26 results about "Synthetic spectrum" patented technology

The invention relates to the technical field of gas detection, and provides a doped gas concentration detection method and system based on a gas analyzer. The method comprises the following steps: constructing a standard spectrum database under the same pressure and temperature as a to-be-measured site; the method comprises the following steps: introducing sample gas into an optical absorption cell, carrying out wavelength scanning through a tunable diode laser in a plurality of adjacent absorption spectral line intervals containing target gas and interference gas, superposing a high-frequency sinusoidal modulation signal on a driving current, and synchronously detecting a second harmonic component of transmission laser to generate an actual measurement spectrum; fitting the actually measured spectrum with a synthetic spectrum generated on the basis of a standard spectrum database, and outputting a target gas concentration ratio as a detection result when a residual error meets a threshold value, so as to solve the technical problem that the detection precision is reduced due to spectrum overlapping caused by interference gas, and achieve the purpose that the detection precision is reduced by constructing the standard spectrum database and second harmonic spectrum fitting analysis. And the accuracy and the anti-interference capability of gas detection are improved.

The invention discloses a combustion field temperature measurement method and device based on a Raman vibration conversion spectrum, and the method comprises the steps: extracting an equipment instrument function through a cold flow nitrogen spectrum, and carrying out the convolution of a nitrogen theoretical spectrum data set through the instrument function, thereby obtaining a synthetic spectrum; synthesizing a monotonic function curve of the spectral band intensity ratio along with the temperature change through nonlinear fitting; raman scattering light generated by the measurement area is obtained; converting the generated Raman scattering light into a spectral signal; processing the spectral signal, and extracting a nitrogen Raman Stokes signal spectral band intensity ratio after asymmetric Voigt function fitting; and substituting the band intensity ratio into the monotonic function curve, and carrying out back calculation to obtain a temperature measurement result. According to the method, related characteristic information of the Raman vibration-rotation spectrum and the temperature is utilized, and the asymmetric Voigt function is combined to carry out fine fitting on the Raman spectrum, so that the limitation of the existing temperature measurement technology is overcome, and more accurate and quicker combustion field temperature measurement is realized.

The invention discloses a wheat grain moisture content lossless prediction method based on hyperspectral imaging and Wasserstein generative adversarial network data enhancement, which realizes accurate moisture prediction by fusing spectral feature data enhancement and deep learning modeling. Collecting spectral image data of the wheat grains by using a visible light-near infrared and short wave infrared hyperspectral imaging system; a Wasserstein generative adversarial network is adopted to generate synthetic spectrum and moisture data in a differentiated mode, data distribution consistency is verified through t-SNE, and a triple training set is expanded; noise is eliminated in combination with first-order derivative preprocessing, key characteristic wavelengths are screened by using SPA and ReliefF algorithms, and a convolutional neural network regression model is constructed; experiments show that the method achieves # imgabs0 # imgabs1 # RMSEP = 0.5717 in the visible light-near infrared band and # imgabs2 # RMSEP = 1.0009 in the short wave infrared band, and the precision is remarkably improved compared with a traditional extreme learning machine and a back propagation neural network.

A method of Raman spectroscopy for determining concentrations of at least one target component included in a medium including a given combination of multiple components includes: for each component of the medium, providing a reference spectrum of the component; based on Raman peaks included in the reference spectra, identifying Raman peaks that occur within less than a predetermined minimum spectral distance from each other as disturbing peaks; for each component, determining a component spectrum by eliminating each Raman peak included in the reference spectrum of the respective component identified as a disturbing peak; based on the component spectra, determining synthetic spectra of samples of the medium including different concentrations of the components; and based on the synthetic spectra, determining and providing a model for determining concentrations of each target component based on measured spectra of samples of the medium.

The invention relates to a spectrometric measurement method and an analyzer for evaluating variability presented by a measurement spectrum. A spectrometric measurement method includes: presenting, with a plurality of calibration light sources, a known emission spectrum that performs calibration of a plurality of spectrometers; evaluating variability exhibited by the measurement spectrum determined by the calibration spectrometer based on calibration data obtained by these calibrations; determining a reference spectrum of a reference sample presenting a known reference value of the at least one measured object; determining, based on the reference spectrum and the previously evaluated variability, a synthetic spectrum of the reference sample exhibiting the previously evaluated variability; and determining and providing a model for determining a measurement result of the at least one measured object based on the measurement reference spectrum, the synthetic spectrum and the corresponding reference value. An analyzer for evaluating variability presented by a measurement spectrum determined by a calibration spectrometer is configured to perform analog calibration.

