728 results about "Spectroscopy" patented technology

Systems and methods for detecting features, outputting feature representations, determining measurements, and combinations thereof using machine-learned algorithms are provided. In some embodiments, first sample data from a first characterization modality and second sample data from a second characterization modality are provided to a machine-learned algorithm. Improved feature detection, feature representation, and measurement may be realized by using multimodal data with a machine-learned algorithm. In some embodiments, a first characterization modality is an interferometric modality and a second characterization modality is a spectroscopic modality. A first characterization modality may be optical coherence tomography and a second characterization modality may be a diffuse spectroscopy modality, such as near-infrared spectroscopy. Sample data may be intraluminal and / or vascular data useful in characterizing a vascular system of a subject, such as a human.

The invention discloses a visual gas leakage detection system and method based on a laser spectrum, and relates to the technical field of gas detection visualization. According to the visual gas leakage detection system and method based on the laser spectrum, the method comprises the following steps that 1, gas monitoring data are collected and preprocessed; 2, extracting a characteristic spectrum from the gas monitoring data, obtaining a gas spectrum reduction value, and separating an overlapped absorption spectrum; 3, estimating the diffusion trajectory of the gas, and calculating a gas diffusion coefficient estimation value and a diffusion rate estimation value; outputting a gas concentration estimated value; 4, gas leakage source points are calculated, gas mass tracking is carried out, and different gas leakage sources are separated; and 5, generating a scatter diagram to display gas concentrations at different positions, and marking the position of a leakage source. The problem that a complex multi-gas environment cannot be recognized due to the fact that homogeneity and heterogeneity possibly exist in the same environment due to leakage of different types of gases in the prior art is solved.

When x-ray fluorescence (“XRF”) spectroscopy is utilized to classify materials transported on a moving conveyor belt, there is the possibility of the x-ray beam only partially irradiating the material piece, which can result in the capture of an inaccurate XRF spectrum needed to classify the material piece. This can lead to an improper (erroneous) classification and resultant sortation of material pieces (e.g., aluminum alloys). In a material handling system, the area of the intersections between the x-ray beam spots from an x-ray fluorescence system and the material pieces are measured and correspondingly used to correct the measured XRF spectrum associated with each material piece. The material pieces can then be sorted according to the corrected XRF spectrum.

The invention relates to the technical field of garbage recognition, and particularly discloses an intelligent garbage recognition system and method based on multi-modal fusion, a hardware sensing unit is composed of a three-dimensional vision module, a touch sensing module and a near infrared spectrum module, and point cloud data, a pressure distribution matrix and spectrum data of garbage are obtained respectively; and an edge computing unit is equipped to carry an NPU acceleration chip for data processing. And the software module realizes multi-modal feature fusion by using ResNet-50, LSTM (Long Short Term Memory), 1D-CNN (Convolutional Neural Network) and Transform Encoder, and is also provided with a multi-sensor timestamp synchronous controller. And the dynamic feature database starts online learning to update the material feature library when the garbage recognition confidence coefficient is lower than 85%. During feature fusion, a cross-modal feature mapping relation is established, the weight is adjusted through an attention mechanism, and dynamic learning adopts a model lightweight method based on knowledge distillation. And starting an arbitration mechanism to correct the classification result when different modal identification results conflict. According to the method, the advantages of different modal data are fully utilized, and the recognition capability in a complex environment is improved.

The invention relates to an optical fiber coupling dynamic interferometer based on arrayed waveguide grating light splitting. A luminous source and an arrayed waveguide grating of the optical fiber coupling dynamic interferometer are arranged on a motion module. The light emitting source is connected to the input port of the array waveguide grating through an optical fiber, and through the movement module, any one of the multiple output ports of the array waveguide grating is in butt joint with the optical fiber connected to the incident end of the collimation structure. A half-wave plate and a beam splitter prism of the imaging element group are sequentially arranged on an emergent light path of the emergent end of the collimation structure, and the beam splitter prism is used for splitting a light beam passing through the half-wave plate into a reference light beam transmitted along a first light path and a measurement light beam transmitted along a second light path. And the reflecting mirror of the imaging element group reflects the reference light beam back to the beam splitting prism. And the to-be-measured surface reflects the measurement light beam back to the beam splitter prism. The reference beam and the measurement beam reflected back to the beam splitter prism interfere with each other and are imaged on the target surface of the camera. Accurate measurement of the wavefront image of the surface to be measured is realized.

