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arXiv 2024年

作者： Wang, Chaoqian Su, Qi Department of Computational and Data Sciences George Mason University FairfaxVA22030 United States Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

Understanding the emergence of cooperation in spatially structured populations has advanced significantly in the context of pairwise games, but the fundamental theory of group-based public goods games (PGGs) remains less explored. Here, we provide theoretical conditions under which cooperation thrive in spatial PGGs on any population structure, which are accurate under weak selection. We find that PGGs can support cooperation across all kinds of model details and on almost all network structures in contrast to pairwise games. For example, a class of networks that would otherwise fail to produce cooperation, such as star graphs, are particularly conducive to cooperation in spatial PGGs. This fundamental advantage of spatial PGGs derives from reciprocity through second-order interactions, allowing local structures such as the clustering coefficient to play positive roles. We also verify the robustness of spatial PGGs on empirical networks where pairwise games cannot support cooperation, which implies that PGGs could be a universal interaction mode in real-world systems. © 2024, CC BY.

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LU2Net: A Lightweight Network for Real-time Underwater Image Enhancement

arXiv

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arXiv 2024年

作者： Yang, Haodong Xu, Jisheng Lin, Zhiliang He, Jianping The Department of Computer Science Shanghai Jiao Tong University China The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China The School of Ocean and Civil Engineering Shanghai Jiao Tong University State Key Laboratory of Ocean Engineering Shanghai200240 China

Computer vision techniques have empowered underwater robots to effectively undertake a multitude of tasks, including object tracking and path planning. However, underwater optical factors like light refraction and absorption present challenges to underwater vision, which cause degradation of underwater images. A variety of underwater image enhancement methods have been proposed to improve the effectiveness of underwater vision perception. Nevertheless, for real-time vision tasks on underwater robots, it is necessary to overcome the challenges associated with algorithmic efficiency and real-time capabilities. In this paper, we introduce Lightweight Underwater Unet (LU2Net), a novel U-shape network designed specifically for real-time enhancement of underwater images. The proposed model incorporates axial depthwise convolution and the channel attention module, enabling it to significantly reduce computational demands and model parameters, thereby improving processing speed. The extensive experiments conducted on the dataset and real-world underwater robots demonstrate the exceptional performance and speed of proposed model. It is capable of providing well-enhanced underwater images at a speed 8 times faster than the current state-of-the-art underwater image enhancement method. Moreover, LU2Net is able to handle real-time underwater video enhancement. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

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A Secure Parameter Self-Tuning control Scheme for Vehicle Platoons Under DoS Attacks

A Secure Parameter Self-Tuning Control Scheme for Vehicle Pl...

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IEEE International Conference on Cyber Technology in Automation, control, and intelligent Systems

作者： Qiaoni Han Guowen Cheng Zhenning Tang Chang Liu Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin University Tianjin China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Center for Biomedical Engineering School of Information Science and Technology Fudan University Shanghai China

In order to mitigate the adverse impact of denial of service (DoS) attacks, this work designs a parameter self-tuning controller based on fuzzy rules for vehicle platoons with vehicle-to-vehicle (V2V) communications. Aiming at achieving the cooperative adaptive cruise control (CACC) of the vehicles with a required inter-vehicle distance and the same velocity, while considering the possible DoS attacks, we propose a parameter self-tuning longitudinal controller combining linear control and fuzzy control to enhance the stability and resilience of the platoon systems. Firstly, by analyzing the dynamic characteristics of vehicles and the mechanism of DoS attacks, we establish the dynamic model of vehicle platoons and model the impact of random DoS attacks as a constant data transmission delay, whose probability distribution can be defined using the Bernoulli random variable. Then, a linear controller based on fuzzy control is designed for longitudinal control to guarantee that the platoon keeps a reference velocity and the specified inter-vehicle distance under DoS attacks. Finally, thorough simulations are performed and the detailed results show the performance of the secure longitudinal control scheme for vehicle platoons in terms of stability and security.

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Digital-Twin-Based Clock Synchronization in Industrial IoT Systems

Digital-Twin-Based Clock Synchronization in Industrial IoT S...

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IEEE International Conference on Cyber Technology in Automation, control, and intelligent Systems

作者： Ting Cheng Shuai Liu Qimin Xu Shanying Zhu Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China School of Control Science and Engineering Shandong University Jinan China

Close and efficient cooperation between devices in the Industrial Internet of Things (IIoT) requires precise clock synchronization as a prerequisite. The uncertainty of IIoT networks and the complexity of industrial environments have led to less precise clock synchronization achieved by traditional algorithms and higher network resource consumption. This paper proposes a digital-twin-based clock synchronization scheme for IIoT systems. By building the digital model for each local clock to predict their clock skew, the information interaction required to achieve synchronization is reduced. In order to model the clock accurately, this paper proposes to use the extended state Kalman filter to handle uncertain dynamics and unknown measurement bias to improve the estimation accuracy of clock skew further. The simulation results show that the proposed algorithm can achieve high- precision clock synchronization while reducing the consumption of network resources.

