Pub Date : 2021-11-25DOI: 10.1109/anzcc53563.2021.9628361
H. Patel, V. Shah
Fuzzy Inference Systems (FIS) are now widely employed to regulate a wide range of safety-critical engineering systems. The main reason for this is that FIS may be created using expert human knowledge. Furthermore, with the introduction of Type-2 Fuzzy Logic, the ability to handle uncertainty offers an alluring improvement for fault-tolerant abilities in fault-tolerant control methods, and, in fact, recent studies have shown that the use of Interval Type-2 Fuzzy Inference Systems (IT2 FIS) provides better results than Type-1 Fuzzy Inference Systems (T1 FIS).The current research intends to suggest a novel strategy employing Shadowed Type-2 Fuzzy Inference System (ST2 FIS) for significant features in fault-tolerant capability in the Passive Fault-Tolerant Control (PFTC) method based on the performance increase shown by IT2 FIS. The ST2 FIS is based on the ideas of Shadowed Fuzzy Sets and is an approximation of General Type-2 Fuzzy Inference Systems (GT2 FIS). The fundamental rationale for utilising ST2 FIS instead of GT2 FIS is that the computational cost of GT2 FIS is too high for this application. The simulation results for the FTC using IT2 FIS and FTC using ST2 FIS for Coupled Conical Tank Level Control (CCTLC) system with 30 % and 40% loss of effectiveness in the main actuator are presented in this study. In addition, the article compares the proposed FTC approach using ST2 FIS to FTC using IT2 FIS in terms of fault-recovery time.
{"title":"General Type-2 Fuzzy Logic Systems Using Shadowed Sets: A New Paradigm Towards Fault-Tolerant Control","authors":"H. Patel, V. Shah","doi":"10.1109/anzcc53563.2021.9628361","DOIUrl":"https://doi.org/10.1109/anzcc53563.2021.9628361","url":null,"abstract":"Fuzzy Inference Systems (FIS) are now widely employed to regulate a wide range of safety-critical engineering systems. The main reason for this is that FIS may be created using expert human knowledge. Furthermore, with the introduction of Type-2 Fuzzy Logic, the ability to handle uncertainty offers an alluring improvement for fault-tolerant abilities in fault-tolerant control methods, and, in fact, recent studies have shown that the use of Interval Type-2 Fuzzy Inference Systems (IT2 FIS) provides better results than Type-1 Fuzzy Inference Systems (T1 FIS).The current research intends to suggest a novel strategy employing Shadowed Type-2 Fuzzy Inference System (ST2 FIS) for significant features in fault-tolerant capability in the Passive Fault-Tolerant Control (PFTC) method based on the performance increase shown by IT2 FIS. The ST2 FIS is based on the ideas of Shadowed Fuzzy Sets and is an approximation of General Type-2 Fuzzy Inference Systems (GT2 FIS). The fundamental rationale for utilising ST2 FIS instead of GT2 FIS is that the computational cost of GT2 FIS is too high for this application. The simulation results for the FTC using IT2 FIS and FTC using ST2 FIS for Coupled Conical Tank Level Control (CCTLC) system with 30 % and 40% loss of effectiveness in the main actuator are presented in this study. In addition, the article compares the proposed FTC approach using ST2 FIS to FTC using IT2 FIS in terms of fault-recovery time.","PeriodicalId":246687,"journal":{"name":"2021 Australian & New Zealand Control Conference (ANZCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124320803","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 : 2021-11-25DOI: 10.1109/anzcc53563.2021.9628290
Yuta Araki, K. Uchiyama, Kai Masuda
This paper describes the method for reconfiguring formations when some UAVs fail in a formation flight. We proposed fault detection using correlation coefficient and formation reconfiguration by applying the optimal allocation problem in this method. The correlation coefficient can detect faults by examining the similarity between the commanded value of the control input and the actual input. The optimal assignment problem can ensure a one-to-one assignment of UAVs to new target positions and minimize the total distance moved during the reconfiguration. Numerical simulation is performed to confirm the effectiveness of the proposed method for the formation flight of fixed-wing UAVs.
