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

作者： Ding, Wenwen Li, Juanjuan Qin, Rui Guan, Sangtian Wang, Fei-Yue Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems The State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Macau University of Science and Technology Faculty of Innovation Engineering Macau China Institute of Automation The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350337020

The rise of Artificial Intelligence for Science (AI4S) has highlighted the importance and urgency of ensuring open-ness, fairness, impartiality, diversity, and sustainability in scientific systems. Existing scientific systems, referred to as Centralized Science (CeSci), are built on centralized organizational structures and top-down institutional frameworks, which are lagging behind the development and practical requirements of AI4S. To address these limitations, AI4S needs to embrace a new scientific organizational and operational paradigm, namely Decentralized Science (DeSci). It can provide strong support to AI4S via effectively addressing issues such as information silos, biases, unfair distribution, and monopolies and promoting multidisciplinary, interdisciplinary, and trans disciplinary cooperation in science. Based on these considerations, this paper presents the framework of AI4S based on DeSci and explores its potential application scenarios and research issues. The research can provide effective guidance for the development of scientific systems. © 2023 IEEE.

关键词： Artificial Intelligence for Science (AI4S) ChatGPT Decentralized Autonomous Organizations and Operations (DAOs) Decentralized Science (De-Sci) Generative Artificial Intelligence (GAI) Intelligent Science

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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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A comparative study of data-driven battery capacity estimation based on partial charging curves

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Journal of Energy Chemistry 2024年第1期88卷 409-420,I0010页

作者： Chuanping Lin Jun Xu Delong Jiang Jiayang Hou Ying Liang Xianggong Zhang Enhu Li Xuesong Mei State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina Shaanxi Key Laboratory of Intelligent Robots School of Mechanical EngineeringXi’an Jiaotong UniversityXi’an 710049ShaanxiChina Department of Electrical Engineering and Automation Luoyang Institute of Science and TechnologyLuoyang 471023HenanChina Wuhan Institute of Marine Electric Propulsion China State Shipbuilding Corporation LimitedWuhan 430064HubeiChina Gresgying Digital Technology Ltd. Xi’an 710086ShaanxiChina

With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.

关键词： Lithium-ion battery Partial charging curves Capacity estimation Data-driven Sampling frequency

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FTEKFNet: Hybridizing Physical and Data-driven Estimation Algorithms for Vehicle State

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Wang, Yan Hu, Fengqing Tian, Cheng Li, Panshuo Huang, Hailong Yin, Guodong Huang, Chao Department of Industrial and System Engineering The Hong Kong Polytechnical University Hong Kong Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnical University Hong Kong School of Automation and the Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou China School of Mechanical Engineering Southeast University Nanjing China

Sideslip angle and vehicle velocity are crucial for both traditional and autonomous vehicles. They play essential roles in chassis stability control, as well as in tasks such as path planning and tracking control. However, these states cannot be directly measured by onboard sensors, therefore various vehicle state estimation algorithms have been developed. Most of these algorithms assume that the noise characteristics are known, ignoring the impact of missing measurement data, and cannot simultaneously handle the effects of colored noise and white noise. To address these issues, we propose a fault-tolerant extended Kalman filter network (FTEKFNet), which integrates both physics-based and data-driven methods for vehicle state estimation. Based on the Fault Tolerant Extended Kalman Filter (FTEKF) iterative framework, a pre-trained artificial neural network is utilized to directly predict the Kalman gain, and it is combined with FTEKF to form FTEKFNet. Experimental results under different conditions demonstrate that FTEKFNet can simultaneously deal with unknown noise and data loss problems and has good adaptability to color noise. The estimation performance of the proposed algorithm is better than the traditional FTEKF and EKF methods. IEEE

关键词： Extended Kalman filters

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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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Optimal Multi-agent Collision-Free Path Planning with Temporal Logic Constraints 5th

Optimal Multi-agent Collision-Free Path Planning with Tempo...

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5th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2021

作者： Zheng, Yiwei Liu, Zhaocong Lai, Aiwen Yu, Xiao Lan, Weiyao Department of Automation Xiamen University Fujian Xiamen361005 China The Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control Engineering Beijing100094 China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9789811939976

In this paper, we present a novel optimal multi-agent collision-free path planning algorithm (OMPATL) for the problem of multi-robot path planning with temporal logic constraints. Given a high-level mission specification in the form of linear temporal logic (LTL), OMPATL can automatically synthesize a set of trajectories for the multi-agent system (MAS), which achieves the collision avoidance among agents and minimizes the cost of the maximum traveling time between successive satisfactions. To synthesize the optimal trajectories, the motion of all agents, together with the mission area, is modeled as a weighted team transition system (TS) with a set of atomic propositions (APs). If the collision avoidance were not required, the optimal trajectories would be easily synthesized by an algorithm such as shortest paths. However, once the multi-agent systems are required to be safety-critical and collision-free, these trajectories may become infeasible. In OMPATL, a strategy is proposed for adding a set of transitions including "wait" and "go-back" to each individual TS based on the optimal run which does not consider collision avoidance. Then, the states and transitions with collisions can be removed such that the optimal collision-free trajectories are founded by the synthesis algorithm. The synthesized run balances collision avoidance, mission fulfillment, and optimality. The optimal run is synthesized offline and online adjustments are not needed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Motion planning

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An Error Correcting Output Code Algorithm Based on Hierarchical Clustering for Lithology Identification 43

An Error Correcting Output Code Algorithm Based on Hierarchi...

