• Structural Engineering and Mechanics
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• v.38 no.1
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• pp.81-97
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• 2011

In this research, the wavelet transform is used to analyze time response of a cracked beam carrying moving mass for damage detection. In this respect, a new damage detection method based on the combined use of continuous and discrete wavelet transforms is proposed. It is shown that this method is more capable in making damage signature evident than the traditional two approaches based on direct investigation of the wavelet coefficients of structural response. By the proposed method, it is concluded that strain data outperforms displacement data at the same point in revealing damage signature. In addition, influence of moving mass-induced terms such as gravitational, Coriolis, centrifuge forces, and pure inertia force along the deflection direction to damage detection is investigated on a sample case. From this analysis it is concluded that centrifuge force has the most influence on making both displacement and strain data damage-sensitive. The Coriolis effect is the second to improve the damage-sensitivity of data. However, its impact is considerably less than the former. The rest, on the other hand, are observed to be insufficient alone.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.301-303
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• 2000

This paper presents Kohonen neural network and wavelet transform analysis based technique for industrial peak load forecasting for the purpose of peak demand control. Firstly, one year of historical load data were sorted and clustered into several groups using Kohonen neural network and then wavelet transforms are adopted using the Biorthogonal mother wavelet in order to forecast the peak load of one hour ahead. The 5-level decomposition of the daily industrial load curve is implemented to consider the weather sensitive component of loads effectively. The wavelet coefficients associated with certain frequency and time localization is adjusted using the conventional multiple regression method and the components are reconstructed to predict the final loads through a six-scale synthesis technique.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.1
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• pp.1-10
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• 2013

In JPEG2000, the Cohen-Daubechies-Feauveau (CDF) 9/7-tap wavelet filter is implemented using the conventional lifting scheme. On the other hand, this wavelet filter has two problems: the filter coefficients remain complex, and the conventional lifting scheme does not consider the image edges in the coding process. This paper proposes an effective lifting scheme to solve these problems. For this purpose, optimal 9/7-tap wavelet filters were designed in two steps. In the first step, the appropriate filter coefficients were selected. In the second step, a median operator was employed to consider the image edges. The experimental results with the median lifting scheme and the combination of filter optimization with the median lifting show that the proposed methods outperform the well-known CDF 9/7-tap wavelet filter of JPEG2000 on the edge-dominant images.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2395-2398
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• 2003

This paper focuses on lossless medical image compression methods for medical images that operate on two-dimensional(2D) reversible integer wavelet transform. We offer an application of the Set Partitioning in Hierarchical Trees(SPIHT) algorithm [1][3][9] to medical images, using a 2D wavelet decomposition and a 2D spatial dependence tree. The wavelet decomposition is accomplished with integer wavelet filters implemented with the lifting method, where careful scaling and truncations keep the integer precision small and the transform unitary. We have tested our encoder on medical images using different integer filters. Results show that our algorithm with certain filters performs as well and sometimes better in lossless coding than previous coding systems using 2D integer wavelet transforms on medical images.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.173-174
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• 2008

Wavelet transform is a new method for power system analysis. On the basis of extensive investigation, optimal mother wavelets for the detection of leakage current are chosen. The recommended mother wavelet is 'Daubechies 4' wavelet. This paper proposes a technique for modeling toe finding point of leakage current in distribution system using wavelet transform and EMTP MODELS.

• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.239-241
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• 1999

This paper proposes a novel wavelet transform and Kohonen neural network based technique for short-time load forecasting of power systems. Firstly. Kohonen Self-organizing map(KSOM) is applied to classify the loads and then the Daubechies D2, D4 and D10 wavelet transforms are adopted in order to forecast the short-term loads. The wavelet coefficients associated with certain frequency and time localisation are adjusted using the conventional multiple regression method and then reconstructed in order to forecast the final loads through a four-scale synthesis technique. The outcome of the study clearly indicates that the proposed composite model of Kohonen neural network and wavelet transform approach can be used as an attractive and effective means for short-term load forecasting.

• Proceedings of the IEEK Conference
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• summer
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• pp.249-253
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• 2004

Wavelet transform (WT) has attracted most engineers and scientists because of its excellent properties. The coherence of practical approach and a theoretical basis not only solves currently important problems, but also gives the potential of formulating and solving completely new problems. It has been show that multi-resolution analysis of Wavelet transforms is good solution in speech analysis and threshold of wavelet coefficients has near optimal noise reduction property for many classes of signals. This paper proposed applications of wavelet in speech processing: pitch detection, voice-unvoice (V -UV) decision, denoising with the detailed algorithms and results.

• Proceedings of the Korean Society Of Semiconductor Equipment Technology
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• 2004.05a
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• pp.217-222
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• 2004

This paper focuses on lossy medical image compression methods for medical images that operate on two-dimensional(2D) integer wavelet transform. We offer an application of the Set Partitioning in Hierarchical Trees(SPIHT) algorithm to medical images, using a 2D wavelet decomposition and a 2D spatial dependence tree. The wavelet decomposition is accomplished with integer wavelet filters implemented with the lifting method, where careful scaling and truncations keep the integer precision small and the transform unitary. We have tested our encoder on medical images using different integer filters. Results show that our algorithm with certain filters performs as well and is sometimes better lossy coding using 2D integer wavelet transforms on medical images.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.10
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• pp.473-478
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• 2001

Image processings using the two-dimensional wavelet transform (2DWT) have been a very active research area in recent years because the 2DWT possess many good properties. However, the discrete 2DWT can not be used for pattern recognition directly because it does not have the translation property. In this paper, we show why conventional discrete two-dimensional wavelet transforms cannot be used for pattern recognitions directly. Then, we propose a new method that makes it possible to use discrete 2DWT to pattern recognition without modification of standard pyramidal algorithms. The main idea of our method is to postprocess the wavelet transformed images using the second-order neural network. To justify the validity of the method, evaluations with test images were performed. The effectiveness of the method can be shown by the evaluation results.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.225-228
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• 1996

ECG data are used for the diagnostic purposes with many clinical situations, especially heart disease. In this paper, an efficient ECG data compression technique by wavelet transform and high-speed vector quantization on PMS-B algorithm is proposed. In general, ECG data compression techniques are divided into two categories: direct and transform methods. The direct data compression techniques are AZTEC, TP, CORTES, FAN and SAPA algorithms, besides the transform methods include K-L, Fourier, Walsh, and wavelet transforms. In this paper, we applied wavelet analysis to the ECG data. In particular, vector quantization on PMS-B algorithm to the wavelet coefficients in the higher frequency regions, but scalar quantized in the lower frequency regions by PCM. Finally, the quantized indices were compressed by LZW lossless entropy encoder. As the result of simulation, it turns out to get sufficient compression ratio while keeping clinically acceptable PRD.