In this article, we study the optimal control of stochastic differential equations with random impulses. We optimize the performance index and add the influence of random impulses to the performance index with a random compensation function. Using the idea of stochastic analysis and dynamic programming principle, a new Hamilton–Jacobi–Bellman (HJB) equation is obtained, and the existence and uniqueness of its viscosity solution are proved.
{"title":"Optimal control of stochastic differential equations with random impulses and the Hamilton–Jacobi–Bellman equation","authors":"Qian‐Bao Yin, Xiao‐Bao Shu, Yu Guo, Zi‐Yu Wang","doi":"10.1002/oca.3139","DOIUrl":"https://doi.org/10.1002/oca.3139","url":null,"abstract":"In this article, we study the optimal control of stochastic differential equations with random impulses. We optimize the performance index and add the influence of random impulses to the performance index with a random compensation function. Using the idea of stochastic analysis and dynamic programming principle, a new Hamilton–Jacobi–Bellman (HJB) equation is obtained, and the existence and uniqueness of its viscosity solution are proved.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140839885","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}
M. Jagadeesh Kumar, Kumar Rahul, Pappula Sampath Kumar, Jiten K. Chavda
This article proposes a Fire Hawk Optimizer (FHO) technique for photovoltaic fed grid connected wireless electric vehicle battery‐charger. The optimization issues are solved by the FHO across a countless endless exploring space. The main aim of the proposed technique is to enhance the efficiency, reduce energy demand, improve communication amid the receiver and transmitter sides of electric vehicle (EV) and reduce range anxiety. A photovoltaic (PV) panel, an energy storage unit (ESU), and electric vehicles are part of the proposed topology. Each unit is separately regulated, and the converter of energy storage unit uses a voltage‐regulation mechanism to guarantee that the direct current bus voltage is kept in nominal‐level when operating in various circumstances. An essential requirement for the quick commercialization of EVs is the ability to charge them. Moreover, the charging‐station smartly uses grid power in the event that the battery storage is empty and the generation of solar photovoltaic array is not available. The inverter is tuned using the proposed technique. The FHO method is done in MATLAB software and it evaluated their performance. The proposed methodology provides higher efficiency of 91% than the existing techniques.
{"title":"An efficient hybrid approach based design of photovoltaic fed grid integrated wireless electric vehicle battery charger","authors":"M. Jagadeesh Kumar, Kumar Rahul, Pappula Sampath Kumar, Jiten K. Chavda","doi":"10.1002/oca.3137","DOIUrl":"https://doi.org/10.1002/oca.3137","url":null,"abstract":"This article proposes a Fire Hawk Optimizer (FHO) technique for photovoltaic fed grid connected wireless electric vehicle battery‐charger. The optimization issues are solved by the FHO across a countless endless exploring space. The main aim of the proposed technique is to enhance the efficiency, reduce energy demand, improve communication amid the receiver and transmitter sides of electric vehicle (EV) and reduce range anxiety. A photovoltaic (PV) panel, an energy storage unit (ESU), and electric vehicles are part of the proposed topology. Each unit is separately regulated, and the converter of energy storage unit uses a voltage‐regulation mechanism to guarantee that the direct current bus voltage is kept in nominal‐level when operating in various circumstances. An essential requirement for the quick commercialization of EVs is the ability to charge them. Moreover, the charging‐station smartly uses grid power in the event that the battery storage is empty and the generation of solar photovoltaic array is not available. The inverter is tuned using the proposed technique. The FHO method is done in MATLAB software and it evaluated their performance. The proposed methodology provides higher efficiency of 91% than the existing techniques.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140839669","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}
In this article, the problem of adaptive optimal tracking control is studied for nonlinear strict‐feedback systems. While not directly measurable, the states of these systems are subject to both time‐varying and asymmetric constraints. Bypassing the conventional barrier Lyapunov function method, the constrained system is transformed into its unconstrained counterpart, thereby obviating the need for feasibility conditions. A specially designed reinforcement learning (RL) algorithm, featuring an observer‐critic‐actor architecture, is deployed in an adaptive optimal control scheme to ensure the stabilization of the converted unconstrained system. Within this architecture, the observer estimates the unmeasurable system states, the critic evaluates the control performance, and the actor executes the control actions. Furthermore, enhancements to the RL algorithm lead to relaxed conditions of persistent excitation, and the design methodology for the observer overcomes the restrictions imposed by the Hurwitz equation. The Lyapunov stability theorem is applied for two primary purposes: to ascertain the boundedness of all signals within the closed‐loop system, and to ensure the accuracy of the output signal in tracking the desired reference trajectory. Finally, numerical and practical simulations are provided to corroborate the effectiveness of the proposed control strategy.
