Tian Ling, Shuo Tian, Songyuheng Gao, Zhixue Xing, J. Lai, Zhenzhai Li
In order to reduce the incidence of traffic accidents, the use of computer vision to identify vehicles and passers-by in the process of driving can achieve the effect of assisting driving. This paper mainly introduces the performance improvement brought by the introduction of the SPP module in YOLO-V3 for object recognition. Model training is performed on the VOC dataset based on YOLO-V3-SPP. Finally, 300 photos were used to test the accuracy of the algorithm. The results show that the recognition accuracy of YOLO-V3-SPP for vehicles and pedestrians can reach 94.19% and 90.68%, and the accuracy of YOLO-V3 is improved by nearly ten under the same equipment. percentage point. The research on this technology can effectively reduce the probability of traffic accidents and provide reference value for the future driving safety warning field.
{"title":"An improvement of vehicle and passerby recognition based on YOLO-V3 algorithm","authors":"Tian Ling, Shuo Tian, Songyuheng Gao, Zhixue Xing, J. Lai, Zhenzhai Li","doi":"10.1117/12.2671229","DOIUrl":"https://doi.org/10.1117/12.2671229","url":null,"abstract":"In order to reduce the incidence of traffic accidents, the use of computer vision to identify vehicles and passers-by in the process of driving can achieve the effect of assisting driving. This paper mainly introduces the performance improvement brought by the introduction of the SPP module in YOLO-V3 for object recognition. Model training is performed on the VOC dataset based on YOLO-V3-SPP. Finally, 300 photos were used to test the accuracy of the algorithm. The results show that the recognition accuracy of YOLO-V3-SPP for vehicles and pedestrians can reach 94.19% and 90.68%, and the accuracy of YOLO-V3 is improved by nearly ten under the same equipment. percentage point. The research on this technology can effectively reduce the probability of traffic accidents and provide reference value for the future driving safety warning field.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124998359","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}
Aiming at the problem of tracking and controlling the motion path of industrial robots in the process of research, design and development, this paper will take the common six-axis industrial robots as the research object, take advantage of the application advantages of VR technology, 3D modeling technology and Web3D interactive technology, take 3ds Max as the modeling tool and Unity3D virtual reality engine as the development platform, and build a virtual reality simulation experiment system of industrial robots from the perspective of visual interaction between virtual robots and real robots, so as to provide a comprehensive and feasible solution for the research of virtual motion simulation and control of industrial robots. The whole system adopts B/S architecture and completes the design and deployment of the whole function according to MVC mode in APS.NET environment, so as to support users with different roles to test the functions of each component module of industrial robot in virtual reality environment, and also simulate the trajectory planning and motion effect control of industrial robot in different scenes. The system will greatly improve the research and development efficiency of industrial robots, increase the efficiency and flexibility of industrial robots, break through the limitations of traditional testing methods on time and space, and provide experience and reference for the intelligent development of industrial robots.
{"title":"Application of virtual reality technology in motion simulation and control of industrial robot","authors":"Wei Zhao","doi":"10.1117/12.2672640","DOIUrl":"https://doi.org/10.1117/12.2672640","url":null,"abstract":"Aiming at the problem of tracking and controlling the motion path of industrial robots in the process of research, design and development, this paper will take the common six-axis industrial robots as the research object, take advantage of the application advantages of VR technology, 3D modeling technology and Web3D interactive technology, take 3ds Max as the modeling tool and Unity3D virtual reality engine as the development platform, and build a virtual reality simulation experiment system of industrial robots from the perspective of visual interaction between virtual robots and real robots, so as to provide a comprehensive and feasible solution for the research of virtual motion simulation and control of industrial robots. The whole system adopts B/S architecture and completes the design and deployment of the whole function according to MVC mode in APS.NET environment, so as to support users with different roles to test the functions of each component module of industrial robot in virtual reality environment, and also simulate the trajectory planning and motion effect control of industrial robot in different scenes. The system will greatly improve the research and development efficiency of industrial robots, increase the efficiency and flexibility of industrial robots, break through the limitations of traditional testing methods on time and space, and provide experience and reference for the intelligent development of industrial robots.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122662286","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}
Haoqing Li, Xiaohao Huang, C. Pan, Chunlei Yang, Jinbao Wang
As a key indicator in ship design, many major incidents of ship sinking are related to the ship's damaged stability. The process of calculating the damaged stability becomes more and more complex and time-consuming on account of more and more stringent specification standards. A two-stage design step is used in this article to realize the calculation of ship’s damaged stability under various watertight bulkhead fast. Firstly, a multi-layer feed-forward neural network model was designed for the predictive regression of a ship's damaged stability using the location of the watertight bulkhead as a variable. Secondly, the relationship between each watertight bulkhead variant and the damaged stability A-value is analyzed. After that, with hydrostatic curve calculation based on the inlet simulation and the interaction between watertight bulkheads considered, a multilayer feed-forward neural network model based on the attention mechanism is designed, which could predict the regression of the damaged stability A-value and analyze bulkhead weights. Finally, the validity of the model was verified by the data, in which the mean value of the prediction error MAE (mean absolute error) was at 2.67×10-4 and the computation time was greatly reduced.
