• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 학술대회 논문집 정보 및 제어부문
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• pp.195-196
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• 2008

ECG 기반의 부정맥 자동 분류에 관한 연구는 지난 수십 년간 다양한 방법으로 연구되어 왔다. 많은 연구들이 부정맥을 구별해 낼 수 있는 특징 벡터를 찾아내기 위해 연구하였으나, 피험자의 ECG 특징이 각기 다르기 때문에 부정맥으로 인한 차이와 개인 간 차이를 구별하기 어려웠다. 생체데이터는 그 특성상 서로 다른 특징을 갖고 있으며, 다양한 특징을 가진 사람들에게 적용하기 위한 범용성과 부정맥 검출의 정확성 사이에 교환적 관계를 갖게 된다. 특히 ECG 데이터의 경우 사람 식별 데이터로 사용하고자 하는 연구가 있을 정도로 개인 간 편차가 분명하다. wavelet 분석방법은 다양한 mother wavelet을 사용할 수 있다는 점을 큰 장점으로 가지고 있으며, wavelet parameterization 기법을 사용하여 임의의 직교 wavelet basis를 발생시킬 수 있다. 본 논문은 wavelet parameterization을 사용하여 개인 간의 ECG 파형의 차이를 상쇄시키고, 부정맥의 차이만을 부각시킴으로써 ECG 기반의 부정맥 자동 분류 성능을 높이고자 하는데 목적이 있다.

• 전기전자학회논문지
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• 제4권2호
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• pp.233-240
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• 2000

This paper presents fuzzy clustering and wavelet transform analysis based technique for the industrial hourly load forecasting fur the purpose of peak demand control. Firstly, one year of historical load data were sorted and clustered into several groups using fuzzy clustering and then wavelet transform is 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 five-scale synthesis technique. The outcome of the study clearly indicates that the proposed composite model of fuzzy clustering and wavelet transform approach can be used as an attractive and effective means for the industrial hourly peak load forecasting.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.3050-3052
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• 2000

Recently. the subject of "wavelet analysis" has drawn much attention from both mathematical and engineering application fields such as Signal Processing, Compression/ Decomposition, Statistics and etc. Analogous to Fourier analysis, wavelets is a versatile tool with very rich mathematical content and great potential for applications. Specially, wavelet transform uses localizable various mother wavelet functions in time-frequency domain. In this paper, discrimination analyses of acquired electrical current signals for each and mixed loads were tried by using Morlet wavelet transform. Their representative loads were classified as TV, DRY, REF, and FL.

• 한국산업정보학회논문지
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• 제22권1호
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• pp.41-51
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• 2017

This Paper Describes a Method for the Evaluation of Sleep Apnea, Namely, the Peak Signal-to-noise ratio (PSNR) of Wavelet Transformed Electroencephalography (EEG) Data. The Purpose of this Study was to Investigate EEG Properties with Regard to Differences between Sleep Spindles and K-complexes and to Characterize Obstructive Sleep Apnea According to Sleep Stage. We Examined Non-REM and REM Sleep in 20 Patients with OSA and Established a New Approach for Detecting Sleep Apnea Base on EEG Frequency Changes According to Sleep Stage During Sleep Apnea Events. For Frequency Bands Corresponding to A3 Decomposition with a Sampling Applied to the KC and the Sleep Spindle Signal. In this Paper, the KC and Sleep Spindle are Ccalculated using MSE and PSNR for 4 Types of Mother Wavelets. Wavelet Transform Coefficients Were Obtained Around Sleep Spindles in Order to Identify the Frequency Information that Changed During Obstructive Sleep Apnea. We also Investigated Whether Quantification Analysis of EEG During Sleep Apnea is Valuable for Analyzing Sleep Spindles and The K-complexes in Patients. First, Decomposition of the EEG Signal from Feature Data was Carried out using 4 Different Types of Wavelets, Namely, Daubechies 3, Symlet 4, Biorthogonal 2.8, and Coiflet 3. We Compared the PSNR Accuracy for Each Wavelet Function and Found that Mother Wavelets Daubechies 3 and Biorthogonal 2.8 Surpassed the other Wavelet Functions in Performance. We have Attempted to Improve the Computing Efficiency as it Selects the most Suitable Wavelet Function that can be used for Sleep Spindle, K-complex Signal Processing Efficiently and Accurate Decision with Lesser Computational Time.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
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• pp.427-430
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• 2002

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 FFT (Fast Fourier Transform).

