Single-sensor multispectral imaging technology has been widely used in computer vision, mechanical diagnosis, cultural history protection and other industries due to its convenience and low cost. Single-sensor multispectral imaging can only generate a single mosaic image, so an efficient method is needed to convert mosaic images into multispectral images. Based on the concept of pseudo-panchromatic image, a 9-band multispectral imaging system is designed in this paper. We directly estimate the pseudo-panchromatic image from the mosaic image and use the correlation between the pseudo panchromatic image and each channel to generate a multispectral image by guided filtering and residual interpolation. The experimental results show that the multispectral images obtained by our method are superior to the other two methods in objective and subjective evaluation.
{"title":"Sparse signal recovery for multispectral demosaicking using pseudo-panchromatic image","authors":"Ronghao Liao, Shifu Zhou, Guangyuan Wu","doi":"10.1117/12.3000913","DOIUrl":"https://doi.org/10.1117/12.3000913","url":null,"abstract":"Single-sensor multispectral imaging technology has been widely used in computer vision, mechanical diagnosis, cultural history protection and other industries due to its convenience and low cost. Single-sensor multispectral imaging can only generate a single mosaic image, so an efficient method is needed to convert mosaic images into multispectral images. Based on the concept of pseudo-panchromatic image, a 9-band multispectral imaging system is designed in this paper. We directly estimate the pseudo-panchromatic image from the mosaic image and use the correlation between the pseudo panchromatic image and each channel to generate a multispectral image by guided filtering and residual interpolation. The experimental results show that the multispectral images obtained by our method are superior to the other two methods in objective and subjective evaluation.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128365637","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}
Guobing Liu, Zhenliang Chen, Limin Xiao, Yixiang Li
Design a three-dimensional digital intelligent patrol system for substation based on digital twin technology, and intelligently patrol the substation equipment to effectively excavate the potential safety hazards of the substation. The physical entity layer uses cameras and sensors to collect the image of substation equipment, parameter data and environmental information, and carries out effective preprocessing of the acquired data; The digital twin virtual model layer calls the relevant data of the physical entity layer, and uses SolidWorks, 3D MAX and Unity3D software to build the digital twin virtual model of the substation; The application layer plans the patrol path and identifies the electronic tag of the equipment according to the substation data sent by the network transmission layer. On this basis, the patrol module applies the Super SAB neural network equipment state perception and prediction method to effectively perceive and predict the status of the substation equipment, and visualizes the patrol results on the human-computer interaction layer. The experimental results show that the system can effectively inspect the substation equipment, present the inspection results through virtual vision, intelligently inspect the substation equipment, and apply it to practical work, which can achieve better intelligent inspection results in the substation.
{"title":"Three-dimensional digital intelligent patrol inspection system for substation based on digital twin technology","authors":"Guobing Liu, Zhenliang Chen, Limin Xiao, Yixiang Li","doi":"10.1117/12.3000823","DOIUrl":"https://doi.org/10.1117/12.3000823","url":null,"abstract":"Design a three-dimensional digital intelligent patrol system for substation based on digital twin technology, and intelligently patrol the substation equipment to effectively excavate the potential safety hazards of the substation. The physical entity layer uses cameras and sensors to collect the image of substation equipment, parameter data and environmental information, and carries out effective preprocessing of the acquired data; The digital twin virtual model layer calls the relevant data of the physical entity layer, and uses SolidWorks, 3D MAX and Unity3D software to build the digital twin virtual model of the substation; The application layer plans the patrol path and identifies the electronic tag of the equipment according to the substation data sent by the network transmission layer. On this basis, the patrol module applies the Super SAB neural network equipment state perception and prediction method to effectively perceive and predict the status of the substation equipment, and visualizes the patrol results on the human-computer interaction layer. The experimental results show that the system can effectively inspect the substation equipment, present the inspection results through virtual vision, intelligently inspect the substation equipment, and apply it to practical work, which can achieve better intelligent inspection results in the substation.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124259874","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}
Achieving automatic sorting of garbage through a mechanical arm depends on accurate recognition and localization of garbage. In this paper, we propose a garbage sorting method based on an adaptive deep neural network. The method addresses the limitations of YOLOv5 object detection algorithm, such as the fixed number of anchor boxes and the inability of the feature fusion network to adjust according to the target scale. Our proposed method introduces an object detection algorithm based on an adaptive deep neural network. We use the adaptive K-means clustering algorithm to automatically determine the initial clustering center and the number of clusters, extract features of multiple scales using the feature extraction backbone network, and automatically adjust the structure and feature fusion times of the adaptive feature fusion network based on the clustering results of adaptive K-means. We test the proposed algorithm and YOLOv5 object detection algorithm on a self-made garbage classification dataset. The experiments demonstrate that our proposed adaptive deep neural network reduces the model parameters of YOLOv5 by 27.03%, improves the detection speed by 18%, and enhances the detection accuracy by 0.7%. Finally, we transplant the adaptive deep neural network to the garbage sorting platform and use it for real-time garbage sorting.