{"title":"Development of neural network model based on attention mechanism applied to the prediction of ship damaged stability","authors":"Haoqing Li, Xiaohao Huang, C. Pan, Chunlei Yang, Jinbao Wang","doi":"10.1117/12.2671282","DOIUrl":"https://doi.org/10.1117/12.2671282","url":null,"abstract":"As a key indicator in ship design, many major incidents of ship sinking are related to the ship's damaged stability. The process of calculating the damaged stability becomes more and more complex and time-consuming on account of more and more stringent specification standards. A two-stage design step is used in this article to realize the calculation of ship’s damaged stability under various watertight bulkhead fast. Firstly, a multi-layer feed-forward neural network model was designed for the predictive regression of a ship's damaged stability using the location of the watertight bulkhead as a variable. Secondly, the relationship between each watertight bulkhead variant and the damaged stability A-value is analyzed. After that, with hydrostatic curve calculation based on the inlet simulation and the interaction between watertight bulkheads considered, a multilayer feed-forward neural network model based on the attention mechanism is designed, which could predict the regression of the damaged stability A-value and analyze bulkhead weights. Finally, the validity of the model was verified by the data, in which the mean value of the prediction error MAE (mean absolute error) was at 2.67×10-4 and the computation time was greatly reduced.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127745547","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}
At present, the academic community has carried out some research on knowledge reasoning using Reinforcement Learning (RL), which has achieved good results in multi-hop reasoning. However, these methods often need to manually design the reward function to adapt to a specific dataset. For different datasets, the reward function in RL-based methods needs to be manually adjusted to obtain good performance. To solve this problem, an agent training model combined with Generative Adversarial Networks (GAN) is proposed. The model consists of two modules: a generative adversarial inference engine and a sampler. The sampler uses a policy-based bidirectional breadth-first search method to find the demonstration path, and the agent uses the reward considering the information of the neighborhood entities as the initial reward function. After sufficient adversarial training between the agent and the discriminator, the policy-based agent can find evidence paths that match the demonstration distribution and synthesize these evidence paths to make predictions. Experiments show that the model achieves better results in both fact prediction and link prediction tasks.
{"title":"Reinforcement learning multi-hop reasoning method with GAN network","authors":"Zhicai Gao, Xiaoze Gong, Yongli Wang","doi":"10.1117/12.2671176","DOIUrl":"https://doi.org/10.1117/12.2671176","url":null,"abstract":"At present, the academic community has carried out some research on knowledge reasoning using Reinforcement Learning (RL), which has achieved good results in multi-hop reasoning. However, these methods often need to manually design the reward function to adapt to a specific dataset. For different datasets, the reward function in RL-based methods needs to be manually adjusted to obtain good performance. To solve this problem, an agent training model combined with Generative Adversarial Networks (GAN) is proposed. The model consists of two modules: a generative adversarial inference engine and a sampler. The sampler uses a policy-based bidirectional breadth-first search method to find the demonstration path, and the agent uses the reward considering the information of the neighborhood entities as the initial reward function. After sufficient adversarial training between the agent and the discriminator, the policy-based agent can find evidence paths that match the demonstration distribution and synthesize these evidence paths to make predictions. Experiments show that the model achieves better results in both fact prediction and link prediction tasks.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128138263","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 view of the large volume and complex structure of electromagnetic space big data, it is difficult to store and retrieve spectrum data using traditional databases and knowledge graph. Due to the abstractness and space-time characteristics of electromagnetic spectrum data, the use of event forms can better represent the spectrum data, and also make people and machines better understand. Based on the knowledge graph and the concept of events, this paper constructs the spectrum event knowledge graph (EMS-DEKG) and compares several methods of spectrum data retrieval through experiments, which shows that the EMS-DEKG method improves the stability and timeliness of electromagnetic space big data storage and retrieval.
