Pub Date : 2021-12-14DOI: 10.1109/CDC45484.2021.9683507
F. Galarza-Jimenez, J. Poveda, Ronny J. Kutadinata, Lele Zhang, E. Dall’Anese
Motivated by the shallow concavity properties that emerge in certain response maps in the context of optimization problems in transportation systems, we study the stability properties of a class of hybrid accelerated extremum seeking (HAES) dynamics interconnected with dynamic plants in the loop. In particular, we establish suitable semi-global practical asymptotic stability properties for different classes of cost functions, as well as tuning conditions for the hybrid extremum seeking algorithm. Additionally, we implement the HAES to optimize the performance of a self-organizing traffic light system (SOTL) in a class of smart transportation systems. We show that the dynamic momentum mechanism incorporated by the HAES can significantly reduce the convergence time in the optimization process compared to the traditional extremum seeking algorithms based on gradient descent flows.
{"title":"Self-Optimizing Traffic Light Control Using Hybrid Accelerated Extremum Seeking","authors":"F. Galarza-Jimenez, J. Poveda, Ronny J. Kutadinata, Lele Zhang, E. Dall’Anese","doi":"10.1109/CDC45484.2021.9683507","DOIUrl":"https://doi.org/10.1109/CDC45484.2021.9683507","url":null,"abstract":"Motivated by the shallow concavity properties that emerge in certain response maps in the context of optimization problems in transportation systems, we study the stability properties of a class of hybrid accelerated extremum seeking (HAES) dynamics interconnected with dynamic plants in the loop. In particular, we establish suitable semi-global practical asymptotic stability properties for different classes of cost functions, as well as tuning conditions for the hybrid extremum seeking algorithm. Additionally, we implement the HAES to optimize the performance of a self-organizing traffic light system (SOTL) in a class of smart transportation systems. We show that the dynamic momentum mechanism incorporated by the HAES can significantly reduce the convergence time in the optimization process compared to the traditional extremum seeking algorithms based on gradient descent flows.","PeriodicalId":229089,"journal":{"name":"2021 60th IEEE Conference on Decision and Control (CDC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132893706","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 : 2021-12-14DOI: 10.1109/CDC45484.2021.9683152
Dániel Fényes, B. Németh, P. Gáspár
The paper presents a data-driven control design for a steering system, which is based on variable-geometry suspension. The proposed control system into a hierarchical structure with different roles on each layer is ordered, i.e., low-level and high-level controls are designed. The low-level controller is responsible for the realization of the steering angle, while the high-level controller guarantees the trajectory tracking of the vehicle. The low-level controller based on a polytopic model is designed, which model structure through a data-driven identification approach is carried out. The effectiveness and the operation of the hierarchical control structure through a Hardware-in-the-Loop (HiL) simulation are demonstrated. In the HiL simulation environment, the lateral dynamics of the vehicle by the CarMaker software is modeled, and the motion of the variable-geometry suspension through a testbed is realized.
{"title":"Data-driven modeling and control design in a hierarchical structure for a variable-geometry suspension test bed","authors":"Dániel Fényes, B. Németh, P. Gáspár","doi":"10.1109/CDC45484.2021.9683152","DOIUrl":"https://doi.org/10.1109/CDC45484.2021.9683152","url":null,"abstract":"The paper presents a data-driven control design for a steering system, which is based on variable-geometry suspension. The proposed control system into a hierarchical structure with different roles on each layer is ordered, i.e., low-level and high-level controls are designed. The low-level controller is responsible for the realization of the steering angle, while the high-level controller guarantees the trajectory tracking of the vehicle. The low-level controller based on a polytopic model is designed, which model structure through a data-driven identification approach is carried out. The effectiveness and the operation of the hierarchical control structure through a Hardware-in-the-Loop (HiL) simulation are demonstrated. In the HiL simulation environment, the lateral dynamics of the vehicle by the CarMaker software is modeled, and the motion of the variable-geometry suspension through a testbed is realized.","PeriodicalId":229089,"journal":{"name":"2021 60th IEEE Conference on Decision and Control (CDC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133697016","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 : 2021-12-14DOI: 10.1109/CDC45484.2021.9682840
Xuwei Yang, Minyi Huang
We consider linear quadratic (LQ) Stackelberg games with a major player (leader) and N minor players (followers) and derive two master equations in a mean field limit model. We show the resulting decentralized strategies are time consistent by adapting a procedure introduced by Ekeland and Lazrak (2006).
