The search for pulsars is an important area of study in modern astronomy. The amount of collected pulsar data is increasing exponentially as the performance of modern radio telescopes improves, necessitating the improvement of the original pulsar search methods. Artificial intelligence techniques are currently being used in pulsar candidate identification tasks. However, improving the accuracy of pulsar candidate identification using artificial intelligence techniques remains a challenge. Because the amount of collected data is so large, the number of real pulsar samples is very limited, which leads to a serious sample imbalance problem. Many existing methods ignore this issue, making it difficult for the model to reach the optimal solution. A framework combining generative adversarial networks and residual networks is proposed to greatly alleviate the problem of sample inequality. The framework first generates stable pulsar images using generative adversarial networks and then designs a deep neural network model based on residual networks to identify pulsar candidates using intra-block and inter-block residual connectivity. The ResNet approach has a better ability to fit the data than the CNN approach and can achieve the extraction of features with more classification ability with a smaller dataset. Meanwhile, the data expanded by the high-quality simulated samples generated by the generative adversarial network can provide richer identification features and improve the identification accuracy for pulsar candidates.
{"title":"Pulsar identification based on generative adversarial network and residual network","authors":"Zelun Bao, Guiru Liu, Yefan Li, Yanxi Xie, Yang Xu, Zifeng Zhang, Qian Yin, Xin Zheng","doi":"10.20517/ces.2022.30","DOIUrl":"https://doi.org/10.20517/ces.2022.30","url":null,"abstract":"The search for pulsars is an important area of study in modern astronomy. The amount of collected pulsar data is increasing exponentially as the performance of modern radio telescopes improves, necessitating the improvement of the original pulsar search methods. Artificial intelligence techniques are currently being used in pulsar candidate identification tasks. However, improving the accuracy of pulsar candidate identification using artificial intelligence techniques remains a challenge. Because the amount of collected data is so large, the number of real pulsar samples is very limited, which leads to a serious sample imbalance problem. Many existing methods ignore this issue, making it difficult for the model to reach the optimal solution. A framework combining generative adversarial networks and residual networks is proposed to greatly alleviate the problem of sample inequality. The framework first generates stable pulsar images using generative adversarial networks and then designs a deep neural network model based on residual networks to identify pulsar candidates using intra-block and inter-block residual connectivity. The ResNet approach has a better ability to fit the data than the CNN approach and can achieve the extraction of features with more classification ability with a smaller dataset. Meanwhile, the data expanded by the high-quality simulated samples generated by the generative adversarial network can provide richer identification features and improve the identification accuracy for pulsar candidates.","PeriodicalId":72652,"journal":{"name":"Complex engineering systems (Alhambra, Calif.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67657452","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}
A. Formenti, G. Bucca, Asad Ali Shahid, D. Piga, L. Roveda
Hybrid impedance/admittance control aims to provide an adaptive behavior to the manipulator in order to interact with the surrounding environment. In fact, impedance control is suitable for stiff environments, while admittance control is suitable for soft environments/free motion. Hybrid impedance/admittance control, indeed, allows modulating the control actions to exploit the combination of such behaviors. While some work has addressed the proposed topic, there are still some open issues to be solved. In particular, the proposed contribution aims: (i) to satisfy the continuity of the interaction force in the switching from impedance to admittance control when a feedforward velocity term is present; and (ii) to adapt the switching parameters to improve the performance of the hybrid control framework to better exploit the properties of both impedance and admittance controllers. The proposed approach was compared in simulation with the standard hybrid impedance/admittance control in order to show the improved performance. A Franka EMIKA panda robot was used as a reference robotic platform to provide a realistic simulation.
{"title":"Improved impedance/admittance switching controller for the interaction with a variable stiffness environment","authors":"A. Formenti, G. Bucca, Asad Ali Shahid, D. Piga, L. Roveda","doi":"10.20517/ces.2022.16","DOIUrl":"https://doi.org/10.20517/ces.2022.16","url":null,"abstract":"Hybrid impedance/admittance control aims to provide an adaptive behavior to the manipulator in order to interact with the surrounding environment. In fact, impedance control is suitable for stiff environments, while admittance control is suitable for soft environments/free motion. Hybrid impedance/admittance control, indeed, allows modulating the control actions to exploit the combination of such behaviors. While some work has addressed the proposed topic, there are still some open issues to be solved. In particular, the proposed contribution aims: (i) to satisfy the continuity of the interaction force in the switching from impedance to admittance control when a feedforward velocity term is present; and (ii) to adapt the switching parameters to improve the performance of the hybrid control framework to better exploit the properties of both impedance and admittance controllers. The proposed approach was compared in simulation with the standard hybrid impedance/admittance control in order to show the improved performance. A Franka EMIKA panda robot was used as a reference robotic platform to provide a realistic simulation.","PeriodicalId":72652,"journal":{"name":"Complex engineering systems (Alhambra, Calif.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67657038","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}
Qian Xu, Chutian Yu, Xiang Yuan, Zao Fu, Hongzhe Liu
This paper presents a power dispatch strategy combining the main grid and distributed generators based on aggregative game theory and the Cournot price mechanism. Such a dispatch strategy aims to increase the electricity under the power shortage situation. Under the proposed strategy, this paper designs a discrete-time algorithm fusing the estimation technique and the Digging method to solve the power shortage problem in a distributed way. The distributed algorithm can provide privacy protection and information safety and improve the power grid's extendibility. Moreover, the simulation results show that the proposed algorithm has favorable performance and effectiveness in the numerical example.
