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In Memory of Wolter J. Fabrycky: A Pioneer of systems engineering and US-Sino Academic Exchange

IEEE/CAA Journal of Automatica Sinica 2025年第5期12卷 839-840页

作者： Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China

The passing of Professor Wolter “Wolt” Fabrycky, an outstanding member and great leader, is a big loss to our international systems engineering professional community. “Wolt was a legend in the systems engineering community with his teaching, advising, and dissemination of knowledge through the books he authored.”, as stated by Professor Eileen Aken, a former student of Wolt and the head of the Virginia Tech's Grado Department of Industrial and systems Engineeirng where Wolt had served and led for 30 years and retired as John L. Lawrence Professor emeritus.

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Efficient Facial Expression Recognition Network Based on Spot-Adaptive Knowledge Distillation 26

Efficient Facial Expression Recognition Network Based on Spo...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Wen, Xin Liu, Zhen-Tao Chen, Dan School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9798331521950

Facial expression recognition plays a key role in promoting the development of comprehensive intelligence and building friendly human-computer interaction. Due to the interference of feature noise in expression data, the lightweight facial expression recognition model with fewer parameters is difficult to learn more expression features through simple training, which limits the improvement of its recognition performance. An efficient facial expression recognition network based on Spot-adaptive Knowledge Distillation is proposed in this paper. Inspired by VoVNetV2, the network designed in this paper is lightweightly improved using Depthwise Separable Convolution and the parameter-free SimAM attention mechanism, reducing the number of parameters to 0.21 M. To further improve the recognition accuracy of the model, Spot-adaptive Knowledge Distillation is employed to improve the characterization ability of the model. The recognition accuracies of the student network designed in this paper on the KDEF and RAF-DB datasets are 93.05% and 81.17% respectively after spot-adaptive distillation. © 2025 IEEE.

关键词： Facial Expression Recognition Knowledge Distillation Lightweight Neural Network

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Improving Collision-Free Success Rate For Object Goal Visual Navigation Via Two-Stage Training With Collision Prediction

arXiv

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arXiv 2025年

作者： Lian, Shiwei Zhang, Feitian Robotics and Control Laboratory Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory of Turbulence and Complex Systems Peking University Beijing100871 China

The object goal visual navigation is the task of navigating to a specific target object using egocentric visual observations. Recent end-to-end navigation models based on deep reinforcement learning have achieved remarkable performance in finding and reaching target objects. However, the collision problem of these models during navigation remains unresolved, since the collision is typically neglected when evaluating the success. Although incorporating a negative reward for collision during training appears straightforward, it results in a more conservative policy, thereby limiting the agent’s ability to reach targets. In addition, many of these models utilize only RGB observations, further increasing the difficulty of collision avoidance without depth information. To address these limitations, a new concept—collision-free success is introduced to evaluate the ability of navigation models to find a collision-free path towards the target object. A two-stage training method with collision prediction is proposed to improve the collision-free success rate of the existing navigation models using RGB observations. In the first training stage, the collision prediction module supervises the agent’s collision states during exploration to learn to predict the possible collision. In the second stage, leveraging the trained collision prediction, the agent learns to navigate to the target without collision. The experimental results in the AI2-THOR environment demonstrate that the proposed method greatly improves the collision-free success rate of different navigation models and outperforms other comparable collision-avoidance methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Moving-Window Integrated Physics-Data-Based Modeling of Four-Wheel Vehicle Lateral Dynamics

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Automotive Innovation 2025年 1-11页

作者： Wei, Wenpeng Wang, Jinxiang Pi, Dawei Zhuang, Weichao He, Tianyi Yin, Guodong School of Mechanical Engineering Southeast University JiangSu Nanjing 211189 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University JiangSu Nanjing 211189 China Safety and Intelligence of Vehicle Equipment Research Center Nanjing University of Science and Technology JiangSu Nanjing 210094 China Department of Mechanical and Aerospace Engineering Utah State University Logan 84322 UT United States

