11 results about "Approximate entropy" patented technology

The invention discloses an industrial sewage water quality soft measurement method. The method comprises the following steps: collecting sewage water quality data of a sewage plant; carrying out feature extraction on the input data by adopting an AHSICLasso method; decomposing the variable signal into a plurality of symplectic geometric components by using symplectic geometric mode decomposition (SGMD), and then performing secondary decomposition on the decomposed high-frequency nonlinear components through singular spectrum analysis (SSA); carrying out complexity quantification on the decomposed multi-mode component by utilizing approximate entropy so as to evaluate the dynamic characteristics of the multi-mode component; according to a quantization result, reconstructing the multi-mode component; using a time step adaptive dynamic selection mechanism and approximate entropy to screen components at past moments, and using CBO to optimize component weights; the AHSICLasso feature extraction data, the screened components at the past moment and the components obtained after secondary decomposition are input into an OfficANet model; the method comprises the following steps: optimizing hyper-parameters of an EfficANet model by using a CBO collider, introducing an adaptive attention weight mechanism into a GTVA module of the EfficANet model for improvement, and learning and predicting a reconstructed multi-modal component to realize soft measurement of total nitrogen in industrial sewage; according to the invention, high-precision and real-time prediction of the total nitrogen concentration of the industrial sewage is realized.

This invention relates to the field of healthcare informatics technology, specifically to a dynamic prediction system for the risk of delayed chest closure after surgery in children with congenital heart disease. It collects high-frequency vital signs signals during surgery, imaging report text, and clinical structured data; extracts the maximum Lyapunov exponent, correlation dimension, and approximate entropy as chaotic features; quantifies the ambiguous semantics in the imaging report into membership values; uses Dempster-Shafer evidence theory to fuse multimodal features and encode uncertainty; constructs a dynamic ensemble learning model, detects concept drift using ADWIN and updates it online, dynamically weights it based on Shapley values; uses multi-objective reinforcement learning to dynamically optimize the risk threshold; and generates counterfactual explanations to provide individualized intervention suggestions. This invention achieves accurate and dynamic early warning of the need for delayed chest closure.

The application discloses a kind of drop lightning arrester multi-source signal discrimination and alarm method, it is related to the on-line monitoring and fault early warning technical field of power system equipment, the application can effectively separate high-frequency sub-band signal by wavelet packet decomposition technology, adapt to non-stationary vibration environment, ensure the robustness of feature extraction;The introduction of approximate entropy and peak factor and other characteristics, quantifies the randomness and impact of signal, can sensitively capture the subtle changes in the fatigue accumulation process of lead wire;Single-class classification model trained only by healthy sample is used, such as support vector data description, decision boundary is constructed, so that the model has high sensitivity to abnormal state, and can early warning before lead wire fracture occurs;This kind of evaluation mode based on healthy baseline avoids the false alarm problem of traditional threshold method in complex environment.

A kind of permanent magnet synchronous motor bearing fault diagnosis method based on motor current analysis method, first acquisition permanent magnet synchronous motor stator U, V phase current signal, calculate W phase current;Three-phase current is obtained by space vector dimension reduction two-phase current under rectangular coordinate system, and two-phase current is carried out vector normalization processing;Based on the mechanism of action of bearing fault to current signal and the characteristics of current signal, the signal after space vector dimension reduction is carried out variation modal decomposition, the approximate entropy of modal component is calculated and the feature matrix is formed;The improved standard artificial bee colony algorithm makes the bee colony type mutually transform according to the optimal solution, and changes the bee colony initialization population generation rule, the optimal classification model is obtained by using the optimized adaptive variable type algorithm to optimize machine learning model parameter;Using optimal model, the test set in sample set is classified according to fault type, and compared with label to obtain test set accuracy rate;The present application has the advantages of high accuracy, high diagnostic efficiency and the like.

The application discloses a kind of electric vehicle direct current charging fault diagnosis model training method, diagnosis method, it is related to electric vehicle charging facility test and diagnosis technical field.The electric vehicle direct current charging fault diagnosis model training method, diagnosis method injects 1kHz-100kHz wide frequency test signal, synchronously collects direct current voltage V (t) and current I (t) signal, calculates impedance amplitude spectrum |Z (f)|, forms three-way analysis signal;To each signal parallel extraction wavelet energy entropy, wavelet singular spectrum entropy, approximate entropy, power spectrum entropy four kinds of time-frequency domain entropy features, constructs 12-dimensional fusion feature vector, innovatively introduces four-dimensional confidence evaluation system (maximum probability confidence, probability distribution entropy confidence, classification interval confidence, feature space distance confidence), according to fault type characteristics Dynamic allocation weight, realize comprehensive confidence fusion;Finally, based on confidence threshold, three-level decision output is carried out.

This application belongs to the field of battery thermal runaway prediction technology, and discloses a method, apparatus, device, and medium for battery thermal runaway prediction based on segmented features. The method includes: acquiring the voltage and specified data of each cell in the battery; slicing the specified data according to a preset dimension segmentation rule to obtain slice datasets for each dimension; sorting the cell voltages of each frame of data in each slice dataset, and calculating the mode of the sorted values ​​of each cell per preset time unit under each slice dimension; extracting time-series features, including complexity features, approximate entropy features, average absolute change values, and coefficient of variation, based on the time-series data of the mode of the sorted values ​​of each cell; and predicting the probability of battery thermal runaway based on the time-series features. This application improves the accuracy of battery thermal runaway prediction through multi-dimensional slicing, mode statistics of sorted values, and extraction of time-series features, providing strong support for battery safety management.

