Pub Date : 2018-06-01DOI: 10.1109/ICIVC.2018.8492755
Pei An, Yanchao Liu, Wei Zhang, Z. Jin
With the development of lunar exploration technology, vision-based localization and navigation technology has become a research focus in the field of lunar rover. This paper proposes an image-based method for localization and mapping with a lunar rover. The motion of the camera represents the movement of the lunar rover. Based on the images acquired by the camera, the relative pose of the camera and 3D landmarks are obtained using the multi-view geometry and the bundle adjustment optimization methods. The prior knowledge of the lunar rover movement is not required. In addition, this paper also proposes a grid-based feature extraction method to solve the problem of uneven feature extraction and mis-matching. The algorithm in this paper has been tested in real time in a large image dataset. Finally, the error analysis of the estimated pose obtained from the experiment and the real trajectory proves the excellent performance of the algorithm.
{"title":"Vision-Based Simultaneous Localization and Mapping on Lunar Rover","authors":"Pei An, Yanchao Liu, Wei Zhang, Z. Jin","doi":"10.1109/ICIVC.2018.8492755","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492755","url":null,"abstract":"With the development of lunar exploration technology, vision-based localization and navigation technology has become a research focus in the field of lunar rover. This paper proposes an image-based method for localization and mapping with a lunar rover. The motion of the camera represents the movement of the lunar rover. Based on the images acquired by the camera, the relative pose of the camera and 3D landmarks are obtained using the multi-view geometry and the bundle adjustment optimization methods. The prior knowledge of the lunar rover movement is not required. In addition, this paper also proposes a grid-based feature extraction method to solve the problem of uneven feature extraction and mis-matching. The algorithm in this paper has been tested in real time in a large image dataset. Finally, the error analysis of the estimated pose obtained from the experiment and the real trajectory proves the excellent performance of the algorithm.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129077739","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 : 2018-06-01DOI: 10.1109/ICIVC.2018.8492797
Hu Xiaomei, Li Minghang, Wang Chuan, Yang Xu, Wei Chenjun
In order to establish the simulation model of dust evolution and reveal the evolution process of dust, Shanghai University is selected as the simulation area, the method of Kinetic Monte Carlo is used to simulate the dust particles in the virtual campus, OpenGL and C language are used so as to realize the visualization of dust evolution simulation model. The collection data and simulation data are compared at different locations in the campus, and the results prove the validity of dust evolution simulation model. Based on the visualization results of dust evolution simulation, the relationships among wind speed, simulation time, vegetation effect and accumulation of dust particles on the ground or the motion of dust particles in the vertical surface are revealed. Visualization of dust evolution simulation model will provide a valid reference for dust control.
{"title":"Visualization of Dust Evolution Simulation Model in Campus Environment","authors":"Hu Xiaomei, Li Minghang, Wang Chuan, Yang Xu, Wei Chenjun","doi":"10.1109/ICIVC.2018.8492797","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492797","url":null,"abstract":"In order to establish the simulation model of dust evolution and reveal the evolution process of dust, Shanghai University is selected as the simulation area, the method of Kinetic Monte Carlo is used to simulate the dust particles in the virtual campus, OpenGL and C language are used so as to realize the visualization of dust evolution simulation model. The collection data and simulation data are compared at different locations in the campus, and the results prove the validity of dust evolution simulation model. Based on the visualization results of dust evolution simulation, the relationships among wind speed, simulation time, vegetation effect and accumulation of dust particles on the ground or the motion of dust particles in the vertical surface are revealed. Visualization of dust evolution simulation model will provide a valid reference for dust control.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132454308","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 : 2018-06-01DOI: 10.1109/ICIVC.2018.8492884
Mandan Zhao, X. Hao, Gaochang Wu
This paper addresses the problem of disparity map accurate estimation in the cross-scale reference-based light field, which consists several low-quality images arranged around one central high-resolution (HR) image. In the framework, we use a HR image-guidance CNN (HRIG-CNN) for estimating the disparity map in the HR level. Specifically, we first calculate the coarse disparity map using our cross-pattern strategy, which can blend the multiple disparity maps. And then, we refine this coarse disparity map using HRIG-CNN for obtaining high-quality disparity map, which contains detail information and preserve edge information. With the HR image guidance, our HRIG-CNN achieves state-of-the-art for obtaining disparity map in such hybrid light field condition. In the end, we provide both quantitative and qualitative evaluations on different methods, and demonstrate the high performance and robustness of the proposed framework compared with the state-of-the-arts algorithms.
