Pub Date : 2019-06-03DOI: 10.1109/CCDC.2019.8832533
Jidong Jin
Traditional social network analysis was often used for connected or strongly connected networks. Current social network analysis, such as online social network analysis, will target such networks, which have many members and include a large number of connected or strongly connected subnetworks. Using the concept of group (subnetwork), we can convert large-scale networks into small-scale networks with nodes as groups. A theoretical model for the influence between groups is given in this paper.
{"title":"Social Network Structure: Groups and Their Influence","authors":"Jidong Jin","doi":"10.1109/CCDC.2019.8832533","DOIUrl":"https://doi.org/10.1109/CCDC.2019.8832533","url":null,"abstract":"Traditional social network analysis was often used for connected or strongly connected networks. Current social network analysis, such as online social network analysis, will target such networks, which have many members and include a large number of connected or strongly connected subnetworks. Using the concept of group (subnetwork), we can convert large-scale networks into small-scale networks with nodes as groups. A theoretical model for the influence between groups is given in this paper.","PeriodicalId":254705,"journal":{"name":"2019 Chinese Control And Decision Conference (CCDC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127272402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-03DOI: 10.1109/CCDC.2019.8832897
X. Hong, Wei Wang, Quanli Liu
Recently, vision-based target positioning technology has received considerable attention in the field of computer vision. The positioning technology has many advantages such as faster speed, higher accuracy and more stable and reliable positioning results. Therefore, it plays an important role in the fields of robot technology, military reconnaissance, geographic survey and field measurement. Based on the positioning technology, the paper designs an embedded vision system with DM816x microprocessor (the processing module) and CCD camera equipped with an infrared filter (the acquisition module), which realizes the recognition and positioning of the fire. Moreover, with 200 groups of experiments in a warehouses, the fire detection system has an alarm accuracy rate over 98%, and positioning accuracy of fire is far higher than the national standard of China. In addition, an automatic fire protection system is proposed, which consists of the fire detector and the water cannon, then it can automatically extinguish the fire when a fire is detected.
{"title":"Design and Realization of Fire Detection Using Computer Vision Technology","authors":"X. Hong, Wei Wang, Quanli Liu","doi":"10.1109/CCDC.2019.8832897","DOIUrl":"https://doi.org/10.1109/CCDC.2019.8832897","url":null,"abstract":"Recently, vision-based target positioning technology has received considerable attention in the field of computer vision. The positioning technology has many advantages such as faster speed, higher accuracy and more stable and reliable positioning results. Therefore, it plays an important role in the fields of robot technology, military reconnaissance, geographic survey and field measurement. Based on the positioning technology, the paper designs an embedded vision system with DM816x microprocessor (the processing module) and CCD camera equipped with an infrared filter (the acquisition module), which realizes the recognition and positioning of the fire. Moreover, with 200 groups of experiments in a warehouses, the fire detection system has an alarm accuracy rate over 98%, and positioning accuracy of fire is far higher than the national standard of China. In addition, an automatic fire protection system is proposed, which consists of the fire detector and the water cannon, then it can automatically extinguish the fire when a fire is detected.","PeriodicalId":254705,"journal":{"name":"2019 Chinese Control And Decision Conference (CCDC)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125580294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-03DOI: 10.1109/CCDC.2019.8832885
Qiangqiang Ye, Xueqin Yang, Chaobo Chen, Jingcheng Wang
This paper investigates the characteristics of dynamic nonlinearity and correlation of water quality parameter information, as well as the gradient disappearance and gradient explosion caused by the training data of traditional RNN network model, etc. The long short-term memory network structure (LSTM) is introduced to optimize the structure of RNN network and the connection weight and threshold of hidden layer. A new water quality parameter prediction model of LSTM-RNN network based on improved RNN network structure is proposed by setting the number of storage units in the hidden layer of the network, the number of structural layers of the network model, and adjusting the time window size of the data training set. Combined with the water quality monitoring data of the River in Shanghai, the model is used to predict and verify the main pollutant index COD (potassium permanganate index) in the River. The simulation results show that compared with the traditional GM (grey model) and RNN network water quality prediction model, the sample approximation accuracy and generalization ability of the training prediction based on LSTM-RNN network model is higher and better than that of the traditional GM (grey model) and RNN network model. Good comprehensive prediction performance of river water quality is presented.
