• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.1
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• pp.26-32
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• 2007

This study was carried out fusion-welded butt joints in SWS 490A high strength steel subjected to tensile test that load-deflection curve. The windowed or short-time Fourier transform(WFT or STFT) makes possible for the analysis of non-stationary or transient signals into a joint time-frequency domain and the wavelet transform(WT) is used to decompose the acoustic emission(AE) signal into various discrete series of sequences over different frequency bands. In this paper, for acoustic emission signal analysis to use a continuous wavelet transform, in which the Gabor wavelet base on a Gaussian window function is applied to the time-frequency domain. A wavelet transform is demonstrated and the plots are very powerful in the recognition of the acoustic emission features. As a result, the technique of acoustic emission is ideally suited to study variables which control time and stress dependent fracture or damage process in metallic materials.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.342-348
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• 2005

This study was carried out fusion-welded butt joints in SWS 490A high strength steel subjected to tensile test that load-deflection curve. The windowed or short-time Fourier transform (WFT or SIFT) makes possible for the analysis of non-stationary or transient signals into a joint time-frequency domain and the wavelet transform (WT) is used to decompose the acoustic emission (AE) signal into various discrete series of sequences over different frequency bands. In this paper, for acoustic emission signal analysis to use a continuous wavelet transform, in which the Gabor wavelet base on a Gaussian window function is applied to the time-frequency domain. A wavelet transform is demonstrated and the plots are very powerful in the recognition of the acoustic emission features. As a result, the technique of acoustic emission is ideally suited to study variables which control time and stress dependent fracture or damage process in metallic materials.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.5
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• pp.33-40
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• 2004

In this paper, we constructs face database by using wavelet comparison, and compare face recognition rate by using principle component analysis (Principal Component Analysis : PCA) algorithm. General face recognition method constructs database, and do face recognition by using normalized size. Proposed method changes image of normalized size (92${\times}$112) to 1 step, 2 step, 3 steps to wavelet compression and construct database. Input image did compression by wavelet and a face recognition experiment by PCA algorithm. As well as method that is proposed through an experiment reduces existing face image's information, the processing speed improved. Also, original image of proposed method showed recognition rate about 99.05%, 1 step 99.05%, 2 step 98.93%, 3 steps 98.54%, and showed that is possible to do face recognition constructing face database of large quantity.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.4
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• pp.442-449
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• 2002

EZW (Embedded Zerotree Wavelet) is an efficient algorithm to encode wavelet-transformed image. In this algorithm, each coefficient of wavelet transformed image is given one of the specific symbols and encoded according to its significant priority. In this paper, we analysis the occurrence conditions of symbols in EZW and propose a modified EZW algorithm. In the proposed algorithm, the significance of an IZ (Isolated Zero) symbol is determined by the additional conditions as well as its absolute value. The occurrence of IZ symbols is decreased and the required bits for insignificant IZ symbols is saved, so we obtained good quality of the reconstructed image.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.707-719
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• 2018

Various time series representation methods have been proposed for efficient time series clustering and classification. Lin et al. (DMKD, 15, 107-144, 2007) proposed a symbolic aggregate approximation (SAX) method based on symbolic representations after approximating the original time series using piecewise local mean. The performance of SAX therefore depends heavily on how well the piecewise local averages approximate original time series features. SAX equally divides the entire series into an arbitrary number of segments; however, it is not sufficient to capture key features from complex, large-scale time series data. Therefore, this paper considers data-adaptive local constant approximation of the time series using the unbalanced Haar wavelet transformation. The proposed method is shown to outperforms SAX in many real-world data applications.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.278-278
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• 2020

감조하천이 위치한 연안 지역의 경우, 강우 및 태풍과 발생과 동시에 만조위가 겹치게 되면 큰 홍수 피해를 입는 지역이다. 감조하천은 조석의 영향으로 인해 물의 흐름 및 수위가 주기적으로 진동하는 특성을 보이고 있다. 조석 현상은 주로 기조력에 의한 주기적인 운동이 발생하지만, 풍속, 저기압 등의 영향도 함께 포함되어 있다. 연안 지역에 대한 홍수 위험 관리를 위해, 본 연구에서는 연안 지역 내 위치한 조위 관측소의 조위 자료를 주기적인 운동을 보이는 조석 성분과 확률론적인 운동을 보이는 파고 성분으로 분리하고자 하였다. 자료 내 각각 세부적인 특성을 확인하기 위해 주파수 대역별 필터링이 가능한 이산형 웨이블릿 변환을 통해 자료를 6단계로 분해하였다. 분해된 각 성분 별 주기성 및 주파수 분석을 실시하여 조석 성분 및 파고 성분으로 분리하였으며, 최종적으로 자료 내 각각 66% 및 34%의 비중을 차지하고 있음을 확인하였다. 본 연구의 결과를 활용한다면, 파고의 영향을 고려한 연안 지역의 홍수 관리의 기초자료로 활용할 수 있을 것으로 판단된다.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.4
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• pp.308-315
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• 2009

Because the attenuation of AE signal in composite materials is relatively higher than that of metallic materials, it is required to develop a damage assessment technique less affected by the attenuation property of composite materials in order to use AE sensing as a damage detection method. In the signal processing procedure, it is profitable to use the leading wave that arrives first because the leading wave is less influenced by the boundary conditions. Using wavelet transform, we investigated the frequency characteristics of impact induced AE signals focused on the leading wave in advance and chose the key factors to discriminate the damaged condition quantitatively. In this research, we established a damage assessment technique using the sharing percentage of the wavelet detail components of AE signal, and conducted a low-velocity impact test on composite laminates to confirm the feasibility of the proposed signal processing method.

• Journal of the Korean earth science society
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• v.30 no.6
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• pp.692-698
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• 2009

Wavelet transform has been widely used in terms that it may overcome the shortcoming of conventional Fourier transform. Fourier transform has its difficulty to explain how the transformed domain, frequency, is related with time. Traditional semblance technique in Fourier transform was devised to compare two time series on the basis of their phase as a function of frequency. But this method is known not to work well for the non-stationary signal. In this study, we present two applications of the wavelet-based semblance method to geophysical data. Firstly, we show filtered geomagnetic signal remained with components of high correlation to each observatory. Secondly, highly correlated residual signal of gravity and magnetic survey data, which are also filtered by this semblance method, is present.

• 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 Internet Computing and Services
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• v.22 no.3
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• pp.45-52
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• 2021

Although many studies have been conducted to identify unknown attacks in cyber security intrusion detection systems, studies based on outliers are attracting attention. Accordingly, we identify outliers by defining categories for unknown attacks. The unknown attacks were investigated in two categories: first, there are factors that generate variant attacks, and second, studies that classify them into new types. We have conducted outlier studies that can identify similar data, such as variants, in the category of studies that generate variant attacks. The big problem of identifying anomalies in the intrusion detection system is that normal and aggressive behavior share the same space. For this, we applied a technique that can be divided into clear types for normal and attack by discrete wavelet transformation and detected anomalies. As a result, we confirmed that the outliers can be identified through One-Class SVM in the data reconstructed by discrete wavelet transform.