Pub Date : 2019-08-01DOI: 10.1109/ICSGEA.2019.00084
J. Wenjie, He Zhihong
Contraposing to the security of cloud storage service network and data sharing characteristics, a ciphertext access control mechanism based on CP-ABE algorithm is proposed. The access control mechanism is studied from two aspects: access control and access control architecture. The corresponding data structure of security algorithm is provided, and its simulation and performance analysis are performed. The security mechanism ensures the security of data in cloud storage system under open environment and reduces the complexity of privilege management through attribute management, under the premise that service providers are not credible. The cloud storage system designed and implemented in this paper achieves the original design object and performs well in all aspects, which is of great importance to the safe and effective storage of user's information and data in the era of large data.
{"title":"Big Data Encryption Storage System Design Under Cloud Environment","authors":"J. Wenjie, He Zhihong","doi":"10.1109/ICSGEA.2019.00084","DOIUrl":"https://doi.org/10.1109/ICSGEA.2019.00084","url":null,"abstract":"Contraposing to the security of cloud storage service network and data sharing characteristics, a ciphertext access control mechanism based on CP-ABE algorithm is proposed. The access control mechanism is studied from two aspects: access control and access control architecture. The corresponding data structure of security algorithm is provided, and its simulation and performance analysis are performed. The security mechanism ensures the security of data in cloud storage system under open environment and reduces the complexity of privilege management through attribute management, under the premise that service providers are not credible. The cloud storage system designed and implemented in this paper achieves the original design object and performs well in all aspects, which is of great importance to the safe and effective storage of user's information and data in the era of large data.","PeriodicalId":201721,"journal":{"name":"2019 International Conference on Smart Grid and Electrical Automation (ICSGEA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133603250","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 : 2019-08-01DOI: 10.1109/ICSGEA.2019.00110
Xin Chen, Jing Li
To further improve the detection accuracy of SSD object detection algorithm, in this paper, a high efficient single shot multibit detector (HE-SSD) algorithm is proposed, which based on SSD for solving the low accuracy of classical single-stage object detection SSD algorithm. Firstly, an efficient and dense network is designed to improve the detection accuracy. Secondly, in order to improve the robustness of the algorithm and solve the problem of positive and negative sample imbalance in the detection process, the Focal Loss function is used to suppress the weight of the easily classified samples in the loss function. Finally, the accuracy of SSD algorithm for small object detection is improved by data augmentation. In the experiment, the network structure is deployed through the Pytorch deep learning framework, compared the effects of SGD and Adabound optimization methods on training loss to verify the superiority of convergence of the proposed algorithm. The experimental results show that HE-SSD algorithm is more accurate than SSD in PASCAL VOC dataset.
{"title":"Research on an Efficient Single-Stage Multi-object Detection Algorithm","authors":"Xin Chen, Jing Li","doi":"10.1109/ICSGEA.2019.00110","DOIUrl":"https://doi.org/10.1109/ICSGEA.2019.00110","url":null,"abstract":"To further improve the detection accuracy of SSD object detection algorithm, in this paper, a high efficient single shot multibit detector (HE-SSD) algorithm is proposed, which based on SSD for solving the low accuracy of classical single-stage object detection SSD algorithm. Firstly, an efficient and dense network is designed to improve the detection accuracy. Secondly, in order to improve the robustness of the algorithm and solve the problem of positive and negative sample imbalance in the detection process, the Focal Loss function is used to suppress the weight of the easily classified samples in the loss function. Finally, the accuracy of SSD algorithm for small object detection is improved by data augmentation. In the experiment, the network structure is deployed through the Pytorch deep learning framework, compared the effects of SGD and Adabound optimization methods on training loss to verify the superiority of convergence of the proposed algorithm. The experimental results show that HE-SSD algorithm is more accurate than SSD in PASCAL VOC dataset.","PeriodicalId":201721,"journal":{"name":"2019 International Conference on Smart Grid and Electrical Automation (ICSGEA)","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116394233","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 : 2019-08-01DOI: 10.1109/ICSGEA.2019.00027
Li Chenyan, Nie Jing, Su Hui-Wei
With the gradually wide application of Internet of things on green building, Internet of Building Energy System (iBES) has gained more and more attention and application in the green building. Based on conception, technology and standard of the Internet of things, it acquires building energy consumption data through a series of sensors in the Intelligent Gateway (IG) and unifies a data standard. After data aggregation and software process, an effective building consumption data report can be provided in time, further adjusting building energy consumption in order to attain the goal of energy saving and consumption reducing. Application results show that, using the Internet of things technology for building power adjustment, reduce energy consumption, reduce carbon dioxide emissions, are a valuable technique.
