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Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

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Variable impedance control method for robot contact force based on TD3 algorithm

Variable impedance control method for robot contact force ba...

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Chinese Automation Congress (CAC)

作者： Xin Shen Kaiwei Ma Mingxing Yang Shuang Gao Shuo Chao Fengyu Xu College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Anhui Province Key Laboratory of Special Heavy Load Robot Maanshan China

As a common method of traditional force control, impedance control has been widely used in robot machining field. However, impedance control with fixed parameters cannot dynamically adapt to the manufacturing environment. This paper presents a variable impedance control method for contact forces based on the TD3 (Twin Delayed Deep Deterministic Policy Gradient) algorithm. The method builds on the original impedance model to obtain the optimal impedance parameters for each stage by stochastic exploration of the continuous action space, reducing the reliance on human experience and multiple attempts in the selection of traditional impedance parameters. In this paper, gantry robot is taken as the research object. The simulation results show that the variable impedance control method has faster adjustment time and more stable, and the average position tracking error is about 0.11cm and the force tracking error is about 0.01N.

关键词： Adaptation models Heuristic algorithms Simulation Force Neural networks Stochastic processes Process control

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Disturbance Rejection in Lower-Limb Rehabilitation Robot with Parameter Uncertainties

Disturbance Rejection in Lower-Limb Rehabilitation Robot wit...

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Intelligent systems, International Workshop on

作者： He Wang Jinhua She Feng Wang Juan Zhao Seiichi Kawata School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9798350353303

ISBN: (纸本)9798350353310

This paper presents a method integrated equivalent-input-disturbance (EID) and repetitive control (RC) to mitigate the effects of non-linearity and uncertainty in a pedaling rehabilitation robot. The repetitive controller is designed to ensure accurate steady-state tracking of periodic signal. Meanwhile, the EID estimator, combined with a state observer, is responsible for estimating and compensating for the impacts of non-linearity, parameter uncertainties, and disturbances caused by patients with lower-limb impairments. We design a Lyapunov stability function to assess the stability of system. The parameters of the control gain are obtained by linear matrix inequality (LMI). The results confirm the effectiveness of the proposed method.

关键词： Uncertain systems Accuracy Uncertainty Simulation Observers Assistive robots Stability analysis Steady-state Robots Lyapunov methods

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TSN-GReID: Transformer-based Siamese Network for Group Re-Identification

TSN-GReID: Transformer-based Siamese Network for Group Re-Id...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Xiaoyan Lu Weijie Sheng Xinde Li School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing China

ISBN: (纸本)9781665476881

Group re-identification (GReID) is an important yet less-studied task. GReID focuses on associating the group images across non-overlapping cameras. The key challenges of GReID include layout variation, membership changes, and occlusion. Most existing methods focus on the group variation but ignore the occlusion that frequently occurs in the group. In practical scenarios, so many activities like conversing, queuing, or fighting consist of groups. To this end, we design a novel Transformer-based Siamese Network for GReID (TSN-GReID) for joint learning of classification and correspondence tasks to learn more robust group features for group layout and membership changes. Furthermore, we put forward an original regrouping random patch module(RRPM) which respectively regroups the member patch embedding and member-level local features to generate group features with improved discrimination ability and more diversified coverage to deal with occlusion. Experimental results demonstrate the effectiveness of our approach, which significantly outperforms state-of-the-art methods by 4.6 % Rank-1 on the CUHK-SYSU Group (CSG) dataset and by 7.1% Rank-1 on the DukeMTMC Group dataset.

关键词： Automation Layout Transformers Cameras Task analysis Robots

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Improving the Adaptability of sEMG-Based Gesture Recognition Using Variational Mode Decomposition and Transfer Learning

Improving the Adaptability of sEMG-Based Gesture Recognition...

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Pervasive and Intelligent Computing (PICom), IEEE Conference on

作者： Hao Wu Feng Wang Juan Zhao Seiichi Kawata Jinhua She School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9798331522742

ISBN: (纸本)9798331522759

Surface electromyography (sEMG) based gesture recognition has received broad attention and application in rehabilitation areas. sEMG signals exhibit strong user dependence properties among users with different physiology, causing the inapplicability of the recognition model on new users. Transfer learning (TL) is a representative method to reducing user gaps by utilizing features already learned by pre-trained models. However, TL uses a large amount of training data due to the discrepancy of sEMG among different users, which increases the training burden. In this paper, a multi-user adaptive network (MUAN) is devised to decompose the insensitive features among different users to improve gesture recognition accuracy for new users, which is based on variational modal decomposition (VMD), convolutional neural network (CNN), and TL. Ninapro dataset is used to evaluate the adaptability of MUAN and the training burden on new users. Experimental results show that MUAN outperforms CNN and TL, and reduces the training burden for new users. MUAN has the potential to provide a robust and generalized HMI system for clinical applications.

关键词： Training Adaptation models Adaptive systems Transfer learning Training data Gesture recognition Electromyography Physiology Convolutional neural networks Signal resolution

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A Research on Attitude control of Quadrotor UAV Based on Equivalent-Input-Disturbance Approach and Model Predictive control

A Research on Attitude Control of Quadrotor UAV Based on Equ...

