Pub Date : 2018-07-01DOI: 10.1109/ICCIA.2018.00040
Xiaotian Wang, Wan-chao Ma, Kai Zhang, Jie Yan
Image complexity metric is an important part of automatic target recognition(ATR) performance evaluation, the relationship between infrared image complexity metric and target recognition is studied, which is important for infrared imaging system performance prediction and evaluation and the performance comparison of target recognition algorithms. Aiming at this problem, an automatic target recognition infrared image complexity metric method is proposed. Firstly, the infrared imaging mechanism is analyzed to find the main factors affecting target recognition. The image complexity is defined from the similarity degree of target and clutter and the submergence degree of target and clutter, which clarify for the influence of target recognition. To increase the universality of image complexity, the concept of feature space was introduced. Finally, the weighted processing and statistical formula F1-Score is used to combine the three indexes, the complexity of the frame image is established. The experimental results show that the proposed metric is more valid than traditional metrics, such as SV and SCR, has a strong correlation with automatic target recognition algorithm, while the values are in better agreement with the actual situation.
{"title":"Complexity Metric of Infrared Image for Automatic Target Recognition","authors":"Xiaotian Wang, Wan-chao Ma, Kai Zhang, Jie Yan","doi":"10.1109/ICCIA.2018.00040","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00040","url":null,"abstract":"Image complexity metric is an important part of automatic target recognition(ATR) performance evaluation, the relationship between infrared image complexity metric and target recognition is studied, which is important for infrared imaging system performance prediction and evaluation and the performance comparison of target recognition algorithms. Aiming at this problem, an automatic target recognition infrared image complexity metric method is proposed. Firstly, the infrared imaging mechanism is analyzed to find the main factors affecting target recognition. The image complexity is defined from the similarity degree of target and clutter and the submergence degree of target and clutter, which clarify for the influence of target recognition. To increase the universality of image complexity, the concept of feature space was introduced. Finally, the weighted processing and statistical formula F1-Score is used to combine the three indexes, the complexity of the frame image is established. The experimental results show that the proposed metric is more valid than traditional metrics, such as SV and SCR, has a strong correlation with automatic target recognition algorithm, while the values are in better agreement with the actual situation.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"1 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":"130611514","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.2018.00037
Yicong Hu, Kai Qiao, Linyuan Wang, Li Tong, Chi Zhang, Hui Gao, Bin Yan
The computation encoding models, used to predict human brain activity from natural image stimuli, can be performed as a function simulator of human vision information process. In the traditional computational encoding models for human V2 cortex, due to the lack of higher visual feature and information processing hierarchy, it is difficult to achieve expected predict performance. Here, activated by the properties of CNN, we trained a CNN as an encoding model for human V2 cortex, which can be trained for predicting stimuli-evoked response measured by functional magnetic resonance imaging. The results reveal that the CNN-based encoding model can achieve a higher performance, proves that CNN have advantages in encoding higher visual areas. This finding provides a new framework for the human vision encoding models and helps to further understand of the human vision mechanism from the computational point view.
{"title":"A CNN-Based Computational Encoding Model for Human V2 Cortex","authors":"Yicong Hu, Kai Qiao, Linyuan Wang, Li Tong, Chi Zhang, Hui Gao, Bin Yan","doi":"10.1109/iccia.2018.00037","DOIUrl":"https://doi.org/10.1109/iccia.2018.00037","url":null,"abstract":"The computation encoding models, used to predict human brain activity from natural image stimuli, can be performed as a function simulator of human vision information process. In the traditional computational encoding models for human V2 cortex, due to the lack of higher visual feature and information processing hierarchy, it is difficult to achieve expected predict performance. Here, activated by the properties of CNN, we trained a CNN as an encoding model for human V2 cortex, which can be trained for predicting stimuli-evoked response measured by functional magnetic resonance imaging. The results reveal that the CNN-based encoding model can achieve a higher performance, proves that CNN have advantages in encoding higher visual areas. This finding provides a new framework for the human vision encoding models and helps to further understand of the human vision mechanism from the computational point view.","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":"126951748","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.2018.00033
J. Preethi
Brain cancers are the second most common disease in children. The radiologist plays a vital role in diagnosing a disease. Manual classification is a time consuming process and can cause human errors. Our objective is to develop a fully automated classification method for identification of brain cancers. Methods: This paper proposes a Bio Inspired Hybrid Krill Herd-Extreme Learning Machine (ELM) Network which classifies the Brain images into one of the classes namely normal image, Astrocytoma cancer, Meningioma cancer or Oligidendroglioma cancer. The most essential part of the research is to find the local and global features from the brain cancer images. In this proposed method, both Local Binary Patterns (LBP) and Gray Level Co-occurrence Matrix (GLCM) features are used for feature extraction. The real time brain database is obtained from Jansons MRI Diagnostic centre Erode during November 1, 2013 to December 31, 2014 consisting of 400 images with their ages ranging from 20 to 65 years. In our experiment, 85 samples aretaken for training and the remaining 15 samples are taken for testing. Initially, the local feature information is extracted using LBP method and the overall global features are extracted using GLCM method. By these methods, the brain images are fully illustrated using local and global features. Then the statistical technique is used for feature sub selection where the variance of each features are calculated. The selected features from statistical technique is fed as inputs to the ELM Neural Network classifier where the weights are optimized using Krill Herd algorithm.Results: This proposed hybrid approach achieves 98.9% accuracy when compared with other traditional techniques.
