Pub Date : 2021-12-14DOI: 10.1109/CDC45484.2021.9683204
J. A. Dulce-Galindo, Lucas V. R. Alves, G. Raffo, P. Pena
The ability to hide sensitive information is important in many contexts such as multi-agent systems’ communications, industry 4.0, among others. In this paper, we deal with weak versions of known state-based opacity properties by using synchronizing automata to enforce such properties. A case study is presented in the context of the communication of multi-agent systems, where we aim to hide the leader from an intruder. Using synchronizing automata, initial-state and initial-and-final state opacity are enforced even if the intruder has full observation of the events of the system.
{"title":"Enforcing State-Based Opacity using Synchronizing Automata","authors":"J. A. Dulce-Galindo, Lucas V. R. Alves, G. Raffo, P. Pena","doi":"10.1109/CDC45484.2021.9683204","DOIUrl":"https://doi.org/10.1109/CDC45484.2021.9683204","url":null,"abstract":"The ability to hide sensitive information is important in many contexts such as multi-agent systems’ communications, industry 4.0, among others. In this paper, we deal with weak versions of known state-based opacity properties by using synchronizing automata to enforce such properties. A case study is presented in the context of the communication of multi-agent systems, where we aim to hide the leader from an intruder. Using synchronizing automata, initial-state and initial-and-final state opacity are enforced even if the intruder has full observation of the events of the system.","PeriodicalId":229089,"journal":{"name":"2021 60th IEEE Conference on Decision and Control (CDC)","volume":"29 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":"127247538","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/CDC45484.2021.9683410
Theodoros Tsatsanifos, A. Clark, Claire Walton, I. Kaminer, Q. Gong
We theoretically and numerically study the problem of optimal control of large-scale autonomous systems under explicitly adversarial conditions, including probabilistic destruction of agents during the simulation. Large-scale autonomous systems often include an adversarial component, where different agents or groups of agents explicitly compete with one another. An important component of these systems that is not included in current theory or modeling frameworks is random destruction of agents in time. In this case, the modeling and optimal control framework should consider the attrition of agents as well as their position. We propose and test three numerical modeling schemes, where survival probabilities of all agents are smoothly and continuously decreased in time, based on the relative positions of all agents during the simulation. In particular, we apply these schemes to the case of agents defending a high-value unit from an attacking swarm. We show that these models can be successfully used to model this situation, provided that attrition and spatial dynamics are coupled. Our results have relevance to an entire class of adversarial autonomy situations, where the positions of agents and their survival probabilities are both important.
{"title":"Modeling Large-Scale Adversarial Swarm Engagements using Optimal Control","authors":"Theodoros Tsatsanifos, A. Clark, Claire Walton, I. Kaminer, Q. Gong","doi":"10.1109/CDC45484.2021.9683410","DOIUrl":"https://doi.org/10.1109/CDC45484.2021.9683410","url":null,"abstract":"We theoretically and numerically study the problem of optimal control of large-scale autonomous systems under explicitly adversarial conditions, including probabilistic destruction of agents during the simulation. Large-scale autonomous systems often include an adversarial component, where different agents or groups of agents explicitly compete with one another. An important component of these systems that is not included in current theory or modeling frameworks is random destruction of agents in time. In this case, the modeling and optimal control framework should consider the attrition of agents as well as their position. We propose and test three numerical modeling schemes, where survival probabilities of all agents are smoothly and continuously decreased in time, based on the relative positions of all agents during the simulation. In particular, we apply these schemes to the case of agents defending a high-value unit from an attacking swarm. We show that these models can be successfully used to model this situation, provided that attrition and spatial dynamics are coupled. Our results have relevance to an entire class of adversarial autonomy situations, where the positions of agents and their survival probabilities are both important.","PeriodicalId":229089,"journal":{"name":"2021 60th IEEE Conference on Decision and Control (CDC)","volume":"174 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":"133788557","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/CDC45484.2021.9683416
Syed Aseem Ul Islam, Khaled F. Aljanaideh, T. Nguyen, I. Kolmanovsky, D. Bernstein
This paper considers system identification in the presence of an unmeasured, unknown, and unmatched multitone harmonic disturbance with completely unknown spectrum. It is shown that the identified model possesses spurious poles at the disturbance frequencies that are cancelled by coincident, spurious zeros. Although the presence of the spurious poles is expected, this paper shows that the free response of the identified model is identical—in frequencies, amplitudes, and phases—to the free-plus-forced response of the true system. Consequently, by retaining—rather than cancelling—the coincident, spurious poles and zeros, the identified model has the ability to forecast the future response to an unknown harmonic input over a prediction horizon during which the harmonic disturbance persists. A numerical example illustrates the usefulness of this property to model predictive control with concurrent system identification for rejecting unmeasured, unknown, and unmatched harmonic disturbances with completely unknown spectrum.
