Pub Date : 2020-09-01DOI: 10.1109/ICITE50838.2020.9231437
Hao Gao, Yadong Zhang, Jin Guo
To improve the service level of urban rail transit, the mechanism of multi-train tracking in the moving block signaling system is researched and the calculation method of minimum headway between successive trains is proposed. Due to the extra running time supplements, the minimum headway can be further compressed by multi-step braking operation of trains during the phase of entering station. To find the theoretical minimum headway with the constraint of punctual arriving, an optimization model which take driving strategy as decision variable is constructed. A dynamic programming based searching approach is proposed to solve the optimization model. A case study of Yizhuang urban line in Beijing is conducted to verify the effectiveness of the optimization model and the algorithm.
{"title":"Calculation and Optimization of Minimum Headway in Moving Block System","authors":"Hao Gao, Yadong Zhang, Jin Guo","doi":"10.1109/ICITE50838.2020.9231437","DOIUrl":"https://doi.org/10.1109/ICITE50838.2020.9231437","url":null,"abstract":"To improve the service level of urban rail transit, the mechanism of multi-train tracking in the moving block signaling system is researched and the calculation method of minimum headway between successive trains is proposed. Due to the extra running time supplements, the minimum headway can be further compressed by multi-step braking operation of trains during the phase of entering station. To find the theoretical minimum headway with the constraint of punctual arriving, an optimization model which take driving strategy as decision variable is constructed. A dynamic programming based searching approach is proposed to solve the optimization model. A case study of Yizhuang urban line in Beijing is conducted to verify the effectiveness of the optimization model and the algorithm.","PeriodicalId":112371,"journal":{"name":"2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129800314","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}
In order to explore the mechanism of salinized soil disease, the real subgrade in Hexi area, Gansu Province, China was selected and the health monitoring system of salinized soil subgrade was established. The response effect of the test system was good. In this paper, the temperature and moisture content of the system are se-lected to study the water temperature of the subgrade. The results show that the dissipation of the residual heat of the subgrade changes steadily with the seasons in winter. The distribution of subgrade moisture con-tent is affected by temperature gradient of subgrade, partition layer and pavement cover. The research results have important guiding significance for the establishment of monitoring methods of saline soil subgrade and the research on the change law of subgrade water temperature,
{"title":"Study on the Health Monitoring Technology of Saline Soil Subgrade and the Distribution Law of Subgrade Water Temperature","authors":"Dongna Li, Xingjun Zhang, JiHao Luan, Luchun Yan, Shanglin Song, Yongning Wang","doi":"10.1109/ICITE50838.2020.9231496","DOIUrl":"https://doi.org/10.1109/ICITE50838.2020.9231496","url":null,"abstract":"In order to explore the mechanism of salinized soil disease, the real subgrade in Hexi area, Gansu Province, China was selected and the health monitoring system of salinized soil subgrade was established. The response effect of the test system was good. In this paper, the temperature and moisture content of the system are se-lected to study the water temperature of the subgrade. The results show that the dissipation of the residual heat of the subgrade changes steadily with the seasons in winter. The distribution of subgrade moisture con-tent is affected by temperature gradient of subgrade, partition layer and pavement cover. The research results have important guiding significance for the establishment of monitoring methods of saline soil subgrade and the research on the change law of subgrade water temperature,","PeriodicalId":112371,"journal":{"name":"2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130354996","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 : 2020-09-01DOI: 10.1109/ICITE50838.2020.9231332
Weilong Chen, Q. Luo, Wei Li, Lei Gong
Geographically rebalancing the capacity utilization of high-speed railway network is an effective method to reduce the idleness of network transportation capacity and optimize the railway operation organization. A mode, i.e. “originating from stations outside downtown before a temporary stop at stations in the city center” is proposed in this paper, with an intention to make full use of capacity of the high-speed railway stations and lines. Besides the target regarding the capacity, another target related to maximizing the flow of high-speed rail passengers in the whole city is also used in the optimization model, intending to make the high-speed rail service convenient to the passengers in the whole city. Lpsolve in c # is used to solve the optimization problem. The proposed mode and its corresponding optimization model as well as the way of solving it is finally validated by the data from Shenzhen, China. The results show that the optimization problem can achieve its optimized train operation plan within 10s under the proposed mode, in which 80% of the capacity of stations and lines in Shenzhen can be utilized in a geographically balanced way. The proposed organization mode and model for checking its superior effects can function as a decision support for the expansion of terminal stations and lines.
