Pub Date : 2020-11-18DOI: 10.1109/iciibms50712.2020.9336202
Yang Tan, Ke Zhao, Tong Chen
Facial expression, powerful non-verbal signals, contains abundant personal information and social communication information. Accurately identifying these signals is critical to the success of interpersonal communication. Studies have shown that both children and adults tend to confuse surprise with fear rather than sadness, anger, or disgust. However, the studies of fear and surprise expression recognition using network pattern based on MEG is only a few. In this study, we monitored the brain activity of 6 subjects as they perform a recognition task of fear and surprise, and subsequently constructed a network of brain functional connections. By using rank sum test and random forest, the most discriminative and representative 6 FCs from 2278 FCs were selected. The group of these 6 FCs can give a best prediction performance of 78.56%. Additionally, we also found that people tend to refer to surprise as fear when distinguishing between fear and surprise.
{"title":"The predominant functional connections of recognizing fear and surprise expression: a MEG study","authors":"Yang Tan, Ke Zhao, Tong Chen","doi":"10.1109/iciibms50712.2020.9336202","DOIUrl":"https://doi.org/10.1109/iciibms50712.2020.9336202","url":null,"abstract":"Facial expression, powerful non-verbal signals, contains abundant personal information and social communication information. Accurately identifying these signals is critical to the success of interpersonal communication. Studies have shown that both children and adults tend to confuse surprise with fear rather than sadness, anger, or disgust. However, the studies of fear and surprise expression recognition using network pattern based on MEG is only a few. In this study, we monitored the brain activity of 6 subjects as they perform a recognition task of fear and surprise, and subsequently constructed a network of brain functional connections. By using rank sum test and random forest, the most discriminative and representative 6 FCs from 2278 FCs were selected. The group of these 6 FCs can give a best prediction performance of 78.56%. Additionally, we also found that people tend to refer to surprise as fear when distinguishing between fear and surprise.","PeriodicalId":243033,"journal":{"name":"2020 5th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132499630","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 : 2020-11-18DOI: 10.1109/ICIIBMS50712.2020.9336404
Yukinori Hetsugi, Tadashi Sakata, Y. Ueda
We have developed a speech compensation system for dysarthria. The system aims at improving the phonological properties of vowels without losing speaker individuality. We propose a method for phonological control of vowels using a standardized space to control vowels in the normalized articulation space, normalized for speaker individuality. The method maps an original dysarthric speaker's normalized articulation space to a standardized space, then from the standardized space to the target speaker's normalized articulation space assuming normality to improve the phonological properties of vowels. We confirm phonological control of vowels by performing a processing simulation, comparison different target speakers and a processing simulation using a dummy original speaker as a dysarthria.
{"title":"A Phonological Control Method on A Speech Compensation System for Dysarthria Using A Standardized Space","authors":"Yukinori Hetsugi, Tadashi Sakata, Y. Ueda","doi":"10.1109/ICIIBMS50712.2020.9336404","DOIUrl":"https://doi.org/10.1109/ICIIBMS50712.2020.9336404","url":null,"abstract":"We have developed a speech compensation system for dysarthria. The system aims at improving the phonological properties of vowels without losing speaker individuality. We propose a method for phonological control of vowels using a standardized space to control vowels in the normalized articulation space, normalized for speaker individuality. The method maps an original dysarthric speaker's normalized articulation space to a standardized space, then from the standardized space to the target speaker's normalized articulation space assuming normality to improve the phonological properties of vowels. We confirm phonological control of vowels by performing a processing simulation, comparison different target speakers and a processing simulation using a dummy original speaker as a dysarthria.","PeriodicalId":243033,"journal":{"name":"2020 5th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132403777","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 : 2020-11-18DOI: 10.1109/ICIIBMS50712.2020.9336424
Wenju Li, Qianwen Ma, Wenchao Tian, Xinyuan Na
To solve the problem of information loss caused by point cloud segmentation using voxels. A method of transforming point cloud into graph data and using double filter graph convolution network for segmentation is proposed. The first filter is for point clouds to reduce the number of nodes in the graph. Considering the feature as a signal, the convolution is defined in the spectral domain using a Laplacian matrix, and the Chebyshev polynomial is used to reduce the computational complexity of the matrix decomposition. The second filter is a low-pass filter for the Chebyshev polynomial, which reduce the computation. Finally, the 2D data is detected using CNN to optimizes the segmented result. Experiments were performed on the ShapeNet dataset to demonstrate the efficiency of the method.
