219 results about "Scientific instrument" patented technology

Systems or techniques are provided for facilitating retrieval augmented generative question and answer boosting. In various embodiments, a system can access a plain text question regarding a scientific instrument. In various aspects, the system can generate, via a large language model that references a document-graph repository, a structured or unstructured answer for the plain text question. In various instances, the document-graph repository can comprise a plurality of document-graphs that respectively correspond to a plurality of technical documents. In various cases, for a first document-graph that corresponds to a first technical document, leaf nodes of the first document-graph can represent respective text blocks written in the first technical document, and non-leaf nodes of the first document-graph can respectively represent a document title, one or more section headings, and one or more scientific instrument identifiers written in the first technical document and beneath which the respective text blocks are nested.

Systems or techniques are provided for facilitating improved digital twin calibration for scientific instruments. In various embodiments, a system can synchronize, via execution of a state-wide Bayesian filter, a parametric state of a digital twin with a physical state of a scientific instrument. In various instances, the state-wide Bayesian filter can comprise a set of calibration iterations, each of which can comprise a Bayesian update to an entirety of the parametric state based on an iteration-common observable that is exhibitable by the scientific instrument and simulatable by the digital twin. In various cases, the scientific instrument can be a charged-particle microscope, the parametric state of the digital twin can be an aberration coefficient vector of the charged-particle microscope, and the iteration-common observable can be a Fourier transform of a convergent beam electron diffraction pattern of an amorphous carbon specimen captured by the charged-particle microscope.

In some embodiments, a scientific instrument includes an electron-beam column configured to scan an electron beam across a sample and a segmented electron detector configured to receive diffracted beams produced by diffraction of the electron beam in the sample. The segmented electron detector has a plurality of segments arranged in a two-dimensional array, with each of the segments being configured to generate a respective output signal representing a respective integrated flux of electrons received thereat. The scientific instrument also includes an electronic controller configured to receive a set of frames from the segmented electron detector, each of the frames representing a respective set of output signals generated by the segments in response to an electron diffraction pattern projected onto the segmented electron detector, and further configured to communicate with a computing device programmed to generate a strain map of the sample based on the set of frames.

The invention relates to an SEM (scanning electron microscope) and CT (computed tomography) compatible solid propellant mechanical property testing device and method, belongs to the technical field of precise scientific instruments and material micro-mechanical property testing, and solves the problems that in the prior art, due to the fact that imaging equipment is single, and image quality becomes poor due to creeping accumulation, micro-scale failure information of propellants is incomplete and inaccurate to obtain. In the mechanical property testing device, the in-situ mechanical testing device performs mechanical loading and load holding on the propellant sample, the SEM obtains the surface appearance of the propellant sample, the double-source double-probe industrial CT obtains the internal three-dimensional structure of the propellant sample, and the in-situ mechanical testing device is transmitted between the transfer cabin main cavity and the electron microscope cabin through the transfer channel. And the vacuum transfer cabin containing the in-situ mechanical testing device is transferred between the transfer channel and the scanning turntable of the double-source double-probe industrial CT by the mechanical arm. According to the invention, more comprehensive and accurate mesoscopic failure information of the propellant sample can be obtained while less time cost is paid.

Systems or techniques are provided for facilitating intelligent maintenance for scientific instruments. In various embodiments, a scientific instrument can comprise a chromatograph-equipped mass spectrometer. In various aspects, the scientific instrument can determine, based on an electronic counter or readback sensor associated with the chromatograph-equipped mass spectrometer failing to satisfy a threshold, whether performance of a maintenance task on the chromatograph-equipped mass spectrometer is warranted. In various instances, the scientific instrument can schedule, in response to a determination that the performance of the maintenance task is warranted, a time or date for the performance of the maintenance task, wherein the time or date can be predicted by a machine learning model based on an operational history of the chromatograph-equipped mass spectrometer. In various cases, the scientific instrument can prepare for the performance of the maintenance task, by adjusting, prior to the time or date, actuatable hardware of the chromatograph-equipped mass spectrometer.

Systems or techniques are provided for facilitating intelligent maintenance for scientific instruments. In various embodiments, a scientific instrument can comprise a chromatograph-equipped mass spectrometer. In various aspects, the scientific instrument can determine, based on an electronic counter or readback sensor associated with the chromatograph-equipped mass spectrometer failing to satisfy a threshold, whether performance of a maintenance task on the chromatograph-equipped mass spectrometer is warranted. In various instances, the scientific instrument can schedule, in response to a determination that the performance of the maintenance task is warranted, a time or date for the performance of the maintenance task, wherein the time or date can be predicted by a machine learning model based on an operational history of the chromatograph-equipped mass spectrometer. In various cases, the scientific instrument can prepare for the performance of the maintenance task, by adjusting, prior to the time or date, actuatable hardware of the chromatograph-equipped mass spectrometer.

