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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： XingLin Li Xinde Li Chuanfei Hu Huaping Liu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Nanjing Center for Applied Mathematics Nanjing China Southeast University Shenzhen Research Institute Shenzhen China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Given the difficulty of recognizing ambiguous emotions in facial expression recognition tasks, we propose a visual-language model named CAER-CLIP to address this challenge. The proposed CAER-CLIP standed for Context-Aware Emotion Recognition (CAER), and were incorporated structure of the Contrastive Language–Image Pre-training (CLIP) model as promising alternative to classifier. There are two parts in CAER-CLIP model. In the visual part, facial expressions and contextual information of the image are simultaneously extracted to obtain the final feature embeddings, which are then used as a learnable “class” token for text-image pairing with desired module. In the textual part, we use text labels for emotion recognition classes as input. The outputs were merged to participate the comparative study to generated parameters of the model. The experiments demonstrate the effectiveness of the proposed method and show that our CAER-CLIP outperforms the state-of-the-art results on the CAER benchmark. The ablation experiment verified the effectiveness of both the classifier-based and text-based (ours without classifier) models, demonstrating that our method with the CAER-CLIP structure performed better, and the incorporation of a text encoder in the deep network model architecture effectively enhancing recognition accuracy.

关键词： Training Emotion recognition Visualization Accuracy Text recognition Face recognition Benchmark testing Feature extraction Data mining Context modeling

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Differential Steering control for 6 × 6 Wheel-drive Mobile Robot 26

Differential Steering Control for 6 × 6 Wheel-drive Mobile ...

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26th International Conference on Automation and Computing, ICAC 2021

作者： Zhao, Hongqiang Luo, Chao Xu, Yongkang Li, Jiehao Central South University State Key Laboratory of High Performance Complex Manufacturing School of Mechanical and Electrical Engineering ChangSha China Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing China

ISBN: (纸本)9781860435577

The 6 × 6 Wheel-drive mobile robot is widely used in the field of special operations, and there is the phenomenon of slipping in the process of steering. To study the steering accuracy and stability of mobile robot, the dynamic model of 6 × 6 Wheel-drive Mobile Robot is established, and the sliding mode control strategy of differential steering based on mobile robot is proposed. Firstly, taking the torque of six wheels and the operator's direction control as input, the three-dimensional attitude and the relative position of the steering center of the mobile robot are obtained by using the position and attitude sensor, to determine the reference steering angle of the mobile robot. Secondly, considering the relationship between the position error and the velocity error, a recursive sliding surface is designed, and a neural network is introduced to approach the uncertain part of the mobile robot model. Then, a nonlinear gain function is applied to construct a dynamic surface control law. Finally, through the Trucksim/Simulink co-simulation model, the nonlinear gain recursive sliding mode dynamic surface adaptive control method for mobile robot trajectory tracking can improve the tracking speed and control precision, and the control force and torque are smooth and reasonable. The results show that the method is effective in improving the steering accuracy and stability of mobile robot. © 2021 Chinese Automation and Computing Society in the UK-CACSUK.

关键词： Mobile robots

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Tunable and Functional Ionic Liquids Embedded Hyper-Crosslinked Polymers as H2o-Tolerant Adsorbents for Highly Efficient Gaseous Toluene Capture

SSRN

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SSRN 2024年

作者： Nie, Xiongfei Qiu, Xinjie Zhang, Ruina Ke, Quanli Liu, Huayan Zhang, Xiangping Lu, Hanfeng Cui, Guokai Zhang, Suojiang Innovation Team of Air Pollution Control Institute of Catalytic Reaction Engineering State Key Laboratory Breeding Base of Green Chemistry Synthesis Technology College of Chemical Engineering Zhejiang University of Technology Hangzhou310014 China Beijing Key Laboratory of Ionic Liquids Clean Process CAS Key Laboratory of Green Process and Engineering State Key Laboratory of Multiphase Complex Systems Institute of Process Engineering Chinese Academy of Sciences Beijing100190 China

Developing advanced sorbents for efficient aromatic VOCs sorption from humid conditions are highly desired. In this work, a series of tunable and functional ionic liquids embedded hyper-crosslinked polymers (ILHCPs) containing different structures of anion-functional ionic liquids (ILs) with 1,4-dichloroxylene (DCX) as the crosslinker were designed and prepared for highly efficient gaseous toluene adsorption under humidity. These ILHCPs could achieve high surface area (600~900 m2 g−1), high micropore volume (0.1~0.3 cm3 g−1), small average pore size (4~5 nm), and large water contact angle (up to 110°) through tuning the anion structures and the initial IL:DCX molar ratio. Gaseous toluene adsorption isotherms on typical ILHCPs were tested under 298, 308, and 318 K, and a dual-site Langmuir-Freundlich (DSLF) model was used for well describing adsorption isotherms, which can be divided into two contributions for site I and site II. The isosteric heats of gaseous toluene adsorption were calculated. Furthermore, toluene adsorption under different humidity conditions were also tested. Compared with DCX-based self-condensed HCP, toluene adsorption capacity of ILHCPs remained steadily (270 mg g−1) even under 80% humidity. To our best knowledge, this is the first example of tuning functional ILHCPs for efficient H2O-tolerant gaseous toluene adsorption. The method may also open a door to achieving high adsorption efficiency of other volatile organic compounds by functional IL-based porous materials. © 2024, The Authors. All rights reserved.