This paper presents an Integrated physics-data-based (IPDB) modeling of lateral vehicle dynamics with moving-window data snapshots. The IPDB model encodes the fundamental physical principle of four-wheel vehicle motions and simultaneously carries out the adaptiveness of the data-driven approach. Specifically, the traditional physics-based lateral dynamics considering four-wheel interaction are first derived into an affine linear-parameter-varying model, in which the vehicle-related parameters and motion variables are separated. Then, by using the Kronecker product, the IPDB model, directly formulated by the data snapshots in the moving-window fashion, is obtained for system representation. As a result, the IPDB technique rendered model is physically interpretable. The impacts of moving window length on modeling performance are numerically studied. The IPDB model accuracy is validated with data from CarSim simulations and experiments with passenger vehicles under various scenarios. It is further demonstrated that the proposed IPDB model is more data-efficient than other data-driven methods since it only uses the data snapshot in the moving window to update the model recursively. This characteristic enables the IPDB method with online modeling capability to adapt to varying driving scenarios. © The Author(s) 2025.

关键词： Autonomous vehicles Four-wheel vehicle dynamics Integrated physics-data-based modeling Linear parameter varying system

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Unraveling tensor structures in correct-by-design controller synthesis

arXiv

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arXiv 2025年

作者： Wang, Ruohan Sun, Zhiyong Haesaert, Sofie Department of Electrical Engineering Control Systems Group Eindhoven University of Technology Netherlands Department of Mechanics and Engineering Science State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China

Formal safety guarantees on the synthesis of controllers for stochastic systems can be obtained using correct-by-design approaches. These approaches often use abstractions as finite-state Markov Decision Processes (MDPs). As the state space of these MDPs grows, the curse of dimensionality makes the computational and memory cost of the probabilistic guarantees, quantified with dynamic programming, scale exponentially. In this work, we leverage decoupled dynamics and unravel, via dynamic programming operations, a tree structure in the Canonical Polyadic Decomposition (CPD) of the value functions. For discrete-time stochastic systems with syntactically co-safe linear temporal logic (scLTL) specifications, we provide provable probabilistic safety guarantees and significantly alleviate the computational burden. We provide numerical studies and validations of the theoretical results on several typical case studies and showcase that the uncovered tree structure enables efficient reductions in the computational burden. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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BeltLineNet: A Shape-Prior-Guided Lightweight Network for Real-Time Deviation Detection in Circular Pipe Conveyors

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IEEE Sensors Journal 2025年第11期25卷 20024-20036页

作者： Zhao, Long Su, Jinhui Zhong, Yusheng Xie, Weiwei Su, Jinya Chen, Xisong Chen, Congyan Li, Shihua Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Nanjing210096 China Fujian Longking Intelligent Conveying Engineering Co. Ltd Longyan China

Belt deviation in circular pipe conveyor systems could lead to material spillage, environmental contamination, reduced efficiency, and accelerated belt wear. Real-time belt deviation detection is crucial for ensuring safe and efficient operation. However, existing methods, primarily developed for flat-belt systems, heavily rely on distinct belt-background semantics, making them unsuitable for circular conveyors where indistinct target features, motion blur, and occlusions pose significant challenges. To address these issues, we reformulate the deviation detection task as an object detection problem, proposing BeltLineNet, a lightweight, real-time deviation detection network specifically designed for circular pipe conveyors. Our method integrates explicit feature learning with a coarse-to-fine global feature fusion mechanism, enhancing belt line representation under complex conditions. Additionally, a shape-prior loss strategy is introduced to improve supervision during training, ensuring more accurate detection of elongated targets. The model is also optimized for real-time deployment through layer-adaptive pruning, achieving a balance between accuracy and computational efficiency. Extensive comparative experiments involving 8 state-of-the-art (SOTA) feature extraction networks and 6 feature enhancement strategies on a self-collected dataset under industrial conditions demonstrate that BeltLineNet surpasses SOTA feature extraction and fusion networks, improving average precision (AP) by 8.23% before pruning and 6.33% after pruning. © 2001-2012 IEEE.

