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SSRN 2024年

作者： Li, Yaqian Han, Jiaqi Li, Haibin Zhang, Wenming College of Electrical Engineering Yanshan University Hebei Qinhuangdao066000 China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Hebei Qinhuangdao066000 China

The pantograph is the key structure in the railway Pantograph-OCS system, acting as the switch for the railway traction power supply system. So it’s crucial to detect the pantograph's lifting and lowing states for production efficiently and safely. However, due to the pantograph’s special slender structure and complex application scenarios, it occupies few pixels and is susceptible to background interference, making accurate real-time detection a challenging task. In this work, we aim to propose a generalized model, designed for detecting, branch-like objects with thin structure and multi-scale size such as pantographs. To this end, we propose the Branch-YOLO based on YOLOv8, which is improved in two stages, feature extraction and feature fusion, to simultaneously enhance perception. First, we introduce BrLayer is consist of BranchConv and EFAC(Extend receptive field attention Convolution), BranchConv can adaptively capture features of thin and tortuous local structures,EFAC is able to assist in expanding the effective receptive field. Subsequently, we propose a feature fusion network (BranchNet) to integrate multilevel semantic information and multi-scale features, which significantly reduces background interference in detecting slender structure objects and enhances the ability to perceive changes in object scale. Besides, we propose the Lightweight Cross-Kernel Convolution layer (LckLayer) and introduce the Feature Decomposition module (FdM) in BranchNet for lightweight design, reducing the computational overhead and enhancing model efficiency. Our proposed Branch-YOLO not only achieves the best performance on the multi-scale pantograph datasets but also attains an outstanding 42.9% AP on the COCO val2017 datasets, with 5.9M parameters and 16.8 GFLOPs. © 2024, The Authors. All rights reserved.

关键词： Religious buildings

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Trajectory Optimization of Manipulator for Loading and Unloading the Ladle Nozzle Based on Decomposition Multi-Objective Improved Whale Optimization Algorithm 13

Trajectory Optimization of Manipulator for Loading and Unloa...

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13th IEEE International Conference on CYBER Technology in Automation, control, and Intelligent Systems, CYBER 2023

作者： Zhao, Dongliang Fang, Yiming Yang, Chen Xin, Jingliang Yanshan University Key Lab of Industrial Computer Control Engineering Hebei Province Qinhuangdao China Eng. Res. Center of the Education Ministry for Intelligent Control System and Intelligent Equipment Yanshan University Hebei Province Qinhuangdao China Hebei Iron and Steel Group Hebei Province Tangshan China

ISBN: (纸本)9798350315196

In recent years, industrial manipulators are widely used in factories. To solve the problems of low efficiency and high joint jitter in ladle nozzle loading and unloading, a new trajectory optimization method is proposed. Firstly, a manipulator model is established on the base of mechanical construction and parameters of KR1000L750. And the 3-5-3 segmented polynomial interpolation approach is used to design the trajectory. Then a trajectory optimization model is proposed based on time and impact as optimization objectives. Secondly, the traditional whale optimization algorithm is improved by introducing a nonlinear adaptive convergence factor to balance the exploration and development of the algorithm. The improved algorithm has also added a new search mechanism during the exploration phase. To reduce the waste of exploration resources, the information on the current optimal solution is added by the Cauchy mutation method. Then, a decomposition-based multi-objective improved whale optimization algorithm (DMOIWOA) is proposed. The algorithm combines a penalty-based boundary intersection approach with the improved whale optimization algorithm. Six representative optimization algorithms are selected for comparison on the test functions. The DMOIWOA has evident superiority in convergence and distribution. Finally, the algorithm is applied to the trajectory optimization model to obtain a better Pareto front. In conclusion, the DMOIWOA can reasonably optimize the manipulator trajectory, improving the manipulator operation efficiency and reducing the manipulator jitter. © 2023 IEEE.