Proposed is a gamma spectrum separation system for a whole-body counter for generating artificial neural network training data by separating a single-nuclide spectrum from a spectrum in which multiple nuclides are mixed. According to the present disclosure, the present disclosure includes an efficiency calculation unit configured to obtain an efficiency for each of channels A, B, and C of a whole-body counter, a count rate contribution amount calculation unit configured to calculate a count rate contribution amount of each channel by multiplying the efficiency of each channel by a value obtained by multiplying a mixed source for generating the artificial neural network training data by a gamma-ray emission rate of each nuclide, and a normalization unit configured to subtract the count rate contribution amount from the count rate of each channel measured by the nuclide and then perform normalization to obtain a normalized spectrum of the nuclide. Accordingly, a single-nuclide spectrum can be separated from a spectrum in which multiple nuclides are mixed, and the single-nuclide spectrum can be used to generate a synthetic spectrum for artificial intelligence training.

The invention discloses a soil salinity inversion method and device based on a hybrid generative adversarial network, and the method comprises the steps: constructing a dual-band and a triple-band of each soil sample, and calculating a second spectral index of each dual-band and a third spectral index of each triple-band; screening dual wavebands according to the first Pearson's correlation coefficient and a preset threshold value, and screening three wavebands according to the second Pearson's correlation coefficient and the preset threshold value; and training the feature hybrid generative adversarial network by using the single wave band, the first coordinate and the first spectral index, and generating a plurality of first synthetic spectral samples and second coordinates according to the screened double wave bands, the second spectral index, the first coordinate, the screened three wave bands and the third spectral index. According to the method, the quality, diversity and number of the second synthetic spectrum samples are improved, the problem that the generalization ability of the soil salinity prediction performance optimal model is weak is solved, and the soil salinity prediction precision of the soil salinity prediction performance optimal model is improved.

The application discloses a handheld spectral sensor signal fidelity method and a related device, and relates to the technical field of spectral detection. The method comprises the following steps: acquiring a measured spectral signal measured by a handheld spectral sensor; identifying a deviation prediction signal based on the measured spectral signal and a deviation signal identification model; and determining a spectral signal after removing the deviation prediction signal based on the measured spectral signal and the deviation prediction signal, wherein the deviation signal identification model is obtained by training a machine learning model based on a sensor deviation feature training set; the sensor deviation feature training set comprises m*n information pairs, each information pair comprises synthetic spectral information and label information; the synthetic spectral information is obtained by synthesizing one unbiased reference information in a set of unbiased reference information and one deviation information in a set of deviation information; the deviation information comprises deviation information generated under the influence of an environment and hardware; and the application can improve the signal fidelity precision of the handheld spectral sensor.

This invention discloses a method and apparatus for soil salinity inversion based on a hybrid generative adversarial network (GAN). The method includes: constructing dual-band and tri-band features for each soil sample, and calculating a second spectral index for each dual-band and a third spectral index for each tri-band; selecting dual-band features based on a first Pearson correlation coefficient and a preset threshold, and selecting tri-band features based on a second Pearson correlation coefficient and a preset threshold; training the hybrid GAN using single-band features, a first coordinate, and a first spectral index; and generating multiple first synthetic spectral samples and second coordinates based on the selected dual-band features, second spectral index, first coordinate, selected tri-band features, and third spectral index. This invention improves the quality, diversity, and quantity of the second synthetic spectral samples, solves the problem of weak generalization ability of the best-performing soil salinity prediction model, and improves the accuracy of soil salinity prediction by the best-performing model.

This application relates to the field of lighting technology, and discloses a spectral control method, device, computer equipment, and storage medium, including receiving a brightness adjustment command, the brightness adjustment command carrying target brightness level information; searching for the driving parameters of each light-emitting channel from a preset driving parameter table according to the target brightness level information, or calculating the driving parameters of each light-emitting channel according to a preset algorithm; outputting the driving parameters to a multi-channel LED driving circuit, so that the driving circuit adjusts the driving current or driving duty cycle of each channel, controlling the multi-channel LED to output a composite spectrum that matches the target brightness level and has high color recognition; the composite spectrum has a preset target spectral structure at the target brightness level, so as to solve the technical problem that the color rendering effect and brightness perception of existing 2700k–3500k warm white LED light sources deteriorate in low-illuminance environments.

