111 results about "Nonlinear feature extraction" patented technology

The invention provides an underground water environment automatic monitoring super station state supervision method and system. The method comprises the following steps: collecting a multi-source dynamic time sequence data set and carrying out time-space alignment; generating a dynamic characteristic index set by using a nonlinear dynamic characteristic extraction method; based on the index set, constructing a self-adaptive cooperative measurement network to perform anomaly monitoring, and generating an anomaly detection result and a cooperative control instruction; generating real-time monitoring and early warning information by using a dynamic threshold adjustment algorithm; and generating an adaptive control instruction and a dynamic resource allocation scheme by using a neural network adaptive control strategy and a resource scheduling optimization algorithm. According to the method, a dynamic characteristic index set is generated, a C-C method is adopted to reconstruct a high-dimensional phase space, a Wolf algorithm and a G-P algorithm are combined to calculate related indexes and dimensions, and a multi-scale fractal mode is analyzed through an R / S analysis method and wavelet transform. The methods capture complex dynamic behaviors of the underground water system, and solve the problem that the traditional method is insufficient in non-linear feature extraction capability.

The invention discloses a sleep state monitoring method based on a GM-GP algorithm, and relates to the technical field of intelligent health monitoring, and the method comprises the steps: collecting a multi-modal signal in real time through a physiological signal collection device, and carrying out the preprocessing; electroencephalogram signals are separated from the preprocessed multi-mode signals, nonlinear feature extraction is carried out, and correlation dimensions of the electroencephalogram signals are calculated; the correlation dimensions of the electroencephalogram signals are input into a GM model for modeling, and dynamic change characteristics of different sleep stages are captured; introducing a hybrid memory enhancement network, and performing trend prediction on the time sequence of the electroencephalogram signal and the multi-modal signal; based on the prediction result of the hybrid memory enhancement network and the optimized sleep staging result, detecting the time proportion and quantity of the user in different sleep stages in real time; according to the invention, trend prediction is carried out on the multi-modal physiological signals by adopting the hybrid memory enhancement network, so that the real-time performance and continuity of sleep state monitoring are ensured, and the stability and reliability of overall monitoring are improved.

The invention discloses a hydraulic pump fault prediction method and system, and relates to the technical field of hydraulic system fault diagnosis and prediction.The hydraulic pump fault prediction method comprises the steps that a vibration sensor signal of a hydraulic pump is collected, and the vibration sensor signal is converted into time domain vibration data according to a preset sampling frequency; performing wavelet packet decomposition on the time domain vibration data to obtain energy coefficients of different frequency bands, and selecting the frequency bands corresponding to the first three maximum energy coefficients as characteristic frequency bands; extracting characteristic indexes in the characteristic frequency band, constructing a fault characteristic vector, training the fault characteristic vector, and establishing a hydraulic pump fault prediction model; and inputting the fault feature vector of a to-be-tested hydraulic pump into the hydraulic pump fault prediction model, and predicting the fault type of the hydraulic pump. According to the method, the fault type and degree of the hydraulic pump are accurately predicted, and the challenges of data imbalance and nonlinear feature extraction in hydraulic pump fault diagnosis are effectively solved, so that early prediction and early warning of the fault of the hydraulic pump are realized.

The invention relates to a non-linear feature extraction-based roadside end radar and camera fused three-dimensional target detection method and device, and a medium. The three-dimensional target detection method comprises the steps of synchronously completing data enhancement and downsampling of an image and a point cloud; extracting high-dimensional nonlinear features by using an encoder improved by a Kolmogov-Arnod network, and projecting the high-dimensional nonlinear features to a unified aerial view space; establishing cross-modal dependence by using multi-head cross attention; carrying out weight fusion by using nonlinear convolution to generate an integrated aerial view feature map; and the decoder and the detection head output the three-dimensional coordinate, the size and the category of the target. Compared with the prior art, the Kolmogorov-Arnod network is introduced to perform nonlinear enhancement on the image and the point cloud encoder, the cross-modal weight is dynamically calculated through multi-head cross attention in the aerial view space, and finally, the weighted fusion is completed by using the KANs convolution. Therefore, the three-dimensional detection precision in a complex traffic scene is remarkably improved while the consistency of the receptive field is ensured.

