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8th International Conference on Advances in Computing and Data sciences, ICACDS 2024

作者： Thanh, Bui Dang Anh, Mac Tuan Khanh, Giap Dang Dong, Trinh Cong Huong, Nguyen Thanh School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Viet Nam Institute for Control Engineering and Automation Hanoi University of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9783031709050

In Vietnam, rice stands as a vital food source for the whole population, and because of that timely identification and management of rice plant diseases is extremely crucial. Among the diseases that appear in Vietnam, blast leaf, leaf folder, and brown spot are among the most prevalent ailments that harm rice plants, directly impacting cultivation. To address this problem, deep learning, a cutting-edge solution can be used to detect plant diseases. In this paper, we present an approach utilizing Deep learning to identify illnesses affecting rice leaves. YOLOv8 is employed and trained with various strategies of Stochastic Gradient Descent with Warm Restart (SGDR) to identify diseases on rice leaves. The result of the proposed method is assessed using 309 images. The experimental findings reveal an accuracy of 88.6%, indicating superior performance compared to alternative methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Plant diseases

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Online Vehicle Forensics Method of Responsible Party for Accidents Based on LSTM-BiDBN External Intrusion Detection

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Journal of Shanghai Jiaotong university(science) 2024年第6期29卷 1161-1168页

作者： LIU Wen XU Jianxin YANG Genke CHEN Yuanfang Ningbo Industrial Internet Institute Ningbo 315000ZhejiangChina Ningbo Artificial Intelligence Institute of Shanghai Jiao Tong University Ningbo 315000ZhejiangChina Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China School of Cyberspace Hangzhou Dianzi UniversityHangzhou 310018China

Vehicle data is one of the important sources of traffic accident digital *** propose a novel method using long short-term memory-deep belief network by binary encoding(LSTM-BiDBN)controller area network identifier(CAN ID)to extract the event sequence of CAN IDs and the semantic of CAN IDs *** of detecting attacks only aimed at a specific CAN ID,the proposed method fully considers the potential interaction between electronic control *** this means,we can detect whether the vehicle has been invaded by the outside,to online determine the responsible party of the *** use our LSTM-BiDBN to distinguish attack-free and abnormal situations on *** results show that our proposed method is more effective in identifying anomalies caused by denial of service attack,fuzzy attack and impersonation attack with an accuracy value of 97.02%,a false-positive rate of 6.09%,and a false-negative rate of 1.94%compared with traditional methods.

关键词： digital forensics deep belief network(DBN) long short-term memory(LSTM) binary encoding controller area network identifier(CAN ID) responsible party

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Impact Time Cooperative Guidance for Multi-missile System Based on Incremental Learning

Impact Time Cooperative Guidance for Multi-missile System Ba...

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第42届中国控制会议

作者： Xuheng Li Jianglong Yu Xiwang Dong Zhang Ren Zhuo Feng Biao Liang School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing Institute of Control and Electronic Technology

An impact time cooperative guidance method based on incremental learning is proposed for multi-missile cooperative guidance problem in the attack scenario. Firstly, a guidance model is established to represent the relationship of the missiles and the target. Secondly, an impact time coordinated guidance law based on the consensus method is designed. Then, time-to-go of the missile is predicted using an incremental learning algorithm, so that the accuracy of time-to-go can be better than the results calculated by traditional methods. Finally, the trained neural network is used to predict time-to-go. The results show that the algorithm can be used in the cooperative guidance law to complete the coordination in time, which is superior to the traditional guidance rate in coordination and has advantage in the scene of large maneuvering target.

