Support vector machine (SVM) is a classification model, which learns the decision surface that maximizes the margin in the feature space. Such a decision surface has a good classification ability for unknown new samples. In real-world applications, the data set usually contains many noises and outliers, which will affect the learning of the decision surface, thus the maximum margin cannot be obtained, and the generalization ability of SVM will be reduced. In this paper, we introduce an adjacency factor to each input point to characterize the local neighbor relationship between each point. Weighting each sample point by the adjacency factor can let different sample points make different contributions to the learning of the decision surface. Thus, we can filter out the influence of noises and outliers on the decision surface by this weighting method. We propose this new method namely local neighborhood reliability weighted support vector machine (LN-SVM).
{"title":"Local Neighborhood Reliability Weighted Support Vector Machine","authors":"Yunlong Gao, Yisong Zhang, Baihua Chen, Yuhui Xiong","doi":"10.1109/IAI50351.2020.9262215","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262215","url":null,"abstract":"Support vector machine (SVM) is a classification model, which learns the decision surface that maximizes the margin in the feature space. Such a decision surface has a good classification ability for unknown new samples. In real-world applications, the data set usually contains many noises and outliers, which will affect the learning of the decision surface, thus the maximum margin cannot be obtained, and the generalization ability of SVM will be reduced. In this paper, we introduce an adjacency factor to each input point to characterize the local neighbor relationship between each point. Weighting each sample point by the adjacency factor can let different sample points make different contributions to the learning of the decision surface. Thus, we can filter out the influence of noises and outliers on the decision surface by this weighting method. We propose this new method namely local neighborhood reliability weighted support vector machine (LN-SVM).","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117270776","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 : 2020-10-23DOI: 10.1109/IAI50351.2020.9262207
K. Mitsugi, M. Deng
This paper presents a method of operator-based nonlinear temperature control for a calorimeter using a Peltier device. The Peltier device has nonlinear characteristic and so it is not easy to design a controller which satisfies desired performance. Based on the concept of the Lipschitz operator and the robust right coprime factorization condition, nonlinear temperature controllers are designed for the model, and the closed-loop system's robust stability is guaranteed. Moreover, a tracking operator is designed to ensure the temperature tracking performance. Finally, simulation and experimental results are presented to show the effectiveness of the proposed design method.
{"title":"Operator-Based Robust Nonlinear Control for Calorimetric Power Loss Measurement System Using Peltier Device","authors":"K. Mitsugi, M. Deng","doi":"10.1109/IAI50351.2020.9262207","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262207","url":null,"abstract":"This paper presents a method of operator-based nonlinear temperature control for a calorimeter using a Peltier device. The Peltier device has nonlinear characteristic and so it is not easy to design a controller which satisfies desired performance. Based on the concept of the Lipschitz operator and the robust right coprime factorization condition, nonlinear temperature controllers are designed for the model, and the closed-loop system's robust stability is guaranteed. Moreover, a tracking operator is designed to ensure the temperature tracking performance. Finally, simulation and experimental results are presented to show the effectiveness of the proposed design method.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123436368","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 : 2020-10-23DOI: 10.1109/IAI50351.2020.9262158
Liandong Zhang, Z. Cai, Lim Joo Ghee
The digital twin application in manufacturing is mainly based on the virtual simulation model of a digital twin to build a solid model, which is applied to the product processing and assembly to achieve precise production control. This paper presents a virtual commissioning digital twin model for the modularized automatic assembly system running in our lab. First, the Siemens NX MCD software tool is used to develop the virtual commissioning digital twin model for the system. Then the different working scenarios are simulated and implemented in the virtual physical simulation environment. The data from the proposed virtual commissioning digital twin model is collected and trained with 6 different machine learning algorithm such as Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Gaussian Naive Bayes (NB) and Support Vector Machines (SVM). The advantage of our newly developed virtual commissioning model is that it is able to simulate different working conditions without risk and cost-free. It is also convenient to mimic the worsening working status and failed operation scenarios which need long time to collect for the real system. We use the collected data as input for the machine learning to implement the system monitoring and predicting. The machine learning results for 6 learning algorithms are presented and it shows the possibilities and advantages of our proposed virtual commissioning digital twin model.
