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5th International Conference on System Reliability and Safety engineering, SRSE 2023

作者： Liu, Weiming Chen, Cen Wang, Zhifei Zhang, Xiaosheng Zhai, Guofu Zheng, Wei Engineering & Automation Harbin Institute of Technology Department of Electrical Harbin China Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (纸本)9798350305944

The energy meter is a widely used digital-analog electronic system. However, metering accuracy is inevitably influenced by factors such as ambient temperature, load current, and component tolerance. This paper introduces a method utilizing digital twin models to analyze the consistency of metering accuracy in a new single-phase smart meter prototype. An electro-thermal coupling model incorporating a temperature compensation algorithm is established, and the metering accuracy is solved using a surrogate model. By employing the Monte Carlo method, a batch of product samples is generated to determine the impact of varying ambient temperature and load current conditions on the metering accuracy's consistency within the maximum operating range. The results of this study provides a valuable foundation for robust design and reliability optimization of the energy meter. © 2023 IEEE.

关键词： Smart meters

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Performance Comparison of Lightweight CNN Models for Drone Collision Avoidance Dataset 20

Performance Comparison of Lightweight CNN Models for Drone C...

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20th International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2023

作者： Zufar, Rifqi Nabila Banjerdpongchai, David Chulalongkorn University Center of Excellence in Intelligent Control Automation of Process Systems Department of Electrical Engineering Faculty of Engineering 254 Phayathai Road Bangkok Pathumwan10330 Thailand

ISBN: (纸本)9798350310467

Collision Avoidance System (CAS) is important for drone safety. CAS consists of three steps e.g., obstacle sensing, collision prediction, and collision avoidance. Collision prediction enables drones to gain information, process information, and estimate whether the object has a risk of collision. Convolution Neural Network (CNN) is one of the methods that can be employed for collision prediction. However, CNN is a method that needs a large data in the training. Dario Pedro et al. provided a dataset called the CoLANet dataset that consists of VDOs of collision drones. Subsequently, they proposed a new algorithm called Neural Network Pipeline which has a Convolution Neural Network (CNN) part to extract the feature from a couple of images. CNN extracts images by using MobileNetV2 as a pre-trained model. They chose MobileNetV2 due to training performance from another dataset. This paper aims to assess the performance of lightweight CNN models using the CoLANet dataset. The models will be trained on the Keras library with parameters of fewer than ten million. The models will be validated by Confusion Matrix and Receiver Operating Characteristics. In conclusion, we examine which pre-trained CNN model has the best performance and suggest ongoing work. © 2023 IEEE.

关键词： Drones

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Deep Learning-based Moving Object Segmentation:Recent Progress and Research Prospects

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Machine Intelligence Research 2023年第3期20卷 335-369页

作者： Rui Jiang Ruixiang Zhu Hu Su Yinlin Li Yuan Xie Wei Zou College of Information Engineering Shanghai Maritime UniversityShanghai201306China School of Computer Science and Technology East China Normal UniversityShanghai200062China Research Center of Precision Sensing and Control Institute of AutomationChinese Academy of SciencesBeijing100190China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing100190China

Moving object segmentation(MOS),aiming at segmenting moving objects from video frames,is an important and challenging task in computer vision and with various *** the development of deep learning(DL),MOS has also entered the era of deep models toward spatiotemporal feature *** paper aims to provide the latest review of recent DL-based MOS methods proposed during the past three ***,we present a more up-to-date categorization based on model characteristics,then compare and discuss each category from feature learning(FL),and model training and evaluation *** FL,the methods reviewed are divided into three types:spatial FL,temporal FL,and spatiotemporal FL,then analyzed from input and model architectures aspects,three input types,and four typical preprocessing subnetworks are *** terms of training,we discuss ideas for enhancing model *** terms of evaluation,based on a previous categorization of scene dependent evaluation and scene independent evaluation,and combined with whether used videos are recorded with static or moving cameras,we further provide four subdivided evaluation setups and analyze that of reviewed *** also show performance comparisons of some reviewed MOS methods and analyze the advantages and disadvantages of reviewed MOS methods in terms of ***,based on the above comparisons and discussions,we present research prospects and future directions.

关键词： Moving object segmentation(MOS) change detection background subtraction deep learning(DL) video understanding

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Unscented Kalman Filter for State of Charge Estimation of Lithium Titanate Battery 20

Unscented Kalman Filter for State of Charge Estimation of Li...

