Pub Date : 2018-07-01DOI: 10.1109/iccia.2018.00010
R. Syahputra, K. Komarudin, A. R. Destyanto
VRPTF is one of many various kinds of Vehicle Routing Problem (VRP) which is still slightly discussed. As logistic is always an issue for both developed and developing country, this research aims to compare whether or not VRPTF is suitably applied to logistic distribution in Indonesia. As we know, Indonesia is one of the archipelagic countries, so distributing goods across the island requires a high cost. The purpose of this paper is giving cost comparison by using VRPTF method, Hub-and-Spoke method, and Capacitated VRP method, so countries that may experience similar case can apply this method and get the optimize route model. This study was conducted by taking case study from a company that produces a non-perishable product and generate using Open Solver and VBA program, both in Excel. This research indicates that VRPTF method is giving optimize cost than other methods.
{"title":"Optimization of Distribution Route with Vehicle Routing Problem with Transshipment Facilities (VRPTF)","authors":"R. Syahputra, K. Komarudin, A. R. Destyanto","doi":"10.1109/iccia.2018.00010","DOIUrl":"https://doi.org/10.1109/iccia.2018.00010","url":null,"abstract":"VRPTF is one of many various kinds of Vehicle Routing Problem (VRP) which is still slightly discussed. As logistic is always an issue for both developed and developing country, this research aims to compare whether or not VRPTF is suitably applied to logistic distribution in Indonesia. As we know, Indonesia is one of the archipelagic countries, so distributing goods across the island requires a high cost. The purpose of this paper is giving cost comparison by using VRPTF method, Hub-and-Spoke method, and Capacitated VRP method, so countries that may experience similar case can apply this method and get the optimize route model. This study was conducted by taking case study from a company that produces a non-perishable product and generate using Open Solver and VBA program, both in Excel. This research indicates that VRPTF method is giving optimize cost than other methods.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116086702","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 : 2018-07-01DOI: 10.1109/iccia.2018.00014
Sifan Su, Cui Zhu, Wenjun Zhu, L. Kaunda
In this paper, a temporal-spatial fusion model is proposed for PM2.5 concentration prediction. The model uses historical PM2.5 concentration and meteorological data as input of the model to make hourly predictions of PM2.5 concentration. This model consists of three parts: 1) Long short-term memory neural network predictor based on time dimension, 2) Artificial neural network predictor based on spatial dimension, 3) Model tree predictor based on temporal-spatial fusion. This method combines the forecast results of two dimensions in space and time dynamically, as the spatial and temporal correlation of data is considered. Experimental results show this model performs better than predicting from a single dimension, confirming the effectiveness of the model.
{"title":"A Prediction of PM2.5 Concentration Based on Temporal-Spatial Fusion Model","authors":"Sifan Su, Cui Zhu, Wenjun Zhu, L. Kaunda","doi":"10.1109/iccia.2018.00014","DOIUrl":"https://doi.org/10.1109/iccia.2018.00014","url":null,"abstract":"In this paper, a temporal-spatial fusion model is proposed for PM2.5 concentration prediction. The model uses historical PM2.5 concentration and meteorological data as input of the model to make hourly predictions of PM2.5 concentration. This model consists of three parts: 1) Long short-term memory neural network predictor based on time dimension, 2) Artificial neural network predictor based on spatial dimension, 3) Model tree predictor based on temporal-spatial fusion. This method combines the forecast results of two dimensions in space and time dynamically, as the spatial and temporal correlation of data is considered. Experimental results show this model performs better than predicting from a single dimension, confirming the effectiveness of the model.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130502676","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 : 2018-07-01DOI: 10.1109/ICCIA.2018.00049
Hyunwoo Choi
Though more and more people are feeling pain in the neck due to computer and smartphone usage, few equipment is available to measure neck fatigue. In this paper, we use radio frequency wireless power transmission (RF WPT) method to allow a small battery to be used while allowing continuous measurement for big data. Miniaturized electromyogram (EMG) sensor system with Arduino Pro mini can be lightly attached to the neck, giving the user notification of posture correction or stretching needs without additional neck fatigue. Information collected by EMG sensor system is sent to the users, which can prevent turtle neck syndrome and reduce the neck fatigue by long working hours.
