Pub Date : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455626
Youdao Ma, Wenhan Zhang, Xinyang Liu, Zhenhua Wang, Yi Shen
This paper studies the data-driven fault symptoms generation and augmentation for satellite attitude control system via an approximate model technique and a generative adversarial network. An approximate model is determined to fit the input and output data of satellite attitude control system. Based on the designed model, a small number of addictive fault symptoms and multiplicative fault symptoms are generated. To obtain abundant symptom data, the generative adversarial network is introduced to augment the fault symptoms. Finally, numerical simulation results are presented to demonstrate the effectiveness of the proposed method.
{"title":"Data-Driven Fault Symptoms Generation and Augmentation for Satellite Attitude Control System","authors":"Youdao Ma, Wenhan Zhang, Xinyang Liu, Zhenhua Wang, Yi Shen","doi":"10.1109/DDCLS52934.2021.9455626","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455626","url":null,"abstract":"This paper studies the data-driven fault symptoms generation and augmentation for satellite attitude control system via an approximate model technique and a generative adversarial network. An approximate model is determined to fit the input and output data of satellite attitude control system. Based on the designed model, a small number of addictive fault symptoms and multiplicative fault symptoms are generated. To obtain abundant symptom data, the generative adversarial network is introduced to augment the fault symptoms. Finally, numerical simulation results are presented to demonstrate the effectiveness of the proposed method.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133875714","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455479
Jin Yang, Qishui Zhong, Kaibo Shi, S. Zhong, Shengzhi Han
In this paper, the sampled-data consensus problem of nonlinear multiagent systems (MASs) with time-varying delays is investigated. Compared with the widely used sampled-data controller, a proportional integral type (PI-type) protocol utilizing the information of neighbors considering the effects of memory delay is adopted. Then, by adequately considering characteristic about the time-varying delays, an improved time-varying quadratic type of Lyapunov-Krasovskii functional (LKF) is developed. Besides, augmented state vectors and two-sided looped-functional approach are adopting to constructed the LKF, some relaxed matrices in the LKF are not necessarily positive definite. Furthermore, some sufficient criteria are derived to ensure the consistency of the MASs. By solving a series of linear matrix inequalities, the desired memory PI-type sampled-data control gain matrices are obtained. Finally, the numerical examples are presented to illustrate the theoretical results.
{"title":"Memory-Based PI-Type Sampled-Data Consensus Control for Nonlinear Multiagent Systems with Time-Varying Delays","authors":"Jin Yang, Qishui Zhong, Kaibo Shi, S. Zhong, Shengzhi Han","doi":"10.1109/DDCLS52934.2021.9455479","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455479","url":null,"abstract":"In this paper, the sampled-data consensus problem of nonlinear multiagent systems (MASs) with time-varying delays is investigated. Compared with the widely used sampled-data controller, a proportional integral type (PI-type) protocol utilizing the information of neighbors considering the effects of memory delay is adopted. Then, by adequately considering characteristic about the time-varying delays, an improved time-varying quadratic type of Lyapunov-Krasovskii functional (LKF) is developed. Besides, augmented state vectors and two-sided looped-functional approach are adopting to constructed the LKF, some relaxed matrices in the LKF are not necessarily positive definite. Furthermore, some sufficient criteria are derived to ensure the consistency of the MASs. By solving a series of linear matrix inequalities, the desired memory PI-type sampled-data control gain matrices are obtained. Finally, the numerical examples are presented to illustrate the theoretical results.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124674380","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 order to attenuate the influence of the uncertainties of high altitude parafoil and environment on trajectory tracking control, active disturbance rejection control (ADRC) is used to regulate the trajectory of the high-altitude wind power parafoil. Linear extended state observer (LESO) is designed to estimate and compensate for nonlinear disturbances of the system. The simulation results show that this method has good control precision and fast-tracking velocity.
