Pub Date : 2021-11-08DOI: 10.1109/IAI53119.2021.9619352
Wei Ding, Jin‐Xi Zhang
In this paper, we propose a deep transfer learning-based intelligent diagnosis approach for malignant tumors on mammography. An image segmentation algorithm is developed to remove the background, noise, and other redundancy in the image, for improving the learning efficiency. Based on the GoogleNet after training, we apply the transfer learning technique to the processed image. In this way, the accuracy of the classification model is improved. The experiment results show that the accuracy of our image segmentation algorithm is 100%, using only one-third of the data in training; the accuracy of our training approach is with the highest and average accuracy of 83% and 70%, respectively, by 2 × 104 iterations; and the area under the receiver operating characteristic curve is 0.77. These results are superior to those obtained by the existing methods.
{"title":"Deep Transfer Learning-Based Intelligent Diagnosis of Malignant Tumors on Mammography","authors":"Wei Ding, Jin‐Xi Zhang","doi":"10.1109/IAI53119.2021.9619352","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619352","url":null,"abstract":"In this paper, we propose a deep transfer learning-based intelligent diagnosis approach for malignant tumors on mammography. An image segmentation algorithm is developed to remove the background, noise, and other redundancy in the image, for improving the learning efficiency. Based on the GoogleNet after training, we apply the transfer learning technique to the processed image. In this way, the accuracy of the classification model is improved. The experiment results show that the accuracy of our image segmentation algorithm is 100%, using only one-third of the data in training; the accuracy of our training approach is with the highest and average accuracy of 83% and 70%, respectively, by 2 × 104 iterations; and the area under the receiver operating characteristic curve is 0.77. These results are superior to those obtained by the existing methods.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126880753","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 : 2021-11-08DOI: 10.1109/IAI53119.2021.9619254
Haodong Wang, Wenying Xu, Jianquan Lu
Average consensus is the key basis of distributed collective behaviors of multi-agent systems. Almost all the existing average consensus algorithms require exact values of agents, under which the privacy of nodes is likely to be revealed to honest-but-curious neighbors. In this paper, we are concerned with the average consensus issue without loss of privacy of agents over a general directed network. A privacy-preserving push-sum algorithm is constructed for each agent based on state decomposition, where each agent sends its partial states instead of exact states to its neighbors. Such an algorithm not only guarantees the asymptotic average consensus but also preserves the initial value of each agent from disclosure. Finally, a numerical example is provided to verify the effectiveness of our algorithm.
{"title":"Privacy-Preserving Push-sum Average Consensus Algorithm over Directed Graph Via State Decomposition","authors":"Haodong Wang, Wenying Xu, Jianquan Lu","doi":"10.1109/IAI53119.2021.9619254","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619254","url":null,"abstract":"Average consensus is the key basis of distributed collective behaviors of multi-agent systems. Almost all the existing average consensus algorithms require exact values of agents, under which the privacy of nodes is likely to be revealed to honest-but-curious neighbors. In this paper, we are concerned with the average consensus issue without loss of privacy of agents over a general directed network. A privacy-preserving push-sum algorithm is constructed for each agent based on state decomposition, where each agent sends its partial states instead of exact states to its neighbors. Such an algorithm not only guarantees the asymptotic average consensus but also preserves the initial value of each agent from disclosure. Finally, a numerical example is provided to verify the effectiveness of our algorithm.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115007502","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 : 2021-11-08DOI: 10.1109/IAI53119.2021.9619442
F. A. Hossain, Youmin Zhang
Although Fully Convolutional Networks (FCNs) have been proven to be a very powerful tool in deep learning-based image segmentation, they are still too computationally expensive to be incorporated into mobile platforms such as Unmanned Aerial Vehicles (UAVs) for real-time performance. While significant efforts have been made to make the encoder side of a FCN more efficient, the decoder side, which involves upsampling the feature maps, is still overlooked in comparison. This paper proposes two new efficient upsampling techniques, “Reversed Depthwise Separable Transposed Convolution (RDSTC)” and “Compression-Expansion Transposed Convolution (CETC)”. U-Net architecture and UAV-captured forest pile fire images have been used to evaluate the performance of these new efficient upsampling techniques. RDSTC and CETC achieve Dice scores of 0.8815 and 0.8832 respectively, outperforming commonly used bilinear interpolation and original transposed convolution, while significantly reducing the number of upsampling computations. The results of this paper demonstrate that upsampling operation in a deep learning architecture can be made more efficient without degradation in performance.
