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2025 Australian and New Zealand Control Conference, ANZCC 2025

作者： Wu, Yue Zhong, Yuan Yu, Haitao Wang, Jiong Yan, Huaimin Wang, Jingcheng Dai, Xingyuan Zhao, Hongxia Lv, Yisheng Beijing Intelligent Transportation Development Center China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9798331532192

This paper proposes a deep learning-based hierarchical warning method designed to detect and respond to abnormal ride-hailing vehicle clustering. By integrating previously developed traffic forecasting and anomaly detection techniques, the method can differentiate varying degrees of clustering severity and trigger appropriate actions. The proposed approach fuses multi-source traffic data, utilizing a convolution-based spatio-temporal feature extraction module to predict ride-hailing vehicle arrival times, and implements a hierarchical warning system based on predefined thresholds. Validation results show that the proposed method can assess the difference between predicted and actual arrival times to evaluate the model’s confidence. On a real historical dataset from beijing, significant deviations were used to identify abnormal ride-hailing behavior, achieving accurate graded warnings. © 2025 IEEE.

关键词： Data fusion

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Accurate and Real-time Hierarchical Ensemble Network for Activity Classification in Construction Worker

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Zuo, Guoyu Wu, Qifei Gao, Wenbin Li, Cheng Sun, Liangkun Yu, Shuangyue Beijing University of Technology School of Information Science and Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China

Accurate and real-time locomotion classification is crucial for exoskeletons to assist construction workers in completing multiple tasks. However, state-of-the-art algorithms for classifying multiple activities face multifaceted challenges in both accuracy and real-time capability. In addition, advanced studies typically provide a single solution based on certain sensor combinations, which may have an indirect impact on different assistive devices (e.g., an algorithm using feet IMUs is not suited for bilateral portable hip exoskeletons or unilateral knee exoskeletons), limiting its practicality and applicability in diverse applications. To fill these two gaps, first, we developed a novel hierarchical ensemble network framework that can accurately and real-time classify 11 typical lower limb activities of construction workers. Second, building upon this hierarchical ensemble network framework, we developed 6 configurations wearing IMU sensors on different body segments, which are potentially used for different wearable devices. Experimental results with leave-one-out cross-validation obtained from 10 able-bodied subjects validated the effectiveness of the proposed algorithm. Compared to the baseline ANN-based algorithm, our algorithm under 6 configurations on average was able to improve accuracy, precision, recall, and F1-score by 4.97%, 3.40%, 4.97%, and 5.31%, respectively, and reduce the number of parameters and inference time by 71.86% and 47.85%, respectively. This study showcases multiple solutions with different wearable sensor configurations, offering high accuracy and strong real-time performance for classifying multiple activities, which can be deployed to controllers for multiple types of assistive devices targeting construction workers. © 2013 IEEE.

关键词： Exoskeleton (Robotics)

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An Isochron-Based Solution to Pursuit–Evasion Games of Two Heterogeneous Players

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IEEE Transactions on Automatic Control 2025年第5期70卷 2811-2826页

作者： Li, Shuai Wang, Chen Xie, Guangming Peking University State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Beijing100871 China Peking University National Engineering Research Center of Software Engineering Beijing100871 China Peking University Center for Multi-Agent Research Institute for Artificial Intelligence Beijing100871 China

In this article, we study a pursuit–evasion game between two players with heterogeneous kinematics, where the pursuer is with damped double-integrator dynamics and the evader is with single-integrator dynamics. The pursuer aims at capturing the evader as soon as possible, while the evader wants to avoid or delay the capture. Traditional methods to solve pursuit–evasion games rely on the Hamilton–Jacobi–Isaacs (HJI) equations and retrogressive path equations, which are very complicated and nonintuitive, thus failing to obtain a complete solution. To overcome these challenges, we develop an intuitive isochron-based method to thoroughly analyze all possible situations of the game and a concise geometric approach to calculate the optimal strategies, providing a complete solution to this game. Specifically, the isochron-based method effectively leverages three main factors: the players' motion capability, the pursuer's capture capability, and the players' states. Based on these, we analyze the players' superiority and the geometrical features of their isochrones and the intersections, thus acquiring concise conditions that determine the game's outcome. For the success-capture cases, we propose a new geometric approach to calculate the target points of the players and then obtain the closed-loop state feedback optimal pursuit and evasion strategies. We then get the corresponding value function and provide a validation using the HJI equation. For the success-evasion cases, we exploit the intersection of the players' isochrones to design some effective evasion strategies, which ensure that the evader can always avoid or delay the capture. Finally, some numerical simulations are carried out to validate the effectiveness and applicability of our results. © 1963-2012 IEEE.

