS), EDGE, GPRS, CDMA, WiMAX, LTE, EV-DO, etc. In one or more embodiments, a first group of circuits of the interface device 406 may be dedicated to wireless communication, and a second group of circuits of the interface device 406 may be dedicated to wired communication.

[0085] Computing device 400 may include battery / power circuitry 408. Battery / power circuitry 408 may include one or more energy storage devices (e.g., batteries or capacitors) and / or circuitry for coupling components of computing device 400 to an energy source separate from computing device 400 (e.g., AC line power).

[0086] Computing device 400 may include display device 410 (eg, multiple display devices). Display device 410 may include any visual indicator, such as a heads-up display, a computer monitor, a projector, a touch screen display, a liquid crystal display (LCD), a light emitting diode display, or a flat panel display.

[0087] The computing device 400 may include other input / output (I / O) devices 412. For example, the other I / O devices 412 may include one or more audio output devices (e.g., speakers, headphones, earbuds, alarms, etc.), one or more audio input devices (e.g., microphones or microphone arrays), a positioning device (e.g., a GPS device that communicates with a satellite-based system to receive the location of the computing device 400 as is known in the art), an audio codec, a video codec, a printer, a sensor (e.g., a thermocouple or other temperature sensor, a humidity sensor, a pressure sensor, a vibration sensor, an accelerometer, a gyroscope, etc.), an image capture device such as a camera, a keyboard, a cursor control device such as a mouse, a stylus, a trackball, or a touchpad, a barcode reader, a quick response (QR) code reader, or a radio frequency identification (RFID) reader.

[0088] The computing device 400 may have any suitable form factor suitable for its applications and settings, such as a handheld or mobile computing device (e.g., a cell phone, a smart phone, a mobile Internet device, a tablet computer, a laptop computer, a netbook computer, an ultrabook computer, a personal digital assistant (PDA), an ultra-mobile personal computer, etc.), a desktop computing device, or a server computing device or other networked computing component.

[0089] Next reference Figure 5 and Figure 6 , in one or more embodiments, Figure 5 and Figure 6 The non-limiting systems 500 and / or 600 shown in FIG. 1 and / or the systems thereof may also include computing environments (such as Fig. 20 One or more computers and / or computing-based elements described in the computing environment 2000 shown in FIG. In one or more of the described embodiments, the computers and / or computing-based elements may be combined to implement the combined Figure 5 and / or Figure 6 and / or used in combination with one or more of the systems, devices, components, and / or computer-implemented operations illustrated and / or described in other figures described herein.

[0090] First turn Figure 5 , which illustrates a block diagram of an example, non-limiting system 500 that may include a material analysis system 502. The material analysis system 502 may facilitate a process of generating a synthetic spectrum of a target component based on one or more spectra defining known components, wherein the known components have material compositions that are related to the target component. In one or more embodiments, the material analysis system 502 may be comprised, at least in part, of a computing device 400.

[0091] Note that the material analysis system 502 is only briefly described in detail to provide Figure 6 The introduction of the more complex and / or extensive material analysis system 602 shown. That is, the following will be relative to Figure 6 Non-limiting system 600 provides further details regarding processes that may be performed by one or more embodiments described herein.

[0092] Still reference Figure 5 , the material analysis system 502 may include at least a memory 504, a bus 505, a processor 506, a determination component 514, and a generation component 520. The processor 506 may be the same as, included in, or different from the processor 402. The memory 504 may be the same as, included in, or different from the storage device 404.

[0093] Using the above components, the material analysis system 502 can employ both the synthetic spectrum and the measured spectrum of the target component or selected known components to generate a more accurate synthetic spectrum (e.g., a resulting synthetic spectrum) for the target component than can be generated from the initial component composition data of the target component alone. In practice, the generation of the resulting synthetic spectrum can be most particularly based on the identification of known components that are nearest neighbors of the target component, such as having related material compositions, such as using component weight percentages.

[0094] The determination component 514 can generally determine a known component 552 (e.g., a nearest neighbor) having at least one component 554 that is the same as a component 564 of the target component 562. As used herein, components 554 and 564 refer to atomic elements. For example, the known component 552 and the target component 562 can each include the component silicon (Si).

[0095] The generation component 520 can generally generate a resulting (e.g., second) synthetic spectrum 580 of the target component 562 based on the baseline synthetic spectrum 558 of the known component 552 (e.g., as a baseline for the known component 552) and the initial (e.g., first) synthetic spectrum 566 of the target component 562. In one or more embodiments, the known component 552 and the data defining the known component 552 can be obtained from a database 540 that is communicatively connected to the material analysis system 502.

[0096] The determining component 514 and the generating component 520 are operatively coupled to the processor 506, which is operatively coupled to the memory 504. The operative coupling may be provided by the bus 505. The processor 506 may facilitate the execution of the determining component 514 and the generating component 520. The determining component 514 and the generating component 520 may be stored in the memory 504.

[0097] In general, the non-limiting system 500 can employ any suitable communication method (e.g., electronic, telecommunication, Internet, infrared, fiber optic, etc.) to provide communication between the material analysis system 502, a data source defining the target component and known components, and any device associated with a user entity that will use the resulting synthetic spectrum 580 of the target component 562.

[0098] Next turn Figure 6 , a non-limiting system 600 that may include a material analysis system 602 is shown. For the sake of brevity, repeated descriptions of similar elements and / or processes employed in various embodiments are omitted. Figure 5 The description of the embodiments may be applicable to Figure 6 Similarly, regarding Figure 6 The description of the embodiments may be applicable to Figure 5 implementation plan.

[0099] Generally speaking, material analysis system 602 can facilitate the process of generating a synthetic spectrum of a target component based on one or more spectra defining known components having material compositions related to the target component. In one or more embodiments, material analysis system 602 can be comprised at least in part of computing device 400.

[0100] One or more communications between one or more components of the non-limiting system 600 may be provided via wired and / or wireless means, including but not limited to using a cellular network, a wide area network (WAN) (e.g., the Internet), and / or a local area network (LAN). Suitable wired or wireless technologies for supporting communications may include, but are not limited to, Wireless Fidelity (Wi-Fi), Global System for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX), Enhanced General Packet Radio Service (Enhanced GPRS), Third Generation Partnership Project (3GPP) Long Term Evolution (LTE), Third Generation Partnership Project 2 (3GPP2) Ultra Mobile Broadband (UMB), High Speed ​​Packet Access (HSPA), Zigbee and other 802.XX wireless technologies and / or traditional telecommunication technologies, Session Initiation Protocol (SIP), RF4CE protocol, WirelessHART protocol, 6LoWPAN (Ipv6 over Low Power Wireless Area Network), Z-Wave, Advanced and / or Adaptive Networking Technology (ANT), Ultra-Wideband (UWB) standard protocol and / or other proprietary and / or non-proprietary communication protocols.

[0101] The material analysis system 602 may be associated with, such as accessible via, a cloud computing environment.

[0102] The material analysis system 602 may include multiple components. These components may include a memory 604, a processor 606, a bus 605, a determination component 614, a linear combination component 616, a synthesis component 618, a generation component 620, and a database maintenance component 622. Using these components, the material analysis system 602 may output one or more resulting synthesized spectra defining a target component 662 or an identification of the target component 662 in response to a query about an unknown material (e.g., a target component). The query may be requested by an entity, such as an administrator entity of a mining system or other system.

[0103] Next, the discussion turns to the processor 606, memory 604, and bus 605 of the material analysis system 602. For example, in one or more embodiments, the material analysis system 602 may include a processor 606 (e.g., a computer processing unit, a microprocessor, a classical processor, a quantum processor, and / or the like). In one or more embodiments, as described herein with or without reference to one or more figures of one or more embodiments, the components associated with the material analysis system 602 may include one or more computer and / or machine readable, writable, and / or executable components and / or instructions, which may be executed by the processor 606 to provide the performance of one or more processes defined by such components and / or instructions. In one or more embodiments, the processor 606 may include a determination component 614, a linear combination component 616, a synthesis component 618, a generation component 620, and a database maintenance component 622.

[0104] In one or more embodiments, the material analysis system 602 may include a computer readable memory 604 operably connected to a processor 606. The memory 604 may store computer executable instructions that, when executed by the processor 606, may cause the processor 606 and / or one or more other components of the material analysis system 602 (e.g., a determination component 614, a linear combination component 616, a synthesis component 618, a generation component 620, and a database maintenance component 622) to perform one or more actions. In one or more embodiments, the memory 604 may store computer executable components (e.g., a determination component 614, a linear combination component 616, a synthesis component 618, a generation component 620, and a database maintenance component 622).

[0105] The material analysis system 602 and / or its components as described herein can be communicatively, electrically, operatively, optically, and / or otherwise coupled to each other via a bus 605. The bus 605 can include one or more of a memory bus, a memory controller, a peripheral bus, an external bus, a local bus, a quantum bus, and / or another type of bus that can employ one or more bus architectures. One or more of these examples of the bus 605 can be employed.

[0106] In one or more embodiments, the material analysis system 602 can be coupled (e.g., communicatively, electrically, operatively, optically, and / or the like) to one or more external systems (e.g., an electrical output generating system not shown, one or more output targets, and / or output target controllers), sources, and / or devices (e.g., classical and / or quantum computing devices, communication devices, and / or the like), such as via a network. In one or more embodiments, one or more of the components of the material analysis system 602 and / or the non-limiting system 600 can reside in the cloud and / or can reside in a local computing environment (e.g., at a specified location).

[0107] In addition to the processor 606 and / or memory 604 described above, the material analysis system 602 may also include one or more computer and / or machine readable, writable and / or executable components and / or instructions that, when executed by the processor 606, may provide for the performance of one or more operations defined by such components and / or instructions.

[0108] Turning now to the additional components of the material analysis system 602 (e.g., determination component 614, linear combination component 616, synthesis component 618, generation component 620, and database maintenance component 622), generally speaking, the material analysis system 602 can perform a set of processes that can be divided into three steps: nearest neighbor determination, initial synthetic spectrum generation, and resulting synthetic spectrum generation.

[0109] Turning first to the determination component 614 , the component can generally determine that a known component 652 (eg, nearest neighbor) has one or more components 654 (eg, component 654 ) in common as a component 664 (eg, component 664 ) of a target component 662 .

[0110] More specifically, based on a set 650 of known components (such as components obtained from a database 640 or one or more other databases), the determination component 614 can determine that one of the components in the set 650 is a nearest neighbor of the set 650. A "nearest neighbor" can be a known component having a composition / composition that is most similar to a target component 662. In one or more embodiments, the determination can be based on component weight percentages. That is, the component weight percentage of the selected nearest neighbor known component 652 can be closer to the component weight percentage of the target component than other known components in the set 650 of known components. In one or more embodiments, the known components 652 and the data defining the known components 652 can be obtained from a database 640 that is communicatively connected to the material analysis system 602.

[0111] The nearest neighbor determination performed by the determination component 614 can generally verify whether the increment of the composition weight percentage of the known composition 652 and the increment of the composition weight percentage of the target composition 662 meet a selected threshold value defining a selected composition relationship between the known composition 652 and the target composition 662. The selected threshold value can be defined by the user entity or the material analysis system 602.

