A Graph-Based Semi-Supervised Approach for Few-Shot Class-Incremental Modulation Classification

With the successive application of deep learning(DL)in classification tasks,the DL-based modulation classification method has become the preference for its state-of-the-art performance.Nevertheless,once the DL recognition model is pre-trained with fixed classes,the pre-trained model tends to predict incorrect results when identifying incremental classes.Moreover,the incremental classes are usually emergent without label information or only a few labeled samples of incremental classes can be obtained.In this context,we propose a graphbased semi-supervised approach to address the fewshot classes-incremental(FSCI)modulation classification problem.Our proposed method is a twostage learning method,specifically,a warm-up model is trained for classifying old classes and incremental classes,where the unlabeled samples of incremental classes are uniformly labeled with the same label to alleviate the damage of the class imbalance problem.Then the warm-up model is regarded as a feature extractor for constructing a similar graph to connect labeled samples and unlabeled samples,and the label propagation algorithm is adopted to propagate the label information from labeled nodes to unlabeled nodes in the graph to achieve the purpose of incremental classes recognition.Simulation results prove that the proposed method is superior to other finetuning methods and retrain methods.

Semi-supervised learning based hybrid beamforming under time-varying propagation environments

Hybrid precoding is considered as a promising low-cost technique for millimeter wave(mm-wave)massive Multi-Input Multi-Output(MIMO)systems.In this work,referring to the time-varying propagation circumstances,with semi-supervised Incremental Learning(IL),we propose an online hybrid beamforming scheme.Firstly,given the constraint of constant modulus on analog beamformer and combiner,we propose a new broadnetwork-based structure for the design model of hybrid beamforming.Compared with the existing network structure,the proposed network structure can achieve better transmission performance and lower complexity.Moreover,to enhance the efficiency of IL further,by combining the semi-supervised graph with IL,we propose a hybrid beamforming scheme based on chunk-by-chunk semi-supervised learning,where only few transmissions are required to calculate the label and all other unlabelled transmissions would also be put into a training data chunk.Unlike the existing single-by-single approach where transmissions during the model update are not taken into the consideration of model update,all transmissions,even the ones during the model update,would make contributions to model update in the proposed method.During the model update,the amount of unlabelled transmissions is very large and they also carry some information,the prediction performance can be enhanced to some extent by these unlabelled channel data.Simulation results demonstrate the spectral efficiency of the proposed method outperforms that of the existing single-by-single approach.Besides,we prove the general complexity of the proposed method is lower than that of the existing approach and give the condition under which its absolute complexity outperforms that of the existing approach.

Semi-supervised surface defect detection of wind turbine blades with YOLOv4

Timely inspection of defects on the surfaces of wind turbine blades can effectively prevent unpredictable accidents.To this end,this study proposes a semi-supervised object-detection network based on You Only Looking Once version 4(YOLOv4).A semi-supervised structure comprising a generative adversarial network(GAN)was designed to overcome the difficulty in obtaining sufficient samples and sample labeling.In a GAN,the generator is realized by an encoder-decoder network,where the backbone of the encoder is YOLOv4 and the decoder comprises inverse convolutional layers.Partial features from the generator are passed to the defect detection network.Deploying several unlabeled images can significantly improve the generalization and recognition capabilities of defect-detection models.The small-scale object detection capacity of the network can be improved by enhancing essential features in the feature map by adding the concurrent spatial and channel squeeze and excitation(scSE)attention module to the three parts of the YOLOv4 network.A balancing improvement was made to the loss function of YOLOv4 to overcome the imbalance problem of the defective species.The results for both the single-and multi-category defect datasets show that the improved model can make good use of the features of the unlabeled images.The accuracy of wind turbine blade defect detection also has a significant advantage over classical object detection algorithms,including faster R-CNN and DETR.