{"title":"Research of Carbon Emission Reduction on the Green Building Based on the Internet of Things","authors":"Li Chenyan, Nie Jing, Su Hui-Wei","doi":"10.1109/ICSGEA.2019.00027","DOIUrl":"https://doi.org/10.1109/ICSGEA.2019.00027","url":null,"abstract":"With the gradually wide application of Internet of things on green building, Internet of Building Energy System (iBES) has gained more and more attention and application in the green building. Based on conception, technology and standard of the Internet of things, it acquires building energy consumption data through a series of sensors in the Intelligent Gateway (IG) and unifies a data standard. After data aggregation and software process, an effective building consumption data report can be provided in time, further adjusting building energy consumption in order to attain the goal of energy saving and consumption reducing. Application results show that, using the Internet of things technology for building power adjustment, reduce energy consumption, reduce carbon dioxide emissions, are a valuable technique.","PeriodicalId":201721,"journal":{"name":"2019 International Conference on Smart Grid and Electrical Automation (ICSGEA)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116716503","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 : 2019-08-01DOI: 10.1109/icsgea.2019.00019
Wang Zuchuan, Yao Haiting, Wu Xiaoye
This paper reports the research results of high power silicon carbide Schottky barrier diode (SiC SBD) in three aspects, namely the quality and type selection of SiC materials, device structure of SBD and the manufacturing process of SiC devices. Besides, the key processes of manufacturing SiC SBD, i.e. p-type ion implantation and activation process, ohmic contact process, Schottky metal preparation process, and passivation layer preparation process, are analyzed in detail. The paper introduces the preparation method of SiC SBD with a withstand voltage of 1200V, a current density of more than 120A/cm2 and a junction capacitance of less than 0.4pf, proposing a new technical route and process flow for preparation of high-power SiC SBD.
{"title":"Technical Research on High Power Silicon Carbide Schottky Barrier Diode","authors":"Wang Zuchuan, Yao Haiting, Wu Xiaoye","doi":"10.1109/icsgea.2019.00019","DOIUrl":"https://doi.org/10.1109/icsgea.2019.00019","url":null,"abstract":"This paper reports the research results of high power silicon carbide Schottky barrier diode (SiC SBD) in three aspects, namely the quality and type selection of SiC materials, device structure of SBD and the manufacturing process of SiC devices. Besides, the key processes of manufacturing SiC SBD, i.e. p-type ion implantation and activation process, ohmic contact process, Schottky metal preparation process, and passivation layer preparation process, are analyzed in detail. The paper introduces the preparation method of SiC SBD with a withstand voltage of 1200V, a current density of more than 120A/cm2 and a junction capacitance of less than 0.4pf, proposing a new technical route and process flow for preparation of high-power SiC SBD.","PeriodicalId":201721,"journal":{"name":"2019 International Conference on Smart Grid and Electrical Automation (ICSGEA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114617039","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 : 2019-08-01DOI: 10.1109/ICSGEA.2019.00038
Sijie Chen, C. Zhai, Zewei Li, Jiaxin Zhang, Xinghua Pan
In order to effectively improve the problems existing in the current road traffic information acquisition device, such as too much equipment, low utilization ratio, serious information overlap and low detection rate, the integrated design and transformation of the traditional traffic information acquisition device is carried out, and a new type of traffic information acquisition device, the integrated traffic information detector, which integrates radar acquisition technology, video acquisition technology and Radio Frequency Identification (RFID) is designed. The integrated traffic information detector of lightning network can not only collect the intersection required by the road comprehensively, accurately and in real time, but also through the multi-source data fusion processing of the information collected by radar, video and RFID reader. Through information, and can adapt to a variety of complex detection environment. At the same time, it also fully considers the general direction of traffic in the future fifth generation mobile communication technology (5G: 5th-Generation) environment, equipped with wireless network module suitable for 5G, combined with the high capacity, low delay and high reliable high speed transmission rate under the future 5G network, to further promote the popularization and development of vehicle networking technology in the future.
