Pub Date : 2020-07-01DOI: 10.23919/ACC45564.2020.9147536
A. Ghaffari
Keep-in operational envelopes are essential to maintain the safety of autonomous systems such as unmanned aerial vehicles (UAVs). System constraints, including actuator saturation, dramatically affect the maneuverability of the system inside the operational envelope. Moreover, sate-of-the-art safety control depends heavily on the specifications of the operational envelope. Thus, this paper presents a modular technique to transform safety envelopes into low and high barriers along position and velocity axes. The proposed safety envelope guarantees safety and asymptotic stability simultaneously. The closed-form solution of the safety rule is obtained in the form of allowable high and low control limits, which are calculated adaptively. Thus, the design scalability is improved, and control tuning effort is minimized. Furthermore, it is shown that the proposed safety design seamlessly integrates with an existing motion control algorithm with minimum modification. Numerical simulations are conducted to verify the effectiveness of the proposed algorithm on a quadrotor drone.
{"title":"Operational Safety Control for Unmanned Aerial Vehicles Using Modular Barrier Functions","authors":"A. Ghaffari","doi":"10.23919/ACC45564.2020.9147536","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147536","url":null,"abstract":"Keep-in operational envelopes are essential to maintain the safety of autonomous systems such as unmanned aerial vehicles (UAVs). System constraints, including actuator saturation, dramatically affect the maneuverability of the system inside the operational envelope. Moreover, sate-of-the-art safety control depends heavily on the specifications of the operational envelope. Thus, this paper presents a modular technique to transform safety envelopes into low and high barriers along position and velocity axes. The proposed safety envelope guarantees safety and asymptotic stability simultaneously. The closed-form solution of the safety rule is obtained in the form of allowable high and low control limits, which are calculated adaptively. Thus, the design scalability is improved, and control tuning effort is minimized. Furthermore, it is shown that the proposed safety design seamlessly integrates with an existing motion control algorithm with minimum modification. Numerical simulations are conducted to verify the effectiveness of the proposed algorithm on a quadrotor drone.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116041806","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 : 2020-07-01DOI: 10.23919/ACC45564.2020.9147960
Wenjie Song, Shixian Liu, Yujun Li, Yi Yang, C. Xiang
For the intelligent sequential decision-making tasks like autonomous driving, decisions or actions made by the agent in a short period of time should be smooth enough or not too choppy. In order to help the agent learn smooth actions (steering, accelerating, braking) for autonomous driving, this paper proposes the smooth actor-critic algorithm for both deterministic policy and stochastic policy systems. Specifically, a regularization term is added to the objective function of actorcritic methods to constrain the difference between neighbouring actions in a small region without affecting the convergence performance of the whole system. Then, the theoretical analysis and proof for the modified methods are conducted so that it can be theoretically guaranteed in terms of iterative improvements. Moreover, experiments in different simulation systems also prove that the methods can generate much smoother actions and obtain more robust performance for reinforcement learning-based End-to-End autonomous driving.
{"title":"Smooth Actor-Critic Algorithm for End-to-End Autonomous Driving","authors":"Wenjie Song, Shixian Liu, Yujun Li, Yi Yang, C. Xiang","doi":"10.23919/ACC45564.2020.9147960","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147960","url":null,"abstract":"For the intelligent sequential decision-making tasks like autonomous driving, decisions or actions made by the agent in a short period of time should be smooth enough or not too choppy. In order to help the agent learn smooth actions (steering, accelerating, braking) for autonomous driving, this paper proposes the smooth actor-critic algorithm for both deterministic policy and stochastic policy systems. Specifically, a regularization term is added to the objective function of actorcritic methods to constrain the difference between neighbouring actions in a small region without affecting the convergence performance of the whole system. Then, the theoretical analysis and proof for the modified methods are conducted so that it can be theoretically guaranteed in terms of iterative improvements. Moreover, experiments in different simulation systems also prove that the methods can generate much smoother actions and obtain more robust performance for reinforcement learning-based End-to-End autonomous driving.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123436975","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 : 2020-07-01DOI: 10.23919/acc45564.2020.9147916
A. Goel, D. Bernstein
In many applications of state estimation, it is efficient to confine the output-error injection to a prescribed subspace of the state space. This paper considers this problem by applying the unscented Kalman filter and retrospective cost state estimator (RCSE) to linear and nonlinear systems with subspace-constrained state correction. As an application of these techniques, parameter estimation is considered for linear and nonlinear systems with unknown parameters, where the output- error injection is confined to the subspace corresponding to the states representing the unknown parameters.