The invention relates to the technical field of spectral analysis and concentration quantitative detection, and discloses an LIBS quantitative analysis method, device and system based on a thin plate spline regression algorithm. The method comprises the steps that multiple sets of sample data are obtained, and each set of sample data comprises corresponding target sample concentration, laser energy and spectral intensity; constructing a concentration quantitative model about the concentration, the laser energy and the spectral intensity based on a spline function; determining an optimal penalty parameter by adopting a cross validation method; based on the optimal penalty parameter, solving parameters of the concentration quantitative model through a minimization objective function to obtain a constructed concentration quantitative model; and performing quantitative analysis on a sample with unknown concentration by using the constructed concentration quantitative model. Thus, the output energy of the laser is incorporated into the core model, and an I-E-C nonlinear coupling relationship is constructed, so that the problem of calibration deviation caused by laser energy fluctuation is reduced, and the precision improvement of concentration quantification in a complex scene is realized.

The invention belongs to the technical field of terahertz detection, and discloses a terahertz metamaterial enhanced spectrum detection system. Comprising the following steps: constructing a metamaterial enhancement factor tensor matrix according to the interaction between terahertz waves and a metamaterial; performing local field enhancement and surface plasma resonance optimization on the target sample based on the metamaterial enhancement factor tensor matrix to obtain a feature enhancement spectroscopy mapping function; constructing a multi-dimensional detection feature space based on a feature enhancement spectroscopy mapping function; a spectral signal enhancement topology network is formed based on the multi-dimensional detection feature space; enhancing the topological network based on the spectral signal, and generating an enhanced spectral feature discrimination function; obtaining a high-precision terahertz spectrum detection result based on the enhanced spectrum characteristic discrimination function; according to the invention, internal correlation between spectral characteristics can be mined, and comprehensive description of sample information is enhanced, so that high-precision terahertz spectrum detection is realized.

An optical device includes: a first optical system; and a second optical system. The first optical system includes a first light splitting device configured to split measurement light emitted from a first light source into a first measurement light and a second measurement light and then mix the split measurement light, a first mirror configured to add a first modulation signal by being moved in an entering direction of the first measurement light and reflecting the first measurement light, a second mirror configured to reflect the second measurement light, and a first light receiving device configured to receive the measurement light including a sample-derived signal and the first modulation signal and output a first light receiving signal. The second optical system includes a second light source configured to emit laser light, an optical modulator configured to add a second modulation signal to the laser light, and a second light receiving device configured to receive the laser light including a displacement signal generated by reflection on the first mirror and the second modulation signal and output a second light receiving signal.

The invention discloses a correction method for saturation spectral line intensity in a laser spectrum, and relates to the technical field of spectral data analysis, the method comprises the following steps: identifying saturation characteristics in the spectrum, determining a saturation point based on an intensity threshold, and recording the original intensity of the saturation point; determining a target spectrum peak wavelength range containing a saturation point; eliminating saturation points from the wavelength range of the target spectrum peak, and retaining unsaturation points; fitting the unsaturated point through a nonlinear function to obtain the maximum fitting strength; if the maximum fitting strength is lower than the original strength, performing strength correction by adopting a linear extension mode by taking an unsaturated point as a reference, and otherwise, performing strength correction directly based on a fitting model; and outputting the corrected spectral intensity data, and calculating the maximum intensity of the spectral peak and the integral area of the spectral peak. According to the method, the accuracy of saturation judgment and the reliability of a correction result are improved, and a direct intensity data basis and core derivative parameters are provided for quantitative analysis.

The invention discloses a mixing parameter regulation and control method and system for an SBS-T modified asphalt mixture and a terminal. The method comprises the following steps: analyzing a modification mechanism through a scanning electron microscope and X-ray photoelectron spectroscopy combined technology, dividing three stages of rapid melting and the like, and establishing a temperature-viscosity model; constructing a multi-scale capture system by using an atomic force microscope, a Fourier transform infrared spectrum and a dynamic shear rheometer; designing a five-factor three-level test matrix by adopting a response surface method, and establishing a road performance prediction model in combination with a BP neural network; mixing parameters are optimized in a multi-objective mode based on a genetic algorithm, and dynamic correction is achieved through an Internet of Things sensor and Kalman filtering. The system comprises a mechanism analysis module, a multi-scale detection module, a terminal integrated storage unit, a processing unit and a man-machine interaction unit. According to the scheme, the limitation of traditional single-factor analysis is broken through, microscopic and macroscopic collaborative optimization and dynamic parameter regulation and control are achieved, and the construction adaptability and precision are improved.