关键词： Gas adsorption

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A Lightweight Object Detection Network for Industrial Robot Based YOLOv5

A Lightweight Object Detection Network for Industrial Robot ...

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Chinese Automation Congress (CAC)

作者： Tao Zhong Yahui Gan Zao Han Huan Gao Ang Li Key Laboratory of Measurement and Control of Complex Systems of Engineering School of Automation Southeast University Ministry of Education NanJing China Nanjing YingQi Intelligent Technology Co. Ltd. Intel Joint Research Institute on Intelligent Edge Computing NanJing China

With the rapid development of deep learning, it has been widely applied in fields such as computer vision, natural language processing, and robotics. Despite the superior performance of deep learning in object detection, most industrial vision robots still rely on traditional object detection methods due to computational constraints of robot controllers. In order to improve the performance of object detection for vision robots, a lightweight 3D object detection network based on You Only Look Once version 5 (YOLOv5) is proposed for satisfying industrial production. YOLOv5 work to efficiently object detection using deep convolutional networks. In terms of model deployment, we adopt a novel OpenVINO-based model deployment approach. The OpenVINO framework significantly enhances the inference speed of models by model optimization and compression. Our model achieves a 70% reduction in inference time compared to the baseline model on CPU. The effectiveness of the proposed method is demonstrated through experiments.

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GCN-Transformer Autoencoder with Knowledge Distillation for Unsupervised Video Anomaly Detection

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Journal of Advanced Computational Intelligence and Intelligent Informatics 2025年第3期29卷 659-667页

作者： Yan, Mingchao Xiong, Yonghua She, Jinhua School of Automation China University of Geosciences No.388 Lumo Road Hongshan District Hubei Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems No.388 Lumo Road Hongshan District Hubei Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education No.388 Lumo Road Hongshan District Hubei Wuhan430074 China School of Engineering Tokyo University of Technology 1404-1 Katakura Hachioji Tokyo192-0982 Japan

Video anomaly detection is crucial in intelligent surveillance, yet the scarcity and diversity of abnormal events pose significant challenges for supervised methods. This paper presents an unsupervised framework that integrates graph attention networks (GATs) and Transformer architectures, combining masked autoencoders (MAEs) with self-distillation training. GATs are utilized to model spatial and inter-frame relationships, while Transformers capture long-range temporal dependencies, overcoming the limitations of traditional MAE and self-distillation approaches. The model employs a two-stage training process: first, a lightweight MAE combined with a GAT-Transformer fusion constructs a knowledge distillation module;second, the student autoencoder is optimized by integrating a graph convolutional autoencoder and a classification head to identify synthetic anomalies. We evaluate the proposed method on three representative datasets—ShanghaiTech Campus, UBnormal, and UCSD Ped2—and achieve promising results. © Fuji Technology Press Ltd.

关键词： Network coding

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An Efficient Learning control Framework with Sim-to-Real for String-Type Artificial Muscle-Driven Robotic systems

arXiv

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arXiv 2024年

作者： Tao, Jiyue Zhang, Yunsong Rajendran, Sunil Kumar Zhang, Feitian The Robotics and Control Laboratory Department of Advanced Manufacturing and Robotics College of Engineering the State Key Laboratory of Turbulence and Complex Systems Peking University Beijing100871 China The BSS Technologies Inc. Takoma ParkMD20912 United States

Robotic systems driven by artificial muscles present unique challenges due to the nonlinear dynamics of actuators and the complex designs of mechanical structures. Traditional model-based controllers often struggle to achieve desired control performance in such systems. Deep reinforcement learning (DRL), a trending machine learning technique widely adopted in robot control, offers a promising alternative. However, integrating DRL into these robotic systems faces significant challenges, including the requirement for large amounts of training data and the inevitable sim-to-real gap when deployed to real-world robots. This paper proposes an efficient reinforcement learning control framework with sim-to-real transfer to address these challenges. Bootstrap and augmentation enhancements are designed to improve the data efficiency of baseline DRL algorithms, while a sim-to-real transfer technique, namely randomization of muscle dynamics, is adopted to bridge the gap between simulation and real-world deployment. Extensive experiments and ablation studies are conducted utilizing two string-type artificial muscle-driven robotic systems including a two degree-of-freedom robotic eye and a parallel robotic wrist, the results of which demonstrate the effectiveness of the proposed learning control strategy. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Optimal synchronization control formulti-agent systems with input saturation:a nonzero-sum game

