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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Zhao, Fuyu Gao, Weinan Liu, Tengfei Jiang, Zhong-Ping Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Liaoning Shenyang110819 China New York University Tandon School of Engineering Department of Electrical and Computer Engineering BrooklynNY11201 United States

ISBN: (纸本)9798350316339

This paper presents a novel policy iteration (PI) based event-triggered cooperative adaptive cruise control (CACC) approach for vehicular platoons, which are composed of autonomous vehicles. An observer is established to observe the signal of leading vehicle. To reduce computation and communication resources, event-triggered based adaptive optimal controllers are designed by using the PI algorithm without relying on the knowledge of system dynamics. Sufficient conditions are introduced to achieve the leader-to-formation stability (LFS) of the vehicular platoons. Simulation results are presented to verify the effectiveness of the proposed design. © 2024 IEEE.

关键词： Adaptive cruise control

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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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Distributed data-driven fault detection for industrial interconnected systems with unknown topology structure ⁎

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IFAC-PapersOnLine 2024年第4期58卷 670-675页

作者： Jingjing Gao Xu Yang Xian Zhou Qing Li Jian Huang Jiarui Cui Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing PR China School of Computer and Communication Engineering University of Science and Technology Beijing PR China

Driven by the enhancing requirements for ensuring stable operation of complex industrial processes, this paper investigates a distributed data-driven fault detection (FD) method for industrial interconnected systems. The topology structure of the industrial interconnected system is presumed to be unknown. To this end, the input/output (I/O) data-set model for the interconnected system and the distributed input/output data-set models for the subsystems are derived first. Then, the topology structure of the interconnected system is reconstructed through data-driven realization of gap metric by utilizing the subspace Identification method. Subsequently, the distributed data-driven fault detection method with enhanced information exchange topology is developed. Finally, we validate the feasibility and efficiency of the proposed method by conducting a case study on a chemical process consisting of four continuous-stirred tank reactors (CSTRs).

关键词： Fault detection industrial interconnected systems subspace Identification method gap metric data-driven

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On a novel tracking differentiator design based on iterative learning in a moving window

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Control Theory and Technology 2023年第1期21卷 46-55页

作者： Xiangyang Li Rafal Madonski Zhiqiang Gao Senping Tian Key Laboratory of Autonomous Systems and Network Control Ministry of EducationSchool of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510641China Energy and Electricity Research Center Jinan UniversityZhuhai 519070China Department of Electrical and Computer Science Cleveland State UniversityClevelandOH 44115USA

Differential signals are key in control engineering as they anticipate future behavior of process variables and therefore are critical in formulating control laws such as proportional-integral-derivative(PID).The practical challenge,however,is to extract such signals from noisy measurements and this difficulty is addressed first by *** in the form of linear and nonlinear tracking differentiator(TD).While improvements were made,TD did not completely resolve the conflict between the noise sensitivity and the accuracy and timeliness of the *** two approaches proposed in this paper start with the basic linear TD,but apply iterative learning mechanism to the historical data in a moving window(MW),to form two new iterative learning tracking differentiators(IL-TD):one is a parallel IL-TD using an iterative ladder network structure which is implementable in analog circuits;the other a serial IL-TD which is implementable digitally on any computer *** algorithms are validated in simulations which show that the proposed two IL-TDs have better tracking differentiation and de-noise performance compared to the existing linear TD.

关键词： Tracking differentiator(TD) Iterative learning Iterative learning tracking differentiator(IL-TD) Active disturbance rejection control(ADRC)-Two-dimensional system(2-D system)

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UAD-DPL: An Unknown Encrypted Attack Detection Method Based on Deep Prototype Learning 27th

UAD-DPL: An Unknown Encrypted Attack Detection Method Based ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Chen, Liang-Chen Gao, Shu Liu, Bao-Xu Zhang, Xu-Yao School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430063 China Institute of Automation NLPR Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Computer China University of Labor Relations Beijing100048 China Key Laboratory of Network Assessment Technology Chinese Academy of Sciences Beijing100093 China

