• Title/Summary/Keyword: Noise Identification

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Extension and Appication of Total Least Squares Method for the Identification of Bilinear Systems

  • Han, Seok-Won;Kim, Jin-Young;Sung, Koeng-Mo
    • The Journal of the Acoustical Society of Korea
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    • v.15 no.1E
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    • pp.59-64
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    • 1996

  • When the input-output record is available, the identification of a bilinear system is considered. It is assumed that the input is noise free and the output is contaminated by an additive noise. It is further assumed that the covariance matrix of the noise is known up to a factor of proportionality. The extended generalized total least squares (e-GTLS) method is proposed as one of the consistent estimators of the bilinear system parameters. Considering that the input is noise-free and that bilinear system equation is linear with respect to the system parameters, we extend the GTLS problem. The extended GTLS problem is reduced to an unconstrained minimization problem, and is solved by the Newton-Raphson method. We compare the GTLS method and the e-GTLS method in the point of the accuracy of the estimated system parameters.

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NOISE SOURCE IDENTIFICATION WITH INCREASED SPATIAL RESOLUTION

  • Gade, Svend;Hald, Jorgen;Ginn, Bernard
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2012.10a
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    • pp.636-642
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    • 2012

  • Delay-and-sum (DAS) Planar Beamforming has been a widely used Noise Source Identification Technique for the last decade. It is a quick one shot measurement technique being able to map sources that are larger than the array itself. The spatial resolution is proportional to distance between array and source, and inversely proportional to wavelength, thus the resolution is only good at medium to high frequencies. Improved algorithms using iterative de-convolution techniques offers up to ten times better resolution. The principle behind these techniques is described in this paper, as well as measurement examples from the automotive industry are presented.

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Damage assessment of a bridge based on mode shapes estimated by responses of passing vehicles

  • Oshima, Yoshinobu;Yamamoto, Kyosuke;Sugiura, Kunitomo
    • Smart Structures and Systems
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    • v.13 no.5
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    • pp.731-753
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    • 2014

  • In this study, an indirect approach is developed for assessing the state of a bridge on the basis of mode shapes estimated by the responses of passing vehicles. Two types of damages, i.e., immobilization of a support and decrease in beam stiffness at the center, are evaluated with varying degrees of road roughness and measurement noise. The assessment theory's feasibility is verified through numerical simulations of interactive vibration between a two-dimensional beam and passing vehicles modeled simply as sprung mass. It is determined that the damage state can be recognized by the estimated mode shapes when the beam incurs severe damage, such as immobilization of rotational support, and the responses contain no noise. However, the developed theory has low robustness against noise. Therefore, numerous measurements are needed for damage identification when the measurement is contaminated with noise.

  • https://doi.org/10.12989/sss.2014.13.5.731 인용 KSCI

Lofargram analysis and identification of ship noise based on Hough transform and convolutional neural network model (허프 변환과 convolutional neural network 모델 기반 선박 소음의 로파그램 분석 및 식별)

  • Junbeom Cho;Yonghoon Ha
    • The Journal of the Acoustical Society of Korea
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    • v.43 no.1
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    • pp.19-28
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    • 2024

  • This paper proposes a method to improve the performance of ship identification through lofargram analysis of ship noise by applying the Hough Transform to a Convolutional Neural Network (CNN) model. When processing the signals received by a passive sonar, the time-frequency domain representation known as lofargram is generated. The machinery noise radiated by ships appears as tonal signals on the lofargram, and the class of the ship can be specified by analyzing it. However, analyzing lofargram is a specialized and time-consuming task performed by well-trained analysts. Additionally, the analysis for target identification is very challenging because the lofargram also displays various background noises due to the characteristics of the underwater environment. To address this issue, the Hough Transform is applied to the lofargram to add lines, thereby emphasizing the tonal signals. As a result of identification using CNN models on both the original lofargrams and the lofargrams with Hough transform, it is shown that the application of the Hough transform improves lofargram identification performance, as indicated by increased accuracy and macro F1 scores for three different CNN models.

  • https://doi.org/10.7776/ASK.2024.43.1.019 인용 PDF