The invention discloses a method for suppressing common-mode noise of a CPT magnetometer, which can remarkably improve the performance of the CPT magnetometer by introducing a differential detection technology and an adaptive signal processing method. A beam of coherent laser is configured, two beams of light generated by a spectroscope respectively pass through different paths, one beam of light passes through an atomic gas chamber, the other beam of light does not pass through the atomic gas chamber, and a high-sensitivity photoelectric detector is used for collecting signals. A differential signal of two signals is generated by using a differential amplifier, so that the influence of common-mode noise is effectively eliminated. Then, the differential signal is subjected to adaptive filtering through a digital signal processor, and the signal quality is further improved. Experiments show that the method can significantly improve the measurement precision and stability of the CPT magnetometer, and has a wide application prospect.

The invention relates to the technical field of grading gangue selection of coal gangue, in particular to a grading gangue selection method of coal gangue, which comprises the following steps: acquiring mineral texture data through a multispectral imaging device, analyzing the spatial distribution of aluminum and silicon elements in combination with an X-ray diffractometer, and generating a three-dimensional component map through spatial registration and fusion with infrared heat value data; impurities are dynamically removed through sulfur element spectrum detection, and the air pressure and the material flow of winnowing equipment are adjusted by matching roasting process parameters; components of the sorted waste are analyzed through an X-ray fluorescence spectrometer, particle size data are obtained through a laser particle analyzer, a genetic algorithm is input to iteratively optimize detection channel combination and sampling density, a resource distribution instruction is generated, and equipment parameter correction is fed back. According to the method, aluminum-silicon ratio critical value accurate identification, sorting parameter dynamic adaptation and waste recycling path optimization are realized, and the problem of overburning caused by insufficient quantitative analysis of element distribution in the prior art is solved.

Apparatuses and methods for microscopic analysis of a sample by simultaneously characterizing infrared absorption characteristics of a plurality of spatially resolved locations are described herein. These apparatuses and methods improve sampling times while collecting microscopic data regarding composition of a sample across a wide field.

The methods described herein provide improvements in composite infrared absorption imaging. The scanning approach described herein reduces erroneous measurements caused by thermal drift and optical interference that occurs between light scattering from the top surface of the sample and light scattering from the underlying substrate. Embodiments described herein relate to imaging and spectroscopy, and more particularly, to improvements in photo-thermal imaging and spectroscopy systems, and techniques for obtaining spectral information characterizing optical properties and / or materials or chemical compositions of a sample, such as information related to infrared (IR) absorption spectroscopy.

The invention discloses a design and implementation method of a near-infrared light-splitting super lens, and relates to the technical field of super lenses. The super lens is composed of a rotational symmetric structure, so that polarization insensitivity is achieved, incident broadband light can be dispersed to different positions of the same focal plane according to different wavelengths through ingenious optical design, an effective light splitting effect is achieved, and the polarization insensitivity of the super lens is improved by adjusting geometric parameters and shapes of the super lens nano structure units. The positions of light with different wavelengths on a focal plane can be accurately controlled; in the optimization process, a particle swarm optimization algorithm is adopted to reduce errors, and performance attenuation and optical distortion caused by dispersion can be effectively reduced by adjusting dispersion characteristics and geometric parameters of the nano-structure units, so that the precision and reliability of light splitting are improved, and the light splitting precision is improved. The identification capability of the laser detection system is greatly improved, and integration and miniaturization are realized.