ISBN: (纸本)9783031781681

In the field of cybersecurity, a significant concern is the potential for unknown attacks to occur at any moment. The primary obstacle faced by intrusion detection systems lies in the identification of unknown attacks and the differentiation of such threats from benign network traffic and known attack patterns. Many current deep learning approaches struggle to discern the underlying features of these new attack types, especially in the case of encrypted attacks, resulting in inherent inaccuracies. To tackle these challenges, a novel unknown attack detection method based on deep prototype learning (UAD-DPL) is proposed to improve the accuracy of unknown encrypted attack detection. Firstly, a spatiotemporal fusion feature extraction network for encrypted traffic is employed to enhance the feature representation ability. Subsequently, an innovative prototype-based deep feature space learning model is proposed, which utilizes discriminative loss and open loss training to ensure that the learned encrypted traffic features meet the predetermined prior distribution. Finally, an unknown attack recognition approach is designed to effectively identify both known and unknown attacks, and a three-stage training strategy is proposed to train the model. Experimental results indicate that the UAD-DPL method is highly proficient in detecting both known and unknown attacks in encrypted traffic. Moreover, UAD-DPL exhibits promising application potential in open-set network intrusion detection systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Network intrusion

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Recent Developments in Event-Triggered Control of Nonlinear Systems:An Overview

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无人系统（英文） 2023年第1期11卷 27-56页

作者： Pengpeng Zhang Tengfei Liu Jie Chen Zhong-Ping Jiang Department of Control Science & Engineering Frontiers Science Center for Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819P.R.China Department of Electrical and Computer Engineering New York University370 Jay StreetBrooklynNY 11201USA

Event-triggered control is a most popular paradigm for transferring feedback information in an economical"as needed"*** study of event-triggered control can be traced back to the *** significant advances on the topic of control over networks and the topic of nonlinear control systems over the last two decades,event-triggered control has quickly emerged as a major theoretical subject in control *** of event-triggered control are wide-spread ranging from embedded control systems and industrial control processes to unmanned systems and cyber-physical transportation *** this paper,we first review developments in the synthesis of event-triggered sampling *** event triggering mechanisms,such as static event trigger,dynamic event trigger,time-regularized event trigger,and event trigger with positive threshold offsets,are systematically ***,we study how to design a stabilizing controller that is robust with respect to the sampling ***,we review some recent results in the directions of self-triggered control,event-triggered tracking control and cooperative control,and event-triggered control of stochastic systems and partial differential equation *** applications of event-triggered control are also discussed.

关键词： Event-triggered control event-triggered sampling mechanism event-triggered controller

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A Novel Approach with Fusion Edge Prediction Strategy for Table Structure Recognition 11

A Novel Approach with Fusion Edge Prediction Strategy for Ta...

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11th International Conference on Information Systems and Computing Technology, ISCTech 2023

作者： Liu, Yingli Zhang, Guangtao Shen, Tao Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650093 China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China

ISBN: (纸本)9798350342406

Table, as an information carrier with high information density and direct display form, are widely used in scientific literature and social production documents. Accurately identifying tables from various documents will significantly promote the intelligent application of documents. The table's structural information is crucial for comprehending how the text content in the table relates to one another, but teaching a computer to fully understand a table's structure is still a very difficult undertaking. In this research work, we provided a unique edge-based method to recognize table structures. Specifically, we define the inter-cell relationships as an edge prediction problem and leverage graph convolutional networks to learn representations of the cells. These learned representations are then utilized to directly predict the connections between the cells. The experimental results conducted on three publicly available datasets for table structure recognition provide compelling evidence supporting the feasibility and usefulness of our presented methodology. © 2023 IEEE.

