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Temporal-Spatial Scheduling of Energy and Computation Resources for Charging and Computing Service Vehicles

IEEE internet of things Journal 2025年

作者： Rong, Shichu Deng, Xiongtian Zhong, Weifeng Huang, Xumin Kang, Jiawen Xie, Shengli Yuen, Chau Guangdong University of Technology School of Automation Key Laboratory of Intelligent Detection and Manufacturing Internet of Things Ministry of Education Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Guangzhou510006 China Guangdong University of Technology School of Automation 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Nanyang Technological University School of Electrical and Electronic Engineering Singapore639798 Singapore

The growing adoption of electric vehicles (EVs) and expansion of internet-of-things (IoT) in-vehicle applications enhance vehicle intelligence and connectivity but also drive higher demand for both charging and computing services. Charging and Computing Stations (CCSs), integrating bidirectional chargers and edge computing servers and allowing optimal joint energy-computation management, has been taken as an effective solution to address this demand. This paper introduces a new concept called charging and computing service vehicle (CCSV) fleets, which are equipped with high-capacity batteries and edge servers, serving as mobile resources to support the stationary CCSs at different locations in a wide area. We propose a two-timescale model integrating temporal-spatial scheduling, charging/discharging management, and computation task offloading of the CCSV fleets. Our goal is to minimize the total system cost by optimizing the energy-computation coordination between the mobile CCSV fleets and the stationary CCSs. We construct an extended time-space network (TSN) with congestion nodes, providing a clearer depiction of the time-varying congestion conditions in the traffic network. For practical implementation, we develop a heuristic based on the convex-concave procedure (CCP) and penalty alternating direction method (PADM) to solve the problem quickly. Simulation results in a traffic network based on Guangzhou city demonstrate that the proposed model effectively leverages the mobility and multi-dimensional resources of the CCSV fleets to reduce the system cost significantly. © 2014 IEEE.

关键词： Traffic congestion

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Kernel Bayesian tensor ring decomposition for multiway data recovery

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Neural Networks 2025年 189卷 107500页

作者： Huang, Zhenhao Zhou, Guoxu Qiu, Yuning Chen, Xinqi Zhao, Qibin School of Automation Guangdong University of Technology Guangzhou510006 China Key Laboratory of Intelligent Detection and the Internet of Things in Manufacturing Ministry of Education Guangdong University of Technology Guangzhou510006 China RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangdong University of Technology Guangzhou510006 China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China

Tensor ring (TR) decomposition has emerged as the prevailing method for tensor completion. Earlier approaches have situated TR decomposition within a probabilistic framework, yielding satisfactory outcomes. However, these methods ignore side information or are inherently incapable of leveraging it. In response to this challenge, we propose a variational inference-based kernel Bayesian TR (VKBTR) method that integrates side information, low-rankness, and sparse learning. By incorporating kernel matrices into the TR factors, we can effectively leverage the intrinsic properties of the data (e.g., the smoothness in images and videos) to improve performance across different tasks. Additionally, by introducing a sparsity-inducing hierarchical prior on the latent factors, the proposed method enables automatic selection of the TR rank. Leveraging the variational inference algorithm enables us to achieve the update of posterior parameters effectively. Extensive experiments conducted on synthetic data, color images, face images, and color video data have shown that, with the assistance of side information, VKBTR significantly improves performance in completion tasks compared to other state-of-the-art methods. © 2025 Elsevier Ltd

关键词： Variational techniques

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Solo Adaptation for Distributed Synchronization With Application to Event-Triggered Linear Multi-Agent Systems

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IEEE Transactions on Automatic Control 2025年第6期70卷 4225-4232页

作者： Chen, Ci Zheng, Zhiyang Lewis, Frank L. Xie, Kan Xie, Shengli Guangdong University of Technology Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education School of Automation Guangzhou510006 China Guangdong University of Technology School of Automation Guangdong Guangzhou510006 China The University of Texas at Arlington UTA Research Institute Fort Worth76118 United States Guangdong University of Technology 111 Center for Intelligent Batch Manufacturing Based on IoT Technology School of Automation Guangzhou China Guangdong University of Technology Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Ministry of Education School of Automation Guangzhou510006 China