虽然越来越多的人因为使用电脑和智能手机而感到颈部疼痛，但很少有设备可以测量颈部疲劳。在本文中，我们使用射频无线电力传输(RF WPT)方法，允许使用小电池，同时允许对大数据进行连续测量。使用Arduino Pro mini的小型化肌电图(EMG)传感器系统可以轻轻附着在颈部，在不增加颈部疲劳的情况下通知用户姿势纠正或拉伸需求。肌电传感器系统采集到的信息发送给使用者，可以预防龟颈综合征，减少长时间工作造成的颈部疲劳。
{"title":"EMG Sensor System for Neck Fatigue Assessment Using RF Wireless Power Transmission","authors":"Hyunwoo Choi","doi":"10.1109/ICCIA.2018.00049","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00049","url":null,"abstract":"Though more and more people are feeling pain in the neck due to computer and smartphone usage, few equipment is available to measure neck fatigue. In this paper, we use radio frequency wireless power transmission (RF WPT) method to allow a small battery to be used while allowing continuous measurement for big data. Miniaturized electromyogram (EMG) sensor system with Arduino Pro mini can be lightly attached to the neck, giving the user notification of posture correction or stretching needs without additional neck fatigue. Information collected by EMG sensor system is sent to the users, which can prevent turtle neck syndrome and reduce the neck fatigue by long working hours.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"13 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120972843","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 : 2018-07-01DOI: 10.1109/ICCIA.2018.00025
Ying Li, Fanjun Li
Real-time prediction of NOx is important for the control of NOx emission from a coal-fired power plant. This paper presents a NOx prediction method based on deep extreme learning machine. First, an improved deep extreme learning machine is proposed. Then, a NOx prediction model is designed based on the proposed method. Finally, the model is evaluated by using the actual data. Simulations results show that the proposed method is effective.
{"title":"NOx Prediction Method Based on Deep Extreme Learning Machine","authors":"Ying Li, Fanjun Li","doi":"10.1109/ICCIA.2018.00025","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00025","url":null,"abstract":"Real-time prediction of NOx is important for the control of NOx emission from a coal-fired power plant. This paper presents a NOx prediction method based on deep extreme learning machine. First, an improved deep extreme learning machine is proposed. Then, a NOx prediction model is designed based on the proposed method. Finally, the model is evaluated by using the actual data. Simulations results show that the proposed method is effective.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122681374","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 : 2018-07-01DOI: 10.1109/ICCIA.2018.00022
P. Hájek, V. Olej
Intuitionistic fuzzy inference systems are used to model the uncertainty associated with positive and negative information and preferences. Here, we propose a novel intuitionistic fuzzy inference system of the Takagi-Sugeno-Kang type with genetic tuning. A genetic fuzzy apriori algorithm is used to obtain both the set of if-then rules and the initial values of the premise parameters. Then, a genetic algorithm is applied to tune the premise and consequent parameters of the intuitionistic fuzzy inference system. We demonstrate the effectiveness of the proposed system for predicting corporate financial performance and show that the system has higher prediction accuracy than state-of-the-art fuzzy inference systems.
{"title":"Intuitionistic Fuzzy Inference System with Genetic Tuning for Predicting Financial Performance","authors":"P. Hájek, V. Olej","doi":"10.1109/ICCIA.2018.00022","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00022","url":null,"abstract":"Intuitionistic fuzzy inference systems are used to model the uncertainty associated with positive and negative information and preferences. Here, we propose a novel intuitionistic fuzzy inference system of the Takagi-Sugeno-Kang type with genetic tuning. A genetic fuzzy apriori algorithm is used to obtain both the set of if-then rules and the initial values of the premise parameters. Then, a genetic algorithm is applied to tune the premise and consequent parameters of the intuitionistic fuzzy inference system. We demonstrate the effectiveness of the proposed system for predicting corporate financial performance and show that the system has higher prediction accuracy than state-of-the-art fuzzy inference systems.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115283260","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 : 2018-07-01DOI: 10.1109/ICCIA.2018.00013
Lianbo Zhu, Xu Meng, Zhenqun Shi, Yilei Huang
In China, owing to some promotion policies of prefabricated buildings, the enterprises of precast concrete components grow by leaps and bounds. However, there are lots of influence factors of selecting PC factories' locations. It's a complex and multi-criteria decision-making problems. How to make a right decision is very important because the location will affect the operation cost and core competence. The paper puts forward four primary criterion: the geographical factors, economic factors, social and environmental factors on the basis of summarizing the related research results, combining with the experience of the experts and the characteristics of the precast concrete components techniques. There are total 19 secondary criteria under the primary ones. Then the paper presents a fuzzy group decision-making model based on the triangular fuzzy numbers. Finally an example is given to prove the model's validity and scientificity. The research result will provide a new method and idea to select the precast concrete factories' locations.
