The Tujia nationality's brocade (short as Tujia brocade or Xilankapu) is one of the Tujia traditional handicrafts; it has been widely used in Tujia people's daily life, especially for the people reside in the YouShui River Basin. Tujia brocade not only has many varieties, manifestations and performance styles, but also very rich design patterns, these exhibits aesthetic sentiment and national consciousness. It is important effect on the deep excavation of Tujia brocade culture and virtual design by analyzing the compositional structure and structural parameters of Tujia brocade, The paper deconstructs and analysis the structures of Tujia brocade, discusses the hierarchical composition and structure parameter in the analysis of a large number of traditional classic patterns. It develops Tujia brocade structure simulation and interactive design system based on Unity 3D technology, which simulates innovative patterns and presents them visually by changing the compositional structure parameters values, these vector diagrams of Tujia brocade could be directly used in the intelligent machine production.
{"title":"On compositional structure simulation and interactive design of Tujia brocade","authors":"Gang Zhao, Yawen Chen, Bingbing Di, Shuai Lu, Yali Yu, Hui Zan","doi":"10.1109/DDCLS.2017.8068130","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068130","url":null,"abstract":"The Tujia nationality's brocade (short as Tujia brocade or Xilankapu) is one of the Tujia traditional handicrafts; it has been widely used in Tujia people's daily life, especially for the people reside in the YouShui River Basin. Tujia brocade not only has many varieties, manifestations and performance styles, but also very rich design patterns, these exhibits aesthetic sentiment and national consciousness. It is important effect on the deep excavation of Tujia brocade culture and virtual design by analyzing the compositional structure and structural parameters of Tujia brocade, The paper deconstructs and analysis the structures of Tujia brocade, discusses the hierarchical composition and structure parameter in the analysis of a large number of traditional classic patterns. It develops Tujia brocade structure simulation and interactive design system based on Unity 3D technology, which simulates innovative patterns and presents them visually by changing the compositional structure parameters values, these vector diagrams of Tujia brocade could be directly used in the intelligent machine production.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132560595","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 : 2017-05-01DOI: 10.1109/DDCLS.2017.8068135
H. Yuan, Yuan Gao, X. Dai, L. Yu
It presents a sliding mode strategy integrated with rear angle and yaw moment to control the four-wheel-steering vehicle. The slip angle and yaw rate of vehicle gravity center are controlled variables. One input of sliding mode controller is the front steering angle which is measured by sensor, while others are estimated values of disturbance bound and the errors of slip angle and yaw rate. Furthermore, the disturbance bound estimator, and sliding mode controller of rear wheel angle and yaw moment are designed based on the dynamic model and ideal vehicle steering model. The results show that the sliding mode control strategy presents good performance and robustness under different driving conditions. After changing vehicle parameters, it found that maneuverability and stability of the vehicle was guaranteed through tracking the yaw rate and zero degree of side slip angle.
{"title":"Four-wheel-steering vehicle control via sliding mode strategy","authors":"H. Yuan, Yuan Gao, X. Dai, L. Yu","doi":"10.1109/DDCLS.2017.8068135","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068135","url":null,"abstract":"It presents a sliding mode strategy integrated with rear angle and yaw moment to control the four-wheel-steering vehicle. The slip angle and yaw rate of vehicle gravity center are controlled variables. One input of sliding mode controller is the front steering angle which is measured by sensor, while others are estimated values of disturbance bound and the errors of slip angle and yaw rate. Furthermore, the disturbance bound estimator, and sliding mode controller of rear wheel angle and yaw moment are designed based on the dynamic model and ideal vehicle steering model. The results show that the sliding mode control strategy presents good performance and robustness under different driving conditions. After changing vehicle parameters, it found that maneuverability and stability of the vehicle was guaranteed through tracking the yaw rate and zero degree of side slip angle.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133979840","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 : 2017-05-01DOI: 10.1109/DDCLS.2017.8068044
Jiaxi Chen, Junmi Li, Jinsha Li
This paper investigates the adaptive consensus problem of first-order linearly parameterized multi-agent systems (MASs) with imprecise communication topology structure. T-S fuzzy models are presented to describe leader-followers MASs with imprecise communication topology structure, and a fuzzy distributed adaptive iterative learning control protocol is proposed. With the dynamic of leader unknown to any of the agent, the proposed protocol guarantees that the follower agents can track the leader uniformly on [0, T] for consensus problem. A numerical example is provided to show the effectiveness of the theoretical results.