{"title":"Adaptive State Estimation with Subspace-Constrained State Correction","authors":"A. Goel, D. Bernstein","doi":"10.23919/acc45564.2020.9147916","DOIUrl":"https://doi.org/10.23919/acc45564.2020.9147916","url":null,"abstract":"In many applications of state estimation, it is efficient to confine the output-error injection to a prescribed subspace of the state space. This paper considers this problem by applying the unscented Kalman filter and retrospective cost state estimator (RCSE) to linear and nonlinear systems with subspace-constrained state correction. As an application of these techniques, parameter estimation is considered for linear and nonlinear systems with unknown parameters, where the output- error injection is confined to the subspace corresponding to the states representing the unknown parameters.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123503711","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 : 2020-07-01DOI: 10.23919/ACC45564.2020.9147459
Prateek Munankarmi, Xin Jin, F. Ding, Changhong Zhao
With increasing penetration of renewable energy resources, the flexibility of operating behind-the-meter (BTM) resources plays a key role in enhancing grid reliability and resilience. Residential buildings with home energy management systems (HEMS) can provide desired flexibility for the distribution system operator (DSO) while considering customer comfort and preferences. This paper discusses a methodology to quantify the flexibility of BTM resources of residential buildings using HEMS. First, we propose a model predictive control framework to formulate the flexibility band comprising nominal, upper, and lower demand profiles. Second, the paper proposes a dispatch method for HEMS to compute the control signals for each BTM resource (e.g., air conditioner, water heater, home battery system) upon receiving a flexibility service request from the DSO. The case study provides insight into the flexibility provided at the whole-home level with different user preferences and seasons. The results demonstrate that HEMS is capable of providing flexibility service at the request of the DSO while delivering primary services to the building occupants.
{"title":"Quantification of Load Flexibility in Residential Buildings Using Home Energy Management Systems","authors":"Prateek Munankarmi, Xin Jin, F. Ding, Changhong Zhao","doi":"10.23919/ACC45564.2020.9147459","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147459","url":null,"abstract":"With increasing penetration of renewable energy resources, the flexibility of operating behind-the-meter (BTM) resources plays a key role in enhancing grid reliability and resilience. Residential buildings with home energy management systems (HEMS) can provide desired flexibility for the distribution system operator (DSO) while considering customer comfort and preferences. This paper discusses a methodology to quantify the flexibility of BTM resources of residential buildings using HEMS. First, we propose a model predictive control framework to formulate the flexibility band comprising nominal, upper, and lower demand profiles. Second, the paper proposes a dispatch method for HEMS to compute the control signals for each BTM resource (e.g., air conditioner, water heater, home battery system) upon receiving a flexibility service request from the DSO. The case study provides insight into the flexibility provided at the whole-home level with different user preferences and seasons. The results demonstrate that HEMS is capable of providing flexibility service at the request of the DSO while delivering primary services to the building occupants.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121957677","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 : 2020-07-01DOI: 10.23919/ACC45564.2020.9147262
Trevor Avant, K. Morgansen
In this paper, we consider the task of estimating the state of dynamic object by applying an unscented filter to pose estimates generated by a neural network. To incorporate the rotational state of the system into the filter, we use a parameterization of the tangent space of the group of rotation matrices SO(3). We then characterize the noise in the pose estimation neural network by considering simple motions of the object, as well as using a Monte Carlo approach. Finally, using synthetically generated images, we show in simulation how the unscented filter can improve the accuracy of the pose estimates from the neural network.
{"title":"Rigid Body Dynamics Estimation by Unscented Filtering Pose Estimation Neural Networks","authors":"Trevor Avant, K. Morgansen","doi":"10.23919/ACC45564.2020.9147262","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147262","url":null,"abstract":"In this paper, we consider the task of estimating the state of dynamic object by applying an unscented filter to pose estimates generated by a neural network. To incorporate the rotational state of the system into the filter, we use a parameterization of the tangent space of the group of rotation matrices SO(3). We then characterize the noise in the pose estimation neural network by considering simple motions of the object, as well as using a Monte Carlo approach. Finally, using synthetically generated images, we show in simulation how the unscented filter can improve the accuracy of the pose estimates from the neural network.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122166289","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 : 2020-07-01DOI: 10.23919/ACC45564.2020.9148035
Audrey Schanen, J. Dumon, N. Meslem, A. Hably
In this paper, the problem of take-off and landing of an airborne wind energy system is addressed. The solution explored is to equipe the airborne wing of the system with a multicopter drone in order to perform the take-off and land maneuvers, even in the absence of wind. The proposed model with the proposed control strategy is implemented and tested in a numerical environment. The results show efficiency of the proposed control law and its robustness with respect to modelling errors and wind gusts.
