Pub Date : 2022-08-19DOI: 10.1109/CCET55412.2022.9906319
Jiaquan Zhang, Pengfei Li, Xin'an Wang, Yong Zhao
To further improve the accuracy of disparity estimation in ill-posed regions and weak texture regions, in this paper we propose HFMANet: which is a stereo matching method based on hierarchical feature fusion and multi-scale cost aggregation. Specifically, we first propose a hierarchical feature fusion module, which innovatively fuses low-level features and high-level features to obtain rich semantic information while retaining the edge information of the image. Secondly, we propose a multi-scale cost aggregation module to extract rich global context information. At the same time, the layer-by-layer fusion optimization helps increase the receptive field to capture more structural information, reduce the dependence on local information, and help the disparity estimation of ill-posed regions and weak-textured regions. Comprehensive experiments are conducted on the SceneFlow and KITTI datasets, and achieve competitive results, which proves the effectiveness of the proposed method.
{"title":"Hierarchical Feature Fusion and Multi-scale Cost Aggregation for Stereo Matching","authors":"Jiaquan Zhang, Pengfei Li, Xin'an Wang, Yong Zhao","doi":"10.1109/CCET55412.2022.9906319","DOIUrl":"https://doi.org/10.1109/CCET55412.2022.9906319","url":null,"abstract":"To further improve the accuracy of disparity estimation in ill-posed regions and weak texture regions, in this paper we propose HFMANet: which is a stereo matching method based on hierarchical feature fusion and multi-scale cost aggregation. Specifically, we first propose a hierarchical feature fusion module, which innovatively fuses low-level features and high-level features to obtain rich semantic information while retaining the edge information of the image. Secondly, we propose a multi-scale cost aggregation module to extract rich global context information. At the same time, the layer-by-layer fusion optimization helps increase the receptive field to capture more structural information, reduce the dependence on local information, and help the disparity estimation of ill-posed regions and weak-textured regions. Comprehensive experiments are conducted on the SceneFlow and KITTI datasets, and achieve competitive results, which proves the effectiveness of the proposed method.","PeriodicalId":329327,"journal":{"name":"2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127554159","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 : 2022-08-19DOI: 10.1109/CCET55412.2022.9906352
Guocong Feng, Tingting Mu, Huahui Lyu, Hang Yang, Yuyang Lai, Huijuan Li
Smart grids are envisioned as the next-generation electricity grids. The data measured from the smart grid is very sensitive. It is thus highly necessary to adopt data access control in smart grids to guarantee the security and privacy of the measured data. Due to its flexibility and scalability, attribute-based encryption (ABE) is widely utilized to realize data access control in smart grids. However, most existing ABE solutions impose a heavy decryption overhead on their users. To this end, we propose a lightweight attribute-based encryption scheme for data access control in smart grids by adopting the idea of computation outsourcing. Under our proposed scheme, users can outsource a large amount of computation to a server during the decryption phase while still guaranteeing the security and privacy of the data. Theoretical analysis and experimental evaluation demonstrate that our scheme outperforms the existing schemes by achieving a very low decryption cost.
