Prediction of approaching trains based on H‐ranks of track vibration signals

IF 8.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2024-10-01 DOI:10.1111/mice.13349

Ugne Orinaite, Rafal Burdzik, Vinayak Ranjan, Minvydas Ragulskis

引用次数: 0

Abstract

This paper introduces a method for forecasting the arrival of trains by analyzing track vibration signals. The proposed algorithms, based on H‐ranks of track vibration signals, can generate early alerts for approaching trains. These algorithms are robust to additive noise and environmental conditions. The theoretical foundation of the method involves the application of matrix operations to detect significant changes in vibration patterns, indicating an approaching train.

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基于轨道振动信号的 H 级预测驶近的列车

本文介绍了一种通过分析轨道振动信号预报列车到达的方法。所提出的算法基于轨道振动信号的 H-等级，可对即将到来的列车发出早期警报。这些算法对加性噪声和环境条件具有鲁棒性。该方法的理论基础包括应用矩阵运算来检测振动模式的显著变化，从而指示列车即将到来。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computer-Aided Civil and Infrastructure Engineering 工程技术-工程：土木

CiteScore

17.60

自引率

19.80%

发文量

146

审稿时长

1 months

期刊介绍： Computer-Aided Civil and Infrastructure Engineering stands as a scholarly, peer-reviewed archival journal, serving as a vital link between advancements in computer technology and civil and infrastructure engineering. The journal serves as a distinctive platform for the publication of original articles, spotlighting novel computational techniques and inventive applications of computers. Specifically, it concentrates on recent progress in computer and information technologies, fostering the development and application of emerging computing paradigms. Encompassing a broad scope, the journal addresses bridge, construction, environmental, highway, geotechnical, structural, transportation, and water resources engineering. It extends its reach to the management of infrastructure systems, covering domains such as highways, bridges, pavements, airports, and utilities. The journal delves into areas like artificial intelligence, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, internet-based technologies, knowledge discovery and engineering, machine learning, mobile computing, multimedia technologies, networking, neural network computing, optimization and search, parallel processing, robotics, smart structures, software engineering, virtual reality, and visualization techniques.