A Novel Graph Structure Learning Based Semi-Supervised Framework for Anomaly Identification in Fluctuating IoT Environment

With the rapid development of Internet of Things(IoT)technology,IoT systems have been widely applied in health-care,transportation,home,and other fields.However,with the continuous expansion of the scale and increasing complexity of IoT systems,the stability and security issues of IoT systems have become increasingly prominent.Thus,it is crucial to detect anomalies in the collected IoT time series from various sensors.Recently,deep learning models have been leveraged for IoT anomaly detection.However,owing to the challenges associated with data labeling,most IoT anomaly detection methods resort to unsupervised learning techniques.Nevertheless,the absence of accurate abnormal information in unsupervised learning methods limits their performance.To address these problems,we propose AS-GCN-MTM,an adaptive structural Graph Convolutional Networks(GCN)-based framework using a mean-teacher mechanism(AS-GCN-MTM)for anomaly identification.It performs better than unsupervised methods using only a small amount of labeled data.Mean Teachers is an effective semi-supervised learning method that utilizes unlabeled data for training to improve the generalization ability and performance of the model.However,the dependencies between data are often unknown in time series data.To solve this problem,we designed a graph structure adaptive learning layer based on neural networks,which can automatically learn the graph structure from time series data.It not only better captures the relationships between nodes but also enhances the model’s performance by augmenting key data.Experiments have demonstrated that our method improves the baseline model with the highest F1 value by 10.4%,36.1%,and 5.6%,respectively,on three real datasets with a 10%data labeling rate.

Model Change Active Learning in Graph-Based Semi-supervised Learning

Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier.A challenge is to identify which points to label to best improve performance while limiting the number of new labels."Model Change"active learning quantifies the resulting change incurred in the classifier by introducing the additional label(s).We pair this idea with graph-based semi-supervised learning(SSL)methods,that use the spectrum of the graph Laplacian matrix,which can be truncated to avoid prohibitively large computational and storage costs.We consider a family of convex loss functions for which the acquisition function can be efficiently approximated using the Laplace approximation of the posterior distribution.We show a variety of multiclass examples that illustrate improved performance over prior state-of-art.

Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical imaging.Traditional deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical images.Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature representations.Additionally,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering performance.To address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss weights.Extensive experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC algorithm.The results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these datasets.Furthermore,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.

Decentralized Semi-Supervised Learning for Stochastic Configuration Networks Based on the Mean Teacher Method

The aim of this paper is to broaden the application of Stochastic Configuration Network (SCN) in the semi-supervised domain by utilizing common unlabeled data in daily life. It can enhance the classification accuracy of decentralized SCN algorithms while effectively protecting user privacy. To this end, we propose a decentralized semi-supervised learning algorithm for SCN, called DMT-SCN, which introduces teacher and student models by combining the idea of consistency regularization to improve the response speed of model iterations. In order to reduce the possible negative impact of unsupervised data on the model, we purposely change the way of adding noise to the unlabeled data. Simulation results show that the algorithm can effectively utilize unlabeled data to improve the classification accuracy of SCN training and is robust under different ground simulation environments.

Semi-supervised least squares support vector machine algorithm:application to offshore oil reservoir

At the early stages of deep-water oil exploration and development, fewer and further apart wells are drilled than in onshore oilfields. Supervised least squares support vector machine algorithms are used to predict the reservoir parameters but the prediction accuracy is low. We combined the least squares support vector machine （LSSVM） algorithm with semi-supervised learning and established a semi-supervised regression model, which we call the semi-supervised least squares support vector machine （SLSSVM） model. The iterative matrix inversion is also introduced to improve the training ability and training time of the model. We use the UCI data to test the generalization of a semi-supervised and a supervised LSSVM models. The test results suggest that the generalization performance of the LSSVM model greatly improves and with decreasing training samples the generalization performance is better. Moreover, for small-sample models, the SLSSVM method has higher precision than the semi-supervised K-nearest neighbor （SKNN） method. The new semi- supervised LSSVM algorithm was used to predict the distribution of porosity and sandstone in the Jingzhou study area.