{"title":"Adaptive optimized backstepping tracking control for full‐state constrained nonlinear strict‐feedback systems without using barrier Lyapunov function method","authors":"Boyan Zhu, Ning Xu, Guangdeng Zong, Xudong Zhao","doi":"10.1002/oca.3136","DOIUrl":"https://doi.org/10.1002/oca.3136","url":null,"abstract":"In this article, the problem of adaptive optimal tracking control is studied for nonlinear strict‐feedback systems. While not directly measurable, the states of these systems are subject to both time‐varying and asymmetric constraints. Bypassing the conventional barrier Lyapunov function method, the constrained system is transformed into its unconstrained counterpart, thereby obviating the need for feasibility conditions. A specially designed reinforcement learning (RL) algorithm, featuring an observer‐critic‐actor architecture, is deployed in an adaptive optimal control scheme to ensure the stabilization of the converted unconstrained system. Within this architecture, the observer estimates the unmeasurable system states, the critic evaluates the control performance, and the actor executes the control actions. Furthermore, enhancements to the RL algorithm lead to relaxed conditions of persistent excitation, and the design methodology for the observer overcomes the restrictions imposed by the Hurwitz equation. The Lyapunov stability theorem is applied for two primary purposes: to ascertain the boundedness of all signals within the closed‐loop system, and to ensure the accuracy of the output signal in tracking the desired reference trajectory. Finally, numerical and practical simulations are provided to corroborate the effectiveness of the proposed control strategy.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140839657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper studies the distributed optimization problem (DOP) of discrete time‐varying linear multi‐agent systems (MASs), in which the global objective function is formed by a sum of local convex objective functions. Firstly, a DOP with discrete time‐varying MASs is considered, in which the time‐varying linear matrix satisfies a certain equality constraint. To solve this problem, a novel discrete‐time distributed optimization algorithm (DOA) with event‐triggered communication mechanism (ETCM) is proposed. Secondly, by constructing the error dynamical system and using a series of inequality techniques, some sufficient conditions for achieving consensus and obtaining the optimal solution are established. It is found that the considered MAS has generality and the proposed DOA has the advantage of reducing communication burden. Finally, a numerical simulation is presented to verify the validity of theoretical results.