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20th International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2023

作者： Dardchuntuk, Joshua Chun-Ken Banjerdpongchai, David Faculty of Engineering Chulalongkorn University Center of Excellence in Intelligent Control Automation of Process Systems Department of Electrical Engineering Pathumwan 254 Phayathai Road Bangkok10330 Thailand

ISBN: (纸本)9798350310467

An accurate estimation of the state of charge (SoC) of lithium titanate (LTO) batteries is required for their effective operation and management. In this study, we propose an unscented Kalman filter (UKF) approach for estimating the SoC of LTO batteries, which are challenging to assess due to the nonlinear voltage-SoC relationship and aging impact. Our approach uses a state and measurement model based on LTO's electrochemical characteristics and employs sigma points and weights to address nonlinearities. According to the findings of our research, the UKF-based methodology has high accuracy, rapid convergence, and resilience to discharge rate, outperforming or matching the capabilities of existing state-of-the-art approaches. This work provides a novel and effective solution for LTO battery SoC estimation, useful for applications in electric vehicles, energy storage, and smart grid energy systems. © 2023 IEEE.

关键词： Battery management systems

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Comparative Performance of CNN and Transformer Architectures in Infrared Small Target Detection 23

Comparative Performance of CNN and Transformer Architectures...

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23rd International Conference on control, automation and systems, ICCAS 2023

作者： Choi, Lammi Chung, Won Young Lee, In Ho Park, Chan Gook Automation and Systems Research Institute Seoul National University Department of Aerospace Engineering Seoul08826 Korea Republic of

ISBN: (纸本)9788993215274

Infrared (IR) small target detection is a challenging task due to the low resolution of IR cameras, the presence of noise and variability, and the similarity of thermal characteristics between small targets and surrounding objects. Deep learning has shown significant improvements in IR small target detection by capturing complex patterns and features. In deep learning-based algorithms, there are convolutional neural networks (CNN) and Transformer-based architectures that utilize attention concepts. This paper focuses on the benefits when Transformer-based architecture, like the Swin UNet, is applied to IR small target detection. So, a comparative simulation is conducted to evaluate the performance of a CNN model and a Transformer model in the context of object detection. The CNN models utilized in the experiment are UNet, UNet++, and UNet 3+, while the Transformer model employed is the Swin UNet. As a result, the experiments demonstrate that the performance of UNet 3+ and Swin UNet are similar. The results indicate that the Transformer-based architecture does not always outperform the existing CNN-based model. © 2023 ICROS.

关键词： Object detection

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Parallel Driving with Big Models and Foundation Intelligence in Cyber-hysical-ocial Spaces

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Research 2024年第2期2024卷 1-17页

作者： Xiao Wang Jun Huang Yonglin Tian Chen Sun Lie Yang Shanhe Lou Chen Lv Changyin Sun Fei-Yue Wang School of Artificial Intelligence Anhui UniversityHefeiChina Macao University of Science and Technology MacaoChina State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina MVSLab Department of Mechanical and Mechatronics EngineeringUniversity of Waterloo200 University Ave WestWaterlooON N2L3G1Canada School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingaporeSingapore

Recent years have witnessed numerous technical breakthroughs in connected and autonomous vehicles (CAVs). On the one hand, these breakthroughs have significantly advanced the development of intelligent transportation systems (ITSs);on the other hand, these new traffic participants introduce more complex and uncertain elements to ITSs from the social space. Digital twins (DTs) provide real-time, data-driven, precise modeling for constructing the digital mapping of physical-world ITSs. Meanwhile, the metaverse integrates emerging technologies such as virtual reality/mixed reality, artificial intelligence, and DTs to model and explore how to realize improved sustainability, increased efficiency, and enhanced safety. More recently, as a leading effort toward general artificial intelligence, the concept of foundation model was proposed and has achieved significant success, showing great potential to lay the cornerstone for diverse artificial intelligence applications across different domains. In this article, we explore the big models embodied foundation intelligence for parallel driving in cyber-physical-social spaces, which integrate metaverse and DTs to construct a parallel training space for CAVs, and present a comprehensive elucidation of the crucial characteristics and operational mechanisms. Beyond providing the infrastructure and foundation intelligence of big models for parallel driving, this article also discusses future trends and potential research directions, and the ?S?goals of parallel driving.

关键词： breakthrough integrate corners

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Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 47-63页

作者： Yongliang Yang Zihao Ding Rui Wang Hamidreza Modares Donald C.Wunsch Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation&Electrical EngineeringUniversity of Science and Technology BeijingBeijing 100083China School of Automation Beijng Institute TechnologyBeijing 100081China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Mechanical Engineering Department Michigan State UniversityEast LansingMI 48824 USA Department of Electrical&Computer Engineering Missouri University of Science&TechnologyRollaMO 65401 USA

In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the *** presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller *** task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop,while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the ***-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance *** the inner-loop,a velocity-free filter is designed to avoid the requirement of end-effector velocity *** this basis,an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task *** simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.

