• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.369-388
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• 2023

In this paper, we develop the two-step procedure that detects and estimates the position of structural changes for multivariate nonstationary time series, either on mean parameters or second-order structures. We first investigate the presence of mean structural change by monitoring data through the aggregated cumulative sum (CUSUM) type statistic, a sequential procedure identifying the likely position of the change point on its trend. If no mean change point is detected, the proposed method proceeds to scan the second-order structural change by modeling the multivariate nonstationary time series with a multivariate locally stationary Wavelet process, allowing the time-localized auto-correlation and cross-dependence. Under this framework, the estimated dynamic spectral matrices derived from the local wavelet periodogram capture the time-evolving scale-specific auto- and cross-dependence features of data. We then monitor the change point from the lower-dimensional approximated space of the spectral matrices over time by applying the dynamic principal component analysis. Different from existing methods requiring prior information on the type of changes between mean and covariance structures as an input for the implementation, the proposed algorithm provides the output indicating the type of change and the estimated location of its occurrence. The performance of the proposed method is demonstrated in simulations and the analysis of two real finance datasets.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.766-776
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• 2015

It is well known that series arc faults in Low Voltage DC (LVDC) distribution system occur at unintended points of discontinuity within an electrical circuit. These faults can make circuit breakers not respond timely due to low fault current. It, therefore, is needed to detect the series fault for protecting circuits from electrical fires. This paper proposes a novel scheme to detect the series arc fault using Wavelet Singular Value Decomposition (WSVD) and state diagram. In this paper, the fault detector developed is designed by using three criterion factors based on the RMS value of Singular value of Approximation (SA), Sum of the absolute value of Detail (SD), and state diagram. LVDC distribution system including AC/DC and DC/DC converter is modeled to verify the proposed scheme using ElectroMagnetic Transient Program (EMTP) software. EMTP/MODELS is also utilized to implement the series arc model and WSVD. Simulation results according to various conditions clearly show the effectiveness of the proposed scheme.

• Journal of applied mathematics & informatics
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• v.14 no.1_2
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• pp.267-275
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• 2004

While the convergence of the classical Fourier series has been well known, the rate of its convergence is not well acknowledged. The results regarding the rate of convergence of the Fourier series and wavelet expansions can be found in the book of Walter[5]. In this paper, we give the rate of convergence of hybrid sampling series associated with orthogonal wavelets.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.3
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• pp.362-368
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• 2009

Electrical fires have been occurred continuously in spite of installing ELB. Therefore the concern with the electrical arc-fault that cause the fire has growing. This paper measured series arc fault currents by the method of arc generator test in UL standard 1699. The used analysis methods in this paper are three different ways using DWT(discrete wavelet transform) those are frequently used for the arc fault current signal analysis. The arc fault detection probability is 100 % by method using noise-energy/shoulder-duration ratio of approximation coefficient. As these results, the variation of noise-energy and shoulder-duration ratio of approximation coefficient are founded important factors for the analysis of arc fault.

• The Journal of Information Systems
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• v.18 no.3
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• pp.351-373
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• 2009

An index fund is a collective investment scheme that aims to replicate the movements of an index of a specific financial market regardless of market conditions. An index fund is a popular investment alternative because it is much cheaper to run than an active fund and it performs better than actively managed funds. This paper illustrates the usefulness of wavelet analysis in constructing an index fund. The wavelet analysis can decompose the time series data in frequency domain as well as in time domain. The major findings of this paper are as follows. First, the beta coefficient that represents the systematic risk has the scale dependent property. This result can provide important information to the investors with various investment time frequency. Investors can use the betas corresponding to their investment frequencies among the various scale betas estimated by wavelet analysis. Second, we can find the usefulness of wavelet analysis in constructing index fund because the wavelet technique gives less tracking error(difference between the index performance and the index fund performance) than the traditional constructing techniques. The result of this study implies that the wavelet techniques can be an important analytic method to the other financial markets such as option market, futures market, bond markets and currency market.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1215-1221
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• 1999

A high speed digital distance relaying algorithm based on a Wavelet Transformation is proposed. To obtain stable phasor values very quickly, first, a lowpass filter which has low cutoff frequency is used. Secondly, db2(Daubechies 2) Wavelet which has the data window of 4 samples is used. A FIR filter which removes the DC-offset component in current relaying signals is applied. In accordance with a series of tests, the operation time of the relaying algorithm is less than 3/4 cycles after faults in a 80 [km], 154[kV], 60[Hz] over-head transmission line system.

• Journal of Korean Society on Water Environment
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• v.22 no.5
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• pp.831-837
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• 2006

The present study applies wavelet transform for the extraction of various periodicities which are included in TOC and pH time series of water quality and rainfall data. The primary objective of the present study is to detect the relationships between the respective data through the correlation analysis using the approximation components which are decomposed by wavelet transform. The results reveal the approximation components of TOC and pH in the 5th level of wavelet transform can explain more than 99% of the whole energy for the raw data respectively and there are considerably high correlation between the approximation components of the respective data used for the study even through no significant correlation between the raw data has been detected.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.1812-1821
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• 2014

Wind has been a rapidly growing renewable power source for the last twenty years. Since wind behavior is chaotic in nature, its forecasting is not easy. At the same time, developing an accurate forecasting method is essential when wind farms are integrated into the power grid. In fact, wind speed forecasting tools can solve issues related to grid stability and reserve allocation. In this paper 30 hours ahead wind speed profile forecast is proposed using Adaptive Wavelet Neural Network (AWNN). The implemented AWNN uses a Mexican hat mother Wavelet, and Morlet Mother Wavelet for seven, eight and nine levels decompositions. For wind speed forecasting, the time series data on wind speed has been gathered from the National Renewable Energy Laboratory (NREL) website. In this work, hourly averaged 10-min wind speed data sets for the year 2004 in the Midwest ISO region (site number 7263) is taken for analysis. Data sets are normalized in the range of [-1, 1] to improve the training performance of forecasting models. Total 8760 samples were taken for this forecasting analysis. After the forecasting phase, statistical parameters are calculated to evaluate system accuracy, comparing different configurations.

• 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.

• Journal of the Korean Society of Safety
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• v.15 no.2
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• pp.1-7
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• 2000

This paper presents the approach involving fault detection and diagnosis of gears using pattern recognition and Wavelet transform. It describes result of the comparison between KDI (Kullback Discrimination Information) with the nearest neighbor classification rule as one of pattern recognition methods and Wavelet transform to know a way to detect and diagnosis of gear damages experimentally. To model the damages 1) Normal (no defect), 2) one tooth is worn out, 3) All teeth faces are worn out 4) One tooth is broken. The vibration sensor was attached on the bearing housing. This produced the total time history data that is 20 pieces of each condition. We chose the standard data and measure distance between standard and tested data. In Wavelet transform analysis method, the time series data of magnitude in specified frequency (rotary and mesh frequency) were earned. As a result, the monitoring system using Wavelet transform method and KDI with nearest neighbor classification rule successfully detected and classified the damages from the experimental data.