期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到174篇文章
< 1 2 … 9 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Model Change Active Learning in Graph-Based Semi-supervised Learning 认领 引用
1
作者 Kevin S.Miller Andrea L.Bertozzi 《Communications on Applied Mathematics and Computation》 EI 2024年第2期1270-1298,共29页
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 bes... 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. 展开更多
关键词 Active learning Graph-based methods Semi-supervised learning(SSL) Graph Laplacian
暂未订购 下载PDF
Enhanced semi-supervised learning for top gas flow state classification to optimize emission and production in blast ironmaking furnaces 认领 引用
2
作者 Song Liu Qiqi Li +3 位作者 Qing Ye Zhiwei Zhao Dianyu E Shibo Kuang 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2026年第1期204-216,共13页
Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate ... Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate this challenge,we present an enhanced semi-supervised learning approach based on the Mean Teacher framework,incorporating a novel feature loss module to maximize classification performance with limited labeled samples.The model studies show that the proposed model surpasses both the baseline Mean Teacher model and fully supervised method in accuracy.Specifically,for datasets with 20%,30%,and 40%label ratios,using a single training iteration,the model yields accuracies of 78.61%,82.21%,and 85.2%,respectively,while multiple-cycle training iterations achieves 82.09%,81.97%,and 81.59%,respectively.Furthermore,scenario-specific training schemes are introduced to support diverse deployment need.These findings highlight the potential of the proposed technique in minimizing labeling requirements and advancing intelligent blast furnace diagnostics. 展开更多
关键词 blast furnace gas flow state semi-supervised learning mean teacher feature loss
暂未订购 下载PDF
Data-driven estimation of surrounding rock areal fracture intensity with semi-supervised learning using measurement-while-drilling data 认领 引用
3
作者 Jun Wang Qian Fang +2 位作者 Weiguo He Guoli Zheng Jiayao Chen 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第6期4723-4741,共19页
The areal fracture intensity(P21)of the surrounding rock is a critical indicator for evaluating tunnel excavation stability.However,conventional methods often fail to capture geological conditions ahead of the tunnel ... The areal fracture intensity(P21)of the surrounding rock is a critical indicator for evaluating tunnel excavation stability.However,conventional methods often fail to capture geological conditions ahead of the tunnel face.With the development of measurement-while-drilling(MWD)technology,real-time acquisition of surrounding rock information has become feasible.Therefore,establishing a relationship between MWD data and the P21 is of significant importance.This study proposes a semisupervised deep learning framework that combines a multi-autoencoder unit training module(MAUT)with a multi-branch feature extraction network(MBEN).By integrating MWD,construction,and blasting data as dataset input,the proposed model is applied to the Yangjiawopu tunnel.The predicted P21 values exhibited a relative error of less than 10%,confirming the model's superior predictive performance and engineering applicability.Ablation studies demonstrate that both the MAUT module and feature fusion enhance model accuracy.The Shapley Additive exPlanations(SHAP)analysis highlights excavation length,water inflow,and mechanical specific energy(MSE)as key predictors.The model effectively leverages unlabeled data and provides robust geological insights,offering a data-driven approach for early fracture detection and risk assessment ahead of the tunnel face. 展开更多
关键词 Tunnel Areal fracture intensity Measurement while drilling(MWD) Semi-supervised learning
暂未订购 下载PDF
Federated Semi-Supervised Learning Based on Feature Space Fusion 认领 引用
4
作者 Zhe Ding Hao Yi +6 位作者 Wenrui Xie Ming Zhang Yuxuan Xiao Qixu Wang Qing Chen Zhiguang Qin Dajiang Chen 《Computers, Materials & Continua》 SCIE EI 2026年第5期2062-2076,共15页