{"title":"A garbage sorting method using an adaptive deep neural network","authors":"Shuo Xu, Kai Cao, Li Wang, Jie Shen","doi":"10.1117/12.3001567","DOIUrl":"https://doi.org/10.1117/12.3001567","url":null,"abstract":"Achieving automatic sorting of garbage through a mechanical arm depends on accurate recognition and localization of garbage. In this paper, we propose a garbage sorting method based on an adaptive deep neural network. The method addresses the limitations of YOLOv5 object detection algorithm, such as the fixed number of anchor boxes and the inability of the feature fusion network to adjust according to the target scale. Our proposed method introduces an object detection algorithm based on an adaptive deep neural network. We use the adaptive K-means clustering algorithm to automatically determine the initial clustering center and the number of clusters, extract features of multiple scales using the feature extraction backbone network, and automatically adjust the structure and feature fusion times of the adaptive feature fusion network based on the clustering results of adaptive K-means. We test the proposed algorithm and YOLOv5 object detection algorithm on a self-made garbage classification dataset. The experiments demonstrate that our proposed adaptive deep neural network reduces the model parameters of YOLOv5 by 27.03%, improves the detection speed by 18%, and enhances the detection accuracy by 0.7%. Finally, we transplant the adaptive deep neural network to the garbage sorting platform and use it for real-time garbage sorting.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":"506 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115888192","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}
The accuracy of tourists traffic prediction plays a critical role in scenic area management. Traditional methods of forecasting tourist attraction traffic rely heavily on static historical data, often ignoring important factors that affect the flow of tourists. This process is usually time-consuming. However, with the emergence of deep technology, it is now possible to use real-time data collection and analysis to design a temporal and spatial representation of data sources. And a deep learning-based tourist flow recognition model combined with dynamic time-bending distance indicators and a temporal feature recognition method with temporal data clustering analysis is designed. The method can use location big data to analyze traffic temporal types and identify traffic spatial distribution features, and the analysis results can help traffic and facility management in scenic areas.
{"title":"Deep learning-based crowd recognition for tourist attractions in different periods","authors":"Xiaoyan Fang","doi":"10.1117/12.3001368","DOIUrl":"https://doi.org/10.1117/12.3001368","url":null,"abstract":"The accuracy of tourists traffic prediction plays a critical role in scenic area management. Traditional methods of forecasting tourist attraction traffic rely heavily on static historical data, often ignoring important factors that affect the flow of tourists. This process is usually time-consuming. However, with the emergence of deep technology, it is now possible to use real-time data collection and analysis to design a temporal and spatial representation of data sources. And a deep learning-based tourist flow recognition model combined with dynamic time-bending distance indicators and a temporal feature recognition method with temporal data clustering analysis is designed. The method can use location big data to analyze traffic temporal types and identify traffic spatial distribution features, and the analysis results can help traffic and facility management in scenic areas.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":"52 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132025864","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}
Due to the characteristics of water and the particles in water, the underwater images will have problems of color deviation, low contrast and uneven brightness. Therefore, a statistical approach based on variance is proposed in this paper to correct color-biased images and then equalize the image brightness using the Gamma algorithm. Secondly, a local adaptive contrast enhancement algorithm improved by contrast code images and a restricted contrast adaptive histogram equalization algorithm are used to improve the contrast of underwater images. Finally, two different contrast enhanced images after color correction are fused by a multi-scale fusion algorithm to obtain high-quality underwater images. The results of the comparison experiments with some existing underwater image enhancement algorithms show that our algorithm can effectively improve the quality of underwater images.
{"title":"Underwater image color correction and adaptive contrast algorithm improvement based on fusion algorithm","authors":"Hengjun Zhu, Tianluo Wang, Lihao Ma","doi":"10.1117/12.3000979","DOIUrl":"https://doi.org/10.1117/12.3000979","url":null,"abstract":"Due to the characteristics of water and the particles in water, the underwater images will have problems of color deviation, low contrast and uneven brightness. Therefore, a statistical approach based on variance is proposed in this paper to correct color-biased images and then equalize the image brightness using the Gamma algorithm. Secondly, a local adaptive contrast enhancement algorithm improved by contrast code images and a restricted contrast adaptive histogram equalization algorithm are used to improve the contrast of underwater images. Finally, two different contrast enhanced images after color correction are fused by a multi-scale fusion algorithm to obtain high-quality underwater images. The results of the comparison experiments with some existing underwater image enhancement algorithms show that our algorithm can effectively improve the quality of underwater images.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123161859","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}
According to the World Health Organization, the current global death toll from road traffic accidents is as high as 1.3 million annually. The main cause of road traffic accidents is poor road conditions, and potholes on roads are the most serious type of road diseases. Therefore, timely detection and treatment of road potholes is very necessary. This paper proposes a method based on the use of YOLOv7 deep learning model to detect potholes on the road. At the same time, CBAM attention mechanism and optimization of loss function are added on the basis of this method. Combined with the idea of transfer learning, the improved YOLOv7 network is trained. The final test results are significantly improved compared with other road potholes detection models. F1 score is 78%, Precision value can reach 85.81%, and mAP value can reach 83.02%.