关键词： Trajectories

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Two-Player Multiagent Graphical Games with Reinforcement Learning*

Two-Player Multiagent Graphical Games with Reinforcement Lea...

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industrial Cyber-Physical Systems (ICPS)

作者： Bosen Lian Jiacheng Wu Electrical and Computer Engineering Department Auburn University Auburn AL USA State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (数字)9798350363012

ISBN: (纸本)9798350363029

This paper studies the synchronization problem of two-player multiagent systems through reinforcement learning methods. A Nash-minmax strategy is formulated, where the interactions of two players in the same agent are non-zero-sum, while interactions of players between agents are zero-sum games. We propose an offline model-based reinforcement learning algorithm to identify Nash solutions for players within each agent, as well as the worst control solutions for players in neighboring antagonistic agents. On this basis, a data-driven off-policy algorithm is provided to alleviate the requirement for accurate system dynamics in the offline algorithm. Besides, the convergence of the proposed algorithms is analyzed. Finally, simulation results verify the effectiveness of the designed algorithms.

关键词： System dynamics Heuristic algorithms Simulation Games Reinforcement learning Cyber-physical systems Synchronization

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Distributed Bilevel Optimization via Adaptive Penalization with Time-Scale Separation

arXiv

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arXiv 2024年

作者： Niu, Youcheng Xu, Jinming Sun, Ying Chai, Li Chen, Jiming State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Zhejiang University Hangzhou310027 China School of Electrical Engineering and Computer Science Pennsylvania State University PA16802 United States

This paper studies a class of distributed bilevel optimization (DBO) problems with a coupled inner-level subproblem. Existing approaches typically rely on hypergradient estimations involving computationally expensive Hessian information. To address this, we propose an equivalent constrained reformulation by treating the inner-level subproblem as an inequality constraint, and introduce an adaptive penalty function to properly penalize both inequality and consensus constraints based on subproblem properties. Moreover, we propose a loopless distributed algorithm, AHEAD, that employs multiple-timescale updates to solve each subproblem asymptotically without requiring Hessian information. Theoretically, we establish convergence κ4 rates of O((1−ρ)2K1/3 ) for nonconvex-strongly-convex cases and κ2 O((1−ρ)2K2/3 ) for distributed min-max problems. Our analysis shows the clear dependence of convergence performance on bilevel heterogeneity, the adaptive penalty parameter, and network connectivity, with a weaker assumption on heterogeneity requiring only bounded first-order heterogeneity at the optimum. Numerical experiments validate our theoretical findings. Copyright © 2024, The Authors. All rights reserved.

关键词： Consensus algorithm

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Improved Lightweight YOLOv5 Based on GhostNet and Its Application in Target Board Detection for Ladle Nozzle Positioning

Improved Lightweight YOLOv5 Based on GhostNet and Its Applic...

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第43届中国控制会议

作者： Fengfei Si Yiming Fang Hao Ju Dongliang Zhao Jingliang Xin Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Engineering Research Center of the Education Ministry for Intelligent Control System and Intelligent Equipment Yanshan University Tangshan Branch HBIS Company Limited

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The detection of the target board of ladle nozzle positioning is the basis of automatic loading and unloading of ladle nozzle by ladle robot in continuous casting. Due to the complex situation of high temperature, dust, backlight and so on, the target board of ladle nozzle positioning is prone to false detection or low detection accuracy, and it is difficult to take into account the contradiction between rapidity and detection accuracy. Therefore, this paper proposes an improved lightweight algorithm GVP-YOLOv5(YOLOv5 with GhostNet, VoVGSCSP and Pruning), which takes into account both detection accuracy and rapidity, to detect the target board for ladle nozzle positioning. Firstly, Ghost module is used to improve the backbone network of YOLOv5 as the backbone feature extraction network to reduce the number of parameters and calculation of the backbone. Then in the neck, the GSConv module is used to design a thin neck structure, which can ensure the multi-scale feature fusion capability and further reduce the complexity of the network. Finally, the model pruning technology is used to prune the whole model, and the channels with small scaling factor are removed to compress the model to the greatest extent, so as to meet the requirements of industrial field application deployment. The experimental results show that the lightweight GVP-YOLOv5 algorithm proposed in this paper can complete the real-time detection of the target board in complex industrial scenarios. Compared with the original YOLOv5s algorithm, the number of parameters in the model is reduced by 82.6%, and the size of the model is significantly reduced to 19.4%. At the same time, the detection accuracy is maintained at more than 95% on mAP@50:95 and the detection speed is effectively improved.

