153 results about "Linear discriminant analysis" patented technology

The invention discloses a Raman spectrum training and prediction algorithm SPP-1D of a multichannel one-dimensional convolutional neural network based on spatial pyramid pooling. According to the method, local features of spectral data are extracted through 1D-CNN, and features are pooled on multiple scales by using spatial pyramid pooling (SPP), so that global feature representation is generated. Compared with the traditional principal component analysis (PCA) and linear discriminant analysis (LDA) methods, the method can effectively capture the complex nonlinear relationship of the spectral data and enhance the anti-noise performance. The SPP-1D supports multi-channel input, a plurality of characteristic channels of the Raman spectrum can be processed at the same time, correlation and complementarity between the channels are fused, and more comprehensive characteristic description is achieved. The algorithm is suitable for application scenes such as single substance identification, complex mixture component analysis, spectral imaging and the like, and the precision, robustness and generalization ability of Raman spectrum data processing are remarkably improved. According to the invention, deep learning and a signal processing technology are creatively combined, and an efficient spectral analysis tool is provided for the fields of material science, chemical analysis and biomedicine.

Disclosed in the present invention are a cumulative pressure stress effect evaluation system and method. The system comprises: an animal modeling module, configured to construct various pressure stress level models by means of applying different durations of pressure to a target model animal; a spontaneous behavior capture module, configured to capture spontaneous behaviors of the target model animal corresponding to different pressure stress levels to obtain corresponding action recognition data, and construct a first data set according to a corresponding relationship between the action recognition data and the pressure stress levels; a behavior phenotype analysis module, configured to use linear discriminant analysis to perform dimension reduction on the first data set, so as to obtain a second data set in a discrimination space; and a pressure degree evaluation module, configured to use the second data set to train a machine learning model, so as to obtain an optimized cumulative pressure stress effect evaluation model. The present invention can be used for cumulative pressure stress effect evaluation, is suitable for precise medicine, and can achieve behavior phenotype difference recognition induced by the same pressure stress source and different pressure degrees.

The invention discloses a voiceprint characterization method and system for resisting voice conversion and voice synthesis based on deep learning. Relates to the field of speech recognition and biological feature security. Comprising the steps of 1, acquiring an input voice sample signal in an automatic speaker verification system, and extracting an FBANK feature of the voice sample signal; 2, performing depth feature extraction on the FBANK features by using a convolutional neural network (CNN) to obtain a depth feature vector; 3, processing the depth feature vector by using a recurrent neural network (RNN), and generating a forged identity vector of the voice; and 4, classifying the counterfeit identity vectors by using a linear discriminant analysis (LDA) module, and outputting a judgment result. According to the invention, the accuracy of counterfeit detection can be effectively improved in both clean and noise environments.

A photovoltaic power generation associated physical quantity mining method based on historical time series data analysis, and a related apparatus. The method comprises: acquiring historical time series data of multiple dimensions during photovoltaic power generation; calculating degrees of mutual information between the historical time series data of the multiple dimensions of photovoltaic power and the photovoltaic power; selecting the historical time series data of which the degree of mutual information satisfies a set requirement to serve as data related to photovoltaic data; and using a linear discriminant analysis (LDA) method to perform feature dimension reduction on the selected historical time series data to obtain data having undergone dimension reduction processing. In the present invention, by using a maximal information coefficient (MIC) feature selection method, data most related to photovoltaic power generation is selected from original feature variables, and then by using an LDA-based feature dimension reduction method, high-dimensional data is mapped to a lower-dimensional space. By means of the MIC feature selection method and the LDA-based feature dimension reduction method, the accuracy of photovoltaic power generation prediction is effectively improved.

