检索结果-内蒙古大学图书馆

Contrastive Conditional Adversarial Autoencoder with Class-specific Forces for Imbalanced Open-set Fault Detection

IEEE Transactions on Automation Science and engineering 2025年 22卷 15757-15767页

作者： Zuo, Zhonglin Meng, Tao Li, Zheng Zhang, Hao Liu, Tong Chen, Zhansheng Pang, Zhibo Zhejiang University College of Control Science and Engineering Hangzhou310027 China Zhejiang University School of Aeronautics and Astronautics Hangzhou310027 China Hainan Institute of Zhejiang University Sanya572025 China Chongqing University School of Automation Chongqing400044 China University of Sheffield Department of Computer Science SheffieldS1 4DP United Kingdom Shanghai Aerospace Space Technology Co. Ltd Shanghai201109 China ABB Corporate Research Sweden Department of Automation Technology Vasteras Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm Sweden

In real-world industrial scenarios, fault detection faces the widely recognized challenge of data imbalance, which not only refers to the scarcity of fault data but also includes the imbalance in healthy data. This article is concerned with imbalanced open-set fault detection (IOSFD), a practical yet challenging scenario in industrial applications where multiple healthy operating conditions and multiple fault types are imbalanced. In this article, we propose a new contrastive conditional adversarial autoencoder for IOSFD. It constructs an end-to-end unified model based on multi-class known healthy and faulty data to address the reliance of traditional methods on fault samples, while optimizing with class-specific weighted forces to ensure equal attention to imbalanced known classes. Input and feature reconstruction conditioned on operating modes are utilized to learn a compact decision plane and achieve both unknown fault detection and known data classification. Significantly, we formulate the optimization objective of conditional reconstruction based on contrastive learning and introduce adversarial training to further enhance the model’s performance. The effectiveness of the proposed method is validated through real-world pipeline leak detection and Tennessee-Eastman multi-fault detection. © 2004-2012 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Curiosity and Affect-Driven Cognitive Architecture for HRI

引用

IEEE Transactions on Affective Computing 2025年

作者： Berto, Leticia Tanevska, Ana Cirne, Azamor Costa, Paula Simoes, Alexandre Gudwin, Ricardo Rea, Francesco Colombini, Esther Sciutti, Alessandra State University of Campinas Institute of Computing Campinas Brazil Italian Institute of Technology CONTACT Unit Genoa Italy Uppsala University Department of Information Technology Uppsala Sweden Federal University of Paraiba Department of Finance and Accounting João Pessoa Brazil State University of Campinas School of Electrical and Computer Engineering Campinas Brazil São Paulo State University Department of Control and Automation Engineering Sorocaba Brazil

This study explores how humans and cognitive robots with different value systems and motivations understand each other's needs in free-form interactions. We developed a cognitive architecture that links sensing and perception to internal motivation and an intrinsic value system for determining actions. Inspired by young children's needs, this architecture includes three drives: learning, interaction, and recharging, each with varying dependence on the human partner. We aimed to assess how experimentally changing the importance of these drives within a fixed architecture affects interaction dynamics with human partners (acting as caregivers) and their understanding of the robot's needs. By adjusting the learning and interaction drives, we created two robot profiles: Playful, which prioritizes environmental exploration and playfulness to reduce boredom, and Social, which focuses on social interaction through touch and visual contact to increase comfort. Our findings show that changing the importance of these drives produces distinct behaviors and human perceptions. Robot behaviors matched their profiles, and participants adapted their responses accordingly. Participants identified and attributed distinct traits to each robot without knowing the specific profiles. Despite variability among human partners, the robots, especially the playful one, were generally well understood by most participants. © 2025 IEEE. All rights reserved.

关键词： Robot learning

来源：评论

学校读者我要写书评

暂无评论

A Survey on Network Management for xANET: Evolution, Challenges, Future Directions

引用

IEEE Communications Surveys and Tutorials 2025年

作者： Li, Tong Yang, Chungang Wang, Yao Cai, Lin Anpalagan, Alagan Han, Zhu Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China National Key Laboratory of Multi-domain Data Collaborative Processing and Control Xi'an710068 China Hangzhou Institute of Technology of Xidian University Hangzhou311231 China University of Victoria Department of Electrical and Computer Engineering VictoriaBCV8P 5C2 Canada Ryerson University Department of Electrical and Computer Engineering TorontoONM5B 2K3 Canada University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446701 Korea Republic of

