Pub Date : 2017-11-01DOI: 10.1109/ICSAI.2017.8248418
Yonghua Wang, Jian Yang, Pin Wan, Y. Xiao, Weisen Zeng
Spectrum sensing is the primary task of cognitive radio technology. How to monitor the radio signal in space real-timely and efficiently is a hotspot and difficult point of the research about spectrum sensing technology. In order to improve the experimental teaching effect and students' understanding of cognitive radio technology, this paper builds a spectrum sensing platform based on LabVIEW and USRP, which is useful to train students' practical ability and innovation ability of spectrum sensing technology. By using energy detection method, we carry out dynamic spectrum sensing experiment and spectral scanning experiments. Dynamic spectrum sensing experiments show the spectrum usage in the 95MHz-105MHz and 950MHz-960MHz frequency bands. Spectrum scanning experiments are conducted on the 70MHz-170MHz and 900MHz-1GHz frequency bands and show the spectrum usage of that two bands.
{"title":"Design of spectrum sensing experiments based on LabVIEW and USRP","authors":"Yonghua Wang, Jian Yang, Pin Wan, Y. Xiao, Weisen Zeng","doi":"10.1109/ICSAI.2017.8248418","DOIUrl":"https://doi.org/10.1109/ICSAI.2017.8248418","url":null,"abstract":"Spectrum sensing is the primary task of cognitive radio technology. How to monitor the radio signal in space real-timely and efficiently is a hotspot and difficult point of the research about spectrum sensing technology. In order to improve the experimental teaching effect and students' understanding of cognitive radio technology, this paper builds a spectrum sensing platform based on LabVIEW and USRP, which is useful to train students' practical ability and innovation ability of spectrum sensing technology. By using energy detection method, we carry out dynamic spectrum sensing experiment and spectral scanning experiments. Dynamic spectrum sensing experiments show the spectrum usage in the 95MHz-105MHz and 950MHz-960MHz frequency bands. Spectrum scanning experiments are conducted on the 70MHz-170MHz and 900MHz-1GHz frequency bands and show the spectrum usage of that two bands.","PeriodicalId":285726,"journal":{"name":"2017 4th International Conference on Systems and Informatics (ICSAI)","volume":"274 1-2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116849006","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 : 2017-11-01DOI: 10.1109/ICSAI.2017.8248445
Yuzhen Cao, Yixiang Guo, Hui Yu, Xuyao Yu
Traditional method of epileptic seizure detection could not avoid the process of manually selecting the features. Recently, the development of deep learning technology has provided a new direction. This paper introduces a new method of the seizure detection based on EEG signal using the short time Fourier transform(STFT) and convolution neural network(CNN). And the paper verifies the feasibility of this method through the actual research data and parameter setting. Afterwards, the method of single threshold is adopted to combine the multi-channel results. Then, the comparison with the classical method using the support vector machine(SVM) has been done, which shows that the approach presented in this paper is better. And the experimental result on single channel is that the average accuracy is 86%. In addition, the method of the multi-channel could increase the average accuracy to 90% and the average true positive rate(TPR) to 96.5% while decrease the average false positive rate(FPR) to 7%. All of those indexes reveal the high performance and stability of the approach for the epileptic seizure detection.
