Pub Date : 2019-12-01DOI: 10.1109/ICICIP47338.2019.9012166
Fu Wentao, X. Longfei, Ji Yuanfa, Sun Xiyan
In order to realize the high-precision positioning of the pseudo-satellite system, a loop for synchronizing the carrier phase of the 4-channel transmitting end of the pseudo-satellite system is designed based on the traditional pseudo-satellite system, and the phase correction is completed to ensure the realtime synchronization of the carrier phase at the transmitting end. By studying the structure of the traditional pseudo-satellite transmitting end, the correction phase of the carrier phase of each channel is added at the pseudo-satellite transmitting end, and the carrier phase of the 4-channel transmitting end of the pseudo-satellite system is realized on the hardware platform with FPGA+DSP as the core. Synchronize. The pseudo-satellite system with closed-loop modified carrier phase is compared with the traditional pseudo-satellite transmitter, and the results of the test are analyzed. The experimental results show that the carrier phase correction loop can effectively synchronize the carrier of each channel of the pseudo-satellite transmitter. The phase is such that the positioning accuracy is increased from 3 cm to about 1 cm.
{"title":"Implementation of Carrier Phase Synchronization Technology in Pseudo Satellite Transmitter","authors":"Fu Wentao, X. Longfei, Ji Yuanfa, Sun Xiyan","doi":"10.1109/ICICIP47338.2019.9012166","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012166","url":null,"abstract":"In order to realize the high-precision positioning of the pseudo-satellite system, a loop for synchronizing the carrier phase of the 4-channel transmitting end of the pseudo-satellite system is designed based on the traditional pseudo-satellite system, and the phase correction is completed to ensure the realtime synchronization of the carrier phase at the transmitting end. By studying the structure of the traditional pseudo-satellite transmitting end, the correction phase of the carrier phase of each channel is added at the pseudo-satellite transmitting end, and the carrier phase of the 4-channel transmitting end of the pseudo-satellite system is realized on the hardware platform with FPGA+DSP as the core. Synchronize. The pseudo-satellite system with closed-loop modified carrier phase is compared with the traditional pseudo-satellite transmitter, and the results of the test are analyzed. The experimental results show that the carrier phase correction loop can effectively synchronize the carrier of each channel of the pseudo-satellite transmitter. The phase is such that the positioning accuracy is increased from 3 cm to about 1 cm.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"778 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123008359","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 : 2019-12-01DOI: 10.1109/ICICIP47338.2019.9012184
Lei Wang, Chuan Wang, Kun Wang, He Wang, Zhao Liu, Wenwu Yu
This paper investigates the distributed load frequency control problem for a class of multi-area power systems (MAPSs). Each agent in the MAPSs has the ability of information processing and learning, and there exists information interaction between the adjacent neighboring ones. A controller is designed to ensure the interconnected power system to be uniformly ultimately bounded (UUB) with a bounded mismatched load disturbance. Then, two decoupled conditions are derived so that the control gains can be obtained with only local information. Finally, some simulations are given to verify the correctness of the theoretical results.
{"title":"Distributed Load Frequency Control for Multi-Area Power Systems","authors":"Lei Wang, Chuan Wang, Kun Wang, He Wang, Zhao Liu, Wenwu Yu","doi":"10.1109/ICICIP47338.2019.9012184","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012184","url":null,"abstract":"This paper investigates the distributed load frequency control problem for a class of multi-area power systems (MAPSs). Each agent in the MAPSs has the ability of information processing and learning, and there exists information interaction between the adjacent neighboring ones. A controller is designed to ensure the interconnected power system to be uniformly ultimately bounded (UUB) with a bounded mismatched load disturbance. Then, two decoupled conditions are derived so that the control gains can be obtained with only local information. Finally, some simulations are given to verify the correctness of the theoretical results.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122832346","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 this work, we study learning behavior analysis for automatic evaluation of the classroom teaching. We define five classroom learning behaviors including listen, fatigue, hand-up, sideways and read-write, and construct a class-room learning behavior dataset named as ActRec-Classroom, which includes five categories with 5,126 images in total. With the aid of convolutional neural network (CNN), we propose a classroom learning behavior analysis system framework. Firstly, Faster R-CNN is used to detect human body. Then OpenPose is used to extract key points of human skeleton, faces and fingers. Finally, a CNN based classifier is designed for action recognition. Extensive experiments validate the proposed system. The validation accuracy reaches 92.86% on average, and it meets the need of learning behavior analysis in the real classroom teaching environment.
