• Structural Engineering and Mechanics
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• v.22 no.2
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• pp.185-195
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• 2006

Nowadays it is very necessary to generate artificial accelerograms because of lack of adequate earthquake records and vast usage of time-history dynamic analysis to calculate responses of structures. According to the lack of natural records, the best choice is to use proper artificial earthquake records for the specified design zone. These records should be generated in a way that would contain seismic properties of a vast area and therefore could be applied as design records. The main objective of this paper is to present a new method based on wavelet theory to generate more artificial earthquake records, which are compatible with target spectrum. Wavelets are able to decompose time series to several levels that each level covers a specific range of frequencies. If an accelerogram is transformed by Fourier transform to frequency domain, then wavelets are considered as a transform in time-scale domain which frequency has been changed to scale in the recent domain. Since wavelet theory separates each signal, it is able to generate so many artificial records having the same target spectrum.

• Ocean Systems Engineering
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• v.4 no.3
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• pp.201-213
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• 2014

Wave run-up is an important issue in offshore engineering, which is tightly related to the loads on the marine structures. In this study, a series of physical experiments have been performed to investigate the wave run-up around a vertical cylinder in transitional water depth. The wave run-ups of regular waves, irregular waves and focused waves have been presented and the characteristics in frequency domain have been investigated with the FFT and wavelet transform methods. This study focuses on the nonlinear features of the wave run-up and the interaction between the wave run-up and the cylinder. The results show that the nonlinear interaction between the waves and the structures might result wave run-up components of higher frequencies. The wave run-ups of the moderate irregular waves exhibit 2nd order nonlinear characteristics. For the focused waves, the incident waves are of strong nonlinearity and the wavelet coherence analysis reveals that the wave run-up at focal moment contains combined contributions from almost all the frequency components of the focused wave sequence and the contributions of frequency components up to 4th order harmonic levels are recommended to be included.

• Journal of Astronomy and Space Sciences
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• v.25 no.1
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• pp.1-18
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• 2008

We present a detailed period analysis for the F-type primary of ${\in}$ Aurigae by means of Fourier and wavelet algorithm. After collecting all available data which have been observed for around 160 years (1842 - 2006) from various international databases and published references we selected only data obtained during outside eclipse among them again. As a result of analysis using CLEANest and WWZ(weighted wavelet Z-transform) several frequencies including two clear periods ($67^d\;and\;123^d$) were found. In contrast to previous results that the periods vary irregularly it seems that the primary of ${\in}$ Aurigae is double mode or multiperiodic pulsator. The presence of the two periods and their ratio indicates that the high-mass interpretation of the variable could be valid. Also better understanding of the mechanisms driving the light variability of F-type supergiant stars requires continual series of photometric and radial velocity measurements in outside eclipse of this star.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2192-2197
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• 2005

This paper describes how to diagnose SBR plant equipment using time-series data mining. It shows the equipment diagnostics based upon vibration signals that are acquired from each device for process control. Data transform techniques including two data preprocessing skills and data mining methods were employed in the data analysis. The proposed method is not only suitable for SBR equipment, but is also suitable for other industrial devices. The experimental results performed on a lab-scale SBR plant show a good equipment-management performance.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.4
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• pp.271-276
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• 2021

Arc fault detector based on multilevel DWT with analysis of high-frequency noise components over 100 kHz is proposed in this study to improve the performance in detecting serial arcs and distinguishing them from inverter noise in PV systems. PV inverters generally operate at a frequency range of 20-50 kHz for switching operation and maximum power tracking control, and the effect of these frequency components on the signal for arc detection leads to negative arc detection. High-speed ADC and multilevel DWT are used in this study to analyze frequency components above 100 kHz. Such high frequency components are less influenced by inverter noise and utilized to detect as well as separate DC series arc from inverter noise. Arc detectors identify the input current of PV inverters using a Rogowski coil. The sensed signal is filtered, amplified, and used in 800kSPS ADC and DWT analysis and arc occurrence determination in DSP. An arc detection simulation facility in UL1699B was constructed and AFD tests the proposed detector were conducted to verify the performance of arc detection and performance of distinction of the negative arc. The satisfactory performance of the arc detector meets the standard of arc detection and extinguishing time of UL1699B with an arc detection time of approximately 0.11 seconds.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1083-1093
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• 2021

