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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

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OpenLS-DGF: An Adaptive Open-Source Dataset Generation Framework for Machine Learning Tasks in Logic Synthesis

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Ni, Liwei Wang, Rui Liu, Miao Meng, Xingyu Lin, Xiaoze Liu, Junfeng Luo, Guojie Chu, Zhufei Qian, Weikang Yang, Xiaoyan Xie, Biwei Li, Xingquan Li, Huawei Chinese Academy of Sciences State Key Lab of Processors Institute of Computing Technology Beijing100190 China Pengcheng Laboratory Shenzhen518055 China University of Chinese Academy of Sciences Beijing101408 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing100049 China Peking University School of Computer Science Center for Energy-Efficient Computing and Applications Beijing100871 China Ninbo University Faculty of Electrical Engineering and Computer Science Ninbo315211 China Shanghai Jiao Tong University University of Michigan-Shanghai Jiao Tong University Joint Institute MoE Key Laboratory of Artificial Intelligence Shanghai200240 China Hangzhou Dianzi University School of Electronics and Information Engineering Hangzhou311121 China

This paper introduces OpenLS-DGF, an adaptive logic synthesis dataset generation framework, to enhance machine learning (ML) applications within the logic synthesis process. Previous dataset generation flows were tailored for specific tasks or lacked integrated ML capabilities. While OpenLS-DGF supports various machine learning tasks by encapsulating the three fundamental steps of logic synthesis: Boolean representation, logic optimization, and technology mapping. It preserves the original information in both Verilog and ML-friendly GraphML formats. The Verilog files offer semi-customizable capabilities, enabling researchers to insert additional steps and incrementally refine the generated dataset. Furthermore, OpenLS-DGF includes an adaptive circuit engine that facilitates the final dataset management and downstream tasks. The generated OpenLS-D-v1 dataset comprises 46 combinational designs from established benchmarks, totaling over 966,000 Boolean circuits. OpenLS-D-v1 supports integrating new data features, making it more versatile for new tasks. This paper demonstrates the versatility of OpenLS-D-v1 through four distinct downstream tasks: circuit classification, circuit ranking, quality of results (QoR) prediction, and probability prediction. Each task is chosen to represent essential steps of logic synthesis, and the experimental results show the generated dataset from OpenLSDGF achieves prominent diversity and applicability. The source code and datasets are available at https://***/Logic-Factory/ACE/blob/master/OpenLS-DGF. © 1982-2012 IEEE.

关键词： Information management

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LineConGraphs: Line Conversation Graphs for Effective Emotion Recognition using Graph Neural Networks

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IEEE Transactions on Affective Computing 2025年

作者： Krishnan, Gokul S. Padi, Sarala Greenberg, Craig S. Ravindran, Balaraman Manocha, Dinesh Sriram, Ram D. IIT Madras Centre for Responsible AI Wadhwani School of Data Science and AI Chennai India National Institute of Standards and Technology GaithersburgMD United States University of Maryland Computer Science and Engineering College ParkMD United States

Emotion Recognition in Conversations (ERC) is an important aspect of affective computing with practical applications in healthcare, education, chatbots, and social media platforms. Previous approaches to ERC analysis involved using graph neural network architectures to model both speaker and long-term contextual information. In this paper, we introduce new models for ERC analysis: the LineConGCN and LineConGAT models, which are constructed using a graph construction strategy for conversations called line conversational graphs (LineConGraphs). LineConGraph is designed to capture short-term conversational context using one previous and future utterance, while also capturing long-term context using GCN or GAT layers without explicitly integrating into the graph construction strategy. We evaluate the performance of our proposed models on two benchmark datasets, rm IEMOCAP and rm {MELD}$, and show that our LineConGAT model outperforms the state-of-the-art methods with an F1-score of rm 64.58% and rm 76.50%. Furthermore, we demonstrate that incorporating sentiment shift information into line conversation graphs further enhances rm {ERC}$ performance in the case of LineConGCN models. We also evaluate the performance of our proposed model by embedding speaker information into LineConGCN and LineConGAT models and show that LineConGAT and LineConGAT with speaker embeddings performed equally for ERC analysis. © 2010-2012 IEEE.

