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DRSC: Dual-Reweighted Siamese Contrastive Learning network for Cross-Domain Rotating Machinery Fault Diagnosis With Multi-Source Domain Imbalanced Data

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IEEE Internet of Things Journal 2025年第13期12卷 23678-23693页

作者： Bi, Yuanguo Fu, Rao Jiang, Cunyu Zhang, Xiaoling Li, Fengyun Zhao, Liang Han, Guangjie Northeastern University School of Computer Science and Engineering Shenyang110169 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110169 China Shenyang University of Technology Institute of Artificial Intelligence Shenyang110870 China Shenyang Aerospace University School of Computer Science Shenyang110135 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

To enhance the reliability of rotating machinery, cross-domain fault diagnosis becomes vital for detecting faults under unknown operating conditions. However, multi-source domain imbalanced data present significant challenges, as divergent label distributions across domains cause complex domain-class shifts and degrade the performance of cross-domain fault diagnosis. Moreover, diagnostic models often struggle to learn features from minority classes due to label imbalance within each domain, which may degrade the performance in diagnosing these minority classes. To address these challenges, we propose a Dual-Reweighted Siamese Contrastive learning network (DRSC) for cross-domain fault diagnosis with multi-source domain imbalanced data. In DRSC, we design a Siamese feature extractor based on a wide-kernel convolutional neural network to capture short-term characteristics and leverage the convenience in extracting domain-invariant features. Subsequently, to alleviate domain-class shifts, we design a reweighted contrastive domain-class alignment mechanism that strategically pulls domain-class pairs together while pushing other health conditions away. Finally, to enable the diagnostic model to learn from minority health conditions, a reweighted health condition classifier is developed by assigning higher weights to the minority classes. Evaluation results on two public datasets illustrate DRSC outperforms comparison models in cross-domain fault diagnosis. © 2014 IEEE.

关键词： Convolutional neural networks

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Offloading Mode Hidden for Delay Sensitive Tasks in Edge Computing networks 5

Offloading Mode Hidden for Delay Sensitive Tasks in Edge Com...

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5th International Seminar on Artificial Intelligence, networking and information Technology, AINIT 2024

作者： Zhang, Guowei Du, Jiayuan Qufu Normal University School of Cyber Science and Engineering Qufu China Key Laboratory of Computing Power Network and Information Security Jinan China

ISBN: (纸本)9798350385557

Edge computing has been developed to support local services with lower burden to the core network. However, privacy issues deteriorate when terminal users enjoy the high-quality services with the price of sending their data to multiple service nodes (SNs) scattered throughout the network. Existing researches concerning this problem are almost all based on the traditional security technologies including encryption and authentication, which are too complicated to be well supported by the terminal nodes (TNs) in edge networks due to the poor local processing capabilities. In view of the above, this article proposes an offloading mode hidden (OMH) algorithm, which reduces the risk level by equalizing the task offloading modes of different TNs. Gaming process is adopted to achieve the risk-minimized offloading solutions for TNs. Numerical results demonstrate that comparing with the Greedy algorithm, the proposed OMH algorithm can achieve lower task offloading risk with higher efficiency and provide more TNs with satisfied services. © 2024 IEEE.

关键词： Edge computing

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Review of Static Image Compression Algorithms 7

Review of Static Image Compression Algorithms

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7th IEEE Advanced information Technology, Electronic and Automation Control Conference, IAEAC 2024

作者： Liang, Yanxia Jia, Tong Li, Na Liu, Xin Jiang, Jing Lu, Guangyue Zhao, Meng Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an China Xi'an Eurasia University College of Information Engineering Xi'an China

ISBN: (纸本)9798350339161

With the increasing development of digital image communication technology, the requirements for image quality, such as texture details, color levels, and dynamic range, are gradually increasing. This presents unprecedented challenges to the tradeoff between the reliability and effectiveness of image compression technology. Static image compression standards are an integration of phased research achievements in image compression algorithms and provide guidance for the study of image compression. Firstly, static image compression algorithms are introduced in three types: discrete cosine transform (DCT) coding-based methods, prediction coding-based methods, and discrete wavelet transform (DWT) coding-based methods, based on the existing literatures and the latest achievements. The basic idea is elaborated for each typical algorithm. Secondly, the advantages and disadvantages of these algorithms are analyzed, compared, and summarized. Thirdly, research on static image compression in the past three years is detailed and analyzed, which is divided into three technical fields: image steganography, high fidelity image processing, and optimization of image processing performance. Finally, the challenges in each field are proposed as well. Some potential future research work is advised. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Spatio-temporal Constrained Geomagnetic Indoor Localization with Arbitrary Walking Speed

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IEEE Internet of Things Journal 2025年第12期12卷 21740-21756页