{"title":"A Bio Inspired Hybrid Krill Herd-Extreme Learning Machine Network Based on LBP and GLCM for Brain Cancer Tissue Taxonomy","authors":"J. Preethi","doi":"10.1109/ICCIA.2018.00033","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00033","url":null,"abstract":"Brain cancers are the second most common disease in children. The radiologist plays a vital role in diagnosing a disease. Manual classification is a time consuming process and can cause human errors. Our objective is to develop a fully automated classification method for identification of brain cancers. Methods: This paper proposes a Bio Inspired Hybrid Krill Herd-Extreme Learning Machine (ELM) Network which classifies the Brain images into one of the classes namely normal image, Astrocytoma cancer, Meningioma cancer or Oligidendroglioma cancer. The most essential part of the research is to find the local and global features from the brain cancer images. In this proposed method, both Local Binary Patterns (LBP) and Gray Level Co-occurrence Matrix (GLCM) features are used for feature extraction. The real time brain database is obtained from Jansons MRI Diagnostic centre Erode during November 1, 2013 to December 31, 2014 consisting of 400 images with their ages ranging from 20 to 65 years. In our experiment, 85 samples aretaken for training and the remaining 15 samples are taken for testing. Initially, the local feature information is extracted using LBP method and the overall global features are extracted using GLCM method. By these methods, the brain images are fully illustrated using local and global features. Then the statistical technique is used for feature sub selection where the variance of each features are calculated. The selected features from statistical technique is fed as inputs to the ELM Neural Network classifier where the weights are optimized using Krill Herd algorithm.Results: This proposed hybrid approach achieves 98.9% accuracy when compared with other traditional techniques.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"22 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":"116360973","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.2018.00057
Jungyu Ahn, Ju-hong Lee
Due to the random walk property of the financial time series, it is very difficult to develop a system that solves real financial application problems. However, if we obtain a time series cluster with a high degree of co-movement, it will be very useful for developing financial application systems. This paper proposes a clustering method that finds time series clusters with higher degrees of co-movement than the existing time series clustering algorithms. There is a problem in that clusters generated by the existing time series clustering algorithms contain too much noise with a low degree of co-movement. We propose a clustering method that solves the problem. This method is performed in the following steps. In the Data Preprocessing step, it performs Average Scaling, Weighted Time Series Transformation, Dimension Reduction, and Cluster Diameter Estimation. In the Clustering Step, it performs Preclustering and Refinement. Experiments show that our clustering method has higher performance than the existing time series clustering algorithms in finding clusters with high degree of co-movement.