The invention relates to the technical field of electromyographic signal analysis and processing, in particular to a low-delay electromyographic signal processing method based on multi-channel fusion, which comprises the following steps of: acquiring full-band electromyographic signals by using a surface electrode array, and screening channels by adopting a mode of combining a Pearson's correlation coefficient and mutual information; the method comprises the following steps: setting dual thresholds, combining signal strength and electrode impedance detection, then performing 20-500Hz Butterworth band-pass filtering, Z-Score standardization and normalization processing on screened signals to improve the signal quality, optimizing sliding window parameters through the Parseval theorem and a dynamic time warping algorithm, balancing the real-time performance and the feature extraction effect, and improving the accuracy and the accuracy of feature extraction. And finally, time domain, frequency domain and time-frequency domain multidimensional features are extracted in each time window, and feature vectors are generated through principal component analysis and linear discriminant analysis fusion and dimension reduction. The invention aims to solve the problems of poor real-time performance and low feature identification degree of the existing electromyographic signal processing method.

The invention relates to the technical field of intracranial pressure monitoring, in particular to a craniocerebral injury patient secondary brain injury early warning method based on multi-modal fusion, which comprises the following steps: acquiring intracranial pressure, cerebral blood flow, electroencephalogram signals and brain tissue oxygen partial pressure signals, carrying out denoising and normalization processing, constructing a multi-modal normalized time sequence data set, and carrying out multi-modal fusion on the basis of the multi-modal normalized time sequence data set; and inputting a linear discriminant analysis model to generate a time sequence difference vector, constructing a difference driving map based on a Bayesian dynamic network model, identifying abrupt change nodes, outputting a risk early warning section set, and carrying out trend stability analysis and reconstruction optimization. According to the method, the abnormal state recognition sensitivity is improved through normalized processing of multi-modal signals, enhancement of signal fusion capability, quantitative capture of direction change, reconstruction of spatial relationship between signals, modeling of dynamic transition probability, recognition of abrupt change nodes, combination of time difference vector change, evaluation of signal linkage trend, positioning of risk sections and extraction of parameter features; and the early warning and intervention precision is improved.

The invention discloses a lightweight optical fiber vibration intrusion event identification method and system for perimeter security and protection, and belongs to the technical field of optical fiber sensing technology and mode identification, and the method comprises the steps: collecting an original vibration signal of a perimeter monitoring region through a distributed optical fiber vibration sensing system; performing wavelet threshold de-noising preprocessing on the original vibration signal to obtain a de-noised signal; performing feature extraction on the denoised signal, and constructing a high-dimensional feature vector; performing dimension reduction processing on the high-dimensional feature vector by using a linear discriminant analysis method to obtain a low-dimensional classification feature vector; and inputting the low-dimensional classification feature vector into a pre-trained lightweight convolutional neural network model for classification and identification, and outputting a corresponding intrusion event category. According to the method, through cooperation of front-end LDA dimension reduction and a rear-end lightweight network, the model parameter quantity and calculation overhead are greatly reduced while high recognition precision is guaranteed, efficient real-time deployment on edge equipment is achieved, and the method is suitable for intrusion detection in perimeter security and protection.

The invention discloses a method for identifying freshness of different parts of pork and application. The method comprises the following steps: firstly, constructing a PCA-LDA model for identifying fresh pork and frozen-thawed pork, wherein the PCA-LDA model comprises the following steps: respectively obtaining mass spectrum fingerprint data of the fresh pork and the frozen-thawed pork; and based on a principal component analysis method and a linear discriminant analysis method, constructing a PCA-LDA model by using the mass spectrum fingerprint data of the fresh pork and the mass spectrum fingerprint data of the frozen and thawed pork. Secondly, a pork sample to be detected is identified on the basis of the PCA-LDA model, the result shows that the identification accuracy reaches 99% or above, the identification efficiency is high, the single-time determination identification speed is 6 seconds per sample, fresh pork and frozen-thawed pork at different parts can be rapidly and accurately distinguished, and the method has an extremely good application prospect in pork product identification.