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 춘계학술대회
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• pp.391-394
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• 1997

In this paper, we considered newly the use of wavelet transform in order to improve the troubles of the established methods for the analysis of ultrasound echo signals. We made the phantoms of 13.2g, 19.8g, 26.4g, 33.0g, 39.8g by ourselves, and extracted the only pulse-echo signals that reflected through the mediums using windowing technique. For determining the characterized value, the signals were wavelet transformed, absoluted, and integral calculated. As the result, we acquired characterized value of each signals, and acknowledged the differences among them except of some datas. But this will be improved by advanced work as sellecting a proper mother wavelet, a method of making phantoms, correcting the various errors, etc. We expect that wavelet transform is powerful for analysis of ultrasound signals.

• 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.

• 한국공작기계학회논문집
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• 제13권6호
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• pp.10-16
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• 2004

The detection and analysis of chatter behaviour in endmilling is very complex and difficult so it is necessary to detect and diagnose this chatter phenomenon clearly. This paper presents a new method for detecting the abnormal chatter in endmilling operation, based on the wavelet transform. Using AR spectrum the data that has chatter phenomenon was verified and the fundamental property of chatter and its characteristics in endmilling by using the wavelet transform is reviewed. This result obtained by wavelet transform proves the possibility and reliability of detecting the chatter in endmilling operation.

• 융합신호처리학회논문지
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• 제7권4호
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• pp.155-160
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• 2006

본 논문에서는 웨이브릿 변환 영역의 4개의 서브밴드와 임계값 처리를 이용하는 계층적 블록 매칭 알고리즘을 제안하였다. 제안한 알고리즘은 다분해능의 첫 번째 레벨에서 웨이브릿 변환 영역의 4개 서브밴드를 이용함으로써 복원된 영상의 PSNR 성능을 향상시켰고, 움직임 벡터에 대한 임계값 처리를 하여 계산량을 줄였다. Carphone 영상과 Mother and Daughter 영상에 대한 실험에서 기존의 계층적 움직임 추정 알고리즘과 비교하여 임계값을 0으로 하였을 경우에 계산량은 최대 16%까지 증가하였으나 복원된 영상의 PSNR 성능은 최대 0.16dB 정도 향상된 결과를 보였고, 임계값을 증가시킴에 따라서 계산량은 최대 8%정도 줄고 복원된 영상의 PSNR은 비슷한 성능을 보였다.

• 조명전기설비학회논문지
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• 제21권3호
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• pp.75-81
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• 2007

Wavelet 변환은 신호를 분석하고 해석하는데 효과적인 수학적 도구로 알려져 여러 응용분야에서 다양한 연구가 진행되고 있다. Wavelet 변환은 Fourier 변환과 유사한 측면도 있으나, Fourier 변환과는 달리 다양한 Wavelet 모함수를 사용함으로써 해석 속도가 빠르고, 시간-주파수 영역에서 국재화가 가능하다는 특징을 가지고 있을뿐만 아니라 고주파 성분에 대해선 시간 분해능이 높고, 저주파 성분에 대해선 주파수 분해능이 좋다는 장점을 가지고 있으므로, 전력계통의 다양한 고장 전류의 판별에 적극 이용할 수 있을 것으로 생각된다. 본 논문에서는 고장 전류의 특성을 해석하는데 용이한 복소형의 Morlet Wavelet 모함수를 사용하여 전력계통의 고장기록장치로부터 얻어지는 선로의 전류 데이터를 Wavelet 변환하였고, 이로부터 다양한 고장 모드를 판별할 수 있었다. 실험 결과 Wavelet 변환을 이용하여 선로의 고장 모드를 판별하는 것이 기존의 고속 Fourier 변환을 이용하는 것보다 특징점 고찰에 더욱 유용하다는 것을 확인할 수 있었다.