{"title":"River Water Quality Parameters Prediction Method Based on LSTM-RNN Model","authors":"Qiangqiang Ye, Xueqin Yang, Chaobo Chen, Jingcheng Wang","doi":"10.1109/CCDC.2019.8832885","DOIUrl":"https://doi.org/10.1109/CCDC.2019.8832885","url":null,"abstract":"This paper investigates the characteristics of dynamic nonlinearity and correlation of water quality parameter information, as well as the gradient disappearance and gradient explosion caused by the training data of traditional RNN network model, etc. The long short-term memory network structure (LSTM) is introduced to optimize the structure of RNN network and the connection weight and threshold of hidden layer. A new water quality parameter prediction model of LSTM-RNN network based on improved RNN network structure is proposed by setting the number of storage units in the hidden layer of the network, the number of structural layers of the network model, and adjusting the time window size of the data training set. Combined with the water quality monitoring data of the River in Shanghai, the model is used to predict and verify the main pollutant index COD (potassium permanganate index) in the River. The simulation results show that compared with the traditional GM (grey model) and RNN network water quality prediction model, the sample approximation accuracy and generalization ability of the training prediction based on LSTM-RNN network model is higher and better than that of the traditional GM (grey model) and RNN network model. Good comprehensive prediction performance of river water quality is presented.","PeriodicalId":254705,"journal":{"name":"2019 Chinese Control And Decision Conference (CCDC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126728159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-03DOI: 10.1109/CCDC.2019.8833410
Shanen Yu, X. Jia, Wenhui Liu
In this paper, the problem of stabilization is studied by output feedback, for a class of uncertain nonlinear cascade systems under weaker conditions. A high-gain observer based controller has been proposed to solve the problem of global output feedback stabilization for more general uncertain systems. Finally, an example is given to illustrate the usefulness of our results.
{"title":"Adaptive stabilization of uncertain cascade nonlinear systems via output feedback","authors":"Shanen Yu, X. Jia, Wenhui Liu","doi":"10.1109/CCDC.2019.8833410","DOIUrl":"https://doi.org/10.1109/CCDC.2019.8833410","url":null,"abstract":"In this paper, the problem of stabilization is studied by output feedback, for a class of uncertain nonlinear cascade systems under weaker conditions. A high-gain observer based controller has been proposed to solve the problem of global output feedback stabilization for more general uncertain systems. Finally, an example is given to illustrate the usefulness of our results.","PeriodicalId":254705,"journal":{"name":"2019 Chinese Control And Decision Conference (CCDC)","volume":"306 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116256107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-03DOI: 10.1109/CCDC.2019.8832928
Wenhui Liu, Xiaojing Qi, X. Jia, Fei Xie
This paper deals with the adaptive control problem for a class of nonlinear systems with mismatched disturbances. First, a nonlinear disturbance observer is constructed to estimate the mismatched external disturbances. Then, based on adding a power integrator technique, an adaptive control scheme is proposed for the nonlinear systems with uncertain parameters. It is proved that the designed adaptive controller ensures the global input-to-state stability of the closed-loop system. Finally, a simulation example is utilized to validate the applicability of the proposed control technique.
{"title":"Adaptive control for a class of nonlinear systems with uncertain parameters and mismatched disturbances","authors":"Wenhui Liu, Xiaojing Qi, X. Jia, Fei Xie","doi":"10.1109/CCDC.2019.8832928","DOIUrl":"https://doi.org/10.1109/CCDC.2019.8832928","url":null,"abstract":"This paper deals with the adaptive control problem for a class of nonlinear systems with mismatched disturbances. First, a nonlinear disturbance observer is constructed to estimate the mismatched external disturbances. Then, based on adding a power integrator technique, an adaptive control scheme is proposed for the nonlinear systems with uncertain parameters. It is proved that the designed adaptive controller ensures the global input-to-state stability of the closed-loop system. Finally, a simulation example is utilized to validate the applicability of the proposed control technique.","PeriodicalId":254705,"journal":{"name":"2019 Chinese Control And Decision Conference (CCDC)","volume":"63 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126757755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-03DOI: 10.1109/CCDC.2019.8832545
X. Qin, Tingting Wang
The problems of tracking moving target of quad-rotor UAV involve target tracking in image sequence and flight control of UAV. Because Camshift algorithm is easily disturbed by background with similar color and has poor robustness to occlusion interference when tracking objects in image sequence. A new tracking algorithm based on Kalman filter and Camshift algorithm with multi-feature fusion is proposed. On this basis, the horizontal displacement between the target and the UAV is calculated as the control input, and a position-attitude outer-inner loop controller is designed to ensure the target near the center of view of the camera in order to track the target efficiently. Experimental and simulation results verify the performance of the algorithm.