关键词： Target board detection Ladle nozzle positioning YOLOv5 lightweight GhostNet thin neck structure channel pruning

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Exact Algorithm for Batch Scheduling with Many-to-Many Job-Tool Matching and Job Release Time Constraints

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IEEE Transactions on Automation Science and engineering 2025年 22卷 16499-16511页

作者： Li, Shengchao Liu, Shixin Zhao, Ziyan Zhou, Mengchu Northeastern University State Key Laboratory of Synthetical Automation for Process Industries College of Information Science and Engineering Shenyang 110819 China Northeastern University at Qinhuangdao Hebei Key Laboratory of Industrial Software for Advanced Planning and Scheduling of Complex Industrial Processes School of Control Engineering Qinhuangdao 066004 China New Jersey Institute of Technology Department of Electrical and Computer Engineering Newark 07102 NJ United States

Job-dependent tool switching is necessary in many batch processing systems (BPSs). Heterogeneous tool demand and extra time consumption for tool switch bring great challenge for high-performance production scheduling in BPSs. In this study, we present a novel batch scheduling problem derived from a satellite vibration test system. Different from basic scheduling problems in BPSs with tool switching, it has complications caused by many-to-many task-tool matching and job release time constraints. Specifically, each job has predetermined candidate tools. One of them should be selected and equipped to process a given job. Unlike sequence-dependent setup time that is only related to two adjacent jobs, tool switching time is additionally dependent on the tools selected by both jobs from their respective candidate tools. Both job sequence and tool selections need to be determined to minimize makespan. Mixed-integer linear programming (MILP) and constraint programming (CP) models are developed for formulating the problem. We prove that the problem is NP hard. To obtain exact solutions, we propose a novel branch-and-bound algorithm (B&B) based on the analysis and extraction of problem properties. It integrates a domain reduction mechanism, branching strategies, and dominance rules. Among them, the domain reduction mechanism reduces the number of jobs to be scheduled;the branching strategies accelerate solving speed;and the dominance rules save the memory consumption. Ablation studies and key influencing factor experiments are performed. The results show that the proposed B&B can optimally solve much larger instances than state-of-the-art methods (including some heuristics). It can optimally solve industrial-size problems in a short time, implying that it can well satisfy the performance demand of a satellite vibration test system and thus advancing the filed of optimal scheduling for BPSs. © 2004-2012 IEEE.

关键词： Batch scheduling branch-and-bound algorithm heterogenerous tool demand mixed-integer linear programming satellite vibration test system

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MPC-Based Trajectory Tracking control Strategy Incorporating Autonomous Lane-Changing Mechanism for Intelligent Vehicles

MPC-Based Trajectory Tracking Control Strategy Incorporating...

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Conference on Vehicle control and Intelligence (CVCI)

作者： Dongxue Zhang Huiwen Liu Xiaohong Jiao Ting Zhang Hebei Key Laboratory of Industrial Computer Control Engineering Yanshan University Qinhuangdao Hebei Province China School of Vehicle and Mobility Tsinghua University Beijing China