[0112] In one or more embodiments, the selected threshold can be generally defined as being based on an error metric between a pair of vectors comprising component weight percentages.

[0113] For example, in one or more embodiments, the selected threshold value can be defined as the increment of the output of the sum of the absolute deviation of the components of the target component and the absolute deviation of the components of the known components. In one or more embodiments, the selected threshold value can be defined as the increment of the output of the sum of the relative deviations of the components of the target component and the components of the known components. For example, for a single element, an exemplary relative error method can be defined by Error=abs(element 1-element 2) / element 1.

[0114] As another example, temporarily turning to Figure 7 6 and nearest neighbor determination process 700, the determination by determination component 614 can be illustrated based on exemplary known components 652A, 652B, and 652C.

[0115] The target composition 662 is shown as having composition weight percentages 704 of 4% iron (Fe), 2% carbon (C), and 94% oxygen (O). The known composition 652A is shown as having composition weight percentages 702 of 10% Fe, 82% O, and 8% Ag. The increments of these composition weight percentages 702 and 704 can be determined generally using absolute value deviations as shown in Equation 1 and, in particular, Equation 2 below.

[0116] Equation 1: |%Fe T -%Fe K | and |%O T -%O K |=Δ%Fe and Δ%O; total deviation = Δ%Fe+Δ%O.

[0117] Equation 2: |4%-10%| of Fe, |94%-82%| of O, |2%-0%| of C, and |8%-0%| of Ag = 6% of Fe, 12% of O, 2% of C, and 8% of Ag; Total deviation = 6% + 12% + 2% + 8% = 28%.

[0118] In one or more embodiments, elements C and Ag may be omitted.

[0119] Differently, the known composition 652B is shown as having a composition weight percentage 702 of 3% Fe and 97% O, compared to a composition weight percentage 704 of the target composition 662 of 4% Fe, 2% C, and 94% O. Therefore, the increments of these composition weight percentages 702 and 704 may be determined using an absolute value deviation as specifically shown in Equation 3 below.

[0120] In one or more embodiments, element C may be omitted.

[0121] Equation 3: |4%-3%| of Fe, |94%-97%| of O, and |2%-0%| of C = 1% of Fe, 3% of O, and 2% of C; Total deviation = 1% + 3% + 2% = 6%.

[0122] Thus, the individual incremental composition weight percentages of known composition 652B (eg, individually for O and individually for Fe) are all lower than the individual incremental composition weight percentages of known composition 652 A. Thus, known composition 652 / nearest neighbor 710 is known composition 652B.

[0123] Additionally, the sum of the deviations for known component 652B (6%) is lower than the sum of the deviations for known component 652A (28%).

[0124] Note that the total deviation for the known composition 652C is 184%.

[0125] In one or more other embodiments, another suitable approach based at least in part on component weight percentages, or not based at all on component weight percentages, may be employed.

[0126] Turning briefly to linear component composition 616, in the event that no sum of deviations satisfies a selected threshold for the sum of deviations, and / or in the event that the entity desires, an exemplary known component can be based on a combination (e.g., aggregation) of two or more known components. The two or more known components can be obtained by determination component 614 from the same or different databases 640. Based on obtaining two or more known components, linear combination component 616 can perform aggregation, such as component weight percentages of two or more known components. In one or more embodiments, aggregation can include averaging a first component weight percentage of a first element in two or more known components, averaging a second component weight percentage of a second element in two or more known components, and so on. This can produce a combination of known components having a component weight percentage that is averaged individually for each different element of the combination of known components.

[0127] For example, for the spectrum of the mineral albite (NaAlSi3O8), a single synthesis yields O=48.8%, Na=8.7%, Al=10.3% and Si=32.1%. This can be made up of a set of three reference components (here minerals) SiO2, Na2O and Al2O3, where for example the proportions of SiO2, Al2O3 and Na2O are about 68.7%, 19.5% and 11.9%. These numbers can scale the measured spectra of these references and thus produce a synthetic spectrum that is close to the target. Given that Na2O is a rare compound, this component may not be present in an accessible database of known components, and the remaining contributions of Na and O may have been obtained synthetically. The other compounds SiO2 and Al2O3 are common and are therefore more likely to be present in accessible databases.

[0128] In this case, a combination of known components may be employed as one of the comparison samples for determining the nearest neighbor 710, as described above.

[0129] When the nearest neighbor is determined 710, the first stage of identification is completed, where the first stage is the nearest neighbor determination. The second stage includes the generation of the initial synthetic spectrum. For the second stage, the discussion now turns to the synthesis component 618.

[0130] The synthesis component 618 can generally synthetically generate a baseline synthetic spectrum 658 of the known component 652 and an initial synthetic spectrum 665 of the target component 662. These synthetically generated spectra can then be used to help identify the target component 662. That is, the initial synthetic spectrum 665 of the target component 662 will be an initial estimate of the identity of the target component 662, and subsequent additional processes are performed by the generation component 620 to provide a more accurate identity of the target component 662.

[0131] Generally speaking, the baseline synthetic spectrum 658 can be based on the sum of the component weight percentages of the components of the known composition 652 and a matrix correction procedure, such as, but not limited to, ZAF analysis, PAP analysis, Armstrong analysis, or XPHI analysis, to allow the sum to be scaled in a set of iterations of summing and subsequent scaling. Figure 8 With the synthetic spectrum determination process 800, the first step may include determining an initial weight percent composition 802 of the components of the known composition 652. For example, given a known composition with a weight percent composition of 3% Fe and 97% O, a 3% Fe spectrum and a 97% O spectrum may be aggregated. The result is the sum of the weight percent composition of the components of the composition 804.

[0132] In one embodiment, ZAF analysis may be performed on the results 804 and thereby obtain analysis output 806. ZAF analysis may refer to a quantitative analysis that takes into account three effects of characteristic X-ray intensity, including atomic number (Z) effect, absorption (A) effect, and fluorescence excitation (F) effect.

[0133] In one or more other embodiments, PAP analysis can be used, as described above. The PAP (Pouchou and Pichoir) matrix correction procedure can be used in energy dispersive X-ray spectroscopy (EDS) to improve the accuracy of quantitative analysis in thin samples. It solves the X-ray absorption and fluorescence effects that can affect the measured X-ray intensity. The PAP method can involve iteratively calculating the correction factor for each element in the sample based on theoretical models and experimental data, allowing researchers to obtain more reliable elemental composition information from the EDS spectrum of thin materials. The correction provided by the PAP method helps compensate for these effects and produces more accurate and precise quantitative results in EDS analysis.

[0134] The PAP (Pouchou and Pichoir) matrix correction procedure and the ZAF (Z (atomic number), A (absorption), F (fluorescence)) method can both be used with energy dispersive X-ray spectroscopy (EDS) for quantitative analysis. The difference between these methods lies in the way they correct for X-ray absorption and fluorescence effects. While the ZAF method handles these corrections separately, the PAP method combines absorption and fluorescence corrections into a single unified correction scheme. This integration allows the PAP method to provide more accurate and reliable elemental quantification, especially for thin samples.

[0135] For example, using a ZAF analysis (e.g., ZAF method), the analysis output 806 may provide 1% Fe and 99% O. From this output 806, a set of scalings 808 for the components of the composition may be generated. In the example below, this set of scalings 808 is Fe 3x and O 0.9x. For example, the following steps of Equations 4 and 5 may be performed, where Equation 4 is a general set of steps and Equation 5 continues the exemplary composition of FE and O. Note that the symbols of the ZAF analysis below may be generally replaced with one or more matrix correction procedures, or more specifically with another type of matrix correction procedure.

[0136] Equation 4 (performed by synthesis component 618):

[0137] a) ZAF analysis output giving the composition weight percentage of elements;

[0138] b) given an initial percentage determined for the element;

[0139] c) Calculate a new scaling factor for element 1: initial component weight percentage / ZAF analysis percentage = scaling factor of x (e.g., 0.1X, 2.4X, etc.);

[0140] d) Calculate the new scaling ratio of element 2: initial component weight percentage / ZAF analysis percentage = scaling ratio of x;

[0141] e) calculating a new scaling factor for the attached element;

[0142] f) applying the new scaling to the original percentages: (element 1: scaling of initial component weight percentage * x, and element 2: scaling of initial component weight percentage * x; and

[0143] g) Output scaled sum = scale element 1 and scale element 2

[0144] h) Repeat steps a to f according to the threshold iteration count.

[0145] Equation 5 (performed by synthesis component 618):

[0146] a) A given ZAF analysis outputs 1% Fe and 99% O;

[0147] b) given initial percentages of 3% Fe and 97% O;

[0148] c) Calculate the new Fe ratio: 3% / 1%=3x;

[0149] d) Calculate the new O ratio: 97% / 99%=0.9x;

[0150] e) Apply the new scaling to the original percentages: (Fe: 3%*3x and O:

[0151] 97%*0.9x); and

[0152] f) Output scaled sum = 9% Fe and 95% O.

[0153] g) Repeat steps a to f according to the threshold iteration count.

[0154] As described above, the scaled sum 810 of the component weight percentages of the components of the known composition is 9% Fe and 95% O, which is different from the given initial percentages of 3% Fe and 97% O.

[0155] In another example, a PAP analysis may be performed to obtain an analysis output 806 , a scaling 808 , and a scaled sum 810 .

[0156] As described above, the calculation of the new scaling factor 808 and the resulting scaled sum 810 may be repeated multiple times until at least a selected threshold iteration count is met. The threshold may be determined by the user entity or the synthesis component 618. For example, Figure 8 As shown in decision step 812 of , if the specified iteration count has been met, the synthetic spectrum determination process 800 can proceed to generate and output a scaled synthetic spectrum 814 for the known components 652 and based on the scaled sum 810 of the component weight percentages. If the specified iteration count has not been reached, the synthetic spectrum determination process 800 can proceed to re-execute the steps of outputting the analysis output 806, scaling 808, and the scaled sum 810.

[0157] Note that the output of the synthetic spectrum determination process 800 is the baseline synthetic spectrum 658 , which is the final scaled synthetic spectrum 814 after all iterations of the process 800 are completed.

[0158] The same synthetic spectrum determination process 800 may be completed for the target component 662 to output an initial synthetic spectrum 665 , which will be the final scaled synthetic spectrum 814 after all iterations of the process 800 are completed.

[0159] After determining the initial synthetic spectrum 665 of the target component 662 and the baseline synthetic spectrum 658 of the known component 652, the third stage of identification can be performed. The third stage can generate a resulting synthetic spectrum 680 (eg, a corrected synthetic spectrum for the target component 662).

[0160] That is, turning next to the generation component 620, the component can generally: Based on the baseline synthetic spectrum 658 of the known component 652 (e.g., as a baseline for the known component 652) and the initial (e.g., first) synthetic spectrum 665 of the target component 662, a resulting (e.g., second) synthetic spectrum 680 of the target component 662 can be generally generated. In one or more embodiments, the known component 652 and the data defining the known component 652 can be obtained from a database 640 that is communicatively connected to the material analysis system 602.

[0161] However, first, before outputting the resulting synthetic spectrum 680 of the target component 662, the generation component 620 performs two intermediate steps.