The invention discloses a steam turbine fault diagnosis method and system based on a knowledge graph, and the method comprises the following steps: S1, synchronously collecting the vibration signal, the iron abrasive particle concentration, the oil viscosity and working condition parameters of a steam turbine bearing in a steam turbine, and relates to the technical field of steam turbine fault diagnosis. In order to solve the problems that a turbine bearing serving as a core rotating part is prone to abrasion faults, and early abrasion such as surface microcracks and oil film degradation concealment is high, vibration signals, iron abrasive particle concentration, oil viscosity and working condition parameters are synchronously collected to generate multi-source data streams with aligned timestamps, nonlinear feature extraction is combined, and a multi-source data stream with aligned timestamps is obtained. By constructing the initial knowledge graph, mapping the extracted features to the initial knowledge graph and generating the dynamic knowledge graph based on the graph neural network, time sequence reasoning of the bearing abrasion state is achieved, so that the early abrasion features of the turbine bearing can be obtained, and early warning is conducted according to the early abrasion features of the turbine bearing.

The invention discloses a large model dynamic batch processing method based on sequence splicing. The method comprises the following steps: receiving input sequences of a plurality of users in a batch; converting the input sequence into a corresponding token sequence; carrying out heterogeneous splicing on all token sequences along a sequence length dimension to form a unified joint token; the spliced joint tokens pass through a normalization layer, standardization operation is executed on the joint tokens, and data distribution is unified; carrying out linear projection on the joint token through a shared linear transformation layer to generate a joint query vector, a joint key vector and a joint value vector, and splitting the joint query vector, the joint key vector and the joint value vector into sub-vector groups corresponding to each user; executing multi-head attention calculation to obtain attention output of the user; and carrying out linear transformation on the attention output, and inputting a transformed result into a shared MLP to carry out nonlinear feature extraction and enhancement so as to obtain a final output corresponding to each user. According to the method, the problems of efficiency bottleneck and resource consumption when a large model processes mass data are effectively solved.

The invention relates to the field of computer big data, and discloses a self-adaptive parallel computing high-dimensional data dimension reduction and classification optimization system which comprises a quantum tensor decomposition module, a dynamic routing module, a heterogeneous computing classification module and a feedback regulation and control module. The quantum tensor decomposition module realizes nonlinear feature extraction of high-dimensional data through super-adjacency tensor modeling and entanglement entropy constraint; the dynamic routing module generates an adaptive communication path based on pulse time sequence weight optimization and quantum key verification; the heterogeneous calculation classification module dynamically schedules a quantum processor according to data characteristics, and FPGA and GPU resources execute mixed gradient aggregation; and the feedback regulation and control module guarantees the robustness of the system through multi-dimensional parameter closed-loop correction and a multi-stage fault-tolerant mechanism. All the modules cooperate to form a closed loop of dimension reduction, transmission, classification and regulation, the problems of feature extraction distortion, low resource utilization rate and poor dynamic environment adaptability in the prior art are solved, and the real-time performance and reliability of high-dimensional data processing are remarkably improved.

The invention relates to a multi-temporal optical remote sensing image thick cloud removal model, method and device. The model comprises a feature extraction module, a feature fusion module and an image reconstruction module which are connected in sequence. The feature extraction module is used for sequentially performing nonlinear feature extraction, channel number adjustment and multi-scale feature extraction on the multi-temporal satellite image to obtain multi-scale features; the feature fusion module is used for sequentially performing scale compression, summation operation and self-attention-based convolution modulation operation on the multi-scale features to obtain global information; and the image reconstruction module is used for performing up-sampling and affine transformation on the global information in sequence to obtain modulation features, and performing low-frequency information-to-high-frequency information processing and high-frequency information reconstruction processing on the modulation features in sequence to obtain a cloudless image. According to the method, the spectral information recovery and semantic information retention capability under thick cloud shielding is improved, and the cloud removal performance is further improved.

