Pub Date : 2019-11-01DOI: 10.1109/ANZCC47194.2019.8945626
Yitao Yan, J. Bao, Biao Huang
This paper develops some dissipativity conditions for linear time-invariant (LTI) systems in the behavioural framework. The behaviour of a system is characterised by its persistently exciting trajectories. For the dissipativity conditions, both the supply rate and the storage function are represented using quadratic difference forms (QdFs) using past steps. We show that it is possible to define an LTI system of arbitrary length using trajectories with low order of excitation. The system can be defined in a similar way as an image representation and the dissipativity conditions can hence be derived using a similar logic. The conditions are presented in the form of linear matrix inequalities (LMIs).
{"title":"Dissipativity Analysis for Linear Systems in the Behavioural Framework","authors":"Yitao Yan, J. Bao, Biao Huang","doi":"10.1109/ANZCC47194.2019.8945626","DOIUrl":"https://doi.org/10.1109/ANZCC47194.2019.8945626","url":null,"abstract":"This paper develops some dissipativity conditions for linear time-invariant (LTI) systems in the behavioural framework. The behaviour of a system is characterised by its persistently exciting trajectories. For the dissipativity conditions, both the supply rate and the storage function are represented using quadratic difference forms (QdFs) using past steps. We show that it is possible to define an LTI system of arbitrary length using trajectories with low order of excitation. The system can be defined in a similar way as an image representation and the dissipativity conditions can hence be derived using a similar logic. The conditions are presented in the form of linear matrix inequalities (LMIs).","PeriodicalId":322243,"journal":{"name":"2019 Australian & New Zealand Control Conference (ANZCC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121489707","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 : 2019-11-01DOI: 10.1109/ANZCC47194.2019.8945748
Jing-Zhi Fu, Y. Nazarathy, S. Moka, P. Taylor
We consider the multi-armed restless bandit problem (RMABP) with an infinite horizon average cost objective. Each arm of the RMABP is associated with a Markov process that operates in two modes: active and passive. At each time slot a controller needs to designate a subset of the arms to be active, of which the associated processes will evolve differently from the passive case. Treated as an optimal control problem, the optimal solution of the RMABP is known to be computationally intractable. In many cases, the Whittle index policy achieves near optimal performance and can be tractably found. Nevertheless, computation of the Whittle indices requires knowledge of the transition matrices of the underlying processes, which are sometimes hidden from decision makers. In this paper, we take first steps towards a tractable and efficient reinforcement learning algorithm for controlling such a system. We setup parallel Q-learning recursions, with each recursion mapping to individual possible values of the Whittle index. We then update these recursions as we control the system, learning an approximation of the Whittle index as time evolves. Tested on several examples, our control outperforms naive priority allocations and nears the performance of the fully-informed Whittle index policy.
{"title":"Towards Q-learning the Whittle Index for Restless Bandits","authors":"Jing-Zhi Fu, Y. Nazarathy, S. Moka, P. Taylor","doi":"10.1109/ANZCC47194.2019.8945748","DOIUrl":"https://doi.org/10.1109/ANZCC47194.2019.8945748","url":null,"abstract":"We consider the multi-armed restless bandit problem (RMABP) with an infinite horizon average cost objective. Each arm of the RMABP is associated with a Markov process that operates in two modes: active and passive. At each time slot a controller needs to designate a subset of the arms to be active, of which the associated processes will evolve differently from the passive case. Treated as an optimal control problem, the optimal solution of the RMABP is known to be computationally intractable. In many cases, the Whittle index policy achieves near optimal performance and can be tractably found. Nevertheless, computation of the Whittle indices requires knowledge of the transition matrices of the underlying processes, which are sometimes hidden from decision makers. In this paper, we take first steps towards a tractable and efficient reinforcement learning algorithm for controlling such a system. We setup parallel Q-learning recursions, with each recursion mapping to individual possible values of the Whittle index. We then update these recursions as we control the system, learning an approximation of the Whittle index as time evolves. Tested on several examples, our control outperforms naive priority allocations and nears the performance of the fully-informed Whittle index policy.","PeriodicalId":322243,"journal":{"name":"2019 Australian & New Zealand Control Conference (ANZCC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132707494","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 : 2019-11-01DOI: 10.1109/ANZCC47194.2019.8945695
Wansik Choi, C. Ahn
The physics and data-based methods are used to predict the trajectory of vehicles. To improve prediction performance, we suggest data-based methods using a deep learning model and a simple integration method. The integration method is the weighted sum, and the weights are extracted from the root mean square error of two methods. It shows enhanced results by taking the strength of both methods. The root mean square error of 0 to 3 seconds is less than 3 meter, and 3 to 6 seconds is less than 6 meter.