{"title":"Epileptic seizure auto-detection using deep learning method","authors":"Yuzhen Cao, Yixiang Guo, Hui Yu, Xuyao Yu","doi":"10.1109/ICSAI.2017.8248445","DOIUrl":"https://doi.org/10.1109/ICSAI.2017.8248445","url":null,"abstract":"Traditional method of epileptic seizure detection could not avoid the process of manually selecting the features. Recently, the development of deep learning technology has provided a new direction. This paper introduces a new method of the seizure detection based on EEG signal using the short time Fourier transform(STFT) and convolution neural network(CNN). And the paper verifies the feasibility of this method through the actual research data and parameter setting. Afterwards, the method of single threshold is adopted to combine the multi-channel results. Then, the comparison with the classical method using the support vector machine(SVM) has been done, which shows that the approach presented in this paper is better. And the experimental result on single channel is that the average accuracy is 86%. In addition, the method of the multi-channel could increase the average accuracy to 90% and the average true positive rate(TPR) to 96.5% while decrease the average false positive rate(FPR) to 7%. All of those indexes reveal the high performance and stability of the approach for the epileptic seizure detection.","PeriodicalId":285726,"journal":{"name":"2017 4th International Conference on Systems and Informatics (ICSAI)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117023246","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 : 2017-11-01DOI: 10.1109/ICSAI.2017.8248527
R. Marik, Matej Cibula
Pattern and anomaly detection in time series or sequences is an active field of research. Most of the work in the field is dedicated to multivariate sequences of real numbers or they only focus on pattern recognition in simple sequences. This work introduces hidden Markov models and applies new methods which are suitable for the processing of binary or categorical multivariate sequences in which individual elements are considered to be expressions of the same system. Subsequently, proposed methods are applied in the analysis of the dataset of viziers and their titles from the fourth to the sixth dynasties of ancient Egypt.
{"title":"Multi-attribute sequence interpretation using HMM","authors":"R. Marik, Matej Cibula","doi":"10.1109/ICSAI.2017.8248527","DOIUrl":"https://doi.org/10.1109/ICSAI.2017.8248527","url":null,"abstract":"Pattern and anomaly detection in time series or sequences is an active field of research. Most of the work in the field is dedicated to multivariate sequences of real numbers or they only focus on pattern recognition in simple sequences. This work introduces hidden Markov models and applies new methods which are suitable for the processing of binary or categorical multivariate sequences in which individual elements are considered to be expressions of the same system. Subsequently, proposed methods are applied in the analysis of the dataset of viziers and their titles from the fourth to the sixth dynasties of ancient Egypt.","PeriodicalId":285726,"journal":{"name":"2017 4th International Conference on Systems and Informatics (ICSAI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128423900","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 : 2017-11-01DOI: 10.1109/ICSAI.2017.8248531
Ruocheng Wang, Yanhua Liu
With the growth of micro-blog users, the number of micro-blog text is also showing an explosive growth trend. Faced with such a large amount of text data, how to effectively retrieve useful information is very important for micro-blog users. This paper proposes a method combining traditional TF-IDF computing and LDA topic model. First, we compute by TF-IDF to find micro-blog articles about word frequency similarity. Then we use the LDA topic model approach to filter out micro-blog articles with similar themes. Experimental results show that using the integrated search method, users can retrieve more suitable user's actual needs micro-blog articles.
{"title":"The method of micro-blog article retrieval based on text similarity","authors":"Ruocheng Wang, Yanhua Liu","doi":"10.1109/ICSAI.2017.8248531","DOIUrl":"https://doi.org/10.1109/ICSAI.2017.8248531","url":null,"abstract":"With the growth of micro-blog users, the number of micro-blog text is also showing an explosive growth trend. Faced with such a large amount of text data, how to effectively retrieve useful information is very important for micro-blog users. This paper proposes a method combining traditional TF-IDF computing and LDA topic model. First, we compute by TF-IDF to find micro-blog articles about word frequency similarity. Then we use the LDA topic model approach to filter out micro-blog articles with similar themes. Experimental results show that using the integrated search method, users can retrieve more suitable user's actual needs micro-blog articles.","PeriodicalId":285726,"journal":{"name":"2017 4th International Conference on Systems and Informatics (ICSAI)","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124675294","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 : 2017-11-01DOI: 10.1109/ICSAI.2017.8248350
Marius Geitle, R. Olsson
In this paper, we automatically improve the competitive differential evolution algorithm through automatic programming. The improved algorithm outperforms the original for over 73% of the 50-dimensional CEC 2014 problems and is worse for less than 17% of the problems when comparing using a Wilcoxon rank-sum test. The evolutionary automatic programming system ADATE that is used in this paper systematically searches for better programs by evaluating millions of candidate programs. The candidates are graded by first evaluating on a small training set consisting of five synthetic optimization problems, with well performing candidates being evaluated more extensively on a larger and more computationally expensive validation set with 100 problems. Thus, we use one evolutionary algorithm to rewrite the source code of another evolutionary algorithm. The results show that the techniques introduced in this paper are capable of improving the heuristics of contemporary numerical optimization algorithms.