The invention relates to a near-infrared coal quality quantitative analysis model construction method, and belongs to the technical field of coal quality analysis. In order to solve the problems that a traditional coal quality analysis method is tedious in operation, time-consuming, destructive and difficult to meet rapid nondestructive detection requirements, the invention aims to realize rapid, accurate and quantitative detection and analysis of moisture, ash and volatile components in coal by combining a near infrared spectrum analysis technology with a deep learning algorithm. The method comprises the following specific steps: collecting and preparing a coal sample according to the national standard, and measuring moisture, ash and volatile components; the method comprises the following steps: acquiring spectral data by using a near infrared spectrometer, marking the spectral data, performing smoothing, multiplicative scatter correction and normalization processing, and dividing a training set and a test set according to a Kennard-Stone algorithm; and constructing a cascaded integrated deep learning prediction model of CNN-LSTM, and performing training, evaluation and optimization to predict coal quality indexes. According to the method, data are obtained based on a strict national standard experiment method, the accuracy of quantitative analysis of coal quality and the robustness of the model are improved by combining the near infrared spectrum technology with the deep learning model, and powerful support is provided for quality control and reasonable utilization of resources in the coal industry.

A Raman-infrared spectroscopy analysis combination device including: a light source; a plate for fixing a sample; a stage on which the plate is placed; an objective optical element for obtaining Raman light; an objective optical element for obtaining reflected infrared light; a Raman light detection system; an infrared light detection system; a driving unit for adjusting a positional relationship between a position of the plate and the objective optical elements; a switching unit that switches between the detection systems; and a control unit for controlling the driving unit, the switching unit and the optical imaging elements. At least one of the plate and the stage is provided with a marker for adjusting the positional relationship, and the control unit controls the driving unit to adjust the positional relationship between the position of the plate and the objective optical elements based on a position of the marker.

The invention discloses a thermal detection system and a chiral substance identification method, and relates to the technical field of micro-nano optics, the thermal detection system and the chiral substance identification method, based on the structure of the thermal detection system and the steps of the chiral substance identification method, a chiral substance to be detected is configured into a corresponding solution to be detected; a solution to be detected is sampled and placed in the absorber, the surface plasmon effect generated when the metal resonance layer receives chiral light irradiation is utilized, a corresponding thermal signal is output, then the thermal signal is captured and analyzed through the thermal detection system, and high-precision identification of chiral substances is achieved. Compared with a traditional circular dichroism and circular polarization fluorescence spectrophotometer device, the technical scheme provided by the invention is simple in structure, convenient to operate and relatively low in requirements on the concentration and the volume of a detected sample.

The invention discloses an LIBS (laser-induced breakdown spectroscopy) technology-based probe gun for online detection of molten steel components, comprising: a cylindrical shell, an upper cylinder opening of which is provided with an optical fiber receiving head electrically connected with a spectrometer; the laser emitting system is used for emitting high-energy pulse laser to penetrate through a lower barrel opening of the barrel-shaped shell to excite atoms on the surface of the molten steel, and plasma is formed; the signal acquisition system is used for coupling a spectral signal emitted by the plasma to the optical fiber receiving head, and an acquisition light path is formed between the plasma and the optical fiber receiving head; and the gas protection system is used for carrying out coaxial purging on the laser action area and the signal acquisition area so as to form an inert gas protection barrier around the plasma and on the peripheral side of the acquisition light path. The probe gun adopts inert gas to protect a barrier, has multiple effects of inhibiting generation of an oxide layer, reducing local temperature, reducing steam scattering and weakening continuous background radiation, and can improve the signal acquisition capability of the probe gun.

The invention discloses an ultra-wideband femtosecond optical parametric oscillator capable of being quickly tuned, and relates to the technical field of optical parametric oscillators. The oscillator comprises a frequency doubling device, an optical parametric oscillator resonant cavity device and a dispersion compensation chirp mirror pair module. According to the invention, the original phase matching bandwidth is further broken through by adopting a non-collinear phase matching mode that the transmission included angle between the pump light and the signal light is 2.5 degrees, and rapid wavelength tuning can be realized only by finely tuning the cavity length under the condition that the angle of the nonlinear crystal is not adjusted; the broadband tunable optical parametric oscillator can break through the bottlenecks in the aspects of wavelength tuning range, wavelength tuning speed and the like of the existing near-infrared optical parametric oscillator, and provides a novel light source with larger wavelength tuning range and higher tuning speed for the fields needing broadband tunable light sources, such as spectroscopy, micro-nano processing, optical frequency combs and the like.

