145 results about "Neural network classification" patented technology

The invention discloses a battery state evaluation method based on machine learning, and the method comprises the steps: collecting battery data in a plurality of battery use environments, and marking a battery state; carrying out sample generation on the collected battery data by adopting an SMOTE algorithm based on low-rank matrix interpolation; forming an expanded data set by the generated data and the original data, training the data pair feature extraction model, and optimizing the training process of the feature extraction model by adopting a self-adaptive oscillation method in the training process; inputting the features corresponding to the battery state data extracted by the feature extraction model into a classifier model of a Riemann neural network classification algorithm based on fractional derivation to train the classifier model; and inputting the collected battery data of the to-be-evaluated battery into the trained feature extraction model for feature extraction, and inputting the extracted features into the trained classifier model for classification to obtain the state of the to-be-evaluated battery. According to the invention, the health state evaluation precision of the battery can be improved.

The invention discloses an error diagnosis classification and intelligent review planning method based on multi-dimensional analysis, which is suitable for a personalized learning support system. The method comprises six steps of error collection, automatic labeling, error attribution, capability evaluation, knowledge point correlation analysis and dynamic review planning, and integrates OCR recognition, TF-IDF feature extraction, convolutional neural network classification, knowledge graph modeling and Q-learning intelligent strategies. The system can realize accurate classification and reason diagnosis of wrong questions based on answering behaviors and cognitive features of students, optimizes a review plan in combination with a forgetting curve and habit error, supports an online and offline mixed review mode, and significantly improves learning efficiency and personalized adaptation ability.

The invention discloses an airborne radar identification method based on multi-scale signal decomposition and depth feature fusion, belongs to the technical field of radar target identification, is used for unmanned aerial vehicle radar target identification, and comprises the following steps: decomposing an original dynamic radar cross section signal into a plurality of intrinsic mode components by adopting an adaptive noise complete set empirical mode decomposition method; self-adaptive wavelet soft threshold denoising is carried out on all the components in a unified mode so as to improve the signal-to-noise ratio and suppress background interference; effective components are screened out by calculating correlation coefficients of all intrinsic mode components and original signals, a radar cross section signal sequence after noise reduction is reconstructed, and multilevel feature fusion is carried out; and inputting the feature vector into a stacked auto-encoder deep neural network classification model, and introducing a particle swarm optimization algorithm to optimize the classification model. According to the method, the signal-to-noise ratio of the signal and the recognition accuracy of the classification model in a low signal-to-noise ratio environment are remarkably improved, and high-precision recognition and classification of different types of radar cross section targets are realized.

The invention relates to the field of die forging press anomaly detection, in particular to a large die forging press anomaly detection method, which comprises the steps of collecting multi-dimensional time sequence training data of a die forging press, aligning timestamps and processing missing values; dividing the training data into a plurality of segments through a sliding window, and dividing the operation stages of the die forging press into five classes; performing tagging processing on the data fragments, calculating a Pearson's correlation coefficient matrix, and generating a corresponding correlation threshold matrix; training a neural network classification model based on the training data; collecting detection data of the die forging press in real time, and judging the current operation stage; selecting, calculating and comparing a correlation threshold matrix and a real-time correlation coefficient matrix according to the current operation stage; and if the real-time correlation coefficient is lower than the correlation threshold matrix coefficient, abnormal early warning is triggered. According to the method, anomaly detection is carried out on the sensor correlation in different stages, so that the accuracy of anomaly detection of a correlation algorithm is improved, and the false alarm rate and the missing report rate are reduced.

The present invention belongs to the technical field of mechanical fault diagnosis and signal processing, and specifically relates to a bearing fault diagnosis method, system, equipment and medium based on a multi-channel analog filter feature network. The method constructs a multi-channel analog filter feature extraction network, uses multiple parallel bandpass filter channels to extract bearing vibration signal characteristics, and combines an analog neural network classifier to perform fault classification. Signal preprocessing includes removing DC components, amplitude normalization and segmented processing; the filter passband parameters and classifier parameters are jointly optimized through a particle swarm algorithm, with classification accuracy and decision confidence as fitness functions. The system includes signal acquisition, preprocessing, multi-channel filtering, feature extraction, analog neural network classification and result display modules, achieving low-power, high-real-time bearing fault diagnosis. The present invention solves the problems of high power consumption and large delay of traditional digital processing methods, and is suitable for long-term monitoring and large-scale deployment in industrial sites.

