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63rd IEEE Conference on Decision and control, CDC 2024

作者： Liu, Aixin Wang, Lin Chen, Guanrong Guan, Xinping Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China City University of Hong Kong Department of Electrical Engineering Hong Kong

ISBN: (纸本)9798350316339

This study investigates the impact of multi-bit function perturbations (MFPs) on the steady-state distribution of Boolean networks (BNs), with a focus on robust stability. First, the algebraic formulation of BNs under MFPs is introduced. Then, a novel stability criterion is proposed, demonstrating that monotonically decreasing perturbations can effectively ensure the stability of BNs against MFPs. A significant advantage of this approach is that it enables robust stability assessment without remodeling the perturbed BNs. Applying this methodology to a simplified Boolean model of the lactose operon in Escherichia coli reveals that MFPs can serve as strategic tools to promote stability within BNs, rather than being merely disruptive. © 2024 IEEE.

关键词： Stability criteria

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A Cloud-Edge-Based Multi-Label Learning and Fast Clustering Method for Emerging New PV Faults 13

A Cloud-Edge-Based Multi-Label Learning and Fast Clustering ...

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13th IEEE International Conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2023

作者： Liu, Qi Yang, Bo Liu, Yuxiang Ma, Kai Guan, Xinping Shanghai Jiao Tong University Ministry of Education Key Laboratory of System Control and Information Processing Department of Automation Shanghai200240 China School of Electrical Engineering Yanshan University Qinhuangdao066004 China

ISBN: (纸本)9798350315196

Aiming at accurate identification of emerging new faults in PV systems, a novel PV fault diagnosis framework based on cloud-edge collaboration is designed. Model updates are deployed on the cloud servers, while real-time diagnosis is implemented at the edge devices. A multi-label learning model is developed to extract fault features efficiently, and a threshold scheme is further designed for feature enhancement. The obtained features are processed by K-Means clustering and the One-Class Support Vector Machine (OCSVM) methods, which can accurately diagnose existing types and identify unknown faults. For analysis and demonstration, the Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) methods are used for dimensionality reduction and visualization. Numerical results validate the effectiveness of the proposed method. © 2023 IEEE.

关键词： K-means clustering

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UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 430-445页

作者： Jiawen Kang Junlong Chen Minrui Xu Zehui Xiong Yutao Jiao Luchao Han Dusit Niyato Yongju Tong Shengli Xie IEEE School of Automation Guangdong University of TechnologyGuangzhou 510006China 111 Center for Intelligent Batch Manufacturing based on IoT Technology Guangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China School of Computer Science and Engineering Nanyang Technological UniversitySingaporeSingapore Pillar of Information Systems Technology and Design Singapore University of Technology and DesignSingaporeSingapore College of Communication Engineering Army Engineering University of PLANanjing 210007China National Natural Science Foundation of China Beijing 100085China Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of EducationGuangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangzhou 510006China

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach

关键词： Avatar blockchain metaverses multi-agent deep reinforcement learning transformer UAVs

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Collision-Free Path Planning with Kinematic Constraints in Urban Scenarios

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Journal of Shanghai Jiaotong university(Science) 2021年第5期26卷 731-738页

作者： WANG Liang WANG Bing WANG Chunxiang Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai Jiao Tong UniversityShanghai 200240China

In urban driving scenarios,owing to the presence of multiple static obstacles such as parked cars and roadblocks,planning a collision-free and smooth path remains a challenging *** addition,the path-planning problem is mostly non-convex,and contains multiple local ***,a method for combining a sampling-based method and an optimization-based method is proposed in this paper to generate a collision-free path with kinematic constraints for urban *** sampling-based method constructs a search graph to search for a seeding path for exploring a safe driving corridor,and the optimization-based method constructs a quadratic programming problem considering the desired state constraints,continuity constraints,driving corridor constraints,and kinematic constraints to perform path *** experimental results show that the proposed method is able to plan a collision-free and smooth path in real time when managing typical urban scenarios.