{"title":"Vehicle Trajectory Prediction with Integrating a Physics based Method and a Data-based Method","authors":"Wansik Choi, C. Ahn","doi":"10.1109/ANZCC47194.2019.8945695","DOIUrl":"https://doi.org/10.1109/ANZCC47194.2019.8945695","url":null,"abstract":"The physics and data-based methods are used to predict the trajectory of vehicles. To improve prediction performance, we suggest data-based methods using a deep learning model and a simple integration method. The integration method is the weighted sum, and the weights are extracted from the root mean square error of two methods. It shows enhanced results by taking the strength of both methods. The root mean square error of 0 to 3 seconds is less than 3 meter, and 3 to 6 seconds is less than 6 meter.","PeriodicalId":322243,"journal":{"name":"2019 Australian & New Zealand Control Conference (ANZCC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132109465","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 : 2019-11-01DOI: 10.1109/ANZCC47194.2019.8945670
Jiayin Chen, H. Nurdin
Recent efforts to develop hybrid quantum-classical algorithms for solving combinatorial problems have rekindled interest in revisiting heuristic classical optimization algorithms and exploring possibilities for improving them. A popular approach for finding good solutions to combinatorial problems is local search. In spite of its efficiency, if the search space is rugged, local search often gets trapped in unsatisfactory local optima. On the other hand, global search meta-heuristic algorithms, such as classical simulated annealing, guarantee asymptotic convergence in probability distribution to global optima. Despite its theoretical appeal, practical performance of classical simulated annealing is often sensitive to implementation details. In this paper, we revisit classical simulated annealing and propose a generalization in which the annealing is guided by a sequentially modified cost function. We prove asymptotic convergence to global optima and give an example choice of the modified cost function. We test the proposed algorithm with this example modified cost function on the traveling salesman problem. Numerical results suggest that the performance of this method is more robust to the initial temperature choice. Furthermore, the method demonstrates a significant efficiency gain without compromising its performance.
{"title":"Generalized Simulated Annealing with Sequentially Modified Cost Function for Combinatorial optimization Problems","authors":"Jiayin Chen, H. Nurdin","doi":"10.1109/ANZCC47194.2019.8945670","DOIUrl":"https://doi.org/10.1109/ANZCC47194.2019.8945670","url":null,"abstract":"Recent efforts to develop hybrid quantum-classical algorithms for solving combinatorial problems have rekindled interest in revisiting heuristic classical optimization algorithms and exploring possibilities for improving them. A popular approach for finding good solutions to combinatorial problems is local search. In spite of its efficiency, if the search space is rugged, local search often gets trapped in unsatisfactory local optima. On the other hand, global search meta-heuristic algorithms, such as classical simulated annealing, guarantee asymptotic convergence in probability distribution to global optima. Despite its theoretical appeal, practical performance of classical simulated annealing is often sensitive to implementation details. In this paper, we revisit classical simulated annealing and propose a generalization in which the annealing is guided by a sequentially modified cost function. We prove asymptotic convergence to global optima and give an example choice of the modified cost function. We test the proposed algorithm with this example modified cost function on the traveling salesman problem. Numerical results suggest that the performance of this method is more robust to the initial temperature choice. Furthermore, the method demonstrates a significant efficiency gain without compromising its performance.","PeriodicalId":322243,"journal":{"name":"2019 Australian & New Zealand Control Conference (ANZCC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114715975","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 : 2019-11-01DOI: 10.1109/anzcc47194.2019.8945661
Yeayoung Park, Hyun-Sik Nam, C. Ahn
The usual model embedded in MPC for steering control is vehicle dynamics without steering dynamics because such steering dynamics are ignorable in usual maneuvers. However, the dynamics of the steering system need to consider when a maneuver requires high angular velocity. Therefore, this paper proposes MPC model include steering and vehicle model that can provide information on disturbances and voltage.
