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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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Passivity-Based control of Tower Vibration of Offshore Wind Turbines 34

Passivity-Based Control of Tower Vibration of Offshore Wind ...

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第三十四届中国控制会议

作者： REN Lina YANG Huanhuan LIU Shuangshuang LIU Fucai Key Lab of Industrial Computer Control Engineering of Hebei Yanshan University

ISBN: (纸本)9781467374439

Considering the influence of the tower vibration such as fore-aft/ side-to-side in the offshore wind turbine, the Passivity-based control(PBC) method is proposed to suppress the tower vibration based on the offshore Permanent Magnet Synchronous Generator(PMSG). The PCH model of the tower is established and the energy compensation of the system is adopted to increase the damping of the tower. In comparison with PID controller, the PBC method not only suppresses the vibration of the tower effectively while reduce the fatigue load of tower, but also reduces the influence of maximum capture of wind energy and the drive-train torsional vibration, stabilizing the output power of the generator. The effectiveness and superiority of the PBC method proposed is verified by the theoretical analysis and the simulations by comparing with PID.

关键词： Permanent Magnet Synchronous Generator(PMSG) fore-aft tower vibration side-to-side tower vibration PCH model Passivity-Based control(PBC) PID

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Distributed containment control for multi-robot system based on binocular vision and RBF neural network

International Journal of Control and Automation

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International Journal of control and Automation 2015年第9期8卷 139-152页

作者： Shao, Nuan Li, Huiguang Liu, Le Li, Guoyou Key Lab of Industrial Computer Control Engineering of Hebei Province College of Electrical Engineering Yanshan University Qinhuangdao China

Aiming at the distributed containment control problem for Multi-Robot system with dynamic leaders, we use binocular vision as the sensing device, so firstly, a binocular visual model for dynamic target is established by introducing the pseudo depth variable, and it is used to provide Real-Time status information for the followers. Then, in the case that each follower can only get information from part of the leaders, even individual follower can not get any information from the leaders directly, the topological relationship among robots is built by using the graph theory. Once more, RBF neural networks are employed to approximate the uncertainties in the system model of followers, and L2robust control laws are used to restrain the influences of the approximation errors. Finally, a simulation is carried out on multiple 2 Degrees-Of-Freedom (DOF) robots, and results validate the designed controllers can drive the followers converge to the convex hull spanned by the leaders asymptotically and realize synchronization motion ultimately. © 2015 SERSC.

关键词： Multipurpose robots

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Dynamics Modeling and Robust Trajectory Tracking control for a Class of Hybrid Humanoid Arm Based on Neural Network

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Chinese Journal of Mechanical engineering 2009年第3期22卷 355-363页

作者： WANG Yueling JIN Zhenlin College of Mechanical Engineering Yanshan University Qinhuangdao 066004 China Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao 066004 China

In order to solve the problem of trajectory tracking for a class of novel serial-parallel hybrid humanoid arm（HHA）, which has parameters uncertainty, frictions, disturbance, abrasion and pulse forces derived from motors, a multistep dynamics modeling strategy is proposed and a robust controller based on neural network（NN）-adaptive algorithm is designed. At the first step of dynamics modeling, the dynamics model of the reduced HHA is established by Lagrange method. At the second step of dynamics modeling, the parameter uncertain part resulting mainly from the idealization of the HHA is learned by adaptive algorithm. In the trajectory tracking controller, the radial basis function（RBF） NN, whose optimal weights are learned online by adaptive algorithm, is used to learn the upper limit function of the total uncertainties including frictions, disturbances, abrasion and pulse forces. To a great extent, the conservatism of this robust trajectory tracking controller is reduced, and by this controller the HHA can impersonate mostly human actions. The proof and simulation results testify the validity of the adaptive strategy for parameter learning and the neural network-adaptive strategy for the trajectory tracking control.

关键词： hybrid humanoid arm dynamic modeling neural network adaptive control trajectory tracking

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Design of Robotic Visual Servo control Based on Neural Network and Genetic Algorithm

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International Journal of Automation and computing 2012年第1期9卷 24-29页

作者： Hong-Bin Wang Mian Liu Key Lab of Industrial Computer Control Engineering of Hebei Province Institute of Electrical EngineeringYanshan UniversityQinhuangdao 066004PRC

A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation （BP） neural network is used to make a direct transition from image feature to joint angles without requiring robot kinematics and camera calibration. To speed up the convergence and avoid local minimum of the neural network, this paper uses a genetic algorithm to find the optimal initial weights and thresholds and then uses the BP Mgorithm to train the neural network according to the data given. The proposed method can effectively combine the good global searching ability of genetic algorithms with the accurate local searching feature of BP neural network. The Simulink model for PUMA560 robot visual servo system based on the improved BP neural network is built with the Robotics Toolbox of Matlab. The simulation results indicate that the proposed method can accelerate convergence of the image errors and provide a simple and effective way of robot control.