关键词： Graph neural networks

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Multimodal Evidential Learning for Open-World Weakly-Supervised Video Anomaly Detection

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IEEE Transactions on Multimedia 2025年

作者： Huang, Chao Huang, Weiliang Jiang, Qiuping Wang, Wei Wen, Jie Zhang, Bob Shenzhen Campus of Sun Yat-sen University School of Cyber Science and Technology Shenzhen518000 China University of Macau PAMI Research Group Department of Computer and Information Science China Ningbo University Faculty of Information Science and Engineering Ningbo China Harbin Institute of Technology Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen518055 China

Efforts in weakly-supervised video anomaly detection center on detecting abnormal events within videos by coarse-grained labels, which has been successfully applied to many real-world applications. However, a significant limitation of most existing methods is that they are only effective for specific objects in specific scenarios, which makes them prone to misclassification or omission when confronted with previously unseen anomalies. Relative to conventional anomaly detection tasks, Open-world Weakly-supervised Video Anomaly Detection (OWVAD) poses greater challenges due to the absence of labels and fine-grained annotations for unknown anomalies. To address the above problem, we propose a multi-scale evidential vision-language model to achieve open-world video anomaly detection. Specifically, we leverage generalized visual-language associations derived from CLIP to harness the full potential of large pre-trained models in addressing the OWVAD task. Subsequently, we integrate a multi-scale temporal modeling module with a multimodal evidence collector to achieve precise frame-level detection of both seen and unseen anomalies. Extensive experiments on two widely-utilized benchmarks have conclusively validated the effectiveness of our method. The code will be made publicly available. © 1999-2012 IEEE.

关键词： Self-supervised learning

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MSPL: Multi-modal Statistical Prompt Learning for New Energy Equipment Defect Recognition

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IEEE Internet of Things Journal 2025年

作者： Wang, Zhenwei Wang, Pengfei Han, Guangjie Zhou, Chengjie Lin, Qing Zhang, Jianxin Fan, Guangjie Zhang, Qiang Dalian University of Technology School of Computer Science and Technology Dalian116024 China Dalian116024 China Shenyang Huayan Electric Technology Co Ltd Shenyang110180 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China Deputy Director of the Yantai Big Data Center and Senior Engineer Yantai264003 China Dalian Minzu University School of Computer Science and Engineering Dalian116600 China

Monitoring renewable energy devices is crucial for the timely detection of faults and the improvement of system stability, making it a key component of the Internet of Things (IoT) ecosystem. However, existing intelligent algorithms and IoT methods rely on edge devices with limited computational capacity, which requires balancing real-time performance and accuracy. Additionally, most methods require initial training on large-scale data using cloud platforms or high-performance servers, leading to high initial costs. To address these challenges, we propose Multi-modal Statistical Prompt Learning (MSPL) for new energy equipment defect recognition on IoT edge devices. This method enables rapid learning with only a few samples, avoiding the need for large-scale centralized training. Specifically, MSPL uses the pre-trained Contrastive Language-Image Pre-training model as its backbone, leveraging text-based conceptual information to enhance the understanding of visual inputs. A Statistical Query (SQ) module is implemented at the end of the backbone to extract distinctive features from the outputs, integrating these features with soft prompts to customize them for the defect recognition task. Since the learnable parameters are limited to soft prompts added at the end of the backbone, MSPL restricts gradient backpropagation to this point. This improves parameter and memory efficiency, making it more suitable for scenarios with limited computational capacity on IoT edge devices. Experimental results of MSPL on two renewable energy equipment defect datasets and edge devices indicate that it meets real-time processing requirements while maintaining high accuracy, outperforming other methods. © 2025 IEEE.

关键词： Zero-shot learning

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Augmented FCN: rethinking context modeling for semantic segmentation

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science China(Information sciences) 2023年第4期66卷 193-211页

作者： Dong ZHANG Liyan ZHANG Jinhui TANG School of Computer Science and Engineering Nanjing University of Science and Technology College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics

The effectiveness of modeling contextual information has been empirically shown in numerous computer vision tasks. In this paper, we propose a simple yet efficient augmented fully convolutional network(AugFCN) by aggregating content-and position-based object contexts for semantic ***, motivated because each deep feature map is a global, class-wise representation of the input,we first propose an augmented nonlocal interaction(AugNI) to aggregate the global content-based contexts through all feature map interactions. Compared to classical position-wise approaches, AugNI is more efficient. Moreover, to eliminate permutation equivariance and maintain translation equivariance, a learnable,relative position embedding branch is then supportably installed in AugNI to capture the global positionbased contexts. AugFCN is built on a fully convolutional network as the backbone by deploying AugNI before the segmentation head network. Experimental results on two challenging benchmarks verify that AugFCN can achieve a competitive 45.38% mIoU(standard mean intersection over union) and 81.9% mIoU on the ADE20K val set and Cityscapes test set, respectively, with little computational overhead. Additionally, the results of the joint implementation of AugNI and existing context modeling schemes show that AugFCN leads to continuous segmentation improvements in state-of-the-art context modeling. We finally achieve a top performance of 45.43% mIoU on the ADE20K val set and 83.0% mIoU on the Cityscapes test set.

关键词： semantic segmentation context modeling long-range dependencies attention mechanism

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Joint Beamforming Design for Integrated Sensing and Communication Systems with Hybrid-Colluding Eavesdroppers

arXiv

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arXiv 2025年

作者： Liu, Meiding Zhou, Zhengchun Shi, Qiao Li, Guyue Liu, Zilong Fan, Pingzhi Lee, Inkyu School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China Key Laboratory of Analytical Mathematics and Applications [Fujian Normal University Ministry of Education China Purple Mountain Laboratories for Network and Communication Security Nanjing210096 China School of Cyber Science and Engineering Southeast University Nanjing210096 China School of Computer Science and Electronics Engineering University of Essex ColchesterCO4 3SQ United Kingdom Key Laboratory of Information Coding and Transmission Southwest Jiaotong University Chengdu611756 China School of Electrical Engineering Korea University Seoul02841 Korea Republic of

In this paper, we consider the physical layer security (PLS) problem for integrated sensing and communication (ISAC) systems in the presence of hybrid-colluding eavesdroppers, where an active eavesdropper (AE) and a passive eavesdropper (PE) collude to intercept the confidential information. To ensure the accuracy of sensing while preventing the eavesdropping, a base station transmits a signal consisting of information symbols and sensing waveform, in which the sensing waveform can be also used as artificial noise to interfere with eavesdroppers. Under this setup, we propose an alternating optimization-based two stage scheme (AO-TSS) for improving the sensing and communication performance. In the first stage, based on the assumptions that the perfect channel state information (CSI) of the AE and statistical CSI of the PE are known, the communication and sensing beamforming problem is formulated with the objective of minimizing the weighted sum of the beampattern matching mean squared error (MSE) and cross-correlation, subject to the secure transmission constraint. To tackle the non-convexity, we propose a semi-definite relaxation (SDR) algorithm and a reduced-complexity zero-forcing (ZF) algorithm. Then, the scenarios are further extended to more general cases with imperfect AE CSI and unknown PE CSI. To further improve the communication performance, the second-stage problem is developed to optimize the secrecy rate threshold under the radar performance constraint. Finally, numerical results demonstrate the superiority of the proposed scheme in terms of sensing and secure communication. © 2025, CC BY.

关键词： Mean square error

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Development of Fast Prediction System of User Action for a Fully Autonomous Smart Home System

Development of Fast Prediction System of User Action for a F...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： N.R. Rajalakshmi L. Sharmila S. Prabu T. Veeramakali G. Arulkumaran Pranshu Chourasia Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India School of Computing and Information Technology REVA UNIVERSITY Bangalore Karnataka India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The HVAC system, energy storage building, distributed power supply, and other equipment are integrated into the scheduling algorithm, which is aimed at reducing household electricity consumption. It is also assumed that users can provide energy to the grid according to their own conditions. Taking electricity cost and comfort level as optimization targets, a home energy optimization control model for the coordinated management of hybrid energy sources is built. A smart scheduling mechanism based on the improved adaptive particle swarm optimization approach is proposed in order to derive the best time intervals for electric appliances, necessary power for the control of the room temperature for every time frame, and power for charging and discharging of the storage battery at various moments. Simulation results show that through the incorporation of distributed photovoltaic power generation, backup storage by battery, and home energy optimization control, the system efficiently balances between user comfort and electricity consumption. This offers great technical support to the development of home energy management systems. By using time-of-use electricity price for energy acquisition and supply, the optimization control goal is minimizing both power use and cost as well as preserving comfort levels. The hybrid energy management's proposed home energy optimization control model uses an adaptive particle swarm optimization algorithm to find the optimal operation schedules of the electrical appliances, the required power for temperature control in a room, and the charge/discharge power level of the storage battery at each time interval. As per the optimization principle, the proposed dynamic programming algorithm converts the multi-stage problem into a sequence of single-stage problems and solves them separately. This method successfully resolves intricate problems that cannot be addressed through greedy algorithms or divide-and-conquer. In this research, management ac