[0162] Now turn to Fig. 9 In the process 900 for generating a synthetic spectrum of a target component, the first step includes generating an intermediate synthetic relationship 678 by the generating component 620 .

[0163] That is, based on the nearest neighbor known component 710, a baseline synthetic spectrum 658 is generated by the synthesis component 618 for the known component 652. A measured spectrum 656 of the known component 652 can also be obtained, such as by the generation component 620, such as from a database (e.g., database 640). It is noted that the measured spectrum 656 is a spectrum measured by a scientific instrument such as an EDS device based on a sample of a known source of the known component 652 (e.g., a sample whose composition is accurately known).

[0164] To generate the intermediate synthetic relationship 678, the initial synthetic spectrum 665 of the target component 662 and the baseline synthetic spectrum 658 of the known component 652 may be employed. That is, the intermediate synthetic relationship 678 is determined as a relationship defined by the increments of these two synthetic spectra 665 and 658.

[0165] Discuss Now FIG. 10A to FIG. 10D . First turn Fig. 10A , which shows the process of determining the intermediate synthesis relationship 1000, and still refers to Fig. 9 , more specifically, the intermediate synthetic relationship 678 is based on an increment of the first photon quantity 1001 of the portion of the baseline synthetic spectrum 658 of the known component 652 and an increment of the second photon quantity 1002 of the portion of the initial synthetic spectrum 665 of the target component 662. That is, the portion of the baseline synthetic spectrum 658 of the known component 652 represents a different energy range of the baseline synthetic spectrum 658 of the known component 652, and the portion of the initial synthetic spectrum 665 of the target component 662 represents a different energy range of the initial synthetic spectrum 665 of the target component 662.

[0166] Even more specifically, determining the increment includes determining a first digital array (e.g., Fig. 10B The difference between a column, or vector, in (a) and a second digital array, the first digital array being indexed within a set of energy ranges and corresponding to a first number of photons 1001, and the second digital array being indexed within the same set of energy ranges and corresponding to a second number of photons 1002.

[0167] For example, turning Fig. 10C , shows an exemplary initial synthetic spectrum 665 of a target component 662. The x-axis represents the energy of the spectrum (e.g., in kiloelectron volts or keV), and the y-axis represents the number of photons at each energy of the x-axis. The data from this exemplary initial synthetic spectrum 665 of the target component 662 can be converted into an array of the number of photons in each energy range of multiple energy ranges of the x-axis energy. The energy ranges can be equal, and the default range for each energy range can cover any suitable number. As shown in FIG. Fig. 10BAs shown in the first column of the set of arrays 1050, the energy range is set to 0.02 keV per energy range. Each of the photon number arrays 1001 to 1006 is indexed within the same energy range set in the first column.

[0168] Note that for simplicity, the first array column only extends to 0.6keV, rather than Fig. 10C The full range of the energy range of the spectrum (with the x-axis extending to 8.0 keV).

[0169] Furthermore, although the visualization of the collection 1050 of arrays is useful to the user entity and can be generated by the generation component 620, such as for display in the display area 302 ( Figure 3 ), but the generation component 620 determines that the intermediate composite relationship 678 or the aggregated composite spectrum 680 does not require a visual version of the set 1050 of arrays to be generated.

[0170] Next in Fig. 10B Shown in column 2 is an array of known photon counts 1006 for a measured spectrum of a target component 662. While it may not be possible to obtain a measured spectrum of a target component or a sample of a given source (and therefore use the material analysis system 602) when using the material analysis system 602, FIG. 10A to FIG. 10D In the example of , the array in column 2 provides proof of concept compared to the final result at column 7, which corresponds to the resulting synthetic spectrum 680 of the target component 662.

[0171] Fig. 10B The third column of the array set 1050 is shown from Fig. 10C An array of second photon quantities 1002 of a portion (eg, energy range) of an exemplary initial synthetic spectrum 665 of a target component 662 is obtained by spectral plot.

[0172] exist Fig. 10B Also shown at column 4 is an array of first photon quantities 1001 for a portion (eg, energy range) of a corresponding baseline synthetic spectrum 658 of a known component 652 .

[0173] Fig. 10BColumn 5 of is an array of third photon counts 1003 for a portion (e.g., energy range) of the intermediate composite relationship 678. This column 5 array is the result (e.g., increment) of subtracting the first photon count 1001 (of the composite spectrum 658 of the known component 652) from the second photon count 1002 (of the composite spectrum 665 of the target component 662). That is, the increment or subtraction performed by the generation component 620 is performed for each energy range (e.g., each row of the set 1050 of arrays) relative to columns 3 and 4. That is, each individual photon count of 1001 (column 4) is subtracted from the individual photon count of the corresponding / corresponding (e.g., same row) 1002 (column 3) to obtain the photon count 1003 of the column 5 array. In other words, column 3 - column 4 = column 5 or equation 6.

[0174] Equation 6: Initial synthetic spectrum 665 of target component 662 −baseline synthetic spectrum 658 of known component 652 =intermediate synthetic relationship 678 .

[0175] Furthermore, as will be described in detail below, column 5+column 6=column 7 or Equation 7.

[0176] Equation 7: Intermediate synthetic relationship 678 −Measured spectrum 656 of known component 652 =Aggregated synthetic spectrum 679 =Resulting synthetic spectrum 680 of target component 662 .

[0177] More specifically, column 3 - column 4 + column 6 = column 7 or Equation 8.

[0178] Equation 8: Initial synthetic spectrum 665 of target component 662 −baseline synthetic spectrum 658 of known component 652 +measured spectrum 656 of known component 652 =aggregated synthetic spectrum 679 =resulting synthetic spectrum 680 of target component 662 .

[0179] Turning to the second step mentioned above, the generation component 620 can generate the aggregated synthetic spectrum 679 based on the intermediate synthetic relationship 678 .

[0180] To generate the aggregated synthetic spectrum 679, the intermediate synthetic relation 678 and the measured spectrum 656 of the known component 652 may be employed. That is, the aggregated synthetic spectrum 679 is determined as the sum (eg, the aggregation) of these two aspects 678 and 656.

[0181] Still reference Fig. 9 and FIG. 10A to FIG. 10D, more specifically, the aggregated composite spectrum 679 is based on the sum of the third photon quantity 1003 of the portion of the intermediate composite relationship 678 and the fourth photon quantity 1004 of the portion of the measured spectrum 656 of the known component 652. That is, the portion of the intermediate composite relationship 678 represents a set of energy ranges, and the portion of the measured spectrum 656 of the known component 652 represents a different energy range of the known component 652 (e.g., the same set of energy ranges).

[0182] Even more specifically, determining the sum includes summing a third digital array and a fourth digital array, the third digital array being indexed in a third energy range and corresponding to a third number of photons 1003 ( Fig. 10B ), the fourth digital array is indexed in the fourth energy range and corresponds to the fourth photon quantity 1004 ( Fig. 10B 6th column of ).

[0183] like Fig. 10B As shown, column 6 is an array of fourth photon counts 1004 for a portion (eg, energy range) of a corresponding measured spectrum 656 of a known component 652 .

[0184] Then, column 7 is an array of fifth photon quantities 1005 of a portion (e.g., energy range) of the aggregate spectrum 679, which is the resulting composite spectrum 680 of the target component 662. Column 7 is the result of the aggregation (e.g., summation) performed by the generation component 620 on each individual photon quantity 1003 in column 5 (e.g., in each row of the set 1050 of arrays) and each corresponding individual photon quantity 1004 (column 6). As described above, column 5 + column 6 = column 7 or equation 7.

[0185] It should be appreciated that in one or more embodiments, generation component 620 need not separately calculate intermediate composite relationship 678, but rather omits this step relative to the use of Equation 8.

[0186] Finally, based on the photon count 1005 , a resulting composite spectrum 680 may be generated for output to a user entity of the material analysis system 602 .

[0187] Additionally, upon determining the synthetic spectrum 680 of the target component 662 , an optional fourth stage of identification may be performed. The fourth stage may include self-improvement of the material analysis system 602 by using the resulting synthetic spectrum 680 .

[0188] That is, turning next to the database maintenance component 622, the component can update the data in the database of known components (e.g., database 640). For example, the database 640 can include the measured spectrum 656 and the synthetic spectrum 658 of the known component 652, and the synthetic spectrum 680 of the resulting target component 662 can be added to the database 640 through the database maintenance component 662. In this way, the user entity of the material analysis system 602 can establish a dedicated library of known components for future identification of unknown components (e.g., target components). For example, when the material analysis system 602 is used iteratively in the future, the determination component 614 can determine the resulting synthetic spectrum 680 of the target component 662 as a second known component for determining the resulting synthetic spectrum of the second target component.

[0189] As a brief summary of one or more of the above embodiments, the discussion now turns to Fig.11 1100 and 1150. As shown, the material depicted at the prior method result 1100 is identified as a single component 1102. This is inaccurate relative to the actual physical sample. In contrast, using the systems and methods described herein (e.g., material analysis system 602), the described method result 1150 more accurately shows that the unknown material includes multiple components (e.g., at least two components 1152 and 1154) and does not include only a single component with a 100% weight percentage. In fact, this type of accurate identification may be very important in mining and / or other material procurement fields.

[0190] As another overview, refer to Fig.12 and Fig.13 , showing one or more embodiments described herein (such as Figure 6 Flowchart of an exemplary non-limiting method 1200 of a non-limiting system 600 of the invention that can provide a process for generating a synthetic spectrum that accurately defines a target component, the synthetic spectrum being based on the nearest neighbor known components. Figure 6 The non-limiting method 1200 is described with reference to the non-limiting system 500 of FIG. 1 , but the non-limiting method 1200 may also be applicable to other systems described herein, such as Figure 5 The non-limiting system 600 of FIG. For the sake of brevity, repeated descriptions of similar elements and / or processes employed in various embodiments are omitted.

[0191] At 1202, non-limiting method 1200 may include determining, by a system operatively coupled to a processor (e.g., determination component 214), a known component (e.g., known component 652) having a component (e.g., component 654) that is the same as a target component (e.g., target component 662 having component 664).

[0192] At 1204 , the non-limiting method 1200 can include determining, by the system (eg, the linear combination component 616 ), a known component based on identifying a linear combination of two or more other known components.

[0193] At 1206, non-limiting method 1200 may include determining by the system (e.g., determination component 614) whether the known component is a nearest neighbor (e.g., nearest neighbor 710) of the target component in a set of known components (e.g., known component set 650) (including the known components). In one or more embodiments, the nearest neighbor is a component whose component weight percentage is closer to the component weight percentage of the target component than other known components in the set of known components. If yes, then the method can proceed to step 1208. If not, then the method can return to step 1202.

[0194] At 1208 , the non-limiting method 1200 can include synthetically generating, by the system (eg, the synthesis component 618 ), a first synthetic spectrum of the target component (eg, the initial synthetic spectrum 665 ) by performing steps 1208A and 1208B.

[0195] At 1208A, the non-limiting method 1200 can include determining, by the system (eg, the synthesis component 618), a first sum of component weight percentages of components of the target component based on the weight percentage composition of the components of the target component.

[0196] 1208B, non-limiting method 1200 may include applying, by a system (e.g., synthesis component 618), a scaling of the components of a target component to the weight percent composition of the components of the target component based on results of a matrix correction procedure on a first sum, wherein the application results in generation of a scaled synthetic spectrum that is a first synthetic spectrum of the target component.