The application provides a method for determining the first stage of compressor surge based on variational mode decomposition and approximate entropy, and belongs to the technical field of aero-engines, comprising: arranging dynamic pressure sensors on each pressurization stage of the compressor part of an aero-engine to obtain original signals of each pressurization stage; performing variational mode decomposition on the original signals mixed with the surge signals and modal waves to obtain a plurality of intrinsic modal components containing the surge signals; calculating the approximate entropy of the plurality of intrinsic modal components, determining the decomposition layer number of the variational mode decomposition based on the approximate entropy; reconstructing the original signals under different values of a penalty factor, and determining the penalty factor according to the signal-to-noise ratio of the reconstructed original signals; performing variational mode decomposition on the original signals mixed with the surge signals and modal waves based on the decomposition layer number and the penalty factor to obtain the final intrinsic modal component signals containing the surge signals, and comparing the final intrinsic modal component signals containing the surge signals with the original signals of each pressurization stage to determine the first stage of surge.

The invention discloses a dangerous rock mass damage early recognition method and system based on a three-dimensional approximate entropy index, and relates to the technical field of slope collapse geological disaster monitoring and early warning, and the method comprises the steps: synthesizing XYZ three-direction vibration data of a target dangerous rock mass into a three-dimensional time sequence, and obtaining a dangerous rock mass space position sequence; based on the first preset time sequence window and the second preset time sequence window, a plurality of first subsequences and a plurality of second subsequences of the dangerous rock mass space position sequence are extracted respectively; calculating a three-dimensional approximate entropy index of the target dangerous rock mass based on the plurality of first subsequences and the plurality of second subsequences; and performing damage identification on the target dangerous rock mass based on the change trend of the three-dimensional approximate entropy index. The technical problem that the multi-direction damage of the dangerous rock body is difficult to accurately reflect in the prior art is solved.

The invention relates to an underwater acoustic signal denoising method and system based on SVMD optimization and wavelet threshold improvement, and the method comprises the steps: employing a state optimization algorithm to optimize an SVMD model, enabling an underwater acoustic signal to be decomposed into a plurality of IMF components, and employing the ratio of an approximate entropy to a cross correlation coefficient as a classification parameter, dividing the signal into a pure signal component, a noisy signal component and a noise dominant component; carrying out wavelet decomposition on the noisy signal component, extracting a low-frequency approximation coefficient and high-frequency detail coefficients of each layer, and carrying out threshold denoising on the high-frequency detail coefficients layer by layer by adopting an improved cosine semi-soft threshold function with smoothness control parameter constraint and combining a wavelet threshold adaptively obtained based on a generalized cross validation criterion; and performing wavelet reconstruction on the denoised high-frequency detail coefficient and low-frequency approximation coefficient of each layer, and performing linear superposition on an obtained denoised signal component and a pure signal component to obtain a denoised underwater acoustic signal. According to the method, the dependency of underwater sound denoising on parameters is reduced, and meanwhile, the denoising effect is improved.

The invention belongs to a feature selection method based on fuzzy approximate composite entropy and applies the feature selection method to the technical field of fault diagnosis, and particularly relates to a fault diagnosis method based on feature selection of fuzzy approximate composite entropy, which comprises the following steps of: proposing a fuzzy decision; providing fuzzy lower approximation and fuzzy upper approximation under the fuzzy decision; proposing a lower fuzzy dependency degree based on fuzzy lower approximation and an upper fuzzy dependency degree based on fuzzy upper approximation; combining the lower fuzzy dependency degree, the upper fuzzy dependency degree and a fuzzy entropy theory, and respectively constructing a fuzzy lower approximate composite entropy and a fuzzy upper approximate composite entropy; internal and external attribute importance functions are constructed by using the fuzzy joint approximate composite entropy, and a feature selection algorithm based on the fuzzy joint approximate entropy is provided; and finally completing the fault diagnosis of the rolling bearing based on a fault diagnosis method of feature selection and a classifier. According to the method, the classification precision and the fault diagnosis accuracy can be remarkably improved while redundant features are effectively eliminated.

The invention belongs to the technical field of battery thermal runaway prediction, and discloses a battery thermal runaway prediction method and device based on segmentation characteristics, equipment and a medium. The method comprises the following steps: acquiring voltage and specified data of each monomer in a battery; performing slicing processing on the specified data according to a segmentation rule of a preset dimension to obtain a slice data set of each dimension; sorting the monomer voltage of each frame of data in each slice data set, and counting the sorting value mode of each monomer in each preset time unit under each slice dimension; extracting time sequence features including complexity features, approximate entropy features, average values of absolute change values, variation coefficients and the like based on the order value mode time sequence data of each monomer; and predicting the probability of thermal runaway of the battery based on the time sequence characteristics. Through multi-dimensional slicing, sorting value mode statistics and time sequence feature extraction, the accuracy of battery thermal runaway prediction is improved, and powerful support is provided for battery safety management.