Federated semi-supervised learning(FSSL)has garnered substantial attention for enabling collaborative global model training across multiple clients to address the scarcity of labeled data and to preserve data privacy.... Federated semi-supervised learning(FSSL)has garnered substantial attention for enabling collaborative global model training across multiple clients to address the scarcity of labeled data and to preserve data privacy.However,FSSL is plagued by formidable challenges stemming fromcross-client data heterogeneity,as existing methods fail to achieve effective fusion of feature subspaces across distinct clients.To address this issue,we propose a novel FSSL framework,named FedSPQR,which is explicitly tailored for the label-at-server scenario.On the server side,FedSPQR adopts subspace clustering and fusion method based on the Grassmann manifold to construct a unified global feature space,which is further leveraged to refine the global model.On the client side,the pre-established global feature space acts as a benchmark for aligning the local feature subspaces.Based on the aligned local feature subspaces,integrating self-supervised learning with knowledge distillation facilitates effective local learning to alleviate local bias caused by data heterogeneity.Extensive experiments on two standard public benchmarks confirm that FedSPQR outperforms state-of-the-art(SOTA)baselines by a significant margin. 展开更多
关键词 Federated semi-supervised learning feature space fusion knowledge distillation
暂未订购 下载PDF
Gearbox Fault Diagnosis under Varying Operating Conditions through Semi-Supervised Masked Contrastive Learning and Domain Adaptation 认领 引用
5
作者 Zhixiang Huang Jun Li 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期448-470,共23页
To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervis... To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings. 展开更多
关键词 Gearbox variable working conditions fault diagnosis semi-supervised masked contrastive learning domain adaptation
暂未订购 下载PDF
Semi-supervised learning for AvO inversion with bidirectional spatial feature constraints 认领 引用
6
作者 Yin-Tian Liu Yong Li +2 位作者 Jun-Heng Peng Jian-Yong Xie Xian-Qiong Chen 《Petroleum Science》 SCIE EI CAS CSCD 2026年第5期2501-2526,共26页
Prestack amplitude variation with offset(AvO)inversion using one-dimensionalconvolutional neural networks often lacks lateral continuity.While two-dimensional methods improve this,they are limited to unidirectional sp... Prestack amplitude variation with offset(AvO)inversion using one-dimensionalconvolutional neural networks often lacks lateral continuity.While two-dimensional methods improve this,they are limited to unidirectional spatial correlations from well to non-well locations.To overcome these limitations,we propose a semi-supervised learning approach with bidirectional spatial feature constraints(BSFC-SSL).Our method introduces a label-annihilation operator and a dedicated spatial feature network to establish bidirectional information flow between well and non-well locations,thereby capturing more complex spatial patterns in seismic data.Integrated with semi-supervised learning and low-frequency constraints,theBSFC-SSL framework enhancesboth stability and generalization.Experiments on synthetic and fielddata demonstratethat our method achieves superior lateral continuity and inversion accuracy compared to conventional one-and two-dimensional deep learning techniques. 展开更多
关键词 AVO inversion Deep learning Spatial feature constraints Semi-supervised learning
暂未订购 下载PDF
GRA:Graph-based reward aggregation for cooperative multi-agent reinforcement learning 认领 引用
7
作者 Jingcheng Tang Peng Zhou +1 位作者 He Bai Gangshan Jing 《Journal of Automation and Intelligence》 EI CSCD 2026年第1期46-56,共11页
Multi-agent reinforcement learning(MARL)has proven its effectiveness in cooperative multi-agent systems(MASs)but still faces issues on the curse of dimensionality and learning efficiency.The main difficulty is caused ... Multi-agent reinforcement learning(MARL)has proven its effectiveness in cooperative multi-agent systems(MASs)but still faces issues on the curse of dimensionality and learning efficiency.The main difficulty is caused by the strong inter-agent coupling nature embedded in an MARL problem,which is yet to be fully exploited in existing algorithms.In this work,we recognize a learning graph characterizing the dependence between individual rewards and individual policies.Then we propose a graph-based reward aggregation(GRA)method,which utilizes the inherent coupling relationship among agents to eliminate redundant information.Specifically,GRA passes information among cooperating agents through graph attention networks to obtain aggregated rewards that contribute to the fitting of the value function,making each agent learn a decentralized executable cooperation policy.In addition,we propose a variant of GRA,named GRA-decen,which achieves decentralized training and decentralized execution(DTDE)when each agent only has access to information of partial agents in the learning process.We conduct experiments in different environments and demonstrate the practicality and scalability of our algorithms. 展开更多
