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 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.

A Survey on Supervised,Unsupervised,and Semi-Supervised Approaches in Crowd Counting

Quantifying the number of individuals in images or videos to estimate crowd density is a challenging yet crucial task with significant implications for fields such as urban planning and public safety.Crowd counting has attracted considerable attention in the field of computer vision,leading to the development of numerous advanced models and methodologies.These approaches vary in terms of supervision techniques,network architectures,and model complexity.Currently,most crowd counting methods rely on fully supervised learning,which has proven to be effective.However,this approach presents challenges in real-world scenarios,where labeled data and ground-truth annotations are often scarce.As a result,there is an increasing need to explore unsupervised and semi-supervised methods to effectively address crowd counting tasks in practical applications.This paper offers a comprehensive review of crowd counting models,with a particular focus on semi-supervised and unsupervised approaches based on their supervision paradigms.We summarize and critically analyze the key methods in these two categories,highlighting their strengths and limitations.Furthermore,we provide a comparative analysis of prominent crowd counting methods using widely adopted benchmark datasets.We believe that this survey will offer valuable insights and guide future advancements in crowd counting technology.

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 kernel FCM algorithm for remote sensing image classification

These problems of nonlinearity, fuzziness and few labeled data were rarely considered in traditional remote sensing image classification. A semi-supervised kernel fuzzy C-means （SSKFCM） algorithm is proposed to overcome these disadvantages of remote sensing image classification in this paper. The SSKFCM algorithm is achieved by introducing a kernel method and semi-supervised learning technique into the standard fuzzy C-means （FCM） algorithm. A set of Beijing-1 micro-satellite＇s multispectral images are adopted to be classified by several algorithms, such as FCM, kernel FCM （KFCM）, semi-supervised FCM （SSFCM） and SSKFCM. The classification results are estimated by corresponding indexes. The results indicate that the SSKFCM algorithm significantly improves the classification accuracy of remote sensing images compared with the others.

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.

农业机器人采摘目标识别技术研究——基于FCM模糊聚类算法

介绍了FCM(Fuzzy C-Means)模糊聚类算法的原理,采用权重分配的方法对该算法进行了改进,通过建立模糊的相似矩阵,对目标对象的特征聚类图进行分析,并引入隶属度矩阵对FCM算法进行优化,以加快算法的迭代速度。实验结果表明:农业机器人采用该方法对农作物轮廓分割识别度较高,算法计算效率较快,验证了其可靠性,该方法可用于目标农作物的分割和目标识别。

基于FCM-LSTM的软件运行资源变化规律方法研究

软件在运行过程中会消耗资源,此过程存在两个问题,一是难以确定资源消耗发生变化的拐点,二是同一软件同一模块运行不同任务时,所产生的数据样本数量级差异过大。基于此,提出一种结合FCM和LSTM的算法研究软件运行资源变化规律的方法,利用FCM算法实现不同数量级样本间的群聚,接着把处理后的样本放入LSTM模型进行训练,进而得到资源消耗变化曲线。实验表明,通过资源消耗变化曲线能够确定拐点的类型和其出现的位置区间,进而找到软件运行资源变化规律。另外,通过对比分析,FCM-LSTM模型在解决此问题上的准确率高于其他同类型的传统算法。

多物理场耦合下FCM堆内行为研究

为研究全陶瓷包覆弥散燃料(FCM)堆内行为,评价FCM芯块的安全性能,优化FCM芯块结构设计,本文采用二维特征模型模拟FCM芯块的热-力学行为,通过计算不同位置TRISO颗粒的内压,作为输入条件,模拟缓冲层的变形将核芯和缓冲层去除,解决了多颗粒映射的难题。通过调整FCM结构参数,计算了不同结构对FCM芯块性能的影响,实现了FCM芯块的结构优化。研究结果表明,在压水堆环境下,FCM芯块中基体最高温度随运行时间迅速增大,在100天左右时温度达到稳定最高温度约为1390K,低于SiC分解温度。SiC基体的环向应力较高,最高可达1200 MPa,远高于SiC的断裂强度,因此基体在运行过程中开概率率较大。TRISO颗粒中SiC层的环向应力较小,最大应力约为180 MPa,具有较低的失效概率(9×10^(-5)),保证了SiC层的结构完整性。此外,当无燃料区尺寸为400μm时,SiC层的失效概率约为2.0×10^(-5),保证了SiC层的完整性。因此,本研究建立的FCM芯块分析方法,为FCM芯块的工程应用和结构优化奠定了基础。

基于LOF-FCM算法的船舶航行数据识别

针对传统船舶自动识别系统数据在清洗异常数据和提取停留数据时分别采用不同的识别方式、类型判断阈值需要人为设定、识别效率不佳的局限性,首次提出了一种船舶航行轨迹中停留及异常数据的一体化检测方法。通过分析航行路线的3种数据(停留、异常和航行)异常因子特征,提出基于LOF-FCM的船舶航行数据、停留数据和异常数据一体化检测算法。实验对3类数据进行了识别,模型识别准确率达到了92.69%,有效提高了异常、停留、航行数据的识别能力。结果表明所提方法可一次性实现AIS数据中3种数据的检测,能高效分离出正常船舶航行数据,具有良好的工程应用价值。

基于GWO-FCM的输油泵故障诊断模型自学习框架

随着输油泵场站无人化建设的发展,企业对输油泵故障诊断技术的要求也越来越高。目前,被广泛使用的利用机器学习算法进行输油泵故障诊断的方法都只能针对模型训练集中已包含的几类故障进行诊断,在企业的实际使用中,仍会出现其他不包含在训练集中的故障而不能被正确自动识别、诊断。针对上述问题,设计了一种输油泵故障诊断模型自学习框架,通过信号处理技术结合深度学习提取深层故障特征,提高工业现场数据的可分性;通过模糊C均值聚类结合相似度度量判别已知故障和未知故障,对出现的未知故障模式进行识别和记录;利用频繁出现的未知故障数据重训练模型,在原有诊断功能的基础上提高对未知故障的识别、诊断及学习能力。为验证方法的有效性,使用工业现场采集的输油泵数据进行实验,结果表明,现有诊断方法所提出的输油泵故障诊断模型自学习框架能够实现对未知故障的准确识别。

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.