{"title":"Linear Quadratic Mean Field Stackelberg Games: Master Equations and Time Consistent Feedback Strategies","authors":"Xuwei Yang, Minyi Huang","doi":"10.1109/CDC45484.2021.9682840","DOIUrl":"https://doi.org/10.1109/CDC45484.2021.9682840","url":null,"abstract":"We consider linear quadratic (LQ) Stackelberg games with a major player (leader) and N minor players (followers) and derive two master equations in a mean field limit model. We show the resulting decentralized strategies are time consistent by adapting a procedure introduced by Ekeland and Lazrak (2006).","PeriodicalId":229089,"journal":{"name":"2021 60th IEEE Conference on Decision and Control (CDC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132712611","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 : 2021-12-14DOI: 10.1109/CDC45484.2021.9683031
A. Sahraeekhanghah, Mo Chen
Guaranteed safe online trajectory planning is becoming an increasingly important topic of robotic research, due to the need to react quickly in unknown environments. However, as a result of modelling mismatch, some error during trajectory tracking is inevitable. In this paper, we present Planner-Aware FaSTrack, or PA-FaSTrack, which provides guaranteed Tracking Error Bounds (TEBs) by solving a Hamilton-Jacobi (HJ) variational inequality in the tracking error space. PA-FaSTrack improves upon the state-of-the-art method, FaSTrack [1], by accounting for motion primitives implied by the planning algorithm in the problem formulation. Our method provides a sequence of TEBs, with each TEB corresponding to a segment of the planned path. We also propose necessary modifications to real time tree based planning algorithms in order to make them compatible with the provided TEB sequence. By integrating planning and tracking more closely together, we greatly decrease the degree of conservatism compared to the original FaSTrack, allowing the autonomous system to navigate safely through much narrower spaces. We demonstrate our method using two representative dynamical systems.
{"title":"PA-FaSTrack: Planner-Aware Real-Time Guaranteed Safe Planning","authors":"A. Sahraeekhanghah, Mo Chen","doi":"10.1109/CDC45484.2021.9683031","DOIUrl":"https://doi.org/10.1109/CDC45484.2021.9683031","url":null,"abstract":"Guaranteed safe online trajectory planning is becoming an increasingly important topic of robotic research, due to the need to react quickly in unknown environments. However, as a result of modelling mismatch, some error during trajectory tracking is inevitable. In this paper, we present Planner-Aware FaSTrack, or PA-FaSTrack, which provides guaranteed Tracking Error Bounds (TEBs) by solving a Hamilton-Jacobi (HJ) variational inequality in the tracking error space. PA-FaSTrack improves upon the state-of-the-art method, FaSTrack [1], by accounting for motion primitives implied by the planning algorithm in the problem formulation. Our method provides a sequence of TEBs, with each TEB corresponding to a segment of the planned path. We also propose necessary modifications to real time tree based planning algorithms in order to make them compatible with the provided TEB sequence. By integrating planning and tracking more closely together, we greatly decrease the degree of conservatism compared to the original FaSTrack, allowing the autonomous system to navigate safely through much narrower spaces. We demonstrate our method using two representative dynamical systems.","PeriodicalId":229089,"journal":{"name":"2021 60th IEEE Conference on Decision and Control (CDC)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132750797","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 : 2021-12-14DOI: 10.1109/CDC45484.2021.9683369
Hesameddin Mohammadi, M. Soltanolkotabi, M. Jovanović
Policy gradient algorithms in model-free reinforcement learning have been shown to achieve global exponential convergence for the Linear Quadratic Regulator problem despite the lack of convexity. However, extending such guarantees beyond the scope of standard LQR and full-state feedback has remained open. A key enabler for existing results on LQR is the so-called gradient dominance property of the underlying optimization problem that can be used as a surrogate for strong convexity. In this paper, we take a step further by studying the convergence of gradient descent for the Linear Quadratic Gaussian problem and demonstrate through examples that LQG does not satisfy the gradient dominance property. Our study shows the non-uniqueness of equilibrium points and thus disproves the global convergence of policy gradient methods for LQG.