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Adaptive Multi-Core Real- Time Scheduling Based on Reinforcement Learning

Adaptive Multi-Core Real- Time Scheduling Based on Reinforce...

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International Conference on control and Automation (ICCA)

作者： Yonghui Liang Hui Li Fei Shen Qimin Xu Shuna Hua Shanying Zhu Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Re-search Center of Intelligent Control and Management Shanghai China Key Laboratory of Wireless Sensor Network and Communications Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China North Information Control Research Academy Group Company Ltd. Nanjing China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

With the transformation towards industrial intel-ligence, multi-core processors are increasingly being applied in real-time networked control systems to ensure secure exe-cution of sensing, computing and actuating tasks under time constraints. However, existing scheduling methods result in either low CPU utilization or many missed task deadlines in dynamic systems. In this paper, we propose a two-layer scheduling architecture to address this issue by fully exploring the complex dependency between real-time tasks. To be specific, the local layer determines task execution priorities considering both dependency between tasks and deadline constraints by utilizing a reinforcement learning approach. Moreover, to better utilize the parallel capabilities of multi-core processors and reduce temporal collisions, this paper minimizes the requested core count for the task set based on a greedy strategy. The global layer designs a scheduling algorithm based on the pre-empt method and provides schedulability analysis of multiple task sets. Experimental results validate the correctness of the proposed scheduling approach, and efficiency is demonstrated through comparisons with baseline method.

关键词： Job shop scheduling Multicore processing Scheduling algorithms Networked control systems Reinforcement learning Real-time systems Sensors

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Two-Archive-Based Competitive Swarm Optimizer for Constrained Multiobjective Optimization

Two-Archive-Based Competitive Swarm Optimizer for Constraine...

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Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Lianghao Li Cheng He Jianqing Lin Linqiang Pan Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China

Constrained multiobjective optimization problems (CMOPs) are prevalent in various real-world applications, presenting a formidable challenge to existing evolutionary algorithms when faced with intricate constraints. When solving CMOPs, a crucial concern is achieving a balance between convergence, diversity, and feasibility. To address these challenges, this paper proposes a two-archive-based constrained multiobjective competitive swarm optimizer. The algorithm preserves two collaborative and complementary archives to improve population convergence and feasibility. By implementing a competitive particle swarm mechanism, offspring solutions are generated by drawing upon solutions from both archives, thus capitalizing on their synergistic effect and exceptional information. An adaptive parameter is also used to adjust the bias in choosing the winner in the paired competition during the evolution. The experimental results demonstrate the effectiveness of the proposed algorithm in handling constrained multiobjective optimization problems.

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Research on the detection method of alignment beacon based on the spatial structure feature of train bogies

Research on the detection method of alignment beacon based o...

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2022 Chinese Automation Congress, CAC 2022

作者： Cao, Chong Wan, Shaowei Peng, Gang He, Dingxin Zhong, Sheng Deng, Zhang Guo, Wentao School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China Beijing Railway Institute of Mechanical & Electrical Engineering Group Co. Ltd. Beijing100071 China Wuhan Lisai Technology Co. Ltd. Wuhan430073 China

ISBN: (纸本)9781665465335

The high-speed train inspection robot needs to perform alignment parking and image acquisition at the position of the train bogie, and reliable alignment identification marks are the basis for the inspection robot to achieve highprecision alignment parking. The existing alignment method is mainly based on the way of artificial beacons, in the inspection workshop through manual laying of additional identification marks such as QR codes, magnetic stripes, RFID electronic tags, auxiliary robot alignment parking, but this type of method in beacon maintenance, laying costs and other aspects have certain limitations. Aiming at the problem, this paper proposes a vision-based bogie beacon detection method, which combines the target detection technology with the unique bogie spatial structure feature of the train, and provides reliable identification marks for the alignment and parking of the inspection robot. This method improves the alignment accuracy, reduces the cost of laying and maintaining artificial beacons, and has high application value. © 2022 IEEE.