{"title":"Virtual Commissioning and Machine Learning of a Reconfigurable Assembly System","authors":"Liandong Zhang, Z. Cai, Lim Joo Ghee","doi":"10.1109/IAI50351.2020.9262158","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262158","url":null,"abstract":"The digital twin application in manufacturing is mainly based on the virtual simulation model of a digital twin to build a solid model, which is applied to the product processing and assembly to achieve precise production control. This paper presents a virtual commissioning digital twin model for the modularized automatic assembly system running in our lab. First, the Siemens NX MCD software tool is used to develop the virtual commissioning digital twin model for the system. Then the different working scenarios are simulated and implemented in the virtual physical simulation environment. The data from the proposed virtual commissioning digital twin model is collected and trained with 6 different machine learning algorithm such as Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Gaussian Naive Bayes (NB) and Support Vector Machines (SVM). The advantage of our newly developed virtual commissioning model is that it is able to simulate different working conditions without risk and cost-free. It is also convenient to mimic the worsening working status and failed operation scenarios which need long time to collect for the real system. We use the collected data as input for the machine learning to implement the system monitoring and predicting. The machine learning results for 6 learning algorithms are presented and it shows the possibilities and advantages of our proposed virtual commissioning digital twin model.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122673443","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 : 2020-10-23DOI: 10.1109/IAI50351.2020.9262172
Linhan Qiao, Youmin Zhang, Y. Qu
In this paper, a U-net with feature loss enhanced generative adversarial network (GAN) is designed for the wildfire or smoke images restoration which is captured by unmanned aerial vehicles in a serious environment. Based on the concepts of GAN, feature loss, and fastai API, we firstly crappy the target images, and train a U-net architecture based generator, then load the adaptive loss of discriminator and the mean square error together to train the GAN model. After the GAN, a second U-net grabs the feature loss from an Imagenet pre-trained loss network to generate the GAN output images with one more step. This U-net enhanced the generator of GAN and helped to get the main features in human conception. Comparing with other restoration methods, this model used the adaptive loss to train the GAN and perceptual loss to train the next U-net. Learning rate with simulation annealing helped jumping out of the local minimum. The result proved the good performance of this model.
{"title":"Pre-processing for UAV Based Wildfire Detection: A Loss U-net Enhanced GAN for Image Restoration","authors":"Linhan Qiao, Youmin Zhang, Y. Qu","doi":"10.1109/IAI50351.2020.9262172","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262172","url":null,"abstract":"In this paper, a U-net with feature loss enhanced generative adversarial network (GAN) is designed for the wildfire or smoke images restoration which is captured by unmanned aerial vehicles in a serious environment. Based on the concepts of GAN, feature loss, and fastai API, we firstly crappy the target images, and train a U-net architecture based generator, then load the adaptive loss of discriminator and the mean square error together to train the GAN model. After the GAN, a second U-net grabs the feature loss from an Imagenet pre-trained loss network to generate the GAN output images with one more step. This U-net enhanced the generator of GAN and helped to get the main features in human conception. Comparing with other restoration methods, this model used the adaptive loss to train the GAN and perceptual loss to train the next U-net. Learning rate with simulation annealing helped jumping out of the local minimum. The result proved the good performance of this model.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122792373","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 : 2020-10-23DOI: 10.1109/IAI50351.2020.9262193
Kaixin Wu, Ji-gong Li, Jing Yang, Fanfu Zeng, Jia Liu
This paper presents an approach based on BP neural network for quadrotors that estimates the wind velocity in real-time based on measurement data of its on-board inertial measurement unit (IMU) and GPS only. The proposed method is a gray box modelling method for the real-time wind estimation, avoids oversimplifications and determination of many parameters in the existing dynamic models or aerodynamic models of quadrotors. The nonlinear functional relationship between the wind velocity and the flight parameters provided by the on-board IMU and GPS is established after the training of the BP network, using the data collected from the quadrotor and an anemometer not far away from the quadrotor, and then applied to estimate the wind velocity in real time only with the outputs of the on-board IMU and GPS when the quadrotor is flying. The simulation results show that the proposed method can achieve wind estimation with a root mean square error (RMSE) less than 0.02 m/s.
{"title":"Real-time Wind Estimation with a Quadrotor using BP Neural Network","authors":"Kaixin Wu, Ji-gong Li, Jing Yang, Fanfu Zeng, Jia Liu","doi":"10.1109/IAI50351.2020.9262193","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262193","url":null,"abstract":"This paper presents an approach based on BP neural network for quadrotors that estimates the wind velocity in real-time based on measurement data of its on-board inertial measurement unit (IMU) and GPS only. The proposed method is a gray box modelling method for the real-time wind estimation, avoids oversimplifications and determination of many parameters in the existing dynamic models or aerodynamic models of quadrotors. The nonlinear functional relationship between the wind velocity and the flight parameters provided by the on-board IMU and GPS is established after the training of the BP network, using the data collected from the quadrotor and an anemometer not far away from the quadrotor, and then applied to estimate the wind velocity in real time only with the outputs of the on-board IMU and GPS when the quadrotor is flying. The simulation results show that the proposed method can achieve wind estimation with a root mean square error (RMSE) less than 0.02 m/s.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114274575","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 : 2020-10-23DOI: 10.1109/IAI50351.2020.9262184
Yuan Jiyang, Z. Yanbin, Gao Jian
Intelligent chemical manufacturing industry is the basic industry and pillar industry of the national economy. The development of intelligent chemical manufacturing industry has the characteristics of high technology, high value-added, high intelligence intensiveness, synergy, intelligence, and greenness. Take Shandong Province as a case, Analyze the actual situation of the advantages, disadvantages, opportunities and threats of the development of the intelligent chemical manufacturing industry, Use grey forecasting method based on big data forecast to analyze the development trend of intelligent chemical manufacturing industry, Provide a scientific basis for the formulation of management systems and strategic objectives for the intelligent chemical manufacturing industry.