{"title":"A distributed electricity energy trading strategy under energy shortage environment","authors":"Qian Xu, Chutian Yu, Xiang Yuan, Zao Fu, Hongzhe Liu","doi":"10.20517/ces.2022.20","DOIUrl":"https://doi.org/10.20517/ces.2022.20","url":null,"abstract":"This paper presents a power dispatch strategy combining the main grid and distributed generators based on aggregative game theory and the Cournot price mechanism. Such a dispatch strategy aims to increase the electricity under the power shortage situation. Under the proposed strategy, this paper designs a discrete-time algorithm fusing the estimation technique and the Digging method to solve the power shortage problem in a distributed way. The distributed algorithm can provide privacy protection and information safety and improve the power grid's extendibility. Moreover, the simulation results show that the proposed algorithm has favorable performance and effectiveness in the numerical example.","PeriodicalId":72652,"journal":{"name":"Complex engineering systems (Alhambra, Calif.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67657049","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}
The increase of train speed leads to a violent vibration of the pantograph and overhead system. To evaluate the interaction performance of the pantograph and overhead system, a whole railway dynamics model including the track, vehicle, pantograph, and overhead system is established. The overhead system is represented by the finite element approach using the analytical formulas of nonlinear cable and truss elements. The vehicle is modeled by a multi-rigid-body system with a pantograph installed on its roof. A beam element with elastic foundations is used to model the track, which possesses harmonic and random irregularities. An iterative algorithm is implemented to solve the nonlinear behavior of the coupling model. The nonlinearities in the deformation of overhead system, the contact of the pantograph and contact line, and the contact of the vehicle-track are properly considered. Several numerical simulations are implemented to systematically investigate the influence of the vehicle-track vibration on the dynamic behavior of pantograph and overhead system. The results indicate that the vehicle-track vibration induced by the rail irregularities with large amplitude or certain wavelength can significantly aggravate the interaction performance of pantograph and overhead system.
{"title":"Performance assessment of pantograph and overhead system based on a vertical coupling dynamics model of the railway system","authors":"Yang Song, F. Duan","doi":"10.20517/ces.2022.09","DOIUrl":"https://doi.org/10.20517/ces.2022.09","url":null,"abstract":"The increase of train speed leads to a violent vibration of the pantograph and overhead system. To evaluate the interaction performance of the pantograph and overhead system, a whole railway dynamics model including the track, vehicle, pantograph, and overhead system is established. The overhead system is represented by the finite element approach using the analytical formulas of nonlinear cable and truss elements. The vehicle is modeled by a multi-rigid-body system with a pantograph installed on its roof. A beam element with elastic foundations is used to model the track, which possesses harmonic and random irregularities. An iterative algorithm is implemented to solve the nonlinear behavior of the coupling model. The nonlinearities in the deformation of overhead system, the contact of the pantograph and contact line, and the contact of the vehicle-track are properly considered. Several numerical simulations are implemented to systematically investigate the influence of the vehicle-track vibration on the dynamic behavior of pantograph and overhead system. The results indicate that the vehicle-track vibration induced by the rail irregularities with large amplitude or certain wavelength can significantly aggravate the interaction performance of pantograph and overhead system.","PeriodicalId":72652,"journal":{"name":"Complex engineering systems (Alhambra, Calif.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656986","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}
The correlation relations of batch process variables are quite complex. For local abnormalities, there is a problem that the variant features are overwhelmed. In addition, batch process variables have obvious non-Gaussian distributions. In response to the above two problems, a new multiple subspace monitoring method called principal component analysis multiple subspace support vector data description (PCA-MSSVDD) is proposed, which combines the subspace design of latent variables with the SVDD modeling method. Firstly, PCA is introduced to obtain latent variables for removing redundant information. Secondly, the subspace design result is obtained through K-means clustering. Finally, SVDD is introduced to build the monitoring model. Numerical simulation and penicillin fermentation process prove that the proposed PCA-MSSVDD method has better monitoring performance than traditional methods.