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43rd Chinese control Conference, CCC 2024

作者： Li, Ao Chen, Xi Hunan University of Technology School of Rail Transit Zhuzhou412007 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)9789887581581

Lithology identification serves as a crucial foundation for the precise exploration and safe exploitation of geological resources. However, challenges arise from the inherent imbalance and overlap present in logging data, making the accurate identification of lithology a complex task. Addressing these challenges effectively, this paper introduces a novel algorithm, the Error Correcting Output Code algorithm based on Hierarchical Clustering(HC-ECOC). The proposed algorithm initially formulates a coding matrix using distance and overlap measures between class clusters. Subsequently, it extends the length of the coding matrix by considering both the accuracy of the integrated classifier and the diversity among base classifiers. To evaluate the efficacy of the proposed method, comparisons were conducted with alternative algorithms using UCI datasets and an authentic lithology dataset. The results unequivocally demonstrate the superior performance of our algorithm. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Lithology

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Pager Explosion:Cybersecurity Insights and Afterthoughts

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2359-2362页

作者： Chuan Sheng Wanlun Ma Qing-Long Han Wei Zhou Xiaogang Zhu Sheng Wen Yang Xiang Fei-Yue Wang Swinburne University of Technology Hawthorn VIC 3122Australia IEEE the School of Computer and Mathematical Sciences the University of AdelaideAdelaideSouth Australia 5005Australia the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

PAGER EXPLOSION:THE *** 2024 Lebanon pager explosions represent one of the most unexpected and devastating technological incidents in recent *** September 17 and 18,2024,thousands of pagers and walkie-talkies exploded simultaneously across Lebanon and parts of Syria,resulting in 42 deaths and more than 3500 *** handheld communication devices,previously regarded as secure and low-profile,were rigged with concealed explosives and remotely triggered by attackers.

关键词： injuries thoughts explosion

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Prescribed Performance Evolution control for Quadrotor Autonomous Shipboard Landing

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1151-1162页

作者： Yang Yuan Haibin Duan Zhigang Zeng State Key Laboratory of Virtual Reality Technology and Systems the School of Automation Science and Electrical EngineeringBeihang UniversityBeijing 100083China State Key Laboratory of Virtual Reality Technology and Systems the School of Automation Science and Electrical EngineeringBeihang UniversityBeijing 100083 Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng LaboratoryShenzhen 518000China School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China

The shipboard landing problem for a quadrotor is addressed in this paper,where the ship trajectory tracking control issue is transformed into a stabilization control issue by building a relative position *** guarantee both transient performance and steady-state landing error,a prescribed performance evolution control(PPEC)method is developed for the relative position *** addition,a novel compensation system is proposed to expand the performance boundaries when the input saturation occurs and the error exceeds the predefined *** the wind and wave on the relative position model,an adaptive sliding mode observer(ASMO)is designed for the disturbance with unknown upper *** on the dynamic surface control framework,a shipboard landing controller integrating PPEC and ASMO is established for the quadrotor,and the relative position control error is guaranteed to be uniformly ultimately *** results have verified the feasibility and effectiveness of the proposed shipboard landing control scheme.

关键词： error integrating stabilization

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Analysis of Conjoint Response in Post-stroke Patients Using the Attention-coupled Weighting Method

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Machine Intelligence Research 2025年第2期22卷 352-367页

作者： Jingyao Chen Chen Wang Ningcun Xu Zeng-Guang Hou Liang Peng Pu Zhang Faculty of Innovation Engineering CASIA-MUST Joint Laboratory of Intelligence Science and TechnologyMacao University of Science and TechnologyMacao 999078China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Analysis and Testing Beijing Academy of Science and TechnologyBeijing 100089China Department of Rehabilitation Evaluation China Rehabilitation Research CenterBeijing 100068China

Rehabilitation assessment plays a vital role in the recovery process of post-stroke ***,many studies have focused on the implementation of automated assessment ***,the impact of stroke on the synergistic pattern between different human joints is not well *** this study,we developed an attention-coupled weighting based quantitative analysis model to identify the pathological alteration in the conjoint response structure between stroke patients and healthy participants,thus providing more intrinsic suggestions for the post-stroke rehabilitation *** collaboration with the China Rehabilitation Research Center and the Macao University of Science and technology,we recruited 15 post-stroke patients and 15 healthy participants to complete several actions selected from the Fugl-Meyer assessment(FMA)scale,which is a commonly used post-stroke rehabilitation assessment scale in clinical *** study proposed an attentional coupling weight(ACW)extraction *** filtering the weights in the attentional coupling network using a priori matrix,we can analyse the effect of stroke on the alteration of synergistic structures and the coordination patterns of the upper and lower ***,we used a commonly accepted synergy analysis method,the nonnegative matrix decomposition(NMF)method,for comparison and *** experimental results demonstrated the effectiveness of our method in quantifying the synergistic structure pattern alterations in post-stroke patients,which provides a new targeted option for the rehabilitation process.

关键词： Intelligent assessment conjoint response attentional coupling network spatio-temporal weight extraction.

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