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Design and Demonstrate an Attack Strategy to control a Vehicle’s Computer by Targeting Its electrical Network 1

3rd International Conference on Electronic engineering and Renewable Energy, ICEERE 2022

作者： Karrouchi, Mohammed Messaoudi, Abdelhafid Kassmi, Kamal Nasri, Ismail Atmane, Ilias Blaacha, Jalal Electrical Engineering and Maintenance Laboratory High School of Technology Mohammed First University BP. 473 Oujda Morocco Energy Embedded Systems and Information Processing Laboratory National School of Applied Sciences Mohammed First University Oujda Morocco Department of Physics Faculty of Science Mohammed First University Oujda Morocco

ISBN: (数字)9789811962233

ISBN: (纸本)9789811962226

The functionality of most modern automobiles is controlled by electronic control units (ECUs) that interact with one another via the CAN communication protocol (controller area network), as defined by the ISO 11898 standard, which is one of the fundamental features of most modern cars. This physical bus provides excellent data transfer quality by providing wide propagation reaching all areas of a vehicle in a short amount of time. However, because of the absence of secrecy and ease of access (physical or remote), this protocol does not place a high value on security, which causes the CAN bus control system to be vulnerable. Due to this weakness, it is possible to control the car from the outside and place the vehicle's passengers in jeopardy. The objective of this paper is to describe the existing vulnerability of the security system against attacks, and also a demonstration of a hacking technique used to manage the dashboard of a 2014 DACIA Dokker automobile using the automotive network. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： controllers

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Optimization of Crane Scheduling Time for Steel Plate Outbound Operations Based on Backtracking Algorithm

Optimization of Crane Scheduling Time for Steel Plate Outbou...

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2024 China Automation Congress, CAC 2024

作者： Xiang, Jiang Xu, Zhezhuang Wang, Jinlong Chen, Boyu Zhang, Qingdong Wu, Xiuren Xia, Yuxiong College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China Sansteel Minguang Co. Ltd. Sanming365000 China Fujian Huading Intelligent Manufacturing Technology Co. Ltd. Fuzhou350000 China

ISBN: (纸本)9798350368604

Steel plate is a highly customized product, and steel plates from the same order may be stored in different stacks. However, during the outbound process, the steel plates of the next order can only be dispatched from the warehouse once all plates from the preceding order have been fully completed. In this case, the outbound scheduling of steel plates is a challenging problem since its efficiency and complexity are affected by the shuffling of steel plates. In order to address the challenge, this paper formulates the steel plate outbound scheduling as a dynamic programming model with the aim of minimizing the crane operation time. To solve the problem, an decision tree-based backtracking search algorithm (DTBSA) is developed, in which the search tree is pruned through lower bound functions and dominance rules, so as to reduce the search range of the solution space. Experimental results prove that the proposed method can obtain optimal scheduling results within a reasonable time range. © 2024 IEEE.

关键词： Dynamic programming

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A chattering free consensus controller for multiple Lur'e systems with a non-autonomous leader and directed switching topology

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Science China(Technological Sciences) 2023年第4期66卷 1110-1118页

作者： WANG PeiJun YU WenWu WEN GuangHui YU XingHuo HUANG TingWen School of Mathematics and Statistics Anhui Normal UniversityWuhu 241000China Anhui Province Engineering Laboratory of Intelligent Robot's Information Fusion and Control Anhui Normal UniversityWuhu 241000China School of Mathematics Frontiers Science Center for Mobile Information Communication and SecuritySoutheast UniversityNanjing 210096China Purple Mountain Laboratories Nanjing 21111lChina Department of Electrical Engineering Nantong UniversityNantong 226019China Department of Systems Science School of MathematicsSoutheast UniversityNanjing 210096China School of Engineering RMIT UniversityMelbourne VIC 3001Australia Science Program Texas A&M University at QatarDoha 23874Qatar

Achieving asymptotical cooperative goal for multi-agent systems(MASs)with non-autonomous leaders(i.e.,leaders with nonzero inputs)is a critical but challenging *** approach is to use discontinuous controllers which may cause chattering phenomenon in practical *** to achieve the asymptotical goal via a chattering free cooperative controller remains to be open so *** this paper,an adaptive continuous controller is designed to achieve zero error consensus tracking in multiple Lur’e systems with a non-autonomous leader under directed switching ***,an unknown input observer(UIO)based on relative outputs is given to estimate the relative full *** an adaptive continuous controller is designed by introducing a decay function which remains positive into the term that plays the role of eliminating the impacts of leader’s nonzero ***,by using multiple Lyapunov functions(MLFs)technique,it is proven that zero error consensus tracking can be achieved if the average dwell time(ADT)is greater than a positive ***,theoretical result is verified by performing simulations on Chua’s *** with existing work,the proposed controller can not only achieve asymptotical consensus,but also is chattering free.

关键词： multi-agent system consensus chattering free non-autonomous leader directed switching topology

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Pointer Meter Reading Under Noise Based on Virtual Data Generation

Pointer Meter Reading Under Noise Based on Virtual Data Gene...

