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2022 International Conference on Intelligent Manufacturing and Industrial Big Data, ICIMIBD 2022

作者： Qin, Hao Chen, Zhenxue Lu, Mengxu Cao, Jiaqian Zhao, Hongjian Shandong University School of Control Science and Engineering Jinan China Machinery Industry CO Beijing Research Institute of Automation Beijing China

ISBN: (纸本)9798350320305

Computational pressure limits the application of semantic segmentation in road scenes. We propose the lightweight network TBANet that makes a trade-off between accuracy and speed to deal with this problem. TBANet takes advantage of three branches (a fine-grained branch, an adaptive branch, and a coarse-grained branch) to obtain detailed and abstract information and adaptively supplement useful features for semantic segmentation. In order to exploit the information in the horizontal and vertical directions in images and remove redundant information to speed up, we propose the Asymmetric Pyramid Pooling Module (APPM) and the Asymmetric Factorized Module (AFM). TBANet with only 1M parameters processes 512×1024 images at 143 FPS and achieves 71.6% mIoU on the Cityscapes test set. TBANet stands out from other state-of-the-art networks in terms of accuracy and speed. © 2022 IEEE.

关键词： Semantic Segmentation

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USV Formation Path Planning Based on Behavior Trees and Fast Marching Method 5

USV Formation Path Planning Based on Behavior Trees and Fast...

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5th International Conference on Intelligent control, Measurement and Signal Processing, ICMSP 2023

作者： Cao, Honghao Song, Rui Xu, Junjie Hu, Xinming Bao, Zhuoye Bao, Lingzhi School of Mechatronics and Automation Shanghai University Shanghai China Artificial Intelligence Research Institute Shanghai University Shanghai China School of Computer Engineering and Science Shanghai University Shanghai China

ISBN: (纸本)9798350336030

In the deployment of multiple Unmanned Surface Vehicles (USVs) for collaborative operation, path planning is a crucial component. This paper addresses the path planning problem for USV formations operating in complex marine environments and proposes a multi-USV formation path planning method based on behavior tree formation control strategy and the Fast Marching Method (FMM). By adopting an adaptive formation library based on the FMM velocity potential field and a behavior tree formation obstacle avoidance control strategy, the path planning problem for USV formations in narrow and complex water areas is solved. Experimental validation demonstrates that the proposed method exhibits superior performance in complex marine environments, particularly in narrow obstacle zones. © 2023 IEEE.

关键词： Motion planning

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research on Path Planning of Mobile Robot Based on Reinforcement Learning

Research on Path Planning of Mobile Robot Based on Reinforce...

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2022 Chinese automation Congress, CAC 2022

作者： Ji, Maoyuan Li, Jianxing Li, Shanqiang Liu, Zhenyu Song, Jiang Liu, Lisang Fujian Institute of Engineering Fujian Industrial Automation Technology Research and Development Center Fuzhou China Fujian Institute of Engineering Department of Computer Science and Mathematics Fuzhou China

ISBN: (纸本)9781665465335

With the continuous change of artificial intelligence technology, the study of path planning for mobile robots is no longer limited to traditional path algorithms. Reinforcement learning, as an artificial intelligence algorithm with excellent performance in the field of path planning, has also gradually become the object of research on path planning. In order to find a fast path from the starting point to the end point of a mobile robot in a complex environment, reinforcement learning is used to find a valid path by sensing the environment and continuously receiving rewarding feedback through a method of trial and error learning like humans. Therefore, this paper selects two algorithms to verify their effectiveness on the basis of building a two-dimensional grid map. The final experimental results show that both reinforcement learning algorithms can eventually avoid obstacles and plan a valid path in a complex environment through continuous learning iterations. Q-Learning also reduces the path distance by 10% and the number of convergence iterations by 82% compared to Sarsa. © 2022 IEEE.

关键词： Reinforcement learning

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Prediction of Remaining Useful Life Based on SSA-RVM-PF Method for Lithium-ion Battery 5

Prediction of Remaining Useful Life Based on SSA-RVM-PF Meth...

