Pub Date : 2013-04-16DOI: 10.1109/SIS.2013.6615157
Evelia Lizárraga, O. Castillo, J. Soria, P. Melin
In this paper we describe a new methodology to optimize fuzzy logic controllers using Ant Colony Optimization (ACO); in particular, the fuzzy logic controller for the water tank benchmark problem. The proposed methodology is applied in the optimization of membership function parameters and type of membership functions, using a set of constraints for the construction of the solution matrix of an ACO algorithm.
{"title":"A new methodology for membership function design using Ant Colony Optimization","authors":"Evelia Lizárraga, O. Castillo, J. Soria, P. Melin","doi":"10.1109/SIS.2013.6615157","DOIUrl":"https://doi.org/10.1109/SIS.2013.6615157","url":null,"abstract":"In this paper we describe a new methodology to optimize fuzzy logic controllers using Ant Colony Optimization (ACO); in particular, the fuzzy logic controller for the water tank benchmark problem. The proposed methodology is applied in the optimization of membership function parameters and type of membership functions, using a set of constraints for the construction of the solution matrix of an ACO algorithm.","PeriodicalId":444765,"journal":{"name":"2013 IEEE Symposium on Swarm Intelligence (SIS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124913279","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 : 2013-04-16DOI: 10.1109/SIS.2013.6615154
Y. M, P. S G, Kanagaraj G
The main focus of this paper is to study and develop an efficient learning policy to address the exploration vs. exploitation dilemma in a multi-objective foraging task in swarm robotics domain. An efficient learning policy called FIFO-list is proposed to tackle the above mentioned problem. The proposed FIFO-list is a model-based learning policy which can attain near-optimal solutions. In FIFO-list, the swarm robots maintains a dynamic list of recently visited states. States that are included in the list are banned from exploration by the swarm robots regardless of the Q(s, a) values associated with those states. The FIFO list is updated based on First-In-First-Out (FIFO) rule, meaning the states that enters the list first will exit the list first. The recently visited states will remain in the list for a dynamic number of time-steps which is determined by the desirability of the visited states.
{"title":"Reinforcement learning in swarm-robotics for multi-agent foraging-task domain","authors":"Y. M, P. S G, Kanagaraj G","doi":"10.1109/SIS.2013.6615154","DOIUrl":"https://doi.org/10.1109/SIS.2013.6615154","url":null,"abstract":"The main focus of this paper is to study and develop an efficient learning policy to address the exploration vs. exploitation dilemma in a multi-objective foraging task in swarm robotics domain. An efficient learning policy called FIFO-list is proposed to tackle the above mentioned problem. The proposed FIFO-list is a model-based learning policy which can attain near-optimal solutions. In FIFO-list, the swarm robots maintains a dynamic list of recently visited states. States that are included in the list are banned from exploration by the swarm robots regardless of the Q(s, a) values associated with those states. The FIFO list is updated based on First-In-First-Out (FIFO) rule, meaning the states that enters the list first will exit the list first. The recently visited states will remain in the list for a dynamic number of time-steps which is determined by the desirability of the visited states.","PeriodicalId":444765,"journal":{"name":"2013 IEEE Symposium on Swarm Intelligence (SIS)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114496562","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 : 2013-04-16DOI: 10.1109/SIS.2013.6615181
Ying-Chi Lin, M. Middendorf
A new scheme is proposed for the design of probabilistic population based optimization algorithms for solving combinatorial optimization problems. The new scheme, Simple Probabilistic Population Based Optimization scheme (SPPBO), is used also to classify existing metaheuristics, e.g., the Population-based Ant Colony Optimization algorithm (PACO) and the Simplified Swarm Optimization algorithm (SSO). The classification shows the close relationship between PACO and SSO. This fact has not been recognized in the literature so far. SPPBO is also used to identify new metaheuristics that come up naturally as variants and combinations of PACO and SSO. An experimental study is done to evaluate and compare the different algorithms when applied to the Traveling Salesperson Problem. The results show which parts of the algorithms are helpful for obtaining a good optimization behaviour. In addition to the original PACO and SSO algorithms also some of the new combinations perform very well.