[0197] At 1210 , the non-limiting method 1200 can include synthetically generating, by the system (eg, the synthesis component 618 ), a synthetic spectrum of known composition (eg, the baseline synthetic spectrum 658 ) by performing steps 1210A and 1210B.

[0198] At 1210A, the non-limiting method 1200 can include determining, by the system (eg, the synthesis component 618 ), a first sum of component weight percentages of components of the known composition based on the weight percentage composition of the components of the known composition.

[0199] 1210B, non-limiting method 1200 may include applying, by a system (e.g., synthesis component 618), a scaling of the composition of the known component to the weight percent composition of the composition of the known component based on results of a matrix correction procedure on the first sum, wherein the application results in generation of a scaled synthetic spectrum that is a synthetic spectrum of the known component.

[0200] At 1212 , non-limiting method 1200 can include generating, by the system (eg, generation component 620 ), a second synthetic spectrum of the target component (eg, resulting synthetic spectrum 680 of target component 662 ) based on synthetic spectra of known components and the first synthetic spectrum of the target component.

[0201] At 1214 , the non-limiting method 1200 can include generating, by the system (eg, the generation component 620 ), a second synthetic spectrum of the target component based on the measured spectrum of the known component (eg, the measured spectrum 656 ).

[0202] At 1216, non-limiting method 1200 can include updating, by a system (eg, database maintenance component 622), data in a database of known components (eg, database 640) including the known components and a second synthetic spectrum of the target component.

[0203] At 1218, the non-limiting method 1200 can include determining, by the system (eg, determining component 614), a second synthetic spectrum of the target component as a second known component for determining a resulting synthetic spectrum of the second target component.

[0204] As another overview, refer to Fig.14 and Fig.15 , showing one or more embodiments described herein, for example Figure 6 Flowchart of an exemplary non-limiting method 1400 of a non-limiting system 600 of the present invention that can provide a process for synthesizing a corrected synthetic spectrum (e.g., the resulting synthetic spectrum 680) of a target component (e.g., the target component 662) by a system operatively coupled to a processor. Figure 6 The non-limiting method 1400 is described with reference to the non-limiting system 500 of FIG. 1 , but the non-limiting method 1400 may also be applicable to other systems described herein, such as Figure 5 The non-limiting system 600 of FIG. For the sake of brevity, repeated descriptions of similar elements and / or processes employed in various embodiments are omitted.

[0205] At 1402, the non-limiting method 1400 can include determining, by the system (e.g., the determining component 614), a known component (e.g., the known component 652) that includes the same components as the target component (e.g., the known component's component 654 and the target component's component 664). The known component can include atomic elements, phases of atomic elements, or combinations thereof.

[0206] At 1404, non-limiting method 1400 can include verifying by the system (e.g., determining component 614) whether an increment of a composition weight percentage of a known component and a composition weight percentage of a target component satisfies a selected threshold defining a selected composition relationship between the known component and the target component.

[0207] At 1406 , the non-limiting method 1400 can include determining, by the system (eg, determining component 614 ), the increment as an output of an error score for the difference in component weight percentages of the components of the target component and the components of the known component.

[0208] At 1408, non-limiting method 1400 may include determining by the system (e.g., determination component 614) whether the known component is a nearest neighbor (e.g., nearest neighbor 710) of the target component in a set of known components (e.g., known component set 650) (including the known components). In one or more embodiments, the nearest neighbor is a component whose component weight percentage is closer to the component weight percentage of the target component than other known components in the set of known components. If yes, then the method can proceed to step 1410. If yes, then the method can return to step 1402.

[0209] At 1410, non-limiting method 1400 may include generating, by a system (e.g., generation component 620), an intermediate synthetic relationship (e.g., intermediate synthetic relationship 678) that is a relationship defining increments of a synthetic spectrum of a known component (e.g., baseline synthetic spectrum 658) and increments of a first synthetic spectrum of a target component (e.g., initial synthetic spectrum 665).

[0210] At 1412, non-limiting method 1400 may include determining, by a system (e.g., generation component 620), an intermediate synthesis relationship based on a first photon quantity (e.g., first photon quantity 1001) of a portion of a synthetic spectrum of a known component and a second photon quantity (e.g., second photon quantity 1002) of a portion of the first synthetic spectrum of a target component.

[0211] At 1414 , the non-limiting method 1400 can include aggregating, by the system (eg, the generation component 620 ), the intermediate synthetic relationship and the measured spectra of the known components (eg, the measured spectra 656 ).

[0212] At 1416, non-limiting method 1400 may include aggregating, by the system (e.g., generation component 620), a third photon quantity (e.g., third photon quantity 1003) of a portion of the intermediate synthetic relationship and a fourth photon quantity (e.g., fourth photon quantity 1004) of a portion of a measured spectrum of known composition.

[0213] At 1418 , non-limiting method 1400 can include generating, by a system (eg, evaluation component 222 ), an aggregated synthetic spectrum (eg, aggregated synthetic spectrum 679 ) based on the aggregated and corrected synthetic spectrum as a target component (eg, resulting synthetic spectrum 680 ).

[0214] As another overview, refer to Fig.16 and Fig.17 , showing one or more embodiments described herein, for example Figure 6 600, which can provide a process for synthesizing a spectrum of a target component (e.g., target component 662) by a system operatively coupled to a processor. Figure 6 The non-limiting method 1600 is described with reference to the non-limiting system 500 of FIG. 1 , but the non-limiting method 1600 may also be applicable to other systems described herein, such as Figure 5 The non-limiting system 600 of FIG. For the sake of brevity, repeated descriptions of similar elements and / or processes employed in various embodiments are omitted.

[0215] At 1602, non-limiting method 1600 may include determining by the system (e.g., determination component 614) whether a known component (e.g., known component 652) is a nearest neighbor (e.g., nearest neighbor 710) of a target component in a set of known components (e.g., known component set 650) (including known components). In one or more embodiments, the nearest neighbor is a component whose component weight percentage is closer to the component weight percentage of the target component than other known components in the set of known components. If yes, then the method may proceed to step 1604. If no, then the method may return to step 1602.

[0216] At 1604, the non-limiting method 1600 may include determining, by the system (e.g., the generation component 620), an increment of a first photon count of a portion of a synthetic spectrum of a known component (e.g., the first photon count 1001 of the baseline synthetic spectrum 558 of the known component 652) and an increment of a second photon count of a portion of a first synthetic spectrum of a target component (e.g., the second photon count 1002 of the initial synthetic spectrum 665 of the target component 662), thereby generating an intermediate synthetic relationship (e.g., the intermediate synthetic relationship 678). In one or more embodiments, the portion of the synthetic spectrum of the known component represents a different energy range of the synthetic spectrum of the known component, and the portion of the synthetic spectrum of the target component represents a different energy range of the synthetic spectrum of the target component.

[0217] At 1606, non-limiting method 1600 may include determining an increment by determining, by a system (e.g., generation component 620), a difference between a first digital array indexed within a set of energy ranges and corresponding to a first number of photons of a portion of a synthetic spectrum of a known component and a second digital array indexed within the set of energy ranges and corresponding to a second number of photons of a portion of a first synthetic spectrum of a target component.

[0218] At 1608, the non-limiting method 1600 can include aggregating, by the system (e.g., the generation component 620), a third photon count of a portion of the intermediate composite relationship (e.g., the third photon count 1003) and a fourth photon count of a portion of the measured spectrum of the known component (e.g., the fourth photon count 1004 of the measured spectrum 656 of the known component 652). In one or more embodiments, the portion of the intermediate composite relationship represents a different energy range of the intermediate composite relationship, and the portion of the measured spectrum of the known component represents a different energy range of the measured spectrum of the known component.

[0219] At 1610 , non-limiting method 1600 can include determining, by the system (eg, determining component 614 ), that the measured spectrum has been measured from a sample of an established source of known composition.

[0220] At 1612, non-limiting method 1600 may include aggregating, by the system (e.g., generation component 620), a third digital array indexed within a set of energy ranges and corresponding to a third number of photons for a portion of the intermediate composite relationship and a fourth digital array indexed within the set of energy ranges and corresponding to a fourth number of photons for a portion of a measured spectrum of a known composition.

[0221] At 1614 , the non-limiting method 1600 can include generating, by the system (eg, the generating component 620 ), a second synthetic spectrum of the target component (eg, the resulting synthetic spectrum 680 ) based on the aggregation results.

[0222] Additional Invention Summary

[0223] For simplicity of explanation, the computer-implemented and non-computer-implemented methods provided herein are depicted and / or described as a series of actions. It should be understood that the present invention is not limited by the actions and / or action sequences shown, such as actions can occur in one or more sequences and / or occur simultaneously, and occur together with other actions not presented and described herein. In addition, not all actions shown can be used to implement the computer-implemented and non-computer-implemented methods according to the subject matter. In addition, the computer-implemented and non-computer-implemented methods can alternatively be represented as a series of interrelated states via state diagrams or events. In addition, the computer-implemented methods described hereinafter and throughout this specification can be stored in a manufactured product so that the computer-implemented methods are transported and transferred to a computer. The term "article" as used herein is intended to cover a computer program accessible from any computer-readable device or storage medium.

[0224] The system and / or device has been (and / or will be further) described herein about the interaction between one or more components. Such systems and / or components may include those components or subcomponents specified therein, one or more of the specified components and / or subcomponents and / or additional components. Subcomponents may be implemented as components that are communicatively coupled to other components rather than being included in a parent component. One or more components and / or subcomponents may be combined into a single component that provides aggregate functionality. These components may interact with one or more other components, which are not specifically described herein for the sake of brevity, but are known to those skilled in the art.

[0225] In summary, one or more systems, computer program products, and / or computer-implemented methods provided herein relate to synthetically generating a synthetic spectrum of a target component. The system may include a memory storing computer executable components and a processor executing the computer executable components stored in the memory. The computer executable components may include a determination component and a generation component, the determination component determining a known component having the same component as the target component, and the generation component generating a second synthetic spectrum of the target component based on the synthetic spectrum of the known component and the first synthetic spectrum of the target component.

[0226] In fact, in view of one or more embodiments described herein, practical application of one or more systems, computer-implemented methods and / or computer program products described herein can quickly and efficiently generate a synthetic spectrum of a target component, even in cases where the measured spectrum of such a target component would include two or more distinct peaks. One or more embodiments described herein can improve existing methods to achieve the technical advantage of identifying material components with high accuracy by using known components, particularly their measured and synthetically generated spectra, as a baseline for generating a high-precision spectrum that defines the target component. Then, by using this synthetically generated spectrum (i.e., a non-measured spectrum), the target component can be accurately identified. This can be achieved without having to generate a measured spectrum of the target component from a sample of a given source, because the source of the target component itself has not yet been determined.

[0227] Furthermore, in one or more embodiments, the synthetically generated spectrum can be used for the next known component to subsequently identify the subsequent target component. That is, a synthetically generated spectral library can be established and used for further material analysis purposes, thereby enabling the system to self-improve and more accurately analyze and identify target components.