The invention discloses a nuclear power plant equipment fault diagnosis method, and the scheme can comprise the steps: firstly, collecting normal and fault data of 100% and 80% working conditions, and covering a plurality of crevasse fault types; state monitoring is carried out through KPCA, data are mapped to a high-dimensional space in a non-linear mode through a kernel function, and non-linear features are effectively captured through kernel matrix calculation, feature decomposition and anomaly judgment based on T2 and SPE statistics. A CNN-Bi-LSTM model is adopted in the fault diagnosis link, the CNN extracts local space features, and the Bi-LSTM captures bidirectional time sequence dependence; for a small sample working condition, model parameters trained by a source working condition (sufficient data) are migrated to a target working condition through migration learning, and fine adjustment adaptation is carried out. According to the scheme, nonlinear feature extraction, space-time modeling and transfer learning are fused, the accuracy and robustness of fault recognition under complex working conditions are improved, and the method is particularly suitable for data imbalance and sample scarcity scenes.

The embodiment of the invention discloses a data processing method and device, relates to the technical field of data processing, and can screen out core SNPs with high hereditary characteristics from biological SNPs. The method includes obtaining a first SNP set. And determining a second SNP set from the first SNP set according to the first information of the SNPs. And determining a third SNP set from the second SNP set according to a target feature extraction method and a nonlinear feature extraction method. Wherein the first SNP set comprises N SNPs, the N is a positive integer, the first information comprises at least one of linkage imbalance information or variance, the second SNP set comprises M SNPs, the M is a positive integer, the M is smaller than the N, the N is a positive integer, the first information comprises at least one of linkage imbalance information and variance, and the second information comprises at least one of linkage imbalance information and variance. The target feature extraction method comprises at least one of a filtering type feature extraction method, a wrapping type feature extraction method or an embedded type feature extraction method, the third SNP set comprises H SNPs, and H is a positive integer and is smaller than M.

The invention discloses an intelligent tea mat tasting system based on multispectrum and deep learning, and belongs to the technical field of artificial intelligence, the system comprises a tea mat body, a multispectral sensor, a PLC and a terminal device, the multispectral sensor is arranged at the bottom of the tea mat body and comprises two light sources and two light sensing elements, and the PLC is connected with the terminal device; the PLC receives light signals, converts the light signals into electric signals and transmits the electric signals to the terminal device, and the terminal device comprises a multi-channel signal processing module, a peak value feature extraction module, a chaotic dynamics analysis module, a quality mapping module, a multi-standard comparison module, a probabilistic reasoning module, a recommendation generation module and a display module. A tea soup quality model is established through nonlinear feature extraction of multi-channel spectral signals in combination with chaos dynamics analysis, and tea quality evaluation is realized by utilizing three-dimensional flavor topological mapping and multi-standard library comparison, so that objective quantitative evaluation of tea quality is realized.

The invention discloses a method for detecting the content of salidroside in a rhodiola rosea extracting solution based on artificial intelligence. The method comprises the following steps: S1, performing spectral scanning of different wavebands on an extracting solution sample in a plurality of near-infrared wavelength ranges; s2, performing multi-dimensional interference suppression processing and signal purification; s3, carrying out spectral feature compression calculation to obtain a salidroside feature intensity factor; s4, carrying out salidroside characteristic signal-to-noise ratio enhancement and purity index calculation; s5, predicting the content of salidroside and outputting a content prediction score; and S6, mapping the content prediction score into the final salidroside content. According to the method, spectral signal purification, nonlinear feature extraction, dynamic noise enhancement, artificial intelligence prediction modeling and a secondary recheck mechanism are utilized to realize adaptive analysis of extracting solutions from different sources, so that the detection accuracy and stability are remarkably improved, and rapid and traceable salidroside content detection can be realized under the condition that a large precise instrument is not needed.