关键词： Management accounting Heuristic algorithms Electricity Prediction algorithms Dynamic programming Batteries Temperature control Particle swarm optimization Energy management systems Optimization

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Target Localization for Distributed Hybrid Active-Passive Radars

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Zhou, Qiyu Yuan, Ye Li, Hongbin Lai, Yangming Greco, Maria Sabrina Gini, Fulvio Yi, Wei University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States Fuzhou University School of Advanced Manufacturing Fuzhou350108 China University of Pisa Department of Information Engineering Pisa56122 Italy

Hybrid active-passive radar (HAPR) can effectively reduce the radiation power of active nodes, thus improving the radar system's electromagnetic environmental friendliness and anti-interception capability. Meanwhile, active nodes in HAPR can provide cooperative transmitters with known signals for passive nodes, overcoming performance losses caused by non-cooperative illuminators of opportunity (IOs). Although the advantages of HAPR have been gradually recognized in recent years, there is currently almost no systematic solution to the joint localization problem involving active and passive nodes. To address this problem, we propose two target localization algorithms for distributed HAPRs based on two-step and, respectively, direct localization. In the two-step localization, we establish two geometric equation sets with consistent structures to handle different geometric relationships among the target, transmitters/IOs, and receivers associated with the active and passive measurements. In direct localization, we reduce the high-dimensional joint likelihood function constructed from active and passive received signals into a low-dimensional one through two dimensionality reductions, significantly reducing the complexity. These algorithms combine active and passive received signals, enhancing localization performance. We also derive the Cramér-Rao lower bound (CRLB), which provides a useful performance indicator. The results indicate that the two-step localization algorithm yields suboptimal performance, with localization errors approaching the CRLB only under specific conditions. The direct localization algorithm approaches the CRLB and outperforms systems using only active or passive nodes, thereby confirming its effectiveness and robustness. © 1965-2011 IEEE.

关键词： Transmitters

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Joint deep reinforcement learning strategy in MEC for smart internet of vehicles edge computing networks

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Sustainable Computing: Informatics and Systems 2025年 46卷

作者： Luo, Jiabin Song, Qinyu Guo, Fusen Wu, Haoyuan Som, Hafizan Mat Alahmari, Saad Hoshyar, Azadeh Noori School of Mathematics and Statistics Northeastern University at Qinhuangdao Hebei Qinhuangdao066000 China College of Horticulture China Agricultural University Haidian District Beijing100193 China School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia College of Medicine and Biological Information Engineering Northeastern University Liaoning Shenyang110167 China School of Science and Engineering University of Dundee Scotland DundeeDD14HN United Kingdom Computer and Information Sciences Department Faculty of Science and Information Technology Universiti Teknologi Petronas Malaysia Department of Computer Science Applied College Northern Border University Arar Saudi Arabia Institute of Innovation Science and Sustainability Federation University Australia BrisbaneQLD4000 Australia

The Internet of Vehicles (IoV) has a limited computing capacity, making processing computation tasks challenging. These vehicular services are updated through communication and computing platforms. Edge computing is deployed closest to the terminals to extend the cloud computing facilities. However, the limitation of the vehicular edge nodes, satisfying the Quality of Experience (QoE) is the challenge. This paper developed an imaginative IoV scenario supported by mobile edge computing (MEC) by constructing collaborative processes such as task offloading decisions and resource allocation in various roadside units (RSU) environments that cover multiple vehicles. After that, Deep reinforcement Learning (DRL) is employed to solve the joint optimisation issue. Based on this joint optimisation model, the offloading decisions and resource allocations are gained to reduce the cost obtained in end-to-end delay and expense of resource computation. This problem is formulated based on the Markov Decision Process (MDP) designed functions like state, action, and reward. The proposed model's performance evaluations and numerical results achieve less average delay for 30 vehicle nodes in simulation. © 2025 Elsevier Inc.

关键词： Mobile edge computing

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