{"title":"Fuzzy adaptive iterative learning control for consensus of multi-agent systems with imprecise communication topology structure","authors":"Jiaxi Chen, Junmi Li, Jinsha Li","doi":"10.1109/DDCLS.2017.8068044","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068044","url":null,"abstract":"This paper investigates the adaptive consensus problem of first-order linearly parameterized multi-agent systems (MASs) with imprecise communication topology structure. T-S fuzzy models are presented to describe leader-followers MASs with imprecise communication topology structure, and a fuzzy distributed adaptive iterative learning control protocol is proposed. With the dynamic of leader unknown to any of the agent, the proposed protocol guarantees that the follower agents can track the leader uniformly on [0, T] for consensus problem. A numerical example is provided to show the effectiveness of the theoretical results.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132844843","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 : 2017-05-01DOI: 10.1109/DDCLS.2017.8068094
Wenlong Yao, R. Chi, Boyang Li, Ai-ling Chen
Speed sensorless vector control based on ILC of dynamic positioning system propulsion motor for semi-submersible ship is proposed for the problem of speed fluctuation of the semi-submersible ship propulsion motor which is caused by the external sea conditions and the unknown load disturbances. The speed error compensation is introduced in the algorithm, and the periodic torque ripple of the propulsion motor is reduced by utilizing the error trend and the previous error information. The results show that the speed sensorless vector control based on ILC can effectively suppress the torque ripple of the semi-submersible ship propulsion motor and improve the state observation accuracy of the system. It satisfies the steady-state error requirement of the semi-submersible ship propulsion system and the reliability of the system was improved through comparing with the vector control algorithm based on the classical PI control.
{"title":"Propulsion motor vector control based on ILC for dynamic positioning system","authors":"Wenlong Yao, R. Chi, Boyang Li, Ai-ling Chen","doi":"10.1109/DDCLS.2017.8068094","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068094","url":null,"abstract":"Speed sensorless vector control based on ILC of dynamic positioning system propulsion motor for semi-submersible ship is proposed for the problem of speed fluctuation of the semi-submersible ship propulsion motor which is caused by the external sea conditions and the unknown load disturbances. The speed error compensation is introduced in the algorithm, and the periodic torque ripple of the propulsion motor is reduced by utilizing the error trend and the previous error information. The results show that the speed sensorless vector control based on ILC can effectively suppress the torque ripple of the semi-submersible ship propulsion motor and improve the state observation accuracy of the system. It satisfies the steady-state error requirement of the semi-submersible ship propulsion system and the reliability of the system was improved through comparing with the vector control algorithm based on the classical PI control.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131278008","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 : 2017-05-01DOI: 10.1109/DDCLS.2017.8068066
Zhiqiang Geng, Huachao Gao, Qunxiong Zhu, Yongming Han
Energy and management of complex chemical processes play a crucial role in the sustainable development procedure. In order to analyze the effect of the technology, management level, and production structure having on energy efficiency, we put forward an energy analysis and management method based on index decomposition analysis (IDA). The proposed method can reflect the impact of energy usage by integrating the level of energy activity, energy hierarchy and energy intensity effectively. Meanwhile, energy efficiency improvement, energy consumption reduction and energy-savings can be visually disCovered by the proposed method. Finally, the proposed method is applied for energy management and conservation practices of the ethylene production process. The demonstration analysis of ethylene production has verified the practicality of the proposed method. Moreover, we can propose corresponding improvement for the ethylene production.
