Pub Date : 2015-03-11DOI: 10.1109/PRIA.2015.7161634
Noorollah Karimtabar, Sadegh Pasban, S. Alipour
The electricity consumption forecast is especially important with regard to policy making in developing countries. In this paper, the electricity consumption rate is predicted using the data mining techniques. The datasets that were collected for predicting the electricity consumption are related to Islamic Republic of Iran - Mazandaran province pertaining to the years 1991 to 2013. The research objective is analyzing the electricity consumption rate in recent years and predicting future consumption. According to a study the electricity consumption growth rate between the years 2006 to 2013 and the years 1999 to 2006 equaled 28.41 and 73.53, respectively. The results of the research conducted using the regression model indicate a 2.48 relative error. The output of this prediction shows that the total electricity consumption rate increases about 3.2% annually on average and will reach 7076796 megawatts by the year 2020 that shows a 22.28% growth comparing to the year 2013.
{"title":"Analysis and predicting electricity energy consumption using data mining techniques — A case study I.R. Iran — Mazandaran province","authors":"Noorollah Karimtabar, Sadegh Pasban, S. Alipour","doi":"10.1109/PRIA.2015.7161634","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161634","url":null,"abstract":"The electricity consumption forecast is especially important with regard to policy making in developing countries. In this paper, the electricity consumption rate is predicted using the data mining techniques. The datasets that were collected for predicting the electricity consumption are related to Islamic Republic of Iran - Mazandaran province pertaining to the years 1991 to 2013. The research objective is analyzing the electricity consumption rate in recent years and predicting future consumption. According to a study the electricity consumption growth rate between the years 2006 to 2013 and the years 1999 to 2006 equaled 28.41 and 73.53, respectively. The results of the research conducted using the regression model indicate a 2.48 relative error. The output of this prediction shows that the total electricity consumption rate increases about 3.2% annually on average and will reach 7076796 megawatts by the year 2020 that shows a 22.28% growth comparing to the year 2013.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"18 797 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125157733","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 : 2015-03-11DOI: 10.1109/PRIA.2015.7161614
M. Javanmardi, M. Yazdi, M. Shirazi
In recent years, Compressive Sensing (CS) or sparse representation has been considered as one of the most favorite topics in the areas of Computer Vision. In particular this theory can be widely applied in Visual Tracking applications. Addressing the problem of sparse representation through minimizations methods can play a dominant role in the CS trackers (trackers based on CS theory). In contrast to the previous algorithms which usually solve the problem of minimization by using L1-norm, L0-norm minimization is used directly to achieve sparseness in our proposed method. Simulations and results demonstrate that the proposed method can achieve the same or better accuracy with many less complexity than traditional algorithms which used interior-point method.
{"title":"Fast and robust L0-tracker using compressive sensing","authors":"M. Javanmardi, M. Yazdi, M. Shirazi","doi":"10.1109/PRIA.2015.7161614","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161614","url":null,"abstract":"In recent years, Compressive Sensing (CS) or sparse representation has been considered as one of the most favorite topics in the areas of Computer Vision. In particular this theory can be widely applied in Visual Tracking applications. Addressing the problem of sparse representation through minimizations methods can play a dominant role in the CS trackers (trackers based on CS theory). In contrast to the previous algorithms which usually solve the problem of minimization by using L1-norm, L0-norm minimization is used directly to achieve sparseness in our proposed method. Simulations and results demonstrate that the proposed method can achieve the same or better accuracy with many less complexity than traditional algorithms which used interior-point method.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125965289","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 : 2015-03-11DOI: 10.1109/PRIA.2015.7161650
Ali Fatahbeygi, F. Akhlaghian
In this paper a novel and robust image watermarking algorithm based on block classification and visual cryptography (VC) is presented. The proposed method inserts a watermark pattern without modifying the original host image. First the original image is decomposed into non-overlapping blocks. Then, we use canny edge detection and support vector machine (SVM) classification method to categorize these blocks into smooth and non-smooth (non-edge and edge) classes. The VC technique is used to generate two image shares. A master share that is constructed according to the block classification results and then owner share is generated by comparing master share together with binary watermark according to the (2,2) VC technique. To verify the ownership of the image, watermark can be retrieved by stacking the master share and the owner share. Experimental results show that the proposed watermarking scheme is completely imperceptible and also has high robustness against common image processing attacks.