{"title":"Clustering Method for Financial Time Series with Co-Movement Relationship","authors":"Jungyu Ahn, Ju-hong Lee","doi":"10.1109/ICCIA.2018.00057","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00057","url":null,"abstract":"Due to the random walk property of the financial time series, it is very difficult to develop a system that solves real financial application problems. However, if we obtain a time series cluster with a high degree of co-movement, it will be very useful for developing financial application systems. This paper proposes a clustering method that finds time series clusters with higher degrees of co-movement than the existing time series clustering algorithms. There is a problem in that clusters generated by the existing time series clustering algorithms contain too much noise with a low degree of co-movement. We propose a clustering method that solves the problem. This method is performed in the following steps. In the Data Preprocessing step, it performs Average Scaling, Weighted Time Series Transformation, Dimension Reduction, and Cluster Diameter Estimation. In the Clustering Step, it performs Preclustering and Refinement. Experiments show that our clustering method has higher performance than the existing time series clustering algorithms in finding clusters with high degree of co-movement.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"85 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":"114908083","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.2018.00017
Wai Chong Chia, P. Teh, C. M. Gill
Extracting information from a large number of scientific documents prepared in portable document format (PDF) is a time-consuming process, if all this is to be done without the help of an automated system. However, the missing of structural information in PDF can create a lot of issues during the extraction process. Watermark is one of the objects that can have a negative effect on this. When PDF extraction tool is applied to PDF with watermark, the watermark can affect the order of the text and is often extracted as part of the text. If the text is to be used for analysis in the future, the watermark might affect the accuracy in the results, since they should not be taken into consideration. In this paper, an approach that can be used to overcome the issue above is proposed. The proposed approach makes use of direct text recognition from PDF and optical character recognition (OCR) to produce two version of digital text that can be combined for better accuracy. The results shown that the proposed approach is capable of extracting text from PDF with different watermark patterns.
{"title":"Text Extraction and Categorization from Watermark Scientific Document in Bulk","authors":"Wai Chong Chia, P. Teh, C. M. Gill","doi":"10.1109/ICCIA.2018.00017","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00017","url":null,"abstract":"Extracting information from a large number of scientific documents prepared in portable document format (PDF) is a time-consuming process, if all this is to be done without the help of an automated system. However, the missing of structural information in PDF can create a lot of issues during the extraction process. Watermark is one of the objects that can have a negative effect on this. When PDF extraction tool is applied to PDF with watermark, the watermark can affect the order of the text and is often extracted as part of the text. If the text is to be used for analysis in the future, the watermark might affect the accuracy in the results, since they should not be taken into consideration. In this paper, an approach that can be used to overcome the issue above is proposed. The proposed approach makes use of direct text recognition from PDF and optical character recognition (OCR) to produce two version of digital text that can be combined for better accuracy. The results shown that the proposed approach is capable of extracting text from PDF with different watermark patterns.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"58 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":"114172667","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.2018.00012
R. Syahputra, K. Komarudin, A. R. Destyanto
The focus of national development on the infrastructure sector impacts on the rapid growth of the construction market. High demand and complex business processes make ready-mix concrete producers especially in Jakarta no longer able to rely on route planning and manual scheduling mechanisms, which have been some delays in deliveries that impact on the decline in service level. This research proposes an optimization method based on mixed integer linear programming on route planning mechanism and scheduling of ready-mix concrete delivery developed in Java language with Gurobi optimization library support. The simulation is done using the companys historical data of the research object, which is one of the ready-mix concrete producers in Jakarta. From the four simulations, the best output resulted with a total cost of - 3674 and a gap of 0.49%, where all customer requests are met in the given time window. These results indicate that the optimization model developed in this study can yield the optimum solution for route planning mechanism and ready-mix concrete delivery scheduling.
{"title":"Optimization Model of Ready-Mix Concrete Delivery Route and Schedule: A Case in Indonesia RMC Industry","authors":"R. Syahputra, K. Komarudin, A. R. Destyanto","doi":"10.1109/ICCIA.2018.00012","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00012","url":null,"abstract":"The focus of national development on the infrastructure sector impacts on the rapid growth of the construction market. High demand and complex business processes make ready-mix concrete producers especially in Jakarta no longer able to rely on route planning and manual scheduling mechanisms, which have been some delays in deliveries that impact on the decline in service level. This research proposes an optimization method based on mixed integer linear programming on route planning mechanism and scheduling of ready-mix concrete delivery developed in Java language with Gurobi optimization library support. The simulation is done using the companys historical data of the research object, which is one of the ready-mix concrete producers in Jakarta. From the four simulations, the best output resulted with a total cost of - 3674 and a gap of 0.49%, where all customer requests are met in the given time window. These results indicate that the optimization model developed in this study can yield the optimum solution for route planning mechanism and ready-mix concrete delivery scheduling.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"22 4 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":"132692442","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.2018.00053
Yanming Yang, Huayuan Zhu
Process capability indices are the important tools used in most of the manufacturing industries to check whether the manufactured products meet their quality specifications or not. Process capability analysis requires that the quality characteristic data be normally distributed. In actual production, a lot of stable processes do not necessarily satisfy the assumption of normal distribution. An approach to tackle this problem is to use the appropriate transformation methods to convert these non-normal data. Therefore, a method of converting non-normal data into normal data is proposed so that the data can be analyzed using the process capability indices. In this paper, an improved Box-Cox transformation model is proposed to deal with non-normal data and calculate its process capability indices, and the concrete steps are given. Finally, the method is used to study the actual cases, and the process capability indices are calculated. The effectiveness and practicability of the method are proved by comparison with the actual situation. In this paper, Minitab analysis software is used to assist the realization of this method. It has strong operability and convenience, and can be used to guide production practice.