The invention discloses a power transmission tower attitude monitoring and vibration intrusion identification method based on multi-source data fusion, and the method comprises the steps: carrying out the deep fusion of GPS displacement data and accelerometer inclination angle data, employing a dynamic weight fusion mechanism: adjusting a fusion coefficient alpha in real time according to the GPS signal quality, guaranteeing the accuracy and stability of data fusion, and at the same time, carrying out the real-time adjustment of the fusion coefficient alpha, and carrying out the recognition of the vibration intrusion of a power transmission tower. And continuously optimizing the error propagation model by taking the final inclination angle theta final of the power transmission tower as a state variable, updating the attitude estimation of the power transmission tower, and realizing + / -0.01-degree high-precision attitude monitoring. According to the invention, the system can output a high-precision three-dimensional attitude angle in real time by combining the rapid dynamic response of the accelerometer and the long-term stability of the GPS. Besides, an LCD-MPE (feature extraction technology) is introduced, lightweight neural network classification is combined, vibration signal sampling, noise reduction and abnormal signal segment identification, analysis and extraction are carried out on the transmission tower through an MEMS accelerometer, and second-level detection and behavior type discrimination of invasion behaviors around the transmission tower are realized. The core technical barrier from'inability perception 'to'accurate identification' is overcome, and subversive upgrading of power facility safety protection from'passive disposal 'to'active defense' is promoted.

The invention provides a multi-modal heterogeneous sensor energy efficiency optimization method and device, equipment and a storage medium, and the method comprises the steps: carrying out the lightweight convolutional neural network classification of user motion data collected by an acceleration sensor, and obtaining a scene feature vector; constructing a parameter incidence matrix according to the scene feature vector and the pre-determined parameters of the multiple monitoring sensors; performing resource allocation according to the parameter incidence matrix to obtain a parameter configuration table; and performing three-level scheduling according to the scene feature vector, the parameter configuration table and a preset working mode to obtain a target working mode of the plurality of monitoring sensors, and controlling the plurality of monitoring sensors to work according to the target working mode.

The invention discloses an improved satellite navigation deception detection system based on deep learning fusion multi-level time-frequency analysis and a detection method thereof. The system comprises a time-frequency transformation module, a time-frequency image processing module and a neural network classification module. The time-frequency transformation module comprises time-frequency transformation modes of short-time Fourier transformation, continuous wavelet transformation and Margenauu-Hill transformation, the time-frequency transformation module directly takes an intermediate-frequency digital signal as input, and the defect that the input signals are different due to different front ends is overcome; the time-frequency image processing module is composed of a time-frequency standardization module, a gray processing module and a multi-channel image fusion module. The multi-channel image fusion module fuses three types of time-frequency transformation characteristics, different time-frequency transformation methods show different performance expressions under different signal characteristics and environment conditions, and a multi-level time-frequency analysis method is fused to endow the system with higher signal intelligent adaptability, so that the system is excellent in performance when facing various signal types and complex scenes, and the system is more suitable for various signal types and complex scenes. And the anti-noise performance is improved.

The invention belongs to the technical field of mechanical fault diagnosis and signal processing, and particularly relates to a bearing fault diagnosis method, system and equipment based on a multi-channel analog filtering feature network and a medium. The method comprises the steps that a multi-channel analog filtering feature extraction network is constructed, and bearing vibration signal features are extracted through multiple band-pass filter channels arranged in parallel; and fault classification is carried out in combination with a simulated neural network classifier. The signal preprocessing comprises direct current component removal, amplitude normalization and segmentation processing; filter passband parameters and classifier parameters are jointly optimized through a particle swarm algorithm, and classification accuracy and decision confidence are used as fitness functions. The system comprises a signal acquisition module, a preprocessing module, a multi-channel filtering module, a feature extraction module, a simulation neural network classification module and a result display module, and realizes bearing fault diagnosis with low power consumption and high real-time performance. The method solves the problems that a traditional digital processing method is high in power consumption and large in delay, and is suitable for long-term monitoring and large-scale deployment of an industrial site.