{"title":"Trajectory Tracking Control of High-Altitude Wind Power Parafoil","authors":"Xinyu Long, Mingwei Sun, Minnan Piao, Shengfei Liu, Zengqiang Chen","doi":"10.1109/DDCLS52934.2021.9455649","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455649","url":null,"abstract":"In order to attenuate the influence of the uncertainties of high altitude parafoil and environment on trajectory tracking control, active disturbance rejection control (ADRC) is used to regulate the trajectory of the high-altitude wind power parafoil. Linear extended state observer (LESO) is designed to estimate and compensate for nonlinear disturbances of the system. The simulation results show that this method has good control precision and fast-tracking velocity.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123010773","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455499
Ya-Jun Wu, Hao Tang, Xiao‐Zheng Jin
This paper explores an approach tracking the trajectory of a class of quadrotor aircrafts based on robust adaptive control against bounded disturbances by compensating for the perturbations. According to the Lyapunov stability theorem, the attitude tracking controller is achieved by using the backstepping technique. A simulation example is illustrated to verify the effectiveness of the designed position trajectory tracking controller and robust adaptive attitude trajectory tracking controller.
{"title":"Robust Adaptive Trajectory tracking Control of a Class of Disturbed Quadrotor Aircrafts","authors":"Ya-Jun Wu, Hao Tang, Xiao‐Zheng Jin","doi":"10.1109/DDCLS52934.2021.9455499","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455499","url":null,"abstract":"This paper explores an approach tracking the trajectory of a class of quadrotor aircrafts based on robust adaptive control against bounded disturbances by compensating for the perturbations. According to the Lyapunov stability theorem, the attitude tracking controller is achieved by using the backstepping technique. A simulation example is illustrated to verify the effectiveness of the designed position trajectory tracking controller and robust adaptive attitude trajectory tracking controller.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"20 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113964147","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455643
Yantao Chen, Junqi Yang, Lizhi Cui, Junjie Zhu
In this paper, a kind of optimal controller is proposed to estimate the state of Boolean control networks (BCNs). Different from the standard observer, the optimal state estimation is completed by designing the control input instead of directly using it, where the maximum-minimum method is employed such that the state of BCNs can be uniquely estimated in possible short time steps. A set observer is first proposed to estimate the state of BCNs at any time steps. Based on the set observer, an initial output-dependent reconstructible state tree is developed, where an algorithm is provided to generate the nodes of such tree and can be implemented offline. The optimal control sequence for uniquely determining the state of BCNs is derived from the reconstructible state tree by a breadth-first search algorithm, where the output of BCNs is dynamically employed. An example is given to illustrate the applicability and usefulness of the developed methods.
{"title":"Optimal controller design for state estimation of Boolean control networks","authors":"Yantao Chen, Junqi Yang, Lizhi Cui, Junjie Zhu","doi":"10.1109/DDCLS52934.2021.9455643","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455643","url":null,"abstract":"In this paper, a kind of optimal controller is proposed to estimate the state of Boolean control networks (BCNs). Different from the standard observer, the optimal state estimation is completed by designing the control input instead of directly using it, where the maximum-minimum method is employed such that the state of BCNs can be uniquely estimated in possible short time steps. A set observer is first proposed to estimate the state of BCNs at any time steps. Based on the set observer, an initial output-dependent reconstructible state tree is developed, where an algorithm is provided to generate the nodes of such tree and can be implemented offline. The optimal control sequence for uniquely determining the state of BCNs is derived from the reconstructible state tree by a breadth-first search algorithm, where the output of BCNs is dynamically employed. An example is given to illustrate the applicability and usefulness of the developed methods.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"47 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122846349","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455469
Baiyan Liu, Dan Su, Mei Liu, Yang Shi, Shuai Li
It is necessary to make physical constraints on the joints for the redundant robot motion control in order to avoid damage. In this paper, a discrete-time neural network model with minimum kinetic energy as the performance index is proposed, which has predominant convergence performance. Then, a solution in robot motion control is studied and further transformed into a dynamic quadratic programming (QP) with equality and inequality constraints. In addition, for solving the formulated QP problem, a continuous-time neural network model is designed by introducing the Lagrange multiplier method, and a discrete-time neural network model is obtained by the Euler forward difference formula. Moreover, the simulations on robot motion control are carried out, and the simulative results further substantiate the superiority, thus extending a solution for motion control of redundant robots with double-bound constraints.