The invention discloses a sampling-free sample preparation cross-belt coal quality online detection LIBS (laser-induced breakdown spectroscopy) spectrum system which comprises a belt conveying system and an optical detection system, and a laser range finder is arranged on a branch conveying belt; and the laser range finder is used for detecting and recording the height of the coal in the detection area on the branch conveying belt. An optical detection system based on the LIBS technology is directly erected on the coal conveying belt, the problems that the speed of the conveying belt is high and real-time sampling detection cannot be achieved are solved, rapid and real-time cross-belt coal quality quantitative analysis with high sampling rate is achieved, and the detection accuracy is improved for the problem that the spectral stability is affected by the height change of coal on the belt. And the focal length of the LIBS detection system is quickly adjusted through the height of the sample in the detection area recorded by the laser range finder, so that the stability of the spectrum is improved.

The invention relates to the technical field of brain imaging, in particular to a frequency-domain near-infrared spectrum measurement module and method and frequency-domain near-infrared spectroscopy equipment, comprising a detection unit and a processing unit, the detection unit is used for directly contacting with the head of a user, emitting detection light to a detected tissue and receiving a detection signal formed by reflection of the detected tissue, and the processing unit is used for processing the detection signal; amplitude data and phase data of the detection signal are extracted, and the amplitude data and the phase data are sent to the processing unit after analog-to-digital conversion; and the processing unit is used for calculating optical parameters of the detected tissue based on the converted amplitude data and phase data, and wirelessly transmitting the optical parameters to the target main control unit. Amplitude and phase detection functions are completed by the front-end measurement module in advance, the use of a high-speed ADC and a mixer is avoided, the system complexity is reduced, signal transmission between the measurement module and the main control unit depends on wireless communication, excessive cable connection is not needed, and the brain activity monitoring system is suitable for brain activity monitoring requirements in a dynamic scene.

The invention relates to a multi-quality parameter cooperative detection method and system based on near infrared spectrum. The method comprises the following steps: providing to-be-detected near infrared spectrum data of a to-be-detected substance, loading the to-be-detected near infrared spectrum data into a constructed quality parameter collaborative detection model, carrying out collaborative detection processing by utilizing the quality parameter collaborative detection model, and generating multi-quality parameter information of the to-be-detected substance, during cooperative detection processing, at least performing feature dimension expansion processing, feature extraction processing and feature fusion prediction processing on the near infrared spectrum data to be detected, and generating multi-quality parameter information of the substance to be detected after the feature fusion prediction processing. According to the invention, cooperative detection of multiple quality parameters can be effectively realized, the precision and reliability of multi-quality parameter detection are improved, and the cost and complexity of multi-quality parameter cooperative detection are reduced.

The invention discloses a multispectral combined coal multi-index on-line rapid detection system and a multispectral combined coal multi-index on-line rapid detection method. The multispectral combined coal multi-index on-line rapid detection system comprises a coal sample conveying device, a spectrum fusion analysis device and a control device. The coal sample conveying device is used for conveying coal samples; the spectrum fusion analysis device is used for detecting and analyzing the coal sample and comprises an X-ray spectrum detection unit, a near-infrared detection unit and a laser-induced breakdown spectroscopy unit, the X-ray spectrum detection unit is used for obtaining an X-ray fluorescence spectrum of the coal sample, and the near-infrared detection unit is used for obtaining a near-infrared spectrum of the coal sample; the laser-induced breakdown spectroscopy unit is used for acquiring a plasma spectrum of the coal sample; and the spectrum fusion analysis device is electrically connected with the control device. According to the rapid detection system provided by the embodiment of the invention, online real-time prediction of the caking index, the calorific value, the ash content, the total moisture content, the total sulfur content, the volatile component and the ash fusion point of the coal is realized, and real-time data support is provided for coking coal blending process optimization and metallurgical coke quality control.