{"title":"Development of New Efficient Transposed Convolution Techniques for Flame Segmentation from UAV-captured Images","authors":"F. A. Hossain, Youmin Zhang","doi":"10.1109/IAI53119.2021.9619442","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619442","url":null,"abstract":"Although Fully Convolutional Networks (FCNs) have been proven to be a very powerful tool in deep learning-based image segmentation, they are still too computationally expensive to be incorporated into mobile platforms such as Unmanned Aerial Vehicles (UAVs) for real-time performance. While significant efforts have been made to make the encoder side of a FCN more efficient, the decoder side, which involves upsampling the feature maps, is still overlooked in comparison. This paper proposes two new efficient upsampling techniques, “Reversed Depthwise Separable Transposed Convolution (RDSTC)” and “Compression-Expansion Transposed Convolution (CETC)”. U-Net architecture and UAV-captured forest pile fire images have been used to evaluate the performance of these new efficient upsampling techniques. RDSTC and CETC achieve Dice scores of 0.8815 and 0.8832 respectively, outperforming commonly used bilinear interpolation and original transposed convolution, while significantly reducing the number of upsampling computations. The results of this paper demonstrate that upsampling operation in a deep learning architecture can be made more efficient without degradation in performance.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134559372","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 : 2021-11-08DOI: 10.1109/IAI53119.2021.9619404
Zheng Chai, Chunhui Zhao, Youxian Sun
Deep learning based process monitoring methods are attracting increasing research attention in recent years, which generally assume that the process variables are uniformly sampled. In practice, however, the process data are generally collected at multiple different rates, resulting in structurally-incomplete training data. Under such circumstances, how to build effective deep models to fully mine the multirate sampled data has become a constraint in achieving better process monitoring performance. In this paper, a sequentially-adaptive deep variational model is designed in which the knowledge that existed in variables with different rates is comprehensively extracted through deep generative neural networks. The multirate samples are first divided into multiple data blocks in which each block is collected at a uniform rate. A deep generative model is then constructed to model the uncertain data distribution and extract probabilistic feature representations considering the slowness principle. To restrain the small data problem in the blocks with slow rates, a sequentially-adaptation strategy is designed to adapt the knowledge from the fast blocks with sufficient training data and enhance the overall modeling performance. The effectiveness is demonstrated through a real-world industrial thermal power plant case.
{"title":"A Sequentially-Adaptive Deep Variational Model for Multirate Process Anomaly Detection","authors":"Zheng Chai, Chunhui Zhao, Youxian Sun","doi":"10.1109/IAI53119.2021.9619404","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619404","url":null,"abstract":"Deep learning based process monitoring methods are attracting increasing research attention in recent years, which generally assume that the process variables are uniformly sampled. In practice, however, the process data are generally collected at multiple different rates, resulting in structurally-incomplete training data. Under such circumstances, how to build effective deep models to fully mine the multirate sampled data has become a constraint in achieving better process monitoring performance. In this paper, a sequentially-adaptive deep variational model is designed in which the knowledge that existed in variables with different rates is comprehensively extracted through deep generative neural networks. The multirate samples are first divided into multiple data blocks in which each block is collected at a uniform rate. A deep generative model is then constructed to model the uncertain data distribution and extract probabilistic feature representations considering the slowness principle. To restrain the small data problem in the blocks with slow rates, a sequentially-adaptation strategy is designed to adapt the knowledge from the fast blocks with sufficient training data and enhance the overall modeling performance. The effectiveness is demonstrated through a real-world industrial thermal power plant case.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134191575","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 : 2021-11-08DOI: 10.1109/IAI53119.2021.9619348
Fanfan Li, Lingling Zhang
In this paper, we focus on a class of parabolic equations with nonlocal terms under Neumann boundary conditions. By making some assumptions, we establish the upper and lower bounds of parabolic equation system with the approach of constructing auxiliary functions and a series of differential inequalities. Moreover, an example is given to illustrate the main results.
{"title":"Blow-up phenomenon of parabolic equations with nonlocal terms under Neumann boundary conditions*","authors":"Fanfan Li, Lingling Zhang","doi":"10.1109/IAI53119.2021.9619348","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619348","url":null,"abstract":"In this paper, we focus on a class of parabolic equations with nonlocal terms under Neumann boundary conditions. By making some assumptions, we establish the upper and lower bounds of parabolic equation system with the approach of constructing auxiliary functions and a series of differential inequalities. Moreover, an example is given to illustrate the main results.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133082177","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 : 2021-11-08DOI: 10.1109/IAI53119.2021.9619319
Qian Shi, Hui Zhang
Hydraulic positioning systems are widely used in the areas of transportation, earth moving equipment, aircraft, and industry machinery with heavy duty applications. In these systems, nonlinear friction as a typical disturbance, is difficult to model and will influence the system performance. In this paper, we investigate the robust fractional-order PID (FOPID) control for position tracking of a fluid power ElectroHydraulic Actuator (EHA) system which is one type of hydraulic positioning systems. Firstly, the EHA model with friction force uncertainty is built. Then, the FOPID controller which is tuned by the grey wolf optimizer (GWO) is proposed. In the goal function for GWO, we take the uncertainty limits of friction force into consideration. The FOPID parameters are obtained by minimizing the goal function. The effectiveness of the proposed control approach is validated by simulation results in Matlab.