[0228] These are useful and practical applications of computers, providing enhanced (e.g., improved and / or optimized) data analysis and predictive outputs. In general, such computerized tools can bring concrete and significant technical improvements in the field of materials analysis, and more specifically in materials analysis using EDS.

[0229] In addition, based on the disclosed teachings, one or more embodiments described herein can be employed in real-world systems. For example, as described above, even when the measured spectrum of the target component has multiple distinct peaks, one or more embodiments described herein can accurately identify the target component and enable the spectrum of the target component to be generated in a synthetic manner. Thus, the embodiments disclosed herein provide improvements to scientific instrument technology (e.g., improvements in computer technology to support such scientific instruments, and other improvements).

[0230] Furthermore, the apparatus and / or methods described herein can be implemented in one or more domains to achieve a scaled synthetic spectral output. Indeed, use of the systems described herein can be scalable, such as where multiple input target components can be evaluated and multiple resulting synthetic spectra of the target components can be generated at least partially simultaneously.

[0231] The system and / or device has been (and / or will be further) described herein about the interaction between one or more components. Such systems and / or components may include those components or subcomponents specified therein, one or more of the specified components and / or subcomponents and / or additional components. Subcomponents may be implemented as components that are communicatively coupled to other components rather than being included in a parent component. One or more components and / or subcomponents may be combined into a single component that provides aggregate functionality. These components may interact with one or more other components, which are not specifically described herein for the sake of brevity, but are known to those skilled in the art.

[0232] One or more embodiments described herein may be inherently and / or unavoidably related to computer technology in one or more embodiments and cannot be implemented outside of a computing environment. For example, one or more processes performed by one or more embodiments described herein may provide program and / or program instruction execution more efficiently and more feasible than existing systems and / or technologies, such as with respect to material analysis based on nearest neighbor known components and their spectra. Systems, computer-implemented methods, and / or computer program products that provide the performance of these processes have an important role in the field of material analysis (such as including EDS) and cannot be implemented in a reasonable manner outside of a computing environment as well.

[0233] One or more embodiments described herein may employ hardware and / or software to solve problems that are highly technical, non-abstract, and cannot be performed by a human through a set of mental activities. For example, one or even thousands of humans cannot effectively, efficiently, and / or accurately automatically determine the nearest neighbor known components, generate an initial synthetic spectrum, and / or generate a resulting synthetic spectrum without a material sample of known origin, and one or more embodiments described herein may provide such a process. Furthermore, one or more of these processes cannot be performed by a human or a person holding a pen and paper, as performed by one or more embodiments described herein.

[0234] In one or more embodiments, one or more of the processes described herein can be performed by one or more special-purpose computers (e.g., special-purpose processing units, special-purpose classical computers, special-purpose quantum computers, special-purpose hybrid classical / quantum systems, and / or another type of special-purpose computer) to perform defined tasks associated with one or more of the above-mentioned technologies. One or more embodiments described herein and / or components thereof can be used to solve new problems arising from the advancement of the above-mentioned technologies, the adoption of quantum computing systems, cloud computing systems, computer architectures, and / or other technologies.

[0235] One or more embodiments described herein may be fully operable to perform one or more other functions (eg, fully powered on, fully executed, and / or another function) while also performing one or more of the one or more operations described herein.

[0236] To provide additional inventive context, a list of embodiments and their features is provided below.

[0237] A system comprises a memory storing computer executable components; and a processor executing the computer executable components stored in the memory, wherein the computer executable components comprise: a determination component determining a known component having the same composition as a target component; and a generation component generating a second synthetic spectrum of the target component based on a synthetic spectrum of the known component and a first synthetic spectrum of the target component.

[0238] The system of the preceding paragraphs, wherein the generating component further generates the second synthetic spectrum of the target component based on the measured spectrum of the known component.

[0239] A system according to any of the preceding paragraphs, wherein the determination component determines the known ingredient as a nearest neighbor to the target ingredient in a set of known ingredients including the known ingredient, and wherein the nearest neighbor is an ingredient whose weight percentage is closer to the weight percentage of the target ingredient than other known ingredients in the set of known ingredients.

[0240] The system of any preceding paragraph, further comprising a linear combination component that determines the known component also based on identifying a linear combination of two or more other known components.

[0241] The system described in any of the preceding paragraphs also includes: a synthesis component that synthetically generates the first synthetic spectrum of the target component by performing steps including: determining a first sum of the component weight percentages of the components of the target component based on the weight percent composition of the components of the target component; and applying a scaling ratio of the components of the target component to the weight percent composition of the components of the target component based on the results of a matrix correction procedure on the first sum, wherein the application results in the generation of a scaled synthetic spectrum, and the scaled synthetic spectrum is the first synthetic spectrum of the target component.

[0242] The system described in any of the preceding paragraphs also includes: a synthesis component that synthetically generates the synthetic spectrum of the known component by performing steps including: determining a first sum of the component weight percentages of the components of the known component based on the weight percent composition of the components of the known component; and applying a scaling ratio of the components of the known component to the weight percent composition of the components of the known component based on the results of a matrix correction procedure on the first sum, wherein the application results in the generation of a scaled synthetic spectrum, and the scaled synthetic spectrum is the synthetic spectrum of the known component.

[0243] The system according to any of the preceding paragraphs further includes: a database maintenance component, which updates data in a database of known components, the data including the measured spectrum and the synthetic spectrum of the known component and the second synthetic spectrum of the target component, wherein the determination component determines the second synthetic spectrum of the target component as a second known component to determine a resulting synthetic spectrum of a second target component.

[0244] A computer-implemented method includes synthesizing a corrected synthetic spectrum of a target component by a system operatively coupled to a processor, including: generating, by the system, an intermediate synthetic relationship, the intermediate synthetic relationship being a relationship defining increments of a synthetic spectrum of a known component and increments of a first synthetic spectrum of the target component; aggregating, by the system, the intermediate synthetic relationship and a measured spectrum of the known component; and generating, by the system, an aggregated synthetic spectrum based on the aggregation and as the corrected synthetic spectrum of the target component.

[0245] A computer-implemented method according to the preceding paragraph, wherein the known composition comprises a component that is the same as the target composition, and wherein the same component comprises an atomic element, a molecular element, a phase of an atomic or molecular element, or a combination thereof.

[0246] The computer-implemented method described in any of the preceding paragraphs also includes: the system determining the known component as the nearest neighbor of the target component in a set of known components including the known component, and wherein the nearest neighbor is a component whose component weight percentage is closer to the component weight percentage of the target component than other known components in the set of known components.

[0247] A computer-implemented method according to any preceding paragraph, wherein generating the intermediate synthetic relationship comprises determining the intermediate synthetic relationship based on a first photon count of the portion of the synthetic spectrum of the known component and a second photon count of the portion of the first synthetic spectrum of the target component.

[0248] A computer-implemented method as described in any preceding paragraph, wherein the aggregating further comprises aggregating a third number of photons that is part of the intermediate composite relation and a fourth number of photons that is part of the measured spectrum of the known component.

[0249] The computer-implemented method described in any of the preceding paragraphs further includes: verifying by the system whether the increment of the component weight percentage of the known component and the increment of the component weight percentage of the target component meet a selected threshold defining a selected component relationship between the known component and the target component.

[0250] A computer-implemented method according to any preceding paragraph, wherein the synthesis of the spectrum of the target component further comprises: the system determining the increment as an output of an error score for the difference in composition weight percentages of the composition of the target component and the composition of the known component.

[0251] A computer program product facilitates a process for synthesizing a spectrum of a target component, wherein the program instructions are executable by a processor to cause the processor to: synthesize the spectrum of the target component by the processor, wherein the synthesis includes causing the processor to: determine by the processor an increment of a first number of photons of a portion of a synthesized spectrum of a known component and an increment of a second number of photons of a portion of the first synthesized spectrum of the target component, thereby obtaining an intermediate synthetic relationship; aggregate by the processor a third number of photons of a portion of the intermediate synthetic relationship and a fourth number of photons of a portion of a measured spectrum of the known component; and generate by the processor a second synthetic spectrum of the target component based on the result of the aggregation.

[0252] A computer program product according to the preceding paragraphs, wherein the portion of the synthetic spectrum of the known component represents different energy ranges of the synthetic spectrum of the known component, wherein the portion of the synthetic spectrum of the target component represents different energy ranges of the synthetic spectrum of the target component, wherein the portion of the intermediate synthetic relationship represents different energy ranges of the intermediate synthetic relationship, and wherein the portion of the measured spectrum of the known component represents different energy ranges of the measured spectrum of the known component.

[0253] A computer program product according to any preceding paragraph, wherein determining the increment comprises determining a difference between a first digital array indexed within a set of energy ranges and corresponding to the first number of photons in the portion of the synthetic spectrum of the known component and a second digital array indexed within the set of energy ranges and corresponding to the second number of photons in the portion of the first synthetic spectrum of the target component.

[0254] A computer program product according to any preceding paragraph, wherein the aggregation comprises determining an aggregation of a third digital array and a fourth digital array, wherein the third digital array is indexed within the set of energy ranges and corresponds to the third number of photons of the portion of the intermediate synthetic relationship, and the fourth digital array is indexed within the set of energy ranges and corresponds to the fourth number of photons of the portion of the measured spectrum of the known component.

[0255] A computer program product according to any preceding paragraph, wherein the measured spectrum has been measured from a sample of a known source of the known composition.

[0256] A computer program product according to any preceding paragraph, wherein the program instructions are further executable by the processor to cause the processor to:

[0257] The processor determines the known component as a nearest neighbor to the target component in a set of known components including the known component, and wherein the nearest neighbor is a component whose component weight percentage is closer to the component weight percentage of the target component than other known components in the set of known components.

[0258] Scientific Instrument System Description

[0259] Next turn Fig.18 , which provides Figures 1 to 17 A detailed description of the additional context of one or more embodiments described in . One or more computing devices implementing any of the scientific instrument modules or methods disclosed herein may be part of a scientific instrument system. Fig.18 1800, in which one or more scientific instrument methods or other methods disclosed herein may be performed according to various embodiments described herein. Figure 1 The scientific instrument module 100 and Figure 2 The method 200) may be implemented by one or more of the scientific instrument 1810, the user local computing device 1820, the service local computing device 1830 and / or the remote computing device 1840 of the scientific instrument system 1800.

[0260] Any of the scientific instrument 1810, the user local computing device 1820, the service local computing device 1830, and / or the remote computing device 1840 may include the Figure 4 Any of the embodiments of computing device 400 discussed herein, and any of scientific instrument 1810, user local computing device 1820, service local computing device 1830, and / or remote computing device 1840 may employ any of the embodiments described herein. Figure 4 Any suitable form of one or more of the embodiments of computing device 400 discussed.

[0261] One or more of the scientific instrument 1810, the user local computing device 1820, the service local computing device 1830, and / or the remote computing device 1840 may include a processing device 1802, a storage device 1804, and / or an interface device 1806. The processing device 1802 may take any suitable form, including reference Figure 4 The processor 402 discussed herein may be in the form of any processor. The processing device 1802 in different devices including the scientific instrument 1810, the user local computing device 1820, the service local computing device 1830 and / or the remote computing device 1840 may take the same form or different forms. The storage device 1804 may take any suitable form, including reference Figure 4 The storage device 1804 in different devices included in the scientific instrument 1810, the user local computing device 1820, the service local computing device 1830 and / or the remote computing device 1840 can take the same form or different forms. The interface device 1806 can take any suitable form, including reference Figure 4 Any form of storage device in interface device 406 discussed herein. Interface devices 1806 in different devices included in scientific instrument 1810, user local computing device 1820, service local computing device 1830, and / or remote computing device 1840 may take the same form or different forms.