The invention discloses a demand side resource demand response transaction scale calculation method based on LassoNet and S-MLP, and relates to the demand response field, and the method comprises the steps: classifying demand side resources according to power consumption types; the meteorological factors and the classified demand side resources are input into a LassoNet network model, and feature variables are screened through regularization; and splicing the classified demand side resources and the feature variables, carrying out position coding by adopting a position embedding method to form input data input 1, input 2 and input 3, inputting the input data input 1, input 2 and input 3 into a multi-layer perceptron model MLP, capturing a nonlinear relationship, and completing prediction of the demand response transaction scale of the demand side resources. According to the method, through the feature selection capability of the LassoNet model, the model calculation resources are optimized, and the prediction precision is improved; the position embedding method ensures the structured processing of the data, and enhances the understanding of the model on the complex relationship. Finally, through nonlinear feature extraction of the MLP model, high-precision prediction of the demand side resource response transaction scale is realized, and the stability of the system is improved.

The invention discloses an intelligent positioning method and system for a microseism event. Intelligent positioning of the microseism event is realized by combining a convolutional neural network and probability density function mapping. Comprising the following steps: according to a work area speed model, generating four-dimensional training data representing detector coordinates and seismic phase arrival time and a three-dimensional label representing a seismic source position through Poisson disk sampling and a Gaussian probability density function; designing a convolutional neural network structure, optimizing network weight through back propagation, and constructing a micro-seismic event intelligent positioning model; and inputting actual data into the positioning model to obtain probability density distribution of the seismic source in a three-dimensional space, and finally outputting a high-precision seismic source positioning result through peak value extraction. According to the method, the observation system is creatively integrated into network input, and the strong nonlinear feature extraction capability of the convolutional neural network is combined, so that the generalization capability of the positioning model to different observation systems and the robustness to detector coordinate deviation and arrival time error are remarkably improved.

The invention discloses an anomaly detection method and device based on the receiving power of a low-orbit satellite-borne GNSS receiver, and the method comprises the steps: obtaining a source receiving power sequence of the low-orbit satellite-borne GNSS receiver, and carrying out the enhancement processing based on Doppler frequency shift compensation and ionospheric noise filtering, and then generating a to-be-detected receiving power sequence; performing feature extraction on the to-be-detected receiving power sequence and then generating a receiving power feature vector based on a multi-scale entropy feature and a chaotic feature; and comparing the received power feature vector with a pre-constructed dynamic anomaly threshold to generate an anomaly detection result. According to the method, time sequence mismatching caused by frequency shift is overcome, energy distortion caused by ionosphere disturbance is also remarkably weakened, so that the accuracy and sensitivity of a subsequent nonlinear feature extraction process are guaranteed, and particularly, the robustness and effectiveness of GNSS receiving power anomaly detection can be remarkably improved in a complex dynamic space environment.

The embodiment of the invention relates to the technical field of computer security authentication, in particular to a security authentication method and system based on a secure computer. The method comprises the following steps: carrying out sensing array deployment and data acquisition on a secure computer to obtain calibrated time-space synchronization data; performing temperature and current fusion processing on the calibrated time-space synchronization data to obtain a thermoelectric coupling characteristic spectrum; carrying out point location time sequence correlation analysis on the thermoelectric coupling characteristic spectrum to obtain a point location cross-correlation matrix; performing nonlinear feature extraction on the point location cross-correlation matrix to obtain a nonlinear dynamic feature set; performing dynamic behavior analysis on the nonlinear dynamic feature set to obtain a security calculation behavior dynamic phase spectrum; and performing phase difference calculation on the dynamic phase spectrum of the security calculation behavior to obtain a phase difference vector. According to the method, the early detection capability of threats which are difficult to reproduce and are in a novel hardware level is improved through real-time monitoring and anomaly recognition of computer microcosmic physical characteristics.