{"title":"Visual-based Tracking and Control Algorithm Design for Quadcopter UAV","authors":"X. Qin, Tingting Wang","doi":"10.1109/CCDC.2019.8832545","DOIUrl":"https://doi.org/10.1109/CCDC.2019.8832545","url":null,"abstract":"The problems of tracking moving target of quad-rotor UAV involve target tracking in image sequence and flight control of UAV. Because Camshift algorithm is easily disturbed by background with similar color and has poor robustness to occlusion interference when tracking objects in image sequence. A new tracking algorithm based on Kalman filter and Camshift algorithm with multi-feature fusion is proposed. On this basis, the horizontal displacement between the target and the UAV is calculated as the control input, and a position-attitude outer-inner loop controller is designed to ensure the target near the center of view of the camera in order to track the target efficiently. Experimental and simulation results verify the performance of the algorithm.","PeriodicalId":254705,"journal":{"name":"2019 Chinese Control And Decision Conference (CCDC)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126483975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-03DOI: 10.1109/CCDC.2019.8832414
Zhiyan Han, Jian Wang
For the sake of ameliorating the precision of speech emotion recognition, this paper put forward a new emotion recognition technique based on Deep Learning and Kernel Nonlinear PSVM (Proximal Support Vector Machine) to discern four fundamental human emotion (angry, joy, sadness, surprise). First of all, preprocess speech signal. And then use DBN (Deep Belief Networks) to extract emotional features in speech signal automatically. After that, integrate the DBN automatic features and traditional features (prosody features and quality features) as the total features. Finally, use six Nonlinear Proximal Support Vector Machines to recognize the emotion and use majority voting principle to obtain the final identification result. To assess the new method, we compare the total features, DBN automatic features and traditional features. The experimental results indicate that the total features are better than the other two methods.
{"title":"Speech Emotion Recognition Based on Deep Learning and Kernel Nonlinear PSVM","authors":"Zhiyan Han, Jian Wang","doi":"10.1109/CCDC.2019.8832414","DOIUrl":"https://doi.org/10.1109/CCDC.2019.8832414","url":null,"abstract":"For the sake of ameliorating the precision of speech emotion recognition, this paper put forward a new emotion recognition technique based on Deep Learning and Kernel Nonlinear PSVM (Proximal Support Vector Machine) to discern four fundamental human emotion (angry, joy, sadness, surprise). First of all, preprocess speech signal. And then use DBN (Deep Belief Networks) to extract emotional features in speech signal automatically. After that, integrate the DBN automatic features and traditional features (prosody features and quality features) as the total features. Finally, use six Nonlinear Proximal Support Vector Machines to recognize the emotion and use majority voting principle to obtain the final identification result. To assess the new method, we compare the total features, DBN automatic features and traditional features. The experimental results indicate that the total features are better than the other two methods.","PeriodicalId":254705,"journal":{"name":"2019 Chinese Control And Decision Conference (CCDC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125545523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-03DOI: 10.1109/CCDC.2019.8832476
Tianxiang Mei, Yi Yang, Jianbo Chen, Zhang Guihong, Ziyun Jiao, Long Gao, Xiaolin Ren, Qing Li
In this paper, the mechanism and kinematics of the six degree of freedom joint manipulator PUMA560 where contains the forward kinematics, inverse kinematics and trajectory planning problems of PUMA560 are analyzed by the standard D-H method, and the forward and inverse kinematics transformation formula is derived. The manipulator was modeled with Robotics Toolbox, and the forward kinematics, inverse kinematics solutions and trajectory planning were simulated which can obtain the required data in MATLAB. This paper studies the changes of the main kinematic indexes of the robot in the process of operation, which intuitively reflects the motion state of each rod and end working part of the robot, and provides a theoretical basis for the specific development and control strategy of the manipulator.