{"title":"Evasive Steering Control using Model Predictive Control","authors":"Yeayoung Park, Hyun-Sik Nam, C. Ahn","doi":"10.1109/anzcc47194.2019.8945661","DOIUrl":"https://doi.org/10.1109/anzcc47194.2019.8945661","url":null,"abstract":"The usual model embedded in MPC for steering control is vehicle dynamics without steering dynamics because such steering dynamics are ignorable in usual maneuvers. However, the dynamics of the steering system need to consider when a maneuver requires high angular velocity. Therefore, this paper proposes MPC model include steering and vehicle model that can provide information on disturbances and voltage.","PeriodicalId":322243,"journal":{"name":"2019 Australian & New Zealand Control Conference (ANZCC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122863673","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 : 2019-11-01DOI: 10.1109/ANZCC47194.2019.8945600
M. Macktoobian, D. Gillet, J. Kneib
The target assignment methods applied to robotic fiber positioners are only applicable to homogeneous sets of targets to be observed. However, different batches of robotic fiber positioners available at the focal plane of a telescope can be simultaneously used in various applications. Those applications may be intrinsically different, say, some may seek astronomical observations, whereas the others may focus on space operations such as space debris detection. The current target assignment algorithms cannot handle these heterogeneous scenarios. This paper proposes an efficient multilinear algorithm to assign robotic fiber positioners to heterogeneous targets. We classify the targets based on their required exposure times. We also take the priority of observations compared to other operations into account during an assignment processes. Our algorithm assigns a bundle of robotic positioners to each dynamic target to efficiently detect it. We finally illustrate our algorithm’s application using a simulated example.
{"title":"Heterogeneous Target Assignment to Robotic Fiber Positioner Systems","authors":"M. Macktoobian, D. Gillet, J. Kneib","doi":"10.1109/ANZCC47194.2019.8945600","DOIUrl":"https://doi.org/10.1109/ANZCC47194.2019.8945600","url":null,"abstract":"The target assignment methods applied to robotic fiber positioners are only applicable to homogeneous sets of targets to be observed. However, different batches of robotic fiber positioners available at the focal plane of a telescope can be simultaneously used in various applications. Those applications may be intrinsically different, say, some may seek astronomical observations, whereas the others may focus on space operations such as space debris detection. The current target assignment algorithms cannot handle these heterogeneous scenarios. This paper proposes an efficient multilinear algorithm to assign robotic fiber positioners to heterogeneous targets. We classify the targets based on their required exposure times. We also take the priority of observations compared to other operations into account during an assignment processes. Our algorithm assigns a bundle of robotic positioners to each dynamic target to efficiently detect it. We finally illustrate our algorithm’s application using a simulated example.","PeriodicalId":322243,"journal":{"name":"2019 Australian & New Zealand Control Conference (ANZCC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127858615","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 : 2019-11-01DOI: 10.1109/ANZCC47194.2019.8945677
R. IfedayoOladeji, R. Zamora, T. Lie
The need to enhance the performance of existing transmission network in line with economic and technical constraints is crucial in a competitive market environment. This paper models the total transfer capacity (TTC) improvement using optimally placed thyristor-controlled series capacitors (TCSC). The system states were evaluated using distributed slack bus (DSB) and continuous power flow (CPF) techniques. Adaptable logic relations was modelled based on security margin (SM), steady state and transient condition collapse voltages $(mathrm{U}_{mathrm{s}mathrm{s}}, mathrm{U}_{mathrm{t}mathrm{s}})$ and the steady state line power loss $(mathrm{P}1_{mathrm{s}mathrm{s}})$, through which line suitability index (LSI) were obtained. The fuzzy expert system (FES) membership functions (MF) with respective degrees of memberships are defined to obtain the best states. The LSI MF is defined high between 0.2-0.8 to provide enough protection under transient disturbances. The test results on IEEE 30 bus system show that the model is feasible for TTC enhancement under steady state and N-l conditions.