Speech emotion recognition using semi-supervised discriminant analysis

Semi-supervised discriminant analysis SDA which uses a combination of multiple embedding graphs and kernel SDA KSDA are adopted in supervised speech emotion recognition.When the emotional factors of speech signal samples are preprocessed different categories of features including pitch zero-cross rate energy durance formant and Mel frequency cepstrum coefficient MFCC as well as their statistical parameters are extracted from the utterances of samples.In the dimensionality reduction stage before the feature vectors are sent into classifiers parameter-optimized SDA and KSDA are performed to reduce dimensionality.Experiments on the Berlin speech emotion database show that SDA for supervised speech emotion recognition outperforms some other state-of-the-art dimensionality reduction methods based on spectral graph learning such as linear discriminant analysis LDA locality preserving projections LPP marginal Fisher analysis MFA etc. when multi-class support vector machine SVM classifiers are used.Additionally KSDA can achieve better recognition performance based on kernelized data mapping compared with the above methods including SDA.

Semi-Supervised Learning with Generative Adversarial Networks on Digital Signal Modulation Classification

Deep Learning(DL)is such a powerful tool that we have seen tremendous success in areas such as Computer Vision,Speech Recognition,and Natural Language Processing.Since Automated Modulation Classification(AMC)is an important part in Cognitive Radio Networks,we try to explore its potential in solving signal modulation recognition problem.It cannot be overlooked that DL model is a complex model,thus making them prone to over-fitting.DL model requires many training data to combat with over-fitting,but adding high quality labels to training data manually is not always cheap and accessible,especially in real-time system,which may counter unprecedented data in dataset.Semi-supervised Learning is a way to exploit unlabeled data effectively to reduce over-fitting in DL.In this paper,we extend Generative Adversarial Networks(GANs)to the semi-supervised learning will show it is a method can be used to create a more dataefficient classifier.

Situation assessment for air combat based on novel semi-supervised naive Bayes

A method is proposed to resolve the typical problem of air combat situation assessment. Taking the one-to-one air combat as an example and on the basis of air combat data recorded by the air combat maneuvering instrument, the problem of air combat situation assessment is equivalent to the situation classification problem of air combat data. The fuzzy C-means clustering algorithm is proposed to cluster the selected air combat sample data and the situation classification of the data is determined by the data correlation analysis in combination with the clustering results and the pilots＇ description of the air combat process. On the basis of semi-supervised naive Bayes classifier, an improved algorithm is proposed based on data classification confidence, through which the situation classification of air combat data is carried out. The simulation results show that the improved algorithm can assess the air combat situation effectively and the improvement of the algorithm can promote the classification performance without significantly affecting the efficiency of the classifier.

Semi-supervised multi-layered clustering model for intrusion detection

A Machine Learning (ML)-based Intrusion Detection and Prevention System (IDPS)requires a large amount of labeled up-to-date training data to effectively detect intrusions and generalize well to novel attacks.However,the labeling of data is costly and becomes infeasible when dealing with big data,such as those generated by Intemet of Things applications.To this effect,building an ML model that learns from non-labeled or partially labeled data is of critical importance.This paper proposes a Semi-supervised Mniti-Layered Clustering ((SMLC))model for the detection and prevention of network intrusion.SMLC has the capability to learn from partially labeled data while achieving a detection performance comparable to that of supervised ML-based IDPS.The performance of SMLC is compared with that of a well-known semi-supervised model (tri-training)and of supervised ensemble ML models, namely Random.Forest,Bagging,and AdaboostM1on two benchmark network-intrusion datasets,NSL and Kyoto 2006+.Experimental resnits show that SMLC is superior to tri-training,providing a comparable detection accuracy with 20%less labeled instances of training data.Furthermore,our results demonstrate that our scheme has a detection accuracy comparable to that of the supervised ensemble models.

Fault diagnosis of electric transformers based on infrared image processing and semi-supervised learning

It is crucial to maintain the safe and stable operation of distribution transformers,which constitute a key part of power systems.In the event of transformer failure,the fault type must be diagnosed in a timely and accurate manner.To this end,a transformer fault diagnosis method based on infrared image processing and semi-supervised learning is proposed herein.First,we perform feature extraction on the collected infrared-image data to extract temperature,texture,and shape features as the model reference vectors.Then,a generative adversarial network(GAN)is constructed to generate synthetic samples for the minority subset of labelled samples.The proposed method can learn information from unlabeled sample data,unlike conventional supervised learning methods.Subsequently,a semi-supervised graph model is trained on the entire dataset,i.e.,both labeled and unlabeled data.Finally,we test the proposed model on an actual dataset collected from a Chinese electricity provider.The experimental results show that the use of feature extraction,sample generation,and semi-supervised learning model can improve the accuracy of transformer fault classification.This verifies the effectiveness of the proposed method.