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GGIMN: Gravity Gradient Image Matching Network with Adaptive Adjoint Smoothing for AUV Navigation

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IEEE Transactions on Vehicular Technology 2025年

作者： Li, Tianjiao Wang, Bo Song, Bao Deng, Zhihong Fu, Mengyin Beijing Institute of Technology School of Automation Beijing100081 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin300382 China Beijing Institute of Technology Engineering Research Center of the Ministry of Education for Navigation Guidance and Control Technology School of Automation Beijing100081 China Nanjing University of Science and Technology Nanjing210014 China

Gravity gradient-aided navigation is a promising navigation method for autonomous underwater vehicle (AUV), in which matching algorithm is its key technology. Image matching algorithms have higher matching accuracy and information utilization than single point matching and sequence matching. However, the spatial domain image feature matching ability is weak in areas with insignificant feature changes due to the uneven feature distribution in the gravity gradient reference map. To break through this limitation and solve the problem of insufficient spatial domain image feature matching ability, we propose a gravity gradient image matching network (GGIMN) with adaptive adjoint smoothing for AUV navigation and positioning. First, a training set for GGIMN with equal numbers of positive and negative samples is constructed by preparing the gravity gradient reference map and fitting the gravity gradient measured map. Then, the feature extraction part of MatchNet is expanded to construct a deep learning network with five gravity gradient twin towers in parallel. Subsequently, an adaptive adjoint smoothing method that combines the idea of sequence matching is proposed to solve the problem of unsmooth matching trajectory and further improve the matching accuracy. Finally, the GGIMN is trained offline, and the gravity gradient map is matched and positioned online. The results of marine experiments show that the GGIMN has higher matching and positioning accuracy, and the average positioning error is reduced beyond 70% compared with onedimensional matching algorithms such as single-point iteration and sequence matching. © 1967-2012 IEEE.

关键词： Image matching

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Development and control of Underwater Gliding Robots:A Review

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1543-1560页

作者： Jian Wang Zhengxing Wu Huijie Dong Min Tan Junzhi Yu the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the State Key Laboratory for Turbulence and Complex System Department of Mechanics and Engineering ScienceBIC-ESATCollege of EngineeringPeking UniversityBeijing 100871China

As one of the most effective vehicles for ocean development and exploration,underwater gliding robots(UGRs)have the unique characteristics of low energy consumption and strong ***,by borrowing the motion principles of current underwater robots,a variety of novel UGRs have emerged with improving their maneuverability,concealment,and environmental friendliness,which significantly broadens the ocean *** this paper,we provide a comprehensive review of underwater gliding robots,including prototype design and their key *** the perspective of motion characteristics,we categorize the underwater gliding robots in terms of traditional underwater gliders(UGs),hybrid-driven UGs,bio-inspired UGs,thermal UGs,and ***,their buoyancy driven system,dynamic and energy model,and motion control are concluded with detailed ***,we have discussed the current critical issues and future *** review offers valuable insight into the development of next-generation underwater robots well-suited for various oceanic applications,and aims to gain more attention of researchers and engineers to this growing field.

关键词： Buoyancy driven motion control oceanic applications system development underwater gliding robots

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Federated Intelligence for Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 1-5页

作者： Zhang, Weishan Zhang, Baoyu Jia, Xiaofeng Qi, Hongwei Qin, Rui Li, Juanjuan Tian, Yonglin Liang, Xiaolong Wang, Fei-Yue School of China University of Petroleum Qingdao China Department of Data Management Big Data Centre Beijing Beijing China Technology Company Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

This letter is a brief summary of a series of IEEE TIV's decentralized and hybrid workshops (DHWs) on Federated Intelligence for Intelligent Vehicles. The discussed results are: 1) Different scales of large models (LMs) can be federated and deployed on IVs, and three types of federated collaboration between large and small models can be adopted for IVs. 2) Federated fine-tuning of LMs is beneficial for IVs data security. 3) The sustainability of IVs can be improved through optimizing existing models and continuous learning using federated intelligence. 4) LM-enhanced knowledge can make IVs smarter. IEEE

关键词： Intelligent vehicle highway systems

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A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 812-833页

作者： Xinlei Yi Shengjun Zhang Tao Yang Tianyou Chai Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technologyand also affiliated with the Digital FuturesStockholm 10044Sweden Department of Electrical Engineering University of North TexasDentonTX 76203 USA IEEE State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is *** problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated *** propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost *** show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of *** also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global *** demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.