作者： Liu, Ning Peng, Gezhi Ling, Hua-Bao Niu, Qun He, Tao Sun Yat-sen University Guangdong Key Laboratory of Information Security Technology School of Computer Science and Engineering Guangzhou510006 China Sun Yat-sen University School of Artificial Intelligence Zhuhai519000 China

This paper focuses on the accuracy and practicality of geomagnetic-based indoor localization in large-scale real-world sites. Previous studies often use continuous geomagnetic sequences for better discriminability but ignore that longer sequences, while more accurate, increase response times. Shorter sequences enhance practicality but sacrifice accuracy. Additionally, varying walking speeds can cause sequence variations along the same path, leading to ambiguity. To address these challenges, we propose a spatial-temporal constrained indoor localization model (STC-loc) that enables both accurate and practical localization using geomagnetic sequences collected at arbitrary walking speeds. Specifically, we first design a speed-oriented adaptive normalization module to tackle the walking speed heterogeneity problem. Then to balance accuracy and practicality, we initially employ shorter sequences as input and a self-attention hierarchical structure to efficiently extract the temporal features for initial location estimation. Subsequently, we further apply an Encoder-Decoder based refinement strategy, leveraging spatial contextual for continuous localization to ensure accuracy and robustness. Additionally, a slide-window based two-phase system architecture optimizes processing efficiency and scalability. Experiments on three large-scale real-world sites have demonstrated the superiority of the proposed STC-loc, reducing the localization error by more than 43% with shorter geomagnetic sequences. © 2014 IEEE.

关键词： Geomagnetism

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Defending Against Poisoning Attacks in Federated Prototype Learning on Non-IID Data 18th

Defending Against Poisoning Attacks in Federated Prototype ...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zhang, Jie Zhang, Hongliang Wang, Guijuan Dong, Anming Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250353 China

ISBN: (纸本)9783031714665

Federated learning (FL) is an emerging distributed machine learning paradigm that enables participants to cooperatively train learning tasks without revealing the raw data. However, the distributed nature of FL makes it susceptible to poisoning attacks, especially when the local data of participants are non-independent and identically diatributed (non-IID). Although several defense methods have been proposed to mitigate poisoning attacks, their effectiveness is limited by the specific assumptions about the data distribution. In this work, we propose a new defense strategy, FedAPA (Federated Prototype Learning Against Poisoning Attacks). Specifically, we use abstract class prototypes to communicate between the clients and server, thus effectively alleviating the impact of non-IID data. Moreover, we propose a new abnormal client detection method that aims to mitigate the impact of malicious clients while distinguishing between malicious and benign clients, thereby effectively defending against poisoning attacks. Extensive experiments on different datasets show that FedAPA can effectively resist the poisoning attacks under various data distributions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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An Efficient and Provably Secure SM2 key-Insulated Signature Scheme for Industrial Internet of Things

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computer Modeling in engineering & Sciences 2024年第1期138卷 903-915页

作者： Senshan Ouyang Xiang Liu Lei Liu Shangchao Wang Baichuan Shao Yang Zhao School of Mechanical Engineering Northwestern Polytechnical UniversityXi’anChina Department of Process and Information Technology Chengdu Aircraft Industrial(Group)Co.Ltd.ChengduChina School of Information and Software Engineering The Network and Data Security Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina

With the continuous expansion of the Industrial Internet of Things(IIoT),more andmore organisations are placing large amounts of data in the cloud to reduce ***,the channel between cloud servers and smart equipment is not trustworthy,so the issue of data authenticity needs to be *** SM2 digital signature algorithm can provide an authentication mechanism for data to solve such ***,it still suffers from the problem of key *** order to address this concern,this study first introduces a key-insulated scheme,SM2-KI-SIGN,based on the SM2 *** scheme boasts strong key insulation and secure *** scheme uses the elliptic curve algorithm,which is not only more efficient but also more suitable for IIoT-cloud ***,the security proof of SM2-KI-SIGN is given under the Elliptic Curve Discrete Logarithm(ECDL)assumption in the random oracle.

关键词： key-insulated SM2 algorithm digital signature Industrial Internet of Things(IIoT) provable security

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A Technique for Ensured Cross Chain IBC Transactions Using TPM 2

A Technique for Ensured Cross Chain IBC Transactions Using T...

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2nd International Conference on Big Data and Privacy Computing, BDPC 2024

作者： Wang, YiChuan Yan, YiLiang Liu, XiaoXue Gao, Wen Wang, ZiYan School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (纸本)9798350307597

Cross chain contact across several blockchains becomes increasingly significant in importance with the increased use of blockchain technology. However, security cannot be assured when producing equivalent outputs through cross-chain transmission because it is simple to launch an assault, and without security measures;it is simple to cause the transmission material to be stolen. This article combines TPM, IBC, and associates encryption and decryption techniques for protection to overcome this issue. We have establishes the viability and efficacy of the suggested method through testing and performance analyses. The experimental findings show that our approach can successfully preserve privacy and guarantee the security of data transfer. © 2024 IEEE.