The invention discloses a pork safety traceability quality nondestructive testing method and system for livestock breeding. The method comprises the following steps: constructing a stable detection environment by utilizing a constant temperature and humidity device, a dynamic environment monitoring unit and an optical shielding material; a Fourier transform infrared spectrometer is adopted to collect spectral data, and a non-contact texture detection device is combined to measure physical parameters; denoising, standardization processing and feature fusion are carried out on the collected data, and key variables are extracted through principal component analysis; grouping the samples by using a K-means algorithm, and verifying sample classification by using linear discriminant analysis; quantitative prediction and classification of pork quality are realized based on partial least squares regression and a support vector machine model; the model is applied to an actual scene, real-time processing and visual display are achieved through online data collection, and a data sample library is continuously expanded. The method has the advantages of high detection speed, high precision, wide application range and the like, and can be widely applied to the fields of meat quality detection and food safety.

The invention belongs to the technical field of analytical chemistry, and relates to a method for identifying and detecting endogenous mercaptan of a high-activity oxidized nano-enzyme based on machine learning, which comprises the following steps: firstly, preparing a Mnx (DTPMP) nano-enzyme, then constructing a sensor array of one nano-enzyme, four reaction times, three targets and six parallel samples, and detecting the endogenous mercaptan in the Mnx (DTPMP) nano-enzyme. Constructing a sensor array of one nano enzyme, four reaction times, 7 concentrations and 6 parallel samples for each endogenous mercaptan; and finally, analyzing the data through hierarchical clustering analysis, linear discriminant analysis and a support vector machine to obtain an LDA score chart, an HCA map, an SVM confusion matrix and a concentration prediction map of the endogenous mercaptan. After a sample to be detected is subjected to a chromogenic reaction, the absorbance is measured, and the type and concentration of the endogenous mercaptan of the sample to be detected can be distinguished according to the obtained spectrum. The kit has good anti-interference performance, normal cells and cancer cells can be accurately identified according to the content of glutathione in the cells, and the severity of diseases can be judged according to the content of homocysteine in serum.

The invention provides a data mechanism fusion prediction system and method for loss of an unbalanced transformer of a distribution network, and relates to the technical field of operation and loss reduction of the distribution network. Firstly, electric quantity data such as voltage, current, active power and reactive power of a power distribution network are collected to form a matrix to form an original data set, correlation analysis is carried out on data in the original data set by adopting a mutual information method, and dimensionality reduction is carried out by adopting a linear discriminant analysis method. Taking the output after dimension reduction as the input of transformer loss intelligent prediction, calculating the zero-sequence impedance corresponding to the original data set to obtain the total loss of the transformer under the unbalanced load of the distribution network, and training the transformer loss intelligent prediction model by using the output after dimension reduction and the corresponding total loss of the transformer to obtain the transformer loss intelligent prediction model. And finally, the real-time intelligent prediction of the transformer loss under the unbalanced load of the distribution network can be realized by using the real-time monitoring data of the distribution network.

The present invention relates to a technology for rest state-based user authentication by using an electromyogram signal and an inertia measurement device signal and provides a rest state-based user authentication device using an electromyogram signal and an inertia measurement device signal, which extracts a user's unique electromyogram signal and inertia measurement device signal during a rest state in which a specific operation is not performed, and inputs the extracted signal into a linear discriminant analysis and ensemble classification device to identify and authenticate the user, and a method thereof.

The present invention discloses a method and system for on-demand intelligence distribution, comprising the following steps: (1) receiving superior telegraphic information to obtain action tasks and intelligence information; (2) extracting features: commander features, action task features, and intelligence features; (3) error processing and model optimization; and (4) outputting intelligence in order of interest based on the optimized model. The present invention preprocesses task and intelligence text using an improved linear discriminant analysis method, encodes task and intelligence data using a sparse edge denoising autoencoder, and uses a matrix decomposition algorithm to remove blank portions of the matrix for dimensionality reduction, thereby effectively improving the accuracy of the training model, while also increasing the precision of on-demand intelligence distribution and enhancing the command efficiency of commanders.