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Chinese Automation Congress (CAC)

作者： Jinyang Zhu Zhezhuang Xu Ye Lin College of Electrical Engineering and Automation Fuzhou University Fuzhou China Education Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Using machine vision for meter reading significantly enhances the efficiency of industrial monitoring. However, meters in outdoor environment are often subject to the noise such as rain and fog, which affect the accuracy of meter readings. Additionally, the meter images and labels under noise are hard to obtain. To address these challenges, We propose a method for pointer meter reading under noise based on virtual data generation. Firstly, We utilize Unity to construct an industrial meter scene, which generates the images of meter under noise and theri labels. Secondly, we develop a U-Net network with an ASPP module, which expands the receptive field of network and enhances the ability of segmentation network to capture global semantic information under noise. Finally, the angular method is used to calculate the meter readings. Experimental results show that this method can effectively read meters under noise in outdoor environments.

关键词： Meters Image segmentation Rain Accuracy Noise Semantics Object segmentation Data collection Meter reading Monitoring

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Dynamic Event-Triggered Model Predictive control for Multiple Platoons under Non-Ideal Communications

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IEEE Internet of Things Journal 2025年

作者： Han, Qiaoni Xu, Chengfei Wang, Xiaocheng Yang, Hongjiu Zuo, Zhiqiang Yang, Bo Guan, Xinping Tianjin University Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin300072 China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China Tianjin University School of Electrical and Information Engineering Tianjin300072 China Tianjin Normal University Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission College of Electronic and Communication Engineering Tianjin300387 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China

The uncertainty of wireless communication seriously affects the control performance of the platoon. In this paper, a model predictive control (MPC) method based on dynamic event-triggering is proposed to address the impact of non-ideal communications on multi-vehicle systems. Firstly, a hierarchical platoon architecture combining backbone layer and sub-platoon layers is proposed to achieve efficient coordination among multiple platoons, and a communication-aware control mechanism is designed for each vehicle to effectively mitigate the adverse effects of non-ideal communication scenarios. Secondly, a dynamic event-triggering mechanism is established for each vehicle, which is related to time-varying delay and random packet loss parameters, so as to effectively reduce the frequency of MPC solutions and data transmission. Additionally, based on different communication topologies, distributed model predictive controllers are suggested for backbone layer and sub-platoon layer to achieve precise and comfortable tracking. Then, sufficient conditions are provided to ensure the recursive feasibility of the MPC algorithm and the stability of the platoon system. Finally, extensive simulations are conducted, and the results demonstrate the effectiveness of the proposed algorithm in terms of control performance and low computational complexity under non-ideal communication conditions. © 2014 IEEE.

关键词： Predictive control systems

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Parametric Transformation of Timed Weighted Marked Graphs:Applications in Optimal Resource Allocation

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 179-188页

作者： Zhou He Ziyue Ma Zhiwu Li Alessandro Giua the College of Mechanical and Electrical Engineering Shaanxi University of Science and TechnologyXi’an 710021China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of TechnologyXi’an 710048China the School of Electro-Mechanical Engineering Xidian UniversityXi’an 710071China Institute of Systems Engineering Macao University of Science and TechnologyTaipa 999078MacaoChina the Department of Electrical and Electronic Engineering University of CagliariCagliari 09124Italy

Timed weighted marked graphs are a subclass of timed Petri nets that have wide applications in the control and performance analysis of flexible manufacturing *** to the existence of multiplicities(i.e.,weights)on edges,the performance analysis and resource optimization of such graphs represent a challenging *** this paper,we develop an approach to transform a timed weighted marked graph whose initial marking is not given,into an equivalent parametric timed marked graph where the edges have unitary *** order to explore an optimal resource allocation policy for a system,an analytical method is developed for the resource optimization of timed weighted marked graphs by studying an equivalent ***,we apply the proposed method to a flexible manufacturing system and compare the results with a previous heuristic *** analysis shows that the developed approach is superior to the heuristic approach.

关键词： Discrete event system resource allocation timed Petri net weighted marked graph

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Advancing Orchard Fruit Detection: An Innovative Agricultural Foundation Model Approach

Advancing Orchard Fruit Detection: An Innovative Agricultura...

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2024 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2024

作者： Li, Jiajia Lammers, Kyle Yin, Xunyuan Yin, Xiang He, Long Lu, Renfu Li, Zhaojian Department of Electrical and Computer Engineering Michigan State University MI United States School of Chemical and Biomedical Engineering Nanyang Technological University Singapore Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Department of Agricultural and Biological Engineering Pennsylvania State University United States United States Department of Agriculture Agricultural Research Service East LansingMI United States Department of Mechanical Engineering Michigan State University East LansingMI United States

