Pub Date : 2019-12-01DOI: 10.1109/ICICIP47338.2019.9012155
Chengyong Yan, Wenjun Zhang, Shibo Zhou, Zongyao Xue, Pinglin Wang
Aiming at the uncertainties such as human's subjectivity of evaluation index weight and inconsistent distribution in ship maneuverability evaluation, Cluster Analysis, which is a comprehensive evaluation method of ship maneuverability, is proposed. Based on IMO maneuverability index, this method regards one ship with multiple maneuverability evaluation factors as a point in multidimensional space, uses Euclidean distance to measure the similarity between sample points, and classifies and evaluates ship maneuverability. By analyzing and calculating the maneuverability experimental data of 8 ships, the maneuverability of each ship is ranked. The results show that the method is not only scientific and objective in theory, but also simple and reliable in practical application. It is helpful to improve the objectivity of comprehensive evaluation of ship maneuverability.
{"title":"Comprehensive Evaluation of Ship Maneuverability Based on Cluster Analysis","authors":"Chengyong Yan, Wenjun Zhang, Shibo Zhou, Zongyao Xue, Pinglin Wang","doi":"10.1109/ICICIP47338.2019.9012155","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012155","url":null,"abstract":"Aiming at the uncertainties such as human's subjectivity of evaluation index weight and inconsistent distribution in ship maneuverability evaluation, Cluster Analysis, which is a comprehensive evaluation method of ship maneuverability, is proposed. Based on IMO maneuverability index, this method regards one ship with multiple maneuverability evaluation factors as a point in multidimensional space, uses Euclidean distance to measure the similarity between sample points, and classifies and evaluates ship maneuverability. By analyzing and calculating the maneuverability experimental data of 8 ships, the maneuverability of each ship is ranked. The results show that the method is not only scientific and objective in theory, but also simple and reliable in practical application. It is helpful to improve the objectivity of comprehensive evaluation of ship maneuverability.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123046697","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 : 2019-12-01DOI: 10.1109/ICICIP47338.2019.9012208
Jintao Gong, Guang Chen, Hong-xiang Hu, Wenwu Yu
The parameters identification and synchronization of complex dynamical networks (CDNs) with time-varying delays is respectively investigated in this paper. Firstly, by putting to use Lyapunov functional approach, LaSallei's invariance principleand some inequality techniques, we establish a synchronization criterion for the complex dynamical network with time-varying delays by linear control. In addition, we also designed the parameters identification. Finally, two numerical simulation examples are given to verify the correctness and the effectiveness of the acquired criterion.
{"title":"Parameters Identification and Synchronization of Complex Dynamical Networks with Time-varying Delays via Linear Control","authors":"Jintao Gong, Guang Chen, Hong-xiang Hu, Wenwu Yu","doi":"10.1109/ICICIP47338.2019.9012208","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012208","url":null,"abstract":"The parameters identification and synchronization of complex dynamical networks (CDNs) with time-varying delays is respectively investigated in this paper. Firstly, by putting to use Lyapunov functional approach, LaSallei's invariance principleand some inequality techniques, we establish a synchronization criterion for the complex dynamical network with time-varying delays by linear control. In addition, we also designed the parameters identification. Finally, two numerical simulation examples are given to verify the correctness and the effectiveness of the acquired criterion.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124217350","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}
In view of the improved algorithm MOEA/D-AU based on the framework of the decomposition based multi objective optimization algorithm framework (MOEA/D), an adaptive dynamic selection angle adjustment strategy is introduced to balance between convergence and diversity. This paper proposed an adaptive angle selection multi-objective optimization algorithm, MOEA/D-AAU. The algorithm adaptively adjusts the angle range selection coefficient $G$ in the MOEA/D-AU algorithm by using the appropriate dynamic adjustment strategy, which makes the algorithm focus on the convergent back propagation dispersion in the convergence process. Finally, the performance of proposed algorithm is compared with four the state of the art algorithms on DTLZ and WFG benchmark function. Experiments result demonstrated that MOEA/D-AAU algorithm can achieve better Pareto-optimal solutions and obtain a good convergence and diversity in solution space.