{"title":"Simple probabilistic population based optimization for combinatorial optimization","authors":"Ying-Chi Lin, M. Middendorf","doi":"10.1109/SIS.2013.6615181","DOIUrl":"https://doi.org/10.1109/SIS.2013.6615181","url":null,"abstract":"A new scheme is proposed for the design of probabilistic population based optimization algorithms for solving combinatorial optimization problems. The new scheme, Simple Probabilistic Population Based Optimization scheme (SPPBO), is used also to classify existing metaheuristics, e.g., the Population-based Ant Colony Optimization algorithm (PACO) and the Simplified Swarm Optimization algorithm (SSO). The classification shows the close relationship between PACO and SSO. This fact has not been recognized in the literature so far. SPPBO is also used to identify new metaheuristics that come up naturally as variants and combinations of PACO and SSO. An experimental study is done to evaluate and compare the different algorithms when applied to the Traveling Salesperson Problem. The results show which parts of the algorithms are helpful for obtaining a good optimization behaviour. In addition to the original PACO and SSO algorithms also some of the new combinations perform very well.","PeriodicalId":444765,"journal":{"name":"2013 IEEE Symposium on Swarm Intelligence (SIS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128314484","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 : 2013-04-16DOI: 10.1109/SIS.2013.6615184
Wen-jing Gao, Bo Xing, T. Marwala
Remanufacturability pre-evaluation is an important step at used products consolidation stage. In order to provide a quick sorting speed and reliable evaluation with a long lifespan, radio frequency identification (RFID) system is often employed in practice. A major factor that influences RFID system's reliability is the inaccuracy arising from missing data and reading errors, which are magnified to produce deleterious effects on reliability. A good yet simple solution is to add more redundant components (i.e., RFID readers) to smooth the RFID system's reliability. In this paper, we first formulate our focal scenario as a reliability-redundancy allocation problem (RRAP). Then, one of the recently developed swarm intelligence approach called teaching - learning-based optimization (TLBO), which is based on the effect of the influence of a teacher on the output of learners in a class, is employed to address our focal problem. Simulation results suggest that the proposed TLBO is a viable optimization technique in dealing with the optimization of RFID system's reliability.
{"title":"Teaching - Learning-based optimization approach for enhancing remanufacturability pre-evaluation system's reliability","authors":"Wen-jing Gao, Bo Xing, T. Marwala","doi":"10.1109/SIS.2013.6615184","DOIUrl":"https://doi.org/10.1109/SIS.2013.6615184","url":null,"abstract":"Remanufacturability pre-evaluation is an important step at used products consolidation stage. In order to provide a quick sorting speed and reliable evaluation with a long lifespan, radio frequency identification (RFID) system is often employed in practice. A major factor that influences RFID system's reliability is the inaccuracy arising from missing data and reading errors, which are magnified to produce deleterious effects on reliability. A good yet simple solution is to add more redundant components (i.e., RFID readers) to smooth the RFID system's reliability. In this paper, we first formulate our focal scenario as a reliability-redundancy allocation problem (RRAP). Then, one of the recently developed swarm intelligence approach called teaching - learning-based optimization (TLBO), which is based on the effect of the influence of a teacher on the output of learners in a class, is employed to address our focal problem. Simulation results suggest that the proposed TLBO is a viable optimization technique in dealing with the optimization of RFID system's reliability.","PeriodicalId":444765,"journal":{"name":"2013 IEEE Symposium on Swarm Intelligence (SIS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129441961","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 : 2013-04-16DOI: 10.1109/SIS.2013.6615191
Dipankar Maity, Aritra Chowdhury, S. S. Reddy, B. K. Panigrahi, A. Abhyankar, M. K. Mallick
This paper proposes an informative differential evolution with self adaptive re-clustering (IDE-SAR) technique to solve the optimal energy and spinning reserve scheduling problem of a wind-thermal power system. The goal of the paper is to solve an economic dispatch problem, and to find optimal allocation of energy and spinning reserves among the thermal and wind generators available to serve the demand. The stochastic behavior of wind speed and wind power is represented by Weibull probability density function. The total cost minimization objective includes cost of energy provided by conventional thermal generators and wind generators, cost of reserves provided by conventional thermal generators. It also includes costs due to over-estimation and under-estimation of available wind power. In order to show the effectiveness and feasibility of the proposed frame work, various case studies are presented for conventional and wind-thermal power system considering the provision of spinning reserves.
