• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2007.05a
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• pp.9-28
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• 2007

It is well known that wavelet transform is a signal processing technique which can display the signals on in both time and frequency domain. In this paper, we proposed a new approach based on stationary wavelet transform to provide an enhanced approach for eliminating noise. A 'stationary wavelet transform', where the coefficient sequences are not decimated at each stage, is described. The testing result on sample iris images has shown an enhanced image quality and also show that it has a superior performance than traditional discrete wavelet transform.

• Journal of Advanced Navigation Technology
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• v.20 no.6
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• pp.539-543
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• 2016

In this paper, we study the technique to improve the performance of the aerial images taken by UAV using daubechies stationary wavelet transform. When aerial images taken by UAV were damaged by gaussian noise very commonly applied, the experiment for image performance improvement was performed. It was known that stationary wavelet transform is the transferring solution to the problem occurred by down sampling from DWT also more efficient to remove noise than DWT. Also haar wavelet is discontinuous function so not efficient for smooth signal and image processing. Therefore, this study is confirmed that the noise can be removed by daubechies stationary wavelet and the performance is improved by haar stationary wavelet.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.3
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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.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.4 no.2
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• pp.143-152
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• 2000

As muscular contraction is sustained, the Fourier spectrum of the myoelectric signal is shifted toward the lower frequency. This spectral density is associated with muscle fatigue. This paper describes a quantitative measurement method that performs the measurement of localized muscle fatigue by tracking changes of median frequency based on stationary wavelet transform. Applying to the human masseter muscle, the proposed method offers the much information for muscle fatigue, comparing with the conventional FFT-based method for muscle fatigue measurement.

• Journal of Advanced Marine Engineering and Technology
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• v.32 no.8
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• pp.1309-1314
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• 2008

Seismic data is often contaminated with high-energy, spatially aliased noise, which has proven impractical to attenuate using Fourier techniques. Wavelet filtering, however, has proven capable of attacking several types of localized noise simultaneously regardless of their frequencies. In this study a 2-D stationary wavelet transform is used to decompose seismic data into its wavelet components. A threshold is applied to these coefficients to attenuate high amplitude noise, followed by an inverse transform to reconstruct the seismic trace. The stationary wavelet transform minimizes the phase-shift errors induced by thresholding that occur when the conventional discrete wavelet transform is used.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.369-372
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• 2000

An overview on applications of wavelet transform in power systems presented in this paper. Wavelet transform is capable of making trade-offs between time and frequency resolutions, which is a property that makes it appropriate for the analysis of non stationary signal. In recent years, wavelet transform is widely accepted as a technology offering an alternative way due to its flexibility in representation of non-stationary signal even in power systems. This paper presents various applications of wavelet transform in power systems. Wavelet transform has been used by the authors in the field of power system protection for the classification of transient signals, and forecasting of short term loads and system marginal price and so on. Various research works carried out by many researchers in power systems are summarized.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.505-508
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• 2005

Wavelet scheme can be applied to the various remote sensing problems: conventional multi-resolution image analysis, compression of large image sets, fusion of heterogeneous sensor image and segmentation of features. In this study, we attempted wavelet-based filtering and its analysis. Traditionally, statistical methods and adaptive filter are used to manipulate noises in the image processing procedure. While we tried to filter random noise from optical image and radar image using Discrete Wavelet Transform (DW1) and Stationary Wavelet Transform (SW1) and compared with existing methods such as median filter and adaptive filter. In result, SWT preserved boundaries and reduced noises most effectively. If appropriate thresholds are used, wavelet filtering will be applied to detect road boundaries, buildings, cars and other complex features from high-resolution imagery in an urban environment as well as noise filtering

• Journal of the Earthquake Engineering Society of Korea
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• v.7 no.5
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• pp.11-17
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• 2003

It is well known that the double integration of measured acceleration records is one of the most difficult signal processing, particularly in the measurements on civil engineering structures, The measured accelerations of civil engineering structures are usually non-stationary and contain non-gaussian low-frequency noises, which can be significant causes of numerical instabilities in double Integration, For the de-noising of this kind of signals, wavelet transform can be very effective because of its inherent processing features for non-stationary signals, In this paper, the de-noising algorithm for the double integration is proposed using the modified wavelet transform, which is extended version of ordinary wavelet transform to process non-gaussian and low-frequency noises, using the median filter concept, The example studies show that the integration can be improved by the proposed method.

• Journal of Advanced Navigation Technology
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• v.23 no.4
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• pp.273-280
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• 2019

There are many studies that use structure health monitoring to reduce maintenance costs for aircraft and to increase aircraft utilization. Many studies on FBG sensors are also being conducted. However, if the FBG sensor is installed inside the composite, voids will occur between the layers of the composite, resulting in signal split problem. In addition, the FBG sensor is not affected by electromagnetic waves, but will produce electromagnetic noise caused by electronic equipment during post-processing. In this paper, to reduce the error caused by these noises, the stationary wavelet transform, which has the characteristics of movement immutability and is efficient in nonlinear signal analysis, is presented. And in the above situation, we found that noise rejection performance of stationary wavelet transform was better compared with the wavelet packet transform.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1293-1300
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• 2018

For the disadvantages of multi-scale geometric analysis methods such as loss of definition and complex selection of rules in image fusion, an improved multi-focus image fusion method is proposed. First, the initial fused image is quickly obtained based on the lifting stationary wavelet transform, and a simple normalized cut is performed on the initial fused image to obtain different segmented regions. Then, the original image is subjected to NSCT transformation and the absolute value of the high frequency component coefficient in each segmented region is calculated. At last, the region with the largest absolute value is selected as the postfusion region, and the fused multi-focus image is obtained by traversing each segment region. Numerical experiments show that the proposed algorithm can not only simplify the selection of fusion rules, but also overcome loss of definition and has validity.