关键词 Networked system Multi-agent reinforcement learning Graph-based RL
暂未订购 下载PDF
5G-SliceMatch:A Slice-Aware Semi-Supervised Learning Framework for Malicious Traffic Detection in 5G Networks 认领 引用
8
作者 Jinha Kim Hwankuk Kim 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期1521-1555,共35页
The advent of 5th Generation(5G)mobile networks has introduced Network Slicing as a core mechanism for supporting heterogeneous vertical services—such as enhanced Mobile Broadband(eMBB),Ultra-Reliable LowLatency Comm... The advent of 5th Generation(5G)mobile networks has introduced Network Slicing as a core mechanism for supporting heterogeneous vertical services—such as enhanced Mobile Broadband(eMBB),Ultra-Reliable LowLatency Communication(URLLC),and massive Machine-Type Communication(mMTC)over a shared physical infrastructure,thereby significantly expanding the attack surface at the User Plane Function(UPF).Securing this multi-slice environment requires intrusion detection systems that can simultaneously accommodate the statistical heterogeneity of per-slice traffic and the stringent Quality of Service(QoS)constraints of real-time slices,yet the practical cost of obtaining high-quality labeled traffic in operational 5G cores remains prohibitive.This study proposes 5G-SliceMatch,a Slice-Aware Semi-Supervised Learning framework,to address the challenge of malicious traffic detection in 5G network slicing environments under severe label scarcity.Traditional intrusion detection systems fail to account for the heterogeneous traffic characteristics of different 5G slices,leading to significant performance degradation in resource-constrained slices like URLLC.To overcome this,5G-SliceMatch integrates a Slice-Aware Teacher Model with dedicated classification heads,slice-specific differential Feature Masking,and a progressive Self-Training strategy.Experimental results on the 5G-SliciNdd dataset demonstrate that 5G-SliceMatch consistently outperforms state-of-the-art baselines.Specifically,in an extreme scenario with only 1%of labeled data,5G-SliceMatch achieved an F1-Score of 0.9393,outperforming XGBoost by 2.82%.Moreover,it achieves 98.1%of the performance of a fully supervised XGBoost(100%labels)using only 5%of the data,effectively reducing the manual labeling workload by 95%.This work proves that 5G-SliceMatch-by integrating slice-aware architectural design with efficient semi-supervised learning is critical for achieving high-resolution security visibility while satisfying the stringent QoS requirements of complex 5G network environments. 展开更多
关键词 5G network slicing network IDS semi-supervised learning
暂未订购 下载PDF
Instance selection method for improving graph-based semi-supervised learning 认领 引用 被引量：4
9
作者 Hai WANG Shao-Bo WANG Yu-Feng LI 《Frontiers of Computer Science》 SCIE EI CSCD 2018年第4期725-735,共11页
Graph-based semi-supervised learning is an important semi-supervised learning paradigm. Although graphbased semi-supervised learning methods have been shown to be helpful in various situations, they may adversely affe... Graph-based semi-supervised learning is an important semi-supervised learning paradigm. Although graphbased semi-supervised learning methods have been shown to be helpful in various situations, they may adversely affect performance when using unlabeled data. In this paper, we propose a new graph-based semi-supervised learning method based on instance selection in order to reduce the chances of performance degeneration. Our basic idea is that given a set of unlabeled instances, it is not the best approach to exploit all the unlabeled instances; instead, we should exploit the unlabeled instances that are highly likely to help improve the performance, while not taking into account the ones with high risk. We develop both transductive and inductive variants of our method. Experiments on a broad range of data sets show that the chances of performance degeneration of our proposed method are much smaller than those of many state-of-the-art graph-based semi-supervised learning methods. 展开更多
关键词 graph-based semi-supervised learning performance degeneration instance selection
暂未订购 下载PDF
Graph-based semi-supervised learning 认领 引用 被引量：3
10
作者 Changshui ZHANG Fei WANG 《Frontiers of Electrical and Electronic Engineering in China》 CSCD 2011年第1期17-26,共10页