The invention provides a multi-scale context aggregation and dynamic supervision medical image segmentation method and application thereof, and belongs to the technical field of medical image processing. In order to solve the problems of weak non-linear feature fitting ability, global context missing and unstable training convergence in the prior art, the ResUKAN + network is constructed. According to the method, a residual KAN convolution module is embedded in a full level of an encoder, and nonlinear feature extraction is enhanced by using a B-spline function; a multi-scale context aggregation module is arranged on a bottleneck layer, and dynamic pyramid pooling and a double attention mechanism are fused to capture global dependency; a dynamic auxiliary supervision head is introduced at the tail end of a decoder, complementary features are extracted through a heterogeneous receptive field, and loss calculation is optimized in combination with a dynamic weight mechanism which is exponentially attenuated along with a training period. According to the method, the segmentation precision and robustness of the fuzzy boundary and the multi-scale focus are remarkably improved, and the method is suitable for medical image intelligent diagnosis.

The present invention discloses a real-time super-resolution reconstruction method and system based on historical feature fusion. The method includes constructing a network model; constructing the network model includes: extracting linear features of the current frame to obtain a linear feature map of the current frame; extracting non-linear features and performing feature fusion on the current frame and historical frames to obtain a non-linear feature map after feature fusion; wherein, the historical frames are the first N frames before the current frame, and N is a natural number; the extraction of non-linear features of the historical frames is based on the images after motion compensation of the historical frames; based on the non-linear feature map after feature fusion and the linear feature map of the current frame, a super-resolution reconstructed image of the current frame is obtained. The present invention solves a series of problems existing in current super-resolution technologies, such as poor real-time performance, detail temporal breakage, false colors, poor generality, etc., and takes into account both real-time performance and image quality.

The invention belongs to the technical field of data science and physical field simulation, and discloses an AUV structure stress real-time prediction method based on a convolution auto-encoder. The method comprises the following steps: carrying out nonlinear feature extraction on high-dimensional structure stress field data by adopting a trained convolution auto-encoder to obtain low-dimensional representation; establishing a mapping relation between an input working condition and the low-dimensional representation by adopting a deep learning neural network, and predicting a low-dimensional feature under a new working condition; and reconstructing the predicted low-dimensional features by using a decoder part of the convolutional auto-encoder to obtain high-dimensional structure stress field data. According to the method, the nonlinear characteristics of the flow field can be adaptively extracted, and the accurate mapping relation between the input working condition and the full-field stress response is established, so that high-precision and high-efficiency real-time prediction of the stress field of the AUV structure is realized, and the calculation efficiency and the model generalization ability are remarkably improved.

The invention provides an electroencephalogram signal feature extraction method and device based on frequency domain analysis. According to the method, firstly, signals are adjusted through dynamic filtering according to the characteristics of electroencephalogram signals and noise environment, then time-varying characteristics of the signals are accurately captured through energy distribution extraction on a frequency-time plane, and functional connection between brain areas is predicted by applying a frequency domain coherence analysis technology. And frequency domain nonlinear feature extraction is carried out on the signal in combination with a nonlinear dynamics theory. Finally, the extracted frequency domain features are screened through a feature importance scoring method, a target feature set is obtained, and electroencephalogram signals are recognized and classified based on the target feature set. According to the method, by introducing dynamic filtering, frequency domain coherence analysis and a nonlinear dynamics theory, the problems that in the prior art, non-stationarity processing on electroencephalogram signals is insufficient, time-varying characteristics are ignored, and feature extraction accuracy and calculation efficiency are low are solved.

The invention discloses a distribution network automation equipment group fault diagnosis method and system, equipment and a medium, and relates to the technical field of distribution network automation, and the method comprises the steps: obtaining fault data of a distribution network automation equipment group, carrying out the nonlinear feature extraction of the fault data, inputting the nonlinear feature as an input signal into an echo state network for training, and obtaining an echo state network; and an output value is obtained through a trainable activation function based on differential homeomorphism, parameter optimization is performed on the output value by adopting a tangent search algorithm based on a discrete fractional order method, a trained echo state network is obtained, and fault diagnosis is performed on the distribution network automation equipment group. According to the method, a fixed activation function is replaced by an activation function containing trainable parameters, although the trainable parameters of a hidden layer are increased, the fitting ability and learning ability of the network are greatly improved, and through adjustment of fractional calculus, the convergence speed of the algorithm can be increased, and the robustness of the algorithm is improved. And the speed and the effect of fault diagnosis of the distribution network automation equipment group are realized.