{"title":"The Free Response of an Identified Model of a Linear System with a Completely Unknown Harmonic Disturbance Exactly Forcasts the Free-Plus-Forced Response of the True System Thereby Enabling Adaptive MPC for Harmonic Disturbance Rejection","authors":"Syed Aseem Ul Islam, Khaled F. Aljanaideh, T. Nguyen, I. Kolmanovsky, D. Bernstein","doi":"10.1109/CDC45484.2021.9683416","DOIUrl":"https://doi.org/10.1109/CDC45484.2021.9683416","url":null,"abstract":"This paper considers system identification in the presence of an unmeasured, unknown, and unmatched multitone harmonic disturbance with completely unknown spectrum. It is shown that the identified model possesses spurious poles at the disturbance frequencies that are cancelled by coincident, spurious zeros. Although the presence of the spurious poles is expected, this paper shows that the free response of the identified model is identical—in frequencies, amplitudes, and phases—to the free-plus-forced response of the true system. Consequently, by retaining—rather than cancelling—the coincident, spurious poles and zeros, the identified model has the ability to forecast the future response to an unknown harmonic input over a prediction horizon during which the harmonic disturbance persists. A numerical example illustrates the usefulness of this property to model predictive control with concurrent system identification for rejecting unmeasured, unknown, and unmatched harmonic disturbances with completely unknown spectrum.","PeriodicalId":229089,"journal":{"name":"2021 60th IEEE Conference on Decision and Control (CDC)","volume":"57 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":"115735028","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/CDC45484.2021.9683256
F. Stoican, S. Mihai, B. Ciubotaru
This paper analyzes the structure of the constrained optimization problem induced by a typical Model Predictive Control (MPC) problem. The main idea is to exploit the particularities of the feasible domain (namely, that input/state/output constraints describe in fact zonotopic sets) to: i) efficiently describe the solution as a piecewise affine function with polyhedral support; ii) exploit the combinatorial properties of zonotopes to reduce the number of candidate active sets. The results are tested over a numerical example.
{"title":"Observations on the complexity of the explicit MPC","authors":"F. Stoican, S. Mihai, B. Ciubotaru","doi":"10.1109/CDC45484.2021.9683256","DOIUrl":"https://doi.org/10.1109/CDC45484.2021.9683256","url":null,"abstract":"This paper analyzes the structure of the constrained optimization problem induced by a typical Model Predictive Control (MPC) problem. The main idea is to exploit the particularities of the feasible domain (namely, that input/state/output constraints describe in fact zonotopic sets) to: i) efficiently describe the solution as a piecewise affine function with polyhedral support; ii) exploit the combinatorial properties of zonotopes to reduce the number of candidate active sets. The results are tested over a numerical example.","PeriodicalId":229089,"journal":{"name":"2021 60th IEEE Conference on Decision and Control (CDC)","volume":"418 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":"124198521","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/CDC45484.2021.9683065
Xiaobin Xiong, Yuxiao Chen, A. Ames
We present a stepping stabilization control that addresses external push disturbances on bipedal walking robots. The stepping control is synthesized based on the step-to-step (S2S) dynamics of the robot that is controlled to have an approximately constant center of mass (COM) height. We first learn a linear S2S dynamics with bounded model discrepancy from the undisturbed walking behaviors of the robot, where the walking step size is taken as the control input to the S2S dynamics. External pushes are then considered as disturbances to the learned S2S (L-S2S) dynamics. We then apply the system-level-synthesis (SLS) approach on the disturbed L-S2S dynamics to robustly stabilize the robot to the desired walking while satisfying the kinematic constraints of the robot. We successfully realize the proposed approach on the walking of the bipedal robot AMBER and Cassie subject to push disturbances, showing that the approach is general, effective, and computationally-efficient for robust disturbance rejection.
{"title":"Robust Disturbance Rejection for Robotic Bipedal Walking: System-Level-Synthesis with Step-to-step Dynamics Approximation","authors":"Xiaobin Xiong, Yuxiao Chen, A. Ames","doi":"10.1109/CDC45484.2021.9683065","DOIUrl":"https://doi.org/10.1109/CDC45484.2021.9683065","url":null,"abstract":"We present a stepping stabilization control that addresses external push disturbances on bipedal walking robots. The stepping control is synthesized based on the step-to-step (S2S) dynamics of the robot that is controlled to have an approximately constant center of mass (COM) height. We first learn a linear S2S dynamics with bounded model discrepancy from the undisturbed walking behaviors of the robot, where the walking step size is taken as the control input to the S2S dynamics. External pushes are then considered as disturbances to the learned S2S (L-S2S) dynamics. We then apply the system-level-synthesis (SLS) approach on the disturbed L-S2S dynamics to robustly stabilize the robot to the desired walking while satisfying the kinematic constraints of the robot. We successfully realize the proposed approach on the walking of the bipedal robot AMBER and Cassie subject to push disturbances, showing that the approach is general, effective, and computationally-efficient for robust disturbance rejection.","PeriodicalId":229089,"journal":{"name":"2021 60th IEEE Conference on Decision and Control (CDC)","volume":"15 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":"124267033","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/CDC45484.2021.9682896
Y. Orlov
An asymptotic observer for a linear system, evolving in a Hilbert space, is designed over linear state measurements with time-varying delays. The proposed predictor-based approach reduces the problem to the standard one with non-delayed information on the state, thereby being invariant to the dimensionality of the underlying system. Capabilities of the resulting observer design are illustrated for the linearized Kuramoto-Sivashinsky PDE with periodic boundary conditions and with delayed finite-dimensional measurements.