关键词： autonomous vehicle path planning dynamic searching quadratic programming

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Hybrid Traffic Scheduling Based on Adaptive Time Slot Slicing in Time-Sensitive Networking

Hybrid Traffic Scheduling Based on Adaptive Time Slot Slicin...

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2022 IEEE International Conference on Industrial Technology, ICIT 2022

作者： Wang, Shouliang Xu, Qimin Zhang, Yanzhou Xu, Lei Chen, Cailian Shanghai Jiao Tong University Department of Automation Shanghai China 200240 Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (纸本)9781728119489

The development of the Industrial Internet of Things (IIoT) is increasingly relying on the low-latency and low-jitter information exchange. Time-Sensitive Networking (TSN) guarantees the deterministic transmission by a set of mechanisms, such as Time-Aware Shaper (TAS) and Cyclic Queuing and Forwarding (CQF). However, the deterministic scheduling of hybrid traffic with different Quality of Service (QoS) is still unresolved since traffic performance is hard to meet by a single mechanism or an integrated mechanism with fixed time slot. In this paper, to realize the low-latency and low-jitter hybrid traffic scheduling, an adaptive time slot slicing (ATSS) method is proposed integrating TAS and CQF. With dynamically slicing the time slot to centralize time-triggered (TT) traffic, ATSS eliminates the irregular distribution of TT flow to support multi-hop networks. A resource calculation method based on slot occupancy judgment is devised to reduce the scheduling complexity, which raises the analysis objects into flows instead of frames. To reduce the complexity further, an incremental algorithm with the search boundary of resources is designed. The evaluation results show that ATSS reduces the jitter by 12 times and time cost by 200 times when handling over 125 mixed flows in different network topologies compared to previous work. © 2022 IEEE.

关键词： Jitter

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Distributed Identification With Input Design for Network systems

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IEEE Transactions on control of Network systems 2025年

作者： Mao, Xiangyu He, Jianping Fang, Chongrong Shanghai Jiao Tong University The Dept. of Automation China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

The distributed identification of network systems under local observation has recently become one of the research hot spots. Due to the unobservable influence of other subsystems and the noise, it is difficult for local subsystems to achieve convergent identification under local observations. This paper proposes a distributed identification algorithm for local subsystems to identify their parameter matrices under local observation with a guarantee of convergence. We obtain the necessary and sufficient conditions for the convergence of local identification. We prove that by injecting an independent random signal to the local subsystem, which is easy to implement, using a simple least-squares method to identify local systems can achieve convergent identification for a certain probability. Meanwhile, we give the minimal level of variance of the injected signal that can still guarantee the convergence of the identification. Next, we add the designed input as a vector of Gaussian noises with decreasing variance to a known controller, proving that it can converge to the original controller. Simulations demonstrate the effectiveness of the proposed algorithms. © 2025 IEEE.

关键词： Least squares approximations

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Jointly Learning Time-Structure-History Graph Embedding for Temporal Knowledge Graph Reasoning 19

Jointly Learning Time-Structure-History Graph Embedding for ...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Duan, Hao Jin, Haoyu Chen, Kang Du, Shaochong Fang, Tao Huo, Hong Shanghai Jiao Tong University Department of Automation Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

ISBN: (纸本)9798350346558

The Temporal Knowledge Graph(TKG) reasoning is an imperative task for various applications of TKG. However, reasoning over TKGs which aims to predict future facts has been less explored. Intuitively, entities and relations in TKG change differently over time, and the variations are different. It also can be observed that what happens in the past may affect the future. Apart from these, structure information that exists in the neighborhood of a given entity in a KG has been proven to be important. Inspired by these, a novel graph embedding method for TKG reasoning, called time-structure-history graph embedding (TSHGE), is proposed in this paper. TSHGE consists of three progressive learning units. The first time information learning unit employs a novel time-aware mechanism to encode the time influence on entities and relations. Then a relation-aware GAT (Graph Attention Network) is used in the structure information learning unit to model the 1-hop neighborhoods of all entities in a KG at each timestamp in TKG. Finally, the history information learning unit utilizes GRU (Gated Recurrent Unit) to learn history information from various facts. Then the ultimate embedding representations of entities and relations can be obtained for the prediction of future facts. The evaluations on three public TKG datasets have demonstrated the effectiveness and superiority of TSHGE for predicting future facts. © 2022 IEEE.

