A federated learning-based license plate recognition scheme for 5G-enabled Internet of vehicles
- Kong, Xiangjie, Wang, Kailai, Hou, Mingliang, Hao, Xinyu, Shen, Guojiang, Chen, Xin, Xia, Feng
- Authors: Kong, Xiangjie , Wang, Kailai , Hou, Mingliang , Hao, Xinyu , Shen, Guojiang , Chen, Xin , Xia, Feng
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 17, no. 12 (Dec 2021), p. 8523-8530
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- Description: License plate is an essential characteristic to identify vehicles for the traffic management, and thus, license plate recognition is important for Internet of Vehicles. Since 5G has been widely covered, mobile devices are utilized to assist the traffic management, which is a significant part of Industry 4.0. However, there have always been privacy risks due to centralized training of models. Also, the trained model cannot be directly deployed on the mobile device due to its large number of parameters. In this article, we propose a federated learning-based license plate recognition framework (FedLPR) to solve these problems. We design detection and recognition model to apply in the mobile device. In terms of user privacy, data in individuals is harnessed on their mobile devices instead of the server to train models based on federated learning. Extensive experiments demonstrate that FedLPR has high accuracy and acceptable communication cost while preserving user privacy.
- Authors: Kong, Xiangjie , Wang, Kailai , Hou, Mingliang , Hao, Xinyu , Shen, Guojiang , Chen, Xin , Xia, Feng
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 17, no. 12 (Dec 2021), p. 8523-8530
- Full Text:
- Reviewed:
- Description: License plate is an essential characteristic to identify vehicles for the traffic management, and thus, license plate recognition is important for Internet of Vehicles. Since 5G has been widely covered, mobile devices are utilized to assist the traffic management, which is a significant part of Industry 4.0. However, there have always been privacy risks due to centralized training of models. Also, the trained model cannot be directly deployed on the mobile device due to its large number of parameters. In this article, we propose a federated learning-based license plate recognition framework (FedLPR) to solve these problems. We design detection and recognition model to apply in the mobile device. In terms of user privacy, data in individuals is harnessed on their mobile devices instead of the server to train models based on federated learning. Extensive experiments demonstrate that FedLPR has high accuracy and acceptable communication cost while preserving user privacy.
Exploring human mobility for multi-pattern passenger prediction : a graph learning framework
- Kong, Xiangjie, Wang, Kailai, Hou, Mingliang, Xia, Feng, Karmakar, Gour, Li, Jianxin
- Authors: Kong, Xiangjie , Wang, Kailai , Hou, Mingliang , Xia, Feng , Karmakar, Gour , Li, Jianxin
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 23, no. 9 (2022), p. 16148-16160
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- Description: Traffic flow prediction is an integral part of an intelligent transportation system and thus fundamental for various traffic-related applications. Buses are an indispensable way of moving for urban residents with fixed routes and schedules, which leads to latent travel regularity. However, human mobility patterns, specifically the complex relationships between bus passengers, are deeply hidden in this fixed mobility mode. Although many models exist to predict traffic flow, human mobility patterns have not been well explored in this regard. To address this research gap and learn human mobility knowledge from this fixed travel behaviors, we propose a multi-pattern passenger flow prediction framework, MPGCN, based on Graph Convolutional Network (GCN). Firstly, we construct a novel sharing-stop network to model relationships between passengers based on bus record data. Then, we employ GCN to extract features from the graph by learning useful topology information and introduce a deep clustering method to recognize mobility patterns hidden in bus passengers. Furthermore, to fully utilize spatio-temporal information, we propose GCN2Flow to predict passenger flow based on various mobility patterns. To the best of our knowledge, this paper is the first work to adopt a multi-pattern approach to predict the bus passenger flow by taking advantage of graph learning. We design a case study for optimizing routes. Extensive experiments upon a real-world bus dataset demonstrate that MPGCN has potential efficacy in passenger flow prediction and route optimization. © 2000-2011 IEEE.