本文研究了离散时变线性多代理系统（MAS）的分布式优化问题（DOP），其中全局目标函数由局部凸目标函数之和构成。首先考虑的是离散时变 MAS 的 DOP，其中时变线性矩阵满足一定的相等约束。为解决这一问题，提出了一种具有事件触发通信机制（ETCM）的新型离散时间分布式优化算法（DOA）。其次，通过构建误差动态系统和使用一系列不等式技术，建立了一些达成共识和获得最优解的充分条件。研究发现，所考虑的 MAS 具有通用性，所提出的 DOA 具有减轻通信负担的优势。最后，通过数值模拟验证了理论结果的正确性。
{"title":"Distributed optimization for discrete time‐varying linear multi‐agent systems with event‐triggered communication","authors":"Mingxia Gu, Zhiyong Yu, Haijun Jiang","doi":"10.1002/oca.3128","DOIUrl":"https://doi.org/10.1002/oca.3128","url":null,"abstract":"This paper studies the distributed optimization problem (DOP) of discrete time‐varying linear multi‐agent systems (MASs), in which the global objective function is formed by a sum of local convex objective functions. Firstly, a DOP with discrete time‐varying MASs is considered, in which the time‐varying linear matrix satisfies a certain equality constraint. To solve this problem, a novel discrete‐time distributed optimization algorithm (DOA) with event‐triggered communication mechanism (ETCM) is proposed. Secondly, by constructing the error dynamical system and using a series of inequality techniques, some sufficient conditions for achieving consensus and obtaining the optimal solution are established. It is found that the considered MAS has generality and the proposed DOA has the advantage of reducing communication burden. Finally, a numerical simulation is presented to verify the validity of theoretical results.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140623602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a real‐time optimization method for nonlinear model predictive control (NMPC) of systems governed by partial differential equations (PDEs). The NMPC problem to be solved is formulated by discretizing the PDE system in space and time by using the finite difference method. The proposed method is called the double‐layer Jacobi method, which exploits both the spatial and temporal sparsities of the PDE‐constrained NMPC problem. In the upper layer, the NMPC problem is solved by ignoring the temporal couplings of either the state or costate (Lagrange multiplier corresponding to the state equation) equations so that the spatial sparsity is preserved. The lower‐layer Jacobi method is a linear solver dedicated to PDE‐constrained NMPC problems by exploiting the spatial sparsity. Convergence analysis indicates that the convergence of the proposed method is related to the prediction horizon. Results of a numerical experiment of controlling a heat transfer process show that the proposed method can be two orders of magnitude faster than the conventional Newton's method exploiting the banded structure of NMPC problems.
{"title":"A double‐layer Jacobi method for partial differential equation‐constrained nonlinear model predictive control","authors":"Haoyang Deng, Toshiyuki Ohtsuka","doi":"10.1002/oca.3132","DOIUrl":"https://doi.org/10.1002/oca.3132","url":null,"abstract":"This paper presents a real‐time optimization method for nonlinear model predictive control (NMPC) of systems governed by partial differential equations (PDEs). The NMPC problem to be solved is formulated by discretizing the PDE system in space and time by using the finite difference method. The proposed method is called the double‐layer Jacobi method, which exploits both the spatial and temporal sparsities of the PDE‐constrained NMPC problem. In the upper layer, the NMPC problem is solved by ignoring the temporal couplings of either the state or costate (Lagrange multiplier corresponding to the state equation) equations so that the spatial sparsity is preserved. The lower‐layer Jacobi method is a linear solver dedicated to PDE‐constrained NMPC problems by exploiting the spatial sparsity. Convergence analysis indicates that the convergence of the proposed method is related to the prediction horizon. Results of a numerical experiment of controlling a heat transfer process show that the proposed method can be two orders of magnitude faster than the conventional Newton's method exploiting the banded structure of NMPC problems.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140623484","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}
B. Vigneshwar, M. Syed Ali, R. Perumal, Bandana Priya, Ganesh Kumar Thakur
The challenge of and asynchronous reduced‐order design for Takagi‐Sugeno (T‐S) fuzzy Markovian jump systems (MJSs) with time‐varying delays under the event‐triggered scheme (ETS) is investigated in this dissertation. A distributed event‐triggered strategy is provided on the basis of the specified triggering function to ensure consensus in the system, so effectively reducing data transmission. The existence conditions for a class of Markovian jump discrete‐time systems are determined. Unlike previous results, we present a novel membership function‐dependent fuzzy Lyapunov‐Krasovsikii (L‐K) functional with mode‐dependent integral terms, resulting in a stochastically stable filtering error system with the desired performance. By solving linear matrix inequality (LMIs), the recommended filter parameters are achieved. The proposed reduced‐order filter is demonstrated in numerical examples as effective as it is advantageous.