{"title":"The Accurate Estimation of Disparity Maps from Cross-Scale Reference-Based Light Field","authors":"Mandan Zhao, X. Hao, Gaochang Wu","doi":"10.1109/ICIVC.2018.8492884","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492884","url":null,"abstract":"This paper addresses the problem of disparity map accurate estimation in the cross-scale reference-based light field, which consists several low-quality images arranged around one central high-resolution (HR) image. In the framework, we use a HR image-guidance CNN (HRIG-CNN) for estimating the disparity map in the HR level. Specifically, we first calculate the coarse disparity map using our cross-pattern strategy, which can blend the multiple disparity maps. And then, we refine this coarse disparity map using HRIG-CNN for obtaining high-quality disparity map, which contains detail information and preserve edge information. With the HR image guidance, our HRIG-CNN achieves state-of-the-art for obtaining disparity map in such hybrid light field condition. In the end, we provide both quantitative and qualitative evaluations on different methods, and demonstrate the high performance and robustness of the proposed framework compared with the state-of-the-arts algorithms.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132581223","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 : 2018-06-01DOI: 10.1109/ICIVC.2018.8492725
Abubakar Siddique, Bin Xiao, Weisheng Li, Qamar Nawaz, Isma Hamid
In this work, multi-focus image fusion method has been proposed by using color-principal component analysis (C-PCA). Proposed method consists of different phases. In the first phase, both source images have been converted into three RGB color channels. In the next phase, for each channel, covariance's has been calculated for both images. Special weights have been calculated to generate intermediate images. In the next phase, Convolution has been used with Gaussian blur to make image smooth. Zero-crossing based second order-derivative has been incorporated to calculate edges. In the last phase, images have been decomposed into blocks. Salient features information by using Laplacian of Gaussian and Spatial Frequency of each block have been used to get the fused image. Experimental results show that the proposed method performs well as compare to existing methods by using quality matrices.
{"title":"Multi-Focus Image Fusion Using Block-Wise Color-Principal Component Analysis","authors":"Abubakar Siddique, Bin Xiao, Weisheng Li, Qamar Nawaz, Isma Hamid","doi":"10.1109/ICIVC.2018.8492725","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492725","url":null,"abstract":"In this work, multi-focus image fusion method has been proposed by using color-principal component analysis (C-PCA). Proposed method consists of different phases. In the first phase, both source images have been converted into three RGB color channels. In the next phase, for each channel, covariance's has been calculated for both images. Special weights have been calculated to generate intermediate images. In the next phase, Convolution has been used with Gaussian blur to make image smooth. Zero-crossing based second order-derivative has been incorporated to calculate edges. In the last phase, images have been decomposed into blocks. Salient features information by using Laplacian of Gaussian and Spatial Frequency of each block have been used to get the fused image. Experimental results show that the proposed method performs well as compare to existing methods by using quality matrices.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"231 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132828048","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 : 2018-06-01DOI: 10.1109/ICIVC.2018.8492772
Yiwei Liu, Peirui Bai, Chang Li, Yue Zhao
Level set models are widely used in the image segmentation field. However, the sensitivity of the initial contours and the manual adjustment of the controlling parameters have limited the segmentation performance. To effectively solve this problem, a novel level set model utilizing both intensity and spatial information is proposed in this paper. Firstly, the fuzzy connectedness (FC) algorithm is applied to obtain the appropriate initial contours, and as a result the complexity and computation cost of building initial contours is reduced. Secondly, based on the morphological characteristics of the initial contours and the parameters of fuzzy connectedness, several equations are proposed to automatically estimate the controlling parameters of the level set evolution and avoid human intervention. Finally, the region-scalable fitting (RSF) model is adopted to evolve and obtain the final robust segmentation results. The efficiency and accuracy of the model proposed in this paper is verified by comparing the three quantitative indexes of time, Dice similarity coefficient (DSC) and peak signal to noise ratio (PSNR) with four different initialized level set models.