[0262] The scientific instrument 1810, the user local computing device 1820, the service local computing device 1830, and the remote computing device 1840 can communicate with other elements of the scientific instrument system 1800 via a communication path 1808. The communication path 1808 can communicatively couple the interface device 1806 of different elements of the elements of the scientific instrument system 1800, as shown, and can be a wired or wireless communication path (e.g., according to the present reference Figure 4 The interface device 406 of the computing device 400 may be any of the communication technologies discussed above). Fig.18The particular scientific instrument system 1800 depicted in FIG. 1 includes a communication path between each pair of devices in the scientific instrument 1810, the user local computing device 1820, the service local computing device 1830, and the remote computing device 1840, but this "fully connected" embodiment is merely illustrative, and in various embodiments, various communication paths in the communication paths 1808 may be omitted. For example, in one or more embodiments, the service local computing device 1830 may omit a direct communication path 1808 between its interface device 1806 and the interface device 1806 of the scientific instrument 1810, and may instead communicate with the scientific instrument 1810 via the communication path 1808 between the service local computing device 1830 and the user local computing device 1820 and / or the communication path 1808 between the user local computing device 1820 and the scientific instrument 1810.

[0263] Scientific instrument 1810 may include any suitable scientific instrument, such as an EDS device or a device associated with or connected to an EDS device.

[0264] The user-local computing device 1820 can be a computing device local to a user of the scientific instrument 1810 (e.g., according to any of the embodiments of the computing device 400 discussed herein). In one or more embodiments, the user-local computing device 1820 can also be local to the scientific instrument 1810, but this is not necessarily the case; for example, a user-local computing device 1820 associated with a home, office, or other building associated with a user entity can be remote from the scientific instrument 1810, but in communication with it so that the user entity can use the user-local computing device 1820 to control and / or access data from the scientific instrument 1810. In one or more embodiments, the user-local computing device 1820 can be a laptop, smartphone, or tablet device. In one or more embodiments, the user-local computing device 1820 can be a portable computing device. In one or more embodiments, the user-local computing device 1820 can be deployed in the field.

[0265] The service local computing device 1830 can be a computing device local to the entity serving the scientific instrument 1810 (e.g., according to any of the embodiments of the computing device 400 discussed herein). For example, the service local computing device 1830 can be a device local to the manufacturer of the scientific instrument 1810 or to a third-party service company. In one or more embodiments, the service local computing device 1830 can communicate with the scientific instrument 1810, the user local computing device 1820, and / or the remote computing device 1840 (e.g., via a direct communication path 1808 or via multiple "indirect" communication paths 1808, as described above) to receive data regarding the operation of the scientific instrument 1810, the user local computing device 1820, and / or the remote computing device 1840 (e.g., self-test results of the scientific instrument 1810, calibration coefficients used by the scientific instrument 1810, measurements of sensors associated with the scientific instrument 1810, etc.). In one or more embodiments, the service local computing device 1830 may communicate with the scientific instrument 1810, the user local computing device 1820, and / or the remote computing device 1840 (e.g., via a direct communication path 1808 or via multiple "indirect" communication paths 1808, as described above) to transfer data to the scientific instrument 1810, the user local computing device 1820, and / or the remote computing device 1840 (e.g., to update programming instructions (such as firmware) in the scientific instrument 1810 to initiate the execution of a test or calibration sequence in the scientific instrument 1810, to update programming instructions (such as software) in the user local computing device 1820 or the remote computing device 1840, etc.). A user entity of the scientific instrument 1810 may utilize the scientific instrument 1810 or the user local computing device 1820 to communicate with the service local computing device 1830 to report problems with the scientific instrument 1810 or the user local computing device 1820, to request a technician visit to improve the operation of the scientific instrument 1810, to order consumables or replacement parts associated with the scientific instrument 1810, or for other purposes.

[0266] Remote computing device 1840 can be a computing device remote from scientific instrument 1810 and / or user local computing device 1820 (e.g., according to any of the embodiments of computing device 400 discussed herein). In one or more embodiments, remote computing device 1840 can be included in a data center or other large-scale server environment. In one or more embodiments, remote computing device 1840 can include network attached storage (e.g., as part of storage device 1804). Remote computing device 1840 can store data generated by scientific instrument 1810, perform analysis of data generated by scientific instrument 1810 (e.g., according to programmed instructions), facilitate communication between user local computing device 1820 and scientific instrument 1810, and / or facilitate communication between service local computing device 1830 and scientific instrument 1810.

[0267] In one or more embodiments, the Fig.18 One or more of the elements of the scientific instrument system 1800 shown in FIG. In addition, in one or more embodiments, there may be Fig.18 1800. For example, the scientific instrument system 1800 may include multiple user-local computing devices 1820 (e.g., different user-local computing devices 1820 associated with different user entities or located in different locations). In another example, the scientific instrument system 1800 may include multiple scientific instruments 1810, all of which communicate with the service local computing device 1830 and / or the remote computing device 1840; in such an embodiment, the service local computing device 1830 may monitor these multiple scientific instruments 1810, and the service local computing device 1830 may cause updates or other information to be "broadcasted" to multiple scientific instruments 1810 simultaneously. Different scientific instruments 1810 in the scientific instrument system 1800 may be close to each other (e.g., in the same room) or far away from each other (e.g., on different floors of a building, in different buildings, in different cities, etc.). In one or more embodiments, the scientific instrument 1810 may be connected to an Internet of Things (IoT) stack that allows command and control of the scientific instrument 1810 through web-based applications, virtual or augmented reality applications, mobile applications, and / or desktop applications. Any of these applications may be accessed by a user entity operating a user-local computing device 1820 that communicates with the scientific instrument 1810 through an intermediary remote computing device 1840. In one or more embodiments, the scientific instrument 1810 may be sold by a manufacturer along with one or more associated user-local computing devices 1820 as part of a local scientific instrument computing unit 1812.

[0268] In one or more embodiments, the different scientific instruments 1810 included in the scientific instrument system 1800 can be different types of scientific instruments 1810; for example, one scientific instrument 1810 can be an EDS device, while another scientific instrument 1810 can be an analysis device that analyzes the results of the EDS device. In some such embodiments, the remote computing device 1840 and / or the user's local computing device 1820 can combine data from different types of scientific instruments 1810 included in the scientific instrument system 1800.

[0269] Exemplary Operating Environment

[0270] Fig.1919 is a schematic block diagram of an operating environment 1900 with which the subject matter can interact. The operating environment 1900 includes one or more remote components 1910. The remote component 1910 can be hardware and / or software (e.g., a thread, a process, a computing device). In one or more embodiments, the remote component 1910 can be a distributed computer system connected to a local automatic extension component and / or a program using resources of the distributed computer system via a communication framework 1940. The communication framework 1940 can include wired network devices, wireless network devices, mobile devices, wearable devices, radio access network devices, gateway devices, femtocell devices, servers, etc.

[0271] The operating environment 1900 also includes one or more local components 1920. The local components 1920 can be hardware and / or software (e.g., threads, processes, computing devices). In one or more embodiments, the local components 1920 can include an auto-scaling component and / or a program that connects to a remote distributed computing system via a communication framework 1940 to communicate / use remote resources 1910 and 1920, etc.

[0272] One possible communication between the remote component 1910 and the local component 1920 may take the form of a data packet suitable for transmission between two or more computer processes. Another possible communication between the remote component 1910 and the local component 1920 may take the form of circuit-switched data suitable for transmission between two or more computer processes in a radio time slot. The operating environment 1900 includes a communication framework 1940, which can be used to facilitate communication between the remote component 1910 and the local component 1920, and may include an air interface, such as a UMTS network interface via an LTE network, etc. The remote component 1910 may be operably connected to one or more remote data repositories 1950, such as a hard drive, a solid-state drive, a subscriber identity module (SIM) card, an electronic SIM (eSIM), a device memory, etc., which can be used to store information on the remote component 1910 side of the communication framework 1940. Similarly, the local component 1920 may be operably connected to one or more local data repositories 1930, which can be used to store information on the local component 1920 side of the communication framework 1940.

[0273] Exemplary Computing Environment

[0274] To provide additional context for the various embodiments described herein, Fig. 20The following discussion is intended to provide a brief, general description of a suitable computing environment 2000 in which various embodiments of the embodiments described herein may be implemented. Although the embodiments have been described above in the general context of computer-executable instructions that may be executed on one or more computers, those skilled in the art will recognize that the embodiments may also be implemented in conjunction with other program modules and / or as a combination of hardware and software.

[0275] Generally, program modules include routines, programs, components, data structures, etc. that perform tasks or implement abstract data types. In addition, these methods can be practiced with other computer system configurations, including single-processor or multi-processor computer systems, minicomputers, mainframe computers, Internet of Things (IoT) devices, distributed computing systems, and personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc., each of which can be operably coupled to one or more associated devices.

[0276] The embodiments shown herein can also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0277] Computing devices typically include various media, which may include computer-readable storage media, machine-readable storage media, and / or communication media, the two terms being used differently in this article as shown below. Computer-readable storage media or machine-readable storage media can be any available storage media that can be accessed by a computer, and include volatile and non-volatile media, removable and non-removable media. By way of example and not limitation, computer-readable storage media or machine-readable storage media can be implemented in conjunction with any method or technology for storing information (e.g., computer-readable or machine-readable instructions, program modules, structured data, or unstructured data).

[0278] Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD ROM), digital versatile disc (DVD), Blu-ray disc (BD) or other optical disc storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage, solid-state drives or other solid-state storage devices, or other tangible and / or non-transitory media that can be used to store the desired information. In this regard, the terms "tangible" or "non-transitory" as applied to storage, memory, or computer-readable media herein exclude only the propagation of transient signals themselves as a modifier, and do not disclaim all rights to standard storage, memory, or computer-readable media that are not merely propagation of transient signals themselves.

[0279] Computer-readable storage media may be accessed by one or more local or remote computing devices, such as via access requests, queries, or other data retrieval protocols, for various operations regarding the information stored by the media.

[0280] Communication media typically includes computer readable instructions, data structures, program modules, or other structured or unstructured data in a data signal (such as a modulated data signal, such as a carrier wave or other transport mechanism), and includes any information delivery or transmission media. The term "modulated data signal" or signal refers to a signal whose one or more characteristics are set or changed so as to encode information in one or more signals. By way of example, but not limitation, communication media include wired media (such as a wired network or direct-wired connection) and wireless media (such as acoustic, RF, infrared and other wireless media).

[0281] Still refer to Fig. 20 , an exemplary computing environment 2000 in which one or more embodiments described herein may be implemented includes a computer 2002, which includes a processing unit 2004, a system memory 2006, and a system bus 2008. The system bus 2008 couples system components including, but not limited to, the system memory 2006 to the processing unit 2004. The processing unit 2004 may be any of a variety of commercially available processors. Dual microprocessors and other multi-processor architectures may also be used as the processing unit 2004.