关键词： Forecasting

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Curvature Adaptive control Based Path Following for Automatic Driving Vehicles in Private Area

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Journal of Shanghai Jiaotong university(Science) 2021年第5期26卷 690-698页

作者： SHI Qiang ZHANG Jianlin YANG Ming Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai Jiao Tong UniversityShanghai 200240China

Path following refers to traveling along the desired path with automatic steering control,which is a crucial technology for automatic driving *** in private areas are highly irregular,resulting in a large curvature variation,which reduces the control accuracy of the path following.A curvature adaptive control(CAC)based path-following method was proposed to solve the problem mentioned ***,CAC takes advantage of the complementary characteristics in response to the path curvature fluctuation of pure pursuit and front-wheel feedback and by combining the two methods further enhances the immunity of the control accuracy in response to a curvature *** CAC,the quantitative indices of the path curvature fluctuation and control accuracy were *** model between the path curvature fluctuation and a dynamic parameter was identified using the quantitative index of the control accuracy as the optimization *** experimental results of a real vehicle indicate that the control accuracy of path following is further enhanced by its immunity in response to curvature fluctuation improved by the *** addition,CAC is easy to deploy and requires low demand for hardware resources.

关键词： automatic driving vehicles path following adaptive control

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Integrated Framework for Test and Evaluation of Autonomous Vehicles

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Journal of Shanghai Jiaotong university(Science) 2021年第5期26卷 699-712页

作者： SU Yimin WANG Lin Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai Jiao Tong UniversityShanghai 200240China

Autonomous vehicles must pass effective standard tests to verify their reliability and ***-ingly,it is very important to establish a complete scientific test and evaluation system for autonomous vehicles.A comprehensive framework incorporating the design of test scenarios,selection of evaluation indexes,and estab-lishment of an evaluation system is proposed in this *** aims of the system are to obtain an objective and quantitative score regarding the intelligence of autonomous vehicles,and to form an automated process in the future *** proposed framework is built on a simulation platform to ensure the feasibility of the design and implementation of the test *** design principle for the test scenarios is also *** reduce subjective influences,the proposed framework selects objective indexes from four aspects:safety,comfort,driving performance,and standard *** order relation analysis method is adopted to formulate the index weights,and fuzzy comprehensive evaluation is used to quantify the ***,a numerical example is provided to visually demonstrate the evaluation results for the autonomous vehicles scored by the proposed framework.

关键词： test and evaluation scenario construction evaluation index autonomous vehicles

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Joint Design of control and Transmission for Industrial CPS under Time Sensitive Networking 48

Joint Design of Control and Transmission for Industrial CPS ...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Lu, Xuanzhao Xu, Qimin Zhang, Jinglong Chen, Cailian Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

To improve the performance of industrial cyber-physical systems (ICPS), the joint design of control and transmission has been shown to be an effective mechanism. However, the existing joint design work lack completeness and accuracy, which brings challenges to improving the stability and network resource utilization of ICPS. In this paper, we propose a control and transmission joint design architecture for integrated multi-subsystem ICPS for multi-hop networks. We consider the timeliness of information through state delays, input delays, and event-triggered states in ICPS. To avoid the instability caused by input delay, we design an LQR-based controller and derive the feasible region of delay to guarantee the stability of the control system. Finally, we propose an event-triggered strategy that represents control and transmission costs to minimize the amount of data transfer while ensuring system stability. The evaluation results show that our strategy improves the stability of the control system while reducing the network burden compared with the traditional joint design method. © 2022 IEEE.

关键词： Cyber Physical system

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