@inproceedings{edd2a4d9636b49bdbbccfc22e0f1572d,
title = "Train arrival warning system at railroad crossing using accelerometer sensor and neural network",
abstract = "Currently, there are many railroad crossings with no official guards. This will increase the number of traffic accidents. The trains can produce vibrations that travel through the air or rails, so the waves can be used to predict its presence or position. The detection of the sound can be disturbed by many noises. Therefore, this paper has analyzed railroad vibration using microelectromechanical systems-type accelerometer sensor to recognize the existence and to measure the position of the train as a warning system. Fast Fourier Transform algorithm is used to obtain the frequency spectrum feature. A neural network is used to recognize the vibration patterns of both the train and the non-train. The result of this experiment shows that the Neural Network can recognize the vibrations generated by the train to the non-train with a 100% success rate at a distance of 45 meters. The system is expected to be used as a warning system for the arrival of trains by giving alarm to people around the railway junction.",
author = "Heri Ardiansyah and Muhammad Rivai and Nurabdi, {Luhur Prihadi Eka}",
note = "Publisher Copyright: {\textcopyright} 2018 Author(s).; 4th International Conference on Engineering, Technology, and Industrial Application: Human-Dedicated Sustainable Product and Process Design: Materials, Resources, and Energy, ICETIA 2017 ; Conference date: 13-12-2017 Through 14-12-2017",
year = "2018",
month = jun,
day = "26",
doi = "10.1063/1.5042999",
language = "English",
series = "AIP Conference Proceedings",
publisher = "American Institute of Physics Inc.",
editor = "Nurul Hidayati and Tri Widayatno and Hari Prasetyo and Eko Setiawan",
booktitle = "Human-Dedicated Sustainable Product and Process Design",
}