The recent years have witnessed a surge of interests in graph-based semi-supervised learning(GBSSL).In this paper,we will introduce a series of works done by our group on this topic including:1)a method called linear ... The recent years have witnessed a surge of interests in graph-based semi-supervised learning(GBSSL).In this paper,we will introduce a series of works done by our group on this topic including:1)a method called linear neighborhood propagation(LNP)which can automatically construct the optimal graph;2)a novel multilevel scheme to make our algorithm scalable for large data sets;3)a generalized point charge scheme for GBSSL;4)a multilabel GBSSL method by solving a Sylvester equation;5)an information fusion framework for GBSSL;and 6)an application of GBSSL on fMRI image segmentation. 展开更多
关键词 graph-based semi-supervised learning(GBSSL) linear neighborhood propagation(LNP) point charge model fMRI image segmentation
Enhanced battery life prediction with reduced data demand via semi-supervised representation learning 认领 引用 被引量：2
11
作者 Liang Ma Jinpeng Tian +2 位作者 Tieling Zhang Qinghua Guo Chi Yung Chung 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2025年第2期524-534,I0011,共11页
Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion batteries.Although various machine learning models offer promising predictions,one critical but often overlo... Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion batteries.Although various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for training.Collection of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for years.Here,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL labels.Our approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL labels.The approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge cycles.Our method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional approach.We also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder heads.The projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled data.Our approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices. 展开更多
关键词 Lithium-ion batteries Battery degradation Remaining useful life Semi-supervised learning
暂未订购 下载PDF
Efficient Full-Range Nonlinear Analyses of Structural Systems Based on Heterogeneous Graph Learning 认领 引用 被引量：2
12
作者 Linghan Song Chen Wang Jiansheng Fan 《Engineering》 SCIE EI CSCD 2026年第4期320-336,共17页
Nonlinear analyses possess tremendous significance throughout the entire lifespans of civil structures.In recent years,the interest in leveraging deep learning(DL)to address the efficiency limitations of the tradition... Nonlinear analyses possess tremendous significance throughout the entire lifespans of civil structures.In recent years,the interest in leveraging deep learning(DL)to address the efficiency limitations of the traditional structural analysis methods has increased.However,full-range nonlinear analyses of different structures remain underresearched because of a lack of appropriate data representations and the failure to consider both internal structural information and external load conditions.A heterogeneous graph(HetG)representation scheme that can digitalize arbitrary structural systems with high fidelity is proposed in this study.Furthermore,a composite feature learning framework is developed to enable efficient full-range nonlinear analyses.This framework comprises two main components:①a heterogeneous graph neural network(GNN)-based module that encodes static features into embeddings with full structural semantics and②a sequence-to-sequence(Seq2Seq)module that predicts history-dependent responses using structural embeddings and external stimuli in an end-to-end manner.A computational model named structural analysis based on a graph neural network-nonlinear(StructGNN-N)is implemented based on the proposed methodology and is validated through numerical experiments involving real-world concrete structures.The results show that StructGNN-N successfully reproduces the full-range nonlinear responses of all nodes in the entire structure and exhibits excellent generalizability across structures with diverse topological designs and member configurations.Notably,the developed model achieves a computational efficiency level that is 1000 times greater than that of the traditional elastoplastic history analysis approach using the finite-element(FE)method.A parametric analysis and ablation studies demonstrate the effectiveness of the StructGNN-N architecture.Due to its superior accuracy and computational efficiency,the proposed method holds great potential for use in engineering applications,especially in the context of digital twins.This approach provides an inspiring path for simulating diverse engineering structures with accurate and comprehensive mechanical information in real time. 展开更多