[0282] The system bus 2008 may be any of several types of bus structures and may further interconnect with a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory 2006 includes ROM 2010 and RAM 2012. A basic input / output system (BIOS) may be stored in a nonvolatile memory such as ROM, erasable programmable read-only memory (EPROM), EEPROM, wherein the BIOS contains basic routines such as those that facilitate the transfer of information between elements within the computer 2002 during startup. RAM 2012 may also include high-speed RAM such as static RAM for caching data.

[0283] The computer 2002 also includes an internal hard disk drive (HDD) 2014 (e.g., EIDE, SATA), and may include one or more external storage devices 2016 (e.g., magnetic floppy disk drive (FDD) 2016, memory stick or flash drive reader, memory card reader, etc.). Although the internal HDD 2014 is shown as being located within the computer 2002, the internal HDD 2014 may also be configured for external use in a suitable chassis (not shown). In addition, although not shown in the computing environment 2000, a solid state drive (SSD) may be used to supplement or replace the HDD 2014.

[0284] Other internal or external storage devices may include at least one other storage device 2020 having a storage medium 2022 (e.g., a solid-state storage device, a non-volatile memory device, and / or an optical drive that can read or write from a removable medium (such as a CD-ROM disk, a DVD, a BD, etc.). The external storage device 2016 can be facilitated by a network virtual machine. The HDD 2014, the external storage device 2016, and the storage device (e.g., a drive) 2020 can be connected to the system bus 2008 via a HDD interface 2024, an external storage interface 2026, and a drive interface 2028, respectively.

[0285] The drives and their associated computer-readable storage media provide non-volatile storage of data, data structures, computer-executable instructions, etc. For the computer 2002, the drives and storage media accommodate the storage of any data in a suitable digital format. Although the above description of computer-readable storage media refers to the corresponding type of storage device, other types of storage media that can be read by a computer (whether currently existing or developed in the future) can also be used in the exemplary operating environment, and further, any such storage media can contain computer-executable instructions for performing the methods described herein.

[0286] A number of program modules may be stored in the drives and RAM 2012, including an operating system 2030, one or more applications 2032, other program modules 2034, and program data 2036. All or portions of the operating system, applications, modules, and / or data may also be cached in RAM 2012. The systems and methods described herein may be implemented using various commercially available operating systems or combinations of operating systems.

[0287] Computer 2002 may optionally include emulation technology. For example, a virtual machine hypervisor (not shown) or other intermediary may emulate the hardware environment for operating system 2030, and the emulated hardware may optionally be different from the hardware environment of the operating system 2030. Fig. 20 . In such an embodiment, operating system 2030 may include one of a plurality of virtual machines (VMs) hosted at computer 2002. In addition, operating system 2030 may provide a runtime environment, such as a Java runtime environment or a .NET framework, for application 2032. A runtime environment is a consistent execution environment that allows application 2032 to run on any operating system that includes a runtime environment. Similarly, operating system 2030 may support containers, and application 2032 may be in the form of containers, which are lightweight, independent, executable software packages that include, for example, application code, runtime, system tools, system libraries, and settings.

[0288] In addition, the computer 2002 may be equipped with a security module, such as a trusted processing module (TPM). For example, using a TPM, the boot component hashes the next boot component in time and waits for the result to match the security value before loading the next boot component. This process may be performed at any layer in the code execution stack of the computer 2002, for example, applied to the application execution level or the operating system (OS) kernel level, thereby achieving security of code execution at any level.

[0289] A user entity may enter commands and information into the computer 2002 through one or more wired / wireless input devices (e.g., a keyboard 2038, a touch screen 2040, and a pointing device such as a mouse 2042). Other input devices (not shown) may include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control or other remote control, a joystick, a virtual reality controller and / or a virtual reality headset, a game controller, a stylus, an image input device (e.g., a camera), a gesture sensor input device, a visual movement sensor input device, an emotion or facial detection device, a biometric input device (e.g., a fingerprint or iris scanner), etc. These and other input devices are typically connected to the processing unit 2004 through an input device interface 2044 that may be coupled to the system bus 2008, but may also be connected through other interfaces such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, a Interfaces, etc.

[0290] A monitor 2046 or other type of display device may also be connected to the system bus 2008 via an interface, such as a video adapter 2048. In addition to the monitor 2046, computers typically include other peripheral output devices (not shown), such as speakers, printers, and the like.

[0291] The computer 2002 can operate in a network environment, using logical connections to one or more remote computers, such as remote computer 2050, through wired and / or wireless communications. The remote computer 2050 can be a workstation, server computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer device, or other common network node, and typically includes many or all of the elements described relative to the computer 2002, but only a memory / storage device 2052 is shown for simplicity. The depicted logical connections include wired / wireless connections to a local area network (LAN) 2054 and / or a larger network, such as a wide area network (WAN) 2056. Such LAN and WAN networking environments are common in offices and companies and facilitate the establishment of enterprise-wide computer networks (e.g., intranets), all of which can be connected to a global communication network (e.g., the Internet).

[0292] When used in a LAN networking environment, the computer 2002 can be connected to the local network 2054 through a wired and / or wireless communication network interface or adapter 2058. The adapter 2058 can facilitate wired or wireless communication with the LAN 2054, which can also include a wireless access point (AP) disposed thereon for communicating with the adapter 2058 in a wireless mode.

[0293] When used in a WAN networking environment, the computer 2002 may include a modem 2060 or may be connected to a communication server on the WAN 2056 via other means to establish communications via the WAN 2056, such as via the Internet. The modem 2060 may be internal or external, wired or wireless, and may be connected to the system bus 2008 via the input device interface 2044. In a network environment, program modules shown relative to the computer 2002 or portions thereof may be stored in the remote memory / storage device 2052. The network connections shown are examples only, and other means of establishing a communications link between the computers may be used.

[0294] When used in a LAN or WAN networking environment, the computer 2002 can access a cloud storage system or other network-based storage system in addition to or as an alternative to the external storage device 2016 described above. Generally speaking, the connection between the computer 2002 and the cloud storage system can be established through the LAN 2054 or WAN 2056, such as through an adapter 2058 or a modem 2060, respectively. When the computer 2002 is connected to the associated cloud storage system, the external storage interface 2026 can manage the storage provided by the cloud storage system with the help of the adapter 2058 and / or the modem 2060, just as it manages other types of external storage. For example, the external storage interface 2026 can be configured to provide access to cloud storage sources as if those sources were physically connected to the computer 2002.

[0295] The computer 2002 is operable to communicate with any wireless device or entity that operates in a wireless communication manner (e.g., printers, scanners, desktop and / or portable computers, portable data assistants, communication satellites, any device or location associated with a wirelessly detectable tag (e.g., kiosks, newsstands, store shelves, etc.), and telephones). This may include Wireless Fidelity (Wi-Fi) and Wireless technology. Therefore, the communication can be a defined structure like existing networks, or just an ad hoc communication between at least two devices.

[0296] Additional closing information

[0297] The embodiments described herein may be directed to one or more of the systems, methods, devices and / or computer program products at any possible level of technical detail integration. A computer program product may include a computer-readable storage medium (or multiple media) having a computer-readable program instruction thereon, and the computer-readable program instruction is used to cause a processor to perform various aspects of one or more embodiments described herein. A computer-readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a superconducting storage device and / or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media may also include the following: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device (such as a punch card or a raised structure in a groove with instructions recorded thereon) and / or any suitable combination of the above. Computer-readable storage media as used herein should not be interpreted as transient signals themselves, such as radio waves and / or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides and / or other transmission media (e.g., light pulses transmitted through fiber optic cables), and / or electrical signals transmitted through wires.

[0298] Computer-readable program instructions as described herein can be downloaded from computer-readable storage media to corresponding computing / processing equipment and / or downloaded to external computers or external storage devices via a network (e.g., the Internet, local area network, wide area network and / or wireless network). The network may include copper transmission cables, optical transmission optical fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. Network adapter cards or network interfaces in each computing / processing equipment receive computer-readable program instructions from the network, and forward computer-readable program instructions to be stored in computer-readable storage media in corresponding computing / processing equipment. Computer-readable program instructions for performing the operation of one or more embodiments described herein can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcodes, firmware instructions, state setting data, configuration data for integrated circuits and / or source code and / or object code written in any combination of one or more programming languages ​​(including object-oriented programming languages, such as Smalltalk, C++, etc.) and / or process programming languages ​​(such as "C" programming languages ​​and / or similar programming languages). Computer readable program instructions can be executed entirely on a computer, partially on a computer, executed as an independent software package, partially on a computer and / or partially on a remote computer or entirely on a remote computer and / or server. In the latter case, the remote computer can be connected to the computer and / or can be connected to an external computer (for example, using an Internet service provider through the Internet) through any type of network (including a local area network (LAN) and / or a wide area network (WAN)). In one or more embodiments, an electronic circuit (including, for example, a programmable logic circuit, a field programmable gate array (FPGA) and / or a programmable logic array (PLA)) can execute a computer readable program instruction to personalize the electronic circuit by utilizing the state information of the computer readable program instruction, so as to perform the various aspects of one or more embodiments described herein.

[0299] Reference is made to the flowchart illustration and / or block diagram of the method, device (system) and computer program product according to one or more embodiments described herein to describe various aspects of one or more embodiments described herein. It should be understood that each frame in the flowchart illustration and / or block diagram and the combination of frames in the flowchart illustration and / or block diagram can be realized by computer-readable program instructions. These computer-readable program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer and / or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device can create a device for realizing the function / action specified in one or more frames of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, which can guide a computer, a programmable data processing device and / or other equipment to run in a specific manner, and the computer-readable storage medium storing instructions can include a manufactured product, which includes instructions for various aspects of the function / action specified in one or more frames of the flowchart and / or block diagram. Computer-readable program instructions may also be loaded onto a computer, other programmable data processing devices, and / or other devices to cause a series of operational actions to be performed on the computer, other programmable devices, and / or other devices, thereby producing a computer-implemented process, so that the instructions executed on the computer, other programmable devices, and / or other devices implement the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0300] The flow chart and block diagram in the figure show the possible architecture, function and / or operation of the system, computer-implemented method and / or computer program product according to one or more embodiments described herein. In this regard, each frame in the flow chart or block diagram can represent a module, segment and / or partial instruction, which contains one or more executable instructions for realizing the specified logical function. In one or more alternative embodiments, the function marked in the frame may not occur in the order marked in the figure. For example, two frames displayed continuously can be executed substantially at the same time, and / or sometimes can be executed in reverse order, depending on the function involved. It should also be noted that each block in the block diagram and / or flow chart, and / or the combination of the blocks in the block diagram and / or flow chart can be realized by a dedicated hardware system, which can perform the specified function and / or action, and / or perform one or more combinations of dedicated hardware and / or computer instructions.