{"title":"Learning Behavior Analysis in Classroom Based on Deep Learning","authors":"R. Fu, Tongtong Wu, Zuying Luo, Fuqing Duan, Xuejun Qiao, Ping Guo","doi":"10.1109/ICICIP47338.2019.9012177","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012177","url":null,"abstract":"In this work, we study learning behavior analysis for automatic evaluation of the classroom teaching. We define five classroom learning behaviors including listen, fatigue, hand-up, sideways and read-write, and construct a class-room learning behavior dataset named as ActRec-Classroom, which includes five categories with 5,126 images in total. With the aid of convolutional neural network (CNN), we propose a classroom learning behavior analysis system framework. Firstly, Faster R-CNN is used to detect human body. Then OpenPose is used to extract key points of human skeleton, faces and fingers. Finally, a CNN based classifier is designed for action recognition. Extensive experiments validate the proposed system. The validation accuracy reaches 92.86% on average, and it meets the need of learning behavior analysis in the real classroom teaching environment.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123816531","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 : 2019-12-01DOI: 10.1109/ICICIP47338.2019.9012203
Zhenxing Li, Chengdong Yang, Zhaodong Liu, A. Zhang, Jianlong Qiu
This paper studies the event-triggered consensus problem of second-order uncertain nonlinear multi-agent systems (MASs). Based on the local sampled measurement information, we propose an adaptive event-triggered consensus algorithm. The adaptive algorithm estimates not only the uncertain parameters of agent dynamics but also the global topology information. Hence, our consensus algorithm does not rely on global topology information, that is, the proposed consensus algorithm is full distributed. Moreover, we prove that Zeno behavior is ruled out. Finally, a simulation is given to verify the effectiveness of the proposed algorithm.
{"title":"Fully Distributed Consensus Control for Second-order Uncertain Nonlinear Multi-Agent Systems via an Event-triggered Approach","authors":"Zhenxing Li, Chengdong Yang, Zhaodong Liu, A. Zhang, Jianlong Qiu","doi":"10.1109/ICICIP47338.2019.9012203","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012203","url":null,"abstract":"This paper studies the event-triggered consensus problem of second-order uncertain nonlinear multi-agent systems (MASs). Based on the local sampled measurement information, we propose an adaptive event-triggered consensus algorithm. The adaptive algorithm estimates not only the uncertain parameters of agent dynamics but also the global topology information. Hence, our consensus algorithm does not rely on global topology information, that is, the proposed consensus algorithm is full distributed. Moreover, we prove that Zeno behavior is ruled out. Finally, a simulation is given to verify the effectiveness of the proposed algorithm.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124229017","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 : 2019-12-01DOI: 10.1109/ICICIP47338.2019.9012195
Zhenlun Yang, Kunquan Shi, A. Wu, Meiling Qiu, Xue-meng Wei
This paper presents a novel self-learning hybrid optimization algorithm based on the particle swarm optimization (PSO) algorithm and the salp swarm algorithm (SSA) algorithm, namely HSL-PSO-SSA, for solving the function optimization problems. In HSL-PSO-SSA, three search strategies based on the ideas of PSO and SSA are adopted and a probability model is designed to determine the probability of a search strategy being used to update an individual in the search population. The performance of the HSL-PSO-SSA is investigated on solving the unimodal and multimodal benchmark functions. From the experimental results, it is observed that the proposed HSL-PSO-SSA outperforms the compared algorithms including the standard PSO and the original SSA.