{"title":"A Lightweight Attribute-based Encryption Scheme for Data Access Control in Smart Grids","authors":"Guocong Feng, Tingting Mu, Huahui Lyu, Hang Yang, Yuyang Lai, Huijuan Li","doi":"10.1109/CCET55412.2022.9906352","DOIUrl":"https://doi.org/10.1109/CCET55412.2022.9906352","url":null,"abstract":"Smart grids are envisioned as the next-generation electricity grids. The data measured from the smart grid is very sensitive. It is thus highly necessary to adopt data access control in smart grids to guarantee the security and privacy of the measured data. Due to its flexibility and scalability, attribute-based encryption (ABE) is widely utilized to realize data access control in smart grids. However, most existing ABE solutions impose a heavy decryption overhead on their users. To this end, we propose a lightweight attribute-based encryption scheme for data access control in smart grids by adopting the idea of computation outsourcing. Under our proposed scheme, users can outsource a large amount of computation to a server during the decryption phase while still guaranteeing the security and privacy of the data. Theoretical analysis and experimental evaluation demonstrate that our scheme outperforms the existing schemes by achieving a very low decryption cost.","PeriodicalId":329327,"journal":{"name":"2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131077268","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 : 2022-08-19DOI: 10.1109/CCET55412.2022.9906378
Chenchen Dou, Xue Wang, Rui Dai
The rapid development of smart grid has put forward higher requirements on communication technology. With the rapid development of power wireless communication network, the demand of power business becomes more and more complicated and diverse. However, the current single-standard network has poor coverage, poor power service carrying capacity, and weak transmission stability and security. To address the above problems, this paper explores the network slicing technology for intelligent power distribution services. Based on the analysis of existing network access selection methods, a network selection strategy for quality of service (QoS) assurance of power services is proposed for the characteristics of power distribution services. Combining its own QoS requirements and the characteristics of the network to be accessed, the power service can realize the access selection of the optimal adaptation network. In order to better meet various vertical industry applications, network slicing is required in both the core network and the radio access network, i.e., end-to-end network slicing is realized, and network slicing can be customized to tailor network functions and reasonably allocate network resources according to service scenario requirements. After receiving a request for network slicing, operators need to map the virtual network functions required for network slicing to the underlying physical network, a process that involves the optimal allocation of physical resources.
{"title":"Research on Intelligent Communication Scheduling System Based on Network Slices","authors":"Chenchen Dou, Xue Wang, Rui Dai","doi":"10.1109/CCET55412.2022.9906378","DOIUrl":"https://doi.org/10.1109/CCET55412.2022.9906378","url":null,"abstract":"The rapid development of smart grid has put forward higher requirements on communication technology. With the rapid development of power wireless communication network, the demand of power business becomes more and more complicated and diverse. However, the current single-standard network has poor coverage, poor power service carrying capacity, and weak transmission stability and security. To address the above problems, this paper explores the network slicing technology for intelligent power distribution services. Based on the analysis of existing network access selection methods, a network selection strategy for quality of service (QoS) assurance of power services is proposed for the characteristics of power distribution services. Combining its own QoS requirements and the characteristics of the network to be accessed, the power service can realize the access selection of the optimal adaptation network. In order to better meet various vertical industry applications, network slicing is required in both the core network and the radio access network, i.e., end-to-end network slicing is realized, and network slicing can be customized to tailor network functions and reasonably allocate network resources according to service scenario requirements. After receiving a request for network slicing, operators need to map the virtual network functions required for network slicing to the underlying physical network, a process that involves the optimal allocation of physical resources.","PeriodicalId":329327,"journal":{"name":"2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)","volume":"84 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130699861","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 : 2022-08-19DOI: 10.1109/CCET55412.2022.9906330
Ying Guo, Hong Ge, Jinhong Li
Fake news detection is essential for society, however, implicit state information in features is ignored in multimodal fake news detection, resulting in inefficient of feature. There are also poor domain generality of features problems. So, a Two-Branch Network with Domain Adversarial (TBNDA), is proposed. Firstly, a pre-trained language model is used to encode features on textual information, and the hidden layer of word information and sentence information in the features is extracted separately using a two-branch network. Secondly, a pre-trained residual network model is used to encode the image information, and a two-branch network model is used to extract the different hidden layer image feature information. Finally, a domain adversarial network module is constructed to extract generic features between domains. The accuracy of the proposed model is S9.6% and S4.7% on the Weibo dataset and Twitter dataset respectively. The two-branch network improves the feature representation of images and text, and the domain adversarial network extracts features with generality, enhancing the migration performance of the model and improving the detection of fake news.