{"title":"Take-off and landing of an AWE system using a multicopter","authors":"Audrey Schanen, J. Dumon, N. Meslem, A. Hably","doi":"10.23919/ACC45564.2020.9148035","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9148035","url":null,"abstract":"In this paper, the problem of take-off and landing of an airborne wind energy system is addressed. The solution explored is to equipe the airborne wing of the system with a multicopter drone in order to perform the take-off and land maneuvers, even in the absence of wind. The proposed model with the proposed control strategy is implemented and tested in a numerical environment. The results show efficiency of the proposed control law and its robustness with respect to modelling errors and wind gusts.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":" 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120833263","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 : 2020-07-01DOI: 10.23919/ACC45564.2020.9147970
C. He, Anil Alan, T. Molnár, S. Avedisov, A. Bell, Russell Zukouski, Matthew Hunkler, Jim Yan, G. Orosz
In this work, we integrate two once separate concepts for longitudinal control of heavy duty vehicles: responding to elevation changes to improve fuel economy using preview and reacting to the motion of preceding vehicles using feedback. The two concepts are unified to provide a safe yet fuel efficient connected and automated technology for heavy duty vehicles. First, we establish an integrated control framework of the two concepts based on barrier function theory and then we discuss the detailed control design of each concept. Finally, we demonstrate the benefits of the proposed design against a naive switching controller by experimentally evaluating the performance of a connected automated truck.
{"title":"Improving fuel economy of heavy-duty vehicles in daily driving","authors":"C. He, Anil Alan, T. Molnár, S. Avedisov, A. Bell, Russell Zukouski, Matthew Hunkler, Jim Yan, G. Orosz","doi":"10.23919/ACC45564.2020.9147970","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147970","url":null,"abstract":"In this work, we integrate two once separate concepts for longitudinal control of heavy duty vehicles: responding to elevation changes to improve fuel economy using preview and reacting to the motion of preceding vehicles using feedback. The two concepts are unified to provide a safe yet fuel efficient connected and automated technology for heavy duty vehicles. First, we establish an integrated control framework of the two concepts based on barrier function theory and then we discuss the detailed control design of each concept. Finally, we demonstrate the benefits of the proposed design against a naive switching controller by experimentally evaluating the performance of a connected automated truck.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124091611","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 : 2020-07-01DOI: 10.23919/ACC45564.2020.9147994
Serkan Sarıtaş, G. Dán, H. Sandberg
This paper is concerned with the problem of fault-tolerant estimation in cyber-physical systems. In cyber-physical systems, such as critical infrastructures, networked embedded sensors are widely used for monitoring and can be exploited by an adversary to deceive the control center by modifying measured values. The deception is modeled as a bias; i.e., there is a misalignment between the objective functions of the control center and the adversarial sensor. Different from previous studies, a Stackelberg equilibrium of a cheap talk setup is adapted to the attacker-defender game setting for the first time. That is, the defender (control center), as a receiver, is the leader, and the attacker (adversarial sensor), as a transmitter, is the follower. The equilibrium strategies and the associated costs are characterized for uniformly distributed variables and quadratic objective functions, and an analysis on the uniqueness of the equilibrium is provided. It is shown that the attacker and defender costs at the equilibrium are increasing with the bias and decreasing with the number of quantization levels. Our results surprisingly show that, under certain conditions, the attacker prefers a public bias rather than a private one.