{"title":"A hybird self-learning method based on particle swarm optimization and salp swarm algorithm","authors":"Zhenlun Yang, Kunquan Shi, A. Wu, Meiling Qiu, Xue-meng Wei","doi":"10.1109/ICICIP47338.2019.9012195","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012195","url":null,"abstract":"This paper presents a novel self-learning hybrid optimization algorithm based on the particle swarm optimization (PSO) algorithm and the salp swarm algorithm (SSA) algorithm, namely HSL-PSO-SSA, for solving the function optimization problems. In HSL-PSO-SSA, three search strategies based on the ideas of PSO and SSA are adopted and a probability model is designed to determine the probability of a search strategy being used to update an individual in the search population. The performance of the HSL-PSO-SSA is investigated on solving the unimodal and multimodal benchmark functions. From the experimental results, it is observed that the proposed HSL-PSO-SSA outperforms the compared algorithms including the standard PSO and the original SSA.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"470 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126126778","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 : 2019-12-01DOI: 10.1109/ICICIP47338.2019.9012130
Feng Jiang, Jiawei Yang
Accurate prediction of wind power is essential for efficient use of energy. In this paper, an ensemble learning model of optimization algorithm is proposed. Firstly, the data of wind power are decomposed into a series of signal sets by Ensemble empirical mode decomposition. Then, the least squares support vector machine (LSSVM) optimized by Elephant Herd optimization algorithm (EHO) is used to predict each component signal. Clustering method is utilized to cluster the samples. Finally, the EHO-LSSVM method is used to ensemble the sample results to get the final prediction value. Wind power data of PJM west area are used to study the effects of the hybrid method. The comparison results with eight benchmark models shows that the hybrid model has better performance and smaller error values than all other benchmark models. In conclusion, the proposed ensemble learning model is considerably effective and contains high robustness for the wind power data forecast.
{"title":"Ultra-short-term Wind Power Forecast Using Ensemble Learning and Elephant Herd Optimization Algorithm","authors":"Feng Jiang, Jiawei Yang","doi":"10.1109/ICICIP47338.2019.9012130","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012130","url":null,"abstract":"Accurate prediction of wind power is essential for efficient use of energy. In this paper, an ensemble learning model of optimization algorithm is proposed. Firstly, the data of wind power are decomposed into a series of signal sets by Ensemble empirical mode decomposition. Then, the least squares support vector machine (LSSVM) optimized by Elephant Herd optimization algorithm (EHO) is used to predict each component signal. Clustering method is utilized to cluster the samples. Finally, the EHO-LSSVM method is used to ensemble the sample results to get the final prediction value. Wind power data of PJM west area are used to study the effects of the hybrid method. The comparison results with eight benchmark models shows that the hybrid model has better performance and smaller error values than all other benchmark models. In conclusion, the proposed ensemble learning model is considerably effective and contains high robustness for the wind power data forecast.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126514733","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 : 2019-12-01DOI: 10.1109/ICICIP47338.2019.9012188
Fei Wang, Zichen Wang, Fei Yan, Hong Gu, Yan Zhuang
Recently, rich semantic information has proven to be an enabling factor for a wide variety of applications in mobile robots. In this paper, we explore the integration of semantics into lidar odometry and mapping approaches and present a novel real-time semantic-assisted system. To this end, a sparse 3D-CNN model is designed to perform per-frame semantic segmentation of lidar points. Transformations are then estimated by jointly minimizing the geometric and semantic distances between correspondences. At last, new points are transformed into the world coordinate system and used to update predicted labels in the global semantic map. Experiments show that our system has a better performance in pose error compared with the geometry-based method.
{"title":"A Novel Real-time Semantic-Assisted Lidar Odometry and Mapping System","authors":"Fei Wang, Zichen Wang, Fei Yan, Hong Gu, Yan Zhuang","doi":"10.1109/ICICIP47338.2019.9012188","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012188","url":null,"abstract":"Recently, rich semantic information has proven to be an enabling factor for a wide variety of applications in mobile robots. In this paper, we explore the integration of semantics into lidar odometry and mapping approaches and present a novel real-time semantic-assisted system. To this end, a sparse 3D-CNN model is designed to perform per-frame semantic segmentation of lidar points. Transformations are then estimated by jointly minimizing the geometric and semantic distances between correspondences. At last, new points are transformed into the world coordinate system and used to update predicted labels in the global semantic map. Experiments show that our system has a better performance in pose error compared with the geometry-based method.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121146673","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 : 2019-12-01DOI: 10.1109/ICICIP47338.2019.9012216
Min Kim, K. Kim, H. Youn
Recently, wireless sensor network (WSN) has been drawing a great deal of attention both in academia and industry. Numerous schemes have been developed to maximize the performance and reliability of WSN, and node clustering is commonly employed for efficient management of the sensor nodes. In this paper a novel node clustering scheme is proposed which is based on the correlation between the features collected from the nodes, while the features are weighted using the maximum entropy model. It allows efficient measurement of the similarity between the features, and thus proper node clustering is achieved. Extensive computer simulation demonstrates that the proposed scheme significantly outperforms the existing representative schemes in terms of Adjusted Rand Index, Fowlkes-Mallows Index, and relative effectiveness.
