Pub Date : 2020-10-23DOI: 10.1109/IAI50351.2020.9262217
Mai Peng, Changhe Li
The multi-population method is a common method for solving dynamic optimization problems. However, to design an efficient multi-population method, one of the challenging issues is how to allocate computational resources between populations given a limited computing buget in dynamic environments. This paper designs a contribution-based resource allocation mechanism. In this mechanism, a contribution degree of a population is defined according to the performance of the population, which determines the probability of the population to obtain the computing resource. This mechanism is implemented in an adaptive multi-population method. Experimental results on the moving peaks benchmark show that the algorithm equipped with the resource allocation mechanism outperforms the original algorithms.
{"title":"A Contribution-based Resource Allocation Scheme for Multi-population Methods in Dynamic Environments","authors":"Mai Peng, Changhe Li","doi":"10.1109/IAI50351.2020.9262217","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262217","url":null,"abstract":"The multi-population method is a common method for solving dynamic optimization problems. However, to design an efficient multi-population method, one of the challenging issues is how to allocate computational resources between populations given a limited computing buget in dynamic environments. This paper designs a contribution-based resource allocation mechanism. In this mechanism, a contribution degree of a population is defined according to the performance of the population, which determines the probability of the population to obtain the computing resource. This mechanism is implemented in an adaptive multi-population method. Experimental results on the moving peaks benchmark show that the algorithm equipped with the resource allocation mechanism outperforms the original algorithms.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"117 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":"115804188","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/IAI50351.2020.9262225
Ziquan Yu, H. Badihi, Youmin Zhang, Yajie Ma, B. Jiang, C. Su
In this paper, a fractional-order sliding-mode fault-tolerant tracking control scheme is proposed for a fixed-wing UAV with prescribed performance. The outer-loop position dynamics is first transformed to the second-order nonlinear model. By using neural networks, the unknown nonlinear functions containing actuator faults are identified. Moreover, the minimum learning parameter of neural networks is constructed to reduce the computational burden. Fractional-order calculus is further utilized in the sliding-mode control for improving the fault-tolerant tracking performance. Simulation results are presented to show the effectiveness.
{"title":"Fractional-Order Sliding-Mode Fault-Tolerant Neural Adaptive Control of Fixed-Wing UAV With Prescribed Tracking Performance","authors":"Ziquan Yu, H. Badihi, Youmin Zhang, Yajie Ma, B. Jiang, C. Su","doi":"10.1109/IAI50351.2020.9262225","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262225","url":null,"abstract":"In this paper, a fractional-order sliding-mode fault-tolerant tracking control scheme is proposed for a fixed-wing UAV with prescribed performance. The outer-loop position dynamics is first transformed to the second-order nonlinear model. By using neural networks, the unknown nonlinear functions containing actuator faults are identified. Moreover, the minimum learning parameter of neural networks is constructed to reduce the computational burden. Fractional-order calculus is further utilized in the sliding-mode control for improving the fault-tolerant tracking performance. Simulation results are presented to show the effectiveness.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"9 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":"133143460","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/IAI50351.2020.9262233
Ni Da-peng, Zhang Guo-jin, Jia Ming-xing
A method of operation performance assessment based on the combination of fuzzy classification and Gaussian mixture model is proposed to assess the operation performance for the smelting process of electrofused magnesium furnace. And a method of non-optimal cause tracing based on the combination of contribution diagram and case-based reasoning is used to trace the non-optimal causes. For the qualitative data, the fuzzy classification method is used to transform the qualitative division into the quantitative division. Then, the posterior probability of the Gaussian mixture model of each running state of the online data is calculated to get the evaluation results. When the evaluation result is not optimal, the contribution rate of each variable is compared to obtain the non-optimal variable, and the non-optimal reason is found by case search. Finally, the effectiveness of the proposed method is verified by the simulation platform of electrofused magnesium furnace.
{"title":"Operation Performance Assessment and Non-optimal Reasons Traceability for Melting Process of Fused Magnesium Furnace","authors":"Ni Da-peng, Zhang Guo-jin, Jia Ming-xing","doi":"10.1109/IAI50351.2020.9262233","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262233","url":null,"abstract":"A method of operation performance assessment based on the combination of fuzzy classification and Gaussian mixture model is proposed to assess the operation performance for the smelting process of electrofused magnesium furnace. And a method of non-optimal cause tracing based on the combination of contribution diagram and case-based reasoning is used to trace the non-optimal causes. For the qualitative data, the fuzzy classification method is used to transform the qualitative division into the quantitative division. Then, the posterior probability of the Gaussian mixture model of each running state of the online data is calculated to get the evaluation results. When the evaluation result is not optimal, the contribution rate of each variable is compared to obtain the non-optimal variable, and the non-optimal reason is found by case search. Finally, the effectiveness of the proposed method is verified by the simulation platform of electrofused magnesium furnace.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"21 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":"125922324","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/IAI50351.2020.9262218
Xuejiao Wang, Hao Luo, Kuan Li, Shen Yin, O. Kaynak
Thanks to rapid development of artificial intelligence (AI), a new branch of computer science, modern industry system becomes increasingly intelligent. What's more, mountains of data in industrial process can be saved for data-driven intelligent fault detection and classification. A method of intelligent data-driven fault classification based on stability margin is proposed in this paper, which gives a data-driven stability margin solution. As an important feature, the stability margin, together with the input and output (I/O) data, is input into the LM-BP neural network multi-classifier for fault classification. Moreover, the proposed method is demonstrated to be effective with high accuracy through a DC motor benchmark.
