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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Lv, Qincheng Liu, Xiaofeng Li, Jie Ni, Rongrong Xue, Pujun Song, Siyang College of Information Science and Engineering Hohai University Changzhou China College of Artificial Intelligence and Automation Hohai University Changzhou China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China School of Microelectronics and Control Engineering Changzhou University Changzhou China School of Computer Science University of Exeter Exeter United Kingdom

ISBN: (纸本)9798350368741

Facial reactions convey crucial emotional information and coordinating interpersonal relationships in human dyadic interactions. While existing Multiple Appropriate Facial Reaction Generation (MAFRG) methods focus on generating multiple reasonable facial reactions, none of these approaches combines 2D and 3D facial behaviour information nor account for the influence of individuals' facial identities, leading to inconsistencies in the generated facial reactions and limited capability in capturing subtle variations in facial depth and expression dynamics. This paper proposes a novel Hierarchical Multimodal Decoupling-Fusion (HMDF) framework that decouples 3D facial identity from expression behaviors, eliminating identity-based interference in the reaction generation process, which are integrated with audio-visual features through a cross-attention mechanism. Experiments show that our framework achieved the enhanced diversity and synchrony in the generated facial reactions. © 2025 IEEE.

关键词： 2D 3D facial behaviour fusion Facial identity decoupling Multiple Appropriate Facial Reaction Generation

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Fuzzy-Model-Based Fault-Tolerant control for Stochastic Re-Entrant Manufacturing Systems

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3542-3556页

作者： Zhang, Kexin Gao, Qing Ding, Steven X. Lu, Jinhu Qiu, Jianbin Guo, Yige Beihang University School of Cyber Science and Technology Beijing100191 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Hangzhou Innovation Institute Beihang University Hangzhou310051 China Zhongguancun Laboratory Beijing100095 China Institute for Automatic Control and Complex Systems University of Duisburg-Essen Duisburg47057 Germany Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150001 China Zhongguancun Laboratory Beijing China

This study addresses the problem of guaranteed cost fault-tolerant fuzzy control for multiline re-entrant manufacturing systems (RMSs) against stochastic disturbances and workstation faults. Initially, a nonlinear hyperbolic impulsive partial differential equation model is employed to describe the complex and hybrid dynamics of RMSs suffering from unexpected faults within the working stations, and then the corresponding approximation T-S fuzzy model is constructed. In what follows, with the aid of the parallel distributed compensation fuzzy control scheme, the main results of stability analysis and controller synthesis for the closed-loop re-entrant manufacturing control system are derived using a timer-dependent Lyapunov functional with spatio-temporal auxiliary variables. It is found that by means of the proposed fault-tolerant control approach, the RMS can be effectively and robustly driven to a desired production mode with steady feeding and production rates while the upper bound of a quadratic cost function is minimized. Finally, the effectiveness of the proposed control approach is validated through numerical simulations. © 2013 IEEE.

关键词： Fuzzy control

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A Spiral Coverage Path Planning Algorithm for Nonomnidirectional Robots

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Journal of Field Robotics 2025年第0期

作者： Hou, Taogang Li, Jiaxin Pei, Xuan Wang, Hao Liu, Tianhui Institute of Advanced Control Systems School of Automation and Intelligence Beijing Jiaotong University Beijing Beijing China Institute of Robotics School of Mechanical Engineering and Automation Beihang University Beijing Beijing China

The limited steering capabilities of nonomnidirectional robots introduce significant complexity into complete coverage tasks, often leading to increased path overlap or incomplete coverage of certain areas. Although recent research has made progress in optimizing coverage path planning, redundant coverage or omissions are still prone to occur in the target area to be covered. To address these persistent challenges, we propose a novel spiral coverage method. This approach not only conforms to the kinematic constraints of nonomnidirectional robots but also enhances coverage efficiency by dividing the target area into center and boundary regions and devising tailored coverage strategies for each. This method effectively reduces path redundancy and improves overall area coverage. Furthermore, we introduce a comprehensive metric that combines total path length and area coverage ratio to evaluate coverage efficiency, overcoming the limitations and computational complexity associated with existing metrics. For scenarios where maximizing the area coverage ratio is critical, we have developed a high-coverage-rate turning strategy that ensures 100% coverage. Through simulation tests in six representative areas and actual experiments on airport runways, our method shows an improvement of 0.238%–14.538% in coverage efficiency compared with parallel coverage method and 60.548%–76.339% compared with deep reinforcement learning-based method. Additionally, implementing high-coverage-rate turning strategies improves the area coverage ratio by 2.021%–6.732%. In field experiments, our method reduces execution time by 1.61% compared with parallel coverage method. These results show that our method has a significant effect in improving coverage efficiency and achieving complete coverage goals. © 2025 Wiley Periodicals LLC.