{"title":"Joint energy and spinning reserve dispatch in wind-thermal power system using IDE-SAR technique","authors":"Dipankar Maity, Aritra Chowdhury, S. S. Reddy, B. K. Panigrahi, A. Abhyankar, M. K. Mallick","doi":"10.1109/SIS.2013.6615191","DOIUrl":"https://doi.org/10.1109/SIS.2013.6615191","url":null,"abstract":"This paper proposes an informative differential evolution with self adaptive re-clustering (IDE-SAR) technique to solve the optimal energy and spinning reserve scheduling problem of a wind-thermal power system. The goal of the paper is to solve an economic dispatch problem, and to find optimal allocation of energy and spinning reserves among the thermal and wind generators available to serve the demand. The stochastic behavior of wind speed and wind power is represented by Weibull probability density function. The total cost minimization objective includes cost of energy provided by conventional thermal generators and wind generators, cost of reserves provided by conventional thermal generators. It also includes costs due to over-estimation and under-estimation of available wind power. In order to show the effectiveness and feasibility of the proposed frame work, various case studies are presented for conventional and wind-thermal power system considering the provision of spinning reserves.","PeriodicalId":444765,"journal":{"name":"2013 IEEE Symposium on Swarm Intelligence (SIS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121477387","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 : 2013-04-16DOI: 10.1109/SIS.2013.6615186
Subhodip Biswas, Souvik Kundu, Digbalay Bose, Swagatam Das, P. N. Suganthan, B. K. Panigrahi
Swarm Intelligent algorithms focus on imbibing the collective intelligence of a group of simple agents that can work together as a unit. This research article focus on a recently proposed swarm-based metaheuristic called the Artificial Bee Colony (ABC) algorithm and suggests modifications to the algorithmic framework in order to enhance its performance. The proposed ABC variant shall be referred to as MsABC_Fm (Multi swarm Artificial Bee Colony with Forager migration). MsABC_Fm maintains multiple swarm populations that apply different perturbation strategies and gradually migration of the population from worse performing strategy to the better mode of perturbation is promoted. To evaluate the performance of the algorithm, we conduct comparative study involving 8 algorithms and test the problems on 25 benchmark problems proposed in the Special Session on IEEE Congress on Evolutionary Competition 2005. The superiority of the MsABC_Fm approach is also highlighted statistically.