{"title":"On the lack of gradient domination for linear quadratic Gaussian problems with incomplete state information","authors":"Hesameddin Mohammadi, M. Soltanolkotabi, M. Jovanović","doi":"10.1109/CDC45484.2021.9683369","DOIUrl":"https://doi.org/10.1109/CDC45484.2021.9683369","url":null,"abstract":"Policy gradient algorithms in model-free reinforcement learning have been shown to achieve global exponential convergence for the Linear Quadratic Regulator problem despite the lack of convexity. However, extending such guarantees beyond the scope of standard LQR and full-state feedback has remained open. A key enabler for existing results on LQR is the so-called gradient dominance property of the underlying optimization problem that can be used as a surrogate for strong convexity. In this paper, we take a step further by studying the convergence of gradient descent for the Linear Quadratic Gaussian problem and demonstrate through examples that LQG does not satisfy the gradient dominance property. Our study shows the non-uniqueness of equilibrium points and thus disproves the global convergence of policy gradient methods for LQG.","PeriodicalId":229089,"journal":{"name":"2021 60th IEEE Conference on Decision and Control (CDC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133134924","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 : 2021-12-14DOI: 10.1109/CDC45484.2021.9682877
Raihan Seraj, J. Le Ny, Aditya Mahajan
We study a class of Keynesian beauty contest games where a large number of heterogeneous players attempt to estimate a common parameter based on their own observations. The players are rewarded for producing an estimate close to a certain multiplicative factor of the average decision, this factor being specific to each player. This model is motivated by scenarios arising in commodity or financial markets, where investment decisions are sometimes partly based on following a trend. We provide a method to compute Nash equilibria within the class of affine strategies. We then develop a mean-field approximation, in the limit of an infinite number of players, which has the advantage that computing the best-response strategies only requires the knowledge of the parameter distribution of the players, rather than their actual parameters. We show that the mean-field strategies lead to an ε-Nash equilibrium for a system with a finite number of players. We conclude by analyzing the impact on individual behavior of changes in aggregate population behavior.
{"title":"Mean-field approximation for large-population beauty-contest games","authors":"Raihan Seraj, J. Le Ny, Aditya Mahajan","doi":"10.1109/CDC45484.2021.9682877","DOIUrl":"https://doi.org/10.1109/CDC45484.2021.9682877","url":null,"abstract":"We study a class of Keynesian beauty contest games where a large number of heterogeneous players attempt to estimate a common parameter based on their own observations. The players are rewarded for producing an estimate close to a certain multiplicative factor of the average decision, this factor being specific to each player. This model is motivated by scenarios arising in commodity or financial markets, where investment decisions are sometimes partly based on following a trend. We provide a method to compute Nash equilibria within the class of affine strategies. We then develop a mean-field approximation, in the limit of an infinite number of players, which has the advantage that computing the best-response strategies only requires the knowledge of the parameter distribution of the players, rather than their actual parameters. We show that the mean-field strategies lead to an ε-Nash equilibrium for a system with a finite number of players. We conclude by analyzing the impact on individual behavior of changes in aggregate population behavior.","PeriodicalId":229089,"journal":{"name":"2021 60th IEEE Conference on Decision and Control (CDC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127865984","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 : 2021-12-14DOI: 10.1109/CDC45484.2021.9683371
Chase St. Laurent, Raghvendra V. Cowlagi
We present a breadth-first sensor configuration strategy to find near-optimal placement and sensor field of view (FoV). The strategy couples the sensor configuration procedure directly with the decision making task of planning a path for an agent in an unknown static environment comprised of threats. This coupled sensor configuration and path-planning (CSCP) strategy iteratively uses Gaussian Process Regression to construct a threat field estimate and find a candidate optimal path with minimum threat exposure. The strategy utilizes a unique task-driven information gain (TDIG) metric, which yields the sensor configurations when maximized. Due to the non-convex and non-submodular nature of the problem, we present an approximation for the optimization of the TDIG metric. Finally, we discuss the performance of the breadth-first strategy in contrast to a standard and depth-first strategy as well as traditional information-maximization.
