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Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 32, Number 2(1) (2020)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 549-561
S&M2121 Research Paper of Special Issue https://doi.org/10.18494/SAM.2020.2529 Published: February 10, 2020 Development of Artificial-intelligence Vision System for Measurement On-service Train for Train-track Inspection [PDF] Anakkapon Saenthon, Somyot Kiatwanidvilai, and Suparat Pumyoy (Received June 29, 2019; Accepted January 24, 2020) Keywords: rail, track, artificial intelligence, vision system inspection
We studied and inspected the evaluation of safety during the operation of a rail system. In addition, in order to find patterns in the interaction between wheels and rails, the interaction is investigated using an automatic vision system to analyze the patterns that occur. The model of a faster region with a convolutional neural network (R-CNN) can be used to design artificial intelligence (AI) models with vision systems for detecting abnormal operations that occur, causing rail accidents, and to monitor the measurement of tracks and interactions between wheels and tracks in real time.
Corresponding author: Suparat Pumyoy  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Anakkapon Saenthon, Somyot Kiatwanidvilai, and Suparat Pumyoy, Development of Artificial-intelligence Vision System for Measurement On-service Train for Train-track Inspection, Sens. Mater., Vol. 32, No. 2, 2020, p. 549-561. |
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