{"title":"A new robust semi-blind image watermarking based on block classification and visual cryptography","authors":"Ali Fatahbeygi, F. Akhlaghian","doi":"10.1109/PRIA.2015.7161650","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161650","url":null,"abstract":"In this paper a novel and robust image watermarking algorithm based on block classification and visual cryptography (VC) is presented. The proposed method inserts a watermark pattern without modifying the original host image. First the original image is decomposed into non-overlapping blocks. Then, we use canny edge detection and support vector machine (SVM) classification method to categorize these blocks into smooth and non-smooth (non-edge and edge) classes. The VC technique is used to generate two image shares. A master share that is constructed according to the block classification results and then owner share is generated by comparing master share together with binary watermark according to the (2,2) VC technique. To verify the ownership of the image, watermark can be retrieved by stacking the master share and the owner share. Experimental results show that the proposed watermarking scheme is completely imperceptible and also has high robustness against common image processing attacks.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114521663","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 : 2015-03-11DOI: 10.1109/PRIA.2015.7161615
Z. Shokoohi, Ramin Bahmanjeh, K. Faez
Facial expression is an important channel in human communication. Therefore, the problem of facial expression recognition (FER) attracts the growing attention of the research community in the recent years. In this context, the critical point for is the possibility to detect accurately the emotional features. An effective facial feature descriptor is an important issue in the design of a successful expression recongnition algorithm. Although recently there have been certain progress in this domain, extracting a face feature descriptor stable under changing environment is still a difficult task. In this paper, we illustrate empirically the algorithm of person-independent facial expression recognition based on statistical local features such as Directional gradient Local Pattern (DGLP) and gradient local ternary pattern (GLTP). The combined DGLP and GLTP operator encodes the local texture of an image by computing the gradient magnitudes of local neighborhood as well as the angle of direction of the edge and converts those values into feature vector. The results obtained indicate that the combined DGLP and GLTP method performs better than other methods used for facial expression recognition problems in high-textured facial regions.
{"title":"Expression recognition using directional gradient local pattern and gradient-based ternary texture patterns","authors":"Z. Shokoohi, Ramin Bahmanjeh, K. Faez","doi":"10.1109/PRIA.2015.7161615","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161615","url":null,"abstract":"Facial expression is an important channel in human communication. Therefore, the problem of facial expression recognition (FER) attracts the growing attention of the research community in the recent years. In this context, the critical point for is the possibility to detect accurately the emotional features. An effective facial feature descriptor is an important issue in the design of a successful expression recongnition algorithm. Although recently there have been certain progress in this domain, extracting a face feature descriptor stable under changing environment is still a difficult task. In this paper, we illustrate empirically the algorithm of person-independent facial expression recognition based on statistical local features such as Directional gradient Local Pattern (DGLP) and gradient local ternary pattern (GLTP). The combined DGLP and GLTP operator encodes the local texture of an image by computing the gradient magnitudes of local neighborhood as well as the angle of direction of the edge and converts those values into feature vector. The results obtained indicate that the combined DGLP and GLTP method performs better than other methods used for facial expression recognition problems in high-textured facial regions.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115573029","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 : 2015-03-11DOI: 10.1109/PRIA.2015.7161620
M. Soltani-Sarvestani, Azimifar Zohreh
Color can be a useful feature in many fields of AI that are based on machine vision. Unfortunately, many existing vision system do not use color to its full extent, largely because color-based recognition in outdoor scene is complicated, and existing color machine vision techniques have not been shown to be effective in realistic outdoor images. The problem of color recognition in outdoor is considerable when we are faced with glossy materials like automobiles. There is no powerful method to recognize color of a batch of pixels. Thus, for the first time, we propose a novel method to detect dominant color of a group of pixels. This method has many applications in object color detection especially for glossy objects.
