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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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Outage Performance and Optimal Design of MIMO-NOMA Enhanced Small Cell Networks with Imperfect Channel-State Information

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China Communications 2021年第10期18卷 107-128页

作者： Zheng Shi Hong Wang Yaru Fu Guanghua Yang Shaodan Ma Xinrong Ye School of Intelligent Systems Science and Engineering Jinan UniversityZhuhai 519070China State Key Laboratory of Internet of Things for Smart City and the Department of Electrical and Computer Engineering University of MacaoMacao 999078China School of Communication and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China School of Science and Technology Hong Kong Metropolitan UniversityHong Kong SARChina School of Physics and Electronic Information Anhui Normal UniversityWuhu 241002China

This paper focuses on boosting the performance of small cell networks(SCNs)by integrating multiple-input multiple-output(MIMO)and nonorthogonal multiple access(NOMA)in consideration of imperfect channel-state information(CSI).The estimation error and the spatial randomness of base stations(BSs)are characterized by using Kronecker model and Poisson point process(PPP),*** outage probabilities of MIMO-NOMA enhanced SCNs are first derived in closed-form by taking into account two grouping policies,including random grouping and distance-based *** is revealed that the average outage probabilities are irrelevant to the intensity of BSs in the interference-limited regime,while the outage performance deteriorates if the intensity is sufficiently ***,as the channel uncertainty lessens,the asymptotic analyses manifest that the target rates must be restricted up to a bound to achieve an arbitrarily low outage probability in the absence of the inter-cell ***,highly correlated estimation error ameliorates the outage performance under a low quality of CSI,otherwise it behaves ***,the goodput is maximized by choosing appropriate precoding matrix,receiver filters and transmission *** the end,the numerical results verify our analysis and corroborate the superiority of our proposed algorithm.

关键词： imperfect channel-state information(CSI) multiple-input multiple-output(MIMO) non-orthogonal multiple access(NOMA) small cell networks(SCNs)

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Is Dataset Quality Still a Concern in Diagnosis Using Large Foundation Model?

arXiv

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arXiv 2024年

作者： Lin, Ziqin Li, Heng Li, Zinan Fu, Huazhu Liu, Jiang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Institute of High Performance Computing Agency for Science Technology and Research Singapore Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

Recent advancements in pre-trained large foundation models (LFM) have yielded significant breakthroughs across various domains, including natural language processing and computer vision. These models have been particularly impactful in the domain of medical diagnostic tasks. With abundant unlabeled data, an LFM has been developed for fundus images using the Vision Transformer (VIT) and a self-supervised learning framework. This LFM has shown promising performance in fundus disease diagnosis across multiple datasets. On the other hand, deep learning models have long been challenged by dataset quality issues, such as image quality and dataset bias. To investigate the influence of data quality on LFM, we conducted explorations in two fundus diagnosis tasks using datasets of varying quality. Specifically, we explored the following questions: Is LFM more robust to image quality? Is LFM affected by dataset bias? Can fine-tuning techniques alleviate these effects? Our investigation found that LFM exhibits greater resilience to dataset quality issues, including image quality and dataset bias, compared to typical convolutional networks. Furthermore, we discovered that overall fine-tuning is an effective adapter for LFM to mitigate the impact of dataset quality issues. Copyright © 2024, The Authors. All rights reserved.

关键词： Image quality

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TEVA: Training-Efficient and Verifiable Aggregation for Federated Learning for Consumer Electronics in Industry 5.0

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IEEE Transactions on Consumer Electronics 2024年

作者： Xia, Yuanjun Liu, Yining Chen, Jingxue Liang, Yangfan Khan, Fazlullah Alturki, Ryan Wang, Xiaopei Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software School of Computer and Information Security Guilin541004 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325027 China University of Technology School of Mathematical and Physical Sciences SydneyNSW2007 Australia Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems The Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing314001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China Umm AI-Qura University Makkah Department of Software Engineering College of Computing Saudi Arabia Riverside Department of Computer Science and Engineering University of California CA92521 United States