{"title":"Modelling an Adaptable Multi-Objective Fuzzy Expert System Based Transmission Network Transfer Capacity Enhancement","authors":"R. IfedayoOladeji, R. Zamora, T. Lie","doi":"10.1109/ANZCC47194.2019.8945677","DOIUrl":"https://doi.org/10.1109/ANZCC47194.2019.8945677","url":null,"abstract":"The need to enhance the performance of existing transmission network in line with economic and technical constraints is crucial in a competitive market environment. This paper models the total transfer capacity (TTC) improvement using optimally placed thyristor-controlled series capacitors (TCSC). The system states were evaluated using distributed slack bus (DSB) and continuous power flow (CPF) techniques. Adaptable logic relations was modelled based on security margin (SM), steady state and transient condition collapse voltages $(mathrm{U}_{mathrm{s}mathrm{s}}, mathrm{U}_{mathrm{t}mathrm{s}})$ and the steady state line power loss $(mathrm{P}1_{mathrm{s}mathrm{s}})$, through which line suitability index (LSI) were obtained. The fuzzy expert system (FES) membership functions (MF) with respective degrees of memberships are defined to obtain the best states. The LSI MF is defined high between 0.2-0.8 to provide enough protection under transient disturbances. The test results on IEEE 30 bus system show that the model is feasible for TTC enhancement under steady state and N-l conditions.","PeriodicalId":322243,"journal":{"name":"2019 Australian & New Zealand Control Conference (ANZCC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127442030","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 : 2019-11-01DOI: 10.1109/ANZCC47194.2019.8945527
Zi-jiang Hu, Boon-Chong Seet
In wireless sensor and actuator networks (WSANs), the actuators often need to coordinate their actions in response to an event occurrence. A good coordination among actuators can balance energy consumption, prolong network lifetime, and improve real-time performance of WSANs. However, it can be challenging to determine the best cooperating team of actuators in WSANs. In this paper, distributed game-based control algorithms for actuator-actuator coordination are proposed. The game is played between multiple actuators each time an action task needs to be performed in an event area. Hence, the task allocation and execution problem in WSANs can be transformed into a utility assignment problem in a multi-player coalition game. The proposed algorithms implement effective task execution by determining the best cooperating work team among the actuators through a game theoretic strategy. The algorithms are evaluated in terms of energy consumption, network lifetime and task completion time.