{"title":"MKE Scheme for the Control of Dynamic Constrained Redundant Robots Based on Discrete-time Neural Network","authors":"Baiyan Liu, Dan Su, Mei Liu, Yang Shi, Shuai Li","doi":"10.1109/DDCLS52934.2021.9455469","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455469","url":null,"abstract":"It is necessary to make physical constraints on the joints for the redundant robot motion control in order to avoid damage. In this paper, a discrete-time neural network model with minimum kinetic energy as the performance index is proposed, which has predominant convergence performance. Then, a solution in robot motion control is studied and further transformed into a dynamic quadratic programming (QP) with equality and inequality constraints. In addition, for solving the formulated QP problem, a continuous-time neural network model is designed by introducing the Lagrange multiplier method, and a discrete-time neural network model is obtained by the Euler forward difference formula. Moreover, the simulations on robot motion control are carried out, and the simulative results further substantiate the superiority, thus extending a solution for motion control of redundant robots with double-bound constraints.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"217 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124277142","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455364
Yutai Wei, Zhijun Yang, Youdun Bai
High-precision rotary stages are applied in many fields, but the bearing friction has a negative impact on tracking performance. Rigid-flexible coupling rotary stage, a novel structure for rotary stage, can convert the friction disturbance into elastic force with flexure hinges. In order to avoid the effect of elastic force, active disturbance rejection control (ADRC) is adopted in this paper for its excellent disturbance rejection ability and independence of accurate modelling. In view of the resonance and high-frequency noise of the system, notch and lead filters are combined with ADRC, which is called modified ADRC. The experimental results show that the modified ADRC has a good effect on eliminating elastic force disturbance, and also has the ability to suppress resonance and high-frequency noise.
{"title":"Modified ADRC Design for Rigid-flexible Coupling Rotary Stage with Filters","authors":"Yutai Wei, Zhijun Yang, Youdun Bai","doi":"10.1109/DDCLS52934.2021.9455364","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455364","url":null,"abstract":"High-precision rotary stages are applied in many fields, but the bearing friction has a negative impact on tracking performance. Rigid-flexible coupling rotary stage, a novel structure for rotary stage, can convert the friction disturbance into elastic force with flexure hinges. In order to avoid the effect of elastic force, active disturbance rejection control (ADRC) is adopted in this paper for its excellent disturbance rejection ability and independence of accurate modelling. In view of the resonance and high-frequency noise of the system, notch and lead filters are combined with ADRC, which is called modified ADRC. The experimental results show that the modified ADRC has a good effect on eliminating elastic force disturbance, and also has the ability to suppress resonance and high-frequency noise.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125411762","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455678
Yanjun Liang, Yuanxin Li
This article addresses the asymptotic tracking issues of a one-link manipulator system. To realize the exponentially asymptotic tracking performance, the exponential term has been introduced into the Lyapunov function and the bounds estimation method and the smooth modification function are used to guarantee the zero-error tracking. In addition, the neural networks (NNs) is devised to cope with the uncertain disturbance and unknown nonlinearlities. At last, a simulation example has been shown to verify the raised scheme.
{"title":"Adaptive exponentially asymptotic tracking control for a one-link manipulator","authors":"Yanjun Liang, Yuanxin Li","doi":"10.1109/DDCLS52934.2021.9455678","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455678","url":null,"abstract":"This article addresses the asymptotic tracking issues of a one-link manipulator system. To realize the exponentially asymptotic tracking performance, the exponential term has been introduced into the Lyapunov function and the bounds estimation method and the smooth modification function are used to guarantee the zero-error tracking. In addition, the neural networks (NNs) is devised to cope with the uncertain disturbance and unknown nonlinearlities. At last, a simulation example has been shown to verify the raised scheme.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130281542","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455504
Zeyu Li, Peng Chang, Kai Wang, Pu Wang
In the industrial batch process monitoring domain, the conventional multivariate monitoring methods may not always function well in monitoring faults that have both Non-Linear and Non-Gaussian properties. To enhance the monitoring capability, the adversarial auto-encoder (AAE) was introduced to increase the sensitivity to Non-Gaussian anomalies by projecting non-Gaussian information into a given Gaussian distribution feature space. At the same time, low-dimensional feature space can avoid the problem of “Concentration of measure” and improve the ability to distinguish minor small abnormalities. Therefore, A novel statistic index was constructed in the feature space based on the k-nearest neighbor rule (KNN) to improve the ability of minor fault monitoring. The proposed model is compared with the traditional multivariate statistical process monitoring methods in numerical examples and penicillin fermentation platform, which proves that it has better monitoring ability for minor magnitude and non-Gaussian faults.