Idle trigger diagnosis of scientific instruments. Systems or techniques are provided for facilitating idle trigger diagnostics of scientific instruments. In various embodiments, the scientific instrument may include a mass spectrometer coupled to a chromatograph. In various aspects, the scientific instrument may determine whether the scientific instrument is in an idle period via a temperature sensor of the chromatograph or via a clock of the mass spectrometer. In various examples, the scientific instrument may apply one or more electronic control signals to the mass spectrometer or the chromatograph in response to determining that the scientific instrument is in the idle time period, measure one or more resulting ion spectra via an ion detector of the mass spectrometer, and determining whether the mass spectrometer or the chromatograph is operating properly based on the one or more resulting ion spectra.

Systems or techniques are provided for facilitating spectral image analysis via integration-constrained fitting. In various embodiments, a system can access a spectral image of a specimen captured by a scientific instrument, wherein pixels of the spectral image respectively correspond to energy spectra. In various aspects, the system can fit in pixel-wise fashion a function to the energy spectra, wherein the function comprises a plurality of terms that are additively combined, wherein a first term of the plurality of terms represents a fine structure of the energy spectra, and wherein an integral associated with the first term is constrained to zero. In various instances, the system can segment the spectral image by material, based on the first term.

Disclosed herein are scientific instrument support systems, as well as related methods, computing devices, and computer-readable media. For example, in some embodiments, a system for performing scientific instrument support is provided. The system, which may be implemented by a common computing device included a scientific instrument or remote from the scientific instrument or implemented by a plurality of computing devices included in the scientific instrument, remote from the scientific instrument, or a combination thereof, is configured to parse, with a data collector service, a template associated with a scientific instrument to extract a schema for the scientific instrument. The system is further configured to query, with the data collector service, the scientific instrument according to the schema. The system is further configured to receive data from the scientific instrument responsive to the query. An automated method performed via a computing device for providing such scientific instrument support is also provided.

The invention provides a scientific instrument purchase type selection method and device based on artificial intelligence, and belongs to the technical field of artificial intelligence, and the method comprises the steps: constructing a knowledge graph of all initial instruments; calculating an evaluation value of each initial instrument under each evaluation dimension; matching in the knowledge graph according to model selection demand information input by a user to obtain a candidate instrument set; extracting evaluation values of each candidate instrument in the candidate instrument set under all evaluation dimensions; performing weighted calculation on the evaluation values corresponding to all the candidate instruments according to dimension weight parameters set by a user to obtain a personalized comprehensive score; and generating a model selection scheme including an instrument recommendation result and data traceability information according to the personalized comprehensive score and associated information in the knowledge graph. According to the method, the knowledge graph fusing the multi-source data is constructed, and the scientific instrument purchase type selection scheme based on artificial intelligence is generated in combination with multi-dimensional evaluation, so that accurate recommendation of scientific instrument purchase type selection based on artificial intelligence is realized.

To provide a scientific instrument with an electron-beam column.SOLUTION: In some embodiments, a scientific instrument includes an electron-beam column configured to scan an electron beam across a sample. The electron-beam column includes a beam blanker configured to gate the electron beam in response to a drive signal. The scientific instrument also includes an electron detector configured to measure a flux of transmitted or scattered electrons having interacted with the sample, and an electronic controller configured to acquire an image of the sample using values of the flux measured with the electron detector for a plurality of electron-beam scan locations. The electronic controller is further configured to cause the drive signal to have a gating frequency at which the image has a moire pattern therein.SELECTED DRAWING: Figure 1

Disclosed herein are scientific instrument support systems, related methods, computing devices and computer-readable media. A method of mitigating distortion of an optical emission spectrum obtained from an optical emission spectrometer is provided. The method may comprise a step of obtaining a spectrum recorded with the spectrometer and a respective one or more condition parameters indicative of an operating condition at a time of recording the spectrum. The method may further comprise a step of providing a model configured to output, in response to the one or more condition parameters, one or more transform parameters of a transformation to be applied to the obtained spectrum. A transformation may be applied in accordance with the obtained one or more transform parameters to the obtained spectrum to mitigate distortion of the spectrum due to a discrepancy between the operating condition and a baseline operating condition.