关键词： Adaptive impedance control data-driven method human-robot interaction(HRI) reinforcement learning velocity-free

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Robust Walking Direction Estimation via Principal Component Pursuit in Pedestrian Dead Reckoning 23

Robust Walking Direction Estimation via Principal Component ...

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23rd International Conference on control, automation and systems, ICCAS 2023

作者： Park, Jae Wook Lee, Jae Hong Park, Chan Gook Automation and Systems Research Institute Seoul National University Department of Aerospace Engineering Seoul08826 Korea Republic of

ISBN: (纸本)9788993215274

In pedestrian dead reckoning (PDR) systems, accurate estimation of the walking direction is of paramount importance for achieving high localization accuracy. Despite numerous research endeavors in this area, a remaining challenge lies in cases where the direction indicated by the sensors does not align with the actual pedestrian's walking direction. In this paper, we address this misalignment problem by focusing on the PCA-based method. While this method exploits the advantages of PCA, it is also subject to its limitations, notably its susceptibility to outliers due to abnormal actions beyond normal walking, which are commonly encountered in PDR scenarios. To ensure robustness against outliers, we propose a novel PCA-based method for estimating the walking direction. Through experiments conducted on various actions, we evaluate the performance of the proposed method. The results demonstrate that our proposed method, which applies stable principal component pursuit for robust PCA, achieves an improved performance with an RMSE of 5.15° compared to classical PCA. Furthermore, the proposed method exhibits stable estimation and robustness across all presented actions and sequences. © 2023 ICROS.

关键词： Principal component analysis

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Path Planning Based on A-Star and Dynamic Window Approach Algorithm for Wild Environment

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Journal of Shanghai Jiaotong university(Science) 2024年第4期29卷 725-736页

作者： DONG Dejin DONG Shiyin ZHANG Lulu CAI Yunze Department of Automation Shanghai Jiao Tong UniversityShanghai200240China Key Laboratory of System Control and Information Processing Ministry of EducationShanghai200240China Shanghai Electro-Mechanical Engineering Institute Shanghai201109China

The path planning problem of complex wild environment with multiple elements still poses *** paper designs an algorithm that integrates global and local planning to apply to the wild environmental path *** modeling process of wild environment map is *** optimization strategies are designed to improve the A-Star in overcoming the problems of touching the edge of obstacles,redundant nodes and twisting paths.A new weighted cost function is designed to achieve different planning ***,the improved dynamic window approach(DWA)is designed to avoid local optimality and improve time efficiency compared to traditional *** the necessary path re-planning of wild environment,the improved A-Star is integrated with the improved DWA to solve re-planning problem of unknown and moving obstacles in wild environment with multiple *** improved fusion algorithm effectively solves problems and consumes less time,and the simulation results verify the effectiveness of improved algorithms above.

关键词： path planning path re-planning wild environment A-Star algorithm dynamic window approach

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Impact Time Constrained Guidance with Bounded Field-of-View and Input

Impact Time Constrained Guidance with Bounded Field-of-View ...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Singh, Swati Kumar, Shashi Ranjan Mukherjee, Dwaipayan Intelligent Systems and Control Lab Department of Aerospace Engineering Indian Institute of Technology Bombay Powai Mumbai400 076 India Department of Electrical Engineering Indian Institute of Technology Bombay Powai Mumbai400 076 India

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This article proposes a guidance scheme for stationary targets to achieve time-constrained interception at a desired time, considering the bounded field-of-view capability of seekerequipped interceptors and the physical limits of the actuator. Actuator saturation is a crucial problem in physical systems, often resulting in performance degradation. However, the actuator limitations are usually known to the designer, therefore, augmenting these constraints into the guidance design may enhance performance. The time-to-go error-based approach is used to satisfy the field-of-view constraint;further, the engagement kinematics are augmented with an input saturation model to incorporate the lateral acceleration constraints. Subsequently, the guidance strategy that constrains the lateral acceleration and the time-to-go values within their respective bounds is derived using Lyapunov stability concepts and the backstepping technique. Furthermore, a multi-stage approach is suggested to expand the achievable range of impact time. Numerical simulations are performed to validate the efficacy of the proposed scheme for different initial engagement geometries. © 2025, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Backstepping

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