{"title":"Graph Convolution Network with Double Filter for Point Cloud Segmentation","authors":"Wenju Li, Qianwen Ma, Wenchao Tian, Xinyuan Na","doi":"10.1109/ICIIBMS50712.2020.9336424","DOIUrl":"https://doi.org/10.1109/ICIIBMS50712.2020.9336424","url":null,"abstract":"To solve the problem of information loss caused by point cloud segmentation using voxels. A method of transforming point cloud into graph data and using double filter graph convolution network for segmentation is proposed. The first filter is for point clouds to reduce the number of nodes in the graph. Considering the feature as a signal, the convolution is defined in the spectral domain using a Laplacian matrix, and the Chebyshev polynomial is used to reduce the computational complexity of the matrix decomposition. The second filter is a low-pass filter for the Chebyshev polynomial, which reduce the computation. Finally, the 2D data is detected using CNN to optimizes the segmented result. Experiments were performed on the ShapeNet dataset to demonstrate the efficiency of the method.","PeriodicalId":243033,"journal":{"name":"2020 5th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129299869","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 : 2020-11-18DOI: 10.1109/ICIIBMS50712.2020.9336414
A. Lavrova, E. Postnikov
We have used a novel approach to describe pulse flux in the brain lymphatic vessel applying a mathematical analogy to the Barenblatt-Pattle solution of the non-linear diffusion equation that describes gas spreading in porous medium. Such simple mathematical model simulates adequately pulse motion leading to the initial increase of the transversal vessel deformation with following slow lateral distribution. Such approach allows to explain the high velocity motion of the compounds in the brain lymnhatic system.
{"title":"Modelling of the flux in the brain lymphatic vessels using the Barenblatt-Pattle solution","authors":"A. Lavrova, E. Postnikov","doi":"10.1109/ICIIBMS50712.2020.9336414","DOIUrl":"https://doi.org/10.1109/ICIIBMS50712.2020.9336414","url":null,"abstract":"We have used a novel approach to describe pulse flux in the brain lymphatic vessel applying a mathematical analogy to the Barenblatt-Pattle solution of the non-linear diffusion equation that describes gas spreading in porous medium. Such simple mathematical model simulates adequately pulse motion leading to the initial increase of the transversal vessel deformation with following slow lateral distribution. Such approach allows to explain the high velocity motion of the compounds in the brain lymnhatic system.","PeriodicalId":243033,"journal":{"name":"2020 5th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130427480","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 : 2020-11-18DOI: 10.1109/ICIIBMS50712.2020.9336389
Y. Liu, Jinpeng Ren, Chunya Wang, Xinxin Yuan
Deep learning, artificial intelligence and other cutting-edge technologies are constantly being integrated into people's daily lives. Even small vending machines that can be seen everywhere in life have begun to use facial payment methods. The detection and recognition of face images is no longer unattainable, but the analysis and recognition of face information and characteristics (gender, age, race, etc.) is still not fully mature, in order to improve the accuracy of face information recognition In this paper, a face information recognition model is designed. The feature extraction part uses an eight-layer convolutional neural network, and then uses two fully connected modules as the classifiers for gender recognition and age recognition. The experimental results show that the model uses the advantages of the convolutional neural network so that the model can predict the gender and age of the face more accurately.
{"title":"Research And Implementation Of Facialnet Based On Convolutional Neural Network","authors":"Y. Liu, Jinpeng Ren, Chunya Wang, Xinxin Yuan","doi":"10.1109/ICIIBMS50712.2020.9336389","DOIUrl":"https://doi.org/10.1109/ICIIBMS50712.2020.9336389","url":null,"abstract":"Deep learning, artificial intelligence and other cutting-edge technologies are constantly being integrated into people's daily lives. Even small vending machines that can be seen everywhere in life have begun to use facial payment methods. The detection and recognition of face images is no longer unattainable, but the analysis and recognition of face information and characteristics (gender, age, race, etc.) is still not fully mature, in order to improve the accuracy of face information recognition In this paper, a face information recognition model is designed. The feature extraction part uses an eight-layer convolutional neural network, and then uses two fully connected modules as the classifiers for gender recognition and age recognition. The experimental results show that the model uses the advantages of the convolutional neural network so that the model can predict the gender and age of the face more accurately.","PeriodicalId":243033,"journal":{"name":"2020 5th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132096878","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 : 2020-11-18DOI: 10.1109/ICIIBMS50712.2020.9336200
Hanqing Zhao, Hidetaka Nambo
Machine learning and image processing are widely used in various fields, such as, robot vision, object recognition, and automated driving technology. This paper is, the use of cameras to acquire image information, and machine learning methods to analyze and identify the edge boundaries of clothes in the Difficulty in putting on and taking off clothes in the elderly and some patients, especially when going to the toilet. Then, clothing boundary recognition and assistive robots can ameliorate these problems by distinguishing where the edges of the pants are so that they can Determine the position of the waistband of the trousers, and perform dressing and undressing assistance work. The ultimate goal of this research is to be able to apply it to the system control of the clothing donning and doffing device.