关键词 Structural system analysis Heterogeneous graph-based deep learning Full-range response prediction Digital twin Smart structural engineering
暂未订购 下载PDF
PL-HLANet:a semi-supervised approach for tunnel boring machine disc cutter wear prediction 认领 引用
13
作者 Zhaoyang LI Wei TANG +3 位作者 Xinyuan WANG Huxiu XU Huayong YANG Jun ZOU 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2026年第4期317-333,I0001-I0007,I0048,共17页
Unanticipated wear of tunnel boring machine(TBM)disc cutters is a critical factor causing project delays and cost overruns in tunneling engineering.Accurate,real-time prediction of the cutter’s wear state is therefor... Unanticipated wear of tunnel boring machine(TBM)disc cutters is a critical factor causing project delays and cost overruns in tunneling engineering.Accurate,real-time prediction of the cutter’s wear state is therefore essential for enabling predictive maintenance.Data-driven methods,particularly deep learning,have shown promise for this task,but their performance is constrained by the scarcity of high-quality labeled data in practical industrial settings.To address this challenge,we propose a novel,decoupled semi-supervised framework called PL-HLANet.The first component of this framework is a multi-view pseudolabeling(PL)module,which mines high-confidence supervisory signals from massive unlabeled data by leveraging heterogeneous views derived from feature engineering and diverse model architectures;it is followed by a consistency check to ensure label quality.This process effectively augments the training set while correcting for sampling bias.Subsequently,a specialized hierarchical hybrid attention network(HLANet)is used to make predictions.The HLANet organically integrates a temporal convolutional network(TCN)for local feature extraction,a bidirectional long short-term memory(Bi-LSTM)network for capturing temporal dynamics,and a custom attention mechanism for focusing on critical information.Experiments on a realworld tunneling dataset show that PL-HLANet significantly outperforms both supervised and mainstream semi-supervised baselines,such as the Mean Teacher and FixMatch.The framework’s effectiveness is further substantiated by validations of its architectural design and data-driven selection of hyperparameters.Moreover,PL-HLANet has a high inference speed,showcasing its practicality for real-world scenarios.Our work provides an effective solution for machining equipment monitoring in datascarce industrial environments. 展开更多
关键词 Tunnel boring machine(TBM) Cutter wear Semi-supervised learning Pseudo-labeling(PL) Temporal convolutional network Bidirectional long short-term memory(Bi-LSTM) Attention mechanism
暂未订购 下载PDF
RoCoNet:Rotational Contrastive Network for Semi-Supervised Cervical Cell Image Object Detection 认领 引用
14
作者 HUANG Qiubo GONG Runze CHEN Dehua 《Journal of Donghua University(English Edition)》 CAS 2026年第2期82-93,共12页
A semi-supervised learning framework integrating rotational invariance,contrastive learning,and adaptive hybrid thresholds,named rotational contrastive network(RoCoNet),is proposed to enhance the applicability of semi... A semi-supervised learning framework integrating rotational invariance,contrastive learning,and adaptive hybrid thresholds,named rotational contrastive network(RoCoNet),is proposed to enhance the applicability of semisupervised learning for medical cell datasets.Due to the unique sampling approach of cell datasets,input images often contain uncertain rotation angles,which render traditional convolution kernels ineffective in existing semi-supervised detectors.To address this challenge,rotational attention convolution is introduced,offering robustness to rotational transformations.Additionally,cross-feature contrastive loss is proposed to improve upon the contrastive loss used in supervised learning,tackling issues of poor classification performance caused by cell overlap and clustering.An adaptive hybrid threshold is also introduced to stabilize pseudo-label generation during early training.A global threshold,computed by using Gaussian mixture models(GMMs),is applied to refine the local threshold,which helps balance the quantity and quality of pseudo-labels.Experiments on the ThinPrep cytology test(TCT)dataset for cervical cytopathology show that RoCoNet achieves a mean average precision(mAP)of 31.6%with only 10%labeled data,outperforming the baseline method by 8.4%in mAP. 展开更多