{"title":"An Intelligent Fault Classification Method Based on Data-Driven Stability Margin","authors":"Xuejiao Wang, Hao Luo, Kuan Li, Shen Yin, O. Kaynak","doi":"10.1109/IAI50351.2020.9262218","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262218","url":null,"abstract":"Thanks to rapid development of artificial intelligence (AI), a new branch of computer science, modern industry system becomes increasingly intelligent. What's more, mountains of data in industrial process can be saved for data-driven intelligent fault detection and classification. A method of intelligent data-driven fault classification based on stability margin is proposed in this paper, which gives a data-driven stability margin solution. As an important feature, the stability margin, together with the input and output (I/O) data, is input into the LM-BP neural network multi-classifier for fault classification. Moreover, the proposed method is demonstrated to be effective with high accuracy through a DC motor benchmark.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"12 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":"114251072","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/IAI50351.2020.9262208
First A. Jing Wang, Second B. Miao Li, Third C. Yue Qiu, Fourth D. Heng Wang
This paper has presented an idea of using substation and monitoring data to locate the key operations and difficulties in operations of staff working in substation. The analysis also make it possible for related department of majors find out the training needs of staff and technique development needs in power grid. With the help of field data, the conclusion of above analysis will be more clear and objective.
{"title":"In-depth Analysis and Application of Power Grid Data for Location of Task Difficultie","authors":"First A. Jing Wang, Second B. Miao Li, Third C. Yue Qiu, Fourth D. Heng Wang","doi":"10.1109/IAI50351.2020.9262208","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262208","url":null,"abstract":"This paper has presented an idea of using substation and monitoring data to locate the key operations and difficulties in operations of staff working in substation. The analysis also make it possible for related department of majors find out the training needs of staff and technique development needs in power grid. With the help of field data, the conclusion of above analysis will be more clear and objective.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"46 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":"116587142","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/IAI50351.2020.9262228
Weiguo Xia, Hong Liang
This paper studies the evolution of an expressed and private opinion dynamics model with asynchronous updating. It is shown that under some mild connectivity and individual activation conditions, the expressed and private opinions of all individuals in the network converge to a common value exponentially fast when the susceptibility to influence of all individuals is equal to one. A numerical example verifies the theoretical result.
{"title":"An Expressed and Private Opinion Model on Influence Networks","authors":"Weiguo Xia, Hong Liang","doi":"10.1109/IAI50351.2020.9262228","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262228","url":null,"abstract":"This paper studies the evolution of an expressed and private opinion dynamics model with asynchronous updating. It is shown that under some mild connectivity and individual activation conditions, the expressed and private opinions of all individuals in the network converge to a common value exponentially fast when the susceptibility to influence of all individuals is equal to one. A numerical example verifies the theoretical result.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"12 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":"116508048","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/IAI50351.2020.9262190
Zhenyao Zhao, Guangbin Zhang, Min Jiang, Liang Feng, K. Tan
Neural architecture search (NAS) is the process of automatically searching for the best performing neural model on a given task. Designing a neural model requires a lot of time for experts, NAS's automated process effectively solves this problem and makes neural networks easier to promote. Although NAS has achieved excellent performance, its search process is still very time consuming. In this paper, we propose a neural architecture design method based on distribution estimation method called EDNAS, a fast and economical solution to design neural architecture automatically. In EDNAS, we assume that the best performing architecture obeys a certain probability distribution in search space. Therefore, NAS can be transformed to learning this probability distribution. We construct a probability model on the search space, and search for this probability distribution by iterating the probability model. Finally, an architecture that maximizes the performance on a validation set is generated from this probability distribution. Experiment shows the efficiency of our method. On CIFAR-10 dataset, EDNAS discovers a novel architecture in just 4 hours with 2.89% test error, which shows efficent and strong performance.