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Intelligent systems, International Workshop on

作者： Zihao Ning Jinhua She Seiichi Kawata School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9798350353303

ISBN: (纸本)9798350353310

This paper discusses the problem of attitude control of a quadrotor unmanned aerial vehicles (UAV). As external disturbances and system nonlinearities will affect the attitude control of quadcopter UAV, making it difficult for general control methods to achieve satisfactory results. To address this problem, this paper applies the equivalent-input-disturbance (EID) approach and the model predictive control (MPC) to the attitude control of a quadcopter UAV to ensure the reference tracking and suppress the influence of the total disturbances on the attitude of the quadcopter UAV, and the simulation results prove the effectiveness of the method.

关键词： Attitude control Simulation Conferences Autonomous aerial vehicles Prediction algorithms control systems Intelligent systems Quadrotors Predictive control Intelligent control

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Erratum to:Vibration analysis of a gear-rotor-bearing system with outer-ring spalling and misalignment

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Journal of Central South University 2024年第3期31卷 1032-1032页

作者：徐宏阳赵翔马辉罗忠韩清凯闻邦椿 School of Mechanical Engineering and Automation Northeastern UniversityShenyang 110819China Key Laboratory of Vibration and Control of Aero-Propulsion System(Ministry of Education) Northeastern UniversityShenyang 110819China

Because of an unfortunate mistake during the production of this article,Figure 13 was wrongly *** corrected Figure 13 is shown as follows.

关键词： Figure outer analysis

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Research and application of artificial intelligence based webshell detection model: A literature review

arXiv

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arXiv 2024年

作者： Ma, Mingrui Han, Lansheng Zhou, Chunjie Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

Webshell, as the"culprit" behind numerous network attacks, is one of the research hotspots in the field of cybersecurity. However, the complexity, stealthiness, and confusing nature of webshells pose significant challenges to the corresponding detection schemes. With the rise of Artificial Intelligence (AI) technology, researchers have started to apply different intelligent algorithms and neural network architectures to the task of webshell detection. However, the related research still lacks a systematic and standardized methodological process, which is confusing and redundant. Therefore, following the development timeline, we carefully summarize the progress of relevant research in this field, dividing it into three stages: Start Stage, Initial Development Stage, and In-depth Development Stage. We further elaborate on the main characteristics and core algorithms of each stage. In addition, we analyze the pain points and challenges that still exist in this field and predict the future development trend of this field from our point of view. To the best of our knowledge, this is the first review that details the research related to AI-based webshell detection. It is also hoped that this paper can provide detailed technical information for more researchers interested in AI-based webshell detection tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Structural reliability analysis based on improved parallel sampling algorithm

Structural reliability analysis based on improved parallel s...

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10th International Symposium on Test Automation & Instrumentation (ISTAI 2024)

作者： Yifan Yu Liyong Wang Ximing Zhang College of Mechanical & Energy Engineering Beijing University of Technology Beijing People's Republic of China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing People's Republic of China China North Vehicle Research Institute Beijing People's Republic of China

ISBN: (数字)9781837241910

Aiming at the problem of computational inefficiency of traditional reliability analysis methods when dealing with large-scale structural systems, this paper proposes a reliability analysis method that improves the parallel sample algorithm. The method adopts the Kriging model to fit the performance function and introduces the mean square error to evaluate the uncertainty of the sample points; In addition, the positional relationship between the sample points is taken into consideration to avoid overfitting the model; To improve the efficiency of the model fitting, the sample points are divided into multiple clusters using the Mini batch k-means clustering algorithm in parallel computation. Finally, it is verified by multiple random variable cases that the method can significantly improve the computational efficiency while guaranteeing the relative error compared with other reliability analysis methods.

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Efficient IoT Devices Localization Through Wi-Fi CSI Feature Fusion and Anomaly Detection

arXiv

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arXiv 2024年

作者： Li, Yan Yang, Jie Shih, Shang-Ling Shih, Wan-Ting Wen, Chao-Kai Jin, Shi The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Frontiers Science Center for Mobile Information Communication and Security Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China The Institute of Communications Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan The National Mobile Communications Research Laboratory Southeast University Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China

Internet of Things (IoT) device localization is fundamental to smart home functionalities, including indoor navigation and tracking of individuals. Traditional localization relies on relative methods utilizing the positions of anchors within a home environment, yet struggles with precision due to inherent inaccuracies in these anchor positions. In response, we introduce a cutting-edge smartphone-based localization system for IoT devices, leveraging the precise positioning capabilities of smartphones equipped with motion sensors. Our system employs artificial intelligence (AI) to merge channel state information from proximal trajectory points of a single smartphone, significantly enhancing line of sight (LoS) angle of arrival (AoA) estimation accuracy, particularly under severe multipath conditions. Additionally, we have developed an AI-based anomaly detection algorithm to further increase the reliability of LoS-AoA estimation. This algorithm improves measurement reliability by analyzing the correlation between the accuracy of reversed feature reconstruction and the LoS-AoA estimation. Utilizing a straightforward least squares algorithm in conjunction with accurate LoS-AoA estimation and smartphone positional data, our system efficiently identifies IoT device locations. Validated through extensive simulations and experimental tests with a receiving antenna array comprising just two patch antenna elements in the horizontal direction, our methodology has been shown to attain decimeter-level localization accuracy in nearly 90% of cases, demonstrating robust performance even in challenging real-world scenarios. Additionally, our proposed anomaly detection algorithm trained on Wi-Fi data can be directly applied to ultra-wideband, also outperforming the most advanced techniques. Copyright © 2024, The Authors. All rights reserved.

关键词： Smartphones

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