The invention relates to the technical field of urban rainfall pattern recognition, and aims to solve the problems that an existing method depends on an empirical formula, rainfall classification is unstable, and interpretability is insufficient. The method comprises the following steps: acquiring multi-site long-time actually measured rainfall data, and identifying an independent rainfall event based on a rainfall interval threshold value; multi-dimensional features such as rainfall duration and peak rainfall intensity are extracted and standardized; performing hierarchical clustering by adopting a Ward minimum variance method, and determining an optimal clustering number by combining a Clinski-Harabasz index and the like to obtain a rainfall mode; training a linear discriminant analysis (LDA) model by taking a clustering result as a label, and verifying and quickly classifying a new rainfall event; the result is used for flood control scheduling, flood risk assessment and the like. According to the method, rationality is improved based on measured data, classification stability is improved by fusing clustering and discriminant analysis, expandability is high, and intelligent disaster prevention is supported.

The present disclosure provides an array measuring method and interpretation device for ultrasonic detection of middle ear effusion, including an ultrasonic probe, an ultrasonic receiver, an analog-to-digital converter, and an analysis unit. The surface of the mastoid is divided into a plurality of measurement areas, and when ultrasonic is used for non-invasive detection of middle ear effusion, linear discriminant analysis is used for pre-training to find the best detection position and weighting parameters thereof to obtain the accurate evaluation value.

The application discloses a tire force estimation method based on in-utero sensing information and a Bayesian neural network, builds an acceleration type intelligent tire finite element model based on finite element and coordinate system conversion theory; determines the optimal installation position of the acceleration sensor for the three-way tire force by using a Fourier amplitude sensitivity test (FAST); collects acceleration signal-tire force data sets under different test conditions, extracts the acceleration signal in the footprint area, and selects the optimal input features by using a linear discriminant analysis (LDA); processes the acceleration signal-tire force data set after optimal feature selection by using linear normalization theory; builds a tire force estimation algorithm based on a Bayesian neural network according to the characteristics of the training data and the test data, and finally outputs the predicted value and the prediction variance of the tire force. The application has high prediction accuracy, good stability and strong generalization performance, and provides a more reliable technical solution for tire force estimation.

The present application relates to the field of artificial intelligence, and relates to a model trust region-based copyright protection method for smart grid deep learning models. The method comprises: acquiring a smart grid deep model set; for each model in the smart grid deep model set, searching an exclusive dataset for feature data samples having a model prediction value approximate to a model discrimination boundary, so as to obtain a trust region feature point set; performing dimensionality reduction on gradient vectors of the trust region feature point set on the model discrimination boundary according to a linear discriminant analysis method, so as to obtain perturbation vectors of the trust region feature point set; on the basis of predicted label changes of each model before and after the trust region feature point set is combined with the perturbation vectors, generating a model feature identifier set corresponding to the smart grid deep model set; and, on the basis of the model feature identifier set, training a copyright detection model to be trained, so as to obtain a pre-trained copyright detection model.

The invention provides a flower quality grading quality inspection method and system based on AI and image processing, and belongs to the technical field of computer vision and pattern recognizing.The method comprises the steps that an original image of the surface of a flower is collected through high-resolution imaging equipment, and high-frequency sub-band data representing tiny physical characteristics are extracted through multistage discrete wavelet transform; meanwhile, a sliding window is adopted to traverse the image, the color information entropy of a local area is calculated, and a color entropy graph is constructed. And after vectoring and splicing the two types of features, inputting the two types of features into a deep belief network model based on a multilayer restricted Boltzmann machine, and extracting deep abnormal feature codes. And finally, performing linear discriminant analysis on the code by utilizing a classification projection vector based on inter-class and intra-class distance optimization to generate an insect attack infection index, and realizing automatic grading quality inspection of the flower quality according to the insect attack infection index. According to the method, precise recognition and quantitative grading of tiny insect pests and recessive lesions on the surfaces of the fresh flowers are realized.

