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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Xu, Ruijie Chen, Shichao Sun, Wenqiao Lv, Yisheng Luo, Jialiang Tang, Ying Institute of Automation Chinese Academy of Sciences College of Information Science & Technology Beijing University of Chemical Technology The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences China University of Geosciences Beijing School of Information Engineering The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Rowan University Department of Electrical and Computer Engineering Glassboro United States

ISBN: (纸本)9798350337020

Global Navigation Satellite Systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation System (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance. © 2023 IEEE.

关键词： Cubature Kalman Filter Inertial Pedestrian Navigation Zero-Velocity Update

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Internal short circuit fault diagnosis for automotive lithium-ion batteries using an adaptive parameter identification model

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Measurement: Journal of the International Measurement Confederation 2025年 253卷

作者： Tao, Qian Wang, Yiming Tian, Engang Tao, Hongfeng Chen, Hongtian Chen, Yiyang School of Mechanical and Electrical Engineering Soochow University Suzhou215137 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi Jiangsu214122 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China

This paper develops a model-based diagnostic method for internal short circuit (ISC) faults in lithium-ion batteries. This method utilizes a second-order equivalent circuit model (ECM) combined with a recursive least squares method with an adaptive forgetting factor for online parameter identification, with initial parameters optimized by a particle swarm optimization (PSO) algorithm. An extended Kalman filter (EKF) is then employed for state estimation to generate residuals, which are analyzed using the refined cumulative sum (CUSUM) statistical method to detect and diagnose ISC faults. Experimental results show that this method effectively identifies ISC faults in automotive power batteries under various initial state of charge (SOC) levels and fault-free conditions. At 100% initial SOC, fault detection times are 104 seconds for a 15Ω fault, 77 seconds for a 10Ω fault, and 27 seconds for a 1Ω fault;at 90% initial SOC, detection for a 10Ω fault takes 97 seconds, demonstrating high accuracy and robustness. © 2025 Elsevier Ltd

关键词： Lithium-ion batteries

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Two-Step Online Trajectory Planning of a Quadcopter in Indoor Environments with Obstacles

arXiv

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arXiv 2022年

作者： Zimmermann, Martin Vu, Minh Nhat Beck, Florian Nguyen, Anh Kugi, Andreas TU Wien Vienna Austria Center for Vision Automation and Control AIT Austrian Institute of Technology Vienna Austria Department of Computer Science University of Liverpool Liverpool United Kingdom

This paper presents a two-step algorithm for online trajectory planning in indoor environments with unknown obstacles. In the first step, sampling-based path planning techniques such as the optimal Rapidly exploring Random Tree (RRT*) algorithm and the Line-of-Sight (LOS) algorithm are employed to generate a collision-free path consisting of multiple waypoints. Then, in the second step, constrained quadratic programming is utilized to compute a smooth trajectory that passes through all computed waypoints. The main contribution of this work is the development of a flexible trajectory planning framework that can detect changes in the environment, such as new obstacles, and compute alternative trajectories in real time. The proposed algorithm actively considers all changes in the environment and performs the replanning process only on waypoints that are occupied by new obstacles. This helps to reduce the computation time and realize the proposed approach in real time. The feasibility of the proposed algorithm is evaluated using the Intel Aero Ready-to-Fly (RTF) quadcopter in simulation and in a real-world experiment. © 2022, CC BY-NC-ND.

关键词： Motion planning

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Time-synchronized control with the least upper bound of fixed settling time

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Journal of automation and Intelligence 2025年第2期4卷 98-107页

作者： Jiang, Wanyue Ge, Shuzhi Sam Ji, Ruihang Li, Dongyu The Institute for Future & Shandong Key Laboratory of Industrial Control Technology School of Automation Qingdao University Qingdao 266071 China Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore School of Cyber Science and Technology Beihang University Beijing 100191 China

This paper introduces time-synchronized convergence in fixed-time control, where all system states converge to the origin at the same time before a fixed time instant. Sufficient Lyapunov conditions are derived for fixed-time synchronized control (FTSC). An enhanced estimation method for synchronized settling time (ST) is proposed, with an explicit formula for its least upper bound (LUB), which reduces overestimation compared to existing methods. A switching-based technique is incorporated into the controller to avoid singularities while maintaining compatibility with the time-synchronized design. Simulation results validate the fixed-time synchronization properties and the improved ST estimation, demonstrating smoother output trajectories and reduced energy consumption. © 2025 The Authors

关键词： Fixed-time control Settling-time estimation Time-synchronized convergence

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Integration of ANN for Accurate Estimation and control in Wastewater Treatment

