• Journal of the Korea institute for structural maintenance and inspection
• /
• v.9 no.2
• /
• pp.129-136
• /
• 2005

The vehicle loading test under the strict traffic control is generally carried out as a present practice in an evaluation process of the bearing capacity of a bridge. The quasi-static load test is recently proposed to mitigate the traffic condition of test, and analyze the disturbed acceleration time-history data of free vibration due to the ambient traffic on the bridge by Fourier transform to calculate only the natural frequencies of the bridge. The calculated frequencies have some errors due to the analysis technique as well as the influence of ambient traffic loads, and in addition to it is cumbersome to obtain the free vibration data during a quasi-static load test. In this study, the wavelet transform technique using Morlet wavelet is used to analyze the acceleration data recorded during a quasi-static load test on a bridge and calculate the natural frequencies and the modal damping ratios of the bridge. The study results show that the wavelet transform technique is a reliable and reasonable method to analyze test data and obtain the natural frequencies and the modal damping ratios of a bridge regardless of the data types i.e. free or forced vibrations.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
• /
• 2001.05a
• /
• pp.260-265
• /
• 2001

본 연구에서는 연속 웨이블렛 변환을 이용하여 Ringing 현상을 연구하였다. 사용되어진 웨이블렛은 Morlet 웨이블렛이었고, 실험은 파수조에서 수행되었다. 또한 Ringing 현상을 다루고자 쇄파를 발생시켰다. 실험에 쓰인 모델은 수면을 통과하여 수직으로 고정된 원주 실린더였고, 이 실린더에 작용된 힘과 파고가 측정되어졌다. 이들은 연속 웨이블렛 변환으로 분석되어졌고, 이러한 분석으로 얻어진 scalogram 들은 고주파 성분이 쇄파 충격시 만들어진다는 사실을 시간영역상에서 보여주었다. 이는 기존의 스펙트럼 분석에서는 찾기 힘든 것이다. Coherence 분석도 위의 결론을 뒷받침해 주었다.

• Proceedings of the Earthquake Engineering Society of Korea Conference
• /
• 2002.09a
• /
• pp.173-180
• /
• 2002

This paper presents a signal processing procedure to detect damage locations of frame structures by using continuous wavelet transform. Morlet wavelet is used as a mother wavelet in wavelet transform. Wavelet transform has the characteristics that allows the use of long time intervals at more precise low-frequency information, and shorter regions at high-frequency information. By this wavelet transform characteristics, Morlet wavelet may be used to identify the locations of damages in the structures. The numerical case studies show that this method can be applied to detect the damage location under a controlled sweeping load.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.21 no.3
• /
• pp.75-81
• /
• 2007

Recently the subject of "wavelet analysis" has be drawn by both mathematical and engineering application fields such as Signal Processing, Compression/Decomposition, Wavelet-Neural Network, Statistics and etc. Even though its similar to Fourier analysis, wavelet is a versatile tool with much mathematical content and great potential for applications. Especially, wavelet transform uses localizable various mother wavelet functions in time-frequency domain. Therefore, wavelet transform has good time-analysis ability for high frequency component, and has good frequency-analysis ability for low frequency component. Using the discriminative ability is more easy method than other conventional techniques. In this paper, Morlet wavelet transform was applied to discriminate the kind of line fault by acquired data from real power transformation network. The experimental result presented that Morlet wavelet transform is easier, and more useful method than the Fast Fourier Transform(FFT).

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.33 no.8
• /
• pp.753-759
• /
• 2009

Feed-forward neural networks have been widely used as function approximation tools in the context of global approximate optimization. In the present study, a wavelet neural network (WNN) which is based on wavelet transform theory is suggested as an alternative to a traditional back-propagation neural network (BPN). The basic theory of wavelet neural network is briefly described, and approximation performance is tested using a nonlinear multimodal function and a composite rotor blade analysis problem. Laplacian of Gaussian function, Mexican function, and Morlet function are considered during the construction of WNN architectures. In addition, approximation results from WNN are compared with those from BPN.