{"title":"Node Clustering Based on Feature Correlation and Maximum Entropy for WSN","authors":"Min Kim, K. Kim, H. Youn","doi":"10.1109/ICICIP47338.2019.9012216","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012216","url":null,"abstract":"Recently, wireless sensor network (WSN) has been drawing a great deal of attention both in academia and industry. Numerous schemes have been developed to maximize the performance and reliability of WSN, and node clustering is commonly employed for efficient management of the sensor nodes. In this paper a novel node clustering scheme is proposed which is based on the correlation between the features collected from the nodes, while the features are weighted using the maximum entropy model. It allows efficient measurement of the similarity between the features, and thus proper node clustering is achieved. Extensive computer simulation demonstrates that the proposed scheme significantly outperforms the existing representative schemes in terms of Adjusted Rand Index, Fowlkes-Mallows Index, and relative effectiveness.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133730283","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 : 2019-12-01DOI: 10.1109/ICICIP47338.2019.9012182
Erick García López, Wen Yu, Xiaoou Li
It is well known that parallel robots have greater rigidity, higher payload-to-weight ratio and better dynamic performance than serial robots. However, the complex forward kinematics problem and the limited workspace are the main disadvantages of this type of robots. To design a parallel robot to maximize its workspace we need the robot motion models, thus is a very difficult task. The larger the workspace, the more range of movement is available to perform different tasks. In this paper, by using neural network combined with genetic algorithm we propose an optimal design method for the parallel robot, which maximizes the volume of the workspace of parallel robots. The neural network learns the motion model of the robot, the genetic algorithm uses this model to generate the optimal parameters of the robot. As case of the study, the method developed is applied to the Stewart platform to test the effectiveness and efficiency of the algorithm.
{"title":"Optimal Design of a Parallel Robot Using Neural Network and Genetic Algorithm","authors":"Erick García López, Wen Yu, Xiaoou Li","doi":"10.1109/ICICIP47338.2019.9012182","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012182","url":null,"abstract":"It is well known that parallel robots have greater rigidity, higher payload-to-weight ratio and better dynamic performance than serial robots. However, the complex forward kinematics problem and the limited workspace are the main disadvantages of this type of robots. To design a parallel robot to maximize its workspace we need the robot motion models, thus is a very difficult task. The larger the workspace, the more range of movement is available to perform different tasks. In this paper, by using neural network combined with genetic algorithm we propose an optimal design method for the parallel robot, which maximizes the volume of the workspace of parallel robots. The neural network learns the motion model of the robot, the genetic algorithm uses this model to generate the optimal parameters of the robot. As case of the study, the method developed is applied to the Stewart platform to test the effectiveness and efficiency of the algorithm.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134633916","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 : 2019-12-01DOI: 10.1109/ICICIP47338.2019.9012209
Amauri B. Camargo, Yisha Liu, Guojian He, Yan Zhuang
This work is intended to study the stages of exploring, localization and mapping of autonomous mobile robots and vehicles. In addition to the use of integrated and standard software, ROS has the possibility of creating small map data files recorded with the data provided by 2D Light Detection And Ranging (LiDAR) sensors. The low data density favours the increased efficiency during data processing. The metric maps register just enough information to create the topological nodes and edges in a relational map. Extensive experiments in both simulated environments and real-world applications show the effectiveness of the proposed method.
{"title":"Mobile Robot Autonomous Exploration and Navigation in Large-scale Indoor Environments","authors":"Amauri B. Camargo, Yisha Liu, Guojian He, Yan Zhuang","doi":"10.1109/ICICIP47338.2019.9012209","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012209","url":null,"abstract":"This work is intended to study the stages of exploring, localization and mapping of autonomous mobile robots and vehicles. In addition to the use of integrated and standard software, ROS has the possibility of creating small map data files recorded with the data provided by 2D Light Detection And Ranging (LiDAR) sensors. The low data density favours the increased efficiency during data processing. The metric maps register just enough information to create the topological nodes and edges in a relational map. Extensive experiments in both simulated environments and real-world applications show the effectiveness of the proposed method.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114057171","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：