Integration of ANN for Accurate Estimation and Control in Wa...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Andreea Elena Țîru Iulian Vasiliev Larisa Diaconu Ramon Vilanova Daniel Voipan Harsha Ratnaweera Department of Automatic Control and Electrical Engineering "Dunărea de Jos" University of Galaţi Galaţi Romania Department of Automation Systems and Advanced Control Research Autonomous University of Barcelona Barcelona Spain Department of Computer Science and Information Technology "Dunărea de Jos" University of Galaţi Galaţi Romania Faculty of Science and Technology (Realtek) Norwegian University of Life Sciences Ås Norway

The management of wastewater is a significant global concern that calls for innovative solutions to lessen its negative effects on the environment. Conventional techniques of treating wastewater need improvement in order to deal with newly discovered contaminants, which highlights the importance of providing precise estimates of process performance and resource requirements. The worsening water shortage situation requires a paradigm shift in which wastewater is viewed as a useful resource. It is possible to create an economy that is both sustainable and circular by treating and recycling wastewater, putting less pressure on freshwater supplies, and leaving as little of an environmental footprint as possible. This study investigates the use of Artificial Neural Networks (ANNs) as software estimators in the treatment of wastewater, with a particular emphasis on predicting ammonium concentrations in effluent. In order to deal with imbalanced time-series data, the research introduces innovative data pretreatment strategies. These techniques include a Sliding Window protocol, Data Normalization, and a K-Fold training scheme. This illustrates the potential of ANNs to revolutionize wastewater treatment procedures and drive developments in this field. The suggested method demonstrates higher performance when estimating pollutant concentrations, showing the ability of ANNs to do so.

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A Power Split Strategy for a Vehicular Hybrid Energy Storage System Using Adaptive Neuro Fuzzy Inference System

A Power Split Strategy for a Vehicular Hybrid Energy Storage...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Jiao Li Lijun Zhang Longzhi Yang Bo Wang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan China Department of Computer Science and Digital Technologies Faculty of Engineering and Environment Northumbria University Newcastle upon Tyne U.K

This paper presents a real-time power split strategy for a battery-supercapacitor hybrid energy storage system. The objective of the proposed strategy is to alleviate battery degradation through effective supercapacitor utilization. The proposed strategy employs an adaptive neural fuzzy inference system, which is capable of learning a power split strategy from offline optimization results and distributing power in real-time. The performance of the strategy proposed and a low pass filtering technique is compared through simulations. The results demonstrate that the proposed method can effectively reduce battery capacity loss and perform well under unknown driving cycles in real time.

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Reduced Network Cumulative Constraint Violation for Distributed Bandit Convex Optimization under Slater’s Condition

arXiv

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arXiv 2024年

作者： Zhang, Kunpeng Yi, Xinlei Ding, Jinliang Cao, Ming Johansson, Karl H. Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201800 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper studies the distributed bandit convex optimization problem with time-varying inequality constraints, where the goal is to minimize network regret and cumulative constraint violation. To calculate network cumulative constraint violation, existing distributed bandit online algorithms solving this problem directly use the clipped constraint function to replace its original constraint function. However, the use of the clipping operation renders Slater’s condition (i.e, there exists a point that strictly satisfies the inequality constraints at all iterations) ineffective to achieve reduced network cumulative constraint violation. To tackle this challenge, we propose a new distributed bandit online primal–dual algorithm. If local loss functions are convex, we show that the proposed algorithm establishes an O(T max{c,1−c})network regret bound and an O(T 1−c/2) network cumulative constraint violation bound, where T is the total number of iterations and c ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds, the network cumulative constraint violation bound is reduced to O(T 1−c). In addition, if local loss functions are strongly convex, for the case where strongly convex parameters are unknown, the network regret bound is reduced to O(T1−c), and the network cumulative constraint violation bound is O(T1−c/2) and O(T1−c) without and with Slater’s condition, respectively. For the case where strongly convex parameters are known, the network regret bound is further reduced to O)(log(T)), and the network cumulative constraint violation bound is reduced to O(log(T)T) and O(log(T)) without and with Slater’s condition, respectively. To the best of our knowledge, this paper is among the first to establish reduced (network) cumulative constraint violation bounds for (distributed) bandit convex optimization with time-varying constraints under Slater’s condition. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2