{"title":"Research on the construction and application of event based electromagnetic space big data knowledge graph","authors":"Dongsheng Li, Bing Ma, Yuanzhong Ren, K. Li","doi":"10.1117/12.2671438","DOIUrl":"https://doi.org/10.1117/12.2671438","url":null,"abstract":"In view of the large volume and complex structure of electromagnetic space big data, it is difficult to store and retrieve spectrum data using traditional databases and knowledge graph. Due to the abstractness and space-time characteristics of electromagnetic spectrum data, the use of event forms can better represent the spectrum data, and also make people and machines better understand. Based on the knowledge graph and the concept of events, this paper constructs the spectrum event knowledge graph (EMS-DEKG) and compares several methods of spectrum data retrieval through experiments, which shows that the EMS-DEKG method improves the stability and timeliness of electromagnetic space big data storage and retrieval.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133658006","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}
Juan Fang, Qiangang Zheng, Wei-ming Liu, Haibo Zhang
With the development of Reinforcement Learning (RL), it becomes able to solve the continuous action space problem and shows strong ability in dealing with complex nonlinear control problem. Based on the Deep Deterministic Policy Gradient (DDPG) algorithm, a novel scheme of aeroengine acceleration controller is proposed in this paper. According to the characteristics of the engine acceleration stage, the reward function is constructed, and the state parameters are updated in the form of sliding window to reduce the sensitivity of the network to noise. DDPG adopts actor-critic framework, critic calculates value function by the deep neural network, actor outputs action command and forms a closed-loop control system with the engine. The method is verified by digital simulation at ground condition and the results demonstrate that compared with the traditional PID controller, the acceleration time of DDPG controller is reduced by 41.56%. Additionally, the network converges within 400 steps.
{"title":"Optimization control with multi-constraint of aeroengine acceleration process based on reinforcement learning","authors":"Juan Fang, Qiangang Zheng, Wei-ming Liu, Haibo Zhang","doi":"10.1117/12.2671152","DOIUrl":"https://doi.org/10.1117/12.2671152","url":null,"abstract":"With the development of Reinforcement Learning (RL), it becomes able to solve the continuous action space problem and shows strong ability in dealing with complex nonlinear control problem. Based on the Deep Deterministic Policy Gradient (DDPG) algorithm, a novel scheme of aeroengine acceleration controller is proposed in this paper. According to the characteristics of the engine acceleration stage, the reward function is constructed, and the state parameters are updated in the form of sliding window to reduce the sensitivity of the network to noise. DDPG adopts actor-critic framework, critic calculates value function by the deep neural network, actor outputs action command and forms a closed-loop control system with the engine. The method is verified by digital simulation at ground condition and the results demonstrate that compared with the traditional PID controller, the acceleration time of DDPG controller is reduced by 41.56%. Additionally, the network converges within 400 steps.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131926190","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}
Expressway project is usually built in extremely complex natural and cultural environment. The whole process of project implementation management is a continuous and dynamic management practice process, which will be affected by internal and external uncertainties, and may directly affect the benefit and even the survival and development of enterprises. Therefore, this paper studies and analyzes the risk of investment in the highway project and several factors that may affect it. This paper selects the actual situation of 112 expressways in China and analyzes them through 30 different risk indexes. Through constructing multiple linear regression model, the factors that may affect the investment risk of expressway project are analyzed. Finally, there are 20 risk indicators to influence the investment risk of expressway project, and this paper constructs the weight model of expressway investment risk evaluation hierarchy and tries to verify it.
{"title":"Analysis of influencing factors on investment risk of expressway project in China","authors":"Liangjie Wu, Yangyang Li, lianlian shang","doi":"10.1117/12.2671195","DOIUrl":"https://doi.org/10.1117/12.2671195","url":null,"abstract":"Expressway project is usually built in extremely complex natural and cultural environment. The whole process of project implementation management is a continuous and dynamic management practice process, which will be affected by internal and external uncertainties, and may directly affect the benefit and even the survival and development of enterprises. Therefore, this paper studies and analyzes the risk of investment in the highway project and several factors that may affect it. This paper selects the actual situation of 112 expressways in China and analyzes them through 30 different risk indexes. Through constructing multiple linear regression model, the factors that may affect the investment risk of expressway project are analyzed. Finally, there are 20 risk indicators to influence the investment risk of expressway project, and this paper constructs the weight model of expressway investment risk evaluation hierarchy and tries to verify it.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115069218","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 the big data environment, the key is the precise recommendation of learning resources to learners. The core is the in-deep mining of learners’ personalized demands. This study solves this problem by constructing learner personas. Primarily, collect web learning data of learners to cluster them. Then analyze the characteristics of learners to predict their learning intentions and knowledge blind spots. Based on it, generate a clear personalized learning path subsequently. Precise positioning, quickly finding out the learner's ability and quality shortcomings. And completing the accurate recommendation to learners. It will help learners establish a reasonable learning path, and provide more accurate service support. This study will provide a theoretical basis for carrying out big data precision services and meeting the personalized learning needs of learners.