{"title":"Research on the development of intelligent chemical manufacturing industry in Shandong Province based on big data analysis","authors":"Yuan Jiyang, Z. Yanbin, Gao Jian","doi":"10.1109/IAI50351.2020.9262184","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262184","url":null,"abstract":"Intelligent chemical manufacturing industry is the basic industry and pillar industry of the national economy. The development of intelligent chemical manufacturing industry has the characteristics of high technology, high value-added, high intelligence intensiveness, synergy, intelligence, and greenness. Take Shandong Province as a case, Analyze the actual situation of the advantages, disadvantages, opportunities and threats of the development of the intelligent chemical manufacturing industry, Use grey forecasting method based on big data forecast to analyze the development trend of intelligent chemical manufacturing industry, Provide a scientific basis for the formulation of management systems and strategic objectives for the intelligent chemical manufacturing industry.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122036149","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 : 2020-10-23DOI: 10.1109/IAI50351.2020.9262227
Dong Lin, Lijun Zhang, X. Xia
This paper proposes a hierarchical control strategy for greenhouse climate control. The proposed hierarchical control consists of two layers (an upper layer and a lower layer). The upper layer is to generate set points by solving an optimization problem. The objective is to minimize the energy cost under the time-of-use (TOU) tariff while keeping greenhouse climate (temperature, relative humidity and carbon dioxide concentration) within the required range. The lower layer is to track the trajectories obtained by the upper layer. A model predictive controller is designed to address system disturbances and the results are compared with that of an open loop controller. A performance index, relative average deviation (RAD), is introduced to compare the tracking performance of the open loop control and proposed closed-loop model predictive control. Simulation results show that the proposed strategy can reduce 7.86% energy cost compared with the strategy that aims to minimize energy consumption. Moreover, the proposed model predictive control can track reference trajectories better than open loop control under system disturbances.
{"title":"Hierarchical model predictive control of greenhouse climate to reduce energy cost","authors":"Dong Lin, Lijun Zhang, X. Xia","doi":"10.1109/IAI50351.2020.9262227","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262227","url":null,"abstract":"This paper proposes a hierarchical control strategy for greenhouse climate control. The proposed hierarchical control consists of two layers (an upper layer and a lower layer). The upper layer is to generate set points by solving an optimization problem. The objective is to minimize the energy cost under the time-of-use (TOU) tariff while keeping greenhouse climate (temperature, relative humidity and carbon dioxide concentration) within the required range. The lower layer is to track the trajectories obtained by the upper layer. A model predictive controller is designed to address system disturbances and the results are compared with that of an open loop controller. A performance index, relative average deviation (RAD), is introduced to compare the tracking performance of the open loop control and proposed closed-loop model predictive control. Simulation results show that the proposed strategy can reduce 7.86% energy cost compared with the strategy that aims to minimize energy consumption. Moreover, the proposed model predictive control can track reference trajectories better than open loop control under system disturbances.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"2000 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128275209","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 : 2020-10-23DOI: 10.1109/IAI50351.2020.9262220
Kai Shi, Chenni Wu, Yuechen Wang, Hai Yu, Zhiliang Zhu
SCADA data lacks sensory data such as vibration and strain measurement for traditional wind turbine condition monitoring; it is updates in low frequency, one piece of data per 10 minutes in the main, which is also low for failure prediction. Thus it is a tough work to monitoring wind turbines' working condition based on SCADA data. To this end, this paper proposes a wind turbine condition monitoring method based on variable importance of random forest by utilizing the SCADA data. First, to minimize the misjudgment caused by individual outliers, we divide the SCADA time series into segments in unit of time period T. Second, we use decrease accuracy method to calculate the variable importance of random forest, as the feature vector of each segment, which characterizes a turbine's condition. Third, we compare a specific turbine's variable importance with the standard feature of healthy turbines to obtain the proximity of them. Fourth, the monitoring baseline is determined according to 3σ, and the deterioration function is applied to construct the failure probability model. To show the effectiveness, we apply the proposed method to four real cases from wind farms in China.