{"title":"Event‐triggered H∞ and reduced‐order asynchronous filtering for fuzzy Markov jump systems with time‐varying delays","authors":"B. Vigneshwar, M. Syed Ali, R. Perumal, Bandana Priya, Ganesh Kumar Thakur","doi":"10.1002/oca.3127","DOIUrl":"https://doi.org/10.1002/oca.3127","url":null,"abstract":"The challenge of and asynchronous reduced‐order design for Takagi‐Sugeno (T‐S) fuzzy Markovian jump systems (MJSs) with time‐varying delays under the event‐triggered scheme (ETS) is investigated in this dissertation. A distributed event‐triggered strategy is provided on the basis of the specified triggering function to ensure consensus in the system, so effectively reducing data transmission. The existence conditions for a class of Markovian jump discrete‐time systems are determined. Unlike previous results, we present a novel membership function‐dependent fuzzy Lyapunov‐Krasovsikii (L‐K) functional with mode‐dependent integral terms, resulting in a stochastically stable filtering error system with the desired performance. By solving linear matrix inequality (LMIs), the recommended filter parameters are achieved. The proposed reduced‐order filter is demonstrated in numerical examples as effective as it is advantageous.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140560461","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}
In this article, a distributed controller based on adaptive dynamic programming is proposed to solve the minimum loss problem of flywheel energy storage systems (FESS). We first formulate a performance function aiming to reduce total losses of FESS in power distribution applications. Then we use the Hamilton–Jacobi–Bellman (HJB) equation to solve this optimal control problem. The solution of the HJB equation is approximated by neural networks. To achieve distributed control, we estimate the global variables in the HJB equation by using the dynamic average consensus algorithm. A barrier Lyapunov function and a saturation function are introduced to handle the issue of state and input constraints, respectively. Then the stability of the system is proved through the Lyapunov stability analysis. Finally the effectiveness of the proposed strategy is verified by simulations. Simulation results show that FESS can track the power command while minimizing total power losses by interacting with neighbors. The proposed algorithm leads to a loss reduction of compared to the equal power distribution strategy.
{"title":"Minimum loss optimization of flywheel energy storage systems via distributed adaptive dynamic programming","authors":"Feng Xiao, Zikang Ding, Bo Wei, Cong Zhang","doi":"10.1002/oca.3130","DOIUrl":"https://doi.org/10.1002/oca.3130","url":null,"abstract":"In this article, a distributed controller based on adaptive dynamic programming is proposed to solve the minimum loss problem of flywheel energy storage systems (FESS). We first formulate a performance function aiming to reduce total losses of FESS in power distribution applications. Then we use the Hamilton–Jacobi–Bellman (HJB) equation to solve this optimal control problem. The solution of the HJB equation is approximated by neural networks. To achieve distributed control, we estimate the global variables in the HJB equation by using the dynamic average consensus algorithm. A barrier Lyapunov function and a saturation function are introduced to handle the issue of state and input constraints, respectively. Then the stability of the system is proved through the Lyapunov stability analysis. Finally the effectiveness of the proposed strategy is verified by simulations. Simulation results show that FESS can track the power command while minimizing total power losses by interacting with neighbors. The proposed algorithm leads to a loss reduction of compared to the equal power distribution strategy.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140560282","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}
Fuel prices are rising, bringing attention to the utilization of alternative energy sources (RES). Even though load forecasting is more accurate at making predictions than wind power forecasting is. To address the operational challenges with the supply of electricity, wind energy forecasts remain essential. A certain kind of technology has recently been applied to forecast wind energy. On wind farms, a variety of wind power forecasting methods have been developed and used. The main idea underlying recurrent networks is parameter sharing across the multiple layers and neurons, which results in cycles in the network's graph sequence. Recurrent networks are designed to process sequential input. A novel hybrid optimization-based RNN model for wind power forecasting is proposed in this research. Using the SpCro algorithm, a proposed optimization method, the RNN's weights are adjusted. The Crow Search Optimization (CSA) algorithm and the Sparrow search algorithm are combined to form the SpCro Algorithm (SSA). The suggested Algorithm was developed using the crow's memory traits and the sparrow's detecting traits. The proposed system is simulated in MATLAB, and the usefulness of the suggested approach is verified by comparison with other widely used approaches, such as CNN and DNN, in terms of error metrics. Accordingly, the MAE of the proposed method is 45%, 10.02%, 10.04%, 33.58%, 94.81%, and 10.01% higher than RNN, SOA+RNN, CSO+RNN, SSA+DELM, CFU-COA, and GWO+RNN method.