{"title":"Batch color classification using bag of colors and discriminative sparse coding","authors":"M. Soltani-Sarvestani, Azimifar Zohreh","doi":"10.1109/PRIA.2015.7161620","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161620","url":null,"abstract":"Color can be a useful feature in many fields of AI that are based on machine vision. Unfortunately, many existing vision system do not use color to its full extent, largely because color-based recognition in outdoor scene is complicated, and existing color machine vision techniques have not been shown to be effective in realistic outdoor images. The problem of color recognition in outdoor is considerable when we are faced with glossy materials like automobiles. There is no powerful method to recognize color of a batch of pixels. Thus, for the first time, we propose a novel method to detect dominant color of a group of pixels. This method has many applications in object color detection especially for glossy objects.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123380962","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 : 2015-03-11DOI: 10.1109/PRIA.2015.7161651
Farshid Keivanian, N. Mehrshad
Knowing the body fat is an extremely important issue since it affects everyone's health. Although there are several ways to measure the body fat percentage (BFP), the accurate methods are often associated with hassle and/or high costs. Therefore, certain measurements or explanatory variables are used to predict the BFP. This study proposes an intelligent feature subset selection approach with unspecified number of features based on Binary GA and Fuzzy Binary GA algorithms to discover most important variable or feature and facilitate an artificial neural network (ANN) classifier model which is applied for body fat prediction (BFP). The proposed forecasting model is able to effectively predict the BFP with error of ± 3.64031% and the most effective feature of forearm circumference among total twelve features by using Fuzzy Binary GA.
{"title":"Intelligent feature subset selection with unspecified number for body fat prediction based on binary-GA and Fuzzy-Binary-GA","authors":"Farshid Keivanian, N. Mehrshad","doi":"10.1109/PRIA.2015.7161651","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161651","url":null,"abstract":"Knowing the body fat is an extremely important issue since it affects everyone's health. Although there are several ways to measure the body fat percentage (BFP), the accurate methods are often associated with hassle and/or high costs. Therefore, certain measurements or explanatory variables are used to predict the BFP. This study proposes an intelligent feature subset selection approach with unspecified number of features based on Binary GA and Fuzzy Binary GA algorithms to discover most important variable or feature and facilitate an artificial neural network (ANN) classifier model which is applied for body fat prediction (BFP). The proposed forecasting model is able to effectively predict the BFP with error of ± 3.64031% and the most effective feature of forearm circumference among total twelve features by using Fuzzy Binary GA.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132242434","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 : 2015-03-11DOI: 10.1109/PRIA.2015.7161635
Soheila Molaei, M. Keyvanpour
This Time series mining is a new area of research in temporal databases and has been an active area of research with its notable recent progress. Event prediction is one of the main goals of time series mining which have important roles for appropriate decision making in different application area. So far, different studies have been presented in the field of time series mining for meaningful events prediction, which have ample challenges. Lack of systematic identification of challenges causes some obstacles for the development of methods. In this paper, due to the abundance and diversity of challenges in event prediction system on time series, lack of a perfect platform for their systematic identification and removing barriers for the development of methods, a classification is proposed for challenging problems of event prediction system on time series. Also, reviewed and analyzed the application of data mining techniques for solving different challenges in event prediction system on time series. For this goal, the article tries to closely study and categorize related researches for better understanding and to reach a comparison structure that can map data mining techniques into the event prediction challenges. The proposed classification of this paper by introducing systematic challenges can help create different research pivots for the elimination of challenges in different areas of applying and developing methods. We think that this paper can help researchers in event prediction systems on time series for the future activities.
{"title":"An analytical review for event prediction system on time series","authors":"Soheila Molaei, M. Keyvanpour","doi":"10.1109/PRIA.2015.7161635","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161635","url":null,"abstract":"This Time series mining is a new area of research in temporal databases and has been an active area of research with its notable recent progress. Event prediction is one of the main goals of time series mining which have important roles for appropriate decision making in different application area. So far, different studies have been presented in the field of time series mining for meaningful events prediction, which have ample challenges. Lack of systematic identification of challenges causes some obstacles for the development of methods. In this paper, due to the abundance and diversity of challenges in event prediction system on time series, lack of a perfect platform for their systematic identification and removing barriers for the development of methods, a classification is proposed for challenging problems of event prediction system on time series. Also, reviewed and analyzed the application of data mining techniques for solving different challenges in event prediction system on time series. For this goal, the article tries to closely study and categorize related researches for better understanding and to reach a comparison structure that can map data mining techniques into the event prediction challenges. The proposed classification of this paper by introducing systematic challenges can help create different research pivots for the elimination of challenges in different areas of applying and developing methods. We think that this paper can help researchers in event prediction systems on time series for the future activities.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134393746","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 : 2015-03-11DOI: 10.1109/PRIA.2015.7161647
Samira Reyhanian, E. Arbabi
The human body, like all other objects with temperature above the absolute zero, emits electromagnetic wave. The emission of infrared electromagnetic wave from the human face produces thermal images. Thus thermal images can be formed even in dark conditions, in which the formation of the visible image is impossible. However, the majority of the stored images in the recognition systems are visible. Thus, matching the thermal probe and visible gallery images can solve the night time face recognition problem. On the other hand, because of the different formation mechanism of these two types of images, there are lots of challenges in the matching process. Prototype random subspace approach is one of the most successful methods in the area of thermal to visible face recognition. In this paper, we have revised the recognition step of prototype random subspace approach by proposing Weighted Vote Fusion scheme. The proposed strategy has been tested on an available data set and the results show about 9% of improvement in recognition rate, comparing to the original approach.