{"title":"Optimization of Capacity Utilization of High-Speed Railway Network","authors":"Weilong Chen, Q. Luo, Wei Li, Lei Gong","doi":"10.1109/ICITE50838.2020.9231332","DOIUrl":"https://doi.org/10.1109/ICITE50838.2020.9231332","url":null,"abstract":"Geographically rebalancing the capacity utilization of high-speed railway network is an effective method to reduce the idleness of network transportation capacity and optimize the railway operation organization. A mode, i.e. “originating from stations outside downtown before a temporary stop at stations in the city center” is proposed in this paper, with an intention to make full use of capacity of the high-speed railway stations and lines. Besides the target regarding the capacity, another target related to maximizing the flow of high-speed rail passengers in the whole city is also used in the optimization model, intending to make the high-speed rail service convenient to the passengers in the whole city. Lpsolve in c # is used to solve the optimization problem. The proposed mode and its corresponding optimization model as well as the way of solving it is finally validated by the data from Shenzhen, China. The results show that the optimization problem can achieve its optimized train operation plan within 10s under the proposed mode, in which 80% of the capacity of stations and lines in Shenzhen can be utilized in a geographically balanced way. The proposed organization mode and model for checking its superior effects can function as a decision support for the expansion of terminal stations and lines.","PeriodicalId":112371,"journal":{"name":"2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121200534","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 : 2020-09-01DOI: 10.1109/ICITE50838.2020.9231468
Mengling Wu, Chao Chen, Chun Tian, Jiajun Zhou
Precise train stopping is a key technology in train brake control systems. The traditional control research is based on the speed curve following, and this paper proposes a strategy based on the deceleration curve following. After establishing the theory simulation model, this paper designs the ideal curve of target deceleration. In order to follow curve well, the train kinematics model is linearized and an adaptive control algorithm is designed. The simulation analysis shows that the algorithm can guarantee the stopping accuracy and ensure the gentle change of the deceleration. It also shows that the control idea based on the deceleration curve is feasible and has unique advantages, which provides a direction for the subsequent algorithm research.
{"title":"Precise Train Stopping Algorithm Based on Deceleration Control","authors":"Mengling Wu, Chao Chen, Chun Tian, Jiajun Zhou","doi":"10.1109/ICITE50838.2020.9231468","DOIUrl":"https://doi.org/10.1109/ICITE50838.2020.9231468","url":null,"abstract":"Precise train stopping is a key technology in train brake control systems. The traditional control research is based on the speed curve following, and this paper proposes a strategy based on the deceleration curve following. After establishing the theory simulation model, this paper designs the ideal curve of target deceleration. In order to follow curve well, the train kinematics model is linearized and an adaptive control algorithm is designed. The simulation analysis shows that the algorithm can guarantee the stopping accuracy and ensure the gentle change of the deceleration. It also shows that the control idea based on the deceleration curve is feasible and has unique advantages, which provides a direction for the subsequent algorithm research.","PeriodicalId":112371,"journal":{"name":"2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126419528","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 : 2020-09-01DOI: 10.1109/ICITE50838.2020.9231414
Aili Wang, Lu Li, Baotian Dong
In order to classify pedestrians from the mixed multi-objective traffic scene quickly and accurately, this paper proposes an intelligent pedestrian recognition method based on the cascade classifier structure. Using the “from coarse to fine” strategy, a double-layer hierarchical series combination classifier is designed. HGA-BP classifier with two-layer structure is used for pedestrian recognition. Firstly, the candidates are extracted by combining the basic characteristics of the target object shape, in order to quickly eliminate most of the non-target areas, and then use the advanced features of the target to identify the candidate target areas after the processing of the previous classifier. Through the experimental analysis, this method can better classify and identify pedestrians and other negative moving objects, and count the number of pedestrians in the whole traffic scene accurately.
{"title":"Research on Pedestrian Intelligent Recognition Method Based on Cascade Classifier Structure","authors":"Aili Wang, Lu Li, Baotian Dong","doi":"10.1109/ICITE50838.2020.9231414","DOIUrl":"https://doi.org/10.1109/ICITE50838.2020.9231414","url":null,"abstract":"In order to classify pedestrians from the mixed multi-objective traffic scene quickly and accurately, this paper proposes an intelligent pedestrian recognition method based on the cascade classifier structure. Using the “from coarse to fine” strategy, a double-layer hierarchical series combination classifier is designed. HGA-BP classifier with two-layer structure is used for pedestrian recognition. Firstly, the candidates are extracted by combining the basic characteristics of the target object shape, in order to quickly eliminate most of the non-target areas, and then use the advanced features of the target to identify the candidate target areas after the processing of the previous classifier. Through the experimental analysis, this method can better classify and identify pedestrians and other negative moving objects, and count the number of pedestrians in the whole traffic scene accurately.","PeriodicalId":112371,"journal":{"name":"2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128078683","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 : 2020-09-01DOI: 10.1109/ICITE50838.2020.9231430
Wan Liu, Yuanli Gu, Ying Ding, Wenqi Lu, X. Rui, Lu Tao
Short-term traffic flow prediction is playing an important role in the intelligent transportation system. However, exploring high-precision and efficient prediction methods is still a challenge. To capture the spatiotemporal characteristics of traffic flow and accurately perceive the traffic state, a spatial and temporal combination (STC) model was proposed. The radial basis function neural network (RBFNN) was used to capture the spatial characteristics of traffic flow, while the clockwork recurrent neural network (CWRNN) was utilized to predict the temporal characteristics. The prediction accuracy of the model can be further improved by the result fusion based on the spatiotemporal feature prediction model. To verify the accuracy and robustness of the algorithm, the Beijing 3rd Ring Road speed data are used to compare with other models. The results show that the accuracy of the STC algorithm is better than benchmark prediction models at different service levels.