The present invention discloses a graph convolution fusion network for hyperspectral image classification, comprising: image data preprocessing, mainly comprising utilizing methods such as block partitioning, linear discriminant analysis and simple linear iterative clustering to divide segmentation and block data of different scales, and constructing a pixel-level graph structure based on the segmentation and block data; constructing a classification network, comprising a spectral conversion module, a block data graph convolution branch, a block data convolution branch, a segmentation data graph convolution branch, a block data graph convolution feature processing module, a segmentation data feature processing module and a feature fusion module; the block data graph convolution feature processing module improves pixel-level feature expression and enhances classification accuracy by combining a large convolution kernel convolution layer with neighborhood aggregation; the segmentation data feature processing module improves pixel-level feature expression and enhances classification accuracy by utilizing feature similarity weight aggregation; the feature fusion module learns the intrinsic connection between features through void convolution, improves feature fusion effect and enhances classification accuracy. The present invention designs a new convolution and graph convolution fusion neural network to perform hyperspectral image classification. First, block partitioning, linear discriminant analysis and simple linear iterative clustering are used to divide the segmentation and block data of different scales, and a pixel-level graph structure is constructed based on the segmentation and block data. Then, the spectral features of the segmentation and block data are extracted and their spectral dimensions are reduced through the spectral conversion module. Then, convolution and graph convolution are used to extract the spatial features of the segmentation and block data. Then, the block data graph convolution feature processing module is used to process the block data graph convolution features, and the segmentation data feature processing module is used to process the segmentation data features. Finally, the feature fusion module is used to adaptively fuse the features, and the fused features are finally classified.

The invention discloses a tobacco leaf maturity identification method and system based on chromatic values, and relates to the technical field of tobacco leaf identification. The method comprises the following steps: taking five measuring points of three parts of a leaf tip, a leaf middle part and a leaf base of tobacco leaves with different maturity degrees in a tobacco leaf mature picking period, measuring a chromatic value of each point through a colorimeter, carrying out standardization treatment on an original chromatic value, and carrying out statistical analysis and Fisher linear discriminant analysis by utilizing SPSS to obtain a canonical discriminant function and a characteristic value thereof; and the chromatic value indexes of the tobacco leaves with different maturity degrees are substituted into the discrimination function, and the discrimination function classification corresponding to the maximum value in the obtained calculation values is the maturity degree of the tobacco leaf. According to the method, the problem of inaccurate maturity distinguishing caused by subjectivity of traditional manual maturity judgment and external environment influence is effectively solved, objective quantitative evaluation of the maturity of the tobacco leaves is realized, and the method has important significance on selection of tobacco leaf raw materials and stability of product quality in the cigarette industry.

A method for rapidly detecting and identifying food-borne pathogens by using a two-dimensional / zero-dimensional MXene / GQDs Schottky heterojunction fluorescence sensor array based on machine learning comprises the following steps: synthesizing three nitrogen-doped graphene quantum dots through a hydrothermal method, determining the optimal excitation wavelength, concentration, reaction temperature and pH value of the nitrogen-doped graphene quantum dots, and further determining the fluorescence intensity of the nitrogen-doped graphene quantum dots under the optimal condition; constructing a fluorescence sensor array, mixing the three graphene quantum dots with target heavy metal ions, incubating, then measuring fluorescence intensity to obtain fluorescence response data, and normalizing the data to obtain the fluorescence response data. And analyzing by using machine learning methods of a decision tree, a support vector machine, K nearest neighbor, linear discriminant analysis, naive Bayes and a neural network. The prepared fluorescence sensor array can rapidly identify heavy metal ions, is excellent in accuracy, sensitivity and stability, provides an economical and efficient solution for heavy metal ion detection, and has remarkable application potential in environmental monitoring.

The application provides a lightweight identity recognition method combining voiceprint and earprint features. The method comprises the following steps: obtaining voice and ear canal echo signals of a known registered person and a person to be verified; extracting and fusing 13-dimensional mel-frequency cepstral coefficients (MFCC) of the voice and ear canal echo signals, i.e., obtaining voiceprint and earprint fusion features; inputting the fusion features into a lightweight identity recognition model to extract 128-dimensional embedding features of the known registered person and the person to be verified; calculating the similarity of the embedding features of the two types of persons by using a probabilistic linear discriminant analysis (PLDA) method; and determining whether the person to be verified is the known registered person according to the similarity. In the application, the fusion of earprint and voiceprint features improves the recognition performance of the classification model. The lightweight identity recognition model is obtained through a pre-training process, which reduces the equal error rate (EER) and greatly reduces the parameter quantity of the identity recognition model.