{"title":"An improved multi-objective optimization algorithm based on decomposition","authors":"Wanliang Wang, Zheng Wang, Guoqing Li, Senliang Ying","doi":"10.1109/ICICIP47338.2019.9012138","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012138","url":null,"abstract":"In view of the improved algorithm MOEA/D-AU based on the framework of the decomposition based multi objective optimization algorithm framework (MOEA/D), an adaptive dynamic selection angle adjustment strategy is introduced to balance between convergence and diversity. This paper proposed an adaptive angle selection multi-objective optimization algorithm, MOEA/D-AAU. The algorithm adaptively adjusts the angle range selection coefficient $G$ in the MOEA/D-AU algorithm by using the appropriate dynamic adjustment strategy, which makes the algorithm focus on the convergent back propagation dispersion in the convergence process. Finally, the performance of proposed algorithm is compared with four the state of the art algorithms on DTLZ and WFG benchmark function. Experiments result demonstrated that MOEA/D-AAU algorithm can achieve better Pareto-optimal solutions and obtain a good convergence and diversity in solution space.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134278777","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 : 2019-12-01DOI: 10.1109/ICICIP47338.2019.9012165
Zimin Wang, Yumeng Wang, Yuhong Meng, Li Zeng, Zhenbing Liu, Rushi Lan
Ballistocardiogram (BCG) signal is an effective information that can be used to diagnose cardiovascular disease. This paper analyzes a method of learning the Shapelet feature of BCG signal based on ESOINN. Firstly, the original BCG signal is pre-learned using an enhanced self-organizing incremental unsupervised neural network (ESOINN); Then, it's transformed by the shapelet transform algorithm; Finally, the feature selection method is used to select the shapelet feature from the candidate set, and carry out the training of the classifier. The results show that the method can learn the better quality shapelet candidate set, and greatly reduce the number of candidate sets. In addition, the learning time complexity of shapelet features is greatly reduced, and the accuracy of the model is improved.
{"title":"Shapelet Feature Learning Method of BCG Signal Based on ESOINN","authors":"Zimin Wang, Yumeng Wang, Yuhong Meng, Li Zeng, Zhenbing Liu, Rushi Lan","doi":"10.1109/ICICIP47338.2019.9012165","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012165","url":null,"abstract":"Ballistocardiogram (BCG) signal is an effective information that can be used to diagnose cardiovascular disease. This paper analyzes a method of learning the Shapelet feature of BCG signal based on ESOINN. Firstly, the original BCG signal is pre-learned using an enhanced self-organizing incremental unsupervised neural network (ESOINN); Then, it's transformed by the shapelet transform algorithm; Finally, the feature selection method is used to select the shapelet feature from the candidate set, and carry out the training of the classifier. The results show that the method can learn the better quality shapelet candidate set, and greatly reduce the number of candidate sets. In addition, the learning time complexity of shapelet features is greatly reduced, and the accuracy of the model is improved.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133761373","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 : 2019-12-01DOI: 10.1109/ICICIP47338.2019.9012168
Peng Xu, G. Xie, Jin Tao, Minyi Xu
This paper proposes an observer-based event-triggered algorithm for circle formation control problems of first-order multi-agent systems, where the communication topology is modeled by a spanning tree-based directed graph with limited resources. Depending on the trigger threshold of specific measurement error and compared with the norm of a function with states, we apply an event-triggered mechanism to reduce the updates frequency of the controller via observing continually neighbors' state. Sufficient conditions on desired circle formation are derived following the resulting asynchronous network executions converge to the equilibrium points. Both the analysis and numerical simulations show that Zeno behavior can be ruled out under the designed control laws.
{"title":"Observer-based Event-triggered Circle Formation Control for Multi-agent Systems with Directed Topologies","authors":"Peng Xu, G. Xie, Jin Tao, Minyi Xu","doi":"10.1109/ICICIP47338.2019.9012168","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012168","url":null,"abstract":"This paper proposes an observer-based event-triggered algorithm for circle formation control problems of first-order multi-agent systems, where the communication topology is modeled by a spanning tree-based directed graph with limited resources. Depending on the trigger threshold of specific measurement error and compared with the norm of a function with states, we apply an event-triggered mechanism to reduce the updates frequency of the controller via observing continually neighbors' state. Sufficient conditions on desired circle formation are derived following the resulting asynchronous network executions converge to the equilibrium points. Both the analysis and numerical simulations show that Zeno behavior can be ruled out under the designed control laws.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132880569","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 : 2019-12-01DOI: 10.1109/ICICIP47338.2019.9012217
June-Seok Ma, Rong Mo, Miao Chen, L. Cheng, Hongsheng Qi
In recent years, robots are widely used for helping post-stroke patients do rehabilitation training because it can provide long-term, accurate stimulation for motor function recovery. However, how to design a useful robot that can help patients do rehabilitation training such as separate movements and how to establish a human-robot interaction interface to increase the patient's involvement are challenging topics for the hand rehabilitation robot. Therefore, a hand exoskeleton robot has been designed to help the post-stroke patient do hand rehabilitation training with the aid of some advanced control methods. There are two notable features on this robot: 1) the active disturbance rejection controller is utilized to control the robot for a better control performance. Experimental results show that this controller can track the reference better than PID controller and can reject the disturbance as well; and 2) this paper creates a human-robot interaction interface to do active rehabilitation control (mirror-training). Firstly, this paper utilizes the back-propagation neural network to recognize the volunteer's movement intentions (hand gestures) based on surface electromyography (sEMG). Then, the corresponding hand-gesture recognition result is used to control the hand exoskeleton. The result shows that the rehabilitation robot can follow the volunteer's movement intention to fulfill the mirror-training of the patient.