The x ad hoc network (xANET) termed as a family of ad hoc networks, including mobile ad hoc network (MANET), vehicular ad hoc network (VANET), flying ad hoc network (FANET) and satellite ad hoc network (SANET), has found a wide range of applications in providing ubiquitous wireless services. Despite its broad utility, the dynamic nature and lack of a centralized controller pose significant challenges to effective and flexible network management for xANET. Conventional network management protocols face challenges such as scalability, security vulnerabilities, configuration complexity, robustness, and performance resilience. Recent efforts have presented different approaches, focusing on policy-based network management (PBNM) and intent-driven network management (IDNM). However, there is no comprehensive survey to clarify their concepts and classifications. This paper presents a survey of the network management evolution for xANET, covering configuration-based, policy-based and the latest intent-driven approaches. We first introduce the characteristics and applications of xANET. Meanwhile, we investigate the network management concepts and challenges. Moreover, we survey the evolution of management protocols for xANET, including simple network management protocol (SNMP), PBNM, and IDNM. Then, we follow the detailed network management of xANET from configuration-based to policy-based and intent-driven approaches. Through comparative analysis, it is found that IDNM employs a more intelligent management protocol, demonstrating higher efficiency and flexibility in handling complex tasks and dynamic network management. This makes it better suited to addressing the challenges of xANET management. Finally, we summarize the remaining challenges and possible future research directions. © 2025 IEEE.

关键词： Vehicular ad hoc networks

来源：评论

学校读者我要写书评

暂无评论

Mapping cultural heritage sites at risk: A support tool for heritage sites management

引用

Journal of Urban Management 2025年

作者： Brandano, Maria Giovanna Conti, Chiara Modica, Marco Urso, Giulia Gran Sasso Science Institute – Social Sciences Viale Luigi Rendina 26-28 L'Aquila 67100 Italy Department of Computer Control and Management Engineering “Antonio Ruberti” of the University La Sapienza Italy

Under the threat of climate change, the world has become increasingly unsafe, with extreme weather events causing devastation and high economic costs. These impacts are heterogeneous because of the interaction between different regional climate changes and the varying socio-economic characteristics of affected places. Climate change also impacts the management of cultural heritage sites. We then propose a method to identify heritage sites at risk from climate change issues, considering the single components of risk, namely both the natural hazard and the resilient/vulnerable local characteristics that may increase or mitigate the potential damage. The link between the natural and human spheres is, in fact, crucial for the development of suitable mitigation and prevention strategies, particularly relevant for developing countries that suffer from scarce economic resources. Overall, this work aims to provide a method to detect heritage sites at risk and a decision support tool for strategically managing cultural sites. We focus on Italy since it is one of the countries with a higher endowment of cultural heritage. However, thanks to its high replicability, this tool might be exported and adopted in different contexts and scenarios. © 2025 The Authors

关键词： Climate change Cultural heritage Impacts Risks

来源：评论

学校读者我要写书评

暂无评论

Falcon Optimized CNN for the Detection of Alzheimer's Disease in MRI Image

Falcon Optimized CNN for the Detection of Alzheimer's Diseas...

引用

Smart Systems for applications in Electrical Sciences (ICSSES), International Conference on

作者： S. Febeena Ezhil Jothi L. Mahalakshmi P. Elamurugan Naveenkumar Kaliyan J. Swapna R. Rajeshwari Department of Artificial Intelligence and Data Science Adhi College of Engineering And Technology Kanchipuram Department of Biomedical Engineering Kongunadu College of Engineering and Technology Trichy Department of Instrumentation and Control Engineering Sri Manakula Vinayagar Engineering College Puducherry Department of Computer science and Engineering Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Electrical and Electronics Engineering J.P. College of Engineering Ayikudi Tenkasi

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

Degenerative illnesses that lead to the eventual death of brain neurons afflict a large number of people. Compared to the traditional Machine Learning (ML) approach, the recent success in the medical community of classifying Alzheimer's Disease (AD) utilized a Deep Learning (DL) technique eliminates the necessity for manual feature extraction techniques. Although DL methods are widely used, there is currently a dearth of trustworthy and comprehensive research on the quantitative analysis of brain Magnetic Resonance Image (MRI) data. In this paper, an effective AD classification approach is proposed that use MRI to classify brain structure and diagnose AD. Gaussian filter is used to eliminate noises at different degrees of complexity, noisy backgrounds, low contrast, and partial volume effect in the original input image. Next, K-Means clustering segmentation is useful for dividing image into different regions based on pixel intensity values. Key features for categorization are extracted using the Gray Level Co-Occurrence Matrix (GLCM) method. A Convolutional Neural Network (CNN), enhanced by Falcon Optimization Algorithm (FOA), is employed for AD categorization. The proposed system, implemented on the Python platform using the best Alzheimer's MRI dataset, demonstrates an enhanced accuracy of 93.21 %. Additionally, this model-based approach provides a capable avenue for advancing research in the early detection of AD and holds substantial potential in differentiating AD patients from healthy individuals.

