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Communication Atmosphere in Humans and Robots Interaction Based on Fuzzy Analytical Hierarchy Process 36

Communication Atmosphere in Humans and Robots Interaction Ba...

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第36届中国控制会议

作者： Ri Zhang Yong He Zhen-Tao Liu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (纸本)9781538629185

Communication atmosphere in Human-Robot Interaction（HRI） is estimated by integrating emotional states of humans and robots based on the concept of Fuzzy Atmosfleld（FA）,where human emotion is estimated from bimodal communication cues（i.e.,speech and facial expression） and robot emotion is generated by emotional expression *** Analytical Hierarchy Process（FAHP） is used for dynamic weight calculation of emotional states of humans and robots in the FA model,in which a three-level hierarchy is adopted for the analysis of communication atmosphere and four acoustic cues（i.e.,volume,rate,pitch,and duration） are utilized to calculate the dynamic *** is conducted in a multi-modal emotional communication based humans-robots interaction（MEC-HRI） system,by which experimental results demonstrate the validity of proposed FA *** the communication atmosphere can help to facilitate smooth communication between humans and robots by adjusting robot behavior for emotion *** proposal is being planned for an atmosphere representation system for realizing causal communication in HRI and background music is being considered for the FA model.

关键词： Communication Atmosphere Human-Robot Interaction Fuzzy Analytical Hierarchy Process Dynamic Weight

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Experimental Study on Decentralized Concurrent Learning for Multi-Agent System with complex Dynamics 36

Experimental Study on Decentralized Concurrent Learning for ...

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第36届中国控制会议

作者： Ting Fei Xin Chen Min Wu Chi Wang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (纸本)9781538629185

A cooperative multi-agent system entitles some independent agents to complete complex tasks through coordination and *** the dynamics of physical agents are so complex that the environment of learning is indeed stochastic,the paper introduces the decentralized multi-agent reinforcement learning（MARL） algorithm,named as Decentralized Concurrent Learning with Cooperative Policy Exploration（DCL-CPE）,in order to solve cooperative learning within stochastic *** investigate its feasibility in practical multi-agent systems,the box-pushing test with DCL-CPE is designed with a group of two-wheel driven robots acting as learning *** to physical properties,such as nonholonomic dynamics,rolling and sliding frictions,unreliable sense,rigid body collision,etc.,the cooperative learning is a high stochastic learning *** simulation test in Webots shows that DCL-CPE is good at exploring best cooperative policy in a decentralized way,even as state transition and rewards are all stochastic.

关键词： Multi-agent system Decentralized cooperative learning Stochastic environment Webots

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Application of neural network in modeling of activated sludge wastewater treatment process 36

Application of neural network in modeling of activated sludg...

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第36届中国控制会议

作者： Li Jun Zhang Neng Li Jing Jing Zhang Xi Yong Tian School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (纸本)9781538629185

This paper present a prediction model for three different objection(the airflow rate,the carbonaceous biochemical oxygen demand(CBOD) of the effluent,and the total suspend solids(TSS) of the *** model is built by the MLP neural *** accurancy of the prediction result of MLP neural network is compared with the accurancy of the result of trational stational autoregressive model(AR).The conclution is that the percentage error prediction model of the MLP neural network(PE),the fractional deviation(FB),normalized mean square error(NMSE),the mean absolute error(MAE) and mean square error(MSE) prediction model of evaluation index is better than the AR *** other words,the prediction model based on MLP neural network provides a reliable basis for reducing the energy consumption in the activated sludge process of industrial waste water treatment and further improving its effect on the treatment of industrial waste water.

关键词： Industrial waste water treatment Activated sludge process MLP neural network Data mining

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Bat Algorithm Optimizer for Drilling Trajectory Designing under Wellbore Stability Constraints

Bat Algorithm Optimizer for Drilling Trajectory Designing un...