{"title":"Passive Fault-tolerant Estimation under Strategic Adversarial Bias","authors":"Serkan Sarıtaş, G. Dán, H. Sandberg","doi":"10.23919/ACC45564.2020.9147994","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147994","url":null,"abstract":"This paper is concerned with the problem of fault-tolerant estimation in cyber-physical systems. In cyber-physical systems, such as critical infrastructures, networked embedded sensors are widely used for monitoring and can be exploited by an adversary to deceive the control center by modifying measured values. The deception is modeled as a bias; i.e., there is a misalignment between the objective functions of the control center and the adversarial sensor. Different from previous studies, a Stackelberg equilibrium of a cheap talk setup is adapted to the attacker-defender game setting for the first time. That is, the defender (control center), as a receiver, is the leader, and the attacker (adversarial sensor), as a transmitter, is the follower. The equilibrium strategies and the associated costs are characterized for uniformly distributed variables and quadratic objective functions, and an analysis on the uniqueness of the equilibrium is provided. It is shown that the attacker and defender costs at the equilibrium are increasing with the bias and decreasing with the number of quantization levels. Our results surprisingly show that, under certain conditions, the attacker prefers a public bias rather than a private one.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126151575","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 : 2020-07-01DOI: 10.23919/ACC45564.2020.9147738
Sunny Amatya, S. M. R. Sorkhabadi, Wenlong Zhang
This paper explores the gait learning and coordination through physical human-human interaction. The interaction and coordination are modeled as a two-step process: 1) encoding the human gait as a periodic process and 2) adjustment of the periodic gait cycle based on the external forces due to physical interactions. Three-legged walking experiments are conducted with two human dyads. Magnitude and direction of the interaction force, as well as the knee joint angles and ground reaction forces of the tied legs are collected. The knee joint trajectory of the two participants is modeled using dynamic movement primitives (DMP) coupled with force feedback though iterative learning. Gait coordination is modeled as a learning process based on kinematics from the last gait cycle and real-time interaction force feedback. The proposed method is compared with a popular baseline DMP model, which performs batch regression based on data from the previous gait cycle. The proposed model performed better in modeling one pair in the cooperative experiment compared to the baseline algorithm. The results and approaches for improving the algorithm are further discussed.
{"title":"Human Learning and Coordination in Lower-limb Physical Interactions","authors":"Sunny Amatya, S. M. R. Sorkhabadi, Wenlong Zhang","doi":"10.23919/ACC45564.2020.9147738","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147738","url":null,"abstract":"This paper explores the gait learning and coordination through physical human-human interaction. The interaction and coordination are modeled as a two-step process: 1) encoding the human gait as a periodic process and 2) adjustment of the periodic gait cycle based on the external forces due to physical interactions. Three-legged walking experiments are conducted with two human dyads. Magnitude and direction of the interaction force, as well as the knee joint angles and ground reaction forces of the tied legs are collected. The knee joint trajectory of the two participants is modeled using dynamic movement primitives (DMP) coupled with force feedback though iterative learning. Gait coordination is modeled as a learning process based on kinematics from the last gait cycle and real-time interaction force feedback. The proposed method is compared with a popular baseline DMP model, which performs batch regression based on data from the previous gait cycle. The proposed model performed better in modeling one pair in the cooperative experiment compared to the baseline algorithm. The results and approaches for improving the algorithm are further discussed.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124682073","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 : 2020-07-01DOI: 10.23919/ACC45564.2020.9147488
Xin Wang, C. Lough, D. Bristow, R. Landers, E. Kinzel
Selective Laser Melting (SLM) is a common additive manufacturing technique which uses a scanning laser source to fuse metal powder layer by layer. Although complex geometries can be produced, quality and repeatability of parts are still two challenges due to complex physical transformations of the metal powder and highly dynamic temperature fields. Finite Element Models (FEMs) have been developed by researchers in order to predict melt pool behaviors. However, simulations on FEM software are too computationally intensive for real-time control applications. Thus, there arises the need for a control-oriented model of SLM processes. In this paper, a state-space control-oriented layer-to-layer model based on the general heat conduction equation is developed. The layer-to-layer model is constructed to step from one layer’s thermal feature measurement to the next, thus reducing computational complexity to a level suitable for control. To validate the model, an experiment of a rectangular thin part was conducted, and the simulation described the experimental thermal measurements with 5% error in the output.
{"title":"A Layer-to-layer Control-Oriented Model for Selective Laser Melting","authors":"Xin Wang, C. Lough, D. Bristow, R. Landers, E. Kinzel","doi":"10.23919/ACC45564.2020.9147488","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147488","url":null,"abstract":"Selective Laser Melting (SLM) is a common additive manufacturing technique which uses a scanning laser source to fuse metal powder layer by layer. Although complex geometries can be produced, quality and repeatability of parts are still two challenges due to complex physical transformations of the metal powder and highly dynamic temperature fields. Finite Element Models (FEMs) have been developed by researchers in order to predict melt pool behaviors. However, simulations on FEM software are too computationally intensive for real-time control applications. Thus, there arises the need for a control-oriented model of SLM processes. In this paper, a state-space control-oriented layer-to-layer model based on the general heat conduction equation is developed. The layer-to-layer model is constructed to step from one layer’s thermal feature measurement to the next, thus reducing computational complexity to a level suitable for control. To validate the model, an experiment of a rectangular thin part was conducted, and the simulation described the experimental thermal measurements with 5% error in the output.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"123 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129524277","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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