Federated learning (FL) has been widely used for privacy-preserving model updates in Industry 5.0, facilitated by 6G networks. Despite FL's privacy-preserving advantages, it remains vulnerable to attacks where adversaries can infer private data from local models or manipulate the central server (CS) to deliver falsified global models. Current privacy-preserving approaches, primarily based on the FedAvg algorithm, fail to optimize training efficiency for non-independent and identically distributed (non-IID) data. This article proposes training-efficient and verifiable aggregation (TEVA) for FL to resolve these issues. This scheme combines threshold Paillier homomorphic encryption (TPHE), verifiable aggregation, and an optimized double momentum update mechanism (OdMum). TEVA not only leverages TPHE to protect the privacy of local models but also ensures the integrity of the global model through a verifiable aggregation mechanism. Additionally, TEVA integrates the OdMum algorithm to effectively address the challenges posed by non-IID data, promoting rapid model convergence and significantly enhancing overall training efficiency. Security analysis indicates that TEVA meets the requirements for privacy protection. Extensive experimental results demonstrate that TEVA can accelerate model convergence while incurring lower computational and communication overheads. © 1975-2011 IEEE.

关键词： Differential privacy

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Control of an electric vehicle speed using RST controller

Control of an electric vehicle speed using RST controller

引用

Innovative engineering sciences and Technological Research (ICIESTR), International Conference on

作者： Okba Kraa Mohamed Kamel Benbraika Khaled Telli Yacine Belhadi Yassine Himeur Samir Bourekkache Djallal Eddine Zabia Energy Systems Modeling Laboratory Mohamed Khider University Biskra Algeria University of El-Oued El-Oued Algeria College of Engineering and Information University of Dubai Dubai UAE Smart Computer Science Laboratory Mohamed Khider University Biskra Algeria LI3CUB Laboratory Mohamed Khider University Biskra Algeria

ISBN: (数字)9798350348637

ISBN: (纸本)9798350348644

This paper introduces the concept of developing a polynomial RST controller to regulate the velocity of an electric *** computation of the RST polynomials assumes that the electric vehicle’s speed control forms a closed-loop system characterized by a discrete second-order polynomial. This approach is adopted to attain the desired performance and ensure robust stability. This controller significantly improves the vehicle’s overall performance by guaranteeing precise speed control and dynamic response. It also enables the motor and battery to provide sufficient power to reach the target speed. An extensive set of simulation tests and analysis of different driving cycle profiles are used to validate the effectiveness of the suggested controller. This strong RST controller contributes to better EV performance and economy by ensuring greater vehicle dynamics and facilitating optimised power.

关键词： Robust stability Simulation Velocity control Transfer functions Electric vehicles Motors Polynomials Stability analysis Mathematical models Vehicle dynamics

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TiCoSS: Tightening the Coupling between Semantic Segmentation and Stereo Matching within A Joint Learning Framework

arXiv

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arXiv 2024年

作者： Tang, Guanfeng Wu, Zhiyuan Li, Jiahang Zhong, Ping Chen, Xieyuanli Lu, Huimin Fan, Rui The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Shanghai Institute of Intelligent Science and Technology Tongji University The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The Department of Engineering King's College London LondonWC2R 2LS United Kingdom The Department of Computer Science and Technology Central South University China The College of Intelligence Science and Technology National University of Defense Technology China