{"title":"Fault Detection and Formation Flying Reconfiguration of UAVs","authors":"Yuta Araki, K. Uchiyama, Kai Masuda","doi":"10.1109/anzcc53563.2021.9628290","DOIUrl":"https://doi.org/10.1109/anzcc53563.2021.9628290","url":null,"abstract":"This paper describes the method for reconfiguring formations when some UAVs fail in a formation flight. We proposed fault detection using correlation coefficient and formation reconfiguration by applying the optimal allocation problem in this method. The correlation coefficient can detect faults by examining the similarity between the commanded value of the control input and the actual input. The optimal assignment problem can ensure a one-to-one assignment of UAVs to new target positions and minimize the total distance moved during the reconfiguration. Numerical simulation is performed to confirm the effectiveness of the proposed method for the formation flight of fixed-wing UAVs.","PeriodicalId":246687,"journal":{"name":"2021 Australian & New Zealand Control Conference (ANZCC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123460523","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 : 2021-11-25DOI: 10.1109/anzcc53563.2021.9628378
T. Veerendar, Deepak Kumar, V. Sreeram
In this paper, a novel meta-heuristic Colliding bodies optimization-based proportional-integral-derivative controller with derivative filter is presented to solve a single-area power system's load frequency control issues. The controller parameters are determined by employing the integral of time multiplied absolute error as the fitness function. A single-area non-reheat thermal power system is considered for establishing the efficacy of the proposed method. The robustness of the proposed controller is also ascertained by inserting perturbation in system parameters. It is observed from the simulation results that the proposed method provides improved dynamic performance over the existing methods.
{"title":"Colliding Bodies Optimization-based PID Controller for Load Frequency Control of single area power system","authors":"T. Veerendar, Deepak Kumar, V. Sreeram","doi":"10.1109/anzcc53563.2021.9628378","DOIUrl":"https://doi.org/10.1109/anzcc53563.2021.9628378","url":null,"abstract":"In this paper, a novel meta-heuristic Colliding bodies optimization-based proportional-integral-derivative controller with derivative filter is presented to solve a single-area power system's load frequency control issues. The controller parameters are determined by employing the integral of time multiplied absolute error as the fitness function. A single-area non-reheat thermal power system is considered for establishing the efficacy of the proposed method. The robustness of the proposed controller is also ascertained by inserting perturbation in system parameters. It is observed from the simulation results that the proposed method provides improved dynamic performance over the existing methods.","PeriodicalId":246687,"journal":{"name":"2021 Australian & New Zealand Control Conference (ANZCC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125004154","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 : 2021-11-25DOI: 10.1109/anzcc53563.2021.9628203
Lai Wei, R. McCloy, Jie Bao
Motivated by the trend of flexible manufacturing in the process control industry and the uncertain nature of chemical process models, this article aims to achieve offset-free tracking for a family of uncertain nonlinear systems. The proposed control approach employs two main modules: a neural network embedded contraction-based controller to ensure convergence to time-varying references; and an online identification module coupled with a reference generator to provide convergency of the modelled parameters to that of the physical system. The first step in the proposed approach is to provide a guaranteed contraction condition for nonlinear systems, subject to time-varying parametric uncertainty, that are driven by neural network embedded controllers and modelled parameter estimates. The second step is to provide unknown system parameter identification online. By ensuring that uncertain parameter estimates converge to the corresponding physical values, offset-free tracking can be achieved. An illustrative example is included to demonstrate the overall approach.