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第37届中国控制会议

作者： Jiafeng Xu Xin Chen School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences

In the process of deep geological resources exploration, there are often some complex stratigraphic situations. A threedimension drilling trajectory is designed by using the geological environment data. The trajectory should be sure that the target position is reachable. And it can avoid the area prone to be in an accident. In order to solve this problem, this paper discusses a three-dimensional wellbore trajectory design optimization model suitable for shale gas horizontal mining. Considering the wellbore stability and dogleg severity as constraints, the minimum drilling length is taken as the objective function. Depending on the Mohr-Coulomb criterion, the range of azimuth angle and inclination angle of safety trajectory can be determined. It can be seen as a constraint on the wellbore derrick segment. As for the optimization algorithm, bat algorithm（BA） optimizer combined with a penalty function method is applied to this optimization model. Finally, the case study shows that the BA performs well in the wellbore optimization design. It obtains the trade-off result between the safe and efficient drilling demands. And, it provides a basic model idea for the real-time trajectory optimization while drilling.

关键词： Drilling Trajectory Designing Wellbore Stability Bat Algorithm

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Multi-Convolution Neural Networks-Based Deep Learning Model for Emotion Understanding

Multi-Convolution Neural Networks-Based Deep Learning Model ...

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第37届中国控制会议

作者： Luefeng Chen Min Wu Wanjuan Su Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Tokyo Institute of Technology

Multi-convolution neural networks-based deep learning model in combination with multimodal data for emotion understanding is proposed, in which facial expression and body gesture are used to achieve emotional states recognition for emotion understanding. It aims to understand coexistence multimodal information in human-robot interaction by using multi-convolution neural networks, where multilayer convolutions are connected in series and multiple networks are executed in parallel. Moreover,when optimizing the weights of deep neural network by traditional method, it is easy to fall into poor local optimal. To address this problem, a hybrid genetic algorithm with stochastic gradient descent is developed, which has the capacity of inherent implicit parallelism and better global optimization of genetic algorithm so that it can adaptively find the better weights of the *** in order to speed up the convergence of the proposal, the weights optimized by stochastic gradient descent will be taken as a chromosome of genetic algorithms initial population, and it also can be used as a priori knowledge. To verify the effectiveness of the proposal, experiments on benchmark database of spontaneous emotion expressions are developed, and experimental results show that the proposal outperforms the state-of-the-art methods. Meanwhile, the preliminary application experiments are also carried out and the results indicate that the proposal can be extended to human-robot interaction.

关键词： Deep learning emotion understanding convolution neural network facial expression body gesture

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A Detection System of Tool Parameter Using Machine Vision

A Detection System of Tool Parameter Using Machine Vision

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第37届中国控制会议

作者： Hantao Wang Hong Wang School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences

This paper suggests a novel technique for the tool parameter measurement based on machine vision. Tool images are captured by using a machine vision system and the outer contour image of the cutter is obtained by using the machine vision technology. The HALCON image processing library is used as the development platform to build the tool parameters test system. Several algorithms including image segmentation, edge extraction and fitting ellipse determination is used for image *** tool parameters such as external diameter and contour angle of tool edge can be obtained after rebuilding the contour of tool *** proposed scheme is shown to be reliable and effective for the automated tool parameter measurement.

关键词： Machine vision image processing tool parameter

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Hierarchical Prediction Model Based on BP Neural Network for Predicting CO/CO_2 in Iron Ore Sintering Process

Hierarchical Prediction Model Based on BP Neural Network for...