{"title":"A System of Clothing Boundary Recognition Using Machine Learning For Life Support Robots","authors":"Hanqing Zhao, Hidetaka Nambo","doi":"10.1109/ICIIBMS50712.2020.9336200","DOIUrl":"https://doi.org/10.1109/ICIIBMS50712.2020.9336200","url":null,"abstract":"Machine learning and image processing are widely used in various fields, such as, robot vision, object recognition, and automated driving technology. This paper is, the use of cameras to acquire image information, and machine learning methods to analyze and identify the edge boundaries of clothes in the Difficulty in putting on and taking off clothes in the elderly and some patients, especially when going to the toilet. Then, clothing boundary recognition and assistive robots can ameliorate these problems by distinguishing where the edges of the pants are so that they can Determine the position of the waistband of the trousers, and perform dressing and undressing assistance work. The ultimate goal of this research is to be able to apply it to the system control of the clothing donning and doffing device.","PeriodicalId":243033,"journal":{"name":"2020 5th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122370366","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 : 2020-11-18DOI: 10.1109/ICIIBMS50712.2020.9336392
Caleb Jones Shibu, Sujesh Sreedharan, A. Km, C. Kesavadas
In this paper, we have assessed and investigated the classification accuracies of three different techniques to classify functional near-infrared spectroscopy (fNIRS) signals. Signals were extracted from the motor cortex of the brain using a continuous wave multichannel imaging system. The acquired signals were filtered for noise and converted to oxygenated- and deoxygenated- hemoglobin using modified Beer-Lambert law. From the hemodynamic responses statistical features like slope, mean, skewness, kurtosis, peak and variance were extracted, this was trained on a machine learning classifier giving a classification accuracy of 60.66% for support vector machine (SVM) and 57.22% for k nearest neighbor (KNN), likewise from the hemodynamic response we extracted principal component analysis (PCA) vectors and independent component analysis (ICA) vectors, this along with statistical features were trained on the same SVM and KNN classifier yielding a classification accuracy of 71.4% and 71.8% respectively. Instead of handpicking or handcrafting features, if we let deep learning models, in our case, convolutional neural network (CNN) and long short-term memory (LSTM), choose the features and classify them, they gave a jump of 25% accuracy to over 95% for both architectures.
{"title":"Comparison of classification performance of handpicked, handcrafted and automated-features for fNIRS-BCI system","authors":"Caleb Jones Shibu, Sujesh Sreedharan, A. Km, C. Kesavadas","doi":"10.1109/ICIIBMS50712.2020.9336392","DOIUrl":"https://doi.org/10.1109/ICIIBMS50712.2020.9336392","url":null,"abstract":"In this paper, we have assessed and investigated the classification accuracies of three different techniques to classify functional near-infrared spectroscopy (fNIRS) signals. Signals were extracted from the motor cortex of the brain using a continuous wave multichannel imaging system. The acquired signals were filtered for noise and converted to oxygenated- and deoxygenated- hemoglobin using modified Beer-Lambert law. From the hemodynamic responses statistical features like slope, mean, skewness, kurtosis, peak and variance were extracted, this was trained on a machine learning classifier giving a classification accuracy of 60.66% for support vector machine (SVM) and 57.22% for k nearest neighbor (KNN), likewise from the hemodynamic response we extracted principal component analysis (PCA) vectors and independent component analysis (ICA) vectors, this along with statistical features were trained on the same SVM and KNN classifier yielding a classification accuracy of 71.4% and 71.8% respectively. Instead of handpicking or handcrafting features, if we let deep learning models, in our case, convolutional neural network (CNN) and long short-term memory (LSTM), choose the features and classify them, they gave a jump of 25% accuracy to over 95% for both architectures.","PeriodicalId":243033,"journal":{"name":"2020 5th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128529153","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 : 2020-11-18DOI: 10.1109/ICIIBMS50712.2020.9336201
Ming Li, Jian-Kun Lu
A fault diagnosis method based on fractional order PI D control LLC converter is proposed. The main content is to apply the fault tree meth-od to the converter and analyze its fault. First set the LLC converter fault that may occur, select the top events, intermediate and bottom events, and then establish LLC converter fault tree, using the circuit principle analysis and MATLAB simulation, and with the LLC converter under normal working condition of output voltage value comparison, finally, LLC, fault analysis, fault tree to determine the middle and bottom events, so as to find out the LLC converter failure. The comparison of output voltage value through LLC converter not only has faster diagnostic speed, but also has low false diagnosis rate and strong robustness, which plays a very important role in ensuring the normal operation of LLC converter.