The invention relates to the technical field of education information, in particular to a student group classification prediction method, device, system, equipment and medium, and the method comprises the steps: collecting the multi-dimensional data of each student through an online and offline combined mode, and obtaining a multi-dimensional data set; clustering the multi-dimensional data set by adopting a clustering algorithm to obtain a classification result of the student group; and constructing a neural network classification prediction model based on the multi-dimensional data set and the classification result, training the neural network classification prediction model, and inputting the multi-dimensional data of the to-be-tested student into the trained neural network classification prediction model for analysis to obtain the student group category of the student. Therefore, the diversity and comprehensiveness of data sources are ensured through online and offline multi-channel data collection, the accuracy of data analysis is improved by combining a clustering algorithm and a neural network classification prediction model, detailed classification of student groups is realized, the potential value of students is rapidly evaluated, and the students' experience is improved. And the purpose of helping enterprises and educational institutions to carry out strategic planning is achieved.

The invention discloses an arrhythmia detection method based on a pulse neural network. The method comprises the following steps: performing incremental modulation pulse coding and pooling operation on a target electrocardiosignal to obtain a corresponding pulse sequence; and inputting the pulse sequence into a trained multi-level pulse neural network classification model to obtain an arrhythmia detection result. Wherein the multi-level pulse neural network classification model comprises a weight sharing layer and a plurality of cascaded classifiers, the weight sharing layer is used for extracting electrocardiosignal feature information from the pulse sequence, and the plurality of classifiers are used for obtaining an arrhythmia detection result based on the electrocardiosignal feature information. According to the method, on the premise that the accuracy is equivalent, the classification result is more refined, and the requirements for hardware storage resources and computing resources are reduced.

The invention relates to the technical field of network security, in particular to an attack intention behavior recognition method and device, and the method comprises the steps: obtaining a plurality of attack features of a plurality of historical attack gangs, and marking a preset state for each attack feature, so as to obtain a plurality of marked attack features; and training a pre-constructed deep neural network classification model by using the plurality of labeled attack features to obtain a gang attack intention-based recognition model, and recognizing the actual state of the target attack gang by using the gang attack intention-based recognition model. Therefore, the problems that most of existing security products are lack of deep analysis capability, so that comprehensive understanding of behaviors of attackers is insufficient, the real intentions of the attackers are difficult to accurately judge, response can only be performed based on the attacking behaviors, potential threats cannot be actively predicted and recognized and the like are solved.

The invention discloses a method for identifying an epimedium koreanum planting area based on multi-source remote sensing image fusion in the technical field of under-forest planting, and the method comprises the steps: obtaining multi-source remote sensing data of a target area, carrying out the preprocessing, and carrying out the recognition of an epimedium koreanum planting area based on the preprocessed multi-source remote sensing data; extracting multi-source feature data related to under-forest planting of the Korean epimedium, fusing the extracted multi-source features, inputting the fused multi-source features into a deep neural network classification model of a cross-modal attention mechanism, processing the fused multi-source features, and obtaining a deep neural network classification model of the cross-modal attention mechanism; according to the method, multi-source remote sensing data advantages of optics, radars, laser radars and the like are integrated, the limitation of a single data source in under-forest environment monitoring is overcome, and the method has the advantages of being high in accuracy, high in accuracy and high in reliability. And aiming at special growth requirements of the epimedium koreanum, key parameters such as canopy density and gradient are quantified, and accurate evaluation of ecological suitability is realized.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a computer network intrusion detection method and system based on artificial intelligence, and the method comprises the steps: constructing a finite state automaton to carry out the state jump compliance verification of a connection flow, intercepting the illegal flow, only sending the compliance flow into a feature engineering module, and extracting the multi-dimensional behavior features; inputting a deep neural network classification model to determine whether the behavior is an intrusion behavior; according to the technical scheme provided by the invention, the data input to the artificial intelligence model can be ensured to have complete compliance on the protocol level, so that the model is prevented from learning false feature association caused by protocol violation, and the antagonistic attack based on protocol state jump can be effectively resisted on the premise of not depending on adversarial training.