[0301] Although the subject matter has been described above in the general context of computer executable instructions of computer program products running on computers and / or computers, it will be appreciated by those skilled in the art that one or more embodiments herein may also be implemented at least in part in parallel with one or more other program modules. In general, program modules include routines, programs, components and / or data structures that perform specific tasks and / or implement specific abstract data types. In addition, the aforementioned computer-implemented methods may be practiced with other computer system configurations, including single-processor and / or multi-processor computer systems, small computing devices, large computers, and computers, handheld computing devices (e.g., PDAs, phones) and / or based on microprocessors or programmable consumer and / or industrial electronic devices. The various aspects shown may also be practiced in a distributed computing environment, where tasks are performed by remote processing devices connected via a communication network. However, one or more (if not all) aspects of one or more embodiments described herein may be practiced on a stand-alone computer. In a distributed computing environment, program modules may be located in both local memory storage devices and remote memory storage devices.

[0302] As used in this application, the terms "component", "system", "platform" and / or "interface" may refer to and / or may include computer-related entities or entities related to an operating machine having one or more specific functions. The entities described herein may be hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to, a process, a processor, an object, an executable program, a thread of execution, a program, and / or a computer running on a processor. As an example, both an application running on a server and a server may be a component. One or more components may reside in a process and / or an execution thread, and a component may be located on a computer and / or distributed between two or more computers. In another example, corresponding components may be executed from various computer-readable media having various data structures stored thereon. These components may communicate via local and / or remote processes, such as according to signals having one or more data packets (e.g., data from a component may interact with another component in a local system, a distributed system via a signal and / or interact with other systems via a network such as the Internet). As another example, a component may be a device having a specific functionality provided by a mechanical component operated by an electrical or electronic circuit, which is operated by software and / or firmware applications executed by a processor. In this case, the processor may be internal and / or external to the device and may execute at least a portion of a software and / or firmware application. As yet another example, a component may be a device that provides a particular functionality through an electronic component without mechanical parts, where the electronic component may include a processor and / or other means for executing software and / or firmware that at least partially imparts the functionality to the electronic component. In one aspect, the component may simulate an electronic component via, for example, a virtual machine within a cloud computing system.

[0303] In addition, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or". That is, unless otherwise specified or clear from the context, "X employs A or B" is intended to mean any natural inclusive arrangement. That is, if X employs A; X employs B; or X employs both A and B, then "X employs A or B" is satisfied under any of the foregoing examples. In addition, unless otherwise specified or clear from the context to refer to a singular form, the article "a" as used in should generally be interpreted to mean "one or more". As used herein, the terms "example" and / or "exemplary" are used to mean as an example, instance, or illustration. For the avoidance of doubt, the subject matter described herein is not limited to such examples. In addition, any aspect or design described herein as "example" and / or "exemplary" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to exclude equivalent exemplary structures and techniques known to those of ordinary skill in the art.

[0304] As used in this specification, the term "processor" may refer to substantially any computational processing unit and / or device, including but not limited to a single-core processor; a single processor with software multithreaded execution capability; a multi-core processor; a multi-core processor with software multithreaded execution capability; a multi-core processor with hardware multithreading technology; a parallel platform; a parallel platform with distributed shared memory. In addition, a processor may refer to an integrated circuit, an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic controller (PLC), a complex programmable logic device (CPLD), a discrete gate or transistor logic component, a discrete hardware component, and / or any combination thereof, designed to perform the functions described herein. In addition, the processor may utilize nanoscale architectures, such as but not limited to transistors, switches, and / or gates based on molecules and quantum dots, in order to optimize space usage and / or enhance the performance of related equipment. The processor may be implemented as a combination of computational processing units.

[0305] In this document, terms such as "repository", "storage device", "data repository", "data storage device", "database", and substantially any other information storage component related to the operation and functionality of the component are used to refer to a "memory component", an entity embodied in a "memory", or a component that includes a memory. The memory and / or memory components described herein may be volatile memory or non-volatile memory, or may include both volatile memory and non-volatile memory. By way of illustration and not limitation, non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), flash memory, and / or non-volatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM)). Volatile memory may include RAM, which may, for example, act as external cache memory. By way of example and not limitation, RAM can take a variety of forms, such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and / or Rambus dynamic RAM (RDRAM). In addition, the memory components of the systems and / or computer-implemented methods described herein are intended to include, but are not limited to, these and / or any other suitable types of memory.

[0306] The foregoing includes only examples of systems and computer-implemented methods. Of course, it is not possible to describe every conceivable combination of components and / or computer-implemented methods for the purpose of describing one or more embodiments, but one of ordinary skill in the art will recognize that many further combinations and / or permutations of one or more embodiments are possible. In addition, with respect to the use of the terms "including," "having," "having," and the like in the detailed description, claims, appendices, and / or drawings, these terms are intended to be inclusive in a manner similar to the way the term "comprising" is interpreted when used as a transitional word in a claim.

[0307] The description of various embodiments may use the phrases "embodiment," "various embodiments," "one or more embodiments," and / or "some embodiments," each of which may refer to one or more of the same or different embodiments.

[0308] Descriptions of various embodiments have been presented for illustrative purposes, but these descriptions are not intended to be exhaustive or limited to the embodiments described herein. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the embodiments. The terms used herein are selected to best explain the principles of the embodiments, practical applications, and / or technical improvements over technologies on the market, and / or to enable others of ordinary skill in the art to understand the embodiments described herein.

Claims

1. A system comprising: a memory storing computer executable components; and a processor that executes the computer executable components stored in the memory, wherein the computer executable components include: a determination component that determines a known component having the same composition as a target component; and A generating component generates a second synthetic spectrum of the target component based on the synthetic spectrum of the known component and the first synthetic spectrum of the target component. 2 . The system of claim 1 , wherein the generating component further generates the second synthetic spectrum of the target component based on the measured spectrum of the known component.

3. The system according to claim 1, wherein the determining component determines the known component as a nearest neighbor of the target component in a set of known components including the known component, and The nearest neighbor is a component whose component weight percentage is closer to the component weight percentage of the target component than other known components in the set of known components.

4. The system according to claim 1, further comprising: A linear combination component that determines the known component also based on identifying a linear combination of two or more other known components.

5. The system according to claim 1, further comprising: A synthesis component that synthetically generates the first synthetic spectrum of the target component by performing steps including: determining a first sum of component weight percentages of the components of the target component based on the weight percentage composition of the components of the target component; as well as applying a scaling of the components of the target ingredient to the weight percent composition of the components of the target ingredient based on the results of the matrix correction procedure on the first sum, Wherein the applying results in generating a scaled synthetic spectrum, the scaled synthetic spectrum being the first synthetic spectrum of the target component.

6. The system according to claim 1, further comprising: a synthesis component that synthetically generates the synthetic spectrum of the known component by performing steps comprising: determining a first sum of component weight percentages of the components of the known composition based on the weight percentage composition of the components of the known composition; as well as applying a scaling of the components of the known ingredients to the weight percent compositions of the components of the known ingredients based on the results of the matrix correction procedure on the first sum, Wherein said applying results in generation of a scaled synthetic spectrum, said scaled synthetic spectrum being said synthetic spectrum of said known component.

7. The system of claim 1, further comprising: a database maintenance component, the database maintenance component updating data in a database of known components, the data including the measured spectrum and the synthetic spectrum of the known components and the second synthetic spectrum of the target component, The determining component determines the second synthetic spectrum of the target component as a second known component for determining a resulting synthetic spectrum of a second target component.

8. A computer-implemented method comprising: A modified synthetic spectrum of a target component is synthesized by a system operatively coupled to the processor, comprising: generating, by the system, an intermediate synthetic relationship, the intermediate synthetic relationship being a relationship defining an increment of a synthetic spectrum of a known component and an increment of a first synthetic spectrum of the target component; aggregating, by the system, the intermediate synthetic relation and the measured spectra of the known components; and An aggregated synthetic spectrum is generated by the system based on the aggregate and as the modified synthetic spectrum of the target component.

9. The computer-implemented method of claim 8, Wherein the known composition comprises the same component as the target composition, and wherein the same component comprises an atomic element, a molecular element, a phase of an atomic or molecular element, or a combination thereof.

10. The computer-implemented method of claim 8, further comprising: The system determines the known component as a nearest neighbor of the target component in a set of known components including the known component, and The nearest neighbor is a component whose component weight percentage is closer to the component weight percentage of the target component than other known components in the set of known components.

11. The computer-implemented method of claim 8, The generating the intermediate synthesis relationship comprises determining the intermediate synthesis relationship based on a first photon quantity of the portion of the synthetic spectrum of the known component and a second photon quantity of the portion of the first synthetic spectrum of the target component.

12. The computer-implemented method of claim 8, Wherein said aggregating further comprises aggregating a third number of photons of a portion of said intermediate composite relation and a fourth number of photons of a portion of said measured spectrum of said known component.

13. The computer-implemented method of claim 8, further comprising: The system verifies whether the increment in composition weight percentage of the known component and the increment in composition weight percentage of the target component meet selected thresholds defining a selected composition relationship between the known component and the target component.

14. The computer-implemented method of claim 13, wherein said synthesizing said spectrum of said target component further comprises: The increment is determined by the system as an output of an error score of a difference in component weight percentages of a composition of the target component and a composition of the known component.

15. A computer program product facilitating a process for synthesizing a spectrum of a target component, the program instructions being executable by a processor to cause the processor to: synthesizing, by the processor, a spectrum of the target component, wherein the synthesizing comprises causing the processor to: determining, by the processor, an increment of a first photon quantity of a portion of a synthetic spectrum of a known component and an increment of a second photon quantity of a portion of the first synthetic spectrum of a target component, thereby obtaining an intermediate synthetic relationship; aggregating, by the processor, a third number of photons that is part of the intermediate composite relation and a fourth number of photons that is part of the measured spectrum of the known composition; as well as A second composite spectrum of the target component is generated by the processor based on the results of the polymerization.

16. The computer program product according to claim 15, wherein said portions of said synthetic spectrum of said known component represent different photon energy ranges of said synthetic spectrum of said known component, wherein the portions of the composite spectrum of the target component represent different photon energy ranges of the composite spectrum of the target component, wherein said portions of said intermediate synthesis relation represent different photon energy ranges of said intermediate synthesis relation, and Wherein the portions of the measured spectrum of the known component represent different photon energy ranges of the measured spectrum of the known component.

17. The computer program product according to claim 15, Wherein determining the increment comprises determining a difference between a first digital array indexed within a set of energy ranges and corresponding to the first number of photons of the portion of the synthetic spectrum of the known component and a second digital array indexed within the set of energy ranges and corresponding to the second number of photons of the portion of the first synthetic spectrum of the target component.

18. The computer program product according to claim 17, Wherein the aggregation includes determining an aggregation of a third digital array and a fourth digital array, wherein the third digital array is indexed within the energy range set and corresponds to the third number of photons of the portion of the intermediate synthetic relationship, and the fourth digital array is indexed within the energy range set and corresponds to the fourth number of photons of the portion of the measured spectrum of the known component.

19. The computer program product according to claim 18, wherein the measured spectrum has been measured from a sample of a known source of the known composition.

20. The computer program product of claim 15, wherein the program instructions are further executable by the processor to cause the processor to: The processor determines the known component as a nearest neighbor of the target component in a set of known components including the known component, and The nearest neighbor is a component whose component weight percentage is closer to the component weight percentage of the target component than other known components in the set of known components.

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