{"title":"Forecasting wind power using Optimized Recurrent Neural Network strategy with time-series data","authors":"Krishan Kumar, Priti Prabhakar, Avnesh Verma","doi":"10.1002/oca.3122","DOIUrl":"https://doi.org/10.1002/oca.3122","url":null,"abstract":"Fuel prices are rising, bringing attention to the utilization of alternative energy sources (RES). Even though load forecasting is more accurate at making predictions than wind power forecasting is. To address the operational challenges with the supply of electricity, wind energy forecasts remain essential. A certain kind of technology has recently been applied to forecast wind energy. On wind farms, a variety of wind power forecasting methods have been developed and used. The main idea underlying recurrent networks is parameter sharing across the multiple layers and neurons, which results in cycles in the network's graph sequence. Recurrent networks are designed to process sequential input. A novel hybrid optimization-based RNN model for wind power forecasting is proposed in this research. Using the SpCro algorithm, a proposed optimization method, the RNN's weights are adjusted. The Crow Search Optimization (CSA) algorithm and the Sparrow search algorithm are combined to form the SpCro Algorithm (SSA). The suggested Algorithm was developed using the crow's memory traits and the sparrow's detecting traits. The proposed system is simulated in MATLAB, and the usefulness of the suggested approach is verified by comparison with other widely used approaches, such as CNN and DNN, in terms of error metrics. Accordingly, the MAE of the proposed method is 45%, 10.02%, 10.04%, 33.58%, 94.81%, and 10.01% higher than RNN, SOA+RNN, CSO+RNN, SSA+DELM, CFU-COA, and GWO+RNN method.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140560462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article proposes the use of a deep reinforcement learning method—and precisely a variant of the deep deterministic policy gradient (DDPG) method known as twin delayed DDPG, or TD3—for maximum power point tracking in wind energy conversion systems that use permanent magnet synchronous generators (PMSGs). An overview of the TD3 algorithm is provided, together with a detailed description of its implementation and training for the considered application. Simulation results are provided, also including a comparison with a model‐based control method based on feedback linearization and linear‐quadratic regulation. The proposed TD3‐based controller achieves a satisfactory control performance and is more robust to PMSG parameter variations as compared to the presented model‐based method. To the best of the authors' knowledge, this article presents for the first time an approach for generating both speed and current control loops using DRL for wind energy conversion systems.
{"title":"Deep reinforcement learning for PMSG wind turbine control via twin delayed deep deterministic policy gradient (TD3)","authors":"Darkhan Zholtayev, Matteo Rubagotti, Ton Duc Do","doi":"10.1002/oca.3129","DOIUrl":"https://doi.org/10.1002/oca.3129","url":null,"abstract":"This article proposes the use of a deep reinforcement learning method—and precisely a variant of the deep deterministic policy gradient (DDPG) method known as twin delayed DDPG, or TD3—for maximum power point tracking in wind energy conversion systems that use permanent magnet synchronous generators (PMSGs). An overview of the TD3 algorithm is provided, together with a detailed description of its implementation and training for the considered application. Simulation results are provided, also including a comparison with a model‐based control method based on feedback linearization and linear‐quadratic regulation. The proposed TD3‐based controller achieves a satisfactory control performance and is more robust to PMSG parameter variations as compared to the presented model‐based method. To the best of the authors' knowledge, this article presents for the first time an approach for generating both speed and current control loops using DRL for wind energy conversion systems.","PeriodicalId":501055,"journal":{"name":"Optimal Control Applications and Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140560460","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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