关键词： Forecasting

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A Very-Short-Term Online PV Power Prediction Model Based on RAN With Secondary Dynamic Adjustment

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1214-1224页

作者： Zhang, Tengfei Ma, Fumin Peng, Chen Yu, Yang Yue, Dong Dou, Chunxia O'Hare, Gregory M. P. Nanjing University of Posts and Telecommunications College of Automation College of Artificial Intelligence The Jiangsu Engineering Laboratory of Big Data Analysis and Control for Active Distribution Network Nanjing210023 China Nanjing University of Finance and Economics College of Information Engineering Nanjing210023 China Shanghai University School of Mechatronic Engineering and Automation Shanghai200072 China Nanjing University of Posts and Telecommunications Institute of Advanced Technology Nanjing210023 China Trinity College Dublin School of Computer Science and Statistics Dublin 2 Ireland

Photovoltaic (PV) power is progressively being subsumed into power grids. As a consequence, reliable PV power forecasting has become essential in order to ensure the optimal functioning of the power grid. Neural networks remain the dominant prediction model utilized. Conventional neural network forecasting models are wholly dependent upon offline data. Subsequent to offline training, no further structural adjustments can be made during the forecasting process, which therefore they fail to cater for PV power supply fluctuations that are fundamentally dynamic. To address this failing, this article proposes a very-short-term online prediction model based on a resource-allocating network (RAN) incorporating a secondary dynamic adjustment. The RAN is initially trained offline to obtain a basic forecasting model. Thereafter, in an online prediction process, those samples with large prediction errors that exceed a preset value are recorded in a specific buffer. When the set conditions are triggered, the secondary dynamic adjustment strategy is employed, which enables the online prediction model to effectively relearn previously unmodeled samples while shielding external interference. Experimental results obtained from actual testing demonstrate the validity of the secondary dynamic adjustment strategy for online learning while also providing higher accuracy levels from the prediction model. © 2020 IEEE.

关键词： Forecasting

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Multi-objective performance prediction based on a self-constructed Al-Si alloy dataset 11

Multi-objective performance prediction based on a self-const...

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11th International Conference on Information Systems and Computing Technology, ISCTech 2023

作者： Hu, Dan Liu, Yingli Shen, Tao Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650093 China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China

ISBN: (纸本)9798350342406

The combination of data-driven approaches and machine learning techniques has changed the design approach for new materials, propelling materials science research into the realm of the fourth paradigm. Researchers in the field of materials science have increasingly focused on utilizing machine learning methods to uncover the underlying nonlinear relationships within material data. However, there is currently limited research in the field of aluminum-silicon alloys regarding the prediction of alloy performance based on the composition-process-structure relationship. Additionally, there is a lack of data sharing in the aluminum-silicon alloy domain. Therefore, this study proposes a methodology. Firstly, an aluminum-silicon alloy dataset is constructed by mining relevant information from materials literature. Then, high-dimensional features in the dataset are subjected to feature selection techniques for dimensionality reduction. Subsequently, a multilayer perceptron model is applied to investigate the relationship between input features such as alloy composition, preparation process, and microstructural parameters, and output features such as yield strength, ultimate tensile strength, and elongation, enabling performance prediction. © 2023 IEEE.

关键词： Feature Selection

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Models and Basic Algorithmization for VR Simulator of the Industrial Radiography Method 4

Models and Basic Algorithmization for VR Simulator of the In...

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4th International Conference on Control Systems, Mathematical Modeling, automation and Energy Efficiency, SUMMA 2022

作者： Kuvshinnikov, Vladimir Kovshov, Evgeny JSC 'Research and Development Institute of Construction Technology-Atomstroy' Scientific Engineering and Educational Laboratory of Digital Computer Systems and Automation Moscow Russia

ISBN: (数字)9781665456593

ISBN: (纸本)9781665456593

Within the context of contemporary trends in education aimed at increasing the availability of ed-ucational programs through the digitalization of teaching materials, learning processes and knowledge management, and the development of distance learning opportunities and related services, changes are also affecting the field of training, vocational training, retraining, certification and re-certification of technical professionals. Herewith they can pursue both the goal of creating general and special scenarios to prepare readiness for action in emergencies and to comply with safety regulations, and the goal of increasing the level of practical skills in technical means and applying their proper use when performing specialized tasks. The software tool that simulates a radiation laboratory helps to examine a number of important elements of virtual practice for technical professionals, including the testing objects' material physical properties simulation and primary information carriers' physics simulation. This study represents another step toward more accurate calculation of attenuation values during X-ray inspection simulation. © 2022 IEEE.

关键词： Nondestructive examination

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