{"title":"Improving competitive differential evolution using automatic programming","authors":"Marius Geitle, R. Olsson","doi":"10.1109/ICSAI.2017.8248350","DOIUrl":"https://doi.org/10.1109/ICSAI.2017.8248350","url":null,"abstract":"In this paper, we automatically improve the competitive differential evolution algorithm through automatic programming. The improved algorithm outperforms the original for over 73% of the 50-dimensional CEC 2014 problems and is worse for less than 17% of the problems when comparing using a Wilcoxon rank-sum test. The evolutionary automatic programming system ADATE that is used in this paper systematically searches for better programs by evaluating millions of candidate programs. The candidates are graded by first evaluating on a small training set consisting of five synthetic optimization problems, with well performing candidates being evaluated more extensively on a larger and more computationally expensive validation set with 100 problems. Thus, we use one evolutionary algorithm to rewrite the source code of another evolutionary algorithm. The results show that the techniques introduced in this paper are capable of improving the heuristics of contemporary numerical optimization algorithms.","PeriodicalId":285726,"journal":{"name":"2017 4th International Conference on Systems and Informatics (ICSAI)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129474244","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}
A large number of batteries and gas generators in a virtual power plant (VPP) can achieve high profits by providing high-quality frequency regulation services. Therefore, the virtual power plant will participate in the regulation market for those batteries and gas generators. However, uncertainty of intermittent energy in VPP will damage the performance score in settlement and decrease the benefit of VPP. In order to give full play to the high-quality regulation services of VPP, this paper introduces a regulation mechanism for VPP, and we propose a calculation method of VPP's frequency performance. To reduce the influence of fluctuation of intermittent power and load on VPP's regulation performance, an improved VPP regulation control strategy is proposed. Finally, the results show the effectiveness of the control strategy.
{"title":"A regulation strategy for virtual power plant","authors":"Jianlin Yang, Yichao Huang, Haiqun Wang, Yuan Ji, Jingbang Li, Ciwei Gao","doi":"10.1109/ICSAI.2017.8248321","DOIUrl":"https://doi.org/10.1109/ICSAI.2017.8248321","url":null,"abstract":"A large number of batteries and gas generators in a virtual power plant (VPP) can achieve high profits by providing high-quality frequency regulation services. Therefore, the virtual power plant will participate in the regulation market for those batteries and gas generators. However, uncertainty of intermittent energy in VPP will damage the performance score in settlement and decrease the benefit of VPP. In order to give full play to the high-quality regulation services of VPP, this paper introduces a regulation mechanism for VPP, and we propose a calculation method of VPP's frequency performance. To reduce the influence of fluctuation of intermittent power and load on VPP's regulation performance, an improved VPP regulation control strategy is proposed. Finally, the results show the effectiveness of the control strategy.","PeriodicalId":285726,"journal":{"name":"2017 4th International Conference on Systems and Informatics (ICSAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126664035","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 : 2017-11-01DOI: 10.1109/ICSAI.2017.8248340
Guilian Chen, Hao Guo, Jubai An
SAR has become one of the important means of oil spill monitoring. However, oil spills and lookalikes are characterized by dark spots on SAR images. They have similar or identical backscattering coefficients and gray values, which are easy to produce confusion. Aiming at this problem, this paper proposes a deep learning model-Deep Belief Network (DBN), which uses DBN to distinguish oil spills, lookalikes and water. In the experiment, 900 images were collected from the three SAR oil spill images to form a small sample space dataset. The two kinds of texture features such as Tamura and Gray Level-Gradient Co-occurrence Matrix are extracted, and the feature vector with good distinguishing features is selected as the input data of the model. Finally, the classification results are compared with the traditional machine learning method (BP, SVM). The experimental results shows that the DBN model proposed in this paper is superior to these classifiers in classification accuracy.