- Authors: Kong, Xiangjie , Wang, Kailai , Hou, Mingliang , Xia, Feng , Karmakar, Gour , Li, Jianxin
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 23, no. 9 (2022), p. 16148-16160
- Full Text:
- Reviewed:
- Description: Traffic flow prediction is an integral part of an intelligent transportation system and thus fundamental for various traffic-related applications. Buses are an indispensable way of moving for urban residents with fixed routes and schedules, which leads to latent travel regularity. However, human mobility patterns, specifically the complex relationships between bus passengers, are deeply hidden in this fixed mobility mode. Although many models exist to predict traffic flow, human mobility patterns have not been well explored in this regard. To address this research gap and learn human mobility knowledge from this fixed travel behaviors, we propose a multi-pattern passenger flow prediction framework, MPGCN, based on Graph Convolutional Network (GCN). Firstly, we construct a novel sharing-stop network to model relationships between passengers based on bus record data. Then, we employ GCN to extract features from the graph by learning useful topology information and introduce a deep clustering method to recognize mobility patterns hidden in bus passengers. Furthermore, to fully utilize spatio-temporal information, we propose GCN2Flow to predict passenger flow based on various mobility patterns. To the best of our knowledge, this paper is the first work to adopt a multi-pattern approach to predict the bus passenger flow by taking advantage of graph learning. We design a case study for optimizing routes. Extensive experiments upon a real-world bus dataset demonstrate that MPGCN has potential efficacy in passenger flow prediction and route optimization. © 2000-2011 IEEE.
API : an index for quantifying a scholar's academic potential
- Ren, Jing, Wang, Lei, Wang, Kailai, Yu, Shuo, Hou, Mingliang, Lee, Ivan, Kong, Xiangjie, Xia, Feng
- Authors: Ren, Jing , Wang, Lei , Wang, Kailai , Yu, Shuo , Hou, Mingliang , Lee, Ivan , Kong, Xiangjie , Xia, Feng
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 178675-178684
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- Description: In the context of big scholarly data, various metrics and indicators have been widely applied to evaluate the impact of scholars from different perspectives, such as publication counts, citations, ${h}$-index, and their variants. However, these indicators have limited capacity in characterizing prospective impacts or achievements of scholars. To solve this problem, we propose the Academic Potential Index (API) to quantify scholar's academic potential. Furthermore, an algorithm is devised to calculate the value of API. It should be noted that API is a dynamic index throughout scholar's academic career. By applying API to rank scholars, we can identify scholars who show their academic potentials during the early academic careers. With extensive experiments conducted based on the Microsoft Academic Graph dataset, it can be found that the proposed index evaluates scholars' academic potentials effectively and captures the variation tendency of their academic impacts. Besides, we also apply this index to identify rising stars in academia. Experimental results show that the proposed API can achieve superior performance in identifying potential scholars compared with three baseline methods. © 2019 IEEE.
- Authors: Ren, Jing , Wang, Lei , Wang, Kailai , Yu, Shuo , Hou, Mingliang , Lee, Ivan , Kong, Xiangjie , Xia, Feng
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 178675-178684
- Full Text:
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- Description: In the context of big scholarly data, various metrics and indicators have been widely applied to evaluate the impact of scholars from different perspectives, such as publication counts, citations, ${h}$-index, and their variants. However, these indicators have limited capacity in characterizing prospective impacts or achievements of scholars. To solve this problem, we propose the Academic Potential Index (API) to quantify scholar's academic potential. Furthermore, an algorithm is devised to calculate the value of API. It should be noted that API is a dynamic index throughout scholar's academic career. By applying API to rank scholars, we can identify scholars who show their academic potentials during the early academic careers. With extensive experiments conducted based on the Microsoft Academic Graph dataset, it can be found that the proposed index evaluates scholars' academic potentials effectively and captures the variation tendency of their academic impacts. Besides, we also apply this index to identify rising stars in academia. Experimental results show that the proposed API can achieve superior performance in identifying potential scholars compared with three baseline methods. © 2019 IEEE.
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