{"title":"Selection of Prefabricated Concrete Factories' Location Based on Triangular Fuzzy Numbers and Fuzzy Group Decision-Making","authors":"Lianbo Zhu, Xu Meng, Zhenqun Shi, Yilei Huang","doi":"10.1109/ICCIA.2018.00013","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00013","url":null,"abstract":"In China, owing to some promotion policies of prefabricated buildings, the enterprises of precast concrete components grow by leaps and bounds. However, there are lots of influence factors of selecting PC factories' locations. It's a complex and multi-criteria decision-making problems. How to make a right decision is very important because the location will affect the operation cost and core competence. The paper puts forward four primary criterion: the geographical factors, economic factors, social and environmental factors on the basis of summarizing the related research results, combining with the experience of the experts and the characteristics of the precast concrete components techniques. There are total 19 secondary criteria under the primary ones. Then the paper presents a fuzzy group decision-making model based on the triangular fuzzy numbers. Finally an example is given to prove the model's validity and scientificity. The research result will provide a new method and idea to select the precast concrete factories' locations.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116940190","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 : 2018-07-01DOI: 10.1109/ICCIA.2018.00026
Iván Calvo, Mercedes G. Merayo, Manuel Núñez
Recent work has shown that fuzzy bounds are an appropriate mechanism to decide the correctness of systems where some of the parameters governing their behavior have a degree of uncertainty. In order to provide a formalism to specify and analyze this type of systems, an extension of finiteautomata with fuzzy constraints has been introduced. Previous work has provided the theoretical framework and the application methodology. This framework has been used in different areas, in particular, in the analysis of electrocardiograms to detect abnormal patterns of behavior. Although our case studies were fully supported by a dedicated computer program, we missed a tool where the particular features of each system could be easily specified. In this paper we present AUNTY: a tool to AUtomatically aNalyze daTa using fuzzY automata. The tool allows users to graphically represent specifications of behaviors and automatically analyze whether the available data conforms to a specification. Its modular architecture makes the tool suitable to be adapted to a wide range of use cases.
{"title":"AUNTY: A Tool to Automatically Analyze Data Using Fuzzy Automata","authors":"Iván Calvo, Mercedes G. Merayo, Manuel Núñez","doi":"10.1109/ICCIA.2018.00026","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00026","url":null,"abstract":"Recent work has shown that fuzzy bounds are an appropriate mechanism to decide the correctness of systems where some of the parameters governing their behavior have a degree of uncertainty. In order to provide a formalism to specify and analyze this type of systems, an extension of finiteautomata with fuzzy constraints has been introduced. Previous work has provided the theoretical framework and the application methodology. This framework has been used in different areas, in particular, in the analysis of electrocardiograms to detect abnormal patterns of behavior. Although our case studies were fully supported by a dedicated computer program, we missed a tool where the particular features of each system could be easily specified. In this paper we present AUNTY: a tool to AUtomatically aNalyze daTa using fuzzY automata. The tool allows users to graphically represent specifications of behaviors and automatically analyze whether the available data conforms to a specification. Its modular architecture makes the tool suitable to be adapted to a wide range of use cases.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121627537","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 : 2018-07-01DOI: 10.1109/ICCIA.2018.00048
Ryo Hanafusa, T. Okadome
The regression method proposed in this paper determines a regression function for noisy inputs. We represent noisy inputs by using noise and latent noise-free constituent of the noisy input. Given an observed noisy input, the proposed method estimates the posterior of the latent noise-free constituent of it, and represents the posterior using the noise distribution. For the value of the regression function for the noisy input, the method produces the expected value of the Nadaraya–Watson estimator for noiseless inputs, which is constructed from a training dataset consisting of noiseless explanatory values and the corresponding objective values. In addition, a probabilistic generative model is presented for estimating the noise distribution. This enables us to determine the noise distribution parametrically from a single noisy input, using the distribution of the noise-free constituent of the noisy input estimated from the training dataset as a prior. Experiments conducted using artificial and real datasets show that the proposed method suppresses the overfitting of the regression function for noisy inputs and that the root mean squared errors of the predictions are smaller compared with those of an existing method.