We study a solo adaptation-based protocol design problem for distributed synchronization of event-triggered linear multi-agent systems (MAS) over general directed graphs. We show that a solo adaptive gain, a locally static control gain matrix, and a distributed state measurement are sufficient for obtaining adaptive distributed synchronization of linear MAS over directed graphs without any prior knowledge of the global communication network. We then extend the solo adaptation structure to formulate an event-triggered synchronization control protocol. The main feature of our result is that the time for triggering a subsequent event is now computed using the distributed event-triggered state measurement, rather than the continuous-time state measurement. © 1963-2012 IEEE.

关键词： Continuous time systems

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Low-Rank, High-Order Tensor Completion via t- Product-Induced Tucker (tTucker) Decomposition

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Neural computation 2025年第6期37卷 1171-1192页

作者： Li, Yaodong Tan, Jun Yang, Peilin Zhou, Guoxu Zhao, Qibin School of Automation Guangdong University of Technology P.R.C Guangzhou 510006 China Key Laboratory of Intelligent Detection and the Internet of Things in Manufacturing Ministry of Education P.R.C Guangzhou 510006 China School of Automation Guangdong University of Technology P.R.C Guangzhou 51000 China RIKEN Center for Advanced Intelligence Project Wako-shi 351-0198 Japan

Recently, tensor singular value decomposition (t-SVD)-based methods were proposed to solve the low-rank tensor completion (LRTC) problem, which has achieved unprecedented success on image and video inpainting tasks. The t-SVD is limited to process third-order tensors. When faced with higher-order tensors, it reshapes them into third-order tensors, leading to the destruction of interdimensional correlations. To address this limitation, this letter introduces a tproductinduced Tucker decomposition (tTucker) model that replaces the mode product in Tucker decomposition with t-product, which jointly extends the ideas of t-SVD and high-order SVD. This letter defines the rank of the tTucker decomposition and presents an LRTC model that minimizes the induced Schatten-p norm. An efficient alternating direction multiplier method (ADMM) algorithm is developed to optimize the proposed LRTC model, and its effectiveness is demonstrated through experiments conducted on both synthetic and real data sets, showcasing excellent performance. © 2025 Massachusetts Institute of Technology.

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Chemically bonded flexible pressure sensor based on divergent metallic grid with high linearity, low hysteresis, and excellent stability

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Sensors and Actuators A: Physical 2025年 392卷

作者： Luo, Guoxi Zhang, Bingcheng Pi, Diying Li, Min Yang, Ping Zhao, Libo Shandong Laboratory of Advanced Materials and Green Manufacturing at Yantai Yantai265503 China School of Instrument Science and Technology Xi'an Jiaotong University Xi'an710049 China School of Mechanical Engineering Xi'an Jiaotong University Xi'an710049 China Chongqing Key Laboratory of Micro-Nano Systems and Intelligent Sensing Chongqing Academician Workstation Chongqing 2011 Collaborative Innovation Center of Micro/Nano Sensing and Intelligent Ecological Internet of Things Chongqing Technology and Business University Chongqing400067 China

As intelligent robots and smart wearable electronics become commonplace, the demand for flexible pressure sensors is increasing rapidly. Despite the enormous effort in the development of flexible pressure sensor with high sensitivity, limited progress has been made in terms of developing a flexible pressure sensor with high linearity, low hysteresis, and high stability to avoid additional signal processing and signal distortion, and meanwhile promise good accuracy. Herein, we developed a flexible pressure sensor on foundation of the metallic strain effect. The divergent metallic grid was manufactured on polymide (PI) film with the mature magnetron sputtering and lift-off method, and a novel chemical bonding between PI and outer covering polydimethylsiloxane (PDMS) was developed for a flexible encapsulation. Benefiting from the excellent intrinsic properties of rigid sensitive materials and high-strength bonding. The as-fabricated pressure sensors exhibited several superior properties that are characterized by high linearity (linear regression coefficient of R2 = 0.991) in whole sensing range (0–620 kPa), low hysteresis (2.78 %), rapid response/relaxation time (ca. 29/21 ms), remarkable durability (over 13,000 bending and stretching cycles), and excellent stability. The integrated merits of this novel pressure sensor were demonstrated in applications including human physiological signal monitoring, material identification, and pressure space mapping. © 2025 Elsevier B.V.