{"title":"Energy analysis and management method of complex chemical processes based on index decomposition analysis","authors":"Zhiqiang Geng, Huachao Gao, Qunxiong Zhu, Yongming Han","doi":"10.1109/DDCLS.2017.8068066","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068066","url":null,"abstract":"Energy and management of complex chemical processes play a crucial role in the sustainable development procedure. In order to analyze the effect of the technology, management level, and production structure having on energy efficiency, we put forward an energy analysis and management method based on index decomposition analysis (IDA). The proposed method can reflect the impact of energy usage by integrating the level of energy activity, energy hierarchy and energy intensity effectively. Meanwhile, energy efficiency improvement, energy consumption reduction and energy-savings can be visually disCovered by the proposed method. Finally, the proposed method is applied for energy management and conservation practices of the ethylene production process. The demonstration analysis of ethylene production has verified the practicality of the proposed method. Moreover, we can propose corresponding improvement for the ethylene production.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131867874","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 : 2017-05-01DOI: 10.1109/DDCLS.2017.8068101
Yu Hui, R. Chi
This paper explores the question about iterative learning observer design about a kind of nonlinear plants have repetitive operating characteristics. Different from traditional methods, the proposed iterative learning state observer is conducted and updated along the iteration direction. Furthermore, the proposed method has data-driven nature and derives from nonlinear systems directly, where no any model information is required except for the input and output measurements. A simulation case was employed to prove the performance of the given observer.
{"title":"Iterative learning state estimation for nonlinear repetitive process","authors":"Yu Hui, R. Chi","doi":"10.1109/DDCLS.2017.8068101","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068101","url":null,"abstract":"This paper explores the question about iterative learning observer design about a kind of nonlinear plants have repetitive operating characteristics. Different from traditional methods, the proposed iterative learning state observer is conducted and updated along the iteration direction. Furthermore, the proposed method has data-driven nature and derives from nonlinear systems directly, where no any model information is required except for the input and output measurements. A simulation case was employed to prove the performance of the given observer.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"566 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133761121","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 : 2017-05-01DOI: 10.1109/DDCLS.2017.8068060
Jin Xie, Weisheng Chen, Hao Dai
This paper investigates the problem of the distributed cooperative learning over networks via the wavelet approximation. On the basis of the wavelet approximation (WA) theory, the novel distributed cooperative learning (DCL) method, called DCL-WA, is proposed in this paper. The wavelet series is used to approximate the function of network nodes. For the networked systems, DCL method is used to train the optimal weight coefficient matrices of wavelet series, so as to get the best approximation function of network nodes. An illustrative example is presented to show the efficiency of the proposed strategy.
{"title":"Distributed cooperative learning over networks via wavelet approximation","authors":"Jin Xie, Weisheng Chen, Hao Dai","doi":"10.1109/DDCLS.2017.8068060","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068060","url":null,"abstract":"This paper investigates the problem of the distributed cooperative learning over networks via the wavelet approximation. On the basis of the wavelet approximation (WA) theory, the novel distributed cooperative learning (DCL) method, called DCL-WA, is proposed in this paper. The wavelet series is used to approximate the function of network nodes. For the networked systems, DCL method is used to train the optimal weight coefficient matrices of wavelet series, so as to get the best approximation function of network nodes. An illustrative example is presented to show the efficiency of the proposed strategy.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115330044","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 : 2017-05-01DOI: 10.1109/DDCLS.2017.8068173
Yifei Pan, Zehui Mao, Quan Xiao, Xiao He, Y. Zhang
In this paper, a multi-model data trend prediction method is proposed for marine diesel engine to the prognosis of faults. According to the data characteristics, the discrete wavelet transform is used to process the data, which can eliminate the noise of the high-frequency and retain the low-frequency signal. The auto-regression, the gray model, the BP neural network and the radial-based neural network methods are employed to trend prediction and the results are compared. In terms of convergence speed, the autoregressive model has the best performance of the fault prognosis. In terms of fitting error, the neural network model has the best accuracy.