{"title":"Research on SAR oil spill image classification based on DBN in small sample space","authors":"Guilian Chen, Hao Guo, Jubai An","doi":"10.1109/ICSAI.2017.8248340","DOIUrl":"https://doi.org/10.1109/ICSAI.2017.8248340","url":null,"abstract":"SAR has become one of the important means of oil spill monitoring. However, oil spills and lookalikes are characterized by dark spots on SAR images. They have similar or identical backscattering coefficients and gray values, which are easy to produce confusion. Aiming at this problem, this paper proposes a deep learning model-Deep Belief Network (DBN), which uses DBN to distinguish oil spills, lookalikes and water. In the experiment, 900 images were collected from the three SAR oil spill images to form a small sample space dataset. The two kinds of texture features such as Tamura and Gray Level-Gradient Co-occurrence Matrix are extracted, and the feature vector with good distinguishing features is selected as the input data of the model. Finally, the classification results are compared with the traditional machine learning method (BP, SVM). The experimental results shows that the DBN model proposed in this paper is superior to these classifiers in classification accuracy.","PeriodicalId":285726,"journal":{"name":"2017 4th International Conference on Systems and Informatics (ICSAI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126807661","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 : 2017-11-01DOI: 10.1109/ICSAI.2017.8248396
Yutian Wang, Simeng Ma, Hui Wang, Jingling Wang
Nowadays, lots of music software in the market are used broadly by people in daily life. However there are still something to be improved. First of all, considering the large quantity of music, it is difficult for users to search for a song with full information. Second, due to the easy-interference of the mobile network, it may cause a bad experience because of the delay when playing music. In this paper, we designed a streaming media player based on Android to improve these deficiencies. On the one hand, two transfer methods were designed and can be selected by users to fit the network situation. On the other hand, two fuzzy searching methods was compared in this paper, and the Levenshtein distance was finally adopted to retrieve a song with partial information. Furthermore the system was programmed with the Java language, Android system framework and SSH (Struts + Spring + Hibernate) framework. Some necessary modules was contained in the system such as user center, logging in, registering and others. The test results demonstrated that the player performances well in various network situations. The Levenshtein algorithm can find out all of songs which matching the input information with less time and the result of fuzzy searching can be sorted by the correlation value between the search results and the input information.