{"title":"Migrating forager population in a multi-population Artificial Bee Colony algorithm with modified perturbation schemes","authors":"Subhodip Biswas, Souvik Kundu, Digbalay Bose, Swagatam Das, P. N. Suganthan, B. K. Panigrahi","doi":"10.1109/SIS.2013.6615186","DOIUrl":"https://doi.org/10.1109/SIS.2013.6615186","url":null,"abstract":"Swarm Intelligent algorithms focus on imbibing the collective intelligence of a group of simple agents that can work together as a unit. This research article focus on a recently proposed swarm-based metaheuristic called the Artificial Bee Colony (ABC) algorithm and suggests modifications to the algorithmic framework in order to enhance its performance. The proposed ABC variant shall be referred to as MsABC_Fm (Multi swarm Artificial Bee Colony with Forager migration). MsABC_Fm maintains multiple swarm populations that apply different perturbation strategies and gradually migration of the population from worse performing strategy to the better mode of perturbation is promoted. To evaluate the performance of the algorithm, we conduct comparative study involving 8 algorithms and test the problems on 25 benchmark problems proposed in the Special Session on IEEE Congress on Evolutionary Competition 2005. The superiority of the MsABC_Fm approach is also highlighted statistically.","PeriodicalId":444765,"journal":{"name":"2013 IEEE Symposium on Swarm Intelligence (SIS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130843262","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 : 2013-04-16DOI: 10.1109/SIS.2013.6615178
S. Agrawal, S. Swain, Lingraj Dora
This paper presents a pixel based multi focus image fusion technique using independent component analysis (ICA) and bacteria foraging optimization (BFO) algorithm. The basic idea here is to obtain the ICA bases from a set of registered images and optimize them using BFO. The novelty in this paper is that BFO-ICA has not been applied to multi-focus image fusion. The images in the ICA domain are fused and the fused image is then reconstructed using inverse transform. The results are compared with FastICA and PSO-ICA. It is observed that optimizing with BFO yield better result.
{"title":"BFO-ICA based multi focus image fusion","authors":"S. Agrawal, S. Swain, Lingraj Dora","doi":"10.1109/SIS.2013.6615178","DOIUrl":"https://doi.org/10.1109/SIS.2013.6615178","url":null,"abstract":"This paper presents a pixel based multi focus image fusion technique using independent component analysis (ICA) and bacteria foraging optimization (BFO) algorithm. The basic idea here is to obtain the ICA bases from a set of registered images and optimize them using BFO. The novelty in this paper is that BFO-ICA has not been applied to multi-focus image fusion. The images in the ICA domain are fused and the fused image is then reconstructed using inverse transform. The results are compared with FastICA and PSO-ICA. It is observed that optimizing with BFO yield better result.","PeriodicalId":444765,"journal":{"name":"2013 IEEE Symposium on Swarm Intelligence (SIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129791343","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 : 2013-04-16DOI: 10.1109/SIS.2013.6615175
Nikolas J. Unger, B. Ombuki-Berman, A. Engelbrecht
Most optimization algorithms are designed to solve static, unchanging problems. However, many real-world problems exhibit dynamic behavior. Particle swarm optimization (PSO) is a successful metaheuristic methodology which has been adapted for locating and tracking optima in dynamic environments. Recently, a powerful new class of PSO strategies using cooperative principles was shown to improve PSO performance in static environments. While there exist many PSO algorithms designed for dynamic optimization problems, only one cooperative PSO strategy has been introduced for this purpose, and it has only been studied under one type of dynamism. This study proposes a new cooperative PSO strategy designed for dynamic environments. The newly proposed algorithm is shown to achieve significantly lower error rates when compared to well-known algorithms across problems with varying dimensionalities, temporal change severities, and spatial change severities.
{"title":"Cooperative particle swarm optimization in dynamic environments","authors":"Nikolas J. Unger, B. Ombuki-Berman, A. Engelbrecht","doi":"10.1109/SIS.2013.6615175","DOIUrl":"https://doi.org/10.1109/SIS.2013.6615175","url":null,"abstract":"Most optimization algorithms are designed to solve static, unchanging problems. However, many real-world problems exhibit dynamic behavior. Particle swarm optimization (PSO) is a successful metaheuristic methodology which has been adapted for locating and tracking optima in dynamic environments. Recently, a powerful new class of PSO strategies using cooperative principles was shown to improve PSO performance in static environments. While there exist many PSO algorithms designed for dynamic optimization problems, only one cooperative PSO strategy has been introduced for this purpose, and it has only been studied under one type of dynamism. This study proposes a new cooperative PSO strategy designed for dynamic environments. The newly proposed algorithm is shown to achieve significantly lower error rates when compared to well-known algorithms across problems with varying dimensionalities, temporal change severities, and spatial change severities.","PeriodicalId":444765,"journal":{"name":"2013 IEEE Symposium on Swarm Intelligence (SIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121113720","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 : 2013-04-16DOI: 10.1109/SIS.2013.6615176
Manitpal S. Sidhu, P. Thulasiraman, R. Thulasiram
The idea of utilizing nature inspired algorithms to find optimal solutions to various real world NP complete optimization problems has been extensively explored by researchers. One such problem is task matching problem in heterogeneous distributed computing environments like Grid and Cloud. Researchers have explored Swarm Intelligence algorithm, Particle Swarm Optimization (PSO), to find optimal solution for task matching problem. In this study, we investigate the effectiveness of smallest position value (SPV) technique in mapping continuous version of PSO algorithm to the task matching problem in a heterogeneous computing environment. We show that the task matching generated by this technique will result in in-efficient resource utilization. Thus, we present a novel load rebalance based particle swarm optimization heuristic (PSO-LR) for efficient load distribution among available compute nodes even in heterogeneous computing environments.
