Radar emitter signal recognition method based on improved collaborative semi-supervised learning

Rare labeled data are difficult to recognize by using conventional methods in the process of radar emitter recogni-tion.To solve this problem,an optimized cooperative semi-supervised learning radar emitter recognition method based on a small amount of labeled data is developed.First,a small amount of labeled data are randomly sampled by using the bootstrap method,loss functions for three common deep learning net-works are improved,the uniform distribution and cross-entropy function are combined to reduce the overconfidence of softmax classification.Subsequently,the dataset obtained after sam-pling is adopted to train three improved networks so as to build the initial model.In addition,the unlabeled data are preliminarily screened through dynamic time warping(DTW)and then input into the initial model trained previously for judgment.If the judg-ment results of two or more networks are consistent,the unla-beled data are labeled and put into the labeled data set.Lastly,the three network models are input into the labeled dataset for training,and the final model is built.As revealed by the simula-tion results,the semi-supervised learning method adopted in this paper is capable of exploiting a small amount of labeled data and basically achieving the accuracy of labeled data recognition.

Enhanced vision-transformer integrating with semi-supervised transfer learning for state of health and remaining useful life estimation of lithium-ion batteries

The state of health(SOH)and remaining useful life(RUL)of lithium-ion batteries are crucial for health management and diagnosis.However,most data-driven estimation methods heavily rely on scarce labeled data,while traditional transfer learning faces challenges in handling domain shifts across various battery types.This paper proposes an enhanced vision-transformer integrating with semi-supervised transfer learning for SOH and RUL estimation of lithium-ion batteries.A depth-wise separable convolutional vision-transformer is developed to extract local aging details with depth-wise convolutions and establishes global dependencies between aging information using multi-head attention.Maximum mean discrepancy is employed to initially reduce the distribution difference between the source and target domains,providing a superior starting point for fine-tuning the target domain model.Subsequently,the abundant aging data of the same type as the target battery are labeled through semi-supervised learning,compensating for the source model's limitations in capturing target battery aging characteristics.Consistency regularization incorporates the cross-entropy between predictions with and without adversarial perturbations into the gradient backpropagation of the overall model.In particular,across the experimental groups 13–15 for different types of batteries,the root mean square error of SOH estimation was less than 0.66%,and the mean relative error of RUL estimation was 3.86%.Leveraging extensive unlabeled aging data,the proposed method could achieve accurate estimation of SOH and RUL.

An Emotion Analysis Method Using Multi-Channel Convolution Neural Network in Social Networks

As an interdisciplinary comprehensive subject involving multidisciplinary knowledge,emotional analysis has become a hot topic in psychology,health medicine and computer science.It has a high comprehensive and practical application value.Emotion research based on the social network is a relatively new topic in the field of psychology and medical health research.The text emotion analysis of college students also has an important research significance for the emotional state of students at a certain time or a certain period,so as to understand their normal state,abnormal state and the reason of state change from the information they wrote.In view of the fact that convolutional neural network cannot make full use of the unique emotional information in sentences,and the need to label a large number of highquality training sets for emotional analysis to improve the accuracy of the model,an emotional analysismodel using the emotional dictionary andmultichannel convolutional neural network is proposed in this paper.Firstly,the input matrix of emotion dictionary is constructed according to the emotion information,and the different feature information of sentences is combined to form different network input channels,so that the model can learn the emotion information of input sentences from various feature representations in the training process.Then,the loss function is reconstructed to realize the semi supervised learning of the network.Finally,experiments are carried on COAE 2014 and self-built data sets.The proposed model can not only extract more semantic information in emotional text,but also learn the hidden emotional information in emotional text.The experimental results show that the proposed emotion analysis model can achieve a better classification performance.Compared with the best benchmark model gram-CNN,the F1 value can be increased by 0.026 in the self-built data set,and it can be increased by 0.032 in the COAE 2014 data set.

Fusion-Based Deep Learning Model for Hyperspectral Images Classification

A crucial task in hyperspectral image(HSI)taxonomy is exploring effective methodologies to effusively practice the 3-D and spectral data delivered by the statistics cube.For classification of images,3-D data is adjudged in the phases of pre-cataloging,an assortment of a sample,classifiers,post-cataloging,and accurateness estimation.Lastly,a viewpoint on imminent examination directions for proceeding 3-D and spectral approaches is untaken.In topical years,sparse representation is acknowledged as a dominant classification tool to effectually labels deviating difficulties and extensively exploited in several imagery dispensation errands.Encouraged by those efficacious solicitations,sparse representation(SR)has likewise been presented to categorize HSI’s and validated virtuous enactment.This research paper offers an overview of the literature on the classification of HSI technology and its applications.This assessment is centered on a methodical review of SR and support vector machine(SVM)grounded HSI taxonomy works and equates numerous approaches for this matter.We form an outline that splits the equivalent mechanisms into spectral aspects of systems,and spectral–spatial feature networks to methodically analyze the contemporary accomplishments in HSI taxonomy.Furthermore,cogitating the datum that accessible training illustrations in the remote distinguishing arena are generally appropriate restricted besides training neural networks(NNs)to necessitate an enormous integer of illustrations,we comprise certain approaches to increase taxonomy enactment,which can deliver certain strategies for imminent learnings on this issue.Lastly,numerous illustrative neural learning-centered taxonomy approaches are piloted on physical HSI’s in our experimentations.