The application relates to the technical field of positioning device signal processing and positioning precision, and particularly discloses a non-line-of-sight classification and error compensation method and system based on a double convolution neural network, the method comprising the following steps: acquiring channel impulse response data of an ultra-wideband signal, and pre-processing the channel impulse response data to obtain an amplitude sequence in real number form; constructing a double convolution neural network classification model, the double convolution neural network classification model comprising parallel large-kernel convolution blocks and small-kernel convolution blocks, which are respectively used for extracting macroscopic waveform features and microscopic fluctuation features from the amplitude sequence; and performing classification training after fusing the macroscopic waveform features and the microscopic fluctuation features; and based on the trained classification model, the regression compensation of ranging errors of non-line-of-sight signals is performed, macroscopic waveform contour features and microscopic local fluctuation features can be simultaneously extracted from data, and a complementary feature extraction mechanism can more comprehensively describe the differences between LOS / NLOS signals compared with a single-structure network.

The present application relates to a kind of generalization adversarial sample group generation method based on easily confused class feature injection, belong to the technical field of adversarial attack of computer vision field.It introduces the class disturbance update momentum in the direction of easily confused class feature vector in the process of generating class-oriented generalization disturbance sample group, the class disturbance update momentum makes the adjacent easily confused class of adversarial example faster update, to realize the generalization attack to class.The present application only integrates the features of easily confused class in the same class image, forms disturbance template, realizes class-oriented generalization disturbance sample group;Variable step size iterative attack means is used to explore easily confused class in the process of neural network classification, realize easily confused feature injection, while introducing class momentum parameter in the process of iterative attack, improve attack efficiency and alleviate the over disturbance shortcomings of generalization disturbance.

The application discloses a dynamic convolution deep neural network classification method with global and local attention, and belongs to the technical field of image classification and artificial intelligence, wherein a dynamic convolution kernel module with global and local attention is designed and built to improve the traditional convolution operation by analyzing the convolution layer in the deep convolution neural network. The method comprises the following specific steps: S1, dividing a data set into a training set and a test set; S2, designing and building a dynamic convolution kernel module with global and local attention; and S3, training and testing the built dynamic convolution deep neural network with global and local attention on a natural image classification data set, so as to reduce the parameter quantity and the calculation complexity of the network, thereby better solving the image classification problem.

The present invention discloses a method for detecting the operating state of a low-voltage distribution network based on a fusion terminal, which relates to the technical field of distribution networks. The method includes: the fusion terminal acquires the original data set of the low-voltage distribution network; performs data preprocessing on the original data set; extracts features from the preprocessed data; performs feature fusion on the extracted features to obtain a fused feature vector, and divides the training set and the test set; constructs a neural network classification model for training to obtain a state classification model, and optimizes the state classification model, inputs the fused feature vector into the state classification model for classification; selects abnormal state data for detection to identify the types of abnormal states; selects normal state data for health assessment, and comprehensively evaluates the health state of the low-voltage distribution network through health key indicators and a health assessment function. The present invention improves the monitoring accuracy of the low-voltage distribution network, enhances the abnormal detection and health assessment capabilities, and optimizes the resource allocation.

The application belongs to the technical field of image processing, and particularly relates to a defect classification method for automobile injection molding parts based on database comparison, which comprises the following steps: extracting a feature vector fused with multi-scale artificial information and deep learning information for normal, defect and to-be-detected areas; realizing efficient preliminary screening of abnormal areas by calculating a local anomaly factor of the to-be-detected area and comparing the local anomaly factor with a dynamic determination threshold generated based on defect data; and splicing the original feature vector and the quantized local anomaly factor into an enhanced feature vector, inputting the enhanced feature vector into a neural network classification model, and performing accurate defect category determination. The two-stage strategy of dynamic threshold preliminary screening and enhanced feature accurate division improves the accuracy and stability of defect detection of automobile injection molding parts.