Analyzing Cross-domain Transportation Big Data of New York City with Semi-supervised and Active Learning

The majority of big data analytics applied to transportation datasets suffer from being too domain-specific,that is,they draw conclusions for a dataset based on analytics on the same dataset.This makes models trained from one domain(e.g.taxi data)applies badly to a different domain(e.g.Uber data).To achieve accurate analyses on a new domain,substantial amounts of data must be available,which limits practical applications.To remedy this,we propose to use semi-supervised and active learning of big data to accomplish the domain adaptation task:Selectively choosing a small amount of datapoints from a new domain while achieving comparable performances to using all the datapoints.We choose the New York City(NYC)transportation data of taxi and Uber as our dataset,simulating different domains with 90%as the source data domain for training and the remaining 10%as the target data domain for evaluation.We propose semi-supervised and active learning strategies and apply it to the source domain for selecting datapoints.Experimental results show that our adaptation achieves a comparable performance of using all datapoints while using only a fraction of them,substantially reducing the amount of data required.Our approach has two major advantages:It can make accurate analytics and predictions when big datasets are not available,and even if big datasets are available,our approach chooses the most informative datapoints out of the dataset,making the process much more efficient without having to process huge amounts of data.

Semi-supervised Support Vector Regression Model for Remote Sensing Water Quality Retrieving

This paper proposed a semi-supervised regression model with co-training algorithm based on support vector machine, which was used for retrieving water quality variables from SPOT 5 remote sensing data. The model consisted of two support vector regressors (SVRs). Nonlinear relationship between water quality variables and SPOT 5 spectrum was described by the two SVRs, and semi-supervised co-training algorithm for the SVRs was es-tablished. The model was used for retrieving concentrations of four representative pollution indicators―permangan- ate index (CODmn), ammonia nitrogen (NH3-N), chemical oxygen demand (COD) and dissolved oxygen (DO) of the Weihe River in Shaanxi Province, China. The spatial distribution map for those variables over a part of the Weihe River was also produced. SVR can be used to implement any nonlinear mapping readily, and semi-supervis- ed learning can make use of both labeled and unlabeled samples. By integrating the two SVRs and using semi-supervised learning, we provide an operational method when paired samples are limited. The results show that it is much better than the multiple statistical regression method, and can provide the whole water pollution condi-tions for management fast and can be extended to hyperspectral remote sensing applications.

Labeling Malicious Communication Samples Based on Semi-Supervised Deep Neural Network

The limited labeled sample data in the field of advanced security threats detection seriously restricts the effective development of research work.Learning the sample labels from the labeled and unlabeled data has received a lot of research attention and various universal labeling methods have been proposed.However,the labeling task of malicious communication samples targeted at advanced threats has to face the two practical challenges:the difficulty of extracting effective features in advance and the complexity of the actual sample types.To address these problems,we proposed a sample labeling method for malicious communication based on semi-supervised deep neural network.This method supports continuous learning and optimization feature representation while labeling sample,and can handle uncertain samples that are outside the concerned sample types.According to the experimental results,our proposed deep neural network can automatically learn effective feature representation,and the validity of features is close to or even higher than that of features which extracted based on expert knowledge.Furthermore,our proposed method can achieve the labeling accuracy of 97.64%~98.50%,which is more accurate than the train-then-detect,kNN and LPA methodsin any labeled-sample proportion condition.The problem of insufficient labeled samples in many network attack detecting scenarios,and our proposed work can function as a reference for the sample labeling tasks in the similar real-world scenarios.