{"title":"Observer Design in Infinite-dimensional Setting Using Delayed Measurements","authors":"Y. Orlov","doi":"10.1109/CDC45484.2021.9682896","DOIUrl":"https://doi.org/10.1109/CDC45484.2021.9682896","url":null,"abstract":"An asymptotic observer for a linear system, evolving in a Hilbert space, is designed over linear state measurements with time-varying delays. The proposed predictor-based approach reduces the problem to the standard one with non-delayed information on the state, thereby being invariant to the dimensionality of the underlying system. Capabilities of the resulting observer design are illustrated for the linearized Kuramoto-Sivashinsky PDE with periodic boundary conditions and with delayed finite-dimensional measurements.","PeriodicalId":229089,"journal":{"name":"2021 60th IEEE Conference on Decision and Control (CDC)","volume":"32 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":"124421268","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/CDC45484.2021.9683192
Omar M. Sleem, C. Lagoa
Quantization plays an important role as an inter-face between analog and digital environments. Since quantization is a many to few mapping, it is a non-linear irreversible process. This made, in addition of the quantization noise signal dependency, the traditional methods of system identification no longer applicable. In this work, we propose a method for parsimonious system identification when only quantized measurements of the output are observable. More precisely, we develop an algorithm that aims at identifying a low order system that is compatible with a priori information on the system and the collected quantized output information. Moreover, the proposed approach can be used even if only fragmented information on the quantized output is available. The proposed algorithm relies on an ADMM approach to ℓp quasi-norm optimization. Numerical results highlight the performance of the proposed approach when compared to the ℓ1 minimization in terms of the sparsity of the induced solution.
{"title":"Parsimonious System Identification from Quantized Observations","authors":"Omar M. Sleem, C. Lagoa","doi":"10.1109/CDC45484.2021.9683192","DOIUrl":"https://doi.org/10.1109/CDC45484.2021.9683192","url":null,"abstract":"Quantization plays an important role as an inter-face between analog and digital environments. Since quantization is a many to few mapping, it is a non-linear irreversible process. This made, in addition of the quantization noise signal dependency, the traditional methods of system identification no longer applicable. In this work, we propose a method for parsimonious system identification when only quantized measurements of the output are observable. More precisely, we develop an algorithm that aims at identifying a low order system that is compatible with a priori information on the system and the collected quantized output information. Moreover, the proposed approach can be used even if only fragmented information on the quantized output is available. The proposed algorithm relies on an ADMM approach to ℓp quasi-norm optimization. Numerical results highlight the performance of the proposed approach when compared to the ℓ1 minimization in terms of the sparsity of the induced solution.","PeriodicalId":229089,"journal":{"name":"2021 60th IEEE Conference on Decision and Control (CDC)","volume":"23 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":"114763024","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/CDC45484.2021.9682942
Biel Roig-Solvas, M. Sznaier
Semidefinite programs (SDP) are a staple of today’s systems theory, with applications ranging from robust control to systems identification. However, current state-of-the art solution methods have poor scaling properties, and thus are limited to relatively moderate size problems. Recently, several approximations have been proposed where the original SDP is relaxed to a sequence of lower complexity problems (such as linear programs (LPs) or second order cone programs (SOCPs)). While successful in many cases, there is no guarantee that these relaxations converge to the global optimum of the original program. Indeed, examples exists where these relaxations "get stuck" at suboptimal solutions. To circumvent this difficulty in this paper we propose an algorithm to solve SDPs based on solving a sequence of LPs or SOCPs, guaranteed to converge in a finite number of steps to an ε-suboptimal solution of the original problem. We further provide a bound on the number of steps required, as a function of ε and the problem data.