The invention relates to the technical field of block chain power supply management, and discloses a distributed power supply transaction management method of block chain nodes. According to the method, historical normal power supply transaction data is collected to establish a reference database, and the transaction detection period duration is determined according to the data size of a real-time power supply transaction data set. And monitoring node transaction records in a detection period, calculating a transaction frequency average value as a detection threshold value, and identifying suspected abnormal transactions in combination with the reference database. After the suspected abnormal transaction is confirmed, extracting the total transaction amount and the time distribution characteristic to judge whether the transaction is the abnormal transaction, and counting the occurrence frequency to calculate an initial risk coefficient; and the category of the abnormal transaction, the participation node identifier and the behavior pattern are extracted and converted into feature vectors, and malicious transaction node groups are grouped, analyzed and detected through a neural network classification algorithm. And if the malicious group exists, calculating an adjustment coefficient to correct the initial risk coefficient, and dynamically adjusting parameters of the next detection period.

The present application provides a blood cell classification method, in which the impedance pulse signal of the blood sample to be classified is input into a trained target neural network classification model. The target neural network classification model is trained by a large number of impedance pulse signal samples with classification result label information. It has a strong pulse morphology feature extraction capability, so that the classification result of the impedance pulse signal can be determined based on the morphological characteristics of the impedance pulse signal of the blood sample to be classified. Compared with the existing technology, the classification result is more accurate, and it can identify the impedance pulse signal caused by interference factors, and can perform identification and classification based on the impedance pulse signal, with higher real-time performance. In addition, the present application also provides a blood cell analyzer and a storage medium.

The invention discloses a buried pipeline leakage signal collaborative classification system based on a twin neural network. The system comprises a data acquisition module, a signal preprocessing module, a twin neural network classification module, an intelligent decision module and a man-machine interaction module. The twin neural network comprises two sub-networks sharing weights, and the two sub-networks respectively process multi-modal fusion signals of the same pipe section at different moments, so that intelligent detection of pipeline state changes is realized. According to the method, the leakage detection precision can be effectively improved, the false alarm rate is reduced, accurate classification of different leakage types is realized, and reliable guarantee is provided for safe operation of buried pipelines. According to the system, the DTS is matched with the DAS technology, and intelligent identification and classification of pipeline leakage signals are realized by utilizing a laying mode that an optical cable is attached to a buried pipeline in an accompanying manner.

The invention, which belongs to the technical field of unmanned aerial vehicle image identification and classification, discloses a pulse neural network-based unmanned aerial vehicle sudden accident rapid classification method comprising the steps of obtaining an initial data set of an unmanned aerial vehicle sudden accident; preprocessing the initial data set to obtain an unmanned aerial vehicle sudden accident data set; selecting a leakage integral-distribution model as a spiking neuron of the spiking neural network; a spiking neural network classification model SpikeClassifier is constructed; the spiking neural network classification model SpikeClassifier is trained, and the spiking neural network classification model SpikeClassifier after optimization is obtained; and the optimized spiking neural network classification model SpikeClassifier is adopted to carry out prediction classification on the unmanned aerial vehicle sudden accidents. According to the method, the classification accuracy can be kept under the conditions of complex backgrounds and limited resources, and the response speed and efficiency of the model are improved.

An automated golf ball sorting apparatus and method of use is provided. A machine vision system and neural network classification sort golf balls by brand, model, or condition. The system includes a chain-driven elevator with inclined ball platforms that rotate each ball in two planes along a high-friction roller. A camera captures images of each rotating ball, which are classified using a trained convolutional neural network. A microcontroller tracks each ball's progress using infrared sensor interrupts and dynamically assigns it to a solenoid-actuated gate for diversion into a corresponding sort bin. A user loads mixed balls into a hopper; the system lifts, scans, classifies, and sorts them in real time, processing one ball per second with over 90% accuracy. A training mode enables collection of annotated image data using single-type ball loads to refine the neural network.

The application relates to the technical field of artificial intelligence, and provides a classification prediction method based on table data, which comprises the following steps: inputting a characteristic value, a characteristic type corresponding to the characteristic value and statistical information corresponding to the characteristic value into an embedding layer of a neural network classification model to obtain an embedding representation corresponding to the characteristic value; then determining an embedding representation sequence according to the embedding representation; inputting the embedding representation sequence into an encoding layer of the neural network classification model to obtain a semantic vector sequence; inputting the semantic vector sequence into an output layer of the neural network classification model to obtain a prediction result; determining a loss value according to the prediction result and a labeled label, and training the neural network classification model according to the loss value to obtain a target neural network classification model. Through the above scheme, the neural network model can understand the global statistical information of the characteristics like a tree model, the performance and accuracy of the neural network model in performing a classification prediction task based on table data are improved, and the satisfaction of users in the experience of financial products is improved.