{"title":"Wind Turbine Condition Monitoring Based on Variable Importance of Random Forest","authors":"Kai Shi, Chenni Wu, Yuechen Wang, Hai Yu, Zhiliang Zhu","doi":"10.1109/IAI50351.2020.9262220","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262220","url":null,"abstract":"SCADA data lacks sensory data such as vibration and strain measurement for traditional wind turbine condition monitoring; it is updates in low frequency, one piece of data per 10 minutes in the main, which is also low for failure prediction. Thus it is a tough work to monitoring wind turbines' working condition based on SCADA data. To this end, this paper proposes a wind turbine condition monitoring method based on variable importance of random forest by utilizing the SCADA data. First, to minimize the misjudgment caused by individual outliers, we divide the SCADA time series into segments in unit of time period T. Second, we use decrease accuracy method to calculate the variable importance of random forest, as the feature vector of each segment, which characterizes a turbine's condition. Third, we compare a specific turbine's variable importance with the standard feature of healthy turbines to obtain the proximity of them. Fourth, the monitoring baseline is determined according to 3σ, and the deterioration function is applied to construct the failure probability model. To show the effectiveness, we apply the proposed method to four real cases from wind farms in China.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123189283","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 : 2020-10-23DOI: 10.1109/IAI50351.2020.9262186
J. Sun, Z. Yuan, Q. Zhang, X. Dai, D. Cui
With the fast expansion of China's high-speed railway network and the rapid development of information and communication technologies (ICTs) in railways, more and more operation data of the China high-speed railway have been collected and will continue increasing forever. As a valuable asset, the big data of China high-speed railway need effective and dedicated management, which not only helps the railway operators to transform its daily operation to provide better services to passengers, but also has the potential to create new value to the whole industry chain and the stake-holders. The rise of artificial intelligence (AI) and big data technologies open up a new era allowing new application of railway data and the realization of data asset appreciation. This article discusses how AI-driven data management technology may tap and play the value of data assets to convert the value asset of data assets. Since dispatching and transportation are the core business of railways, the data asset management in the new generation of train dispatching system and railway passenger transportation system are investigated from the perspective of the acquisition, analysis and application of China's high-speed railway operation data. Future trends in data analysis technologies and evaluation of data assets are also discussed.
{"title":"Data Asset Management and Analytics in China High Speed Railways: Challenges and Perspectives","authors":"J. Sun, Z. Yuan, Q. Zhang, X. Dai, D. Cui","doi":"10.1109/IAI50351.2020.9262186","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262186","url":null,"abstract":"With the fast expansion of China's high-speed railway network and the rapid development of information and communication technologies (ICTs) in railways, more and more operation data of the China high-speed railway have been collected and will continue increasing forever. As a valuable asset, the big data of China high-speed railway need effective and dedicated management, which not only helps the railway operators to transform its daily operation to provide better services to passengers, but also has the potential to create new value to the whole industry chain and the stake-holders. The rise of artificial intelligence (AI) and big data technologies open up a new era allowing new application of railway data and the realization of data asset appreciation. This article discusses how AI-driven data management technology may tap and play the value of data assets to convert the value asset of data assets. Since dispatching and transportation are the core business of railways, the data asset management in the new generation of train dispatching system and railway passenger transportation system are investigated from the perspective of the acquisition, analysis and application of China's high-speed railway operation data. Future trends in data analysis technologies and evaluation of data assets are also discussed.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114818057","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 : 2020-10-23DOI: 10.1109/IAI50351.2020.9262157
Kazuya Tonomura, M. Deng
In this paper, piezoelectric elements are used to vibration control of a flat plate structure. In order to control vibration more effectively, we propose to divide three piezoelectric actuators into two groups to control the vibration. In details, after describing a mathematical model of the flat plate structure, an operator-based nonlinear control system is designed for the vibration of the trapezoid plate structure. The effectiveness of the proposed method is shown by evaluating the control effect using the coupled analysis tool ANSYS and then comparing the vibration control using two groups of piezoelectric actuators with the previous method by simulation.
{"title":"Nonlinear vibration control of a flat plate structure using multiple piezoelectric devices","authors":"Kazuya Tonomura, M. Deng","doi":"10.1109/IAI50351.2020.9262157","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262157","url":null,"abstract":"In this paper, piezoelectric elements are used to vibration control of a flat plate structure. In order to control vibration more effectively, we propose to divide three piezoelectric actuators into two groups to control the vibration. In details, after describing a mathematical model of the flat plate structure, an operator-based nonlinear control system is designed for the vibration of the trapezoid plate structure. The effectiveness of the proposed method is shown by evaluating the control effect using the coupled analysis tool ANSYS and then comparing the vibration control using two groups of piezoelectric actuators with the previous method by simulation.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128157276","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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