The medium-voltage conversion power supply device is deployed between a main base station and a junction box, under the full-load state of a scientific instrument, if the working voltage of the junction box is kept at 20% or above of the minimum threshold value, a single-preceding-stage module working mode is adopted, and otherwise, a front-and-back-stage module working mode is adopted; when a single-preceding-stage module working mode is adopted, the device comprises a preceding-stage boosting module for performing first-stage boosting on medium-voltage direct current and directly supplying the medium-voltage direct current to a junction box for use through a submarine cable transmission line; when a front-back two-stage module working mode is adopted, the device comprises a front-stage boost module and a back-stage boost-buck module which are connected in series; the pre-stage boosting module performs primary boosting on the medium-voltage direct current and transmits the medium-voltage direct current to the post-stage boosting and reducing module, the post-stage boosting and reducing module injects a communication signal into the submarine cable transmission line and transmits the communication signal back to the pre-stage boosting module, and the pre-stage boosting module activates a secondary boosting function after identification; and the post-stage buck-boost module converts the boosted direct current into medium-voltage direct current and supplies the medium-voltage direct current to the junction box for use.

Scientific instrument support systems, and related methods, apparatuses, computing devices, and computer readable media are disclosed herein. In some embodiments, a support apparatus for a scientific instrument includes an interface device and a processing device. An interface device receives a Lenchi map acquired with a scientific instrument and transmits a control signal for electron beam optics of the scientific instrument. The processing device converts the measured Lengchi map into an input token for the transformer, generates an output token based on a tokenized sentence ending with the input token, and determines an adjustment to the control signal based on the input token and the output token. The input token and the output token belong to a plurality of tokens representing a reference Lengchi map that samples an alignment parameter space of the electron beam optics. The transformer implements an autoregressive masking language model trained on a corpus of reference sentences representing a path through an alignment parameter space to a target alignment state of the electron beam optics.

Disclosed herein are scientific-instrument support systems, as well as related methods, apparatus, computing devices, and computer-readable media. In some embodiments, a support apparatus for a scientific instrument includes an interface device and a processing device. The interface device receives Ronchigrams acquired with the scientific instrument and transmits control signals for the electron-beam optics thereof. The processing device converts a measured Ronchigram into an input token for a transformer, produces an output token based on a tokenized sentence ending with the input token, and determines adjustments to the control signals based on the input and output tokens. The input and output tokens belong to a plurality of tokens representing reference Ronchigrams sampling an alignment parameter space of the electron-beam optics. The transformer implements an autoregressive masked language model trained on a corpus of reference sentences representing paths through the alignment parameter space to a target alignment state of the electron-beam optics.

Systems / techniques are provided for facilitating mass analyzer calibration via reinforcement learning. In various embodiments, a system can predict, via execution of one or more reinforcement learning neural networks on present-time state data of a mass analyzer of a scientific instrument, what adjustments to one or more operational parameters of the mass analyzer would cause the mass analyzer to approach a calibrated state, wherein the one or more operational parameters include an electrode voltage of the mass analyzer or a timing control of the mass analyzer. In various aspects, the system can modify the one or more operational parameters based on the adjustments, thereby causing the mass analyzer to approach the calibrated state.

The present disclosure provides a scientific instrument support apparatus comprising a first logic, a second logic, and a third logic. The first logic subscribes to events from a device driver, receives signature data events including a generated signature event, generates signature data with a specified signature identifier in response to the generated signature event, and transmits the signature data to the second logic. The second logic receives the signature data from the first logic, processes the signature data, writes the processed signature data to an instrument health database, and transmits the signature data to a cloud-based database. The third logic presents a graphical user interface based on the processed signature data. The apparatus enables efficient collection, processing, and presentation of scientific instrument data for improved monitoring and analysis.

The utility model relates to an in-situ torsion experiment device for X-ray CT (Computed Tomography), and belongs to the technical field of scientific instruments for in-situ torsion experiments for X-ray CT. Comprising a driving assembly, a torque sensor and a clamp assembly. Two ends of a shaft of the torque sensor are respectively connected with the driving assembly and a clamp. The driving assembly comprises a motor, an outer shell, a worm gear and a worm, the worm gear and the worm are rotationally arranged in the outer shell and meshed, the upper end of the worm gear is in key connection with the lower end of the torque sensor, and one end of the worm is connected with the output end of the motor. The driving assembly is stable in transmission during an experiment, has a self-locking function, prevents reverse rotation, improves the detection accuracy, is compact in structure, has a large transmission ratio, is suitable for speed reduction, is low in noise and small in vibration, and effectively prevents the influence on other works in a laboratory.