{"title":"Fake News Detection Based on Two-Branch Network and Domain Adversarial","authors":"Ying Guo, Hong Ge, Jinhong Li","doi":"10.1109/CCET55412.2022.9906330","DOIUrl":"https://doi.org/10.1109/CCET55412.2022.9906330","url":null,"abstract":"Fake news detection is essential for society, however, implicit state information in features is ignored in multimodal fake news detection, resulting in inefficient of feature. There are also poor domain generality of features problems. So, a Two-Branch Network with Domain Adversarial (TBNDA), is proposed. Firstly, a pre-trained language model is used to encode features on textual information, and the hidden layer of word information and sentence information in the features is extracted separately using a two-branch network. Secondly, a pre-trained residual network model is used to encode the image information, and a two-branch network model is used to extract the different hidden layer image feature information. Finally, a domain adversarial network module is constructed to extract generic features between domains. The accuracy of the proposed model is S9.6% and S4.7% on the Weibo dataset and Twitter dataset respectively. The two-branch network improves the feature representation of images and text, and the domain adversarial network extracts features with generality, enhancing the migration performance of the model and improving the detection of fake news.","PeriodicalId":329327,"journal":{"name":"2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131051812","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 : 2022-08-19DOI: 10.1109/CCET55412.2022.9906390
Xiang Li, Y. Guo, Meng Yan, Xin Wu
As the key method for demand-side management in power grid, non-intrusive load monitoring (NILM) keep up to the power consumption of various users in real time and provides data to support the formulation of relevant power policies. In order to achieve accurate resident load monitoring, this paper proposes a NILM architecture focus on consistent shape loads (CSL). Loads in CSL meet the following conditions: 1) current waveform images of different load individuals in the same type are highly similar. 2) different types of load waveform images are different in shapes which are distinguishable. Besides, a non-intrusive load monitoring method based on convolutional neural network (CNN) to identify CSL load is proposed and carried out on actual users. Power consumption data of CSL with different operating environments is taken as training samples. The outcome of our experiment shows the effectiveness of the method in accurately distinguishing CSL and high-precision identification which reaches 97.06%. The method ensures the real-time performance and accuracy of load monitoring.
{"title":"Non-intrusive Load Monitoring for Consistent Shape Loads Based on Convolutional Neural Network","authors":"Xiang Li, Y. Guo, Meng Yan, Xin Wu","doi":"10.1109/CCET55412.2022.9906390","DOIUrl":"https://doi.org/10.1109/CCET55412.2022.9906390","url":null,"abstract":"As the key method for demand-side management in power grid, non-intrusive load monitoring (NILM) keep up to the power consumption of various users in real time and provides data to support the formulation of relevant power policies. In order to achieve accurate resident load monitoring, this paper proposes a NILM architecture focus on consistent shape loads (CSL). Loads in CSL meet the following conditions: 1) current waveform images of different load individuals in the same type are highly similar. 2) different types of load waveform images are different in shapes which are distinguishable. Besides, a non-intrusive load monitoring method based on convolutional neural network (CNN) to identify CSL load is proposed and carried out on actual users. Power consumption data of CSL with different operating environments is taken as training samples. The outcome of our experiment shows the effectiveness of the method in accurately distinguishing CSL and high-precision identification which reaches 97.06%. The method ensures the real-time performance and accuracy of load monitoring.","PeriodicalId":329327,"journal":{"name":"2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)","volume":"212 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115772868","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 : 2022-08-19DOI: 10.1109/CCET55412.2022.9906381
Jingyi Wang, Zhengping Li, Lijun Wang, Shujun Guo
The simulation fidelity of the flight simulator is affected by the pros and cons of the washout algorithm. Using the classical washout algorithm with fast feedback, simple structure, and easy adjustment, it is found that the choice of parameters directly impacts the perceived fidelity. ensure Based on the research on the problem of insufficient simulation effect caused by parameter selection, the author decided to analyze the perception error of the human vestibular system and combine it with the limitations of the flight simulator. Then, the intelligent optimization method combining genetic algorithm and particle swarm optimization is used to find the optimal filter parameters and carry out simulation analysis. The results show that the classical washout algorithm with optimized parameters can effectively use the platform space to reduce perceptual error and improve simulation fidelity.