{"title":"A Neural Network-based Contraction Control with Online Parameter Identification for Uncertain Nonlinear Systems","authors":"Lai Wei, R. McCloy, Jie Bao","doi":"10.1109/anzcc53563.2021.9628203","DOIUrl":"https://doi.org/10.1109/anzcc53563.2021.9628203","url":null,"abstract":"Motivated by the trend of flexible manufacturing in the process control industry and the uncertain nature of chemical process models, this article aims to achieve offset-free tracking for a family of uncertain nonlinear systems. The proposed control approach employs two main modules: a neural network embedded contraction-based controller to ensure convergence to time-varying references; and an online identification module coupled with a reference generator to provide convergency of the modelled parameters to that of the physical system. The first step in the proposed approach is to provide a guaranteed contraction condition for nonlinear systems, subject to time-varying parametric uncertainty, that are driven by neural network embedded controllers and modelled parameter estimates. The second step is to provide unknown system parameter identification online. By ensuring that uncertain parameter estimates converge to the corresponding physical values, offset-free tracking can be achieved. An illustrative example is included to demonstrate the overall approach.","PeriodicalId":246687,"journal":{"name":"2021 Australian & New Zealand Control Conference (ANZCC)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127173200","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 : 2021-11-25DOI: 10.1109/anzcc53563.2021.9628299
R. M. D. Silva, D. Eckhard
From a practical point of view, adjusting the controller without having to identify the process model has many advantages, for example, when the process is simple but changes a lot during the operation. In this case, there are many direct data-driven methods in the literature which may be employed to adjust a monovariable controller aiming at reference tracking. However, when the control objective is disturbance rejection or regulation, the designer is left with too few choices. The aim of this paper is to provide one new option and show how it can be applied to those control objectives.
{"title":"Data-driven Correlation Approach Applied to Load Disturbance Rejection in a Thermal Process","authors":"R. M. D. Silva, D. Eckhard","doi":"10.1109/anzcc53563.2021.9628299","DOIUrl":"https://doi.org/10.1109/anzcc53563.2021.9628299","url":null,"abstract":"From a practical point of view, adjusting the controller without having to identify the process model has many advantages, for example, when the process is simple but changes a lot during the operation. In this case, there are many direct data-driven methods in the literature which may be employed to adjust a monovariable controller aiming at reference tracking. However, when the control objective is disturbance rejection or regulation, the designer is left with too few choices. The aim of this paper is to provide one new option and show how it can be applied to those control objectives.","PeriodicalId":246687,"journal":{"name":"2021 Australian & New Zealand Control Conference (ANZCC)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116586423","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 : 2021-11-25DOI: 10.1109/anzcc53563.2021.9628304
E. Boeira, D. Eckhard
This paper addresses the use of the regularization feature on impulse response estimation for systems with colored output noise. Firstly, it is shown that the optimal regularization matrix for this scenario is quite different than the optimal for the white noise case and that there is a direct relationship between the Regularized Weighted Least-Squares with a Bayesian perspective of the identification problem for such case. Also, a new Empirical Bayes method, based on the Bayesian perspective, is introduced to estimate the regularization and noise covariance matrices from data. Finally, a numerical example demonstrates that this new methodology outperforms the traditional Regularized Least-Squares, producing better statistical properties and better results for a model fit measure.
{"title":"Regularized impulse response estimation for systems with colored output noise","authors":"E. Boeira, D. Eckhard","doi":"10.1109/anzcc53563.2021.9628304","DOIUrl":"https://doi.org/10.1109/anzcc53563.2021.9628304","url":null,"abstract":"This paper addresses the use of the regularization feature on impulse response estimation for systems with colored output noise. Firstly, it is shown that the optimal regularization matrix for this scenario is quite different than the optimal for the white noise case and that there is a direct relationship between the Regularized Weighted Least-Squares with a Bayesian perspective of the identification problem for such case. Also, a new Empirical Bayes method, based on the Bayesian perspective, is introduced to estimate the regularization and noise covariance matrices from data. Finally, a numerical example demonstrates that this new methodology outperforms the traditional Regularized Least-Squares, producing better statistical properties and better results for a model fit measure.","PeriodicalId":246687,"journal":{"name":"2021 Australian & New Zealand Control Conference (ANZCC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129511931","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 focuses on the research of the dashboard camera for improving the storage space and object recognition. The experiments showed that the CS method of ISTA-Net (Iterative Shrinkage Thresholding Algorithm with Network) can reduce the storage space by at least 60% and without obviously sacrificing the image quality. Furthermore, the recognition method by YOLOv4 can overcome the variety of environments, which can reach the recognition ratio of over 80% in a small 480x480 pixels. The recognition function can help to quickly catch the key features (ex: car, traffic signal, pedestrian, etc.) in the storage data of the dashboard camera.