关键词： Belt conveyors

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Nonlinear Dynamic Modeling and Analysis of Rigid-Flexible Coupling Spacecrafts with Time-Varying Temperature Distribution

SSRN

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SSRN 2025年

作者： Zhao, Bingxin Gu, Xudong Hu, Rongchun Wu, Sihan Deng, Zichen Department of Engineering Mechanics Northwestern Polytechnical University Xi’an China MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi’an China

When establishing the dynamic model of a rigid-coupled spacecraft, the traditional linear method requires the flexible structure to satisfy the small deformation assumption. However, the wide range of motion of the large spacecraft may lead to nonlinear deformation of the flexible structure. In this paper, the nonlinear dynamic model of the rigid-flexible coupling spacecraft is established during in-orbit operation. Firstly, the kinetic energy of the rigid-flexible spacecraft is derived by incorporating the nonlinear deformations with second-order coupling terms. Secondly, the time-varying temperature distribution along the thickness direction of the flexible structure is derived by using a one-dimensional thermal conduction equation. Then, the nonlinear time-varying coupled dynamics equations of the rigid-flexible coupling spacecraft are obtained by the Hamilton’s variational principle. The discrete equations are derived by using the assumed modal method (AMM). Finally, the results obtained through numerical calculations indicate that the nonlinear model accurately predicts the dynamical stiffening phenomenon as the attitude rotational velocity of the rigid hub increases. At the same time, the effects of the temperature, the orbital radius and structure size on the response amplitude of the spacecraft are obtained. © 2025, The Authors. All rights reserved.

关键词： Kinetics

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Adaptive Cooperative Estimation and Guidance Against Unknown Maneuvering Target

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Liu, Tianrun Chen, Yang-Yang Li, Shihua Southeast University School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Advanced Ocean Institute of Southeast University Nantong226000 China

A critical challenge in missile guidance is the frequent inaccessibility of essential target information by on-board seekers. This paper studies the three-dimensional cooperative guidance issue of multiple missiles simultaneously attacking an unknown maneuvering target. Different from most existing research, the pitch and yaw lead angles of a maneuvering target are entirely unknown, while the position and velocity information is only accessible to neighboring missiles. An adaptive distributed estimator is designed to estimate the target information over a directed communication network, generating a transmitted target with similar maneuverability to the actual target for each missile. Boundedness of the estimation errors are guaranteed within a prescribed time. Subsequently, a modified guidance law that integrates proportional navigation (PN) and coordination components is developed for simultaneously tracking the trajectories of the transmitted targets. The superiority of the developed adaptive cooperative estimation and guidance approach is confirmed through simulations. © 1965-2011 IEEE.

关键词： adaptive estimate Cooperative guidance maneuvering target prescribed-time three-dimensional

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Estimation of Metro Passenger Capacity Based on Passenger-Train Matching Model 3rd

Estimation of Metro Passenger Capacity Based on Passenger-Tr...

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3rd International Conference on Information control, Electrical engineering, and Rail Transit, ICEERT 2023

作者： Mei, Zhenkun Chen, Beijia Liu, Jiajun Shao, Jiayu Zhang, Ning Suzhou Rail Transit Group Co. Ltd. Jiangsu Suzhou215004 China School of Automation Southeast University Jiangsu Nanjing210096 China Key Laboratory of Measurement and Control of Complex Engineering Systems Ministry of Education Southeast University Jiangsu Nanjing210096 China ITS Engineering Research Center Ministry of Education Southeast University Jiangsu Nanjing211189 China

ISBN: (纸本)9789819788903

In the present scenario of the ‘one-tapin-one-tapout’ operation mode in urban rail transportation, determining passenger travel trajectories is challenging, thereby decreasing the effective counting of passengers on trains. In order to address this issue, a method for calculating passenger capacity on trains was developed by analyzing passenger travel information extracted from AFC data and train timetables. First, this research estimated the components of journey time to infer and reconstruct the passenger's specific spatiotemporal travel trajectory. The passenger-train matching model was then used to assign passengers to specific trains and calculate the passenger load of different trains at each station. At last, it took Suzhou Metro Line 3 as an example and analyzed the spatiotemporal distribution of urban rail transit passenger flow on a single line based on the proposed passenger-train model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Urban transportation

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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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