如今，市场上大量的音乐软件被人们在日常生活中广泛使用。然而，仍有一些需要改进的地方。首先，考虑到音乐的数量很大，用户很难搜索到一首信息完整的歌曲。其次，由于移动网络的容易干扰，在播放音乐时可能会因为延迟而造成不好的体验。本文针对这些不足，设计了一款基于Android的流媒体播放器。一方面，设计了两种传输方式，用户可以根据网络情况进行选择。另一方面，本文比较了两种模糊搜索方法，最终采用Levenshtein距离来检索具有部分信息的歌曲。系统采用Java语言、Android系统框架和SSH (Struts + Spring + Hibernate)框架进行编程。系统中包含了用户中心、登录、注册等必备模块。测试结果表明，该播放器在各种网络环境下都表现良好。Levenshtein算法可以在较短的时间内找到与输入信息匹配的所有歌曲，模糊搜索的结果可以根据搜索结果与输入信息的关联值进行排序。
{"title":"Design of a streaming media player based on fuzzy searching","authors":"Yutian Wang, Simeng Ma, Hui Wang, Jingling Wang","doi":"10.1109/ICSAI.2017.8248396","DOIUrl":"https://doi.org/10.1109/ICSAI.2017.8248396","url":null,"abstract":"Nowadays, lots of music software in the market are used broadly by people in daily life. However there are still something to be improved. First of all, considering the large quantity of music, it is difficult for users to search for a song with full information. Second, due to the easy-interference of the mobile network, it may cause a bad experience because of the delay when playing music. In this paper, we designed a streaming media player based on Android to improve these deficiencies. On the one hand, two transfer methods were designed and can be selected by users to fit the network situation. On the other hand, two fuzzy searching methods was compared in this paper, and the Levenshtein distance was finally adopted to retrieve a song with partial information. Furthermore the system was programmed with the Java language, Android system framework and SSH (Struts + Spring + Hibernate) framework. Some necessary modules was contained in the system such as user center, logging in, registering and others. The test results demonstrated that the player performances well in various network situations. The Levenshtein algorithm can find out all of songs which matching the input information with less time and the result of fuzzy searching can be sorted by the correlation value between the search results and the input information.","PeriodicalId":285726,"journal":{"name":"2017 4th International Conference on Systems and Informatics (ICSAI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129236815","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 : 2017-11-01DOI: 10.1109/ICSAI.2017.8248541
L. Fu, Hua Fu, Fei You
This paper defines the base of soft topology, studies the properties of base. We give the definition of the product over the soft topological space, and discuss the relative properties of soft topological product space, and finally generate these results to the soft rough topological space.
{"title":"Soft topological product space","authors":"L. Fu, Hua Fu, Fei You","doi":"10.1109/ICSAI.2017.8248541","DOIUrl":"https://doi.org/10.1109/ICSAI.2017.8248541","url":null,"abstract":"This paper defines the base of soft topology, studies the properties of base. We give the definition of the product over the soft topological space, and discuss the relative properties of soft topological product space, and finally generate these results to the soft rough topological space.","PeriodicalId":285726,"journal":{"name":"2017 4th International Conference on Systems and Informatics (ICSAI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130653801","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 : 2017-11-01DOI: 10.1109/ICSAI.2017.8248561
Baolong Cui, Lianglong Da, Wuhong Guo
Adaptive observation is an efficacious idea by operating additional observation in sensitive area to improve the quality of model forecast. The ETKF (Ensemble Transform Kalman Filter) method has been proved an effective method for identifying sensitive area and widely applied in atmosphere but barely in ocean field. In this paper, an adaptive observation system based on ETKF is applied in East China Sea. Simulations are operated based on the ROMS model data of particular area. Several ETKF method parameters are selected and optimized. Sensitive areas of separate ocean environment parameters are identified through distinct adaptive observation types aimed for different areas. There are a number of enlightening conclusions gained from the analysis of simulations.
{"title":"Research on identifying ocean sensitive area in adaptive observation based on ETKF","authors":"Baolong Cui, Lianglong Da, Wuhong Guo","doi":"10.1109/ICSAI.2017.8248561","DOIUrl":"https://doi.org/10.1109/ICSAI.2017.8248561","url":null,"abstract":"Adaptive observation is an efficacious idea by operating additional observation in sensitive area to improve the quality of model forecast. The ETKF (Ensemble Transform Kalman Filter) method has been proved an effective method for identifying sensitive area and widely applied in atmosphere but barely in ocean field. In this paper, an adaptive observation system based on ETKF is applied in East China Sea. Simulations are operated based on the ROMS model data of particular area. Several ETKF method parameters are selected and optimized. Sensitive areas of separate ocean environment parameters are identified through distinct adaptive observation types aimed for different areas. There are a number of enlightening conclusions gained from the analysis of simulations.","PeriodicalId":285726,"journal":{"name":"2017 4th International Conference on Systems and Informatics (ICSAI)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123932312","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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