A spectrometer apparatus (110) and method of obtaining spectroscopy information about at least one object (112) are disclosed. The spectrometer device (110) comprises: at least one light source (114) for generating illumination light (116) for illuminating the object (112), the light source (114) comprising at least one light emitting diode (118) and at least one luminescent material (120) for converting primary light generated by the light emitting diode (118) into secondary light; ii. At least one broadband detector (128) for detecting detection light (130) within a spectral range at least partially comprising a spectral range of the primary light and the secondary light, the broadband detector (128) is configured to generate at least one primary detector signal upon detection of the detection light (130) within the spectral range of the primary light, where the broadband detector (128) is further configured to generate at least one secondary detector signal upon detection of the detection light (130) within the spectral range of the secondary light; and iii. At least one evaluation unit (136) for evaluating the primary detector signal and the secondary detector signal generated by the broadband detector (128), for determining responsivity information (137) about the broadband detector (128) from one of the primary detector signal or the secondary detector signal, and for deriving spectroscopy information about the object (112) from the other of the primary detector signal or the secondary detector signal by taking into account responsivity information (137) about the broadband detector (128).

The invention provides a biomass combustion in-situ measurement method based on spectrum and flame imaging. The biomass combustion in-situ measurement method comprises the following steps that a biomass sample is pretreated and conveyed to a dropper furnace combustion monitoring system through pneumatic conveying; preheating, and monitoring combustion parameters in real time through a temperature control system and a data acquisition system; the synchronous acquisition system is connected with the Fourier transform infrared spectrometer, the ICCD camera and the intermediate infrared camera; spectral information in a flame heat radiation (200 nm to 20 [mu] m) wave band is obtained, and free radical and smoke component images are collected; and finally, comprehensively processing and analyzing the data, and extracting key parameters such as temperature, radiation characteristics, component concentration and the like. According to the invention, multi-parameter in-situ synchronous measurement is realized, combustion surface temperature and radiation characteristics are accurately calculated in combination with an advanced algorithm, and free radical and flue gas component distribution is quantified through an imaging technology. An efficient and reliable technical means is provided for studying a biomass combustion mechanism, optimizing a combustion process and controlling pollution emission, and the biomass combustion device has important scientific research and application values.

The invention relates to the technical field of gas detection, and discloses an underground pipe gallery leakage gas identification system based on infrared spectroscopic analysis, comprising an acoustic sensor unit passively monitoring a pipe gallery acoustic signal and generating a wake-up instruction and azimuth information, the infrared spectrum analysis module emits infrared beams with specific wavelength combination to a pre-estimated leakage sector according to the azimuth information and receives a returned light signal to generate spectral data; and the control processing unit analyzes the spectral data and judges and confirms a leakage event in cooperation with the azimuth information. According to the method, energy efficiency mode innovation from continuous scanning to event driving is realized through a cooperative mechanism of acoustic triggering and infrared directional detection, and the accuracy and response speed of trace leakage identification in a complex environment are remarkably improved by combining a differential spectrum technology and spatial coding logic verification.

The invention relates to a wide-temperature-range stable dual-band infrared optical filter for an extreme environment and a preparation method thereof. The wide-temperature-range stable dual-band infrared optical filter comprises a front film system, a substrate and a back film system, the front film system and the back film system are formed by alternately overlapping germanium layers and zinc sulfide film layers; the drift distance of the central wavelength of the passband of the infrared optical filter is less than 0.05% within the temperature range of-120 DEG C to + 85 DEG C. According to the invention, the internal stress of the film layer is obviously reduced through the optimized film system design, so that the excellent stability of the optical performance in an extreme temperature environment is realized, the technical problem that some special infrared spectrum detection systems work in a wide temperature range is solved, and a reliable solution is provided for high-end aerospace infrared application.

Provided are a coal testing system and method, and a storage medium. The system comprises: a detection unit, which is used for collecting spectral data of a coal sample under test; a training unit, which is used for constructing, by means of the spectral data, neural networks across neural network dimensions on the basis of a greedy search, and obtaining a coal sample test model by means of performing training on the basis of simulated samples which have been subjected to sample augmentation; and an analysis unit, which is used for executing fusion processing on the spectral data to obtain target spectral data, and on the basis of the coal sample test model, executing target spectral data inference to obtain a coal test result, wherein the detection unit comprises a near-infrared spectroscopy collection module and an X-ray fluorescence spectroscopy collection module.

Methods and systems are disclosed for predictive classification of a raw mass spectrum of a sample, the spectrum resulting from mass spectrometry analysis. The predictive classification is performed by a trained large-scale spectral model, including a self-supervised large-scale base spectral model trained with unidentified sample spectra from a plurality of sources and fine-tuned with a task-specific model trained with identified spectra.