{"title":"Weighted Vote Fusion in prototype random subspace for thermal to visible face recognition","authors":"Samira Reyhanian, E. Arbabi","doi":"10.1109/PRIA.2015.7161647","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161647","url":null,"abstract":"The human body, like all other objects with temperature above the absolute zero, emits electromagnetic wave. The emission of infrared electromagnetic wave from the human face produces thermal images. Thus thermal images can be formed even in dark conditions, in which the formation of the visible image is impossible. However, the majority of the stored images in the recognition systems are visible. Thus, matching the thermal probe and visible gallery images can solve the night time face recognition problem. On the other hand, because of the different formation mechanism of these two types of images, there are lots of challenges in the matching process. Prototype random subspace approach is one of the most successful methods in the area of thermal to visible face recognition. In this paper, we have revised the recognition step of prototype random subspace approach by proposing Weighted Vote Fusion scheme. The proposed strategy has been tested on an available data set and the results show about 9% of improvement in recognition rate, comparing to the original approach.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128037735","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 : 2015-03-11DOI: 10.1109/PRIA.2015.7161621
Ali Tourani, A. Shahbahrami
Vehicle counting process provides appropriate information about traffic flow, vehicle crash occurrences and traffic peak times in roadways. An acceptable technique to achieve these goals is using digital image processing methods on roadway camera video outputs. This paper presents a vehicle counter-classifier based on a combination of different video-image processing methods including object detection, edge detection, frame differentiation and the Kalman filter. An implementation of proposed technique has been performed using C++ programming language. The method performance for accuracy in vehicle counts and classify was evaluated, which resulted in about 95 percent accuracy for classification and about 4 percent error in vehicle detection targets.
{"title":"Vehicle counting method based on digital image processing algorithms","authors":"Ali Tourani, A. Shahbahrami","doi":"10.1109/PRIA.2015.7161621","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161621","url":null,"abstract":"Vehicle counting process provides appropriate information about traffic flow, vehicle crash occurrences and traffic peak times in roadways. An acceptable technique to achieve these goals is using digital image processing methods on roadway camera video outputs. This paper presents a vehicle counter-classifier based on a combination of different video-image processing methods including object detection, edge detection, frame differentiation and the Kalman filter. An implementation of proposed technique has been performed using C++ programming language. The method performance for accuracy in vehicle counts and classify was evaluated, which resulted in about 95 percent accuracy for classification and about 4 percent error in vehicle detection targets.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"57 15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124831490","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 : 2015-03-11DOI: 10.1109/PRIA.2015.7161624
A. Hariri, Soroush Arabshahi, A. Ghafari, E. Fatemizadeh
Medical image stabilization has been widely used for clinical imaging modalities. Registration is a conspicuous stage for stabilizing dynamic medical images. Some of regular methods are sensitive to bias field distortion. Sparse-induced similarity measure (SISM) is a robust registering method against spatially-varying intensity distortions which is not evitable on clinical imaging instruments. This paper presents a method for registering medical images to average of captured images using SISM method to avoid spatially-varying intensity distortions like Bias field. Proposed method is compared with SSD and MI similarity measure based registrations. Results show enhancement in stabilizing medical dynamic images with SISM method.
{"title":"Medical images stabilization using sparse-induced similarity measure","authors":"A. Hariri, Soroush Arabshahi, A. Ghafari, E. Fatemizadeh","doi":"10.1109/PRIA.2015.7161624","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161624","url":null,"abstract":"Medical image stabilization has been widely used for clinical imaging modalities. Registration is a conspicuous stage for stabilizing dynamic medical images. Some of regular methods are sensitive to bias field distortion. Sparse-induced similarity measure (SISM) is a robust registering method against spatially-varying intensity distortions which is not evitable on clinical imaging instruments. This paper presents a method for registering medical images to average of captured images using SISM method to avoid spatially-varying intensity distortions like Bias field. Proposed method is compared with SSD and MI similarity measure based registrations. Results show enhancement in stabilizing medical dynamic images with SISM method.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126567291","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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