关键词： Convex optimization

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Reinforcement Learning with Prior Policy Guidance for Motion Planning of Dual-Arm Free-Floating Space Robot

arXiv

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arXiv 2022年

作者： Cao, Yuxue Wang, Shengjie Zheng, Xiang Ma, Wenke Xie, Xinru Liu, Lei Beijing Institute of Control Engineering China Department of Automation Tsinghua University China Department of Computer Science City University of Hong Kong Hong Kong Qian Xuesen Laboratory of Space Technology China

Reinforcement learning methods as a promising technique have achieved superior results in the motion planning of free-floating space robots. However, due to the increase in planning dimension and the intensification of system dynamics coupling, the motion planning of dual-arm free-floating space robots remains an open challenge. In particular, the current study cannot handle the task of capturing a non-cooperative object due to the lack of the pose constraint of the end-effectors. To address the problem, we propose a novel algorithm, EfficientLPT, to facilitate RL-based methods to improve planning accuracy efficiently. Our core contributions are constructing a mixed policy with prior knowledge guidance and introducing k·k∞ to build a more reasonable reward function. Furthermore, our method successfully captures a rotating object with different spinning speeds. Copyright © 2022, The Authors. All rights reserved.

关键词： Motion planning

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Learning from Human Uncertainty by Choquet Integral for Optic Disc Segmentation 21

Learning from Human Uncertainty by Choquet Integral for Opti...

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4th International Conference on control and computer Vision, ICCCV 2021

作者： Qiu, Hao Su, Pan Jiang, Shanshan Yue, Xingyu Zhao, Yitian Liu, Jiang Cixi Institute of Biomedical Engineering Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences China School of Control and Computer Engineering North China Electric Power University China School of Mechatronic Engineering and Automation Shanghai University Shanghai China Department of Computer Science and Engineering Southern University of Science and Technology China

ISBN: (纸本)9781450390477

Modern deep neural networks are able to beat human annotators in several medical image processing tasks. In practical manual annotation for medical image segmentation tasks, the labels of annotators often show inter-observer variability (IOV) which is mainly caused by annotators' different understandings of expertise. In order to build a trustworthy segmentation system, robust models should consider how to capture uncertainty in samples and labels. Different from the conventional way of handling IOV with label fusion such as majority voting, a fuzzy integral based ensemble framework of multiple deep learning models for optic disc segmentation is proposed. Each component segmentation model is trained with respect to an annotator. Then, a powerful nonlinear aggregation function, the Choquet integral, is employed in form of a neural network to integrate the segmentation results of multiple annotators. The proposed method is validated on the public RIM-ONE dataset consisting of 169 fundus images and each image is annotated by 5 experts. Compared with conventional segmentation ensemble methods, the proposed methods achieves a higher Dice score (98.69%). © 2021 ACM.

关键词： Deep neural networks

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Research on Cardiac Medical Image Segmentation Based on an Improved U-Net Model 3

Research on Cardiac Medical Image Segmentation Based on an I...

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3rd International Conference on Image Processing, computer Vision and Machine Learning, ICICML 2024

作者： Wu, Yuqing Wang, Jun Wang, Rui Wang, Jian Zhou, Kecheng Zhang, Xiaoxin Shenyang University of Chemical Technology Key Laboratory of Industrial Intelligence Technology on Chemical Process School of Computer Science and Technology Liaoning Province Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang 110016 China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang 110016 China Zhongyi Northeast International Hospital Co. Ltd. Department of Emergency Shenyang 110015 China

ISBN: (纸本)9798350355413

This paper presents a cardiac MRI image segmentation model based on an improved U-Net architecture. Accurate segmentation of cardiac MRI images is critical for the diagnosis and treatment of cardiovascular diseases, yet existing U-Net models exhibit limitations in handling complex cardiac structures and multi-scale features. To address these challenges, this paper proposes two key enhancements. First, we propose a Multi-Dimensional Context Attention module, designed to improve the integration of global and local information within the skip connections, thereby enhancing segmentation accuracy for intricate cardiac structures. Furthermore, we propose a Reverse Feature Modulation module, which generates reverse masks and dynamically adjusts feature weights across different classes using adaptive weighting, effectively mitigating class imbalance issues in multi-class segmentation tasks and improving focus on difficult-to-segment regions. Experimental results demonstrate that the proposed model outperforms the standard U-Net on the ACDC dataset, achieving significant improvements in evaluation metrics such as the Dice coefficient. These enhancements underscore the model's efficacy and robustness in complex cardiac image segmentation tasks, offering new technical support for automated cardiovascular disease diagnosis. © 2024 IEEE.

关键词： Cardiac MRI segmentation Feature fusion Improved U-Net Multidimensional Context Awareness Reverse Feature Modulation

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