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Secure Consensus of Two-Time-Scale Markov Jump Multi-agent systems with Partially Unknown Transition Rates Against Hybrid Cyber-Attacks

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Neural Processing Letters 2023年第5期55卷 6089-6108页

作者： Zhang, Guanzheng Wang, Jing Li, Feng Shen, Hao Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Ma’anshan China School of Electrical and Information Engineering Anhui University of Technology Ma’anshan China

This paper addresses the secure consensus control problem for a class of continuous-time two-time-scale Markov jump multi-agent systems under hybrid cyber-attacks, which suffers from both the denial-of-service attacks and deception attacks. Firstly, to make the constructed model fit the practice better, the dynamic of each agent is represented by a two-time-scale system and the transition rate matrix of the Markov chain is assumed to be partially unknown. Then, a secure consensus protocol is designed to deal with the hybrid cyber-attacks and guarantee the average consensus can be achieved with a prescribed $$H_{\infty }$$ performance. Based on the stochastic analysis approach and Lyapunov stability method, some sufficient conditions are given in the form of linear matrix inequalities. Furthermore, the desired secure consensus protocol gains can be obtained by solving a convex optimization problem. Finally, an illustrative example is utilized to demonstrate the effectiveness of the proposed method.

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Thrust analysis of the undulating ribbon-fin for biomimetic underwater robots

Thrust analysis of the undulating ribbon-fin for biomimetic ...

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International Conference on Intelligent control and Information Processing

作者： Wang, Shuo Dong, Xiang Shang, Liu-Ji State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Institute of Intelligent Machines Chinese Academy of Sciences P.O. Box 1130 Hefei City 230031 Anhui Province China

ISBN: (纸本)9781457708145

Biomimetic underwater robots have been paid more and more attention because of high efficiency, high maneuverability and low-noise. The undulating ribbon-fins used by rajiformes and gymnotiformes show better maneuverability than those of other fishes in turbulent water. So the undulating ribbon-fin propulsion is analyzed for biomimetic underwater robot design and optimization in the paper. The geometric model of the ribbon-fin is set up in Cartesian space. The undulating motion of the ribbon-fin is expressed in trigonometric functions. And the ribbon-fin has been divided into many small elements to compute the force acted on the small element. All the forces acted on the elements in one undulating period are summed, and the average thrust for one undulating period of the ribbon-fin is obtained. The simulation results show the influence of several important parameters on the ribbon-fin thrust. And an experimental platform is built and several experiments are carried out. And the difference between the results of simulation and experiment are discussed further. © 2011 IEEE.

关键词： Fins (heat exchange)

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Biomechanics based computer simulation of rural landscape design using remote sensing image technology

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MCB Molecular and Cellular Biomechanics 2024年第4期21卷

作者： Xing, Kun Xia, Yuqing Academy of Art and Design Anhui University of Technology Anhui Ma’anshan243032 China key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Anhui Ma’anshan243032 China

Introduction: The strategy and restoration of rural areas and landscape in biomechanics, with a particular emphasis on cellular and molecular biomechanics, is crucial for sustainable rural landscape design. At the cellular and molecular level, plants’ biomechanical properties, such as the rigidity and elasticity of cell walls, determine their growth patterns and responses to environmental factors. These properties are essential in understanding how plants can be effectively incorporated into the rural landscape to enhance its stability and functionality. Aim: The objective of this research is to develop a novel computer simulation model for rural landscape planning using remote sensing imaging technology. Research methodology: We introduce a novel Adaptive YOLOv7 method driven by Starling Murmuration search. UAVs are used to collect extensive visual data for training the model. By considering cellular and molecular biomechanics, we can analyze how the mechanical forces within plants affect their ability to resist wind, retain water, and interact with the surrounding soil and other organisms. This knowledge can be integrated into the model to better predict the long-term viability and adaptability of different plant species in the rural landscape. The combination of the 3D GIS virtual image strategy model and our proposed model, along with SM optimization, not only improves object identification but also takes into account the biomechanical aspects for more accurate simulations. Crowdsourcing helps in precisely mapping rural landscapes and structures, while the incorporation of biomechanical principles ensures better adaptability to changing environmental and ecological conditions. Findings and Conclusion: Implemented in Python software, our SM-AYOLOv7 model shows excellent performance, with metrics like f1 score (93.64%), recall (92.34%), accuracy (91.72%), and IoU (90.23%). Our method outperforms conventional ones, demonstrating enhanced accuracy and flexibility, e

关键词： computer simulation image processing plant biomechanical properties remote sensing rural landscape design starling murmuration search-driven adaptive YOLOv7 (SM-AYOLOv7)

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Dynamic Maintenance Document management Based on BOM

Dynamic Maintenance Document Management Based on BOM

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International Conference on systems and Informatics (ICSAI)

作者： Chunliu Zhou Shan Ye Hongjun Wang Zhenhua Gao Key Laboratory of Multidisciplinary Management Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Ma'anshan China School of Management Science and Engineering Anhui University of Technology Ma'anshan China

ISBN: (纸本)9781665426251

To realize the efficient management of high-speed train maintenance documents, a document management method based on the Bill of Material (BOM) was proposed. Since the maintenance documents were dynamically changed as the design updated, and the change process was complicated and time-consuming via handwork, the relationship between the Engineering BOM (EBOM) and the Documental BOM (DBOM) was established, which delivered the design change, updated the maintenance documents in real time, and provided the latest documents for on-site maintenance business. Technologies of fuzzy matching, version management and XML were used together to realized the dynamic management of maintenance documents, and an application was displayed with system interfaces to verify the DBOM method.