ISBN: (纸本)9798331302214

Fruit harvesting poses a significant labor and financial burden on the fruit industry, which underscore the urgent need for advancements in robotic harvesting solutions. Despite considerable progress in leveraging deep learning and machine learning techniques for fruit detection, a common shortfall is the inability to swiftly extend the developed models across different orchards and/or various fruit species. Additionally, the limited availability of pertinent data further compounds these challenges. In this work, we introduce MetaFruit, the largest publicly available multi-class fruit dataset, comprising 4,248 images and 248,015 manually labeled instances across diverse U.S. orchards. Furthermore, this study proposes an innovative open-set fruit detection system leveraging advanced Vision Foundation Models (VFMs) for fruit detection that can adeptly identify a wide array of fruit types under varying orchard conditions. This system not only demonstrates remarkable adaptability in learning from minimal data through few-shot learning but also shows the ability to interpret human instructions for subtle detection tasks. The performance of the developed foundation model is comprehensively evaluated across several metrics, outperforming existing state-of-the-art algorithms in our MetaFruit, thereby setting a new benchmark in the field of agricultural technology and robotic harvesting. The MetaFruit dataset (https://***/datasets/jiajiali/metafruit) and detection framework (https://***/JiajiaLi04/FMFruit) are open-sourced to foster future research in vision-based fruit harvesting, marking a significant stride toward addressing the urgent needs of the agricultural sector. © 2024 ASABE Annual International Meeting. All rights reserved.

关键词： Orchards

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UW-DNeRF: Deformable Soft Tissue Reconstruction with Uncertainty-Guided Depth Supervision and Local information Integration

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Shan, Jiwei Zhang, Zixin Li, Hao Hsieh, Cheng-Tai Li, Yirui Wu, Wenhua Wang, Hesheng School of Electronic Information and Electrical Engineering of Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University School of Mechanical Engineering Shanghai200240 China Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China

Reconstructing deformable soft tissues from endoscopic videos is a critical yet challenging task. Leveraging depth priors, deformable implicit neural representations have seen significant advancements in this field. However, depth priors from pre-Trained depth estimation models are often coarse, and inaccurate depth supervision can severely impair the performance of these neural networks. Moreover, existing methods overlook local similarities in input sequences, which restricts their effectiveness in capturing local details and tissue deformations. In this paper, we introduce UW-DNeRF, a novel approach utilizing neural radiance fields for high-quality reconstruction of deformable tissues. We propose an uncertainty-guided depth supervision strategy to mitigate the impact of inaccurate depth information. This strategy relaxes hard depth constraints and unlocks the potential of implicit neural representations. In addition, we design a local window-based information sharing scheme. This scheme employs local window and keyframe deformation networks to construct deformations with local awareness and enhances the model's ability to capture fine details. We demonstrate the superiority of our method over state-of-The-Art approaches on synthetic and in-vivo endoscopic datasets. Code is available at: https://***/IRMVLab/UW-DNeRF. © 2025 IEEE.

关键词： Endoscopy

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Synthesis of Temporally-Robust Policies for Signal Temporal Logic Tasks using Reinforcement Learning

Synthesis of Temporally-Robust Policies for Signal Temporal ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Siqi Wang Shaoyuan Li Li Yin Xiang Yin Department of Automation and Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Institute of Systems Engineering Macau University of Science and Technology Taipa China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper investigates the problem of designing control policies that satisfy high-level specifications described by signal temporal logic (STL) in unknown, stochastic environments. While many existing works concentrate on optimizing the spatial robustness of a system, our work takes a step further by also considering temporal robustness as a critical metric to quantify the tolerance of time uncertainty in STL. To this end, we formulate two relevant control objectives to enhance the temporal robustness of the synthesized policies. The first objective is to maximize the probability of being temporally robust for a given threshold. The second objective is to maximize the worst-case spatial robustness value within a bounded time shift. We use reinforcement learning to solve both control synthesis problems for unknown systems. Specifically, we approximate both control objectives in a way that enables us to apply the standard Q-learning algorithm. Theoretical bounds in terms of the approximations are also derived. We present case studies to demonstrate the feasibility of our approach.

关键词： Measurement Uncertainty Q-learning Approximation algorithms Robustness Logic Task analysis

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Fixed-time adaptive parameter estimation for magnetostrictive actuated positioning system

Fixed-time adaptive parameter estimation for magnetostrictiv...

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第43届中国控制会议

作者： Shengbin Wu Jing Na Yingbo Huang Faxiang Zhang Xinkai Chen Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Yunnan Key Laboratory of Intelligent Control and Application Department of Electronic and Information Systems Shibaura Institute of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

An adaptive parameter estimation method that ensures fixed-time convergence is proposed for micro-nano positioning system driven by magnetostrictive actuator(i.e., magnetostrictive actuated positioning system). Firstly, the hysteresis nonlinearity of the magnetostrictive actuated positioning system is characterized using the generalized Prandtl–Ishlinskii(GPI) ***, the low-pass filter is introduced to design the adaptive estimation law driven by the estimation error, by which the unmeasured intermediate hysteresis variable can be avoided, and the estimation error converges to zero within a fixed time. Specifically,a modified error variable is designed to deal with the influence of the regression vector, thereby improving convergence performance and simplifying parameter adjustment. Finally, the effectiveness of the presented method is verified by simulation and experimental outcomes.

关键词： Adaptive parameter estimation Fixed-time convergence Magnetostrictive actuator Micro/nano positioning system

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