{"title":"Simulation Research on Motion Trajectory of PUMA 560 Manipulator Based on MATLAB","authors":"Tianxiang Mei, Yi Yang, Jianbo Chen, Zhang Guihong, Ziyun Jiao, Long Gao, Xiaolin Ren, Qing Li","doi":"10.1109/CCDC.2019.8832476","DOIUrl":"https://doi.org/10.1109/CCDC.2019.8832476","url":null,"abstract":"In this paper, the mechanism and kinematics of the six degree of freedom joint manipulator PUMA560 where contains the forward kinematics, inverse kinematics and trajectory planning problems of PUMA560 are analyzed by the standard D-H method, and the forward and inverse kinematics transformation formula is derived. The manipulator was modeled with Robotics Toolbox, and the forward kinematics, inverse kinematics solutions and trajectory planning were simulated which can obtain the required data in MATLAB. This paper studies the changes of the main kinematic indexes of the robot in the process of operation, which intuitively reflects the motion state of each rod and end working part of the robot, and provides a theoretical basis for the specific development and control strategy of the manipulator.","PeriodicalId":254705,"journal":{"name":"2019 Chinese Control And Decision Conference (CCDC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130335137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-03DOI: 10.1109/CCDC.2019.8833314
Yufei Wang, Yuanyuan Wang, H. Nguyen
As all ships are required to operate with sufficient reliability and appropriate economy, it is necessary to achieve good controlling at reasonable costs. Autopilot systems have a momentous influence on the performance of ships, enabling them to cruise in various sea conditions without human interventions. This paper introduces a Radial Basis Function Neural Network (RBFNN) based Proportional Integral Differential (PID) autopilot system for a surface vessel. In the proposed control algorithm, the RBFNN trained by adaptive mechanism was utilized to approximate the realistic ship’s behaviours, thereby updating the parameters of the discretising PID based controller in real time, so as to compensate for the environmental disturbances and uncertainties during the ship’s sailing. In order to validate the efficiency of the proposed algorithm, the experiments were conducted in a lake by using the free running model scaled ship ‘Hoorn’. The experimental results indicate that the proposed RBFNN PID based autopilot can decrease the course keeping deviations with reasonable rudder actions.
{"title":"Experimental Study of Intelligent Autopilot for Surface Vessels Based on Neural Network Optimised PID Controller","authors":"Yufei Wang, Yuanyuan Wang, H. Nguyen","doi":"10.1109/CCDC.2019.8833314","DOIUrl":"https://doi.org/10.1109/CCDC.2019.8833314","url":null,"abstract":"As all ships are required to operate with sufficient reliability and appropriate economy, it is necessary to achieve good controlling at reasonable costs. Autopilot systems have a momentous influence on the performance of ships, enabling them to cruise in various sea conditions without human interventions. This paper introduces a Radial Basis Function Neural Network (RBFNN) based Proportional Integral Differential (PID) autopilot system for a surface vessel. In the proposed control algorithm, the RBFNN trained by adaptive mechanism was utilized to approximate the realistic ship’s behaviours, thereby updating the parameters of the discretising PID based controller in real time, so as to compensate for the environmental disturbances and uncertainties during the ship’s sailing. In order to validate the efficiency of the proposed algorithm, the experiments were conducted in a lake by using the free running model scaled ship ‘Hoorn’. The experimental results indicate that the proposed RBFNN PID based autopilot can decrease the course keeping deviations with reasonable rudder actions.","PeriodicalId":254705,"journal":{"name":"2019 Chinese Control And Decision Conference (CCDC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131185354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-03DOI: 10.1109/CCDC.2019.8833301
Jidong Jin
The influence of members in social networks and the influence of members in group decision-making belong to the same type of mathematical problem, that is, the influence of nodes in the network. This paper combines Friedkin social network influence model and DeGroot group decision model under a unified theoretical framework. The unified model allows us to see the relationship between the two models and create conditions for further in-depth application.
{"title":"A Fusion Model of Social Influence and Group Decision","authors":"Jidong Jin","doi":"10.1109/CCDC.2019.8833301","DOIUrl":"https://doi.org/10.1109/CCDC.2019.8833301","url":null,"abstract":"The influence of members in social networks and the influence of members in group decision-making belong to the same type of mathematical problem, that is, the influence of nodes in the network. This paper combines Friedkin social network influence model and DeGroot group decision model under a unified theoretical framework. The unified model allows us to see the relationship between the two models and create conditions for further in-depth application.","PeriodicalId":254705,"journal":{"name":"2019 Chinese Control And Decision Conference (CCDC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128962448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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