{"title":"Integrated Design of Traditional Traffic Information Acquisition Device","authors":"Sijie Chen, C. Zhai, Zewei Li, Jiaxin Zhang, Xinghua Pan","doi":"10.1109/ICSGEA.2019.00038","DOIUrl":"https://doi.org/10.1109/ICSGEA.2019.00038","url":null,"abstract":"In order to effectively improve the problems existing in the current road traffic information acquisition device, such as too much equipment, low utilization ratio, serious information overlap and low detection rate, the integrated design and transformation of the traditional traffic information acquisition device is carried out, and a new type of traffic information acquisition device, the integrated traffic information detector, which integrates radar acquisition technology, video acquisition technology and Radio Frequency Identification (RFID) is designed. The integrated traffic information detector of lightning network can not only collect the intersection required by the road comprehensively, accurately and in real time, but also through the multi-source data fusion processing of the information collected by radar, video and RFID reader. Through information, and can adapt to a variety of complex detection environment. At the same time, it also fully considers the general direction of traffic in the future fifth generation mobile communication technology (5G: 5th-Generation) environment, equipped with wireless network module suitable for 5G, combined with the high capacity, low delay and high reliable high speed transmission rate under the future 5G network, to further promote the popularization and development of vehicle networking technology in the future.","PeriodicalId":201721,"journal":{"name":"2019 International Conference on Smart Grid and Electrical Automation (ICSGEA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128749414","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 : 2019-08-01DOI: 10.1109/ICSGEA.2019.00059
Wei Dequan
In order to extract the contour feature of human anatomy experiment organ image effectively and realize 3D visualization reconstruction of 3D virtual anatomy experiment, an image extraction algorithm of human anatomy experiment organ based on Harris wavelet multi-scale segmentation is proposed. The reconstruction model of anatomical experiment based on three-dimensional virtual is constructed. The Splines biorthogonal wavelet is used to enhance the image of human anatomy experiment organ, and the 3D reconstruction model of the feature sequence and edge contour point of the human anatomy experiment is initialized after the enhancement processing. The affine transform feature detection technique is used to improve the traditional Snake algorithm to enhance the edge virtual information feature points of the image and extract the edge features of the human anatomy experimental organ image effectively. Then, the frame points are arranged according to the intensity of pheromone distribution, and the edge contour feature extraction result of the previous image is used as the initial point of edge extraction in the next image of human anatomy experiment organ, and then the frame points are arranged according to the intensity of pheromone distribution. Achieve the human anatomy experiment organ image edge detection batch processing. The simulation results show that the proposed algorithm can effectively extract the edge contour of the human anatomy experiment organ image, and the edge contour point is closer to the real organ edge of the human anatomy experiment, and the reconstruction of the anatomy experiment can be realized effectively.