{"title":"Research on the construction of learner personas","authors":"Hailan Li, Kongyang Peng, Fengying Shang, Haoli Ren","doi":"10.1117/12.2671043","DOIUrl":"https://doi.org/10.1117/12.2671043","url":null,"abstract":"In the big data environment, the key is the precise recommendation of learning resources to learners. The core is the in-deep mining of learners’ personalized demands. This study solves this problem by constructing learner personas. Primarily, collect web learning data of learners to cluster them. Then analyze the characteristics of learners to predict their learning intentions and knowledge blind spots. Based on it, generate a clear personalized learning path subsequently. Precise positioning, quickly finding out the learner's ability and quality shortcomings. And completing the accurate recommendation to learners. It will help learners establish a reasonable learning path, and provide more accurate service support. This study will provide a theoretical basis for carrying out big data precision services and meeting the personalized learning needs of learners.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114863382","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}
For embedded modern equipment, the current gait recognition algorithm model is difficult to deploy on it due to a large amount of gait frame image data, slow network processing speed, complex structure and low computational efficiency. In this paper, a lightweight convolutional network model integrating the attention mechanism is proposed. The algorithm first performs morphological processing on the image, extracts the gait contour image, and calculates the gait energy image; integrates the attention mechanism with MobileNetV1. The feature information of the image is effectively extracted, and the parameters of the network are reduced. A number of body method validation experiments are conducted in the CAISIA-B gait database of the Chinese Academy of Sciences, and the experimental results are significantly improved with other deep learning models.
{"title":"Human gait recognition algorithm based on MobileNetV1 with attention mechanism","authors":"Jinsha Zhang, Xuedong Zhang","doi":"10.1117/12.2671349","DOIUrl":"https://doi.org/10.1117/12.2671349","url":null,"abstract":"For embedded modern equipment, the current gait recognition algorithm model is difficult to deploy on it due to a large amount of gait frame image data, slow network processing speed, complex structure and low computational efficiency. In this paper, a lightweight convolutional network model integrating the attention mechanism is proposed. The algorithm first performs morphological processing on the image, extracts the gait contour image, and calculates the gait energy image; integrates the attention mechanism with MobileNetV1. The feature information of the image is effectively extracted, and the parameters of the network are reduced. A number of body method validation experiments are conducted in the CAISIA-B gait database of the Chinese Academy of Sciences, and the experimental results are significantly improved with other deep learning models.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117081564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the limitation of computer capacity and energy of equipment, unmanned equipment cannot perform intensive computer tasks well during emergency failure inspection. In order to solve the above problems, this paper proposes a task waste strategy based on Deep Reinforcement Learning (DRL), which is mainly applicable to several UAVs and individual ES scenarios. First of all, an end edge cloud cooperative unloading architecture is built in the edge environment of UAV, and the problem of unloading tasks is classified as an optimization problem to achieve the minimum delay under the limit of the computing and communication resources of the Edge Server (ES). Secondly, the problem is constructed as Markov decision, and Deep Q Network (DQN) is used to solve the optimization problem, and experience playback mechanism and greedy algorithm are introduced into the learning process. Experiments show that the mitigation strategy has lower latency and higher reliability.
{"title":"Offloading strategy for UAV power inspection task based on deep reinforcement learning","authors":"Tong Jin, Gu Minghao, Sha Yun, Deng Fang-ming","doi":"10.1117/12.2671522","DOIUrl":"https://doi.org/10.1117/12.2671522","url":null,"abstract":"Due to the limitation of computer capacity and energy of equipment, unmanned equipment cannot perform intensive computer tasks well during emergency failure inspection. In order to solve the above problems, this paper proposes a task waste strategy based on Deep Reinforcement Learning (DRL), which is mainly applicable to several UAVs and individual ES scenarios. First of all, an end edge cloud cooperative unloading architecture is built in the edge environment of UAV, and the problem of unloading tasks is classified as an optimization problem to achieve the minimum delay under the limit of the computing and communication resources of the Edge Server (ES). Secondly, the problem is constructed as Markov decision, and Deep Q Network (DQN) is used to solve the optimization problem, and experience playback mechanism and greedy algorithm are introduced into the learning process. Experiments show that the mitigation strategy has lower latency and higher reliability.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"13 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115489426","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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