Systems and methods for adjusting scanning patterns in scientific instruments based on electromagnetic interference. One example charged particle instrument includes a chamber supporting a sample, a column coupled to the chamber, a pump configured to establish a vacuum within the chamber, a sensing device configured to detect a measure of a frequency of electromagnetic interference generated via the pump, and a controller including an electronic processor and a memory. The column includes a charged particle source configured to generate a charged particle beam traveling through the column and into the chamber. The charged particle beam is generated according to a scanning pattern. The controller configured to receive, from the sensing device, a signal indicative of the frequency of the electromagnetic interference and adjust the scanning pattern based on the frequency of the electromagnetic interference.

This invention provides a high-end scientific instrument reliability analysis and design method based on SysML, belonging to the field of instrument reliability analysis technology. The method includes: constructing a system model of the target instrument using SysML, the system model including a requirements diagram, internal block diagram, and attribute library; extracting all failure modes from all failure mode libraries and assigning them global numbers to establish a failure mode mapping matrix; performing failure mode and impact analysis (FMEA) for the target instrument based on the system model to obtain a failure mode and impact analysis table; establishing a parameterized reliability model for each functional path; calculating the reliability constraints of each functional path based on the system model and the impact analysis table; and performing reliability analysis based on the measured attributes and reliability constraints of sub-components to generate an optimization scheme. The advantages of this invention are improved accuracy, consistency, and automation of reliability analysis.

Disclosed herein are scientific instrument support systems, related methods, computing devices, and computer-readable media. For example, in some embodiments, a scientific instrument support apparatus includes sample analysis logic to cause a sample to be injected into a gas chromatograph and to obtain a mass spectrum of the sample using a mass spectrometer coupled to the output of the gas chromatograph, the mass spectrometer utilizing a mass calibration or a lock mass correction while obtaining the mass spectrum of the sample; calibration gas logic to control a supply of a calibration gas to the mass spectrometer during the void time of the gas chromatograph; and lock mass calibration logic to obtain a mass spectrum of the calibration gas using the mass spectrometer during the void time of the gas chromatograph and to calculate the mass calibration or the lock mass correction to be used by the sample analysis logic.

This invention provides a method and system for verifying and tracing file fingerprints for scientific instrument data, comprising: Step S1, acquiring user identity information and instrument identifier of the scientific instrument, identifying the original data file through file monitoring and automatically collecting basic metadata to generate labeled metadata; Step S2, calculating the file fingerprint based on the algorithm identifier using a digest algorithm and writing it into a fingerprint database; Step S3, performing fingerprint comparison on the file fingerprints; if the fingerprints are the same, reusing the existing content object identifier and adding a new version record; if they are different, creating a new content object identifier and adding a new version record; and when the same reservation identifier, upload time difference not exceeding a preset threshold, and fingerprint inconsistency are met, creating a version fork and recording the fork reason code and fork reason payload for traceability; Step S4, performing verification processing on the downloaded file. The beneficial effect is that this invention can meet the comprehensive requirements of traceability, auditability, and real-time performance for the security management of scientific instrument data.

The application is suitable for the field of computers, and provides a scientific instrument leasing analysis method and system based on big data, which comprises the following steps: acquiring position information of leasing equipment, wherein the leasing equipment comprises a first leasing equipment and a second leasing equipment; identifying a changed position relationship of the first leasing equipment and the second leasing equipment according to the position information of the leasing equipment; if it is identified that the first leasing equipment or the second leasing equipment to be used is within a mutual visual monitoring range, releasing use locking of a monitored leasing equipment; instructing at least one of the leasing equipment to monitor the monitored leasing equipment and generating monitoring information; and identifying and analyzing the monitoring information based on big data to determine whether there is a damaged use behavior in the monitoring information, wherein the application has the beneficial effects of reducing the labor intensity of operation and maintenance personnel, being capable of guaranteeing the integrity of the leasing equipment as much as possible, and reducing the risk of damage of the leasing equipment.

System and methods are disclosed for a scientific instrument support system. In at least one embodiment, the system includes a sample, a physical asset configured to interact with the sample, and one or more computing devices having executable instructions stored thereon. When executed, the executable instructions cause the one or more computing devices to generate one or more predictions based on simulations of one or more of the sample, the physical asset, or an interaction of the physical asset with the sample, and update a physics-based digital model of the sample based on accuracy of the one or more predictions. The physics-based digital model is used to reconstruct chemical and physical properties of the sample to generate the simulations of the sample.