{"title":"Regression Method for Noisy Inputs Based on Non-Parametric Estimator Constructed from Noiseless Training Data","authors":"Ryo Hanafusa, T. Okadome","doi":"10.1109/ICCIA.2018.00048","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00048","url":null,"abstract":"The regression method proposed in this paper determines a regression function for noisy inputs. We represent noisy inputs by using noise and latent noise-free constituent of the noisy input. Given an observed noisy input, the proposed method estimates the posterior of the latent noise-free constituent of it, and represents the posterior using the noise distribution. For the value of the regression function for the noisy input, the method produces the expected value of the Nadaraya–Watson estimator for noiseless inputs, which is constructed from a training dataset consisting of noiseless explanatory values and the corresponding objective values. In addition, a probabilistic generative model is presented for estimating the noise distribution. This enables us to determine the noise distribution parametrically from a single noisy input, using the distribution of the noise-free constituent of the noisy input estimated from the training dataset as a prior. Experiments conducted using artificial and real datasets show that the proposed method suppresses the overfitting of the regression function for noisy inputs and that the root mean squared errors of the predictions are smaller compared with those of an existing method.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126348170","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 : 2018-07-01DOI: 10.1109/ICCIA.2018.00035
F. Fambrini, D. G. Caetano, C. Moya, Guilherme Ferretti Grissi, Y. Iano
A segmentation and recognition system for thermographic images of electric power distribution network using Artificial Intelligence is proposed in this article. The infrared thermography is usually used to proceed inspections in electrical power distribution lines, assisted by a human operator, which is usually responsible for operating all the equipment, selecting the hottest spots in the image (corresponding to the places needing maintenance), making reports and calling the technical team, which will do the repairs. The proposed automatic diagnosis system aims to replace the manual inspection operation using image processing algorithms. A method of segmentation for thermal images known as JSEG is implemented and tested and a Convolution Neural Network is responsible to recognize the segmented elements. The results show the feasibility of the algorithm, and the monitoring system.
{"title":"Combining Deep Learning and JSEG Cuda Segmentation Algorithm for Electrical Components Recognition","authors":"F. Fambrini, D. G. Caetano, C. Moya, Guilherme Ferretti Grissi, Y. Iano","doi":"10.1109/ICCIA.2018.00035","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00035","url":null,"abstract":"A segmentation and recognition system for thermographic images of electric power distribution network using Artificial Intelligence is proposed in this article. The infrared thermography is usually used to proceed inspections in electrical power distribution lines, assisted by a human operator, which is usually responsible for operating all the equipment, selecting the hottest spots in the image (corresponding to the places needing maintenance), making reports and calling the technical team, which will do the repairs. The proposed automatic diagnosis system aims to replace the manual inspection operation using image processing algorithms. A method of segmentation for thermal images known as JSEG is implemented and tested and a Convolution Neural Network is responsible to recognize the segmented elements. The results show the feasibility of the algorithm, and the monitoring system.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114573504","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 : 2018-07-01DOI: 10.1109/ICCIA.2018.00008
Ziwen Ye, Jianan Sun
Multidimensional computerized adaptive testing is one of the most popular research issues in statistical and psychological measurement. The purpose of this study is to compare several commonly concerned item selection criteria in different typical testing conditions for dichotoumous and polytomous testing data. Two simulation studies were conducted to explore ability parameter estimation accuracy and item exposure rate for these criteria with the assumption of multidimensional two parameter logistic model and multidimensional graded response model could fit the testing data well, individually. Results showed that the criterion of Bayesian A-Optimality generally performs best both for the two item response theory models from the perspective of the above evaluation indices. As for the three-dimensional case based on the two models, A-Optimality was a relatively bad criterion in terms of ability parameter estimation accuracy.
{"title":"Comparing Item Selection Criteria in Multidimensional Computerized Adaptive Testing for Two Item Response Theory Models","authors":"Ziwen Ye, Jianan Sun","doi":"10.1109/ICCIA.2018.00008","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00008","url":null,"abstract":"Multidimensional computerized adaptive testing is one of the most popular research issues in statistical and psychological measurement. The purpose of this study is to compare several commonly concerned item selection criteria in different typical testing conditions for dichotoumous and polytomous testing data. Two simulation studies were conducted to explore ability parameter estimation accuracy and item exposure rate for these criteria with the assumption of multidimensional two parameter logistic model and multidimensional graded response model could fit the testing data well, individually. Results showed that the criterion of Bayesian A-Optimality generally performs best both for the two item response theory models from the perspective of the above evaluation indices. As for the three-dimensional case based on the two models, A-Optimality was a relatively bad criterion in terms of ability parameter estimation accuracy.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129064034","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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