The invention belongs to the technical field of electromechanical system simulation, and particularly relates to an electromechanical simulation model evaluation method based on AI analysis, which comprises the following steps: data preprocessing, AI evaluation engine construction, dynamic index evaluation and closed-loop optimization. An electromechanical simulation model evaluation system based on AI analysis comprises the following layered architecture: a data preprocessing layer, an AI evaluation engine layer, a dynamic evaluation layer and a closed-loop optimization layer, one of kernel principal component analysis or a variational auto-encoder is introduced to carry out nonlinear feature extraction, and game Nash equilibrium is introduced to carry out model weight dynamic allocation. And sliding window anomaly detection is introduced to dynamically adjust the threshold. According to the invention, a'physical rule + AI reasoning 'dual-drive evaluation architecture of the electromechanical simulation model is initiated; the dynamic weight distribution algorithm improves the evaluation efficiency compared with a traditional method; and the three-level indexes converge step by step, so that comprehensive evaluation from local performance to global performance is realized.

The application relates to a code detection method and device, computer equipment and a storage medium. The method comprises the following steps: obtaining a target code to be detected; performing string division on the target code to obtain a target substring sequence; obtaining a substring vector corresponding to each target substring in the target substring sequence to form a vector sequence; performing linear feature extraction on the vector sequence to obtain first extracted features, and performing nonlinear feature extraction on the vector sequence to obtain second extracted features; performing fusion processing on the first extracted features and the second extracted features to obtain fusion features; and performing code detection based on the fusion features to obtain a code detection result corresponding to the target code. A cloud server can use an artificial intelligence-based malicious code detection model to implement the code detection method of the application, thereby achieving the purpose of reducing network attacks. The method can improve the accuracy of malicious code detection.

The invention relates to a data detection method for a distributed photovoltaic system and an energy storage system thereof, and relates to the technical field of photovoltaic energy storage data detection. According to the method, a high-precision distributed photovoltaic and energy storage system data detection model is constructed by combining the powerful nonlinear feature extraction and state reconstruction capability of the deep belief network with the precise setting of key parameters by a second-order oscillation firefly optimization algorithm. Therefore, the system can predict the key operation state of equipment or positions where sensor nodes are not directly installed according to the actually measured data of a limited number of intelligent fusion terminals in the area, so that the deployment number of physical sensors and communication modules is greatly reduced on the hardware level, and the equipment investment and the later maintenance cost are directly reduced.

The present invention discloses a real-time super-resolution reconstruction method and system based on multi-frame fusion. The method includes: constructing a network model; constructing the network model includes: performing linear feature extraction on the current frame to obtain a linear feature map of the current frame; performing non-linear feature extraction and feature fusion on the current frame and historical frames to obtain a non-linear feature map after feature fusion; the historical frames are the first N frames of the current frame, where N is a natural number; based on the non-linear feature map after feature fusion and the linear feature map of the current frame, an image with the target resolution of the current frame is obtained. The present invention effectively solves the defect of the existing super-resolution technology in terms of real-time performance, and improves the quality and visual effect of image reconstruction on the premise of ensuring real-time performance.

The invention discloses a parallel double-hop deep residual BP neural network-based distribution network ground screen corrosion detection method and device, and is used for solving the technical problem of poor corrosion detection accuracy caused by manual detection in most of existing distribution network ground screen corrosion detection methods. The method comprises the following steps: acquiring a plurality of to-be-detected distribution network earth screen images; inputting each to-be-detected distribution network earth screen image into a plurality of preset parallel double-hop deep residual BP neural networks, wherein each preset parallel double-hop deep residual BP neural network comprises a double-hop deep residual model and a BP neural network model; each double-hop depth residual model performs shallow low nonlinear feature extraction on input to-be-detected distribution network earth screen images, and outputs target shallow low nonlinear features corresponding to each to-be-detected distribution network earth screen image; and each BP neural network model performs corrosion detection on the input target shallow low-nonlinearity feature to generate a target distribution network earth screen corrosion detection result corresponding to each to-be-detected distribution network earth screen image.

