@inproceedings{87478113bb5b40449c67ee1d7e070fc0,
title = "Neural Network and Collaborative Computing for Image-based Train Location Data Acquisition",
abstract = "How to improve the performance and safety of the train has long been a question of great interest. The object of this study is to achieve the recognition of train speed and mileage that are provided by the train Driver Machine Interface (DMI) and reduce the time consumed by model training. For recognition part, a Convolutional Neural Network (CNN) and a Connectionist Temporal Classification (CTC) layer have been combined together to achieve an end-to-end recognition. For the part of time-consuming reduction, a collaborative computing was being utilized to offload the training tasks to each edge device. In addition, the process of offloading was modeled with Stochastic Petri Net (SPN). Overall, the recognition accuracy was significantly improved, compared to the traditional Optical Character Recognition (OCR) and CNNs, what is more, the time consumed on model training was also brought down as expect.",
keywords = "Collaborative computing, Connectionist temporal classification, Neural network, Petri nets",
author = "Xiaoqing Wu and Haifeng Song and Peng Wang and Jianfeng Cheng and Datian Zhou and Hairong Dong",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 5th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2021 ; Conference date: 19-01-2022 Through 22-01-2022",
year = "2023",
doi = "10.1007/978-981-19-3998-3\_167",
language = "英语",
isbn = "9789811939976",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "1797--1807",
editor = "Zhang Ren and Yongzhao Hua and Mengyi Wang",
booktitle = "Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control",
address = "德国",
}