关键词： State feedback

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Distributed Task Allocation With Minimum Makespan for Heterogeneous Multiplayer Pursuit–Evasion Games

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IEEE Transactions on Automatic Control 2025年第5期70卷 2827-2842页

作者： Li, Shuai Wang, Chen Sun, Jinan Zhang, Shikun Xie, Guangming Peking University State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Beijing100871 China Peking University National Engineering Research Center of Software Engineering Beijing100871 China Peking University Center for Multi-Agent Research Institute for Artificial Intelligence Beijing100871 China

In the domain of multiplayer pursuit–evasion games, it is crucial to address the practical aspects of the players' heterogeneity, the distributed control manner, and the pursuers' goal of minimum makespan. However, the three topics have received limited attention in existing literature, both separately and in combination. In this article, we address the multiplayer pursuit–evasion game integrating these key topics, where the pursuers with simple motions strive to capture as many evaders, characterized by damped double integrators, as possible, meanwhile minimizing the task makespan. To this end, we establish an overall framework and sequentially tackle four key issues, leading to an effective solution to the entire problem. We first propose a novel isochron-based method to derive the capture condition and acquire the optimal pursuit and evasion strategies. Then, we enhance the usability of the capture condition by geometrically deriving the analytical form of the pursuer's winning region with respect to a given evader. Third, leveraging the concept of winning regions, we determine the lower boundary of pursuers' sensing range, ensuring sufficient information acquisition for evader allocation while avoiding selection conflicts. Finally, we propose a fully distributed allocation algorithm for each pursuer allowing the pursuer team to converge to the optimal evader allocation. By combining these contributions, we successfully provide an effective solution to the entire problem. Various simulations are conducted to show the effectiveness of our proposed methods. © 1963-2012 IEEE.

关键词： Games Resource management Sensors Robot sensing systems Mathematical models Heuristic algorithms Decentralized control Usability Biomimetics Topology

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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Computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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Student academic performance prediction via hypergraph and TabNet

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Journal of Big Data 2025年第1期12卷 1-24页

作者： Yang, Yisheng Zhao, Shiyu An, Sufang Li, Xiaoyong Zhang, Yong Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing University of Technology Beijing China Academic Affairs Office Beijing Open University Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China

Student academic performance prediction plays a very important role in student management. With the development of deep learning, there has been some related research work on predicting student academic performance. Although many factors influence students’ academic performance, students at the same university tend to have similar intellectual levels and learning environments. Therefore, learning behaviors can significantly impact their academic performance. Thus, modeling students’ behavior can enable the prediction of their academic performance. However, existing methods mostly focus on modeling individual student behaviors, neglecting the complex associations among students hidden within campus behavioral data. In reality, the associations between students often involve higher-order, multi-to-multi relationships, rather than simple, pairwise connections. At the same time, most data-driven deep learning models are not interpretable. But in fact, the analysis of behaviors that affect student academic performance is more useful in many cases. To address these issues, this paper proposes a student academic performance prediction model that combines hypergraphs and TabNet. This method first processes and extracts usable behavior features from collected multi-source campus behavior data; secondly, it utilizes K-Nearest Neighbors (KNN) to construct a hypergraph to describe the higher-order associations among students; then, it uses hypergraph convolution to aggregate neighborhood features to learn sample embedding representations; finally, the academic performance of students are predicted by the TabNet model. Experimental results on real student behavior datasets indicate that the precision, recall, and F1 score of the method proposed in this paper are improved compared to baseline methods. Additionally, using hypergraphs and TabNet can help to explain the relationship between student behavior and performance at the feature level.

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LMCBert: An Automatic Academic Paper Rating Model Based on Large Language Models and Contrastive Learning

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IEEE Transactions on Cybernetics 2025年第6期55卷 2970-2979页

作者： Liu, Chuanbin Zhang, Xiaowu Zhao, Hongfei Liu, Zhijie Xi, Xi Yu, Lean Ministry of Education Center for Scientific Research and Development in Higher Education Institutes Beijing100080 China University of Science and Technology Beijing School of Intelligence Science and Technology Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education Beijing100083 China Fudan University School of Software Shanghai200433 China Renmin University Information School Beijing100872 China Sichuan University Business School Chengdu610065 China

The acceptance of academic papers involves a complex peer-review process that requires substantial human and material resources and is susceptible to biases. With advancements in deep learning technologies, researchers have explored automated approaches for assessing paper acceptance. Existing automated academic paper rating methods primarily rely on the full content of papers to estimate acceptance probabilities. However, these methods are often inefficient and introduce redundant or irrelevant information. Additionally, while Bert can capture general semantic representations through pretraining on large-scale corpora, its performance on the automatic academic paper rating (AAPR) task remains suboptimal due to discrepancies between its pretraining corpus and academic texts. To address these issues, this study proposes LMCBert, a model that integrates large language models (LLMs) with momentum contrastive learning (MoCo). LMCBert utilizes LLMs to extract the core semantic content of papers, reducing redundancy and improving the understanding of academic texts. Furthermore, it incorporates MoCo to optimize Bert training, enhancing the differentiation of semantic representations and improving the accuracy of paper acceptance predictions. Empirical evaluations demonstrate that LMCBert achieves effective performance on the evaluation dataset, supporting the validity of the proposed approach. The code and data used in this article are publicly available at https://***/iioSnail/LMCBert. © 2013 IEEE.

