107 results about "Linear discriminant analysis" patented technology

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 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 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 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 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.

The application discloses a kind of overhead transmission line insulator fault detection methods based on deep migration learning, including steps as follows: insulator image sample is obtained by unmanned aerial vehicle, and image sample is preprocessed;According to the method of migration learning, insulator image sample is divided into source domain and target domain, source domain is labeled normal insulator image sample, and target domain is unmarked insulator image sample of fault;Swin-Transformer is selected as feature extractor to extract the insulator features of source domain and target domain, and the insulator features are mapped to the same subspace, then the insulator features of the subspace are learned by linear discriminant analysis method, and then the nearest class prototype and structured prediction method is used to pseudo-label the target domain insulator fault sample subset;Finally, all insulator fault samples in the pseudo-labeled target domain are learned by iteration.The application can accurately and quickly detect the insulator fault in overhead transmission line.

The application discloses a multifunctional system based on a single accelerometer for a wearable device, comprising: an accelerometer module: collecting three-axis acceleration data of a user's arm; a data acquisition and preprocessing module: performing vector norm calculation and data standardization processing on the collected three-axis acceleration data, and extracting relevant features according to different functions; a threshold test module: performing threshold judgment according to the acceleration change and angle characteristics of arm movement, and filtering out invalid actions; an LDA classification module: classifying the data that has undergone preprocessing and threshold test through a linear discriminant analysis model, and identifying different arm movements; a function module: including wrist-lifting screen-on and screen-off, step counting, motion pattern recognition and sleep evaluation functions, and performing corresponding response control according to the classification results. Through the single accelerometer, multiple functions such as wrist-lifting screen-on and screen-off, step counting, motion pattern recognition and sleep evaluation are accurately realized.

The invention discloses a colorimetric sensing array based on hierarchical pore nano-enzyme as well as a construction method and application of the colorimetric sensing array, and belongs to the technical field of analysis and detection. According to the array, two cerium-based MOF nano-enzymes, namely micropore UiO-66 and hierarchical pore HP-UiO-66, are respectively combined with TMB (tetramethylbenzidine) and ABTS (2, 2, 6, 6-tetramethylbenzidine) chromogenic substrates to form a four-channel sensing system. The method comprises the following steps: synthesizing hierarchical pore HP-UiO-66 by a template method; collecting the absorbance of each channel under the characteristic wavelength by using an array, and generating a specific fingerprint spectrum of the to-be-detected object; by combining mode recognition technologies such as linear discriminant analysis and the like, rapid and synchronous distinguishing and quantitative detection of at least eight antioxidants with similar structures such as glycyrrhizic acid and caffeic acid in the traditional Chinese medicine decoction are realized. According to the method, the catalysis and mass transfer efficiency is improved by regulating and controlling the pore structure of the nano-enzyme, the problem of multi-target synchronous recognition in a complex matrix is solved by utilizing array cross response, and the method has the advantages of simplicity and convenience in operation, high flux and good accuracy.

The present invention relates to the technical fields of smart mines and data processing, and discloses a method for fusion of multi-source mine data based on collaborative representation. The method comprises: collecting sensor data, geological parameters, optical and spectral imaging data, audio and video data of the mine, and performing data preprocessing on the data to form a data set; extracting features from the data in the data set using mutual information method and linear discriminant analysis method to obtain feature data; and mapping the feature data to respective representation spaces using collaborative representation method to capture and fuse the data into multimodal data. The present invention can effectively avoid the problem that decision-level data fusion technology is sensitive to the instability and uncertainty of sensor data, and improve the fault tolerance of data fusion.

The invention discloses a bearing fault classification method based on VMD-Kalman filtering fusion denoising and CNN-Transformer. The method comprises the following steps: 1) collecting original vibration signals of a bearing in normal and fault states; 2) decomposing an original signal through variational mode decomposition (VMD), screening an effective intrinsic mode function (IMFs), and eliminating a noise dominant mode; 3) performing secondary denoising on the effective IMFs by adopting Kalman filtering to obtain a fused denoised signal; 4) fusing signals through multiple strategies such as principal component analysis (PCA) and linear discriminant analysis (LDA), and screening an optimal fusion signal in combination with a three-dimensional evaluation criterion; and 5) constructing a CNN-Transform fusion model, cooperatively extracting local high-frequency features and global time sequence dependence features of the signals, and completing bearing fault classification. According to the method, noise is deeply suppressed by fusing a denoising technology, fault features are reserved, comprehensive feature extraction is realized by combining advantages of double models, the accuracy and robustness of bearing fault diagnosis under complex working conditions are remarkably improved, and the method is suitable for accurate fault diagnosis of bearings in industrial scenes.

This invention relates to a method for radio signal identification based on multidimensional feature extraction and feature fusion, belonging to the fields of signal recognition and deep learning technology. The invention collects radio signal data, segments the collected radio signals, and converts them into two-dimensional data. Four instantaneous features are extracted from the radio signals. Time-frequency transformation is performed on the radio signal data, and Shannon entropy feature values ​​are extracted. The extracted instantaneous features and time-frequency features are fused, and a linear discriminant analysis (LDA) dimensionality reduction algorithm is used to reduce the dimensionality of the fused feature data. The processed signal data is then input into a deep learning classification network model for signal identification to obtain its corresponding signal category. This invention combines the ideas of multidimensional feature extraction and feature fusion to achieve high-accuracy radio signal identification. Furthermore, by leveraging the advantages of deep learning technology, it can improve the speed and accuracy of radio signal identification.

The application discloses a PtIrRuCuPd high-entropy alloy nanoscale enzyme and a preparation method and application thereof, and belongs to the field of catalytic materials and analytical chemistry. The high-entropy alloy nanoscale enzyme is a nanoparticle composed of five elements of Pt, Ir, Ru, Cu and Pd, and has a quasi-spherical structure. The noble metal elements Pt, Ir, Ru and Pd are combined with the transition group metal element Cu, a multi-element synergistic effect is caused, the peroxidase activity, the oxidase activity and the laccase activity are improved, and the high-entropy alloy nanoscale enzyme has optimal comprehensive enzyme activity. The high-entropy alloy nanoscale enzyme prepared by the application realizes the synchronous enhancement of POD-like, OXD-like and LAC-like activities, a multi-channel sensing array is constructed, different responses of bisphenol substances are generated through different catalytic paths, and high-selectivity recognition and trace detection of bisphenol A in a complex sample are realized by combining hierarchical clustering analysis and linear discriminant analysis algorithms. The application provides a reference for the design of a novel multi-activity nanoscale enzyme and the development of an advanced sensing array.

The application relates to a method and system for establishing a prediction model of chemical liver toxicity, which comprises the following steps: (1) differentiating human embryonic stem cells or human induced pluripotent stem cells into hepatocyte-like cells and constructing a three-dimensional hepatocyte model; (2) taking miR-122, LDH and Cyto C in the three-dimensional hepatocyte as combined test indexes to judge liver toxicity of a liver toxicity mode compound; (3) classifying the liver toxicity mode compound into three categories through two canonical discriminant functions by a linear discriminant analysis modeling method; and (4) converting the canonical discriminant function into a Fishers discriminant function to obtain a liver toxicity prediction model. The application has the general characteristics of a conventional cell model for evaluating compounds, and can also evaluate the toxicity effect and health risk of the compounds in a high-throughput manner with relatively less manpower.