{"title":"Intelligent optimization Research of the Washout Algorithm for Flight Simulators","authors":"Jingyi Wang, Zhengping Li, Lijun Wang, Shujun Guo","doi":"10.1109/CCET55412.2022.9906381","DOIUrl":"https://doi.org/10.1109/CCET55412.2022.9906381","url":null,"abstract":"The simulation fidelity of the flight simulator is affected by the pros and cons of the washout algorithm. Using the classical washout algorithm with fast feedback, simple structure, and easy adjustment, it is found that the choice of parameters directly impacts the perceived fidelity. ensure Based on the research on the problem of insufficient simulation effect caused by parameter selection, the author decided to analyze the perception error of the human vestibular system and combine it with the limitations of the flight simulator. Then, the intelligent optimization method combining genetic algorithm and particle swarm optimization is used to find the optimal filter parameters and carry out simulation analysis. The results show that the classical washout algorithm with optimized parameters can effectively use the platform space to reduce perceptual error and improve simulation fidelity.","PeriodicalId":329327,"journal":{"name":"2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132233086","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 : 2022-08-19DOI: 10.1109/CCET55412.2022.9906331
Wei Huang, Xiaohong Shi, Qi Xu, Qingshu Li, Peng Yang
Nowadays, deep learning based on detection algorithms have replaced the traditional manual feature extraction target algorithms and have achieved amazing results in many places with their powerful automatic feature extraction capabilities. However, the results are not ideal for the detection of small targets with low resolution and a lot of noise, such as traffic signs. To address the current problems of slow detection speed and low detection accuracy in small target detection, this paper adopts a feedback-driven mechanism to solve the image level imbalance of the input feature space under the original data distribution. At the same time, this paper designs a novel and flexible two-stage traffic sign recognition framework. The complex task of traffic sign detection and recognition is decomposed into two stages: 1) designing a superclass classifier to more accurately separate traffic signs in complex natural scene images; 2) The idea of similarity metric learning is used to design fine-grained classifiers to recognize traffic signs. Finally, to verify the effectiveness of the model, the model was first compared with Faster R-CNN and found to possess higher detection accuracy; then the model was experimented with the R-FCN model on TTIOOK dataset and CCTSDB dataset respectively, and the comparison of the experimental results revealed that the model improved over the R-FCN model in most of the metrics in the traffic sign detection task.
{"title":"Granularity Classification and Feature Fusion Methods in Traffic Sign Detection","authors":"Wei Huang, Xiaohong Shi, Qi Xu, Qingshu Li, Peng Yang","doi":"10.1109/CCET55412.2022.9906331","DOIUrl":"https://doi.org/10.1109/CCET55412.2022.9906331","url":null,"abstract":"Nowadays, deep learning based on detection algorithms have replaced the traditional manual feature extraction target algorithms and have achieved amazing results in many places with their powerful automatic feature extraction capabilities. However, the results are not ideal for the detection of small targets with low resolution and a lot of noise, such as traffic signs. To address the current problems of slow detection speed and low detection accuracy in small target detection, this paper adopts a feedback-driven mechanism to solve the image level imbalance of the input feature space under the original data distribution. At the same time, this paper designs a novel and flexible two-stage traffic sign recognition framework. The complex task of traffic sign detection and recognition is decomposed into two stages: 1) designing a superclass classifier to more accurately separate traffic signs in complex natural scene images; 2) The idea of similarity metric learning is used to design fine-grained classifiers to recognize traffic signs. Finally, to verify the effectiveness of the model, the model was first compared with Faster R-CNN and found to possess higher detection accuracy; then the model was experimented with the R-FCN model on TTIOOK dataset and CCTSDB dataset respectively, and the comparison of the experimental results revealed that the model improved over the R-FCN model in most of the metrics in the traffic sign detection task.","PeriodicalId":329327,"journal":{"name":"2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129758231","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 : 2022-08-19DOI: 10.1109/CCET55412.2022.9906338
Zhiwei Mo, Biao Long
Segment Routing over IPv6 (SRv6) is a source routing technique based on IPv6 data plane, which has been widely concerned. Benefit from its simplicity, scalability and programmability, SRv6 has been proposed to be introduced into mobile core network in 3GPP and IETF. SRv6 related standards and applications include alternative user plane protocol of core network, service function chaining, computing power network and smarter user plane. This paper will make a theoretical analysis of SRv6 and then overview the situation of SRv6 standardization and application. Finally, the development of SRv6 in standardization is prospected. With development of 5G-Advanced and 6G, SRv6 can contribute to leverage Software Defined Network (SDN) and Network Function Virtualization (NFV) in mobile network and facilitate fixed mobile convergence.