关键词： Blockchain

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AR-CNN: an attention ranking network for learning urban perception

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Science China(information Sciences) 2022年第1期65卷 164-174页

作者： Zhetao LI Ziwen CHEN Wei-Shi ZHENG Sangyoon OH Kien NGUYEN Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University School of Data and Computer Science Sun Yat-sen University Department of Computer and Information Engineering Ajou University Graduate School of Engineering Chiba University

An increasing number of deep learning methods is being applied to quantify the perception of urban environments, study the relationship between urban appearance and resident safety, and improve urban appearance. Most advanced methods extract image feature representations from street-level images through conventional visual computation algorithms or deep convolutional neural networks and then directly predict the results using features. Unfortunately, these methods take color and texture information together during processing. Color and texture are prime image features, and they affect human perception and judgment differently. We argue that color and texture should be operated differently; therefore, we formulate an end-to-end learning methodology to process input images according to color and texture information before inputting it into the neural network. The processed images and the original image constitute three input streams for the triad attention ranking convolutional neural network(AR-CNN) model proposed in this *** accordance with the aspects of color and texture, an improved attention mechanism in the convolution layer is proposed. Our objective is to obtain the scores of humans on urban appearance in accordance with the prediction results computed from pairwise comparisons generated by the AR-CNN model.

关键词： ranking network urban perception attribute learning attention network colour and texture

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A New Model for network security Situation Assessment of the Industrial Internet

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computers, Materials & Continua 2023年第5期75卷 2527-2555页

作者： Ming Cheng Shiming Li Yuhe Wang Guohui Zhou Peng Han Yan Zhao College of Computer Science and Information Engineering Harbin Normal UniversityHarbin150025China School of Information Technology Luoyang Normal UniversityLuoyang471934China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai200240China

To address the problem of network security situation assessment in the Industrial Internet,this paper adopts the evidential reasoning(ER)algorithm and belief rule base(BRB)method to establish an assessment ***,this paper analyzes the influencing factors of the Industrial Internet and selects evaluation indicators that contain not only quantitative data but also qualitative ***,the evaluation indicators are fused with expert knowledge and the ER *** to the fusion results,a network security situation assessment model of the Industrial Internet based on the ER and BRB method is established,and the projection covariance matrix adaptive evolution strategy(P-CMA-ES)is used to optimize the model *** method can not only utilize semiquantitative information effectively but also use more uncertain information and prevent the problem of combinatorial ***,it solves the problem of the uncertainty of expert knowledge and overcomes the problem of low modeling accuracy caused by insufficient ***,a network security situation assessment case of the Industrial Internet is analyzed to verify the effectiveness and superiority of the *** research results showthat this method has strong applicability to the network security situation assessment of complex Industrial Internet *** can accurately reflect the actual network security situation of Industrial Internet systems and provide safe and reliable suggestions for network administrators to take timely countermeasures,thereby improving the risk monitoring and emergency response capabilities of the Industrial Internet.

关键词： Industrial internet network security situation assessment evidential reasoning belief rule base projection covariance matrix adaptive evolution strategy

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Internet of Things Intrusion Detection System Based on Convolutional Neural network

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computers, Materials & Continua 2023年第4期75卷 2119-2135页

作者： Jie Yin Yuxuan Shi Wen Deng Chang Yin Tiannan Wang Yuchen Song Tianyao Li Yicheng Li Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjingChina Engineering Research Center of Electronic Data Forensics Analysis NanjingChina Key Laboratory of Digital Forensics Department of Public Security of Jiangsu ProvinceNanjingChina

In recent years, the Internet of Things (IoT) technology has developedby leaps and bounds. However, the large and heterogeneous networkstructure of IoT brings high management costs. In particular, the low costof IoT devices exposes them to more serious security concerns. First, aconvolutional neural network intrusion detection system for IoT devices isproposed. After cleaning and preprocessing the NSL-KDD dataset, this paperuses feature engineering methods to select appropriate features. Then, basedon the combination of DCNN and machine learning, this paper designs acloud-based loss function, which adopts a regularization method to preventoverfitting. The model consists of one input layer, two convolutional layers,two pooling layers and three fully connected layers and one output ***, a framework that can fully consider the user’s privacy protection isproposed. The framework can only exchange model parameters or intermediateresults without exchanging local individuals or sample data. This paperfurther builds a global model based on virtual fusion data, so as to achievea balance between data privacy protection and data sharing computing. Theperformance indicators such as accuracy, precision, recall, F1 score, and AUCof the model are verified by simulation. The results show that the model ishelpful in solving the problem that the IoT intrusion detection system cannotachieve high precision and low cost at the same time.

关键词： Internet of things intrusion detection system convolutional neural network federated learning

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