关键词： Distributed nonconvex optimization linear speedup Polyak-Lojasiewicz(P-L)condition primal-dual algorithm stochastic gradient descent

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Model-Free Extended Q-Learning Method for H∞ Output Tracking control of Networked control Systems with Network Delays and Packet Loss

SSRN

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SSRN 2024年

作者： Hao, Longyan Wang, Chaoli Liang, Dong Li, Shihua Department of Control Science and Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Automation Southeast University Nanjing211189 China

In this paper, the extended Q-learning method is used to study the H∞ output tracking control problem of networked control systems with state delay and data loss. Compared with the existing results, the network control system in this paper contains both network delays and packet loss, as well as external disturbances. To deal with the disturbances, the H∞ control problem is transformed into the maximum and minimum value problem, which is solved by the method of zero-sum game. The packet loss and delay of the state make it difficult to obtain accurate current state information. Therefore, it is necessary to design a new smith predictor that contains delay and packet loss to predict the current state. Using the predicted state, the extended Q-learning algorithm is implemented to solve the H∞ output tracking problem with unknown dynamics of the system. Then, the convergence of the extended Q-learning algorithm is proved. Moreover, the stability and optimality of the proposed method areanalyzed in the theorems. Finally, numerical simulation is performed to verify the effectiveness of the proposed algorithm. © 2024, The Authors. All rights reserved.

关键词： Networked control systems

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Real-time Pricing Mechanism for V2G Using Distributed Bilevel Optimization 63

Real-time Pricing Mechanism for V2G Using Distributed Bileve...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： You, Jiawei Dai, Xinliang Jiang, Yuning Yin, Haoyu Shi, Yuanming Jones, Colin N. ShanghaiTech University School of Information Science and Technology China Epfl Switzerland Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Germany Epfl Automatic Control Laboratory Switzerland Washington University in St. Louis Department of Electrical and Systems Engineering United States

ISBN: (纸本)9798350316339

This paper explores the impact of the burgeoning electric vehicle (EV) presence on distribution grid operations, highlighting the challenges they present to conventional pricing strategies due to their dual role as power consumers and suppliers, coupled with their energy storage capabilities. We propose an advanced real-time pricing model for the electricity market, employing a novel distributed bilevel optimization framework. This framework distinguishes between the distribution system operator (DSO) at the upper level and the EVs at the lower level, each aiming to optimize profit margins. The optimization includes power flow constraints at the upper level to ensure efficient operation within safe system limits, while model predictive control (MPC) is used to optimize lower-level EV responses. Additionally, we provide a rigorous convergence analysis of the proposed bilevel optimization method. Detailed convergence studies and simulation results demonstrate the effectiveness and superiority of the proposed algorithm. © 2024 IEEE.

关键词： Predictive control systems

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Event-Triggered-Based Privacy-Preserving Finite-Time Flocking control for Networked Multi-Agent Systems 63

Event-Triggered-Based Privacy-Preserving Finite-Time Flockin...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Yue, Jiangfeng Qin, Kaiyu Shao, Jinliang Shi, Mengji Li, Weihao Zheng, Wei Xing University of Electronic Science and Technology of China School of Aeronautics and Astronautics Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100089 China Western Sydney University School of Computer Data and Mathematical Sciences SydneyNSW2751 Australia

ISBN: (纸本)9798350316339

Drawing inspiration from the dynamics of biological groups, flocking behavior has captivated interest due to its adaptive, self-organizing, and resilient characteristics. However, the presence of numerous agents in an open network environment raises concerns regarding the potential for information leakage during flocking movements. To address this issue, we present a decentralized output mapping function, executed independently by each individual agent, to ensure the maintenance of flocking behavior, including velocity alignment, cohesion and collision avoidance. Additionally, to enhance resource efficiency and convergence speed, an event-triggered mechanism is employed to mitigate the excessive consumption of bandwidth and computation resources in traditional flocking control algorithms. Moreover, the designed finite-time controller facilitates rapid convergence of flocking behavior. Finally, the efficacy of our proposed approach is corroborated through numerical simulation experiments. © 2024 IEEE.

关键词： Multi agent systems

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