关键词： Robot programming

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RepColor: deep coloring algorithm combining semantic categories

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Optoelectronics Letters 2025年

作者： Lei Sun Dongjie Ju School of Electrical Engineering and Automation， Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Complicated Industry Systems， Tianjin University of Technology

Image coloring is an inherently uncertain and multimodal problem. By inputting a grayscale image into a coloring network， visually plausible colored photos can be generated. Conventional methods primarily rely on semantic information for image colorization. Although effective in coloring images with clear semantic information， these methods still suffer from color contamination and semantic confusion. This is largely due to the limited capacity of convolutional neural networks to effectively learn deep semantic information inherent in *** this paper， we propose a network structure that addresses these limitations by leveraging multi-level semantic information classification and fusion. Additionally， we introduce a global semantic fusion network to combat the issues of color contamination. The proposed coloring encoder accurately extracts object-level semantic information from *** further enhance visual plausibility， we employ a self-supervised adversarial training method. We train the network structure on various datasets with varying amounts of data and evaluate its performance using the ImageNet validation set and COCO validation set. Experimental results demonstrate that our proposed RepColor can generate more realistic images compared to previous approaches， showcasing its high generalization ability.

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Unmanned Air-Ground Vehicles Path Planning Using Model Predictive control

Unmanned Air-Ground Vehicles Path Planning Using Model Predi...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Gu, Yuanwen Wang, Mi Wu, Huaining Li, Chenlong Li, Gang School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Hangzhou International Innovation Institute Beihang University Hangzhou311115 China Hangzhou Innovation Institute Beihang University Hangzhou310051 China Department of Control Science and Engineering Tongji University Shanghai201800 China

ISBN: (纸本)9789819639649

In this article, the problem of air-ground vehicles path planning is investigated. The objective is to navigate each vehicle from its starting position to the intended destination, while avoiding collisions with obstacles and other vehicles. First, by combining the objective functions of unmanned ground vehicle (UGV) and unmanned aerial vehicles (UAVs) with the constraints of system equations, control constraints and obstacle avoidance constraints, the vehicles’ path planning is formulated as multiple model predictive control (MPC) problems. Second, considering the interdependencies inherent in these optimization problems, we employ an assumed state method to decouple them effectively. Third, the control inputs of the UGV and UAVs are obtained by solving quadratic programming problems and thus the vehicles’ path can be obtained. Finally, a simulation example is shown to illustrate the effectiveness of the proposed path planning method. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Predictive control systems

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Based on an improved Zero-DCE++ low-light enhanced object detection algorithm

Based on an improved Zero-DCE++ low-light enhanced object de...

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2024 International Conference on Mechatronic engineering and Artificial Intelligence, MEAI 2024

作者： Bai, Xuecheng Gao, Hongwei Zhao, Zhanpeng Yan, Zhiwen School of Automation and Electrical Engineering Shenyang Ligong University Liaoning Shenyang110159 China Institute of International Engineering Shenyang Ligong University Liaoning Shenyang110159 China China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110159 China Xi'an Modern Control Technology Research Institute Xi'an710100 China

ISBN: (纸本)9781510689176

Low-light image quality often suffers from noise, color distortion, and reduced contrast, challenging accurate object detection. Traditional enhancement methods can lead to over-processing and semantic information loss. This study presents an enhanced low-light image detection model based on an improved Zero-DCE++ algorithm, integrated with YOLOv8 for high-speed, accurate detection. Key improvements include the CBAM attention mechanism to enhance feature extraction in low-light areas, DCNv2 for capturing complex object details, and a Local Region Brightness Curve Enhancement Module to prevent overexposure in well-lit areas. The proposed model was tested on the Night Drone dataset, where it achieved superior performance in image quality and detection accuracy compared to state-of-the-art methods. This approach demonstrates significant potential for applications requiring reliable object detection in challenging low-light conditions. © 2025 SPIE.

关键词： Semantic Segmentation

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Haptic-Aided Navigation Vehicle: Enhancing Obstacle Detection in Blind Spots and Transparent Object Scenarios

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Journal of Shanghai Jiaotong University (Science) 2025年 1-9页

作者： Li, Mingwang Li, Xinde Zhang, Zhentong Zhang, Zeyu Zhao, Haoming School of Software Southeast University Suzhou Jiangsu215000 China School of Automation Southeast University Nanjing210018 China School of Cyber Science and Engineering Southeast University Nanjing211102 China Key Laboratory of Measurement and Control of Complex Systems of Engineering of Ministry of Education Nanjing214135 China Nanjing Center for Applied Mathematics Nanjing211135 China Southeast University Shenzhen Research Institute Shenzhen Guangdong518063 China