{"title":"Game-Theoretic Control for Actuator Coordination in Wireless Sensor and Actuator Networks","authors":"Zi-jiang Hu, Boon-Chong Seet","doi":"10.1109/ANZCC47194.2019.8945527","DOIUrl":"https://doi.org/10.1109/ANZCC47194.2019.8945527","url":null,"abstract":"In wireless sensor and actuator networks (WSANs), the actuators often need to coordinate their actions in response to an event occurrence. A good coordination among actuators can balance energy consumption, prolong network lifetime, and improve real-time performance of WSANs. However, it can be challenging to determine the best cooperating team of actuators in WSANs. In this paper, distributed game-based control algorithms for actuator-actuator coordination are proposed. The game is played between multiple actuators each time an action task needs to be performed in an event area. Hence, the task allocation and execution problem in WSANs can be transformed into a utility assignment problem in a multi-player coalition game. The proposed algorithms implement effective task execution by determining the best cooperating work team among the actuators through a game theoretic strategy. The algorithms are evaluated in terms of energy consumption, network lifetime and task completion time.","PeriodicalId":322243,"journal":{"name":"2019 Australian & New Zealand Control Conference (ANZCC)","volume":"313 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132726966","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 : 2019-11-01DOI: 10.1109/ANZCC47194.2019.8945678
M. Seron, Sorin Olaru, F. Stoican, J. Doná, E. Kofman
For linear, time invariant stable systems with additive state disturbances that are bounded by polytopic sets, we establish connections between the minimal robust positively invariant set (mRPI) and ultimate-bound invariant (UBI) sets. We first identify cases for which the mRPI set is finitely determined. We then apply those cases to address the dual problem of finding (i) the A matrix of an LTI system, (ii) a disturbance set and (iii) a projection matrix, for which a given UBI set is a projection of the mRPI set associated with those three elements. Finally, these results are combined to iteratively compute converging outer approximations of the mRPI set associated with a given system via a sequence of sets that are projections of finitely determined mRPI sets in lifted spaces.
{"title":"On Finitely Determined Minimal Robust Positively Invariant Sets","authors":"M. Seron, Sorin Olaru, F. Stoican, J. Doná, E. Kofman","doi":"10.1109/ANZCC47194.2019.8945678","DOIUrl":"https://doi.org/10.1109/ANZCC47194.2019.8945678","url":null,"abstract":"For linear, time invariant stable systems with additive state disturbances that are bounded by polytopic sets, we establish connections between the minimal robust positively invariant set (mRPI) and ultimate-bound invariant (UBI) sets. We first identify cases for which the mRPI set is finitely determined. We then apply those cases to address the dual problem of finding (i) the A matrix of an LTI system, (ii) a disturbance set and (iii) a projection matrix, for which a given UBI set is a projection of the mRPI set associated with those three elements. Finally, these results are combined to iteratively compute converging outer approximations of the mRPI set associated with a given system via a sequence of sets that are projections of finitely determined mRPI sets in lifted spaces.","PeriodicalId":322243,"journal":{"name":"2019 Australian & New Zealand Control Conference (ANZCC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123918230","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 : 2019-11-01DOI: 10.1109/ANZCC47194.2019.8945788
Kazuhiko Takahashi
This paper presents a quaternion neural network-based controller for a robot manipulator that can be used to investigate the possibility of using quaternion neural networks in practical applications. The quaternion neural network, which synthesises the control input for tracking an end-effector of the robot manipulator to the desired trajectory, assumes the role of an adaptive-type servo controller in a control system. Two types of network, such as feed-forward quaternion neural network and a recurrent quaternion neural network, were used to design servo-level controller and their performances were compared. Numerical simulations for controlling a three-link robot manipulator are performed to evaluate the characteristics of the proposed controllers and to demonstrate the feasibility as well as the effectiveness of the proposed controllers.
{"title":"Remarks on Quaternion Neural Networks with Application to Trajectory Control of a Robot Manipulator","authors":"Kazuhiko Takahashi","doi":"10.1109/ANZCC47194.2019.8945788","DOIUrl":"https://doi.org/10.1109/ANZCC47194.2019.8945788","url":null,"abstract":"This paper presents a quaternion neural network-based controller for a robot manipulator that can be used to investigate the possibility of using quaternion neural networks in practical applications. The quaternion neural network, which synthesises the control input for tracking an end-effector of the robot manipulator to the desired trajectory, assumes the role of an adaptive-type servo controller in a control system. Two types of network, such as feed-forward quaternion neural network and a recurrent quaternion neural network, were used to design servo-level controller and their performances were compared. Numerical simulations for controlling a three-link robot manipulator are performed to evaluate the characteristics of the proposed controllers and to demonstrate the feasibility as well as the effectiveness of the proposed controllers.","PeriodicalId":322243,"journal":{"name":"2019 Australian & New Zealand Control Conference (ANZCC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117322740","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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