{"title":"A Novel Level Set Model Originated from Fuzzy Connectedness Guided Initial Contours","authors":"Yiwei Liu, Peirui Bai, Chang Li, Yue Zhao","doi":"10.1109/ICIVC.2018.8492772","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492772","url":null,"abstract":"Level set models are widely used in the image segmentation field. However, the sensitivity of the initial contours and the manual adjustment of the controlling parameters have limited the segmentation performance. To effectively solve this problem, a novel level set model utilizing both intensity and spatial information is proposed in this paper. Firstly, the fuzzy connectedness (FC) algorithm is applied to obtain the appropriate initial contours, and as a result the complexity and computation cost of building initial contours is reduced. Secondly, based on the morphological characteristics of the initial contours and the parameters of fuzzy connectedness, several equations are proposed to automatically estimate the controlling parameters of the level set evolution and avoid human intervention. Finally, the region-scalable fitting (RSF) model is adopted to evolve and obtain the final robust segmentation results. The efficiency and accuracy of the model proposed in this paper is verified by comparing the three quantitative indexes of time, Dice similarity coefficient (DSC) and peak signal to noise ratio (PSNR) with four different initialized level set models.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133130180","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 : 2018-06-01DOI: 10.1109/ICIVC.2018.8492846
Xianguang Lu, Xuehui Du, Wenjuan Wang
Intrusion detection system is an effective defense tool for finding security events. However, it will produce a large number of false positive alerts, which greatly increases the difficulty of real-time security analysis for the security managers, in actual applications. The periodic alarm produced by the wrong configuration of network devices and services, and the approximately duplicate alarm generated by different IDS for the same attack are important components of false alarm. In this paper, we improved the SNM algorithm and cleaned up the duplicate alarm in the original alarm database, which reduced the scale of the database; On the other hand, we have made statistics on the number of duplicate alarms, so that we can further find periodic alerts and remove false alarms.
{"title":"Network IDS Duplicate Alarm Reduction Using Improved SNM Algorithm","authors":"Xianguang Lu, Xuehui Du, Wenjuan Wang","doi":"10.1109/ICIVC.2018.8492846","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492846","url":null,"abstract":"Intrusion detection system is an effective defense tool for finding security events. However, it will produce a large number of false positive alerts, which greatly increases the difficulty of real-time security analysis for the security managers, in actual applications. The periodic alarm produced by the wrong configuration of network devices and services, and the approximately duplicate alarm generated by different IDS for the same attack are important components of false alarm. In this paper, we improved the SNM algorithm and cleaned up the duplicate alarm in the original alarm database, which reduced the scale of the database; On the other hand, we have made statistics on the number of duplicate alarms, so that we can further find periodic alerts and remove false alarms.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122421971","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 : 2018-06-01DOI: 10.1109/ICIVC.2018.8492863
Xiaofeng Yan, Jie Zhao
Prediction is a common method in data mining. In the prediction method, it can be divided into linear prediction and nonlinear prediction. The multiple linear regression method belongs to the linear regression method, and the neural network algorithm belongs to nonlinear prediction. The neural network algorithm belongs to the computational intelligence algorithm. It depends on the complexity of the system and connects the relations between the internal nodes of the neural network through the weights to process the data information. Based on multiple linear regression and neural network algorithms, this paper proposes a predictive model based on multiple linear regression and neural network, and uses this model to study national economic data. The prediction model proposed in this paper is realized by using the linear prediction result as the input neuron of the neural network. The neural network used in this paper is a radial basis function neural network, hereinafter referred to as RBF neural network.
{"title":"Application of Neural Network in National Economic Forecast","authors":"Xiaofeng Yan, Jie Zhao","doi":"10.1109/ICIVC.2018.8492863","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492863","url":null,"abstract":"Prediction is a common method in data mining. In the prediction method, it can be divided into linear prediction and nonlinear prediction. The multiple linear regression method belongs to the linear regression method, and the neural network algorithm belongs to nonlinear prediction. The neural network algorithm belongs to the computational intelligence algorithm. It depends on the complexity of the system and connects the relations between the internal nodes of the neural network through the weights to process the data information. Based on multiple linear regression and neural network algorithms, this paper proposes a predictive model based on multiple linear regression and neural network, and uses this model to study national economic data. The prediction model proposed in this paper is realized by using the linear prediction result as the input neuron of the neural network. The neural network used in this paper is a radial basis function neural network, hereinafter referred to as RBF neural network.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115872818","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 : 2018-06-01DOI: 10.1109/ICIVC.2018.8492818
Yubing Li, Jinbo Zhang, Peng Gao, Liangcheng Jiang, Ming Chen
Grab Cut algorithm is one of the most popular method in the field of image segmentation. It uses texture information and boundary information of image, and achieves good segmentation results with a small number of user interaction. But there are two significant drawbacks about this algorithm. Firstly, If the background is complex or the background and the object are very similar, the segmentation will not be very good. On the other hand, the relatively slow speed and Complex iterative process of the algorithm are greatly limited its application. In this paper, to develop these aspects, we proposed an improved grab cut algorithm. This algorithm is the combination of grab cut and graph-based image segmentation [1]. After the experiment, the improved algorithm is applied to more complex situation.