This invention discloses a heatstroke prevention and monitoring system based on conscious cognitive behavioral characteristics, belonging to the field of intelligent medical technology. This invention addresses the problems of existing technologies lacking intelligent analysis and exhibiting a certain degree of subjectivity, making it difficult to ensure the effectiveness of heatstroke prevention and monitoring. It analyzes the patient's physiological signals and behavioral data using a linear discriminant analysis model to predict whether the patient has heatstroke. After predicting heatstroke, a weighted average method is used to further calculate the patient's risk index, and different prevention and monitoring strategies are implemented based on the risk index. Based on this, it can effectively identify whether a patient has heatstroke and the risk index, thereby enabling targeted preventative measures to avoid further disease progression. Simultaneously, through intelligent analysis and judgment, it can effectively reduce the incidence of heatstroke, reduce the consumption of medical resources, and lower the socioeconomic burden.

The invention relates to a transformer light gas fault detection method and device based on deep learning, and belongs to the field of transformer fault diagnosis. An integrated classification model based on an improved whale optimization algorithm and adaptive enhancement is constructed, support vector machine hyper-parameters are optimized through Tent chaotic mapping initialization, a Levy flight strategy and a simulated annealing mechanism, and model stability and classification precision are improved in combination with weighted multi-weak classifier fusion and a regularization technology; the method comprises the following steps: collecting light gas data by adopting multiple types of gas sensors, and carrying out data preprocessing and feature dimension reduction by utilizing standardization, principal component analysis and linear discriminant analysis; real-time fault diagnosis and feedback are realized based on a gas concentration and fault type relation formula and an edge computing node, and the timeliness and accuracy of fault diagnosis are improved; the method has high real-time performance and practicability, and can quickly complete detection and fault identification of light gas fault gas of the transformer.

The invention provides a high-voltage circuit breaker voiceprint fault diagnosis method based on matrix-flow, and the method comprises the steps: firstly collecting voiceprint signal samples of a high-voltage circuit breaker in different fault states, and constructing a two-dimensional feature matrix of voiceprint signals through spectrum analysis and Mel-frequency cepstrum coefficient extraction; then, singular value decomposition is utilized to realize subspace representation of the features, and modeling is carried out on a Grassmann manifold; in order to improve the accuracy of similarity measurement between samples, a multi-kernel fusion and experience kernel alignment strategy is adopted to generate an experience alignment kernel matrix fusing internal and external structures of a class. Kernel linear discriminant analysis is carried out on the basis, a low-dimensional feature vector with strong discriminant performance is extracted, and a discriminant area of each fault type is constructed based on feature distribution. And finally, matching the voiceprint sample to be detected with various decision areas through a nearest neighbor principle to realize accurate judgment of the fault type of the circuit breaker. According to the invention, the accuracy, robustness and practicability of voiceprint fault diagnosis are improved.

The present invention provides a classification model for sheep echinococcosis and sheep brucellosis based on serum Raman spectroscopy, a method for establishing the model, and a detection device. The method for establishing the classification model for sheep echinococcosis and sheep brucellosis based on serum Raman spectroscopy comprises: (1) collecting blood from healthy sheep and diseased sheep, extracting serum, and obtaining serum samples; (2) detecting the serum samples using a laser Raman spectrometer to obtain Raman spectral data; (3) performing noise reduction, baseline correction, and normalization on the Raman spectral data to obtain pre-processed spectral data; (4) performing principal component analysis (PCA) dimensionality reduction on the processed spectral data, and then establishing a linear discriminant analysis (LDA) or support vector machine (SVM) classification model to obtain a classification model for sheep echinococcosis and sheep brucellosis. The classification model and detection device established by the present invention based on machine learning algorithms and Raman spectroscopy have potential in screening sheep infected with echinococcosis and sheep infected with brucellosis.