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Frontiers of Information Technology & Electronic engineering 2022年第7期23卷 1010-1019页

作者： Hongyang LI Qinglai WEI School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China The State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

This paper presents a novel optimal synchronization control method for multi-agent systems with input *** multi-agent game theory is introduced to transform the optimal synchronization control problem into a multi-agent nonzero-sum ***,the Nash equilibrium can be achieved by solving the coupled Hamilton–Jacobi–Bellman(HJB)equations with nonquadratic input energy terms.A novel off-policy reinforcement learning method is presented to obtain the Nash equilibrium solution without the system models,and the critic neural networks(NNs)and actor NNs are introduced to implement the presented *** analysis is provided,which shows that the iterative control laws converge to the Nash *** results show the good performance of the presented method.

关键词： Optimal synchronization control Multi-agent systems Nonzero-sum game Adaptive dynamic programming Input saturation Off-policy reinforcement learning Policy iteration

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Speech Emotion Recognition Based on Clustering Assistance

Speech Emotion Recognition Based on Clustering Assistance

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2022 Chinese Automation Congress, CAC 2022

作者： Peng, Zhi-Kun Liu, Zhen-Tao Han, Meng-Ting China University of Geosciences School of Automation 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)9781665465335

Speech emotion recognition (SER) is a key technology to achieve natural human-computer interaction. The development of SER is significantly influenced by the scale of the sample. In recent years, the study of SER has been intensified by introducing data augmentation methods. However, most of these methods directly augment the sample data scale, neglecting the rational analysis and utilization of the feature distribution of samples. In this paper, we propose a new framework for SER based on clustering assistance, which can utilize the feature distribution information of the sample data directly and effectively. It considers the sample proportion of each emotion category in the clusters obtained by clustering, converts it into a probability score, which is called the clustering emotion probability score, and fuses it with the emotion probability score from the simple classification model according to different fusion weighting factors. We evaluated the proposed method with the baseline model on the IEMOCAP dataset. Experimental results show that our method achieves better results than the baseline model in terms of both weighted and unweighted accuracy. © 2022 IEEE.

关键词： K-means clustering

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Experimental Design of Lower-limb Movement Recognition Based on Support Vector Machine 41

Experimental Design of Lower-limb Movement Recognition Based...

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第41届中国控制会议

作者： Wangyang Ge Juan Zhao Feng Wang Chi Xu Zhaohui Yang Jinhua She School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Rehabilitation Union HospitalTongji Medical CollegeHuazhong Univ.Science & Technology School of Engineering Tokyo University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

As a rehabilitation aid,the lower extremity rehabilitation robot usually works together with the patient during the rehabilitation training process,and achieves therapeutic effects by assisting the patient to complete movements within a certain trajectory for *** realize the active training of the rehabilitation robot,the rehabilitation robot needs to be able to recognize the patient's movement intention and cooperate with *** signals contain rich neuromuscular movement information and are often used to extract patients' movement *** this paper,using the principle of human lower extremity force generation,we designed an experiment for lower extremity action recognition of sEMG *** extracting the composition of feature vectors through time-domain feature values,we designed an SVM classifier to achieve the classification of five common lower extremity actions and obtained an average accuracy rate of 90.66% through the experiment,which verified the effectiveness of the experimental design.

关键词： sEMG signal SVM movement recognition lower-limb

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ISAC Prototype System for Multi-Domain Cooperative Communication Networks

arXiv

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arXiv 2024年

作者： Yang, Jie Que, Hang Tao, Du Liang, Le Li, Xiao Wen, Chao-Kai Jin, Shi The Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China The Institute of Communications Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan

Future wireless networks are poised to transform into integrated sensing and communication (ISAC) networks, unlocking groundbreaking services such as digital twinning. To harness the full potential of ISAC networks, it is essential to experimentally validate their sensing capabilities and the role of sensing in boosting communication. However, current prototype systems fall short in supporting multiple sensing functions or validating sensing-assisted communication. In response, we have developed an advanced ISAC prototype system that incorporates monostatic, bistatic, and network sensing modes. This system supports multimodal data collection and synchronization, ensuring comprehensive experimental validation. On the communication front, it excels in sensing-aided beam tracking and real-time high-definition video transmission. For sensing applications, it provides precise angle and range measurements, real-time angle-range imaging, and radio-based simultaneous localization and mapping (SLAM). Our prototype aligns with the 5G New Radio standard, offering scalability for up to 16 user equipments (UEs) in uplink transmission and 10 UEs in downlink transmission. Real-world tests showcase the system’s superior accuracy, with root mean square errors of 2.3 degrees for angle estimation and 0.3 meters (m) for range estimation. Additionally, the estimation errors for multimodal-aided real-time radio SLAM localization and mapping are 0.25 m and 0.8 m, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Cooperative communication

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