RVFLN-based online adaptive semi-supervised learning algorithm with application to product quality estimation of industrial processes

Direct online measurement on product quality of industrial processes is difficult to be realized,which leads to a large number of unlabeled samples in modeling data.Therefore,it needs to employ semi-supervised learning(SSL)method to establish the soft sensor model of product quality.Considering the slow time-varying characteristic of industrial processes,the model parameters should be updated smoothly.According to this characteristic,this paper proposes an online adaptive semi-supervised learning algorithm based on random vector functional link network(RVFLN),denoted as OAS-RVFLN.By introducing a L2-fusion term that can be seen a weight deviation constraint,the proposed algorithm unifies the offline and online learning,and achieves smoothness of model parameter update.Empirical evaluations both on benchmark testing functions and datasets reveal that the proposed OAS-RVFLN can outperform the conventional methods in learning speed and accuracy.Finally,the OAS-RVFLN is applied to the coal dense medium separation process in coal industry to estimate the ash content of coal product,which further verifies its effectiveness and potential of industrial application.

Incremental semi-supervised learning for intelligent seismic facies identification

Intelligent seismic facies identification based on deep learning can alleviate the time-consuming and labor-intensive problem of manual interpretation,which has been widely applied.Supervised learning can realize facies identification with high efficiency and accuracy;however,it depends on the usage of a large amount of well-labeled data.To solve this issue,we propose herein an incremental semi-supervised method for intelligent facies identification.Our method considers the continuity of the lateral variation of strata and uses cosine similarity to quantify the similarity of the seismic data feature domain.The maximum-diff erence sample in the neighborhood of the currently used training data is then found to reasonably expand the training sets.This process continuously increases the amount of training data and learns its distribution.We integrate old knowledge while absorbing new ones to realize incremental semi-supervised learning and achieve the purpose of evolving the network models.In this work,accuracy and confusion matrix are employed to jointly control the predicted results of the model from both overall and partial aspects.The obtained values are then applied to a three-dimensional(3D)real dataset and used to quantitatively evaluate the results.Using unlabeled data,our proposed method acquires more accurate and stable testing results compared to conventional supervised learning algorithms that only use well-labeled data.A considerable improvement for small-sample categories is also observed.Using less than 1%of the training data,the proposed method can achieve an average accuracy of over 95%on the 3D dataset.In contrast,the conventional supervised learning algorithm achieved only approximately 85%.

Semi-supervised LIBS quantitative analysis method based on co-training regression model with selection of effective unlabeled samples

The accuracy of laser-induced breakdown spectroscopy(LIBS) quantitative method is greatly dependent on the amount of certified standard samples used for training. However, in practical applications, only limited standard samples with labeled certified concentrations are available. A novel semi-supervised LIBS quantitative analysis method is proposed, based on co-training regression model with selection of effective unlabeled samples. The main idea of the proposed method is to obtain better regression performance by adding effective unlabeled samples in semisupervised learning. First, effective unlabeled samples are selected according to the testing samples by Euclidean metric. Two original regression models based on least squares support vector machine with different parameters are trained by the labeled samples separately, and then the effective unlabeled samples predicted by the two models are used to enlarge the training dataset based on labeling confidence estimation. The final predictions of the proposed method on the testing samples will be determined by weighted combinations of the predictions of two updated regression models. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples were carried out, in which 5 samples with labeled concentrations and 11 unlabeled samples were used to train the regression models and the remaining 7 samples were used for testing. With the numbers of effective unlabeled samples increasing, the root mean square error of the proposed method went down from 1.80% to 0.84% and the relative prediction error was reduced from 9.15% to 4.04%.

General image classification method based on semi-supervised generative adversarial networks

Generative adversarial networks(GANs) have become a competitive method among computer vision tasks. There have been many studies devoted to utilizing generative network to do generative tasks, such as images synthesis. In this paper, a semi-supervised learning scheme is incorporated with generative adversarial network on image classification tasks to improve the image classification accuracy. Two applications of GANs are mainly focused on: semi-supervised learning and generation of images which can be as real as possible. The whole process is divided into two sections. First, only a small part of the dataset is utilized as labeled training data. And then a huge amount of samples generated from the generator is added into the training samples to improve the generalization of the discriminator. Through the semi-supervised learning scheme, full use of the unlabeled data is made which may contain potential information. Thus, the classification accuracy of the discriminator can be improved. Experimental results demonstrate the improvement of the classification accuracy of discriminator among different datasets, such as MNIST, CIFAR-10.