{"title":"Road pothole detection based on improved YOLOv7","authors":"Jianli Zhang, Jiaofei Lei","doi":"10.1117/12.3000774","DOIUrl":"https://doi.org/10.1117/12.3000774","url":null,"abstract":"According to the World Health Organization, the current global death toll from road traffic accidents is as high as 1.3 million annually. The main cause of road traffic accidents is poor road conditions, and potholes on roads are the most serious type of road diseases. Therefore, timely detection and treatment of road potholes is very necessary. This paper proposes a method based on the use of YOLOv7 deep learning model to detect potholes on the road. At the same time, CBAM attention mechanism and optimization of loss function are added on the basis of this method. Combined with the idea of transfer learning, the improved YOLOv7 network is trained. The final test results are significantly improved compared with other road potholes detection models. F1 score is 78%, Precision value can reach 85.81%, and mAP value can reach 83.02%.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":"409 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116333135","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}
To address the problem of slow inference in the original OpenPose pose estimation model and lower the computing power of the model, this paper first uses MobilenetV3 as backbone to make a lightweight improvement for OpenPose's network, followed by using label fusion correction to further improve the accuracy of the model. These steps make a real-time pose recognition system built on embedded devices on robots possible. The performance of the improved model is verified on the COCO dataset, and the results show that the accuracy of the improved model is not much different from the original OpenPose model, but the detection speed is improved by a factor of 4. Finally, a pose recognition model was trained on the self-built dataset using the skeleton map output from the improved model and validated on the test set, and the experiments indicated that the accuracy of the pose recognition model was 92.5%, which was real-time and suitable for various application scenarios.
{"title":"Human posture recognition based on lightweight OpenPose model","authors":"Zhihao Mei, Shiying Wang, Ke-Ping Pan","doi":"10.1117/12.3000882","DOIUrl":"https://doi.org/10.1117/12.3000882","url":null,"abstract":"To address the problem of slow inference in the original OpenPose pose estimation model and lower the computing power of the model, this paper first uses MobilenetV3 as backbone to make a lightweight improvement for OpenPose's network, followed by using label fusion correction to further improve the accuracy of the model. These steps make a real-time pose recognition system built on embedded devices on robots possible. The performance of the improved model is verified on the COCO dataset, and the results show that the accuracy of the improved model is not much different from the original OpenPose model, but the detection speed is improved by a factor of 4. Finally, a pose recognition model was trained on the self-built dataset using the skeleton map output from the improved model and validated on the test set, and the experiments indicated that the accuracy of the pose recognition model was 92.5%, which was real-time and suitable for various application scenarios.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121338721","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}
The advancement of remote sensing technology has broadened the application scope of remote sensing image data across various fields. Traditional methods, when processing remote sensing images, face limitations in efficiency and generalization capabilities due to their intricate geographical features. In contrast, deep learning segmentation methods exhibit superior performance but struggle with contextual detail loss and multi-scale features. In this paper, we introduce the RSFNet model to tackle these issues. The model employs spatial paths to extract detailed information from low-level features, presents a residual ASPP incorporating an attention mechanism, and utilizes a feature map slicing module to capture small target features. Experimental results show that RSFNet attains 88.38% pixel accuracy (PA) and 81.06% mean intersection over union (mIoU) on the Potsdam dataset, proving its suitability for semantic segmentation of remote sensing images.