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Chinese control Conference

作者： Ben Xu Xin Chen Yang Zhou School of Automation China University of Geosciences Wuhan China Intelligent Automation for Complex Systems Hubei key Laboratory of Advanced Control Wuhan China

The sintering process is one of the most energy-consuming processes in steelmaking, its carbon fuel consumption accounts for 8% to 10% in the steel production process. To find ways of reducing the energy consumption, it is necessary to predict the carbon efficiency. The value of CO/CO_2 in the carbon emission can reflect the utilization of carbon combustion in sintering process. In this study, the CO/CO_2 is taken to be a measure of carbon efficiency and a hierarchical model is built to predict it. Firstly, the physical and chemical reactions and the carbon flow mechanism in the sintering process are analyzed, and the process parameters that affect the CO/CO_2 are determined. Then, the gray relational analysis method is used to analyze the influence factors to determine the relationship between the parameters, and a hierarchical predictive model for CO/CO_2 is established based on the relationship between the parameters. The hierarchical predictive model is divided into two parts: the predictive models for the thermal state parameters and the predictive model for CO/CO_2. The inputs of the predictive models for the thermal state parameters are the raw material parameters and the operating parameters, and the inputs of the predictive model for CO/CO_2 are the predicted values of the predictive models for the thermal state parameters. Finally, the simulation results verify the effectiveness of the proposed modeling method. This method can provide a theoretical basis for the optimization and control of carbon efficiency in the sintering process.

关键词： Predictive models Combustion Correlation Mathematical model Carbon dioxide Chemicals Raw materials

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Design and Application of a Web-based Comprehensive Evaluation and Decision Support System for Tobacco Cultivation Process

Design and Application of a Web-based Comprehensive Evaluati...

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第37届中国控制会议

作者： Gong Chen Yonghua Xiong Wei Chen Hao Zhou School of Automation China University of Geoscience Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Guizhou Tobacco Science Institute

The evaluation and decision support system are widely used in agriculture with the development of information technology. In this paper, a Web-based comprehensive evaluation and decision support system（WCEDSS） is designed for flue-cured tobacco cultivation process. Using the artificial neural networks and some image processing methods, WCEDSS can predict various indicators of tobacco such as yield and flue-cured score, or give decisions at different stages of tobacco cultivation. As the system is developed by the Browser/Server（B/S） architecture, it is possible to make full use of the Internet resources and to facilitate users at any place with access to the Internet. Test results indicates that WCEDSS can achieve good performance. With other auxiliary functions, the system shows potential and extensive practicality in agricultural information management and guidance.

关键词： tobacco Browser/Server architecture evaluate decision support

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Exponential Stability of Highly Nonlinear Neutral Stochastic Differential Delay Equations

Exponential Stability of Highly Nonlinear Neutral Stochastic...

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Chinese control Conference (CCC)

作者： Yinghui Hu Xiaofeng Zong Jinhua She Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Engineering Tokyo University of Technology Tokyo Japan

This paper investigates the mean square and almost sure stability of a class of neutral stochastic differential delay equations with highly nonlinear coefficients. We first examine the regularity of the solution to the highly nonlinear neutral stochastic differential delay systems. Then the explicit stability conditions are obtained by the Lyapunov functional and semi-martingale convergence theorem. Especially, the explicit stability conditions of pure delay neutral stochastic differential delay equations are obtained. It is revealed that the delay term in the drift can contribute to the mean square and almost sure stability.

关键词： Stability criteria Delays Asymptotic stability control theory Differential equations

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Street Object Detection Based on Faster R-CNN

Street Object Detection Based on Faster R-CNN

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第37届中国控制会议

作者： Wendi Cai Jiadie Li Zhongzhao Xie Tao Zhao Kang Lu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Library of Wuhan University of Technology

With the increased number of traffic accidents, the research and development of smart cars have been *** detection of street objects has become one of the important research topics. Generic Model detection algorithm based on Convolution Neural Network(CNN) need to design the training model, while the training and testing of the model will take a lot of time. Transfer Learning is used to fine-tune the pre-trained models, using the Image task datasets of COCO, transferring a generic deep learning model to specific one with different weights and outputs. Furthermore, the CNN structure is adjusted to improve overall performance, and the street environment is trained to the special scene. We compare the results of experiments,and the results showed that the network which is fine-tuned is effective.

关键词： Transfer Learning Faster R-CNN Inception-ResNet-v2 Object Detection

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