{"title":"Reconstruction of Anatomy Experiment Based on 3D Virtual Theory","authors":"Wei Dequan","doi":"10.1109/ICSGEA.2019.00059","DOIUrl":"https://doi.org/10.1109/ICSGEA.2019.00059","url":null,"abstract":"In order to extract the contour feature of human anatomy experiment organ image effectively and realize 3D visualization reconstruction of 3D virtual anatomy experiment, an image extraction algorithm of human anatomy experiment organ based on Harris wavelet multi-scale segmentation is proposed. The reconstruction model of anatomical experiment based on three-dimensional virtual is constructed. The Splines biorthogonal wavelet is used to enhance the image of human anatomy experiment organ, and the 3D reconstruction model of the feature sequence and edge contour point of the human anatomy experiment is initialized after the enhancement processing. The affine transform feature detection technique is used to improve the traditional Snake algorithm to enhance the edge virtual information feature points of the image and extract the edge features of the human anatomy experimental organ image effectively. Then, the frame points are arranged according to the intensity of pheromone distribution, and the edge contour feature extraction result of the previous image is used as the initial point of edge extraction in the next image of human anatomy experiment organ, and then the frame points are arranged according to the intensity of pheromone distribution. Achieve the human anatomy experiment organ image edge detection batch processing. The simulation results show that the proposed algorithm can effectively extract the edge contour of the human anatomy experiment organ image, and the edge contour point is closer to the real organ edge of the human anatomy experiment, and the reconstruction of the anatomy experiment can be realized effectively.","PeriodicalId":201721,"journal":{"name":"2019 International Conference on Smart Grid and Electrical Automation (ICSGEA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129050276","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 : 2019-08-01DOI: 10.1109/ICSGEA.2019.00071
Wang Wenzhen
With the continuous growth of music resources, the problem of recommending suitable music for users has become a research hotspot. In this paper, association rules and music genes are added to music collaborative filtering personalized recommendation system to establish a hybrid recommendation model. The structure of the model is described and the recommendation process and recommendation algorithm of personalized recommendation are described in detail. By analyzing users' interests and preferences for different music gene features, the algorithm comprehensively analyses users' behavior, and uses the similarity of interests among different users to construct the neighborhood relationship among them. The recommendation algorithm is validated by combining two factors, and the expected recommendation results are achieved.
{"title":"Personalized Music Recommendation Algorithm Based on Hybrid Collaborative Filtering Technology","authors":"Wang Wenzhen","doi":"10.1109/ICSGEA.2019.00071","DOIUrl":"https://doi.org/10.1109/ICSGEA.2019.00071","url":null,"abstract":"With the continuous growth of music resources, the problem of recommending suitable music for users has become a research hotspot. In this paper, association rules and music genes are added to music collaborative filtering personalized recommendation system to establish a hybrid recommendation model. The structure of the model is described and the recommendation process and recommendation algorithm of personalized recommendation are described in detail. By analyzing users' interests and preferences for different music gene features, the algorithm comprehensively analyses users' behavior, and uses the similarity of interests among different users to construct the neighborhood relationship among them. The recommendation algorithm is validated by combining two factors, and the expected recommendation results are achieved.","PeriodicalId":201721,"journal":{"name":"2019 International Conference on Smart Grid and Electrical Automation (ICSGEA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130897084","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 : 2019-08-01DOI: 10.1109/ICSGEA.2019.00075
Yongjie Zhu
In order to improve the correct rate and generalization ability of residents' personalized behavioral modeling methods, an innovative behavior modeling method based on artificial intelligence is proposed. Firstly, the definition of modeling method based on artificial intelligence is given, and the corresponding personalized motion behavior library is constructed. Then, the resident individualized behavioral model is constructed, and the resident individualized sports logo is normalized model vector. Then the resident personality is analyzed. The reason for the misclassification of sports innovation behaviors is to eliminate the misclassification. The experimental results show that the algorithm has the advantages of simple implementation, fast processing speed and high accuracy.