关键词： Bond strength (chemical)

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Semi-supervised non-negative Tucker decomposition for tensor data representation

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Science China(Technological Sciences) 2021年第9期64卷 1881-1892页

作者： QIU YuNing ZHOU GuoXu CHEN XinQi ZHANG DongPing ZHAO XinHai ZHAO QiBin School of Automation Guangdong University of TechnologyGuangzhou 510006China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangzhou 510006China Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Ministry of EducationGuangzhou 510006China

Non-negative Tucker decomposition(NTD) has been developed as a crucial method for non-negative tensor data ***, NTD is essentially an unsupervised method and cannot take advantage of label information. In this paper, we claim that the low-dimensional representation extracted by NTD can be treated as the predicted soft-clustering coefficient matrix and can therefore be learned jointly with label propagation in a unified framework. The proposed method can extract the physicallymeaningful and parts-based representation of tensor data in their natural form while fully exploring the potential ability of the given labels with a nearest neighbors graph. In addition, an efficient accelerated proximal gradient(APG) algorithm is developed to solve the optimization problem. Finally, the experimental results on five benchmark image data sets for semi-supervised clustering and classification tasks demonstrate the superiority of this method over state-of-the-art methods.

关键词： tensor factorization non-negative Tucker decomposition semi-supervised learning label propagation

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Fast nonnegative tensor ring decomposition based on the modulus method and low-rank approximation

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Science China(Technological Sciences) 2021年第9期64卷 1843-1853页

作者： YU YuYuan XIE Kan YU JinShi JIANG Qi XIE ShengLi School of Automation Guangdong University of TechnologyGuangzhou 510006China Guangdong-Hong Kong-Macao Joint Laboratory for Smart Manufacturing Guangzhou 510006China Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Ministry of EducationGuangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangdong University of TechnologyGuangzhou 510006China

Nonnegative tensor ring(NTR) decomposition is a powerful tool for capturing the significant features of tensor objects while preserving the multi-linear structure of tensor data. The existing algorithms rely on frequent reshaping and permutation operations in the optimization process and use a shrinking step size or projection techniques to ensure core tensor nonnegativity, which leads to a slow convergence rate, especially for large-scale problems. In this paper, we first propose an NTR algorithm based on the modulus method(NTR-MM), which constrains core tensor nonnegativity by modulus transformation. Second, a low-rank approximation(LRA) is introduced to NTR-MM(named LRA-NTR-MM), which not only reduces the computational complexity of NTR-MM significantly but also suppresses the noise. The simulation results demonstrate that the proposed LRA-NTR-MM algorithm achieves higher computational efficiency than the state-of-the-art algorithms while preserving the effectiveness of feature extraction.

关键词： nonnegative tensor ring decomposition modulus method low-rank approximation

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Approximately orthogonal nonnegative Tucker decomposition for flexible multiway clustering

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Science China(Technological Sciences) 2021年第9期64卷 1872-1880页

作者： QIU YiChun SUN WeiJun ZHANG Yu GU XiaoBo ZHOU GuoXu School of Automation Guangdong University of TechnologyGuangzhou 510006China Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Ministry of EducationGuangzhou 510006China Department of Bioengineering Lehigh UniversityBethlehemPA 18015USA Guangdong-Hong Kong-Macao Joint Laboratory for Smart Manufacturing Guangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangzhou 510006China

High-order tensor data are prevalent in real-world applications, and multiway clustering is one of the most important techniques for exploratory data mining and compression of multiway data. However, existing multiway clustering is based on the K-means procedure and is incapable of addressing the issue of crossed membership degrees. To overcome this limitation, we propose a flexible multiway clustering model called approximately orthogonal nonnegative Tucker decomposition(AONTD). The new model provides extra flexibility to handle crossed memberships while fully exploiting the multilinear property of tensor *** accelerated proximal gradient method and the low-rank compression tricks are adopted to optimize the cost function. The experimental results on both synthetic data and real-world cases illustrate that the proposed AONTD model outperforms the benchmark clustering methods by significantly improving the interpretability and robustness.