The invention provides an electroencephalogram signal-based dysmnesia assessment system, which comprises a signal acquisition module for acquiring an electroencephalogram signal of a target object; the preprocessing module is used for performing 0.5-40Hz band-pass filtering on the electroencephalogram signal of the target object; the signal segmentation module is used for segmenting each channel and determining a plurality of non-overlapping Epoches corresponding to each channel; the feature extraction module is used for performing linear feature extraction and nonlinear feature extraction on each Epoch in each channel, and determining electroencephalogram signal fusion features of the target object based on the linear feature and the nonlinear feature of each Epoch; and the dysmnesia assessment module is used for inputting the electroencephalogram signal fusion features into a trained dysmnesia assessment model, and performing dysmnesia assessment on the target object by using the dysmnesia assessment model to obtain a dysmnesia assessment result of the target object. According to the scheme, dysmnesia evaluation of the target object can be efficiently realized.

The present application relates to the technical field of copper ore sorting, and specifically discloses a photoelectric sorting system, which is provided with an imaging acquisition module to complete ultraviolet image acquisition, first acquires image data through initial parameters and completes initial sorting determination, and relies on a parameter adjustment module to obtain optimal acquisition parameters through self-adaptive adjustment according to the initial result; then uses the optimal parameters to continuously acquire multiple frames of dynamic images, a dynamic correction module combines the motion trajectory of the conveying box with the relationship between the ore and the shielding to complete inter-frame correlation, deviation elimination and error correction, and obtains timing correction features; multi-dimensional linear and nonlinear feature extraction is performed on the correction features, feature fusion is completed, and a weight voting network and a multi-branch network are used for independent prediction and comparison to determine the sorting strategy. The present application can adapt to the parameter sensitivity of copper-containing minerals, effectively eliminate dynamic sorting interference, fully excavate the subtle recognition features of the ore, and greatly improve the sorting accuracy and running stability of low-grade copper ore.

The present application relates to a kind of electroencephalogram classification method based on physical information residual polynomial network, design to be trained network, first with polynomial feature extraction layer carries out nonlinear feature extraction, then through LSTM network capture long-term time series dependence of electroencephalogram, then realize the neural network architecture of E / I path separation, respectively to interface excitatory pathway and inhibitory pathway, then by the feature correlation analysis layer of embedding Wilson-Cowan neural population dynamics equation carries out physical constraint, finally in succession fusion layer, classification layer, classification layer completes the construction of to-be-trained network, then based on each sample formed by each multi-channel electroencephalogram, for to-be-trained network training, obtains electroencephalogram classification model, while guaranteeing electroencephalogram classification prediction accuracy, significantly enhance biological explainability.

The present application relates to a kind of lithium battery SOH and RUL collaborative prediction method based on nonlinear enhancement LSTM, belong to battery life prediction technical field, solve the problem of large amount of electrochemical data demand, single and insufficient feature extraction, waste of computing resources and deep, weak nonlinear feature extraction capability and low SOH and RUL prediction accuracy in prior art.The present application is based on data-driven, through multi-channel convolutional neural network multi-dimensional feature extraction, and based on nonlinear enhancement LSTM model carries out battery sequence data feature extraction, in-depth mining nonlinear feature of battery parameter, SOH evaluation result is used as the input of RUL prediction, using neural network method carries out RUL prediction, the prediction accuracy of SOH and RUL of lithium battery is high;Data feature is fully extracted, and the amount of electrochemical data demand is reduced;Using the same pre-training nonlinear enhancement LSTM model shares input, and the computing resource is greatly saved.