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      https://www.riss.kr/link?id=A106404780

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      다국어 초록 (Multilingual Abstract)

      Indirect tire pressure monitoring system(i-TPMS) monitors the inflation pressures in pneumatic tires by using wheel speed signals acquired from the anti-lock braking system(ABS). Frequency analysis and wheel radius analysis are performed at the same time in order to monitor the tire pressures indirectly. The resonance frequency of the tire torsional vibration decreases as the tire pressure decreases. It is necessary to estimate the resonance frequency in order to detect a reduction in tire pressure. This paper discusses the method of estimating the resonance frequency with the rotational wheel speed signal. First, the toothed wheel error of the wheel speed sensor and the wheel speed corrected with the toothed wheel error are calculated from the wheel speed signals. The engine noise caused by the explosion in the four-stroke of engine is eliminated by the notch filter, which is designed by using the engine rotational speed. Finally, the resonance frequency is evaluated via model parameter estimation based on the autoregressive(AR) model. Vehicle tests have been conducted in order to evaluate the proposed estimation method of resonance frequency. The findings of the vehicle test have shown that the proposed method provides satisfactory performance for the warning indication of tire pressure loss.
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      Indirect tire pressure monitoring system(i-TPMS) monitors the inflation pressures in pneumatic tires by using wheel speed signals acquired from the anti-lock braking system(ABS). Frequency analysis and wheel radius analysis are performed at the same t...

      Indirect tire pressure monitoring system(i-TPMS) monitors the inflation pressures in pneumatic tires by using wheel speed signals acquired from the anti-lock braking system(ABS). Frequency analysis and wheel radius analysis are performed at the same time in order to monitor the tire pressures indirectly. The resonance frequency of the tire torsional vibration decreases as the tire pressure decreases. It is necessary to estimate the resonance frequency in order to detect a reduction in tire pressure. This paper discusses the method of estimating the resonance frequency with the rotational wheel speed signal. First, the toothed wheel error of the wheel speed sensor and the wheel speed corrected with the toothed wheel error are calculated from the wheel speed signals. The engine noise caused by the explosion in the four-stroke of engine is eliminated by the notch filter, which is designed by using the engine rotational speed. Finally, the resonance frequency is evaluated via model parameter estimation based on the autoregressive(AR) model. Vehicle tests have been conducted in order to evaluate the proposed estimation method of resonance frequency. The findings of the vehicle test have shown that the proposed method provides satisfactory performance for the warning indication of tire pressure loss.

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      참고문헌 (Reference)

      1 F. Gustafsson, "Tire Pressure Estimation"

      2 L. Ljung, "System Identification Theory for the User" Prentice Hall 1999

      3 R. K. Thenua, "Simulation and Performance Analysis of Adaptive Filter in Noise Cancellation" 2 (2): 4373-4378, 2010

      4 A. H. El Husseini, "Optimization of the Second Order Autoregressive Model AR(2) for Rayleigh-Jakes Flat Fading Channel Estimation with Kalman Filter" 2017

      5 N. Persson, "Event based Sampling with Application to Spectral Estimation" Department of Electrical Engineering, Linkoping University 2002

      6 N. Persson, "Event Based Sampling with Application to Spectral Estimation" 2002

      7 J. G. Proakis, "Digital Signal Processing" Pearson Education 2-6, 2007

      8 M. S. Lee, "A Consideration on the Safety Standard of the Motor Vehicle Equipped with Tire Pressure Monitoring System" 25-31, 2010

      1 F. Gustafsson, "Tire Pressure Estimation"

      2 L. Ljung, "System Identification Theory for the User" Prentice Hall 1999

      3 R. K. Thenua, "Simulation and Performance Analysis of Adaptive Filter in Noise Cancellation" 2 (2): 4373-4378, 2010

      4 A. H. El Husseini, "Optimization of the Second Order Autoregressive Model AR(2) for Rayleigh-Jakes Flat Fading Channel Estimation with Kalman Filter" 2017

      5 N. Persson, "Event based Sampling with Application to Spectral Estimation" Department of Electrical Engineering, Linkoping University 2002

      6 N. Persson, "Event Based Sampling with Application to Spectral Estimation" 2002

      7 J. G. Proakis, "Digital Signal Processing" Pearson Education 2-6, 2007

      8 M. S. Lee, "A Consideration on the Safety Standard of the Motor Vehicle Equipped with Tire Pressure Monitoring System" 25-31, 2010

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2023 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2020-01-01 평가 등재학술지 유지 (해외등재 학술지 평가) KCI등재
      2018-11-01 평가 SCOPUS 등재 (기타) KCI등재
      2016-01-01 평가 등재학술지 선정 (계속평가) KCI등재
      2015-12-01 평가 등재후보로 하락 (기타) KCI등재후보
      2011-01-01 평가 등재 1차 FAIL (등재유지) KCI등재
      2009-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2007-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2005-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2002-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      1999-07-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.38 0.38 0.38
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.37 0.36 0.793 0.11
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