The present application proposes a face authentication method based on image patch sequence loss, including: S1, obtaining a portrait authentication data set containing training images and their label information; S2, performing data processing on the portrait authentication data set, including: uniformly cutting the training images and disrupting the initial patch sequence set of the training images to obtain the first patch sequence set of the training images; enhancing the first patch sequence set to obtain the second patch sequence set; randomly exchanging the patch blocks in the first patch sequence set and the second patch sequence set of different training images, thereby obtaining a processed portrait authentication data set; S3, using the processed portrait authentication data set to train a neural network classification model; S4, inputting the image to be detected into the neural network classification model. The portrait authentication method of the present application greatly saves training costs and data collection costs, and has a high discrimination accuracy.

This invention provides a dual-model defect detection method, training method, and apparatus based on a Bi-LSTM neural network. The dual-model defect detection method includes: acquiring N frames of infrared thermal images during the cooling process; acquiring a pulsed thermal imaging data sequence of each pixel on the test surface of the workpiece as a function of time from the N frames of infrared thermal images; inputting the pulsed thermal imaging data sequence into a trained Bi-LSTM neural network classification model to obtain a binary classification map representing whether the pixel location is a defect or not; inputting the pulsed thermal imaging data sequence into a trained Bi-LSTM neural network regression model to obtain a defect depth prediction map representing the defect depth; and obtaining the defect distribution of the workpiece from the binary classification map and the defect depth prediction map. This invention does not require prior information such as material thermal properties, reference areas, or characteristic times, and is more convenient to apply.

A kind of construction method and device of depression recognition model based on deep network feature fusion, its method includes: data acquisition;Build the video dataset containing video regional level label;Respectively using C3D neural network and channel attention network to the whole video stream is reinforced feature extraction;The reinforced feature is used RPN to generate region proposal and produces region start mark and end mark and region expression class;All video frames are extracted to multiple scale features including local eccentricity features and global features;Weakly supervised classification is carried out to multiple scale features;The operation result of video stream level feature and picture level feature is classified by neural network, and depression recognition model is obtained.The present application effectively utilizes video label, takes into account global and local information, and combines the advantages of high accuracy and reduced label workload, can become a potential depression recognition auxiliary means, and can be widely applied in the field of automatic processing of mental illness.

The invention discloses an early termination method and device for inter-frame coding segmentation, equipment and a storage medium. The method comprises the following steps: calculating a feature distance between to-be-segmented regions based on coding block features; inputting the feature distance into a pre-trained neural network classification model for segmentation threshold prediction, and outputting a target segmentation threshold of the target coding block; based on the feature distance and the target segmentation threshold, judging whether the target coding block meets a segmentation termination condition or not; and if yes, stopping segmentation of the target coding block. According to the embodiment of the invention, features highly related to CU segmentation are selected by using a machine learning algorithm, features having the greatest influence on a segmentation decision are selected by analyzing texture and depth map information of the CU, and early termination of CU segmentation of a texture video and a depth map is realized based on a threshold value extracted from a training model.

The application discloses a power distribution system fault prediction method based on fractal theory prediction and BP neural network classification, collects current data of a line to be predicted in a power distribution system, and performs fractal prediction. The fractal prediction comprises the following steps: S1, establishing a current history sequence, obtaining an accumulated sum sequence and a measurement scale; S2, performing linear regression on the accumulated sum sequence curve in a double logarithmic coordinate system to obtain a fractal dimension; and S3, performing current value prediction of future time. After the fractal prediction, the actual current value is measured, the difference between the actual value and the predicted value is calculated, the BP neural network is inputted, and a fault prediction result is obtained. The power distribution system fault prediction is performed by adopting the method combining fractal theory prediction and BP neural network classification, the method has the characteristics of high accuracy, and is suitable for fault early warning and troubleshooting of a bidirectional power flow and a multi-party participated smart grid.