Scientific instrument support systems and related methods, computing devices, and computer readable media are disclosed herein. For example, in some embodiments, there is provided a method comprising: generating a sample identifier for a sample; receiving sample information of the sample; and generating a data structure ledger for the sample. The data structure ledger includes an initial ledger block having the sample identifier and the sample information. The method further includes receiving experimental data associated with the sample; experimental ledger blocks are generated in the data structure ledger. The method further includes receiving a request for a report corresponding to the sample; querying the data structure ledger to receive ledger data corresponding to the request; and generating the report based on the ledger data received in response to the query.

Scientific instrument support systems and related methods, computing devices, and computer readable media are disclosed herein. For example, in some embodiments, an apparatus includes a sample introduction device and a mass spectrometer. The mass spectrometer includes a first logic to receive a sample from a queue, the sample including an analyte; a second logic to determine instrumentation method and data analysis parameters based on a property of the analyte; and a third logic to process the sample by applying the instrumentation method and data analysis parameters.

The invention relates to a device and a method for testing surface microscopic performance of a magnetic material under a service condition, and belongs to the field of precise scientific instruments. The device is composed of a test loading module and an in-situ detection module. The test loading module drives a worm gear and a worm through a servo motor to drive a lead screw mechanism to achieve pulling or pressing preloading, and fatigue loading is completed in combination with a piezoelectric stack and a flexible hinge. A modularized high-temperature loading cavity and a liquid nitrogen refrigerating device are adopted to realize high or low-temperature environment construction; the position of the coil or the magnitude of current is accurately adjusted through the air cylinder to achieve accurate application of the magnetic field. The in-situ detection module carries out observation by using a behavior in-situ test method, and supports magnetic domain dynamic observation and in-situ mechanical test. According to the invention, the construction of a more complex coupling loading service condition is realized, the real service behavior of the magnetic material is more accurately simulated, a plurality of methods for observing the microscopic performance of the surface of the magnetic material are provided, and a more accurate observation means is provided for the research on the service performance of the magnetic material.

This application provides a hardware-software heterogeneous data processing system and method for atmospheric sounding lidar, relating to the technical field of embedded architecture for atmospheric remote sensing and scientific instruments. The system includes: a PL (Plug-in) terminal, which performs preliminary processing on lidar signal data to generate target data; a PS (Power Controller) terminal, including an internal cache and a processing core, with a virtual spatial filter configured within the processing core; the target data is stored in the internal cache via an ACP (Automatic Component Processing Filter); the processing core schedules the PL terminal to perform preliminary processing on the digital signal, reads the target data from the internal cache, and runs the virtual spatial filter to filter the target data; the virtual spatial filter performs register-level single-loop fusion of each independent algorithm node, completing the calculation of each independent algorithm within the single loop fusion, and all intermediate data resides in the register file. Relying on the ACP and single-loop fusion architecture, the bus occupancy rate and system latency of data flow are reduced, thereby achieving high throughput and zero bottlenecks at the system level.

Scientific instrument support systems and related methods, computing devices, and computer readable media are disclosed herein. For example, in some embodiments, a scientific instrument support apparatus includes a first logic element to generate a first command frame including a flag having a first value; and a second logic element to transmit the first command frame to a first device. The first value indicates that the first device should ignore an operation indicated by the first command frame. The scientific instrument supporting device comprises a third logic element which is used for determining whether a confirmation signal indicating successful operation is received or not; and a fourth logic element for generating a second command frame including a flag having a second value upon receipt of the acknowledgement signal. The second value indicates that the first device should perform an operation indicated by the second command frame.

The invention discloses a scientific research instrument current state monitoring method and a monitor, and relates to the technical field of electrical variable measurement. The scientific research instrument current state monitoring method comprises the following steps: constructing at least one historical instrument use sequence according to historical working time of each scientific research instrument; according to the current instrument use sequence corresponding to each scientific research instrument, screening from each historical instrument use sequence to obtain at least one reference instrument use sequence matched with the current instrument use sequence; determining a predicted operation condition of the scientific research instrument according to the sequence similarity between the reference instrument use sequence and the current instrument use sequence; inputting the predicted operation condition into a current state model corresponding to the scientific research instrument to obtain a predicted current state of the scientific research instrument; the predicted current state is compared with the actual current state of the scientific research instrument, a current monitoring result of the scientific research instrument is obtained, and the actual current state is collected through an intelligent sensor corresponding to the scientific research instrument in the intelligent sensing system.