{"title":"Fault Diagnosis of LLC Converter Controlled by Fractional Order $PI^{lambda}D^{mu}$ under Fault Tree","authors":"Ming Li, Jian-Kun Lu","doi":"10.1109/ICIIBMS50712.2020.9336201","DOIUrl":"https://doi.org/10.1109/ICIIBMS50712.2020.9336201","url":null,"abstract":"A fault diagnosis method based on fractional order PI D control LLC converter is proposed. The main content is to apply the fault tree meth-od to the converter and analyze its fault. First set the LLC converter fault that may occur, select the top events, intermediate and bottom events, and then establish LLC converter fault tree, using the circuit principle analysis and MATLAB simulation, and with the LLC converter under normal working condition of output voltage value comparison, finally, LLC, fault analysis, fault tree to determine the middle and bottom events, so as to find out the LLC converter failure. The comparison of output voltage value through LLC converter not only has faster diagnostic speed, but also has low false diagnosis rate and strong robustness, which plays a very important role in ensuring the normal operation of LLC converter.","PeriodicalId":243033,"journal":{"name":"2020 5th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"88 2-3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132393381","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 : 2020-11-18DOI: 10.1109/ICIIBMS50712.2020.9336425
ZhengCun Jiang, Wenping Jiang
The change of temperature is closely related to people's life, and the drastic change of the next day's temperature will affect people's normal life, so it is very important to accurately predict the next day's temperature. Information fusion technology is a process of automatic analysis and comprehensive processing of multi-source information in order to complete the required decision-making and evaluation tasks. BP neural network is one of the information fusion algorithms, which can predict the data collected by various sensors. Therefore, the data collected by Canberra sensor, such as maximum temperature, minimum temperature, rainfall and maximum wind speed, are processed, and the BP neural network is constructed to predict the maximum and minimum temperature of the next day. The experimental results show that this method can well predict the maximum and minimum temperature of the next day.
{"title":"Forecast of air temperature based on BP neural network","authors":"ZhengCun Jiang, Wenping Jiang","doi":"10.1109/ICIIBMS50712.2020.9336425","DOIUrl":"https://doi.org/10.1109/ICIIBMS50712.2020.9336425","url":null,"abstract":"The change of temperature is closely related to people's life, and the drastic change of the next day's temperature will affect people's normal life, so it is very important to accurately predict the next day's temperature. Information fusion technology is a process of automatic analysis and comprehensive processing of multi-source information in order to complete the required decision-making and evaluation tasks. BP neural network is one of the information fusion algorithms, which can predict the data collected by various sensors. Therefore, the data collected by Canberra sensor, such as maximum temperature, minimum temperature, rainfall and maximum wind speed, are processed, and the BP neural network is constructed to predict the maximum and minimum temperature of the next day. The experimental results show that this method can well predict the maximum and minimum temperature of the next day.","PeriodicalId":243033,"journal":{"name":"2020 5th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133504860","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 : 2020-11-18DOI: 10.1109/ICIIBMS50712.2020.9336397
Hongbo Hao, F. Dai, Dejin Wang
With the development of the smart home, people are not only satisfied to control the home appliances and lights remotely by pressing the button. If people can make full use of voice as the most effective way to communicate information, it will make the smart home more convenient in control. This paper describes the ARM microprocessor, speech recognition chip, voice broadcast module, and NRF24L01 wireless transceiver module. The voice control system of smart home, which is composed of sensor detection and other main modules, is different from the mainstream smart home control products in the market, such as Xiaomi Intelligent Audio. Its input device is portable wearable. When it is used, what you do is only to touch the button to start the recognition mode. Most importantly, it includes the function of voice broadcast so that it can let users achieve simple interaction.
{"title":"A Study on Smart Home Voice Control Terminal","authors":"Hongbo Hao, F. Dai, Dejin Wang","doi":"10.1109/ICIIBMS50712.2020.9336397","DOIUrl":"https://doi.org/10.1109/ICIIBMS50712.2020.9336397","url":null,"abstract":"With the development of the smart home, people are not only satisfied to control the home appliances and lights remotely by pressing the button. If people can make full use of voice as the most effective way to communicate information, it will make the smart home more convenient in control. This paper describes the ARM microprocessor, speech recognition chip, voice broadcast module, and NRF24L01 wireless transceiver module. The voice control system of smart home, which is composed of sensor detection and other main modules, is different from the mainstream smart home control products in the market, such as Xiaomi Intelligent Audio. Its input device is portable wearable. When it is used, what you do is only to touch the button to start the recognition mode. Most importantly, it includes the function of voice broadcast so that it can let users achieve simple interaction.","PeriodicalId":243033,"journal":{"name":"2020 5th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125284626","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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