{"title":"Research on Resident Personalized Sports Artificial Intelligence System","authors":"Yongjie Zhu","doi":"10.1109/ICSGEA.2019.00075","DOIUrl":"https://doi.org/10.1109/ICSGEA.2019.00075","url":null,"abstract":"In order to improve the correct rate and generalization ability of residents' personalized behavioral modeling methods, an innovative behavior modeling method based on artificial intelligence is proposed. Firstly, the definition of modeling method based on artificial intelligence is given, and the corresponding personalized motion behavior library is constructed. Then, the resident individualized behavioral model is constructed, and the resident individualized sports logo is normalized model vector. Then the resident personality is analyzed. The reason for the misclassification of sports innovation behaviors is to eliminate the misclassification. The experimental results show that the algorithm has the advantages of simple implementation, fast processing speed and high accuracy.","PeriodicalId":201721,"journal":{"name":"2019 International Conference on Smart Grid and Electrical Automation (ICSGEA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130327046","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 : 2019-08-01DOI: 10.1109/ICSGEA.2019.00133
Lingfang Huang
This paper improves the security management and control ability of campus network management, studies the security management control model of campus network management, and puts forward a security evaluation and evading model of campus network management based on big data. The management security data mining is carried out by using the statistical analysis method of campus network transmission traffic, and the constraint distribution model of campus network management security control is constructed. Big data fusion and association rule mining methods are used to evaluate the security of campus network management quantitatively, and the data of campus network management security evaluation are tested by grouping regression, and the correlation dimension characteristic quantity of traffic transmission sequence of campus network management is extracted. This paper analyzes the cross-correlation characteristic quantity of the output traffic of campus network management and evaluates the network security according to the anomaly of the characteristic to realize the optimization control of campus network security management. The simulation results show that the traffic anomaly prediction ability is higher and the network intrusion detection ability is stronger by using this method in campus network security management.
{"title":"Research on Campus Network Security Management Technology Based on Big Data","authors":"Lingfang Huang","doi":"10.1109/ICSGEA.2019.00133","DOIUrl":"https://doi.org/10.1109/ICSGEA.2019.00133","url":null,"abstract":"This paper improves the security management and control ability of campus network management, studies the security management control model of campus network management, and puts forward a security evaluation and evading model of campus network management based on big data. The management security data mining is carried out by using the statistical analysis method of campus network transmission traffic, and the constraint distribution model of campus network management security control is constructed. Big data fusion and association rule mining methods are used to evaluate the security of campus network management quantitatively, and the data of campus network management security evaluation are tested by grouping regression, and the correlation dimension characteristic quantity of traffic transmission sequence of campus network management is extracted. This paper analyzes the cross-correlation characteristic quantity of the output traffic of campus network management and evaluates the network security according to the anomaly of the characteristic to realize the optimization control of campus network security management. The simulation results show that the traffic anomaly prediction ability is higher and the network intrusion detection ability is stronger by using this method in campus network security management.","PeriodicalId":201721,"journal":{"name":"2019 International Conference on Smart Grid and Electrical Automation (ICSGEA)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127240258","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 : 2019-08-01DOI: 10.1109/icsgea.2019.00055
Cheng Zha
Due to the difference of the speaker's language, speech emotion recognition tasks often face the situation that training data are not fully representative of test data. Therefore, the space extended by a kernel function. might not sufficient to describe different properties of data and thus produce a satisfactory decision function. In this wok, we apply multiple kernel learning to recognize the speech emotion of cross-language. Compared to SVM, multiple kernel learning can achieve better performance in cross-language speech emotion recognition tasks.
{"title":"Cross-Language Speech Emotion Recognition via Multiple Kernel Learning","authors":"Cheng Zha","doi":"10.1109/icsgea.2019.00055","DOIUrl":"https://doi.org/10.1109/icsgea.2019.00055","url":null,"abstract":"Due to the difference of the speaker's language, speech emotion recognition tasks often face the situation that training data are not fully representative of test data. Therefore, the space extended by a kernel function. might not sufficient to describe different properties of data and thus produce a satisfactory decision function. In this wok, we apply multiple kernel learning to recognize the speech emotion of cross-language. Compared to SVM, multiple kernel learning can achieve better performance in cross-language speech emotion recognition tasks.","PeriodicalId":201721,"journal":{"name":"2019 International Conference on Smart Grid and Electrical Automation (ICSGEA)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127323426","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：