{"title":"Online monitoring of batch processes combining subspace design of latent variables with support vector data description","authors":"Zhaomin Lv","doi":"10.20517/ces.2021.02","DOIUrl":"https://doi.org/10.20517/ces.2021.02","url":null,"abstract":"The correlation relations of batch process variables are quite complex. For local abnormalities, there is a problem that the variant features are overwhelmed. In addition, batch process variables have obvious non-Gaussian distributions. In response to the above two problems, a new multiple subspace monitoring method called principal component analysis multiple subspace support vector data description (PCA-MSSVDD) is proposed, which combines the subspace design of latent variables with the SVDD modeling method. Firstly, PCA is introduced to obtain latent variables for removing redundant information. Secondly, the subspace design result is obtained through K-means clustering. Finally, SVDD is introduced to build the monitoring model. Numerical simulation and penicillin fermentation process prove that the proposed PCA-MSSVDD method has better monitoring performance than traditional methods.","PeriodicalId":72652,"journal":{"name":"Complex engineering systems (Alhambra, Calif.)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44599436","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}
Digitization and digitalization have already changed our world significantly. Further disruptions are imminent with the ongoing digital transformation, a major component of which is digital twins. As the big data techniques, Internet of Things, cloud computing, and artificial intelligence algorithms advance, the digital twin technology has entered a phase of rapid development. It has been stated to be one of the top ten most promising technologies. Although it is still in its early stages, digital twins are already being widely used in a variety of fields, especially in industry, smart cities, and smart health, which are points that attract most researchers to study. In the literature, there can be seen numerous articles and reviews on digital twins, published every year in these three fields. It is therefore timely, even necessary, to provide an analysis of the published work. This is the motivation behind this article, the focus of which is the major research and application areas of digital twins. The survey first analyzes the recent developments of digital twins, then summarizes the theoretical underpinnings of the technology, and finally concludes with specific developments in various application areas of digital twins. It also discusses the challenges that may be encountered in the future.
{"title":"Developments of digital twin technologies in industrial, smart city and healthcare sectors: a survey","authors":"Daoguang Yang, H. Karimi, O. Kaynak, Yin Shen","doi":"10.20517/ces.2021.06","DOIUrl":"https://doi.org/10.20517/ces.2021.06","url":null,"abstract":"Digitization and digitalization have already changed our world significantly. Further disruptions are imminent with the ongoing digital transformation, a major component of which is digital twins. As the big data techniques, Internet of Things, cloud computing, and artificial intelligence algorithms advance, the digital twin technology has entered a phase of rapid development. It has been stated to be one of the top ten most promising technologies. Although it is still in its early stages, digital twins are already being widely used in a variety of fields, especially in industry, smart cities, and smart health, which are points that attract most researchers to study. In the literature, there can be seen numerous articles and reviews on digital twins, published every year in these three fields. It is therefore timely, even necessary, to provide an analysis of the published work. This is the motivation behind this article, the focus of which is the major research and application areas of digital twins. The survey first analyzes the recent developments of digital twins, then summarizes the theoretical underpinnings of the technology, and finally concludes with specific developments in various application areas of digital twins. It also discusses the challenges that may be encountered in the future.","PeriodicalId":72652,"journal":{"name":"Complex engineering systems (Alhambra, Calif.)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45903568","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}
This paper presents a model predictive control (MPC) method for single-phase three-level grid-connected F-type inverters. The main control objective in grid-connected inverters is to regulate the grid current with low total harmonic distortion. Since the F-type inverter has emerged recently, there is no specific control method developed for this inverter topology in the literature yet. In this paper, the mathematical model of the F-type inverter and the design of model predictive control is presented. Since the dc capacitor voltage balancing is essential for F-type inverters, both current control and dc capacitor voltage controllers are combined in a multi-objective cost function. Thus, the control of the dc and ac sides of the F-type inverter is achieved successfully. The theoretical considerations were verified through simulation studies. The effectiveness of the proposed MPC method was investigated in the steady state as well as dynamic transients under variations in grid current, input dc voltage, and grid voltage. The simulation results show that the grid current and dc capacitor voltages are successfully controlled in all operating conditions.
