Detail publikace
Neural Network-Based Train Identification in Railway Switches and Crossings Using Accelerometer Data
KRČ, R. PODROUŽEK, J. KRATOCHVÍLOVÁ, M. VUKUŠIČ, I. PLÁŠEK, O.
Originální název
Neural Network-Based Train Identification in Railway Switches and Crossings Using Accelerometer Data
Anglický název
Neural Network-Based Train Identification in Railway Switches and Crossings Using Accelerometer Data
Jazyk
en
Originální abstrakt
This paper aims to analyse possibilities of train type identification in railway switches and crossings (S&C) based on accelerometer data by using contemporary machine learning methods such as neural networks. That is a unique approach since trains have been only identified in a straight track. Accelerometer sensors placed around the S&C structure were the source of input data for subsequent models. Data from four S&C at different locations were considered and various neural network architectures evaluated. The research indicated the feasibility to identify trains in S&C using neural networks from accelerometer data. Models trained at one location are generally transferable to another location despite differences in geometrical parameters, substructure, and direction of passing trains. Other challenges include small dataset and speed variation of the trains that must be considered for accurate identification. Results are obtained using statistical bootstrapping and are presented in a form of confusion matrices.
Anglický abstrakt
This paper aims to analyse possibilities of train type identification in railway switches and crossings (S&C) based on accelerometer data by using contemporary machine learning methods such as neural networks. That is a unique approach since trains have been only identified in a straight track. Accelerometer sensors placed around the S&C structure were the source of input data for subsequent models. Data from four S&C at different locations were considered and various neural network architectures evaluated. The research indicated the feasibility to identify trains in S&C using neural networks from accelerometer data. Models trained at one location are generally transferable to another location despite differences in geometrical parameters, substructure, and direction of passing trains. Other challenges include small dataset and speed variation of the trains that must be considered for accurate identification. Results are obtained using statistical bootstrapping and are presented in a form of confusion matrices.
Plný text v Digitální knihovně
Dokumenty
BibTex
@article{BUT168007,
author="Rostislav {Krč} and Jan {Podroužek} and Martina {Pálková} and Ivan {Vukušič} and Otto {Plášek}",
title="Neural Network-Based Train Identification in Railway Switches and Crossings Using Accelerometer Data",
annote="This paper aims to analyse possibilities of train type identification in railway switches and crossings (S&C) based on accelerometer data by using contemporary machine learning methods such as neural networks. That is a unique approach since trains have been only identified in a straight track. Accelerometer sensors placed around the S&C structure were the source of input data for subsequent models. Data from four S&C at different locations were considered and various neural network architectures evaluated. The research indicated the feasibility to identify trains in S&C using neural networks from accelerometer data. Models trained at one location are generally transferable to another location despite differences in geometrical parameters, substructure, and direction of passing trains. Other challenges include small dataset and speed variation of the trains that must be considered for accurate identification. Results are obtained using statistical bootstrapping and are presented in a form of confusion matrices.",
address="Hindawi",
chapter="168007",
doi="10.1155/2020/8841810",
howpublished="online",
institution="Hindawi",
number="1",
volume="2020",
year="2020",
month="november",
pages="1--10",
publisher="Hindawi",
type="journal article in Web of Science"
}