• Proceedings of the Korean Society For Composite Materials Conference
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• 2002.05a
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• pp.114-117
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• 2002

현재 개발 중에 있는 비파괴 검사법인 Tapping Sound Analysis 의 실험적 검증을 위한 연구를 수행하였다. 손상이 없는 복합재료 구조물과 손상이 있는 복합재료 구조물에 대한 타격 실험을 통해 타격음과 타격력을 측정하여 비교하였다. Wavelet packet transform에 근거한 특성 추출법을 이용하여 타격음으로부터 손상 판단을 위한 특성을 추출하였다. 손상이 없는 구조물과 손상이 있는 구조물의 특성을 비교하기 위해, 특성 지수를 정의하였다. 정의된 특성 지수를 이용하여 손상이 없는 구조물과 손상이 있는 구조물의 타격음의 차이를 하나의 실수로 표현하였다.

• Structural Engineering and Mechanics
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• v.76 no.5
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• pp.591-598
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• 2020

Ultrasonic waves provide a non-destructive and sensitive way to monitor the concrete hydration. However, limited works are reported to monitor the evolution of the mechanical parameter at early ages. In this study, modified piezoelectric aggregates are embedded inside a concrete beam to excite and receive primary waves. A hydration index, namely, the variation of ultrasonic waveform (VUW) is developed to characterize the variation of the transmitted waves during the hydration process. The recorded hydration indices are compared with the compressive strength measured by destructive test at different ages. The results show that the VUW is closer to the compressive strength than the other two traditional hydration indices, ultrasonic velocity and wave packet energy. The proposed VUW provides a simple and accurate way to monitor the concrete hydration at early ages.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.4A
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• pp.310-315
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• 2002

The Bark Coherence Function (BCF) defies a coherence function within perceptual domain as a new cognition module, robust to linear distortions due to the analog interface of digital mobile system. Our previous experiments have shown the superiority of BCF over current measures. In this paper, a new BCF suitable for VoIP is developed. The unproved BCF is based on the wavelet series expansion that provides good frequency resolution while keeping good time locality. The proposed Wavelet based Bark Coherence function (WBCF) is robust to variable delay often observed in packet-based telephony such as Voice over Internet Protocol (VoIP). We also show that the refinement of time synchronization after signal decomposition can improve the performance of the WBCF. The regression analysis was performed with VoIP speech data. The correlation coefficients and the standard error of estimates computed using the WBCF showed noticeable improvement over the Perceptual Speech Quality Measure (PSQM) that is recommended by ITU-T.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.1
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• pp.77-85
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• 2008

The objective of the study is to construct a sensor fusion system for tool-condition monitoring (TCM) that will lead to a more efficient and economical drill usage. Drill-wear monitoring has an important attribute in the automatic machining processes as it can help preventing the damage of tools and workpieces, and optimizing the drill usage. In this study, we present the architectures of a multi-layer feed-forward neural network with Levenberg-Marquardt training algorithm based on sensor fusion for the monitoring of drill-wear condition. The input features to the neural networks were extracted from AE, vibration and current signals using the wavelet packet transform (WPT) analysis. Training and testing were performed at a moderate range of cutting conditions in the dry drilling of steel plates. The results show good performance in drill- wear monitoring by the proposed method of sensor fusion and neural network analysis.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.365-373
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• 1999

Recently, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as a model construction process. Irrespective of the efficiency of a learning procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network models. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables for neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2002.10a
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• pp.165-169
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• 2002

현재 개발 중에 있는 비파괴 검사법인 Tapping Sound Analysis 의 검증을 위해 실험으로 측정된 타격음(태핑음)과 수치해석으로 구한 타격음을 비교하였다. 손상이 없는 복합재료 구조물과 손상이 있는 복합재료 구조물을 제작하고 타격 실험을 통해 타격음과 타격력을 측정하였다. 타격음의 수치 모사를 위해 동적접촉 알고리듬을 이용한 유한요소법과 경계요소법을 이용하였다. Wavelet packet transform에 근거한 특성 추출법을 이용하여 타격음으로부터 손상 판단을 위한 특성을 추출하였다. 손상이 없는 구조물과 손상이 있는 구조물의 특성을 비교하기 위해, 특성 지수를 정의하였다. 실험 결과와 수치해석 결과의 비교를 통해 타격음 계산에 사용된 수치모델의 타당성을 밝혔다.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1955-1962
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• 2017