The invention relates to the technical field of coal strategies, and discloses a coal industrial element measurement precision improvement method based on NIRSLIBS, and the method comprises the steps: extracting actual measurement spectral data of a part of collection angles from a full-angle coal reference database, and generating physical prediction spectral data; training a virtual wave field reconstruction model by using a neural radiation field technology; generating a full-angle synthetic spectrum and an information gain ranking based on the virtual wave field reconstruction model and the full-angle coal reference database, and screening a student sampling strategy parameter set; carrying out limited angle spectrum acquisition on coal by using a student sampling strategy, and generating an industrial field full-angle synthetic spectrum in combination with a prediction spectrum of an unacquired angle output by the virtual wave field reconstruction model; and inputting the industrial field full-angle synthetic spectrum into the element content prediction model to obtain a coal element content prediction result, and combining with the correction scheme to obtain a coal industrial element measurement result. According to the invention, high precision and long-term stability of coal industrial element measurement are realized.

This application discloses a spectral synthesis method applied to broiler farming. It generates a synthesized spectrum by combining red and green LEDs, with a green to red light ratio of 2:1 and a color temperature range of 7000K ± 500K. Furthermore, this application also discloses a spectral synthesis device and a broiler farming method. By combining red and green LEDs and adjusting the ratio of green to red light in the spectrum to achieve a 2:1 ratio, this method irradiates the broilers with the synthesized spectrum throughout their entire farming cycle. This stimulates the poultry, maximizing their growth potential, improving immunity, reducing antibiotic use during growth, increasing weight, and lowering the feed conversion ratio, thereby achieving optimal economic benefits.

The invention relates to the technical field of copper bar machining, and discloses a material utilization rate optimized layout and order merging method in the copper bar machining process, and the method comprises the steps: obtaining a machining order set and cutting equipment state parameters, performing feature preprocessing on the part to calculate an effective occupied length; mapping the length to a discrete interval to generate an effective virtual filling degree spectrum, and screening orders by using a comprehensive utility function and a seed adsorption strategy to construct a to-be-processed pool; extracting topological characteristics of a synthetic spectrum of a to-be-treated pond, and adaptively matching a layout solver according to the topological characteristics; and a cutting scheme is generated through a solver, and a machining instruction is generated after pre-execution verification is passed. According to the method, the order combination structure is optimized through the spectrum complementation strategy, and the utilization rate of the copper bar material is remarkably improved while the feasibility of the cutting scheme is ensured in combination with the self-adaptive solution and the physical constraint pre-mapping.

A computer-implemented method for synthesizing spectral data includes the following steps: acquiring a set of spectral data each associating a spectrum with a sample having a given chemical composition, using a spectroscopy method, determining a theoretical model of the distribution of the intensities of the spectrum for each wavelength channel of the spectrum, generating a set of synthetic spectral data by generating, for each wavelength channel of the spectrum, a randomly drawn intensity according to the probability distribution of the theoretical model.

A measurement apparatus 1 according to this disclosure includes a controller 80 and a spectroscope 10 having an optical element in which an aperture 132a to pass light L1 to be measured is formed. The controller 80 executes a first process to generate a synthetic spectrum Sr(i) of the narrowed light L1 by synthesizing at least a first spectrum S1(i) when a beam spot P of the light L1 is at a first position x1 within the aperture 132a and a second spectrum S2(i) when the beam spot P of the light L1 is at a second position x2 within the aperture 132a. The first position x1 includes a position shifted to one side in a predetermined direction D from a reference position x0 of the beam spot P within the aperture 132a. The second position x2 includes a position shifted to the other side from the reference position x0.

A method for one or more modules of one or more processors of a spectral sensor system begins by receiving a plurality of synthetic spectra, where a synthetic spectrum of the plurality of synthetic spectra includes one or more known deviations from a reference spectrum. The method continues by generating a spectral output for each synthetic spectrum of the plurality of synthetic spectra and then training an artificial intelligence engine, using the combined spectral output to generate a trained neural network. The method then continues by calibrating, based on the trained neural network, a spectral response generated by another spectral sensor.

The present application relates to a method for matching AM1.5 standard sunlight spectrum by using multiple light sources, and the simulated spectrum wavelength range is the full spectrum of visible-short wave infrared sunlight spectrum radiation. The present application uses metal halide lamp, halogen lamp and multiple different kinds of high-power LED light sources as the alternative sub-light sources of the solar simulator, and the selected light sources are actually tested to obtain the spectral data of each light source as the alternative light source library. According to the superposition principle of the spectrum, the genetic algorithm is used to seek the best configuration of the light source type and quantity under the condition of meeting the spectral matching degree requirement. The present application overcomes the problems of spectral mismatching deviation and wavelength range limitation in the traditional solar simulation technology, and can realize the full spectrum solar simulation in the range of 350-2500nm at low cost, which is suitable for the research of solar spectrum radiation in the optical remote sensing physical simulation system.