{"title":"Breadth-First Coupled Sensor Configuration and Path-Planning in Unknown Environments","authors":"Chase St. Laurent, Raghvendra V. Cowlagi","doi":"10.1109/CDC45484.2021.9683371","DOIUrl":"https://doi.org/10.1109/CDC45484.2021.9683371","url":null,"abstract":"We present a breadth-first sensor configuration strategy to find near-optimal placement and sensor field of view (FoV). The strategy couples the sensor configuration procedure directly with the decision making task of planning a path for an agent in an unknown static environment comprised of threats. This coupled sensor configuration and path-planning (CSCP) strategy iteratively uses Gaussian Process Regression to construct a threat field estimate and find a candidate optimal path with minimum threat exposure. The strategy utilizes a unique task-driven information gain (TDIG) metric, which yields the sensor configurations when maximized. Due to the non-convex and non-submodular nature of the problem, we present an approximation for the optimization of the TDIG metric. Finally, we discuss the performance of the breadth-first strategy in contrast to a standard and depth-first strategy as well as traditional information-maximization.","PeriodicalId":229089,"journal":{"name":"2021 60th IEEE Conference on Decision and Control (CDC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134465972","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 : 2021-12-14DOI: 10.1109/CDC45484.2021.9683027
Maximilian Schütte, Annika Eichler, H. Schlarb, G. Lichtenberg, H. Werner
In this work, we apply the recent framework of sparsity invariance to a large-scale facility, the European XFEL. With the sparsity invariance framework, the most general known class of convex approximations of distributed controller design problems is characterized. It is shown in previous work that this convex restriction can perform at least as well as all existing approximations for the possibly NP-hard problem of distributed controller design. The optical synchronization system at the European XFEL can be modeled as a distributed system with a tree or chain topology. Whereas in previous work based on the spatial invariance framework either static state feedback or high-order dynamic output feedback controller design is presented, we extend it in this work to static or fixed-order output feedback control and apply it to the system at hand. We show simulation results motivated by the real application and demonstrate that the convex approximation can outperform a local baseline tuning.
{"title":"Decentralized Output Feedback Control using Sparsity Invariance with Application to Synchronization at European XFEL","authors":"Maximilian Schütte, Annika Eichler, H. Schlarb, G. Lichtenberg, H. Werner","doi":"10.1109/CDC45484.2021.9683027","DOIUrl":"https://doi.org/10.1109/CDC45484.2021.9683027","url":null,"abstract":"In this work, we apply the recent framework of sparsity invariance to a large-scale facility, the European XFEL. With the sparsity invariance framework, the most general known class of convex approximations of distributed controller design problems is characterized. It is shown in previous work that this convex restriction can perform at least as well as all existing approximations for the possibly NP-hard problem of distributed controller design. The optical synchronization system at the European XFEL can be modeled as a distributed system with a tree or chain topology. Whereas in previous work based on the spatial invariance framework either static state feedback or high-order dynamic output feedback controller design is presented, we extend it in this work to static or fixed-order output feedback control and apply it to the system at hand. We show simulation results motivated by the real application and demonstrate that the convex approximation can outperform a local baseline tuning.","PeriodicalId":229089,"journal":{"name":"2021 60th IEEE Conference on Decision and Control (CDC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131290068","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 : 2021-12-14DOI: 10.1109/CDC45484.2021.9682835
Masaya Murata, I. Kawano, Koichi Inoue
The ensemble Kalman filter (EnKF) is well-established for discrete state-space models. In this paper, we provide the methodology of applying the EnKF to continuous-discrete (CD) state-space models. The proposed CD EnKF algorithm is a bank of the CD extended Kalman filters for the time update. Then, the observation update is formulated using the Gaussian-sum distributed predicted state probability density function (PDF). We also provide the observation update based on the Dirac’s delta mixture predicted state PDF. The numerical simulation using a benchmark filtering problem called the satellite reentry is conducted to investigate the performance of the CD EnKFs. The performance comparison with the EnKF applied to the discretized model is also made.