关键词： Training Accuracy Magnetic resonance imaging Neurons Magnetic resonance Feature extraction Convolutional neural networks Alzheimer's disease Optimization Testing

来源：评论

学校读者我要写书评

暂无评论

H∞ Delayed Filtering of Markov Jump Fuzzy Systems in Consumer Electronics: Input–Output Analysis

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Aslam, Muhammad Shamrooz Bilal, Hazrat Chang, Wen-Jer Kumar, Neeraj Khan, Izaz Ahmad Vasilakos, Athanasios V. China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou221110 China College of Mechatronics and Control Engineering College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Marine Engineering Keelung202 Taiwan Thapar University Patiala India Bacha Khan University Charsadda Department of Computer Science Pakistan Grimstad Norway

This article investigates the design of robust H∞ filters for Takagi–Sugeno (T–S) fuzzy systems with time-varying delays, with a critical challenge in many consumer electronics applications. We extend existing research by incorporating Markovian jump parameters to model system uncertainties and considering internal–like time–varying delays, which are prevalent in real-world scenarios such as wireless communication and networked control systems in consumer devices. To address the complexities introduced by time-varying delays, we employ a three-term approximation model that more accurately captures the system dynamics compared to traditional approaches. Furthermore, we incorporate the time derivative of the membership functions (MFs) into the filter design, and its impact on system stability and performance. To ensure filter stability, we derive novel stability conditions based on the mode–dependent Lyapunov-Krasovskii functional approach, incorporating constraints on the higher bounds of the MF time derivatives. Leveraging the small–gain theorem with scaling techniques, we propose the design of both full-order and reduced-order H∞ filters, expressed in terms of linear matrix inequalities (LMIs). These LMI–based solutions provide a systematic and computationally efficient approach for filter synthesis. Finally, we validate the effectiveness of our proposed filtering strategies through illustrative examples drawn from relevant consumer electronics applications, such as noise cancellation in audio devices, image stabilization in cameras, and vibration suppression in wearable devices. The results demonstrate the enhanced robustness and performance of the proposed filters in the presence of time-varying delays and uncertainties. © 1975-2011 IEEE.

关键词： Membership functions

来源：评论

学校读者我要写书评

暂无评论

Worst-Case Integrity Attacks and Resilient State Estimation With Partially Secured Measurements

引用

IEEE Transactions on control of Network Systems 2025年

作者： Zhou, Jing Shang, Jun Chen, Tongwen University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China

This paper examines the problem of optimal deception attacks against state estimation with partially secured measurements, where smart sensors transmit innovation sequences to the remote end for information fusion. Due to resource limitations or defensive countermeasures, the adversary can only modify data packets transmitted through unreliable channels. Meanwhile, the attack should be synthesized with sophistication to deceive an anomaly detector. To investigate the vulnerabilities of such estimation systems without feedback links and enhance security performance, the optimal attack policy is derived by formulating and explicitly solving a convex optimization problem, with the goal of maximizing the sum of estimation errors. Subsequently, a novel attack detection and resilient state estimation algorithm is proposed to ensure an acceptable level of estimation accuracy. The theoretical performance metrics, including false alarm rates for the proposed detector, are provided. Finally, the effectiveness of the results is confirmed through numerical examples. © 2014 IEEE.

关键词： Optimal detection

来源：评论

学校读者我要写书评

暂无评论

2M3DF: Advancing 3D Industrial Defect Detection with Multi Perspective Multimodal Fusion Network

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Asad, Mujtaba Azeem, Waqar Jiang, He Mustafa, Hafiz Tayyab Yang, Jie Liu, Wei Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Department of Automation Shanghai200240 China Lahore Garrison University Department of Software Engineering Lahore54000 Pakistan China University of Mining and Technology School of Information and Control Engineering Jiangsu Xuzhou221116 China Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China

In the context of Industrial Anomaly Detection (IAD), ensuring the quality of manufactured products is critical. Traditional 2D based methods often fail to capture anomalies present in complex 3D shapes. For effective anomaly detection in 3D shapes, it is essential to incorporate global semantic context, local geometric structure, and color information of the object. To fully leverage these features, we propose a network named 2M3DF, that leverages knowledge from multi-view RGB images and corresponding point cloud information for enhanced anomaly detection performance. Our model initially employs pre-trained feature extractors that generate local features from multi-view RGB images and corresponding point clouds. The novel inter-modality feature representation and fusion module first adapts these inter-modality features and then effectively aligns and aggregates these multimodality features on a pixel-to-point basis. To learn the normality from point-wise fused multimodal features, we fit a multivariate Gaussian distribution to model the normal feature distribution. Comprehensive experimental evaluations using the MVTec3D-AD and Eyecandies dataset validate the effectiveness of our propose model and demonstrate significant improvements in comparison to existing state-of-the-art methods. Our model achieves a 96.6% mean I-AUROC while delivering real-time results. © 1991-2012 IEEE.