{"title":"Integrated Compressed Sensing and YOLOv4 for Application in Image-storage and Object-recognition of Dashboard Camera","authors":"Jim-Wei Wu, Cheng-Chia Wu, Wen-Shan Cen, Shao-An Chao, Jui-Tse Weng","doi":"10.1109/anzcc53563.2021.9628221","DOIUrl":"https://doi.org/10.1109/anzcc53563.2021.9628221","url":null,"abstract":"This paper focuses on the research of the dashboard camera for improving the storage space and object recognition. The experiments showed that the CS method of ISTA-Net (Iterative Shrinkage Thresholding Algorithm with Network) can reduce the storage space by at least 60% and without obviously sacrificing the image quality. Furthermore, the recognition method by YOLOv4 can overcome the variety of environments, which can reach the recognition ratio of over 80% in a small 480x480 pixels. The recognition function can help to quickly catch the key features (ex: car, traffic signal, pedestrian, etc.) in the storage data of the dashboard camera.","PeriodicalId":246687,"journal":{"name":"2021 Australian & New Zealand Control Conference (ANZCC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122745849","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 : 2021-11-25DOI: 10.1109/anzcc53563.2021.9628293
Tony Dang, Frederik Debrouwere, E. Hostens
Typically, Model Predictive Control (MPC) for highly dynamic systems poses challenges to the computation power needed to optimize the control in real-time. In this paper, we present an explainable methodology to approximate MPCs with low input penalization as a closed form expression, using learning by demonstration. Classical approaches, e.g. using neural networks, result in over-complicated controllers and require huge datasets. In this paper, the prior knowledge on the typical bang-bang behavior of low-input penalized MPC will be exploited to approximate the MPC-law by only sparsely sampling the state space. This is achieved by identifying the switching surface of the sampled MPC-solution using Support Vector Machines (SVMs). The result is a light-weight, interpretable, easy to tune, explicit control law suitable for real-time applications. The methodology is validated in simulation on a benchmark problem from the field of process control (stirred tank reactor), and on a physical set-up of a highly dynamic motion control problem (parallel SCARA). The results, both in simulation and experimentally, show that strong approximation can already be obtained by using very light-weight controllers which, for the SCARA, were able to run on a frequency of at least 2kHz on the experimental setup.