{"title":"Ensemble Kalman Filter for Continuous-Discrete State-Space Models","authors":"Masaya Murata, I. Kawano, Koichi Inoue","doi":"10.1109/CDC45484.2021.9682835","DOIUrl":"https://doi.org/10.1109/CDC45484.2021.9682835","url":null,"abstract":"The ensemble Kalman filter (EnKF) is well-established for discrete state-space models. In this paper, we provide the methodology of applying the EnKF to continuous-discrete (CD) state-space models. The proposed CD EnKF algorithm is a bank of the CD extended Kalman filters for the time update. Then, the observation update is formulated using the Gaussian-sum distributed predicted state probability density function (PDF). We also provide the observation update based on the Dirac’s delta mixture predicted state PDF. The numerical simulation using a benchmark filtering problem called the satellite reentry is conducted to investigate the performance of the CD EnKFs. The performance comparison with the EnKF applied to the discretized model is also made.","PeriodicalId":229089,"journal":{"name":"2021 60th IEEE Conference on Decision and Control (CDC)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131311593","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 : 2021-12-14DOI: 10.1109/CDC45484.2021.9683554
Martin Biel, Vien V. Mai, M. Johansson
We develop a fast smoothing procedure for solving linear two-stage stochastic programs, which outperforms the well-known L-shaped algorithm on large-scale benchmarks. We derive problem-dependent bounds for the effect of smoothing and characterize the convergence rate of the proposed algorithm. The theory suggests that the smoothing scheme can be sped up by sacrificing accuracy in the final solution. To obtain an efficient and effective method, we suggest a hybrid solution that combines the speed of the smoothing scheme with the accuracy of the L-shaped algorithm. We benchmark a parallel implementation of the smoothing scheme against an efficient parallelized L-shaped algorithm on three large-scale stochastic programs, in a distributed environment with 32 worker cores. The smoothing scheme reduces the solution time by up to an order of magnitude compared to L-shaped.
{"title":"A Fast Smoothing Procedure for Large-Scale Stochastic Programming","authors":"Martin Biel, Vien V. Mai, M. Johansson","doi":"10.1109/CDC45484.2021.9683554","DOIUrl":"https://doi.org/10.1109/CDC45484.2021.9683554","url":null,"abstract":"We develop a fast smoothing procedure for solving linear two-stage stochastic programs, which outperforms the well-known L-shaped algorithm on large-scale benchmarks. We derive problem-dependent bounds for the effect of smoothing and characterize the convergence rate of the proposed algorithm. The theory suggests that the smoothing scheme can be sped up by sacrificing accuracy in the final solution. To obtain an efficient and effective method, we suggest a hybrid solution that combines the speed of the smoothing scheme with the accuracy of the L-shaped algorithm. We benchmark a parallel implementation of the smoothing scheme against an efficient parallelized L-shaped algorithm on three large-scale stochastic programs, in a distributed environment with 32 worker cores. The smoothing scheme reduces the solution time by up to an order of magnitude compared to L-shaped.","PeriodicalId":229089,"journal":{"name":"2021 60th IEEE Conference on Decision and Control (CDC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131683386","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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