• 한국정밀공학회지
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• 제17권7호
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• pp.217-225
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• 2000

Scanning Acoustic Microscope (SAM) is used as an important nondestructive test tool in semiconductor reliability evaluation and failure analysis. However, inspections of chip attach adhesive interface fer thin chip has proven difficulty as the reflected signals from the chip top and bottom are superimposed. In this paper, in order to overcome this difficulty, a new signal processing method based on the deconvolution technique combined with the wavelet transform is proposed. The wavelet transform complements a disability of deconvolution technique of which performance largely decreases when the waveform of target signal is not identical to that of reference signal. Performances of the proposed method are demonstrated by through computer simulations using model signal and experiments for the fabricated semiconductor samples, and satisfactory results are obtained.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2000년도 하계학술대회 논문집
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• pp.585-591
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• 2000

The wavelet transform is the most recent technique for processing signals with time-varying spectra. In this paper, the wavelet transform is utilized to improved the assessment and multi-resolution analysis of acoustic emission signals generating in partial discharge. This paper especially deals with the assessment of process statistical parameter using the features extracted from the wavelet coefficients of measured acoustic emission signals in case of applied voltage 20[kv]. Since the parameter assessment using all wavelet coefficients will often turn out leads to inefficient or inaccurate results, we selected that level-3 stage of multi decomposition in discrete wavelet transform. We applied FIR(Finite Impulse Response)digital filter algorithm in discrete to suppression for random noise. The white noise be included high frequency component denoised as decomposition of discrete wavelet transform level-3. We make use of the feature extraction parameter namely, maximum value of acoustic emission signal, average value, dispersion, skewness, kurtosis, etc. The effectiveness of this new method has been verified on ability a diagnosis transformer go through feature extraction in stage of acting(the early period, the last period) .

• Wind and Structures
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• 제15권3호
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• pp.223-245
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• 2012

Multivariate simulation is necessary for cases where non-Gaussian processes at spatially distributed locations are desired. A simulation algorithm to generate non-Gaussian wind pressure fields is proposed. Gaussian sample fields are generated based on the spectral representation method using wavelet transforms method and then mapped into non-Gaussian sample fields with the aid of a CDF mapping transformation technique. To illustrate the procedure, this approach is applied to experimental results obtained from wind tunnel tests on the domes. A multivariate Gaussian simulation technique is developed and then extended to multivariate non-Gaussian simulation using the CDF mapping technique. It is proposed to develop a new wavelet-based CDF mapping technique for simulation of multivariate non-Gaussian wind pressure process. The efficiency of the proposed methodology for the non-Gaussian nature of pressure fluctuations on separated flow regions of different rise-span ratios of domes is also discussed.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.1811-1814
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• 2003

In this paper, a novel watermarking technique is proposed to authenticate the owner-ship of copyright for the digital contents. Using the 2-level DWT(Discrete Wavelet Transform) we divide a specific frequency band into detailed blocks and apply PIM(picture information measure). After the complexity is calculated, the watermark is embedded in only on high complexity areas. Conventional watermarking technique damages to the original image, because it does not consider the feature of the whole area or a specific frequency band. Easily affected by noise and compression, it is difficult to extract the watermark. However, the proposed watermarking technique, considering the complexity of input image, does not damage to the original image Simulation result show that the proposed technique has the robustness of JPEG compression, noise and filtering such as a general signal processing

• 소음진동
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• 제8권5호
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• pp.957-963
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• 1998

A new signal processing technique, the directional harmonic wavelet map(dHWM), is presented to characterize the instantaneous planar motion of a measurement point in a structure from its transient complex-valued vibration signal. It is proven that the directional auto-HWM essentially tracks the shape and directively of the instantaneous planar motion, whereas the phase of the directional cross-HWM indicates its inclination angle. Finally, the technique is suessfully applied to an automobile engine for characterization of its transient motion during crank-on/idling/engine-off.

• 조명전기설비학회논문지
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• 제28권3호
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• pp.84-90
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• 2014

The partial discharge of Cast-resin Transformer has a difficulty to be analyzed, because it is an abnormal condition signal of which stochastic characteristics varies with time variance. In this study, background noise coming from the outside of the cast-resin transformers through ground wire can be removed and only a discharge signal of which defects are simulated can be obtained, using the wavelet transform method, which is a time-frequency domain analysis technique. As a result, it was confirmed that de-noising using the SWT technique is the best efficient among three methods of the wavelet transform techniques.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 A
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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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 추계학술대회 논문집 학회본부 A
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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.

• 대한전기학회논문지:전력기술부문A
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• 제54권12호
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• pp.561-566
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• 2005

This paper introduces a compression technique for power qualify disturbance signal via discrete wavelet transform(DWT). The proposed approach is based on a previous estimation of the stationary component of power quality disturbance signal, so that it could be subtracted from the original signal in order to reduce a dynamic range of signal and generate transient events signal, which is subsequently applied to the compression technique. The compression techniques is performed through the difference signal decomposition, thresholding of wavelet coefficients, and signal reconstruction. It presents the relation between compression efficiency and threshold. It shouts that the wavelet transform leads to a power quality data compression approach with high compression efficiency, small compression error and good de-nosing effect.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1996년도 추계학술대회
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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.