{"title":"A load-rebalance PSO heuristic for task matching in heterogeneous computing systems","authors":"Manitpal S. Sidhu, P. Thulasiraman, R. Thulasiram","doi":"10.1109/SIS.2013.6615176","DOIUrl":"https://doi.org/10.1109/SIS.2013.6615176","url":null,"abstract":"The idea of utilizing nature inspired algorithms to find optimal solutions to various real world NP complete optimization problems has been extensively explored by researchers. One such problem is task matching problem in heterogeneous distributed computing environments like Grid and Cloud. Researchers have explored Swarm Intelligence algorithm, Particle Swarm Optimization (PSO), to find optimal solution for task matching problem. In this study, we investigate the effectiveness of smallest position value (SPV) technique in mapping continuous version of PSO algorithm to the task matching problem in a heterogeneous computing environment. We show that the task matching generated by this technique will result in in-efficient resource utilization. Thus, we present a novel load rebalance based particle swarm optimization heuristic (PSO-LR) for efficient load distribution among available compute nodes even in heterogeneous computing environments.","PeriodicalId":444765,"journal":{"name":"2013 IEEE Symposium on Swarm Intelligence (SIS)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133869443","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 : 2013-04-16DOI: 10.1109/SIS.2013.6615182
Manohar Singh, B. K. Panigrahi, A. Abhyankar, R. Mukherjee, Rupam Kundu
Connection of distributed generation resources in distribution system enhances the availability and reliability of electric power during peak load. However, increasing penetration of distributed generation resources causes protection coordination failure in distribution system. An optimization problem is proposed to determine relay coordination under maximum penetration level of distributed generation by optimally selecting location, parameters and size of distributed generation. The proposed optimization problem is implemented on IEEE 15 node radial system. A meta-heuristic approach based on covariance matrix adaptation evolution strategy directed target to best perturbation algorithm is applied for optimization of relay coordination problem under maximum penetration of distributed generation.
{"title":"Optimal location, size and protection coordination of distributed generation in distribution network","authors":"Manohar Singh, B. K. Panigrahi, A. Abhyankar, R. Mukherjee, Rupam Kundu","doi":"10.1109/SIS.2013.6615182","DOIUrl":"https://doi.org/10.1109/SIS.2013.6615182","url":null,"abstract":"Connection of distributed generation resources in distribution system enhances the availability and reliability of electric power during peak load. However, increasing penetration of distributed generation resources causes protection coordination failure in distribution system. An optimization problem is proposed to determine relay coordination under maximum penetration level of distributed generation by optimally selecting location, parameters and size of distributed generation. The proposed optimization problem is implemented on IEEE 15 node radial system. A meta-heuristic approach based on covariance matrix adaptation evolution strategy directed target to best perturbation algorithm is applied for optimization of relay coordination problem under maximum penetration of distributed generation.","PeriodicalId":444765,"journal":{"name":"2013 IEEE Symposium on Swarm Intelligence (SIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129261607","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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