Induction motors are a workhorse for the industry. The condition monitoring and fault analysis are the main concern for the engineers. The bearing is one of the vital segment of the induction machine and the condition of the whole machine is decided based on the condition of the bearing. In the present paper, the vibration signal of the bearing has been used for the analysis. The first line of action is to perform a statistical analysis of the vibration signal which gives trends in signal. To get the location of a fault in the bearing the second action is to develop an index based on Wavelet Packet Transform node energy named as Bearing Damage Index (BDI). Further, Teager-Kaiser Energy Operator (TKEO) has been calculated from higher index value to get the envelope and finally Power Spectral Density (PSD) has been applied to identify the fault frequencies. A performance index has also been developed to compare the usefulness of the proposed method with other existing methods. The result shows that the strong amplitude of fault characteristics and its side bands help to decide the type of fault present in the recorded signal obtained from the bearing.

• Structural Monitoring and Maintenance
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• v.6 no.3
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• pp.219-235
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• 2019

Scheduled inspections of common crossings are one of the main cost drivers of railway maintenance. Prognostics and health management (PHM) approach and modern monitoring means offer many possibilities in the optimization of inspections and maintenance. The present paper deals with data driven prognosis of the common crossing remaining useful life (RUL) that is based on an inertial monitoring system. The problem of scheduled inspections system for common crossings is outlined and analysed. The proposed analysis of inertial signals with the maximal overlap discrete wavelet packet transform (MODWPT) and Shannon entropy (SE) estimates enable to extract the spectral features. The relevant features for the acceleration components are selected with application of Lasso (Least absolute shrinkage and selection operator) regularization. The features are fused with time domain information about the longitudinal position of wheels impact and train velocities by multivariate regression. The fused structural health (SH) indicator has a significant correlation to the lifetime of crossing. The RUL prognosis is performed on the linear degradation stochastic model with recursive Bayesian update. Prognosis testing metrics show the promising results for common crossing inspection scheduling improvement.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.2
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• pp.45.3-46
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• 2018

Oscillations in a sunspot are easily detected through the Doppler velocity observation. Although the sunspot oscillations look erratic, the wavelet analysis show that they consist of successive wave packets which have strong power near three or five minutes. Previous studies found that 3-min oscillation at the chromosphere is a visual pattern of upward propagating acoustic waves along the magnetic field lines. Resent multi-height observations help this like vertical study, however, we also focus on horizontal facet to extend three dimensional understand of sunspot waves. So, we investigate a fragmented sunspot expected to have complex wave profiles according to the positions in the sunspot observed by the Fast Imaging Solar Spectrograph. We choose 4 points at different umbral cores as sampling positions to determine coherence of oscillations. The sets of cross-correlation with three and five minutes bandpass filters during a single wave packet reveal interesting results. Na I line show weak correlations with some lags, but Fe I and Ni I have strong correlations with no phase difference over the sunspots. It is more remarkable at Ni I line with 3-min bandpass that all sets of cross-correlation look like the autocorrelation. We can interpret this as sunspot oscillations occur spontaneously over a sunspot at photosphere but not at chromosphere. It implies a larger or deeper origin of 3-min sunspot oscillation.

• The Journal of the Acoustical Society of Korea
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• v.42 no.3
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• pp.262-269
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• 2023

In this paper, we propose a Linear Prediction (LP) analysis-based feature for detecting Snapping Shrimp (SS) Noise (SSN) in underwater acoustic data. SS is a species that creates high amplitude signals in shallow, warm waters, and its frequent and loud sound is a major source of noise. The proposed feature takes advantage of the characteristic of SSN, which is sudden and rapidly disappearing, by using LP analysis to detect the exact noise interval and reduce the effects of SSN. The error between the predicted and measured value is large and results in effective SSN detection. To further improve performance, a constant false alarm rate detector is incorporated into the proposed feature. Our evaluation shows that the proposed methods outperform the state-of-the-art MultiLayer-Wavelet Packet Decomposition (ML-WPD) in terms of receiver operating characteristic curve and Area Under the Curve (AUC), with the LP analysis-based feature achieving a higher AUC by 0.12 on average and lower computational complexity.