{"title":"A Study of Non-Normal Process Capability Analysis Based on Box-Cox Transformation","authors":"Yanming Yang, Huayuan Zhu","doi":"10.1109/ICCIA.2018.00053","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00053","url":null,"abstract":"Process capability indices are the important tools used in most of the manufacturing industries to check whether the manufactured products meet their quality specifications or not. Process capability analysis requires that the quality characteristic data be normally distributed. In actual production, a lot of stable processes do not necessarily satisfy the assumption of normal distribution. An approach to tackle this problem is to use the appropriate transformation methods to convert these non-normal data. Therefore, a method of converting non-normal data into normal data is proposed so that the data can be analyzed using the process capability indices. In this paper, an improved Box-Cox transformation model is proposed to deal with non-normal data and calculate its process capability indices, and the concrete steps are given. Finally, the method is used to study the actual cases, and the process capability indices are calculated. The effectiveness and practicability of the method are proved by comparison with the actual situation. In this paper, Minitab analysis software is used to assist the realization of this method. It has strong operability and convenience, and can be used to guide production practice.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"37 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":"132616516","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.2018.00007
{"title":"ICCIA 2018 Reviewers","authors":"","doi":"10.1109/iccia.2018.00007","DOIUrl":"https://doi.org/10.1109/iccia.2018.00007","url":null,"abstract":"","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"32 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":"121112013","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.2018.00001
{"title":"Title Page i","authors":"","doi":"10.1109/iccia.2018.00001","DOIUrl":"https://doi.org/10.1109/iccia.2018.00001","url":null,"abstract":"","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"30 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":"116005772","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.2018.00046
Y. Nozaki, M. Yoshikawa
Artificial intelligence technology such as neural network (NN) is widely used in intelligence module for Internet of Things (IoT). On the other hand, the risk of illegal attacks for IoT devices is pointed out; therefore, security countermeasures such as an authentication are very important. In the field of hardware security, the physical unclonable functions (PUFs) have been attracted attention as authentication techniques to prevent the semiconductor counterfeits. However, implementation of the dedicated hardware for both of NN and PUF increases circuit area. Therefore, this study proposes a new area constraint aware PUF for intelligence module. The proposed PUF utilizes the propagation delay time from input layer to output layer of NN. To share component for operation, the proposed PUF reduces the circuit area. Experiments using a field programmable gate array evaluate circuit area and PUF performance. In the result of circuit area, the proposed PUF was smaller than the conventional PUFs was showed. Then, in the PUF performance evaluation, for steadiness, diffuseness, and uniqueness, favorable results were obtained.
{"title":"Area Constraint Aware Physical Unclonable Function for Intelligence Module","authors":"Y. Nozaki, M. Yoshikawa","doi":"10.1109/ICCIA.2018.00046","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00046","url":null,"abstract":"Artificial intelligence technology such as neural network (NN) is widely used in intelligence module for Internet of Things (IoT). On the other hand, the risk of illegal attacks for IoT devices is pointed out; therefore, security countermeasures such as an authentication are very important. In the field of hardware security, the physical unclonable functions (PUFs) have been attracted attention as authentication techniques to prevent the semiconductor counterfeits. However, implementation of the dedicated hardware for both of NN and PUF increases circuit area. Therefore, this study proposes a new area constraint aware PUF for intelligence module. The proposed PUF utilizes the propagation delay time from input layer to output layer of NN. To share component for operation, the proposed PUF reduces the circuit area. Experiments using a field programmable gate array evaluate circuit area and PUF performance. In the result of circuit area, the proposed PUF was smaller than the conventional PUFs was showed. Then, in the PUF performance evaluation, for steadiness, diffuseness, and uniqueness, favorable results were obtained.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"122 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":"115089759","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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