{"title":"A Spatial and Temporal Combination Model for Traffic Flow: A Case Study of Beijing Expressway","authors":"Wan Liu, Yuanli Gu, Ying Ding, Wenqi Lu, X. Rui, Lu Tao","doi":"10.1109/ICITE50838.2020.9231430","DOIUrl":"https://doi.org/10.1109/ICITE50838.2020.9231430","url":null,"abstract":"Short-term traffic flow prediction is playing an important role in the intelligent transportation system. However, exploring high-precision and efficient prediction methods is still a challenge. To capture the spatiotemporal characteristics of traffic flow and accurately perceive the traffic state, a spatial and temporal combination (STC) model was proposed. The radial basis function neural network (RBFNN) was used to capture the spatial characteristics of traffic flow, while the clockwork recurrent neural network (CWRNN) was utilized to predict the temporal characteristics. The prediction accuracy of the model can be further improved by the result fusion based on the spatiotemporal feature prediction model. To verify the accuracy and robustness of the algorithm, the Beijing 3rd Ring Road speed data are used to compare with other models. The results show that the accuracy of the STC algorithm is better than benchmark prediction models at different service levels.","PeriodicalId":112371,"journal":{"name":"2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134304544","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 : 2020-09-01DOI: 10.1109/ICITE50838.2020.9231379
Sha Wang, Qingyuan Shang, Qi Fang, Fagen Fang
Aiming at the problems of low quality, long cycle and low efficiency in the manual CBTC system testing, an automated testing method based on Robot Framework was proposed in this paper. Taking the temporary speed restriction initialization scenario as an example, the entire process from scenario requirement analysis, test case generation, test case execution and test report generation was analyzed and verified. The results showed that the automated testing system based on Robot Framework could be used for the testing of the CBTC system, which could effectively improve the testing efficiency about 10 times and the missing rate of the problem was reduced to 0.08%. Therefore, automated testing can shorten the development cycle, and save a lot of manpower and material resources.
{"title":"Research on Automated Testing Method of Railway Signaling System","authors":"Sha Wang, Qingyuan Shang, Qi Fang, Fagen Fang","doi":"10.1109/ICITE50838.2020.9231379","DOIUrl":"https://doi.org/10.1109/ICITE50838.2020.9231379","url":null,"abstract":"Aiming at the problems of low quality, long cycle and low efficiency in the manual CBTC system testing, an automated testing method based on Robot Framework was proposed in this paper. Taking the temporary speed restriction initialization scenario as an example, the entire process from scenario requirement analysis, test case generation, test case execution and test report generation was analyzed and verified. The results showed that the automated testing system based on Robot Framework could be used for the testing of the CBTC system, which could effectively improve the testing efficiency about 10 times and the missing rate of the problem was reduced to 0.08%. Therefore, automated testing can shorten the development cycle, and save a lot of manpower and material resources.","PeriodicalId":112371,"journal":{"name":"2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131203836","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 : 2020-09-01DOI: 10.1109/ICITE50838.2020.9231429
Jinxiong Gao, Xiumei Gao, Hongye Yang
In order to accurately predict short-term traffic flow, alleviate traffic congestion and improve traffic operation efficiency, a short-term traffic flow prediction method based on cnn-xgboost is proposed. Combined with the temporal and spatial correlation of short-term traffic flow data, the historical data of this section and adjacent sections are taken as input for prediction. This paper uses convolutional neural networks (CNN) to extract features to reduce data redundancy. An xgboost model with parameters optimized by Drosophila algorithm is proposed for traffic flow prediction. The results show that CNN can effectively extract the traffic flow data under the combination of time and space; compared with SVR, LSTM and other models, the traffic flow prediction error of the improved xgboost model is significantly reduced.