关键词： multiway data analysis nonnegative Tucker decomposition flexible clustering accelerated proximal gradient

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Control Policy Learning Design for Vehicle Urban Positioning via BeiDou Navigation

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Journal of Systems Science & Complexity 2024年第1期37卷 114-135页

作者： QIN Yahang ZHANG Chengye CHEN Ci XIE Shengli LEWIS Frank L School of Automation Guangdong University of TechnologyGuangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangzhou 510006China. Center for Intelligent Batch Manufacturing Based on IoT Technology Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Ministry of EducationGuangzhou 510006China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China. UTA Research Institute The University of Texas at ArlingtonFort WorthTX 76019USA

This paper presents a learning-based control policy design for point-to-point vehicle positioning in the urban environment via BeiDou *** navigating in urban canyons,the multipath effect is a kind of interference that causes the navigation signal to drift and thus imposes severe impacts on vehicle localization due to the reflection and diffraction of the BeiDou ***,the authors formulated the navigation control system with unknown vehicle dynamics into an optimal control-seeking problem through a linear discrete-time system,and the point-to-point localization control is modeled and handled by leveraging off-policy reinforcement learning for feedback *** proposed learning-based design guarantees optimality with prescribed performance and also stabilizes the closed-loop navigation system,without the full knowledge of the vehicle *** is seen that the proposed method can withstand the impact of the multipath effect while satisfying the prescribed convergence rate.A case study demonstrates that the proposed algorithms effectively drive the vehicle to a desired setpoint under the multipath effect introduced by actual experiments of BeiDou navigation in the urban environment.

关键词： BeiDou navigation multipath effect prescribed convergence rate reinforcement learning urban localization.

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Adversarially Robust Deep Multi-View Clustering: A Novel Attack and Defense Framework 41

Adversarially Robust Deep Multi-View Clustering: A Novel Att...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Huang, Haonan Zhou, Guoxu Zheng, Yanghang Qiu, Yuning Wang, Andong Zhao, Qibin School of Automation Guangdong University of Technology Guangzhou China RIKEN AIP Tokyo Japan Key Laboratory of Intelligent Detection and the Internet of Things in Manufacturing Ministry of Education Guangzhou China

Deep Multi-view Clustering (DMVC) stands out as a widely adopted technique aiming at enhanced clustering performance by leveraging diverse data sources. However, the critical issue of vulnerability to adversarial attacks is unexplored due to the lack of well-defined attack objectives. To fill this crucial gap, this paper is the first work to investigate the possibility of adversarial attacks on DMVC models. Specifically, we introduce an adversarial attack with Generative Adversarial Networks (GANs) with the aim to maximally change the complementarity and consistency of multiple views, thus leading to wrong clustering. Building upon this adversarial context, in the realm of defense, we propose a novel Adversarially Robust Deep Multi-View Clustering by leveraging adversarial training. Based on the analysis from an information-theoretic perspective, we design an Attack Mitigator that provides a foundation to guarantee the adversarial robustness of our DMVC models. Experiments conducted on multiview datasets confirmed that our attack framework effectively reduces the clustering performance of the target model. Furthermore, our proposed adversarially robust method is also demonstrated to be an effective defense against such attacks. This work is a pioneer in exploring adversarial threats and advancing both theoretical understanding and practical strategies for robust multiview clustering. Code is available at https://***/libertyhhn/AR-DMVC. Copyright 2024 by the author(s)

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