{"title":"EDNAS: An Efficient Neural Architecture Design based on Distribution Estimation","authors":"Zhenyao Zhao, Guangbin Zhang, Min Jiang, Liang Feng, K. Tan","doi":"10.1109/IAI50351.2020.9262190","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262190","url":null,"abstract":"Neural architecture search (NAS) is the process of automatically searching for the best performing neural model on a given task. Designing a neural model requires a lot of time for experts, NAS's automated process effectively solves this problem and makes neural networks easier to promote. Although NAS has achieved excellent performance, its search process is still very time consuming. In this paper, we propose a neural architecture design method based on distribution estimation method called EDNAS, a fast and economical solution to design neural architecture automatically. In EDNAS, we assume that the best performing architecture obeys a certain probability distribution in search space. Therefore, NAS can be transformed to learning this probability distribution. We construct a probability model on the search space, and search for this probability distribution by iterating the probability model. Finally, an architecture that maximizes the performance on a validation set is generated from this probability distribution. Experiment shows the efficiency of our method. On CIFAR-10 dataset, EDNAS discovers a novel architecture in just 4 hours with 2.89% test error, which shows efficent and strong performance.","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":"132345661","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/IAI50351.2020.9262238
Ning Li, Yaguang Li, W. Xiang
In this paper, an adaptive fuzzy control scheme for two inverted pendulums mounted on two carts with unknown control directions is proposed. A kind of Nussbaum-type functions is designed, with which the effect of multiple unknown control directions can be handled. Fuzzy functions are used to approximate the unknown terms and by combining adaptive laws with backstepping procedure, constructed adaptive fuzzy controller can guarantee the two inverted pendulum systems asymptotically stable and all states in the closed-loop systems are bounded. Finally, numerical simulation results show that two inverted pendulums mounted on two carts can move toward stability.
{"title":"The adaptive fuzzy tracking control for double inverted pendulums in the presence of unknown control directions","authors":"Ning Li, Yaguang Li, W. Xiang","doi":"10.1109/IAI50351.2020.9262238","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262238","url":null,"abstract":"In this paper, an adaptive fuzzy control scheme for two inverted pendulums mounted on two carts with unknown control directions is proposed. A kind of Nussbaum-type functions is designed, with which the effect of multiple unknown control directions can be handled. Fuzzy functions are used to approximate the unknown terms and by combining adaptive laws with backstepping procedure, constructed adaptive fuzzy controller can guarantee the two inverted pendulum systems asymptotically stable and all states in the closed-loop systems are bounded. Finally, numerical simulation results show that two inverted pendulums mounted on two carts can move toward stability.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"10 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":"134461285","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/IAI50351.2020.9262166
Chenjing Meng, Huiping Li
This paper studies the model predictive control problem (MPC) of linear systems with unknown parameters both in system models and measurement models. The method that combines the estimation of system parameters and states with MPC is proposed, where the reinforcement learning (RL) is used to learn the optimal control strategies. Its characteristics are that the control and estimate can proceed simultaneously. Simulation studies verify that the designed algorithm can converge to the optimal linear feedback and the parameters converge as well.
{"title":"Model Predictive Control of Linear Systems with Unknown Parameters","authors":"Chenjing Meng, Huiping Li","doi":"10.1109/IAI50351.2020.9262166","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262166","url":null,"abstract":"This paper studies the model predictive control problem (MPC) of linear systems with unknown parameters both in system models and measurement models. The method that combines the estimation of system parameters and states with MPC is proposed, where the reinforcement learning (RL) is used to learn the optimal control strategies. Its characteristics are that the control and estimate can proceed simultaneously. Simulation studies verify that the designed algorithm can converge to the optimal linear feedback and the parameters converge as well.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"38 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":"128281072","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/IAI50351.2020.9262197
Yu-tao Song, Sheng Yang, Chao Cheng
In industrial processes, it is critical to detect and diagnose failures, process failures, and other abnormal events to achieve safe, efficient operations. In this paper, a non-Gaussian correlation algorithm based on independent component analysis is proposed to monitor the non-Gaussian process variables and non-Gaussian performance variables. First, non-Gaussian information is extracted from the original data center by independent component analysis (ICA). On this basis, the non-gaussian information is divided into non-Gaussian performance-related subspace and non-Gaussian process-related subspace by canonical correlation analysis (CCA). The proposed method can effectively analyze the influence of disturbance and control actions on performance variables under non-gaussian data, and improve the monitoring efficiency of non-gaussian process variables. Finally, a case study is used to illustrate the applicability and effectiveness of this method.
{"title":"A method of Fault Diagnosis of non-Gaussian Property and Performance Correlation Based on Independent Component Analysis","authors":"Yu-tao Song, Sheng Yang, Chao Cheng","doi":"10.1109/IAI50351.2020.9262197","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262197","url":null,"abstract":"In industrial processes, it is critical to detect and diagnose failures, process failures, and other abnormal events to achieve safe, efficient operations. In this paper, a non-Gaussian correlation algorithm based on independent component analysis is proposed to monitor the non-Gaussian process variables and non-Gaussian performance variables. First, non-Gaussian information is extracted from the original data center by independent component analysis (ICA). On this basis, the non-gaussian information is divided into non-Gaussian performance-related subspace and non-Gaussian process-related subspace by canonical correlation analysis (CCA). The proposed method can effectively analyze the influence of disturbance and control actions on performance variables under non-gaussian data, and improve the monitoring efficiency of non-gaussian process variables. Finally, a case study is used to illustrate the applicability and effectiveness of this method.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"6 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":"125598502","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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