As autonomous mobile robots are increasingly deployed in complex environments, traditional vision sensors and LiDAR encounter considerable limitations, particularly in detecting obstacles in blind spots or transparent objects. To address the issue of blind spots, we design a specialized haptic sensing structure and develop the haptic-aided navigation vehicle (HANV). This system integrates haptic sensors and LiDAR to deliver comprehensive perception, significantly enhancing close-range obstacle detection in areas that are typically beyond the range of conventional sensors. To tackle the challenge of transparent obstacles, which are often undetected by both vision and LiDAR sensors, we employ a fusion of haptic sensors and LiDAR. The haptic system provides physical contact feedback, ensuring reliable detection of transparent obstacles such as glass, while LiDAR offers long-range sensing capabilities. This combination enables HANV to navigate effectively in environments with transparent obstacles, overcoming the limitations of traditional sensing systems. Experiment results indicate that the proposed haptic and LiDAR integration substantially improves obstacle detection in both blind spots and environments with transparent obstacles. HANV achieves high success rates, minimal collisions, and efficient obstacle avoidance, particularly excelling in complex, confined spaces where conventional systems prove inadequate. These findings emphasize the efficacy of our approach in enhancing navigation performance in dynamic and challenging environments. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Mobile robots

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Small-Signal Stability Analysis of Flux-controlled Grid-Forming DFIG Under Symmetrical Fault

Small-Signal Stability Analysis of Flux-Controlled Grid-Form...

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International Conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Li, Weibing Wang, Wenhe Hu, Yufei Li, Junxian Hua, Xiaojun Jiang, Dingguo Huang, Meng Tian, Zhen Laboratory of Hydro-Wind-Solar Multi-Energy Control Coordination Wuhan430014 China School of Electrical Engineering and Automation Wuhan University Wuhan430072 China Institute of Science and Technology China Three Gorges Corporation Beijing100038 China

ISBN: (纸本)9789819620791

Due to the dislocation distribution of wind resources and load centers in China, large-scale wind power stations usually need long-distance transmission to connect with the load center, which leads to the weakening of the power grid strength at the access point of wind power stations and frequent short-circuit faults. In this paper, a small signal state space model of flux-controlled grid-forming DFIG grid-connected system is constructed for the continuous condition of symmetrical fault in weak grid scenario, and the oscillation mode analysis of the system is carried out. Based on the fault ride-through requirements and electromagnetic equations of wind turbines, the calculation method of the balanced operating point of DFIG during symmetrical fault duration is derived. Then, based on the system characteristic root trajectory and the participation factor matrix, the small-disturbance stability of the system under different fault conditions and converter control parameters is analyzed, and the influence of each dynamic link on the small-disturbance stability of the system is explored. Finally, the accuracy of the theoretical analysis results in this paper is verified by simulation analysis and comparison, which provides guidance for parameter optimization of DFIG during fault duration. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Wind turbines

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Application of Particle Swarm Optimization Algorithm Based on Adaptive Inertia Adjustment Strategy in Scheduling of the Coking Process 26

Application of Particle Swarm Optimization Algorithm Based o...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Chen, Yiheng Yin, Fan Chen, Xiaochong Ren, Yi Hu, Jie Wu, Min School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9798331521950

The sccheduling for pushing plan during the coking process critically affects the efficiency and stability of production. However, the complexity with mutiple-stage during production makes it difficult to design an efficient coke pushing plan. To address this issue, this paper proposes a scheduling method based on the particle swarm optimization algorithm. Firstly, the fuzzy c-means clustering is utilized to categorize actual operating conditions as either normal or abnormal, thereby facilitating the scheduling of pushing plan under disparate conditions. Subsequently, the scheduling problem for the pushing plan is transformed into a traveling salesman problem, and scheduling models under various conditions are established. Finally, to accelerate the convergence and enhance the algorithm's global search capability, an adaptive inertia adjustment strategy is employed to dynamically regulate the velocity and position of particles. The proposed method has been implemented in the coking process. Through the analysis of application results, the completion coefficient of pushing plan has been increased by 4.25%, demonstrating that the proposed has advantages in scheduling the pushing plan during the actual coking process. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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Maritime Target Saliency Detection for UAV Based on Raptor Vision Perception Enhancement Mechanism

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IEEE Aerospace and Electronic Systems Magazine 2025年

作者： Liu, Yuhang Wei, Chen Duan, Haibin Deng, Yimin Beihang University National Key Laboratory of Aircraft Integrated Flight Control School of Automation Science and Electrical Engineering Beijing100083 China

Inspired by the enhanced perception mechanism of raptor vision in various hunting environments, we propose a biological raptor vision perception enhancement algorithm for Unmanned Aerial Vehicles (UAVs) to detect medium-distance maritime targets in complex and dynamic ocean environments. Firstly, we summarize the various perception enhancement capabilities based on the physiological structure and functional characteristics of the raptor visual system. Secondly, we establish mathematical models for light perception enhancement, foreground perception enhancement, contrast perception enhancement, and perception balance based on the raptor vision's enhanced perception mechanism. Finally, our proposed biological raptor vision perception enhancement method is experimentally tested in different scenarios and compared with seven other algorithms. The results of the comparative experiments and relevant evaluation indicators show that the algorithm effectively retains the feature information of maritime targets while suppressing interference from the environmental background. This maritime target saliency detection algorithm can be applied to the vision system of sea UAVs, providing a feasible solution for the medium-distance recovery of UAVs in complex and dynamic ocean environments. © 1986-2012 IEEE.

关键词： Aircraft detection

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