{"title":"Discrete wavelet transform based data trend prediction for marine diesel engine","authors":"Yifei Pan, Zehui Mao, Quan Xiao, Xiao He, Y. Zhang","doi":"10.1109/DDCLS.2017.8068173","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068173","url":null,"abstract":"In this paper, a multi-model data trend prediction method is proposed for marine diesel engine to the prognosis of faults. According to the data characteristics, the discrete wavelet transform is used to process the data, which can eliminate the noise of the high-frequency and retain the low-frequency signal. The auto-regression, the gray model, the BP neural network and the radial-based neural network methods are employed to trend prediction and the results are compared. In terms of convergence speed, the autoregressive model has the best performance of the fault prognosis. In terms of fitting error, the neural network model has the best accuracy.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117040825","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 : 2017-05-01DOI: 10.1109/DDCLS.2017.8068054
Tingting Meng, Wei He, Deqing Huang, Lung-Jieh Yang, Changyin Sun
In this paper, vibration control is addressed for a Timoshenko beam system with input backlash and external disturbances. By integrating iterative learning control into adaptive control, two dual-loop adaptive iterative learning control schemes are proposed in the presence of the input backlash. Two observers are designed to estimate two bounded terms, which are divided from the backlash inputs. Based on the defined composite energy function, all the signals are proved to be bounded in each iteration. Along the iteration axis, (I) the input backlash is tackled; (II) the transverse displacements and the angle displacements are suppressed to zero; and (III) the spatiotemporally varying disturbance and the time-varying disturbance are rejected. Simulations are provided to manifest the effectiveness of the proposed control laws.
{"title":"Iterative learning control for a timoshenko beam with input backlash","authors":"Tingting Meng, Wei He, Deqing Huang, Lung-Jieh Yang, Changyin Sun","doi":"10.1109/DDCLS.2017.8068054","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068054","url":null,"abstract":"In this paper, vibration control is addressed for a Timoshenko beam system with input backlash and external disturbances. By integrating iterative learning control into adaptive control, two dual-loop adaptive iterative learning control schemes are proposed in the presence of the input backlash. Two observers are designed to estimate two bounded terms, which are divided from the backlash inputs. Based on the defined composite energy function, all the signals are proved to be bounded in each iteration. Along the iteration axis, (I) the input backlash is tackled; (II) the transverse displacements and the angle displacements are suppressed to zero; and (III) the spatiotemporally varying disturbance and the time-varying disturbance are rejected. Simulations are provided to manifest the effectiveness of the proposed control laws.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116369003","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 : 2017-05-01DOI: 10.1109/DDCLS.2017.8068090
Xuhui Bu, Jian Liu, Z. Hou
This paper develops a novel iterative learning parameter identification algorithm for a class of single parameter systems with multi-threshold quantized observations. The identification algorithm is constructed along the iteration axis and it can incorporate the parameter identification ability and the learning ability to deal with unknown time-varying parameters. Based on the recursive form of the estimation error along the iteration axis, it is proved that the convergence of parameter estimation can be guaranteed over the whole finite time interval. A numerical example is given to demonstrate the effectiveness of the algorithms.
{"title":"Iterative learning identification using quantized observations","authors":"Xuhui Bu, Jian Liu, Z. Hou","doi":"10.1109/DDCLS.2017.8068090","DOIUrl":"https://doi.org/10.1109/DDCLS.2017.8068090","url":null,"abstract":"This paper develops a novel iterative learning parameter identification algorithm for a class of single parameter systems with multi-threshold quantized observations. The identification algorithm is constructed along the iteration axis and it can incorporate the parameter identification ability and the learning ability to deal with unknown time-varying parameters. Based on the recursive form of the estimation error along the iteration axis, it is proved that the convergence of parameter estimation can be guaranteed over the whole finite time interval. A numerical example is given to demonstrate the effectiveness of the algorithms.","PeriodicalId":419114,"journal":{"name":"2017 6th Data Driven Control and Learning Systems (DDCLS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123526790","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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