{"title":"Short-term Traffic Flow Prediction Based on Time-space Characteristics","authors":"Jinxiong Gao, Xiumei Gao, Hongye Yang","doi":"10.1109/ICITE50838.2020.9231429","DOIUrl":"https://doi.org/10.1109/ICITE50838.2020.9231429","url":null,"abstract":"In order to accurately predict short-term traffic flow, alleviate traffic congestion and improve traffic operation efficiency, a short-term traffic flow prediction method based on cnn-xgboost is proposed. Combined with the temporal and spatial correlation of short-term traffic flow data, the historical data of this section and adjacent sections are taken as input for prediction. This paper uses convolutional neural networks (CNN) to extract features to reduce data redundancy. An xgboost model with parameters optimized by Drosophila algorithm is proposed for traffic flow prediction. The results show that CNN can effectively extract the traffic flow data under the combination of time and space; compared with SVR, LSTM and other models, the traffic flow prediction error of the improved xgboost model is significantly reduced.","PeriodicalId":112371,"journal":{"name":"2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128875189","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 : 2020-09-01DOI: 10.1109/ICITE50838.2020.9231424
Zhuang Liu, Yuanping Zhu
This paper studies the license plate recognition problem under the complex background and the license plate tilt. Existing methods cannot solve these problems well. This paper proposes an end-to-end rectification network based on deep learning. The model contains three parts: Rectification network, residual module and sequence module, which are responsible for distortion of license plate rectification, image feature extraction and license plate character recognition. In the experiments, we studied the effects of complex backgrounds such as light, rain and snow, and the inclination and distortion of license plates on the accuracy of license plate recognition. The experimental part of this article uses the Chinese Academy of Sciences CCPD dataset, which covers a variety of license plate data in natural scenes. The experimental results show that compared with the existing license plate recognition algorithm, the algorithm in this paper improves significantly the accuracy, and it averages 7.7% in complex scenarios of CCPD dataset.
{"title":"Vehicle License Plate Recognition In Complex Scenes","authors":"Zhuang Liu, Yuanping Zhu","doi":"10.1109/ICITE50838.2020.9231424","DOIUrl":"https://doi.org/10.1109/ICITE50838.2020.9231424","url":null,"abstract":"This paper studies the license plate recognition problem under the complex background and the license plate tilt. Existing methods cannot solve these problems well. This paper proposes an end-to-end rectification network based on deep learning. The model contains three parts: Rectification network, residual module and sequence module, which are responsible for distortion of license plate rectification, image feature extraction and license plate character recognition. In the experiments, we studied the effects of complex backgrounds such as light, rain and snow, and the inclination and distortion of license plates on the accuracy of license plate recognition. The experimental part of this article uses the Chinese Academy of Sciences CCPD dataset, which covers a variety of license plate data in natural scenes. The experimental results show that compared with the existing license plate recognition algorithm, the algorithm in this paper improves significantly the accuracy, and it averages 7.7% in complex scenarios of CCPD dataset.","PeriodicalId":112371,"journal":{"name":"2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124889653","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 : 2020-09-01DOI: 10.1109/ICITE50838.2020.9231337
Fangxiao Tian, Y. Yue
The calculation of the capacity of high-speed railway network is a basis for the evaluation of it, which has not formed a complete set of calculation system. According to the relevant research, this paper summarizes three kinds of general methods for calculating the capacity of high-speed railway network based on different conditions, including traffic distribution model based on K-shortest path, bi-level programming model and train working diagram compression method. By listing these typical models and their algorithms, this paper also summarizes the characteristics of each method, and makes a comparative analysis and screening of them. Then this paper makes a case study, applies each method to actual railway network and analyzes the results, which provides the actual basis for the comparative study of this paper.
{"title":"A Comparative Study on Calculation Methods of Capacity of High-speed Railway Network","authors":"Fangxiao Tian, Y. Yue","doi":"10.1109/ICITE50838.2020.9231337","DOIUrl":"https://doi.org/10.1109/ICITE50838.2020.9231337","url":null,"abstract":"The calculation of the capacity of high-speed railway network is a basis for the evaluation of it, which has not formed a complete set of calculation system. According to the relevant research, this paper summarizes three kinds of general methods for calculating the capacity of high-speed railway network based on different conditions, including traffic distribution model based on K-shortest path, bi-level programming model and train working diagram compression method. By listing these typical models and their algorithms, this paper also summarizes the characteristics of each method, and makes a comparative analysis and screening of them. Then this paper makes a case study, applies each method to actual railway network and analyzes the results, which provides the actual basis for the comparative study of this paper.","PeriodicalId":112371,"journal":{"name":"2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127029360","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：