• Title/Summary/Keyword: 고속열차 잡음

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Classification of Transport Vehicle Noise Events in Magnetotelluric Time Series Data in an Urban area Using Random Forest Techniques (Random Forest 기법을 이용한 도심지 MT 시계열 자료의 차량 잡음 분류)

  • Kwon, Hyoung-Seok;Ryu, Kyeongho;Sim, Ickhyeon;Lee, Choon-Ki;Oh, Seokhoon
    • Geophysics and Geophysical Exploration
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    • v.23 no.4
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    • pp.230-242
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    • 2020
  • We performed a magnetotelluric (MT) survey to delineate the geological structures below the depth of 20 km in the Gyeongju area where an earthquake with a magnitude of 5.8 occurred in September 2016. The measured MT data were severely distorted by electrical noise caused by subways, power lines, factories, houses, and farmlands, and by vehicle noise from passing trains and large trucks. Using machine-learning methods, we classified the MT time series data obtained near the railway and highway into two groups according to the inclusion of traffic noise. We applied three schemes, stochastic gradient descent, support vector machine, and random forest, to the time series data for the highspeed train noise. We formulated three datasets, Hx, Hy, and Hx & Hy, for the time series data of the large truck noise and applied the random forest method to each dataset. To evaluate the effect of removing the traffic noise, we compared the time series data, amplitude spectra, and apparent resistivity curves before and after removing the traffic noise from the time series data. We also examined the frequency range affected by traffic noise and whether artifact noise occurred during the traffic noise removal process as a result of the residual difference.

Performance Comparison of Space-Time Block Coding in High-speed Railway Channel (고속 철도 채널 환경에서 시공간 블록 부호 성능 비교)

  • Park, Seong-Guen;Lee, Jong-Woo;Jeon, Taehyun
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.39C no.3
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    • pp.291-297
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    • 2014
  • Due to the rapid increase in demand for transportation of human and freight in modern railway systems, the CBTC system has been proposed, which is the solution for improvement of the line capacity that has been limited by the conventional track circuit based train control system. In the CBTC system, higher reliability of the communication system should be guaranteed for the safety of passengers and trains. However, due to the inherent characteristics of the wireless channel environment, performance degradations are inevitable. The diversity techniques can increase the reliability of data transmission using multiple antennas. In this paper, we investigate the performance of the STBC in the railway channel environment. Rician fading model is used for the viaduct scenarios which take important roles in the railway system. Also, considered is the Doppler effect which is an important factor in the mobile communication system. Simulations are performed to analyze the performance of the STBC in various channel environments. Results show that the performance degradation due to the phase error in viaduct scenarios is independent of the diversity order but is affected by the constellation of the modulation.

Time-frequency Analysis of Train Vibration Using Order Analysis and Correlation (오더분석 및 상관관계를 활용한 철도차량 진동 데이터의 시간-주파수 분석)

  • Choi, Sung-Hoon;Igusa, Takeru;Park, Choon-Soo
    • Journal of the Korean Society for Railway
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    • v.12 no.6
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    • pp.989-995
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    • 2009
  • Short-time Fourier transforms (STFT) are useful for analyzing signals with harmonics that vary with time. If the variation of the harmonics with time is smooth, such as in kinematic vibrations in vehicles, then it is possible to improve the STFT using order spectra and correlation analysis. In this paper, it is shown how correlation analysis can be performed when the speed signal is noisy or unknown and then it is shown how order spectra become simple to compute after this analysis. The results are illustrated by an analysis of axle and car body vibrations in the prototype high-speed train, HSR-350x.