A computer-implemented method for synthesizing spectral data, comprising the steps of: Acquiring (110) a set of spectral data, each associating a spectrum with a sample having a given chemical composition, by a spectroscopic method; Determining (130) a theoretical model of the intensity distribution of the spectrum for each wavelength channel of the spectrum; Generating (140) a set of synthetic spectral data (150) by generating, for each wavelength channel of the spectrum, an intensity randomly drawn according to the probability distribution of the theoretical model. Figure 1

An embodiment according to the present invention includes an audio decoder configured to decode a quantized spectrum and an envelope representation based on linear prediction coefficients from a data stream for a predetermined frame of consecutive frames. Further, the decoder is configured to locate a zero-quantized portion and a non-zero quantized portion in the quantized spectrum and derive the quantized spectrum by filling the quantized spectrum in the zero-quantized portion of the quantized spectrum with synthetic spectrum data modified in accordance with an envelope representation based on linear prediction coefficients in a first manner, and modifying the quantized spectrum in the non-zero quantized spectrum portion in a second manner in accordance with an envelope representation based on linear prediction parameters. Further, a decoder is configured to reconstruct a predetermined frame using the dequantized spectrum. The audio decoder is configured such that, for the predetermined portion, the modification according to the first manner and the modification according to the second manner are such that the spectrally quantized noise shaping comprises different smoothness characteristics. In addition, a corresponding encoder and method are also disclosed.

In an approach to adaptive library building, a method includes: acquiring one or more spectra for particles from a source material using one or more environmental surveillance sensors; identifying a first set of spectra as an anomalous spectra for an unknown material based on a plurality of spectra for one or more known particles in a particle spectra library; generating a plurality of synthetic spectra for the unknown material using the first set of spectra; creating a synthetic entry with the plurality of synthetic spectra for the unknown material in the particle spectra library; acquiring a second set of spectra for of the unknown material using the synthetic entry and spectra acquired from the one or more environmental surveillance sensors; validating the second set of spectra to be from the same source material as the first set of anomalous spectra; and replacing the synthetic entry with the second set of spectra for the unknown material in the particle spectra library.

The embodiment of the invention provides a spectrum programmable lighting control method and system based on a multi-channel narrow-band light source. The spectrum programmable lighting control method based on the multi-channel narrow-band light source comprises the following steps: acquiring input data for a target expected spectrum input by a client; determining a spectrum coverage range of the target expected spectrum based on the input data, and selecting a plurality of target light sources of which the wavelengths are within the spectrum coverage range from a plurality of narrow-band light sources of the target lamp; corresponding target mixing coefficients are distributed to all the target light sources, the target mixing coefficients are used for adjusting the output proportion of all the target light sources, and the difference between the synthetic spectrum of the multiple target light sources and the target expected spectrum is smaller than a preset range; and controlling the corresponding target light sources to emit light based on the target mixing coefficients. Therefore, the spectrum of the lamp can be flexibly changed according to requirements.

A preparation method for preparing spectrometric determinations of a measurand in a target application using a spectrometer is provided. On the basis of reference data recorded in the target application, a normalized measurand master spectrum with a spectral distribution characteristic of the measurand is determined. On the basis of the measurand master spectrum, synthetic spectra are generated which cover a value range greater than or equal to a value range covered by the reference values. On the basis of the synthetic spectra, information for carrying out the spectrometric determinations, including information comprising a property, a wavelength range, and / or a path length range for an optical path length suitable for carrying out the spectrometric determinations, and / or comprising a calculation rule, with which, on the basis of measurement spectra in the target application, measured values of the measurand are determined.

The application discloses a continuous variable regression prediction-oriented generative adversarial network spectrum data enhancement method, carries out spectrum data acquisition and pretreatment, constructs pre-training networks T1 and T2 of a continuous conditional generative adversarial network and the continuous conditional generative adversarial network, constructs a discriminant network model D by using generated spectrum and experimental true spectrum, trains a generated network model G by sharing the training parameters of the D network, generates new synthetic spectrum according to the new generated network G obtained through training, makes the synthetic spectrum data gradually approach the true experimental spectrum data, judges the similarity of the synthetic spectrum and the true experimental spectrum, inputs preset label values into a label input network to generate corresponding synthetic spectrum when the similarity meets the requirements, and forms a training set of a machine learning prediction model by the synthetic spectrum data and corresponding preset labels and the true experimental spectrum data, so that the machine learning prediction model is trained and tested. The application improves the precision of the machine learning prediction model.