关键词： Railroads

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Interactive Multi-scale Fusion of 2D and 3D Features for Multi-object Tracking

arXiv

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arXiv 2022年

作者： Wang, Guangming Peng, Chensheng Zhang, Jinpeng Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai 200240 China XLAB The Second Academy of China Aerospace Science And Industry Corporation Beijing100854 China

Multiple object tracking (MOT) is a significant task in achieving autonomous driving. Traditional works attempt to complete this task, either based on point clouds (PC) collected by LiDAR, or based on images captured from cameras. However, relying on one single sensor is not robust enough, because it might fail during the tracking process. On the other hand, feature fusion from multiple modalities contributes to the improvement of accuracy. As a result, new techniques based on different sensors integrating features from multiple modalities are being developed. Texture information from RGB cameras and 3D structure information from Lidar have respective advantages under different circumstances. However, it’s not easy to achieve effective feature fusion because of completely distinct information modalities. Previous fusion methods usually fuse the top-level features after the backbones extract the features from different modalities. In this paper, we first introduce PointNet++ to obtain multi-scale deep representations of point cloud to make it adaptive to our proposed Interactive Feature Fusion between multi-scale features of images and point clouds. Specifically, through multi-scale interactive query and fusion between pixel-level and point-level features, our method, can obtain more distinguishing features to improve the performance of multiple object tracking. Besides, we explore the effectiveness of pre-training on each single modality and fine-tuning on the fusion-based model. The experimental results demonstrate that our method can achieve good performance on the KITTI benchmark and outperform other approaches without using multi-scale feature fusion. Moreover, the ablation studies indicates the effectiveness of multi-scale feature fusion and pre-training on single modality. Copyright © 2022, The Authors. All rights reserved.

关键词： Computer vision

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A Visual Kinematics Calibration Method for Manipulator Based on Nonlinear Optimization

A Visual Kinematics Calibration Method for Manipulator Based...

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2022 Chinese Automation Congress, CAC 2022

作者： Peng, Gang Wang, Zhihao Guan, Shangbin Deng, Zhang Guo, Wentao Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Beijing Railway Institute of Mechanical & Electrical Engineering Group Co. Ltd. Beijing China Wuhan Lisai Technology Co. Ltd. Wuhan China

ISBN: (纸本)9781665465335

The traditional kinematic calibration method for manipulators requires precise three-dimensional measuring instruments to measure the end pose, which is not only expensive due to the high cost of the measuring instruments but also not applicable to all manipulators. Another calibration method uses a camera, but the system error caused by the camera's parameters affects the calibration accuracy of the kinematics of the robot arm. In order to solve this problem, this paper reduces the dimensionality of the experimental data and redesigns the objective function to be optimized. That is, the three-dimensional positions of the feature points of the calibration plate under each manipulator attitude corresponding to the actual kinematic model and the classic D-H kinematic model are mapped into the pixel coordinate system, and the sum of Euclidean distance errors of the pixel coordinates of the two is used as the objective function to be optimized. And then, nonlinear optimization and parameter compensation are performed. The experimental results show that the pixel deviation of the end pose corresponding to the optimized D-H kinematic model proposed in this paper and the end pose corresponding to the actual kinematic model in the pixel coordinate system is 0.99 pixels. Compared with the 7.9 deviation pixels between the pixel coordinates calculated by the classic D-H kinematic model and the actual pixel coordinates, the deviation is reduced by nearly 7 pixels for an 87% reduction in error. Therefore, the proposed method can effectively avoid system errors caused by camera parameters in visual calibration, can improve the absolute positioning accuracy of the end of the robotic arm, and has good economy and universality. © 2022 IEEE.

关键词： Pixels

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A Novel Optimization Method for Raw Coal Blending in Coal Preparation Plants Based on Interval Programming

A Novel Optimization Method for Raw Coal Blending in Coal Pr...

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Chinese Automation Congress (CAC)

作者： Jiahui Lei Hanwen Liu Jun Zhao Wei Wang Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China CHN Energy Wuda Coal Processing Co. Ltd. Wuhai China

In the process of coal blending and washing in coal preparation plants, there are various raw coals with different quality that changes greatly, which results in poor quality and low profit. To solve these problems, a novel optimization method for raw coal blending based on interval programming is proposed. Interval numbers are introduced to represent the fluctuation range of ash and sulfur content at each density level of raw coals. A predictive model of quantity and quality of dense medium cyclone products is constructed, which uses Hermite interpolation to obtain washability curves of each raw coal and adopts fitting methods to obtain the distribution curve. In order to obtain optimal coal blending schemes, considering operation and maintenance costs of raw coal feeding line, a mixed integer nonlinear programming (MINLP) model is built, whose objective is to maximize profit with the main constraints of clean coal quality and inventory of raw coal. Interval possibility is introduced to transform constraints with interval number and a customized branch and bound algorithm is used to solve it. In order to verify the effectiveness of the method in this paper, experiments are conducted using manufacturing data from a domestic coal preparation plant. The experimental results indicate that the proposed method is practical and has better economic performance.

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