{"title":"Globally Convergent Low Complexity Algorithms for Semidefinite Programming","authors":"Biel Roig-Solvas, M. Sznaier","doi":"10.1109/CDC45484.2021.9682942","DOIUrl":"https://doi.org/10.1109/CDC45484.2021.9682942","url":null,"abstract":"Semidefinite programs (SDP) are a staple of today’s systems theory, with applications ranging from robust control to systems identification. However, current state-of-the art solution methods have poor scaling properties, and thus are limited to relatively moderate size problems. Recently, several approximations have been proposed where the original SDP is relaxed to a sequence of lower complexity problems (such as linear programs (LPs) or second order cone programs (SOCPs)). While successful in many cases, there is no guarantee that these relaxations converge to the global optimum of the original program. Indeed, examples exists where these relaxations \"get stuck\" at suboptimal solutions. To circumvent this difficulty in this paper we propose an algorithm to solve SDPs based on solving a sequence of LPs or SOCPs, guaranteed to converge in a finite number of steps to an ε-suboptimal solution of the original problem. We further provide a bound on the number of steps required, as a function of ε and the problem data.","PeriodicalId":229089,"journal":{"name":"2021 60th IEEE Conference on Decision and Control (CDC)","volume":"219 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":"114666546","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/CDC45484.2021.9683200
F. Blanchini, P. Bolzern, P. Colaneri, G. Nicolao, G. Giordano
We consider a class of epidemiological models in which a compartmental linear system, including various categories of infected individuals (e.g. asymptomatic, symptomatic, quarantined), is fed back by a positive feedback, representing contagion. The positive feedback gain decreases (in a sort of negative feedback) as the epidemic evolves, due to the decrease in the number of susceptible individuals. We first propose a convergence result based on a special copositive Lyapunov function. Then, we address a major problem for this class of systems: the deep uncertainty affecting parameter values. We face the problem adopting techniques from optimal and robust control theory to assess the sensitivity of the model. For this class of systems, the optimal control solution has a peculiar decoupling property that no shooting procedure is required. Finally, we exploit the obtained bounds to assess the effectiveness of possible epidemic control strategies, including intermittent restrictions adopted during the COVID-19 pandemic.
{"title":"Generalized epidemiological compartmental models: guaranteed bounds via optimal control","authors":"F. Blanchini, P. Bolzern, P. Colaneri, G. Nicolao, G. Giordano","doi":"10.1109/CDC45484.2021.9683200","DOIUrl":"https://doi.org/10.1109/CDC45484.2021.9683200","url":null,"abstract":"We consider a class of epidemiological models in which a compartmental linear system, including various categories of infected individuals (e.g. asymptomatic, symptomatic, quarantined), is fed back by a positive feedback, representing contagion. The positive feedback gain decreases (in a sort of negative feedback) as the epidemic evolves, due to the decrease in the number of susceptible individuals. We first propose a convergence result based on a special copositive Lyapunov function. Then, we address a major problem for this class of systems: the deep uncertainty affecting parameter values. We face the problem adopting techniques from optimal and robust control theory to assess the sensitivity of the model. For this class of systems, the optimal control solution has a peculiar decoupling property that no shooting procedure is required. Finally, we exploit the obtained bounds to assess the effectiveness of possible epidemic control strategies, including intermittent restrictions adopted during the COVID-19 pandemic.","PeriodicalId":229089,"journal":{"name":"2021 60th IEEE Conference on Decision and Control (CDC)","volume":"18 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":"117266127","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/CDC45484.2021.9683572
Xuan-Zhi ZHU, Pedro Casau, C. Silvestre
In this paper, we design a model-based event-triggered controller for networked control of a linear time-invariant (LTI) system using a finite-time observer. Under the framework of hybrid dynamical systems, we show that, if the plant dynamics are detectable and stabilizable, then: 1) the zero error set is globally asymptotically stable and globally finite-time stable for the closed-loop system; 2) the closed-loop system is robust to small state perturbations; 3) the state of the plant converges to a neighborhood of the origin that can be made arbitrarily small; 4) the number of transmissions through the network is finite. We illustrate these results through numerical simulations.
{"title":"Finite-Time Model-Based Event-Triggered Control of LTI Systems","authors":"Xuan-Zhi ZHU, Pedro Casau, C. Silvestre","doi":"10.1109/CDC45484.2021.9683572","DOIUrl":"https://doi.org/10.1109/CDC45484.2021.9683572","url":null,"abstract":"In this paper, we design a model-based event-triggered controller for networked control of a linear time-invariant (LTI) system using a finite-time observer. Under the framework of hybrid dynamical systems, we show that, if the plant dynamics are detectable and stabilizable, then: 1) the zero error set is globally asymptotically stable and globally finite-time stable for the closed-loop system; 2) the closed-loop system is robust to small state perturbations; 3) the state of the plant converges to a neighborhood of the origin that can be made arbitrarily small; 4) the number of transmissions through the network is finite. We illustrate these results through numerical simulations.","PeriodicalId":229089,"journal":{"name":"2021 60th IEEE Conference on Decision and Control (CDC)","volume":"82 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":"117308196","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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