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5th International Symposium on Autonomous Systems, ISAS 2022

作者： Zhou, Meng Wang, Jing Cai, Chang Wang, Chang North China University of Technology School of Electrical and Control Engineering Beijing China Beijing Aerospace Automation Research Institute Beijing China

ISBN: (纸本)9781665487085

To improve the prediction accuracy of the remaining useful life of lithium-ion battery, a novel SSA-RVM-PF prediction method is proposed. The key prediction mechanism of the proposed method is based on the relevance vector machine (RVM) method. Based on the strong regression ability of RVM, the optimal degradation trajectory of battery capacity can be predicted. Meanwhile, a sparrow search algorithm (SSA) is used to optimize the kernel function parameters of RVM method. Furthermore, a particle filtering (PF) strategy is constructed based on the degradation trajectory, in which the state space model. Simulation results show that the proposed prediction method has the characteristics of high fitting degree of state tracking and high prediction accuracy. © 2022 IEEE.

关键词： Lithium-ion batteries

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Theoretical and Computational Investigation on Aerospike Nozzle Equipped with Exhaust Plumes for Supersonic Flow Regimes 1

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International Conference on Mechanical engineering, INCOME 2023

作者： Bindal, Rohi Kattyayan, Shivansh Sonawane, Chandrakant R. Dhanwani, Payal Thombre, Simran Joshi, Vedant Mishra, Ankit Kumar Department of Computer Science Engineering Symbiosis Institute of Technology Maharashtra Pune India Department of Mechanical Engineering Symbiosis Institute of Technology Maharashtra Pune India Department of Aeronautical Engineering Bhagwan Arihant Institute of Technology Gujarat Surat India Department of Aerospace Engineering Sastra University Tamil Nadu Thanjavur India Department of Robotics and Automation Symbiosis Institute of Technology Maharashtra Pune India Department of Research and Development ASTROEX RESEARCH ASSOCIATION Uttar Pradesh Deoria India

ISBN: (数字)9789819738748

ISBN: (纸本)9789819738731

Aerospike nozzles are widely used in aerospace applications due to their high performance, low cost, and low maintenance. They have been widely used to improve the performance of aerospike engines. This paper presents a computational and numerical analysis of the flow behavior of an aerospike nozzles equipped with exhaust plumes under different operating conditions, contributing to the better study and understanding of fluid dynamics for the aerospike nozzles in the field of aerospace engineering. The study uses a three-dimensional, axisymmetric model to simulate the flow within the nozzle, and the results are later used in order to generate better contours and performance. An iterative test has been carried out which in turn used for optimization loop which involves changes in contour shape, spike height, or spike angle to achieve better flow expansion and reduce pressure losses. The paper also discusses the potential future research directions, such as investigating unsteady flow phenomena or exploring advanced turbulence models for more accurate predictions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Aerospace applications

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Hierarchical Multiview Top-k Pooling with Deep-Q-Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2985-2996页

作者： Li, Zhi-Peng Su, Hai-Long Wu, Yong- Zhang, Qin-Hu Yuan, Chang-An Gribova, Valeriya Filaretov, Vladimir Fedorovich Huang, De-Shuang Eastern Institute of Technology Zhejiang Ningbo315201 China University of Science and Technology of China School of Life Sciences Anhui Hefei230026 China Tongji University Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Shanghai201804 China Guangxi Academy of Sciences Institute of Big Data and Intelligent Computing Research Center Nanning530007 China Far Eastern Branch of the Russian Academy of Sciences Institute of Automation and Control Processes Vladivostok690041 Russia

Graph neural networks (GNNs) are extensions of deep neural networks to graph-structured data. It has already attracted widespread attention for various tasks such as node classification and link prediction. Existing research focuses more on graph convolutional neural networks (GCNs). However, it is usually overlooked that graph pooling can obtain graph representations by summarizing and down-sampling node information. Meanwhile, existing graph pooling methods mainly use top-k for node selection, but most of them consider only single view information when scoring nodes, and the k values in top-k are usually selected empirically. This work proposes the hierarchical multiview top-k pooling (HMTPool) with deep-Q-networks, which scores nodes taking into account multiview information (considering graph structure and features) and does not rely on the empirical adaptive selection of the best k value. HMTPool is a two-stage process. It first uses a variant GCN and multilayer perceptron to score the nodes from structural and feature perspectives, respectively and then performs fusion operations on the multiview information scores of the nodes. In addition, to select the optimal pooling ratio of top-k, we propose a deep-Q-network-based top-k (DTop-k) node selection method, which can adaptively select the best pooling ratio without prior knowledge. Experimental results on six TUDatasets and two Benchmarking GNNs datasets demonstrate the effectiveness of our proposed approach. © 2020 IEEE.

关键词： Convolution

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Game Theory and DQN-Based Network Defense Decision-Making for Terminal Security in Power Distribution System 24

Game Theory and DQN-Based Network Defense Decision-Making fo...