{"title":"Mirror-Training of a Cable- Driven Hand Rehabilitation Robot Based on Surface Electromyography (sEMG)","authors":"June-Seok Ma, Rong Mo, Miao Chen, L. Cheng, Hongsheng Qi","doi":"10.1109/ICICIP47338.2019.9012217","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012217","url":null,"abstract":"In recent years, robots are widely used for helping post-stroke patients do rehabilitation training because it can provide long-term, accurate stimulation for motor function recovery. However, how to design a useful robot that can help patients do rehabilitation training such as separate movements and how to establish a human-robot interaction interface to increase the patient's involvement are challenging topics for the hand rehabilitation robot. Therefore, a hand exoskeleton robot has been designed to help the post-stroke patient do hand rehabilitation training with the aid of some advanced control methods. There are two notable features on this robot: 1) the active disturbance rejection controller is utilized to control the robot for a better control performance. Experimental results show that this controller can track the reference better than PID controller and can reject the disturbance as well; and 2) this paper creates a human-robot interaction interface to do active rehabilitation control (mirror-training). Firstly, this paper utilizes the back-propagation neural network to recognize the volunteer's movement intentions (hand gestures) based on surface electromyography (sEMG). Then, the corresponding hand-gesture recognition result is used to control the hand exoskeleton. The result shows that the rehabilitation robot can follow the volunteer's movement intention to fulfill the mirror-training of the patient.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133627489","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}
Marine floating raft aquaculture is widely distributed along the coast in China. Polarimetric synthetic aperture radar (PoISAR) images can distinguish marine aquaculture targets from sea water background, but optical satellite remote sensing images cannot detect these effectively and completely. In this paper, considering the complex character of PoISAR data, a complex-value convolutional neural network is utilized for marine aquaculture recognition, which makes the most of phase information implicit in original complex data to improve detection accuracy. Experiments on actual GF-3 PoISAR images substantiate the effectiveness of the proposed approach.
{"title":"GF-3 PolSAR Marine Aquaculture Recognition Based on Complex Convolutional Neural Networks","authors":"Jianchao Fan, Xinxin Wang, Xiang Wang, Xiaoxin Liu, Jianhua Zhao, Qinghui Meng","doi":"10.1109/ICICIP47338.2019.9012171","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012171","url":null,"abstract":"Marine floating raft aquaculture is widely distributed along the coast in China. Polarimetric synthetic aperture radar (PoISAR) images can distinguish marine aquaculture targets from sea water background, but optical satellite remote sensing images cannot detect these effectively and completely. In this paper, considering the complex character of PoISAR data, a complex-value convolutional neural network is utilized for marine aquaculture recognition, which makes the most of phase information implicit in original complex data to improve detection accuracy. Experiments on actual GF-3 PoISAR images substantiate the effectiveness of the proposed approach.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123565260","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 : 2019-12-01DOI: 10.1109/ICICIP47338.2019.9012175
R. Herzallah
This paper proposes a cautious randomised controller that is derived such that it minimises the discrepancy between the joint distribution of the system dynamics and a predefined ideal joint probability density function (pdf). This distance is known as the Kullback-Leibler divergence. The developed methodology is demonstrated on a class of uncertain stochastic systems that can be characterised by Gaussian density functions. The density function of the dynamics of the system is assumed to be unknown, therefore estimated using the generalised linear neural network models. The analytic solution of the randomised cautious controller is obtained by evaluating the multi-integrals in the Kulback-Leibler divergence cost function. The derived cautious controller minimises to high accuracy the expected value of the Kullback-Leibler divergence taking into consideration the covariance of the dynamics estimated probability density functions.
{"title":"Robust Probabilistic Control for Linear Stochastic Systems with Functional Uncertainty","authors":"R. Herzallah","doi":"10.1109/ICICIP47338.2019.9012175","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012175","url":null,"abstract":"This paper proposes a cautious randomised controller that is derived such that it minimises the discrepancy between the joint distribution of the system dynamics and a predefined ideal joint probability density function (pdf). This distance is known as the Kullback-Leibler divergence. The developed methodology is demonstrated on a class of uncertain stochastic systems that can be characterised by Gaussian density functions. The density function of the dynamics of the system is assumed to be unknown, therefore estimated using the generalised linear neural network models. The analytic solution of the randomised cautious controller is obtained by evaluating the multi-integrals in the Kulback-Leibler divergence cost function. The derived cautious controller minimises to high accuracy the expected value of the Kullback-Leibler divergence taking into consideration the covariance of the dynamics estimated probability density functions.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131175895","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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