{"title":"Approximating Nonlinear Model Predictive Controllers using Support Vector Machines","authors":"Tony Dang, Frederik Debrouwere, E. Hostens","doi":"10.1109/anzcc53563.2021.9628293","DOIUrl":"https://doi.org/10.1109/anzcc53563.2021.9628293","url":null,"abstract":"Typically, Model Predictive Control (MPC) for highly dynamic systems poses challenges to the computation power needed to optimize the control in real-time. In this paper, we present an explainable methodology to approximate MPCs with low input penalization as a closed form expression, using learning by demonstration. Classical approaches, e.g. using neural networks, result in over-complicated controllers and require huge datasets. In this paper, the prior knowledge on the typical bang-bang behavior of low-input penalized MPC will be exploited to approximate the MPC-law by only sparsely sampling the state space. This is achieved by identifying the switching surface of the sampled MPC-solution using Support Vector Machines (SVMs). The result is a light-weight, interpretable, easy to tune, explicit control law suitable for real-time applications. The methodology is validated in simulation on a benchmark problem from the field of process control (stirred tank reactor), and on a physical set-up of a highly dynamic motion control problem (parallel SCARA). The results, both in simulation and experimentally, show that strong approximation can already be obtained by using very light-weight controllers which, for the SCARA, were able to run on a frequency of at least 2kHz on the experimental setup.","PeriodicalId":246687,"journal":{"name":"2021 Australian & New Zealand Control Conference (ANZCC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115913149","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 : 2021-11-25DOI: 10.1109/anzcc53563.2021.9628250
Kazuhiko Takahashi, Eri Tano, M. Hashimoto
This study investigates a training method of a quaternion multi–layer neural network based on a gradient– descent method extended to quaternion numbers. The gradient of the cost function is calculated using the generalised ${mathbb{H}}{mathbb{R}}$ calculus to derive the training rule for the network parameters. Computational experiments for identifying and controlling a discrete–time nonlinear plant were conducted to evaluate the proposed method. The results confirmed the feasibility of using the G ${mathbb{H}}{mathbb{R}}$ calculus in the quaternion neural network and showed the capability of using the quaternion neural network for a control system application.
{"title":"Remarks on Quaternion Multi–Layer Neural Network Based on the Generalised HR Calculus","authors":"Kazuhiko Takahashi, Eri Tano, M. Hashimoto","doi":"10.1109/anzcc53563.2021.9628250","DOIUrl":"https://doi.org/10.1109/anzcc53563.2021.9628250","url":null,"abstract":"This study investigates a training method of a quaternion multi–layer neural network based on a gradient– descent method extended to quaternion numbers. The gradient of the cost function is calculated using the generalised ${mathbb{H}}{mathbb{R}}$ calculus to derive the training rule for the network parameters. Computational experiments for identifying and controlling a discrete–time nonlinear plant were conducted to evaluate the proposed method. The results confirmed the feasibility of using the G ${mathbb{H}}{mathbb{R}}$ calculus in the quaternion neural network and showed the capability of using the quaternion neural network for a control system application.","PeriodicalId":246687,"journal":{"name":"2021 Australian & New Zealand Control Conference (ANZCC)","volume":"332 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115975787","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 investigates the global asymptotic stability of linear time-varying impulsive positive systems (IPSs). Several novel stability criteria of linear time-varying IPSs with different types of impulsive effects are proposed by constructing an indefinite time-varying copositive Lyapunov function. In particular, by using the maximum and average dwell time methods, we discuss the stability of the addressed system with destabilizing impulses and stabilizing impulses, respectively. Moreover, we consider a special case in which the continuous dynamic of the system is not asymptotically stable and the system may contain some destabilizing impulses, and give a slightly stricter stability criterion. Finally, two examples are given to validate the effectiveness of the obtained results.
{"title":"Novel stability conditions of linear time-varying impulsive positive systems based on indefinite Lyapunov functions *","authors":"Niankun Zhang, Peilong Yu, Yuting Kang, Qianqian Zhang","doi":"10.1109/anzcc53563.2021.9628267","DOIUrl":"https://doi.org/10.1109/anzcc53563.2021.9628267","url":null,"abstract":"This paper investigates the global asymptotic stability of linear time-varying impulsive positive systems (IPSs). Several novel stability criteria of linear time-varying IPSs with different types of impulsive effects are proposed by constructing an indefinite time-varying copositive Lyapunov function. In particular, by using the maximum and average dwell time methods, we discuss the stability of the addressed system with destabilizing impulses and stabilizing impulses, respectively. Moreover, we consider a special case in which the continuous dynamic of the system is not asymptotically stable and the system may contain some destabilizing impulses, and give a slightly stricter stability criterion. Finally, two examples are given to validate the effectiveness of the obtained results.","PeriodicalId":246687,"journal":{"name":"2021 Australian & New Zealand Control Conference (ANZCC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128072508","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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