This invention discloses a gold nanoparticle array sensor and its method for rapid identification and detection of multiple chromium species. The gold nanoparticle array sensor includes four probe units: gold nanoparticles modified with small molecule organic compounds containing amino and carboxyl groups, phosphorus-containing inorganic polymers, amino-containing cationic surfactants, and quaternary phosphine-containing cationic surfactants. When rapidly identifying and detecting multiple chromium species, this invention acquires fingerprint spectral information corresponding to different chromium species and uses hierarchical clustering analysis (HCA) and linear discriminant analysis (LDA) algorithms to identify single Cr(III), Cr(VI) ions, Cr(III)-organic complexes, or mixed samples. Simultaneously, it can also perform quantitative analysis of target analytes under low concentration conditions. The method for rapid identification of multiple chromium species using the gold nanoparticle array sensor of this invention has strong identification ability, simple operation, short detection time, high sensitivity, and high selectivity, providing a new approach for the analysis of multiple chromium species in industrial wastewater.

The invention discloses a heat-resistant steel failure diagnosis method based on a sound energy correction laser-induced breakdown spectroscopy technology, and belongs to the technical field of heat-resistant steel failure detection. The method comprises the following steps: firstly, establishing an LIBS experimental platform to synchronously acquire a spectral signal and a sound wave signal of a heat-resistant steel sample, and correcting an original spectrum in real time by utilizing a sound energy value obtained by sound wave main wave band integration to weaken a matrix effect and plasma fluctuation interference; carrying out dimensionality reduction on the corrected high-dimensional spectral data by adopting multiple threshold segmentation in combination with an average spectrum comparison mechanism; and finally, constructing a recursive feature elimination-linear discriminant analysis-particle swarm optimization driven radial basis function kernel support vector machine collaborative model to realize high-precision judgment of the aging grade of the heat-resistant steel. According to the method, the complementary advantage of the opto-acoustic multi-mode signals is utilized, and the method has the characteristics of nondestructive detection, strong anti-interference capability and good model generalization performance, can adapt to complex industrial scenes, and provides reliable technical support for safe operation and maintenance of high-temperature key parts in the industries of electric power, machinery and the like.

The present disclosure is about a battery thermal runaway state monitoring and evaluation method based on linear discriminant analysis. The method includes: obtaining the operating state data of the battery to be monitored; obtaining the historical operating state data of the battery to be monitored in a first time period, and determining the characteristic gas concentration change rate, pressure change rate, temperature change rate and volume change rate of the battery to be monitored in the first time period based on the historical operating state data; using the operating state data to determine the thermal runaway risk index value of the battery to be monitored; inputting the operating state data, concentration change rate, pressure change rate and temperature change rate into a first prediction model to obtain the volume prediction value and volume change rate prediction value in the second time period; determining a first weight value based on a first mapping relationship; determining the product of the thermal runaway risk index value and the first weight value to obtain a comprehensive risk index value, and outputting corresponding early warning information based on the comprehensive risk index value. This solution improves the timeliness of monitoring the thermal runaway state of the battery.

The application discloses a kind of composite gel breaking-directional adsorption purification combined special medical thickening component nitrate rapid detection method, belong to food detection technical field.The application is aimed at special medical food thickening component nitrate detection, colloid structure causes target extraction efficiency to be low, gel breaking effect and heat-sensitive component protection are difficult to give consideration to, method adaptability is poor, matrix interference is serious technical problem, first by LDA linear discriminant analysis model realizes sample classification and targeted gel breaking scheme matching, then adopts differentiating composite gel breaking system to cooperate entropy weight method-TOPSIS dynamic end point determination, by colloid polysaccharide characteristic fragment surface imprinting mesoporous nanometer silicon dioxide directional adsorption purification, finally by 15 N mark temperature-sensitive type isotopic internal standard is combined with three-wavelength in-situ calibration to complete quantitative detection.The method has good detection accuracy and repeatability, is suitable for multi-scene detection requirements, and can meet the quality control requirements of trace nitrate detection of special medical food.