关键词： Normal distribution

来源：评论

学校读者我要写书评

暂无评论

Dual defense:Combining preemptive exclusion of members and knowledge distillation to mitigate membership inference attacks

引用

Journal of Information and Intelligence 2025年第1期3卷 68-90页

作者： Jun Niu Peng Liu Chunhui Huang Yangming Zhang Moxuan Zeng Kuo Shen Yangzhong Wang Suyu An Yulong Shen Xiaohong Jiang Jianfeng Ma He Wang Gaofei Wu Anmin Fu Chunjie Cao Xiaoyan Zhu Yuqing Zhang School of Computer Science and Technology Xidian UniversityXi'an 710071China Information Sciences and Technology Pennsylvania State UniversityPA 16802USA School of Cyberspace Security Hainan UniversityHaikou 570228China School of Hangzhou Institute of Technology Xidian UniversityHangzhou 311231China School of Systems Information Science Future University of HakodateHakodate 041-8655Japan School of Cyber Engineering Xidian UniversityXi'an 710071China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China School of Telecommunications Engineering Xidian UniversityXi'an 710071China School of Computer&Control Engineering University of Chinese Academy of SciencesBeijing 101408China

Membership inference(MI)attacks threaten user privacy through determining if a given data example has been used to train a target *** MI defenses protect the membership privacy through preemptive exclusion of members techniques and knowledge ***,using either of these two defenses alone,the defense effect can still offers an unsatisfactory trade-off between membership privacy and *** that the defense method that directly combines these two defenses is still very limited(e.g.,the test accuracy of the target model is decreased by about 40%(in our experiments)),in this work,we propose a dual defense(DD)method that includes the preemptive exclusion of high-risk member samples module and the knowledge distillation module,which thwarts the access of the resulting models to the private training data twice to mitigate MI *** defense method can be divided into two steps:the preemptive exclusion of high-risk member samples(Step 1)and the knowledge distillation to obtain the protected student model(Step 2).We propose three types of exclusions:existing MI attacks-based exclusions,sample distances of members and nonmembers-based exclusions,and mutual information value-based exclusions,to preemptively exclude the high-risk member *** the knowledge distillation phase,we add ground-truth labeled data to the reference dataset to decrease the protected student model's dependency on soft labels,aiming to maintain or improve its test *** evaluation shows that DD significantly outperforms state-of-the-art defenses and offers a better privacy-utility *** example,DD achieves∼100%test accuracy improvement over the distillation for membership privacy(DMP)defense for ResNet50 trained on *** simultaneously achieves the reductions in the attack effectiveness(e.g.,the TPR@0.01%FPR of enhanced MI attacks decreased by 2.10%on the ImageNet dataset,the membership advantage(MA)of risk score-based attacks d

关键词： Machine learning Membership inference defenses Preemptive exclusion Knowledge distillation

来源：评论

学校读者我要写书评

暂无评论

Decoupling While Coupling: Towards More Accurate Stereo Image Sand Removal Beyond Certainty

Decoupling While Coupling: Towards More Accurate Stereo Imag...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Bingcai Wei Hui Liu Chuang Qian Yi Jia Wangyu Wu Zhishan Li School of Computer Science Wuhan University Wuhan China GNSS Research Center Wuhan Unicersity Wuhan China Intelligent Transport Systems Research Center Wuhan University of Technology Wuhan China College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China University of Liverpool Liverpool UK School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Stereo image sand removal is crucial to improve the perceptual quality for autonomous driving perception. Existing methods often fall short in accurately estimating the uncertainty inherent in degraded images, leading to suboptimal outcomes. To address this, we introduce a novel framework named Decoupling While Coupling(DWC). DWC pioneers the integration of inter-view uncertainty estimation, cross-view uncertainty-aware interaction and block-wise uncertainty representation for superior stereo image sand removal. For cross-view information interaction, we propose an Uncertainty-aware Cross-view Attentive Interaction module(UCAI) to cope with the lack of uncertainty estimation ability in the existing cross-view information interaction mechanism. For the uncertainty perception and information interaction within the inter-view, we propose a Distribution Modeling Coupling Block(DMCB), which transmits the representation of uncertainty between each backbone module. For block-wise uncertainty estimation, we use our proposed Uncertainty-aware Distribution Feature Modulator(UDFM) as the backbone of DWC to modulate the uncertainty inside the neural network itself. Extensive experimental validations on our proposed stereo image sand removal dataset SandST confirm the efficacy of DWC. Our method not only achieves higher PSNR and SSIM, but also exhibits enhanced robustness against various sand degrees and patterns.

关键词： Couplings Uncertainty Neural networks Estimation Signal processing Robustness Stereo vision Image restoration Speech processing Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：