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2024 International Conference on Cloud Computing and Big Data, ICCBD 2024

作者： Zhu, Daohua Zhu, Jiang Wang, Xiaodong Wang, Wei Qi, Longyun Electric Power Research Institute State Grid Jiangsu Electric Power Co. Ltd. China State Grid Electric Power Research Institute Co. China School of Control and Computer Engineering North China Electric Power University China China

ISBN: (纸本)9798400710223

The increase of terminal devices in the automated power distribution system expands the attack surface for malicious adversaries, which can severely impair the reliability and stability of the power grids. As cyber-attacks are empowered by diverse and elaborate techniques, relying solely on traditional passive defense methods is insufficient to address the evolving security threats. It is imperative to develop adaptive and active approaches to defend against the cyber-attacks aimed at terminal devices in the power distribution system. Motivated by this idea, we focus on the network defense decision-making approach. Effective defense strategies not only mitigate the risk of disruptive cyber-attacks, such as data breaches and system compromises, but also ensure the continuous operation of essential services in the power system. Though researchers have made efforts to explore game-theoretic defense-decision methods for power systems, there are still shortcoming in defining rewards and state transitions. In this paper, we utilize the stochastic game to model the confrontation between the attacker and defender. We design a hybrid reward function to describe the rewards in the two-player game from various perspectives. We also introduce a deep Q-network to learn the high-dimensional state transitions and achieve the Nash equilibrium. Experimental results based on the simulated network are presented to demonstrate the effectiveness of the proposed approach. © 2024 ACM.

关键词： Cyber attacks

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Infrastructure-Based Vehicle Localization System for Indoor Parking Lots Using RGB-D Cameras

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Journal of Shanghai Jiaotong university(Science) 2023年第1期28卷 61-69页

作者：曹炳全贺越生庄瀚洋杨明 Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong UniversityShanghai 200240China

Accurate vehicle localization is a key technology for autonomous driving tasks in indoor parking lots,such as automated valet ***,infrastructure-based cooperative driving systems have become a means to realizing intelligent *** this paper,we propose a novel and practical vehicle localization system using infrastructure-based RGB-D cameras for indoor parking *** the proposed system,we design a depth data preprocessing method with both simplicity and efficiency to reduce the computational burden resulting from a large amount of ***,the hardware synchronization for all cameras in the sensor network is not implemented owing to the disadvantage that it is extremely cumbersome and would significantly reduce the scalability of our system in mass ***,to address the problem of data distortion accompanying vehicle motion,we propose a vehicle localization method by performing template point cloud registration in distributed depth ***,a complete hardware system was built to verify the feasibility of our solution in a real-world *** in an indoor parking lot demonstrated the effectiveness and accuracy of the proposed vehicle localization system,with a maximum root mean squared error of 5 cm at 15Hz compared with the ground truth.

关键词： infrastructure-based RGB-D camera vehicle localization point cloud registration

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Online Optimization in Power Systems With High Penetration of Renewable Generation:Advances and Prospects

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 839-858页

作者： Zhaojian Wang Wei Wei John Zhen Fu Pang Feng Liu Bo Yang Xinping Guan Shengwei Mei Key Laboratory of System Control and Information Processing Ministry of Education of ChinaDepartment of AutomationShanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Power System and the Department of Electrical Engineering Tsinghua UniversityBeijing 100084China Institute of High Performance Computing(IHPC) Agency for Science Technology and Research(A*STAR)Singapore 138632Singapore

Traditionally,offline optimization of power systems is acceptable due to the largely predictable loads and reliable *** increasing penetration of fluctuating renewable generation and internet-of-things devices allowing for fine-grained controllability of loads have led to the diminishing applicability of offline optimization in the power systems domain,and have redirected attention to online optimization ***,online optimization is a broad topic that can be applied in and motivated by different settings,operated on different time scales,and built on different theoretical *** paper reviews the various types of online optimization techniques used in the power systems domain and aims to make clear the distinction between the most common techniques *** particular,we introduce and compare four distinct techniques used covering the breadth of online optimization techniques used in the power systems domain,i.e.,optimization-guided dynamic control,feedback optimization for single-period problems,Lyapunov-based optimization,and online convex optimization techniques for multi-period ***,we recommend some potential future directions for online optimization in the power systems domain.

关键词： optimization Lyapunov optimization online convex optimization online optimization optimization-guided control

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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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