关键词： Visual servo image Jacobian back propagation （BP） neural network genetic algorithm robot control

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A study on clutch multi-modal fuzzy control in AMT vehicle starting process

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Journal of Computational Information Systems 2013年第6期9卷 2121-2129页

作者： Zang, Huaiquan Li, Dongdong Key Lab of Industrial Computer Control Engineering of Hebei Province Institute of Electrical and Engineering Yanshan University Qinhuangdao 066004 China

In this paper, mathematical model of relationship between clutch engaging speed and engine output torque is firstly established to solve the problem of clutch engaging speed in AMT vehicle starting process, basing on analysis of the reduced kinetic model of clutch. Then multi-modal fuzzy control algorithm is adopted to precisely predict clutch engagement stage, and then driver's intension is considered to fuzzy control the clutch engaging speed. Finally, simulation modal is established. The simulation results show that, basing on the drivers intension and external environment, the control strategy proposed in this paper not only makes the vehicle start fast and stably, but also reduces slip friction work with the premise of comfort, which extends clutch working life and obtains better control performance in clutch starting process. Copyright © 2013 Binary Information Press.

关键词： Clutches

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Adaptive finite time consensus control of multiple mechanical systems under a switching graph 35

Adaptive finite time consensus control of multiple mechanica...

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第35届中国控制会议

作者： WANG Hongbin ZHOU Zhen WANG Yueling Key Lab of Industrial Computer Control Engineering of Hebei Province Yansnan University

ISBN: (纸本)9781509009107

In this paper,adaptive finite time consensus control problem is investigated for multiple mechanical systems with *** adaptive distributed control algorithms are proposed for the multiple mechanical systems under a switching undirected graph,which is addressed through a novel Markov *** any detailed information of the system model,the adaptive distributed controller has a faster and higher precision tracking *** is shown that the states of the mechanical systems can reach a consensus within finite *** finally,an illustrative simulation on the multiple robotic manipulators is given to demonstrate the effectiveness of the proposed controller.

关键词： Finite time consensus Adaptive control Multiple mechanical system Markov chain

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Iterative learning control for nonlinear systems with uncertain state delay and arbitrary initial error

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控制理论与应用（英文版） 2011年第4期9卷 541-547页

作者： Hongbin WANG Yan WANG Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao Hebei 066004 China

Most of the existing iterative learning control algorithms proposed for time-delay systems are based on the condition that the time-delay is precisely available, and the initial state is reset to the desired one or a fixed value at the start of each operation, which makes great limitation on the practical application of corresponding results. In this paper, a new iterative learning control algorithm is studied for a class of nonlinear system with uncertain state delay and arbitrary initial error. This algorithm needs to know only the boundary estimation of the state delay, and the initial state is updated, while the convergence of the system is guaranteed. Without state disturbance and output measurement noise, the system output will strictly track the desired trajectory after successive iteration. Furthermore, in the presence of state disturbance and measurement noise, the tracking error will be bounded uniformly. The convergence is strictly proved mathematically, and sufficient conditions are obtained. A numerical example is shown to demonstrate the effectiveness of the proposed approach.

关键词： Iterative learning control State-delay system Initial condition Uncertain disturbances

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Friction compensation control of space manipulator considering the effects of gravity 35

Friction compensation control of space manipulator consideri...

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第35届中国控制会议

作者： QIN Li JIA Xiao-jing LIU Cai-feng LIU Fu-cai Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University

ISBN: (纸本)9781509009107

All space manipulators are debugged on the ground and on-orbit serviced in space. Compared with in ground gravity environment their friction characteristics in space microgravity environment have great changes. For sake of tracking the desired trajectory, in both ground and space, considering these characteristics, a friction compensation control method based on an Active Disturbance Rejection control(ADRC) is proposed and its effectiveness is validated on a planar 2-DOF free-floating space manipulator. The friction models and the dynamic models under different gravity environment have been derived based on the torque balance, the Kane equation and the Lagrange method. Particle Swarm Optimization(PSO) is put forward to optimize parameters of ADRC so as to improve the efficiency of designing process. Compared with PD controller in tracking simulation under different gravity environment, the results verify the advantage of the proposed improved control method.

关键词： space manipulator gravity friction compensation ADRC Trajectory tracking PSO

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A novel multi-objective decomposition particle swarm optimization based on comprehensive learning strategy 36

A novel multi-objective decomposition particle swarm optimiz...

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第36届中国控制会议

作者： Lixin Wei Rui Fan Xin Li Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University

ISBN: (纸本)9781538629185

Comprehensive learning particle swarm optimization（CLPSO） algorithm has a good performance in overcoming premature convergence and avoiding getting stuck in local minima, which are shortcomings in particle swarm optimization. It can solve complex, multi-modal of single-objective problems, but it has not such performance in handling multi-objective optimization problems because of the difficulty of selective solution mechanism. In this article, a multi-objective decomposition particle swarm optimization based on comprehensive learning strategy is proposed, which uses a comprehensive learning strategy for multi-objective problems to prevent premature convergence;updates the leading particles by decomposition method to enhance the distribution of solutions;adds the archive to preserve non-dominated solutions, and adopts mutation in archive to avoid falling into local optimum. The proposed approach is compared with three multi-objective evolutionary algorithms and the results indicate that the proposed approach is competitive respect to which it is compared in most of the test problems adopted.

关键词： comprehensive learning particle swarm optimization multi-objective optimization decomposition

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