关键词： Solid modeling Bills of materials XML Data visualization Maintenance engineering Real-time systems Mobile handsets

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Deep Learning-Based LecturePosture Evaluation

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Wuhan University Journal of Natural Sciences 2024年第4期29卷 315-322页

作者： YANG Yifan ZHANG Tao LI Weiyu Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of TechnologyMaanshan 243002AnhuiChina School of Microelectronics and Data Science Anhui University of TechnologyMaanshan 243032AnhuiChina

Computer vision,a scientific discipline enables machines to perceive visual information,aims to supplant human eyes in tasksencompassing object recognition,localization,and *** traditional educational settings,instructors or evaluators evaluate teachingperformance based on subjective ***,with the continuous advancements in computer vision technology,it becomes increasinglycrucial for computers to take on the role of judges in obtaining vital information and making unbiased *** thisbackdrop,this paper proposes a deep learning-based approach for evaluating lecture ***,feature information is extracted fromvarious dimensions,including head position,hand gestures,and body posture,using a human pose estimation ***,a machinelearning-based regression model is employed to predict machine scores by comparing the extracted features with expert-assigned *** correlation between machine scores and human scores is investigated through experiment and analysis,revealing a robustoverall correlation(0.6420)between predicted machine scores and human *** ideal scoring conditions(100 points),approximately51.72%of predicted machine scores exhibited deviations within a range of 10 points,while around 81.87%displayed deviationswithin a range of 20 points;only a minimal percentage of 0.12%demonstrated deviations exceeding the threshold of 50 ***,tofurther optimize performance,additional features related to bodily movements are extracted by introducing facial expression recognitionand gesture recognition *** fusion of multiple models resulted in an overall average correlation improvement of 0.0226.

关键词： deep learning human pose estimation object detection correlation

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A Predictive Tracking control Algorithm for Initial Angle of Brushless DC Motor Based on Reinforcement Learning

A Predictive Tracking Control Algorithm for Initial Angle of...

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Asian control Conference

作者： Yu Zhu Haodong Chen Xihong Fei Kang Wang Jing Wang School of Electrical and Information Engineering Anhui University of Technology Ma'anshan China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Ma’anshan China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

The initial angle detection and tracking control of brushless direct current motor (BLDC) is widely used in military, aviation, industrial, automotive, civil control systems, and household appliances. The motor is a nonlinear system, which is easily affected by external factors, so it is difficult to maintain the stability of the system with fixed control parameters. Therefore, this paper proposes a control method for initial angle tracking of BLDC based on reinforcement learning (RL). In order to determine the initial angle of the surface-mounted direct current (DC) motor, in the traditional Field-Oriented control (FOC) algorithm, the pulsating high-frequency injection algorithm is introduced and combined with the RL theory. According to the given PI parameters, the appropriate rewards are obtained through the RL controller. Then the proportional integral (PI) parameters are dynamically adjusted to make the estimated angle closer to the true angle value. This method avoids the error of angle estimation caused by fixed PI parameters in different situations, and obtains the best results by dynamically adjusting the parameters. Finally, the simulation experiment is carried out to prove the feasibility of the proposed scheme.

关键词： Home appliances Brushless DC motors Heuristic algorithms Estimation Reinforcement learning Prediction algorithms control systems

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Parallel Driving with Big Models and Foundation Intelligence in Cyber-hysical-ocial Spaces

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Research 2024年第2期2024卷 1-17页

作者： Xiao Wang Jun Huang Yonglin Tian Chen Sun Lie Yang Shanhe Lou Chen Lv Changyin Sun Fei-Yue Wang School of Artificial Intelligence Anhui UniversityHefeiChina Macao University of Science and Technology MacaoChina State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina MVSLab Department of Mechanical and Mechatronics EngineeringUniversity of Waterloo200 University Ave WestWaterlooON N2L3G1Canada School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingaporeSingapore

Recent years have witnessed numerous technical breakthroughs in connected and autonomous vehicles (CAVs). On the one hand, these breakthroughs have significantly advanced the development of intelligent transportation systems (ITSs);on the other hand, these new traffic participants introduce more complex and uncertain elements to ITSs from the social space. Digital twins (DTs) provide real-time, data-driven, precise modeling for constructing the digital mapping of physical-world ITSs. Meanwhile, the metaverse integrates emerging technologies such as virtual reality/mixed reality, artificial intelligence, and DTs to model and explore how to realize improved sustainability, increased efficiency, and enhanced safety. More recently, as a leading effort toward general artificial intelligence, the concept of foundation model was proposed and has achieved significant success, showing great potential to lay the cornerstone for diverse artificial intelligence applications across different domains. In this article, we explore the big models embodied foundation intelligence for parallel driving in cyber-physical-social spaces, which integrate metaverse and DTs to construct a parallel training space for CAVs, and present a comprehensive elucidation of the crucial characteristics and operational mechanisms. Beyond providing the infrastructure and foundation intelligence of big models for parallel driving, this article also discusses future trends and potential research directions, and the ?S?goals of parallel driving.

关键词： breakthrough integrate corners

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Talent Prediction Based on Switching Strategy Algorithm of Residual Grey Model

Talent Prediction Based on Switching Strategy Algorithm of R...

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Chinese control and Decision Conference, CCDC

作者： Yuxin Pan Xihong Fei Bo Shan Hao Shen Jing Wang Kang Wang School of Electrical and Information Engineering Anhui University of Technology Ma’Anshan China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Ma’Anshan China Instrumentation Technology and Economy Institute Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The surge in demand for medical equipment necessitates accurate prediction of talent demand in the medical industry, becoming a crucial support for the strategic planning of talent and human resources in universities. However, the difficulty in effectively collecting talent data in the medical equipment industry results in limited availability of talent demand data. At the same time, existing research focuses only on the linear or nonlinear single relationship of talent demand sequence data, thereby reducing the accuracy and robustness of predictions. In this paper, we propose a Switching Strategy Algorithm based on Residual Grey Model (SSA-RGM) to address the above issue. SSA-RGM is a hybrid model that includes a novel residual grey model, commonly used simple linear regression model, and a switching strategy based on optimal fitness. In this model, we introduce a residual operation based on the grey model and, for the first time, incorporate a penalty factor to mitigate the impact of linear shifts in small-sample data within nonlinear systems. Furthermore, we adopt the principle of optimal fitness to select the best model in the hybrid model. Experimental results on six real-world datasets show that SSA-RGM achieves the best talent prediction performance in various settings.

关键词： Industries Biomedical equipment Accuracy Switches Predictive models Strategic planning Prediction algorithms

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Finite-time Robust Fault-tolerant Trajectory Tracking control for QUAVs with Actuator Fault

Finite-time Robust Fault-tolerant Trajectory Tracking Contro...

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Robotics, control and Automation Engineering (RCAE), International Conference on

作者： Rongsheng Xia Chunlei Bu Han Jiang Chery Automobile Stock Limited Company Wuhu China School of Electrical and Information Engineering Anhui University of Technology Ma’Anshan China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Ma’Anshan China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

This paper proposed a finite-time robust fault-tolerant trajectory tracking control strategy for quadrotor unmanned aerial vehicles (QUAVs) suffering from actuator saturation, unknown external disturbance and actuator fault. Firstly, robust compensation terms are used to handle unknown external disturbance in position subsystem. On this basis, parameter adaptive method is introduced to remove the adverse effects of actuator saturation in attitude subsystem. Then, a fault detection mechanism is designed to estimate the actuator fault in real time. Subsequently, a finite-time robust fault-tolerant tracking strategy based on backstepping framework are proposed and and the finite-time convergence of all error signals are ensurend. Finally, a numerical simulation result is displayed to validate the effectiveness of the proposed trajectory tracking control scheme for the QUAVs dynamic system.

关键词： Actuators Fault tolerance Trajectory tracking Fault tolerant systems Stability analysis Real-time systems Robots Numerical stability Quadrotors Convergence

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A method of human-robot collaboration for grinding of workpieces

A method of human-robot collaboration for grinding of workpi...

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IEEE International Conference on Mechatronics and Automation

作者： Jianhua Su Hong Qiao Lijin Xu Ming Wang State Key Lab. of Management and Control for Complex Systems Chinese Academy of Science Beijing P.R. China Anhui Effort Equipment Co. Ltd Hefei Anhui Province China

ISBN: (纸本)9781479970995

This paper presents a strategy of human-robot collaboration in grinding system. A collision-avoidance region, which ensures an operator should not be collision with a robot arm, is firstly detected by tracking the operator motion. Then, a path planning method, which uses control points of NURBS (Non-Uniform Rational B-Splines) to modify the path of the robot, is presented. Hence, whenever a possible collision with the robot and operator is predicted, the robot trajectory could be changed accordingly. Finally, a human-robot collaboration grinding system is built to enable a robot to perform safe grinding operations with the aid of humans.

关键词： Collision avoidance Trajectory Cameras Robot vision systems Service robots Collaboration

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