关键词： Contrastive Learning

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Time-independent Spiking Neuron via Membrane Potential Estimation for Efficient Spiking Neural Networks

Time-independent Spiking Neuron via Membrane Potential Estim...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hanqi Chen Lixing Yu Shaojie Zhan Penghui Yao Jiankun Shao Yunnan Key Laboratory of Intelligent Systems and Computing School of Information Science and Engineering Yunnan University China State Key Laboratory of Explosion Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The computational inefficiency of spiking neural networks (SNNs) is primarily due to the sequential updates of membrane potential, which becomes more pronounced during extended encoding periods compared to artificial neural networks (ANNs). This highlights the need to parallelize SNN computations effectively to leverage available hardware parallelism. To address this, we propose Membrane Potential Estimation Parallel Spiking Neurons (MPE-PSN), a parallel computation method for spiking neurons that enhances computational efficiency by enabling parallel processing while preserving the intrinsic dynamic characteristics of SNNs. Our approach exhibits promise for enhancing computational efficiency, particularly under conditions of elevated neuron density. Empirical experiments demonstrate that our method achieves state-of-the-art (SOTA) accuracy and efficiency on neuromorphic datasets. Codes are available at https://***/chrazqee/MPE-PSN.

关键词： Neuromorphics Computational modeling Neurons Membrane potentials Estimation Spiking neural networks Parallel processing Signal processing Computational efficiency Speech processing

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Recognition and Early Warning of Ride-Hailing Vehicle Aggregation Based on Multi-Source Spatiotemporal Data Fusion

Recognition and Early Warning of Ride-Hailing Vehicle Aggreg...

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Control Conference (ANZCC), Australian and New Zealand

作者： Yue Wu Yuan Zhong Haitao Yu Jiong Wang Huaimin Yan Jingcheng Wang Xingyuan Dai Hongxia Zhao Yisheng Lv Beijing Intelligent Transportation Development Center State Key Laboratory of Multimodal Artificial Intelligence Systems Chinese Academy of Sciences Institute of Automation

ISBN: (数字)9798331532192

ISBN: (纸本)9798331532208

This paper proposes a deep learning-based hierarchical warning method designed to detect and respond to abnormal ride-hailing vehicle clustering. By integrating previously developed traffic forecasting and anomaly detection techniques, the method can differentiate varying degrees of clustering severity and trigger appropriate actions. The proposed approach fuses multi-source traffic data, utilizing a convolution-based spatio-temporal feature extraction module to predict ride-hailing vehicle arrival times, and implements a hierarchical warning system based on predefined thresholds. Validation results show that the proposed method can assess the difference between predicted and actual arrival times to evaluate the model's confidence. On a real historical dataset from beijing, significant deviations were used to identify abnormal ride-hailing behavior, achieving accurate graded warnings.

关键词： Accuracy Fuses Data integration Predictive models Alarm systems Feature extraction Spatiotemporal phenomena Forecasting Anomaly detection

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Large Language Models Meet Symbolic Provers for Logical Reasoning Evaluation

arXiv

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arXiv 2025年

作者： Qi, Chengwen Ma, Ren Li, Bowen Du, He Hui, Binyuan Wu, Jinwang Laili, Yuanjun He, Conghui Beihang University China Shanghai Artificial Intelligence Laboratory China Fudan University China Zhongguancun Laboratory China State Key Laboratory of Intelligent Manufacturing Systems Technology Beijing China

First-order logic (FOL) reasoning, which involves sequential deduction, is pivotal for intelligent systems and serves as a valuable task for evaluating reasoning capabilities, particularly in chain-of-thought (CoT) contexts. Existing benchmarks often rely on extensive human annotation or handcrafted templates, making it difficult to achieve the necessary complexity, scalability, and diversity for robust evaluation. To address these limitations, we propose a novel framework called ProverGen that synergizes the generative strengths of Large Language Models (LLMs) with the rigor and precision of symbolic provers, enabling the creation of a scalable, diverse, and high-quality FOL reasoning dataset, ProverQA. ProverQA is also distinguished by its inclusion of accessible and logically coherent intermediate reasoning steps for each problem. Our evaluation shows that state-of-the-art LLMs struggle to solve ProverQA problems, even with CoT prompting, highlighting the dataset’s challenging nature. We also finetune Llama3.1-8B-Instruct on a separate training set generated by our framework. The finetuned model demonstrates consistent improvements on both in-distribution and out-of-distribution test sets, suggesting the value of our proposed data generation framework. Code available at: https://***/opendatalab/ProverGen Copyright © 2025, The Authors. All rights reserved.

关键词： Benchmarking

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