{"title":"Grab Cut Image Segmentation Based on Image Region","authors":"Yubing Li, Jinbo Zhang, Peng Gao, Liangcheng Jiang, Ming Chen","doi":"10.1109/ICIVC.2018.8492818","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492818","url":null,"abstract":"Grab Cut algorithm is one of the most popular method in the field of image segmentation. It uses texture information and boundary information of image, and achieves good segmentation results with a small number of user interaction. But there are two significant drawbacks about this algorithm. Firstly, If the background is complex or the background and the object are very similar, the segmentation will not be very good. On the other hand, the relatively slow speed and Complex iterative process of the algorithm are greatly limited its application. In this paper, to develop these aspects, we proposed an improved grab cut algorithm. This algorithm is the combination of grab cut and graph-based image segmentation [1]. After the experiment, the improved algorithm is applied to more complex situation.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117281269","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 : 2018-06-01DOI: 10.1109/ICIVC.2018.8492865
Zhiqiang Hu, Meiqi Hu
There are some shortcomings for the cache sensitivity of the index in main memory database, so a new index structure is proposed. T -tree index is studied individually ever before, so as Hash index. Combined with the analysis of the two index structure, a new index structure called the T-Hash tree is introduced. Through analyzing the times of the T -Hash tree cache sensitive and testing the performance of the query, insert, delete operation, the results show that the T -Hash tree can effectively reduce the times of cache sensitive, and as the amount of the data is large, the query, insert, delete efficiency of the T -Hash tree is higher than the T tree.
{"title":"Design and Implementation of T-Hash Tree in Main Memory Data Base","authors":"Zhiqiang Hu, Meiqi Hu","doi":"10.1109/ICIVC.2018.8492865","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492865","url":null,"abstract":"There are some shortcomings for the cache sensitivity of the index in main memory database, so a new index structure is proposed. T -tree index is studied individually ever before, so as Hash index. Combined with the analysis of the two index structure, a new index structure called the T-Hash tree is introduced. Through analyzing the times of the T -Hash tree cache sensitive and testing the performance of the query, insert, delete operation, the results show that the T -Hash tree can effectively reduce the times of cache sensitive, and as the amount of the data is large, the query, insert, delete efficiency of the T -Hash tree is higher than the T tree.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114142718","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 : 2018-06-01DOI: 10.1109/ICIVC.2018.8492830
Qirong Bo, Jun Feng, P. Li, Zhaohui Lv, Jing Zhang
According to gestalt psychology theory, the human brain merges and simplifies unrelated units by some relations through eyes for subsequent cognition. We introduce a new segmentation framework based on gestalt psychology in this paper. An input image is first divided into visual patches using two gestalt principles, similarity and proximity, by a clustering method, and then the visual patches are grouped to form soft tissues by a classification step using the spatial relationship and texture features. We evaluated the proposed method using TCIA database at both sectional level and volumetric level. The experimental results demonstrated the efficiency and robustness of the presented method and indicated its promising applications in the field of medical image processing.
{"title":"Towards Better Soft-Tissue Segmentation Based on Gestalt Psychology","authors":"Qirong Bo, Jun Feng, P. Li, Zhaohui Lv, Jing Zhang","doi":"10.1109/ICIVC.2018.8492830","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492830","url":null,"abstract":"According to gestalt psychology theory, the human brain merges and simplifies unrelated units by some relations through eyes for subsequent cognition. We introduce a new segmentation framework based on gestalt psychology in this paper. An input image is first divided into visual patches using two gestalt principles, similarity and proximity, by a clustering method, and then the visual patches are grouped to form soft tissues by a classification step using the spatial relationship and texture features. We evaluated the proposed method using TCIA database at both sectional level and volumetric level. The experimental results demonstrated the efficiency and robustness of the presented method and indicated its promising applications in the field of medical image processing.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115294874","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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