ISBN: (数字)9798331504892

ISBN: (纸本)9798331504908

Navigating the intricate traffic environment, lane-changing has emerged as a frequent and essential driving maneu-ver for intelligent vehicles. However, it is challenging to guarantee the safety of the intelligent vehicle and the trajectory tracking accuracy against the external environment during lane-changing. Considering these challenges to overcome, the model predictive control (MPC)-based trajectory tracking strategy incorporating an autonomous lane-changing mechanism is proposed in this paper. The autonomous lane-changing mechanism considering lane-changing intention is proposed to ensure that the intelligent vehicle generates safe lane-changing decisions and trajectories. Subsequently, the MPC strategy that considers the physical char-acteristics of the intelligent vehicle is proposed to guarantee the accuracy and smoothness of trajectory tracking. The validation of its effectiveness and advantages is established on the PreScan and MATLAB/Simulink co-simulation platforms.

关键词： Accuracy Intelligent vehicles Trajectory tracking Navigation Vehicle safety Trajectory MATLAB Predictive control

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Bimodal Fusion Network for Basic Taste Sensation Recognition from Electroencephalography and Electromyography

Bimodal Fusion Network for Basic Taste Sensation Recognition...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Han Gao Shuo Zhao Huiyan Li Li Liu You Wang Ruifen Hu Jin Zhang Guang Li State Key Laboratory of Industrial Control Technology Zhejiang University School of Computer and Communication Engineering Changsha University of Science and Technology

Taste sensation can be objectively measured using electroencephalography (EEG) or electromyography (EMG). How-ever, it is still challenging to effectively utilize the complementary information from EEG and EMG signals in taste sensation recognition. This paper proposes a bimodal fusion network (Bi-FusionNet) for recognizing basic taste sensations (sour, sweet, bitter, salty, umami, and blank). Two convolutional backbones with similar structures are designed to separately extract the single-modal features of EEG and EMG. Then, EEG and EMG features are concatenated for bimodal interaction and complementarity. Finally, three loss functions are adopted: a center loss for aggregating intra-class samples, a mean squared error loss for sequence positions for minimizing the difference between signals during the stimulation, and a softmax loss for minimizing the entropy of prediction and true labels. The results on the taste sensation dataset show that bimodal fusion improves recognition performance, and Bi-FusionNet outperforms single-modal methods and other fusion methods. Bi-FusionNet paves the way for the application of multimodal fusion in taste sensation recognition.

关键词： Convolution Speech recognition Brain modeling Feature extraction Electromyography Electroencephalography Entropy

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Global-Local Context Enhanced Yolo for Small Object Detection in Uav Images

SSRN

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SSRN 2025年

作者： Wei, Lixin Han, Kangshuai Li, Xin Che, Haijun Hu, Ziyu Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Hebei Qinhuangdao066000 China Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Hebei Qinhuangdao066000 China

Object detection using Unmanned Aerial Vehicles (UAVs) has emerged as a criti cal application across diverse domains. However, the wide-angle views of drones often result in images containing a high density of small objects, posing chal lenges for object detection such as few learnable features, significant occlusion, and an imbalanced distribution of positive and negative samples. To address these issues, this paper introduces AGLC-YOLO, an enhanced version of the YOLOv7 architecture specifically designed for detecting small objects in UAV images. AGLC-YOLO integrates global and local context information through a Attention guide Global-Local Context Information Extraction (AGLC) module. This module employs parallel dilated convolutions to capture local context infor mation and a transformer-based structure to extract global dependencies, which are then fused using an improved attention mechanism. The network also adds an additional small object detection head to enrich the small object information in the model. Additionally, AGLC-YOLO utilizes an auxiliary bounding box in con junction with the Inner-Wise Intersection over Union (Inner-WIoU) loss function to accelerate the bounding box regression process and improve detection accu racy. Experimental results on the VisDrone and ManipalUav datasets demonstrate that AGLC-YOLO achieves significant improvements over the baseline YOLOv7 model, with anincrease of 3%inAP50and2.7%inAP95ontheVisDronedataset, and 1.9% in AP50 and 2% in AP95 on the ManipalUav dataset. © 2025, The Authors. All rights reserved.

关键词： Convolution

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