• Proceedings of the IEEK Conference
• /
• 2003.07e
• /
• pp.2471-2474
• /
• 2003

The modified discrete cosine transform (MDCT) and its inverse transform (IMDCT) are employed in subband/transform coding schemes as the analysis/synthesis filter bank based on time domain aliasing cancellation (TDAC). And they are the most computational intensive operations in layer III of the MPEG audio coding standard. In this paper, we propose a new efficient algorithm for the MDCT/IMDCT computation. It is based on the MDCT/IMDCT computation algorithm using the discrete cosine transforms (DCTs), and it employs two discrete cosine transform of type II(DCT-II) to compute the MDCT/IMDCT. In addition to, it takes advantage of ability in calculating the MDCT/IMDCT computation, where the length of a data block is divisible by 4. The proposed algorithm in this paper requires less calculation complexity than the existing methods. Also, it can be implemented by the parallel structure,, and its structure is particularly suitable for VLSI realization.

• Proceedings of the IEEK Conference
• /
• 2000.06e
• /
• pp.127-130
• /
• 2000

Classification of Electroencephalogram(EEG) makes one of key roles in the field of clinical diagnosis, such as detection for epilepsy. Spectrum analysis using the fourier transform(FT) uses the same window to signals, so classification rate decreases for nonstationary signals such as EEG's. In this paper, wavelet power spectrum method using wavelet transform which is excellent in detection of transient components of time-varying signals is applied to the classification of three types of Event Related Potential(EP) and compared with the result by fourier transform. In the experiments, two types of photic stimulation, which are caused by eye opening/closing and artificial light, are used to collect the data to be classified. After choosing a specific range of scales, scale-averaged wavelet spectrums extracted from the wavelet power spectrum is used to find features by Back-Propagation(13P) algorithm. As a result, wavelet analysis shows superiority to fourier transform for nonstationary EEG signal classification.

• Journal of IKEEE
• /
• v.4 no.2 s.7
• /
• pp.233-240
• /
• 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.

• Journal of Electrical Engineering and Technology
• /
• v.8 no.1
• /
• pp.70-79
• /
• 2013

In this paper, a comparative study on the frequency estimation methods using IRDWT (Improved Recursive Discrete Wavelet Transform), FRDWT(Fast Recursive Discrete Wavelet Transform), and GCDFT(Gain Compensator Discrete Fourier Transform) is presented. The 345[kV] power system modeling data of the Republic of Korea by EMTP-RV is used to evaluate the performance of the proposed two kinds of RDWT(IRDWT and FRDWT) and GCDFT. The simulation results show that the frequency estimation technique based on FRDWT could be the optimal frequency measurement method, and thus can be applied to FDR(Fault Disturbance Recorder) for wide-area blackout protection or frequency measurement apparatus.

• Structural Engineering and Mechanics
• /
• v.60 no.4
• /
• pp.667-691
• /
• 2016

This study focuses on the damage detection of defect types in plate structures based on wavelet transform (WT) and curvelet transform (CT). In particular, for damage detection of structures these transforms have been developed since the last few years. In recent years, the CT approach has been also introduced in an attempt to overcome inherent limitations of traditional multi-scale representations such as wavelets. In this study, the performance of CT is compared with WT in order to demonstrate the capability of WT and CT in detection of defect types in plate structures. To achieve this purpose, the damage detection of defect types through defect shape in rectangular plate is investigated. By using the first mode shape of plate structure and the distribution of the coefficients of the transforms, the damage existence, the defect location and the approximate shape of defect are detected. Moreover, the accuracy and performance generality of the transforms are verified through using experimental modal data of a plate.

• Journal of Biomedical Engineering Research
• /
• v.13 no.2
• /
• pp.115-124
• /
• 1992

An algorithm has been proposed for the automatic detection of optimal epiand endocardial left ventricular borders from 2-D short axis echocardiogram which is degraded by noise and echo drop out. For the implementation of the algorithm, we modified Ballard's Generalized Hough Transform which can be applicable only for deterministic object border, and newly proposed Fuzzy Hough Transform method. The algorithm presented here allows detection of object whose exact shapes are unknown. The algorithm only requires an approximate model of target object based on anatomical data. To detect the approximate epicardial contour of left ventricle, Fuzzy Hough Transform was applied to the echocardiogram. The optimal epicardial contour was founded by using graph searching method which contains cost function analysis process. Using this optimal epicardial contour and average thickness imformation of left ventricular wall, the approximate endocardial line was founded, and graph searching method was also used to detect optimal endocardial contour.

• Journal of Biomedical Engineering Research
• /
• v.15 no.3
• /
• pp.305-316
• /
• 1994

All physical data in the real world are nonstationary signals that have the time varying statistical characteristics. Although few algorithms suitable to process the nonstationary signals have ever been suggested, these are treated the nonstationary signals under the assumption that the nonstationary signal is a piece-wise stationary signal. Recently, statistical analysis algorithms for the nonstationary signal have concentrated so much interest. In this paper, nonstationary EMG signals are mapped onto the orthogonal wavelet transform domain so that the eigenvalue spread of its autocorrelation matrix could be more smaller than that in the time domain. Then the model in the wavelet transform domain and an algorithm to estimate the model parameters are suggested. Also, an test signal generated by a white gaussian noise and the EMG signal are identified, and the algorithm performance is considered in the sense of the mean square error and the evaluation parameters.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.35 no.2
• /
• pp.106-112
• /
• 2012

Real-life time series characteristic data has significant amount of non-stationary components, especially periodic components in nature. Extracting such components has required many ad-hoc techniques with external parameters set by users in case-by-case manner. In our study, we evaluate whether Hilbert-Huang Transform, a new tool of time-series analysis can be used for effective analysis of such data. It is divided into two points : 1) how effective it is in finding periodic components, 2) whether we can use its results directly in detecting values outside control limits, for which a traditional method such as ARIMA had been used. We use glass furnace temperature data to illustrate the method.

• Economic and Environmental Geology
• /
• v.32 no.5
• /
• pp.495-502
• /
• 1999

Common-mid-point (CMP) seismic data on a dipping layer have have a stacking different from a horizontal layer velocity and the reflection points on data are dispersed to many positions. Therefore, the CMP data are not stacked well by the conventional stacking method using the horizontal layer velocity. The CMP gather can ideally stacked by applying dip-moveout(DMO) processing. Hence, modern seismic processing indludes DMO as an essential routine step. DMO processing techniques are broadly categorized by two, Fourier transform and integral methods, each of which has many different computational schemes. In this study, the dip-decomposition technique of the Fourier transform method is used to test the DMO effect on the synthetic scismic data generated for dipping structures. Each of constnat offset sections NMO corrected by using the layer velocity of the model and DMO processed. The resulting zero-offset sections for many offsets are stacked. The stacked sections with DMO processing show the structural boundaries of the models much better than those without DMO processing.

• Proceedings of the KIEE Conference
• /
• 2007.07a
• /
• pp.1795-1796
• /
• 2007

본 논문은 DWT(Discrete Wavelet Transform)기반의 블라인드 워터마킹 기법을 제안 하였다. DWT를 하였을 때, 두 개의 서브밴드들의 계수 값을 변환하여 워터마크를 삽입한다. 기존에는 워터마크를 로고나 signature등을 많이 사용 하였으나, 여기서는 이차원 바코드인 Data Matrix를 워터마크로 사용 하였다. Data Matrix자체가 오류 검출 및 복원 알고리즘을 가지고 있어, 워터마크 추출 시 비교적 작은 에러는 Data Matrix의 복원 알고리즘에 의해 Data Matrix의 암호화된 정보를 복원 할 수 있다.