关键词 semi-supervised learning rotational invariance contrastive learning object detection cervical cell
暂未订购 下载PDF
Deviation-Guided Attention for Semi-Supervised Anomaly Detection With Contrastive Regularisation 认领 引用
15
作者 Guanglei Xie Xiaochang Hu +4 位作者 Yi Sun Wenzhuo Zhang Yafeng Bu Hao Fu Xin Xu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第1期66-82,共17页
Anomaly detection(AD)aims to identify abnormal patterns that deviate from normal behaviour,playing a critical role in applications such as industrial inspection,medical imaging and autonomous driving.However,AD often ... Anomaly detection(AD)aims to identify abnormal patterns that deviate from normal behaviour,playing a critical role in applications such as industrial inspection,medical imaging and autonomous driving.However,AD often faces a scarcity of labelled data.To address this challenge,we propose a novel semi-supervised anomaly detection method,DASAD(Deviation-Guided Attention for Semi-Supervised Anomaly Detection),which integrates deviation-guided attention with contrastive regularisation to reduce the unreliability of pseudo-labels.Specifically,a deviation-guided attention mechanism is designed to combine three types of deviations:latent embeddings,residual direction vectors and hierarchical reconstruction errors to capture anomaly specific cues effectively,thereby enhancing the credibility of pseudo-labels for unlabelled samples.Furthermore,a class-asymmetric contrastive loss is constructed to promote compact representations of normal instances while preserving the structural diversity of anomalies.Extensive experiments on 8 benchmark datasets demonstrate that DASAD consistently outperforms state-of-the-art methods and exhibits strong generalisation across 6 anomaly detection domains. 展开更多
关键词 anomaly detection deep learning semi-supervised learning
暂未订购 下载PDF
A Novel Semi-Supervised Multi-View Picture Fuzzy Clustering Approach for Enhanced Satellite Image Segmentation 认领 引用
16
作者 Pham Huy Thong Hoang Thi Canh +2 位作者 Nguyen Tuan Huy Nguyen Long Giang Luong Thi Hong Lan 《Computers, Materials & Continua》 SCIE EI 2026年第3期1092-1117,共26页
Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rel... Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rely on large amounts of labeled data,which are costly and time-consuming to obtain,especially in largescale or dynamic environments.To address this challenge,we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering(SS-MPFC)algorithm,which improves segmentation accuracy and robustness,particularly in complex and uncertain remote sensing scenarios.SS-MPFC unifies three paradigms:semi-supervised learning,multi-view clustering,and picture fuzzy set theory.This integration allows the model to effectively utilize a small number of labeled samples,fuse complementary information from multiple data views,and handle the ambiguity and uncertainty inherent in satellite imagery.We design a novel objective function that jointly incorporates picture fuzzy membership functions across multiple views of the data,and embeds pairwise semi-supervised constraints(must-link and cannot-link)directly into the clustering process to enhance segmentation accuracy.Experiments conducted on several benchmark satellite datasets demonstrate that SS-MPFC significantly outperforms existing state-of-the-art methods in segmentation accuracy,noise robustness,and semantic interpretability.On the Augsburg dataset,SS-MPFC achieves a Purity of 0.8158 and an Accuracy of 0.6860,highlighting its outstanding robustness and efficiency.These results demonstrate that SSMPFC offers a scalable and effective solution for real-world satellite-based monitoring systems,particularly in scenarios where rapid annotation is infeasible,such as wildfire tracking,agricultural monitoring,and dynamic urban mapping. 展开更多
关键词 Multi-view clustering satellite image segmentation semi-supervised learning picture fuzzy sets remote sensing
暂未订购 下载PDF
Graph and Transformer-Based Deep Learning Paradigms for DDoS Detection:A Systematic and Critical Survey 认领 引用
17
作者 Noor Mueen Mohammed Ali Hayder Seyed Amin Hosseini Seno +2 位作者 Mehdi Ebady Manaa Hamid Noori Davood Zabihzadeh 《Computers, Materials & Continua》 SCIE EI 2026年第7期1-43,共43页