{"title":"Robust FOPID controller design by GWO for position tracking of an EHA System","authors":"Qian Shi, Hui Zhang","doi":"10.1109/IAI53119.2021.9619319","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619319","url":null,"abstract":"Hydraulic positioning systems are widely used in the areas of transportation, earth moving equipment, aircraft, and industry machinery with heavy duty applications. In these systems, nonlinear friction as a typical disturbance, is difficult to model and will influence the system performance. In this paper, we investigate the robust fractional-order PID (FOPID) control for position tracking of a fluid power ElectroHydraulic Actuator (EHA) system which is one type of hydraulic positioning systems. Firstly, the EHA model with friction force uncertainty is built. Then, the FOPID controller which is tuned by the grey wolf optimizer (GWO) is proposed. In the goal function for GWO, we take the uncertainty limits of friction force into consideration. The FOPID parameters are obtained by minimizing the goal function. The effectiveness of the proposed control approach is validated by simulation results in Matlab.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"688 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133166561","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}
To deal with this problem for tracking the depth-varying trajectory of remotely operated vehicle (ROV), state variables is introduced to system transformation for converting trajectory tracking problem into an optimal control problem. For this system, the H∞ optimal control is added basing on the adaptive dynamic programming algorithm (ADP), and the problem is regarded as the process of a two-player zero-sum differential game. Then we use the critic network to estimate the value function, and propose a online policy iteration algorithm to solve the HJI equation basing on the actor network and the disturbance network. Considering the limited output of the controller, we introduce a non-quadratic functional into the performance index function to solve the saturation problem. By using the Lyapunov stability theorem, we prove that the state of the closed-loop system and the weight estimation error of the neural network are uniformly bounded. Finally, an example is used to prove the feasibility and effectiveness of the method.
{"title":"H∞ optimal tracking control for remotely operated vehicle","authors":"Jinyu Liu, Qiuxia Qu, Baolong Yuan, Yupeng Li, Liangliang Sun, Qinghua Shi, Song Bai, Zupeng Xiao","doi":"10.1109/IAI53119.2021.9619206","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619206","url":null,"abstract":"To deal with this problem for tracking the depth-varying trajectory of remotely operated vehicle (ROV), state variables is introduced to system transformation for converting trajectory tracking problem into an optimal control problem. For this system, the H∞ optimal control is added basing on the adaptive dynamic programming algorithm (ADP), and the problem is regarded as the process of a two-player zero-sum differential game. Then we use the critic network to estimate the value function, and propose a online policy iteration algorithm to solve the HJI equation basing on the actor network and the disturbance network. Considering the limited output of the controller, we introduce a non-quadratic functional into the performance index function to solve the saturation problem. By using the Lyapunov stability theorem, we prove that the state of the closed-loop system and the weight estimation error of the neural network are uniformly bounded. Finally, an example is used to prove the feasibility and effectiveness of the method.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122989700","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 : 2021-11-08DOI: 10.1109/IAI53119.2021.9619272
Hai Yang, Hong Zhu, Yuan Zhao, Yefeng Liu, Yunge Li
In view of the characteristics of dense and non-stationary outliers in remote sensing data of navigation satellite in complex space environment, a method of eliminating outliers in residual test based on time-varying radial basis neural network was proposed. In the method of outliers elimination, the time-varying radial basis neural network (RBF) is firstly modeled according to the telemetry data. After the training network is stable, the residuals of the original sequence and the fitting sequence based on RBF neural network are calculated. Then the residual is tested by the adaptive threshold value to determine the outliers in the telemetry data. Finally, the method is proved to be effective in detecting isolated outliers and speckled outliers by practical application.