{"title":"RSFNet: a method for remote sensing image semantic segmentation based on fully convolutional neural networks","authors":"Chuanhao Wei, Dezhao Kong, Xuelian Sun, Yu Zhou","doi":"10.1117/12.3000799","DOIUrl":"https://doi.org/10.1117/12.3000799","url":null,"abstract":"The advancement of remote sensing technology has broadened the application scope of remote sensing image data across various fields. Traditional methods, when processing remote sensing images, face limitations in efficiency and generalization capabilities due to their intricate geographical features. In contrast, deep learning segmentation methods exhibit superior performance but struggle with contextual detail loss and multi-scale features. In this paper, we introduce the RSFNet model to tackle these issues. The model employs spatial paths to extract detailed information from low-level features, presents a residual ASPP incorporating an attention mechanism, and utilizes a feature map slicing module to capture small target features. Experimental results show that RSFNet attains 88.38% pixel accuracy (PA) and 81.06% mean intersection over union (mIoU) on the Potsdam dataset, proving its suitability for semantic segmentation of remote sensing images.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132507149","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}
Junrong Liu, Kailei Xu, Xiaoguang Jiang, Siyu Chen, Jun Chen
With the underground mining in Yanghuopan mining area, the original stress balance state in the rock mass is broken, causing the rock strata and even the ground surface around the goaf to move and deform, resulting in land subsidence and deformation and damage of surface buildings, affecting industrial and agricultural construction and people's living environment. The application of small baseline Radar Interferometry Technology in land subsidence monitoring provides a new means for the monitoring and analysis of land subsidence in Yanghuopan mining area. In this manuscript, SBAS-INSAR technology is used to monitor the land subsidence caused by underground mining in Yanghuopan mining area. Based on 33 sentinel-1A images from June 2019 to August 2020, the surface deformation center, deformation rate, cumulative deformation variables and other information of Yanghuopan coal mine were obtained, and the surface deformation of the mining area was interpreted and analyzed. The main settlement area of the mining area is located in the east of the mining area. The maximum settlement rate in the mining area is -96mm/y, and the maximum cumulative deformation is -119mm.
{"title":"Application of SBAS-INSAR technology in surface subsidence monitoring in Yanghuopan mining area","authors":"Junrong Liu, Kailei Xu, Xiaoguang Jiang, Siyu Chen, Jun Chen","doi":"10.1117/12.3000767","DOIUrl":"https://doi.org/10.1117/12.3000767","url":null,"abstract":"With the underground mining in Yanghuopan mining area, the original stress balance state in the rock mass is broken, causing the rock strata and even the ground surface around the goaf to move and deform, resulting in land subsidence and deformation and damage of surface buildings, affecting industrial and agricultural construction and people's living environment. The application of small baseline Radar Interferometry Technology in land subsidence monitoring provides a new means for the monitoring and analysis of land subsidence in Yanghuopan mining area. In this manuscript, SBAS-INSAR technology is used to monitor the land subsidence caused by underground mining in Yanghuopan mining area. Based on 33 sentinel-1A images from June 2019 to August 2020, the surface deformation center, deformation rate, cumulative deformation variables and other information of Yanghuopan coal mine were obtained, and the surface deformation of the mining area was interpreted and analyzed. The main settlement area of the mining area is located in the east of the mining area. The maximum settlement rate in the mining area is -96mm/y, and the maximum cumulative deformation is -119mm.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130311598","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}
With the vigorous development of artificial intelligence and related technologies, domestic power generation enterprises have also promoted smart wind power projects. In this paper, a wind turbine condition monitoring system based on adaptive neuro fuzzy interference system (ANFIS) is proposed for the state perception of wind farms. A normal behavior model of ANFIS is established based on common monitoring and data acquisition (SCADA) data to detect abnormal behavior of captured signals and to indicate component failure or malfunction using predictive errors. At the same time, according to the theory of wind farm accident warning, this paper adopts the NJW spectral clustering method for the first time, and implements the group classification of wind field fans. Then, the Elman neural network model is adopted for any unit in a certain group, so as to determine the working conditions of all units in a certain group. This method can effectively improve the efficiency of wind farm accident early warning, and is of great significance for the development of intelligent wind field.
{"title":"Intelligent wind farm state mixed sensing and intelligent warning system","authors":"D. Zhong, Yijin Huang, Jinhe Tian, Shihai Ma","doi":"10.1117/12.3000987","DOIUrl":"https://doi.org/10.1117/12.3000987","url":null,"abstract":"With the vigorous development of artificial intelligence and related technologies, domestic power generation enterprises have also promoted smart wind power projects. In this paper, a wind turbine condition monitoring system based on adaptive neuro fuzzy interference system (ANFIS) is proposed for the state perception of wind farms. A normal behavior model of ANFIS is established based on common monitoring and data acquisition (SCADA) data to detect abnormal behavior of captured signals and to indicate component failure or malfunction using predictive errors. At the same time, according to the theory of wind farm accident warning, this paper adopts the NJW spectral clustering method for the first time, and implements the group classification of wind field fans. Then, the Elman neural network model is adopted for any unit in a certain group, so as to determine the working conditions of all units in a certain group. This method can effectively improve the efficiency of wind farm accident early warning, and is of great significance for the development of intelligent wind field.","PeriodicalId":210802,"journal":{"name":"International Conference on Image Processing and Intelligent Control","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134221628","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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