With the rapid expansion of networked systems,Distributed Denial-of-Service(DDoS)attacks have become a major threat to Internet security and service availability.Due to their limited scalability,incapacity to capture ... With the rapid expansion of networked systems,Distributed Denial-of-Service(DDoS)attacks have become a major threat to Internet security and service availability.Due to their limited scalability,incapacity to capture temporal and relational relationships,and decreased detection accuracy under dynamic and high-volume network traffic,traditional machine learning algorithms frequently fail in large-scale DDoS scenarios.This encourages the application of deep learning techniques that can simulate intricate relationships.This survey systematically reviews graph-based deep learning and Transformer models for DDoS detection.We categorize methods for transforming network traffic into graph representations and analyze key architectures,including GraphSAGE,GCN,GAT,spatio-temporal Transformers,and hybrid GNN-Transformer models.We summarize the evaluation metrics,datasets,feature extraction strategies,and performance trends reported across existing studies.Results indicate that these approaches effectively capture topological and temporal patterns to detect coordinated attacks.Our comparative review shows that these approaches are capable of capturing both topological and temporal patterns in network traffic,enabling more accurate identification of coordinated DDoS attacks reported in the literature.Remaining challenges include explainability,scalability,data imbalance,and limited generalization.The survey’s contributions are a unified taxonomy,comparative analysis,identification of open challenges,and future research directions toward explainable,lightweight,and federated frameworks. 展开更多
关键词 Distributed Denial-of-Service(DDoS)detection Graph Neural Networks(GNNs) transformer architecture deep learning network security graph-based learning
暂未订购 下载PDF
Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning 认领 引用 被引量：21
18
作者 Xueyi LI Jialin LI +1 位作者 Yongzhi QU David HE 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2020年第2期418-426,共9页
In aerospace industry,gears are the most common parts of a mechanical transmission system.Gear pitting faults could cause the transmission system to crash and give rise to safety disaster.It is always a challenging pr... In aerospace industry,gears are the most common parts of a mechanical transmission system.Gear pitting faults could cause the transmission system to crash and give rise to safety disaster.It is always a challenging problem to diagnose the gear pitting condition directly through the raw signal of vibration.In this paper,a novel method named augmented deep sparse autoencoder(ADSAE)is proposed.The method can be used to diagnose the gear pitting fault with relatively few raw vibration signal data.This method is mainly based on the theory of pitting fault diagnosis and creatively combines with both data augmentation ideology and the deep sparse autoencoder algorithm for the fault diagnosis of gear wear.The effectiveness of the proposed method is validated by experiments of six types of gear pitting conditions.The results show that the ADSAE method can effectively increase the network generalization ability and robustness with very high accuracy.This method can effectively diagnose different gear pitting conditions and show the obvious trend according to the severity of gear wear faults.The results obtained by the ADSAE method proposed in this paper are compared with those obtained by other common deep learning methods.This paper provides an important insight into the field of gear fault diagnosis based on deep learning and has a potential practical application value. 展开更多
关键词 Deep learning Gear pitting diagnosis Gear teeth Raw vibration signal Semi-supervised learning Sparse autoencoder
暂未订购 下载PDF
Semi-Supervised Learning with Generative Adversarial Networks on Digital Signal Modulation Classification 认领 引用 被引量：47
19
作者 Ya Tu Yun Lin +1 位作者 Jin Wang Jeong-Uk Kim 《Computers, Materials & Continua》 SCIE EI 2018年第5期243-254,共12页
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 imp... 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. 展开更多
关键词 Deep Learning automated modulation classification semi-supervised learning generative adversarial networks
暂未订购 下载PDF
Fault diagnosis of electric transformers based on infrared image processing and semi-supervised learning 认领 引用 被引量：10
20
作者 Jian Fang Fan Yang +2 位作者 Rui Tong Qin Yu Xiaofeng Dai 《Global Energy Interconnection》 EI CSCD 2021年第6期596-607,共12页
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 ac... 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. 展开更多
关键词 Transformer Fault diagnosis Infrared image Generative adversarial network Semi-supervised learning
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 9 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