{"title":"An Overview of SRv6 Standardization and Application towards 5G-Advanced and 6G","authors":"Zhiwei Mo, Biao Long","doi":"10.1109/CCET55412.2022.9906338","DOIUrl":"https://doi.org/10.1109/CCET55412.2022.9906338","url":null,"abstract":"Segment Routing over IPv6 (SRv6) is a source routing technique based on IPv6 data plane, which has been widely concerned. Benefit from its simplicity, scalability and programmability, SRv6 has been proposed to be introduced into mobile core network in 3GPP and IETF. SRv6 related standards and applications include alternative user plane protocol of core network, service function chaining, computing power network and smarter user plane. This paper will make a theoretical analysis of SRv6 and then overview the situation of SRv6 standardization and application. Finally, the development of SRv6 in standardization is prospected. With development of 5G-Advanced and 6G, SRv6 can contribute to leverage Software Defined Network (SDN) and Network Function Virtualization (NFV) in mobile network and facilitate fixed mobile convergence.","PeriodicalId":329327,"journal":{"name":"2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126908192","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 : 2022-08-19DOI: 10.1109/CCET55412.2022.9906341
Dawei Wang, Xibin Wang, Junhong Wang, Peng Du, Xiaoqiong Yao
An adaptive blind beam-forming algorithm based on array steering vector estimation is proposed to suppress interference signals received by the satellite navigation receivers. The received data are firstly projected to signal plus noise subspace to suppress strong interference in the first stage. Then the output data from the signal plus noise subspace are sent to carrier phase estimation modules, where each satellite navigation signal is tracked to estimate array steering vector. With the estimated steering vector, beams pointing to the direction of navigation satellites can be formed. Simulation results show that the proposed algorithm is valid for interference suppression in presence of interference, and the anti-jamming performance is much better than the power inversion (PI) algorithm and maximum carrier to noise ratio (MCNR) with three successive integrations.
{"title":"GNSS Receiver Anti-jamming Algorithm Based on Estimation of Array Steering Vector","authors":"Dawei Wang, Xibin Wang, Junhong Wang, Peng Du, Xiaoqiong Yao","doi":"10.1109/CCET55412.2022.9906341","DOIUrl":"https://doi.org/10.1109/CCET55412.2022.9906341","url":null,"abstract":"An adaptive blind beam-forming algorithm based on array steering vector estimation is proposed to suppress interference signals received by the satellite navigation receivers. The received data are firstly projected to signal plus noise subspace to suppress strong interference in the first stage. Then the output data from the signal plus noise subspace are sent to carrier phase estimation modules, where each satellite navigation signal is tracked to estimate array steering vector. With the estimated steering vector, beams pointing to the direction of navigation satellites can be formed. Simulation results show that the proposed algorithm is valid for interference suppression in presence of interference, and the anti-jamming performance is much better than the power inversion (PI) algorithm and maximum carrier to noise ratio (MCNR) with three successive integrations.","PeriodicalId":329327,"journal":{"name":"2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127323273","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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