Semantic segmentation and stereo matching, respectively analogous to the ventral and dorsal streams in our human brain, are two key components of autonomous driving perception systems. Addressing these two tasks with separate networks is no longer the mainstream direction in developing computer vision algorithms, particularly with the recent advances in large vision models and embodied artificial intelligence. The trend is shifting towards combining them within a joint learning framework, especially emphasizing feature sharing between the two tasks. The major contributions of this study lie in comprehensively tightening the coupling between semantic segmentation and stereo matching. Specifically, this study introduces three key novelties: (1) a tightly coupled, gated feature fusion strategy, (2) a hierarchical deep supervision strategy, and (3) a coupling tightening loss function. All these technical contributions help tightly couple semantic segmentation and stereo matching tasks, collectively resulting in TiCoSS, a state-of-the-art joint learning framework. Through extensive experiments on the KITTI and vKITTI2 datasets, along with both qualitative and quantitative analyses, we validate the effectiveness of our developed strategies and loss function. Our approach demonstrates superior performance compared to prior arts, with a notable increse in mIoU by over 9%. Our source code will be publicly available at ***/TiCoSS upon publication. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Shuai Xu, Yanqing You, Chaoqun Shao, Mingjie Quek, Tony Q.S. University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China The Chinese University of HongKong Shenzhen Research Institute of Big Data School of Science and Engineering Shenzhen518172 China Fudan University School of Computer Science and Technology 200433 China Beijing100149 China Singapore University of Technology and Design Information Systems Technology and Design 487372 Singapore

Federated learning (FL) has been recognized as a viable solution for local-privacy-aware collaborative model training in wireless edge networks, but its practical deployment is hindered by the high communication overhead caused by frequent and costly server-device synchronization. Notably, most existing communication-efficient FL algorithms fail to reduce the significant inter-device variance resulting from the prevalent issue of device heterogeneity. This variance severely decelerates algorithm convergence, increasing communication overhead and making it more challenging to achieve a well-performed model. In this paper, we propose a novel communication-efficient FL algorithm, named FedQVR, which relies on a sophisticated variance-reduced scheme to achieve heterogeneity-robustness in the presence of quantized transmission and heterogeneous local updates among active edge devices. Comprehensive theoretical analysis justifies that FedQVR is inherently resilient to device heterogeneity and has a comparable convergence rate even with a small number of quantization bits, yielding significant communication savings. Besides, considering non-ideal wireless channels, we propose FedQVR-E which enhances the convergence of FedQVR by performing joint allocation of bandwidth and quantization bits across devices under constrained transmission delays. Extensive experimental results are also presented to demonstrate the superior performance of the proposed algorithms over their counterparts in terms of both communication efficiency and application performance. © 2025 IEEE.

关键词： Federated learning

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AXI-REALM: A Lightweight and Modular Interconnect Extension for Traffic Regulation and Monitoring of Heterogeneous Real-Time SoCs

AXI-REALM: A Lightweight and Modular Interconnect Extension ...

引用

Design, Automation and Test in Europe Conference and Exhibition

作者： Thomas Benz Alessandro Ottaviano Robert Balas Angelo Garofalo Francesco Restuccia Alessandro Biondi Luca Benini Integrated Systems Laboratory ETH Zurich Switzerland Department of Electrical Electronic and Information Engineering University of Bologna Italy Department of Computer Science and Engineering UC San Diego San Diego CA USA Department of Excellence in Robotics & AI Scuola Superiore Sant'Anna Pisa Italy

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

The increasing demand for heterogeneous functionality in the automotive industry and the evolution of chip manufac-turing processes have led to the transition from federated to integrated critical real-time embedded systems (CRTESs). This leads to higher integration challenges of conventional timing predictability techniques due to access contention on shared resources, which can be resolved by providing system-level observability and controllability in hardware. We focus on the interconnect as a shared resource and propose AXI-REALM, a lightweight, modular, and technology - independent real-time extension to industry-standard AXI4 interconnects, available open-source. AXI-REALM uses a credit-based mechanism to distribute and control the bandwidth in a multi-subordinate system on periodic time windows, proactively prevents denial of service from malicious actors in the system, and tracks each manager's access and interference statistics for optimal budget and period selection. We provide detailed performance and implementation cost assessment in a 12nm node and an end-to-end functional case study implementing AXI-REALM into an open-source Linux-capable RISC-V SoC. In a system with a general-purpose core and a hardware accelerator's DMA engine causing interference on the interconnect, AXI-REALM achieves fair bandwidth distribution among managers, allowing the core to recover 68.2 % of its performance compared to the case without contention. Moreover, near-ideal performance (above 95 %) can be achieved by distributing the available bandwidth in favor of the core, improving the worst-case memory access latency from 264 to below eight cycles. Our approach minimizes buffering compared to other solutions and introduces only 2.45 % area overhead compared to the original SoC.