{"title":"The Batch Process Fault Monitoring Using Adversarial Auto-encoder and K-Nearest Neighbor Rule","authors":"Zeyu Li, Peng Chang, Kai Wang, Pu Wang","doi":"10.1109/DDCLS52934.2021.9455504","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455504","url":null,"abstract":"In the industrial batch process monitoring domain, the conventional multivariate monitoring methods may not always function well in monitoring faults that have both Non-Linear and Non-Gaussian properties. To enhance the monitoring capability, the adversarial auto-encoder (AAE) was introduced to increase the sensitivity to Non-Gaussian anomalies by projecting non-Gaussian information into a given Gaussian distribution feature space. At the same time, low-dimensional feature space can avoid the problem of “Concentration of measure” and improve the ability to distinguish minor small abnormalities. Therefore, A novel statistic index was constructed in the feature space based on the k-nearest neighbor rule (KNN) to improve the ability of minor fault monitoring. The proposed model is compared with the traditional multivariate statistical process monitoring methods in numerical examples and penicillin fermentation platform, which proves that it has better monitoring ability for minor magnitude and non-Gaussian faults.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129239389","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 : 2021-05-14DOI: 10.1109/DDCLS52934.2021.9455584
Yuan Xu, Kaiduo Cong, Yang Zhang, Qunxiong Zhu, Yanlin He
In the modern industrial process, the likelihood of the occurrence of multiple faults is higher than that of a single fault Comparing with single faults, the multi-faults problem has higher coupling and complexity, thus it is quite important to establish an effective multi-faults recognition model to ensure process safety. In this paper, a multi-fault recognition model based on reconstructed principal component analysis (RPCA) algorithm and support vector machine ensemble (SVME) classifier is proposed to satisfy the needs. First, obtain the principal component information from the original high-dimensional data space. Second, to solve the loss of local feature information, reconstruct the local structural error of the feature space through the inverse mapping matrix, and then align the error to obtain the reconstructed coordinates. Third, based on the One vs. One (OvO) ensemble strategy, an SVME classifier is constructed for multiple faults recognition. Finally, to verify the performance of the proposed RPCA-SVME model, the simulation experiments are made on a Circle dataset and the Tennessee Eastman process (TEP). The comparison results show that the proposed method can guarantee higher diagnostic accuracy and macro F1 score.
在现代工业过程中，与单一故障相比，多故障问题具有更高的耦合性和复杂性，因此建立有效的多故障识别模型对于保证过程安全具有重要意义。本文提出了一种基于重构主成分分析(RPCA)算法和支持向量机集成(SVME)分类器的多故障识别模型。首先，从原始高维数据空间中获取主成分信息;其次，为了解决局部特征信息的丢失问题，通过逆映射矩阵重构特征空间的局部结构误差，并对误差进行对齐，得到重构的坐标。第三，基于One vs. One (OvO)集成策略，构建了支持向量机多故障识别分类器。最后，为了验证RPCA-SVME模型的性能，在Circle数据集和田纳西伊士曼过程(Tennessee Eastman process, TEP)上进行了仿真实验。对比结果表明，该方法能够保证较高的诊断准确率和宏观F1分数。
{"title":"Research and Application of a Novel RPCA-SVME based Multiple Faults Recognition","authors":"Yuan Xu, Kaiduo Cong, Yang Zhang, Qunxiong Zhu, Yanlin He","doi":"10.1109/DDCLS52934.2021.9455584","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455584","url":null,"abstract":"In the modern industrial process, the likelihood of the occurrence of multiple faults is higher than that of a single fault Comparing with single faults, the multi-faults problem has higher coupling and complexity, thus it is quite important to establish an effective multi-faults recognition model to ensure process safety. In this paper, a multi-fault recognition model based on reconstructed principal component analysis (RPCA) algorithm and support vector machine ensemble (SVME) classifier is proposed to satisfy the needs. First, obtain the principal component information from the original high-dimensional data space. Second, to solve the loss of local feature information, reconstruct the local structural error of the feature space through the inverse mapping matrix, and then align the error to obtain the reconstructed coordinates. Third, based on the One vs. One (OvO) ensemble strategy, an SVME classifier is constructed for multiple faults recognition. Finally, to verify the performance of the proposed RPCA-SVME model, the simulation experiments are made on a Circle dataset and the Tennessee Eastman process (TEP). The comparison results show that the proposed method can guarantee higher diagnostic accuracy and macro F1 score.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130959258","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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