{"title":"Outliers processing method of navigation satellite telemetry data based on time-varying neural network","authors":"Hai Yang, Hong Zhu, Yuan Zhao, Yefeng Liu, Yunge Li","doi":"10.1109/IAI53119.2021.9619272","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619272","url":null,"abstract":"In view of the characteristics of dense and non-stationary outliers in remote sensing data of navigation satellite in complex space environment, a method of eliminating outliers in residual test based on time-varying radial basis neural network was proposed. In the method of outliers elimination, the time-varying radial basis neural network (RBF) is firstly modeled according to the telemetry data. After the training network is stable, the residuals of the original sequence and the fitting sequence based on RBF neural network are calculated. Then the residual is tested by the adaptive threshold value to determine the outliers in the telemetry data. Finally, the method is proved to be effective in detecting isolated outliers and speckled outliers by practical application.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123048478","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 : 2021-11-08DOI: 10.1109/IAI53119.2021.9619346
J. Ye, Yougang Bian, Biao Xu, Z. Qin, Manjiang Hu
In this paper, an online adaptive dynamic programming (ADP) scheme that combines eligibility trace is presented for solving optimal control of discrete-time systems. In contrast with the forward view learning that requires to store additional vectors to update, the backward view learning of the proposed scheme employs online collected data and previous gradient information to update the neural network (NN) parameters at each step, which reduces the computational burden. In order to approximate the cost function more accurately to achieve a better policy improvement direction in the exploration process, the proposed algorithm introduces an independent costate network on the basis of the traditional HDP framework to approximate the costate function. By utilizing the costate as supplement information to estimate the cost function, the estimation accuracy has been greatly improved. Finally, two numerical examples are presented and the simulation results demonstrate the effectiveness and the advantage of computation efficiency of the presented method.
{"title":"Online Optimal Control of Discrete-Time Systems Based on Globalized Dual Heuristic Programming with Eligibility Traces","authors":"J. Ye, Yougang Bian, Biao Xu, Z. Qin, Manjiang Hu","doi":"10.1109/IAI53119.2021.9619346","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619346","url":null,"abstract":"In this paper, an online adaptive dynamic programming (ADP) scheme that combines eligibility trace is presented for solving optimal control of discrete-time systems. In contrast with the forward view learning that requires to store additional vectors to update, the backward view learning of the proposed scheme employs online collected data and previous gradient information to update the neural network (NN) parameters at each step, which reduces the computational burden. In order to approximate the cost function more accurately to achieve a better policy improvement direction in the exploration process, the proposed algorithm introduces an independent costate network on the basis of the traditional HDP framework to approximate the costate function. By utilizing the costate as supplement information to estimate the cost function, the estimation accuracy has been greatly improved. Finally, two numerical examples are presented and the simulation results demonstrate the effectiveness and the advantage of computation efficiency of the presented method.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115718085","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 : 2021-11-08DOI: 10.1109/IAI53119.2021.9619380
Yuan Zhao, Du Ying, Yefeng Liu, Ren Wentao
In the design and analysis of the automatic control system, it is difficult to analyze the relative stability of system because of the complexity of calculating the gain and phase-crossover frequencies. The analysis of the influence of open-loop gain on the relative stability of system needs to solve many more complex equations, which is more difficult. This paper explores a method of system stability analysis based on the goal seek method, and studies the operation steps and points for attention in application of this method. The feasibility and convenience of the method are verified by the analysis of a complex third-order control system. The results show that this method can be used to find out the gain-crossover frequency and the phase-crossover frequency easily, meanwhile the phase margin and gain margin can be obtained easily too. The influence of open-loop gain on them can also be analyzed, and the optimum parameters of open-loop gain can be found. This method will provide a basis for the research of multi-process dynamic collaborative optimization manufacturing under the environment of the workshop Internet of Things.
{"title":"Relative Stability Analysis Method of Systems Based on Goal Seek","authors":"Yuan Zhao, Du Ying, Yefeng Liu, Ren Wentao","doi":"10.1109/IAI53119.2021.9619380","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619380","url":null,"abstract":"In the design and analysis of the automatic control system, it is difficult to analyze the relative stability of system because of the complexity of calculating the gain and phase-crossover frequencies. The analysis of the influence of open-loop gain on the relative stability of system needs to solve many more complex equations, which is more difficult. This paper explores a method of system stability analysis based on the goal seek method, and studies the operation steps and points for attention in application of this method. The feasibility and convenience of the method are verified by the analysis of a complex third-order control system. The results show that this method can be used to find out the gain-crossover frequency and the phase-crossover frequency easily, meanwhile the phase margin and gain margin can be obtained easily too. The influence of open-loop gain on them can also be analyzed, and the optimum parameters of open-loop gain can be found. This method will provide a basis for the research of multi-process dynamic collaborative optimization manufacturing under the environment of the workshop Internet of Things.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114431496","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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