关键词： Bandwidth Interference Real-time systems Hardware Regulation Timing System-on-chip

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Towards Effective Multi-Moving-Camera Tracking: A New Dataset and Lightweight Link Model

arXiv

引用

arXiv 2023年

作者： Zhang, Yanting Wang, Shuanghong Wang, Qingxiang Yan, Cairong Fan, Rui The School of Computer Science and Technology Donghua University Shanghai201620 China The Institute of Integrated Science and Technology Hosei University Tokyo1848584 Japan The College of Electronic & Information Engineering The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China

Ensuring driving safety for autonomous vehicles has become increasingly crucial, highlighting the need for systematic tracking of on-road pedestrians. Most vehicles are equipped with visual sensors, however, the large-scale visual data has not been well studied yet. Multi-target multi-camera (MTMC) tracking systems are composed of two modules: single-camera tracking (SCT) and inter-camera tracking (ICT). To reliably coordinate between them, MTMC tracking has been a very complicated task, while tracking across multiple moving cameras makes it even more challenging. In this paper, we focus on multi-target multimoving-camera (MTMMC) tracking, which is attracting increasing attention from the research community. Observing there are few datasets for MTMMC tracking, we collect a new dataset, called Multi-Moving-Camera Track (MMCT), which contains sequences under various driving scenarios. To address the common problems of identity switch easily faced by most existing SCT trackers, especially for moving cameras due to ego-motion between the camera and targets, a lightweight appearance-free global link model, called Linker, is proposed to mitigate the identity switch by associating two disjoint tracklets of the same target into a complete trajectory within the same camera. Incorporated with Linker, existing SCT trackers generally obtain a significant improvement. Moreover, to alleviate the impact of the image style variations caused by different cameras, a color transfer module is effectively incorporated to extract cross-camera consistent appearance features for pedestrian association across moving cameras for ICT, resulting in a much improved MTMMC tracking system, which can constitute a step further towards coordinated mining of multiple moving cameras. The project page is available at https://***/. Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras

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MIntRec: A New Dataset for Multimodal Intent Recognition

arXiv

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arXiv 2022年

作者： Zhang, Hanlei Xu, Hua Wang, Xin Zhou, Qianrui Zhao, Shaojie Teng, Jiayan State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University School of Information Science and Engineering Hebei University of Science and Technology China

Multimodal intent recognition is a significant task for understanding human language in real-world multimodal scenes. Most existing intent recognition methods have limitations in leveraging the multimodal information due to the restrictions of the benchmark datasets with only text information. This paper introduces a novel dataset for multimodal intent recognition (MIntRec) to address this issue. It formulates coarse-grained and fine-grained intent taxonomies based on the data collected from the TV series Superstore. The dataset consists of 2,224 high-quality samples with text, video, and audio modalities and has multimodal annotations among twenty intent categories. Furthermore, we provide annotated bounding boxes of speakers in each video segment and achieve an automatic process for speaker annotation. MIntRec is helpful for researchers to mine relationships between different modalities to enhance the capability of intent recognition. We extract features from each modality and model cross-modal interactions by adapting three powerful multimodal fusion methods to build baselines. Extensive experiments show that employing the non-verbal modalities achieves substantial improvements compared with the text-only modality, demonstrating the effectiveness of using multimodal information for intent recognition. The gap between the best-performing methods and humans indicates the challenge and importance of this task for the community. Copyright © 2022, The Authors. All rights reserved.

关键词： Character recognition

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