{"title":"Model predictive control for single-phase three-level grid-connected F-type inverters","authors":"N. Guler, H. Komurcugil, S. Biricik, H. Karimi","doi":"10.20517/ces.2021.07","DOIUrl":"https://doi.org/10.20517/ces.2021.07","url":null,"abstract":"This paper presents a model predictive control (MPC) method for single-phase three-level grid-connected F-type inverters. The main control objective in grid-connected inverters is to regulate the grid current with low total harmonic distortion. Since the F-type inverter has emerged recently, there is no specific control method developed for this inverter topology in the literature yet. In this paper, the mathematical model of the F-type inverter and the design of model predictive control is presented. Since the dc capacitor voltage balancing is essential for F-type inverters, both current control and dc capacitor voltage controllers are combined in a multi-objective cost function. Thus, the control of the dc and ac sides of the F-type inverter is achieved successfully. The theoretical considerations were verified through simulation studies. The effectiveness of the proposed MPC method was investigated in the steady state as well as dynamic transients under variations in grid current, input dc voltage, and grid voltage. The simulation results show that the grid current and dc capacitor voltages are successfully controlled in all operating conditions.","PeriodicalId":72652,"journal":{"name":"Complex engineering systems (Alhambra, Calif.)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49344505","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}
This paper proposes the topics of sliding mode control for nonlinear Takagi-Sugeno systems based on a state observer with application to single-link flexible joint robotic. Firstly, a state observer relying on estimated premise variables is constructed, based on which we define an integral-type switching surface function on the estimation space. Secondly, by the equivalent control method, a sliding mode dynamics with an error system is obtained. Then, an adaptive variable structure controller is constructed to make sure that the predefined switching surface will be arrived in finite-time. Furthermore, stability analysis with an 𝐻 1 performance is analyzed for the whole closed-loop system by linear matrix inequality condition. Finally, simulation study based on the robotics is conducted to confirm the validity of the proposed observer-based fuzzy controller.
{"title":"Observer-based integral sliding mode control of nonlinear systems with application to single-link flexible joint robotics","authors":"Qi Liu, Zhen-xiong Cai, Jie Chen, Baoping Jiang","doi":"10.20517/ces.2021.05","DOIUrl":"https://doi.org/10.20517/ces.2021.05","url":null,"abstract":"This paper proposes the topics of sliding mode control for nonlinear Takagi-Sugeno systems based on a state observer with application to single-link flexible joint robotic. Firstly, a state observer relying on estimated premise variables is constructed, based on which we define an integral-type switching surface function on the estimation space. Secondly, by the equivalent control method, a sliding mode dynamics with an error system is obtained. Then, an adaptive variable structure controller is constructed to make sure that the predefined switching surface will be arrived in finite-time. Furthermore, stability analysis with an 𝐻 1 performance is analyzed for the whole closed-loop system by linear matrix inequality condition. Finally, simulation study based on the robotics is conducted to confirm the validity of the proposed observer-based fuzzy controller.","PeriodicalId":72652,"journal":{"name":"Complex engineering systems (Alhambra, Calif.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656771","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}
As one of the critical components of rotating machinery, fault diagnosis of rolling bearings has great significance. Although deep learning is useful in diagnosing rolling bearing faults, it is difficult to diagnose the faults of bearings under multiple operating conditions. To overcome the above-mentioned problem, this paper designs a modular federated learning network for fault diagnosis in multiple working conditions by using dynamic routing technology as the federation strategy for federated learning of the multiple modular neural network. First, according to different working conditions, the collected multi-working condition data are divided into different groups for feeding of modular network to extract the local features under different working conditions. Then, an additional deep neural network is constructed to extract the feature involved in data without working condition division. Finally, the global adaptive feature extraction of each working condition can be obtained by designing a federated strategy based on dynamic routing technology to achieve the weights allocation scheme of the modular neural network. The bearing dataset of Case Western Reserve University is taken as a benchmark dataset to verify the effectiveness of the proposed method.
{"title":"Research on federated learning method for fault diagnosis in multiple working conditions","authors":"F. Zhou, Zhiqiang Zhang, Sijie Li","doi":"10.20517/ces.2021.08","DOIUrl":"https://doi.org/10.20517/ces.2021.08","url":null,"abstract":"As one of the critical components of rotating machinery, fault diagnosis of rolling bearings has great significance. Although deep learning is useful in diagnosing rolling bearing faults, it is difficult to diagnose the faults of bearings under multiple operating conditions. To overcome the above-mentioned problem, this paper designs a modular federated learning network for fault diagnosis in multiple working conditions by using dynamic routing technology as the federation strategy for federated learning of the multiple modular neural network. First, according to different working conditions, the collected multi-working condition data are divided into different groups for feeding of modular network to extract the local features under different working conditions. Then, an additional deep neural network is constructed to extract the feature involved in data without working condition division. Finally, the global adaptive feature extraction of each working condition can be obtained by designing a federated strategy based on dynamic routing technology to achieve the weights allocation scheme of the modular neural network. The bearing dataset of Case Western Reserve University is taken as a benchmark dataset to verify the effectiveness of the proposed method.","PeriodicalId":72652,"journal":{"name":"Complex engineering systems (Alhambra, Calif.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656815","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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