It is an essential to predict water usage for establishing an optimal supply operation plan and reducing power consumption. However, the water usage by consumer has a non-linear characteristics due to various factors such as user type, usage pattern, and weather condition. Therefore, in order to predict the water consumption, we proposed the methodology linking various techniques that can consider non-linear characteristics of water use and we called it as KWD framework. Say, K-means (K) cluster analysis was performed to classify similar patterns according to usage of each individual consumer; then Wavelet (W) transform was applied to derive main periodic pattern of the usage by removing noise components; also, Deep (D) learning algorithm was used for trying to do learning of non-linear characteristics of water usage. The performance of a proposed framework or model was analyzed by comparing with the ARMA model, which is a linear time series model. As a result, the proposed model showed the correlation of 92% and ARMA model showed about 39%. Therefore, we had known that the performance of the proposed model was better than a linear time series model and KWD framework could be used for other nonlinear time series which has similar pattern with water usage. Therefore, if the KWD framework is used, it will be possible to accurately predict water usage and establish an optimal supply plan every the various event.

• Korean Journal of Construction Engineering and Management
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• v.10 no.4
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• pp.50-57
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• 2009

The Korea Standard of Estimate which has been used as the only basis of Cost estimate of public construction projects is failed to reflect the fluctuation of current construction cost. Therefore, the government decided to gradually introduce historical construction cost into cost estimate of public construction projects from 2004 and to reduce the use of Korean Standard of Estimate. This paper presents a series of process and the methodology for computing Actual Cost and analyzing the fluctuation patterns based on not only previous contract prices which made a successful bid but also all of the other bid prices. Also, this paper mainly handles a device for extracting strategic bid price such as low price bid for assuring reliable data and for predicting the construction cost which is built by Wavelet Analysis of Time series Analysis data and Neural Network. It is anticipated that the effective use of the proposed process for estimating actual unit cost would make the cost estimation more current and reasonable.

• Journal of Astronomy and Space Sciences
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• v.25 no.4
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• pp.395-404
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• 2008

We explore the linkage between the long term variability of the Sun and earth's climate change by analysing periodicities of time series of solar proxies and global temperature anomalies. We apply the power spectral estimation method named as the periodgram to solar proxies and global temperature anomalies. We also decompose global temperature anomalies and reconstructed total solar irradiance into each local variability components by applying the EMD (Empirical Mode Decomposition) and MODWT MRA (Maximal Overlap Discrete Wavelet Multi Resolution Analysis). Powers for solar proxies at low frequencies are lower than those of high frequencies. On the other hand, powers for temperature anomalies show the other way. We fail to decompose components which having lager than 40 year variabilities from EMD, but both residuals are well decomposed respectively. We determine solar induced components from the time series of temperature anomalies and obtain 39% solar contribution on the recent global warming. We discuss the climate system can be approximated with the second order differential equation since the climate sensitivity can only determine the output amplitude of the signal.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.4A
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• pp.310-315
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• 2002

The Bark Coherence Function (BCF) defies a coherence function within perceptual domain as a new cognition module, robust to linear distortions due to the analog interface of digital mobile system. Our previous experiments have shown the superiority of BCF over current measures. In this paper, a new BCF suitable for VoIP is developed. The unproved BCF is based on the wavelet series expansion that provides good frequency resolution while keeping good time locality. The proposed Wavelet based Bark Coherence function (WBCF) is robust to variable delay often observed in packet-based telephony such as Voice over Internet Protocol (VoIP). We also show that the refinement of time synchronization after signal decomposition can improve the performance of the WBCF. The regression analysis was performed with VoIP speech data. The correlation coefficients and the standard error of estimates computed using the WBCF showed noticeable improvement over the Perceptual Speech Quality Measure (PSQM) that is recommended by ITU-T.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.7-13
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• 2008

To obtain a shear-wave velocity profile in geotechnical practice, various seismic investigation methods which have their own strength and weakness are being frequently used. Generally, geotechnical site have lateral variation of the properties, so it is needed to determine 2-dimensional shear wave velocity imaging of the site. In this study, harmonic wavelet analysis of wave (HWAW) method is applied to determination of 2-D $V_s$ imaging. HWAW method which is based on time-frequency analysis using harmonic wavelet transform have been developed to determine phase and group velocities of waves. HWAW method uses the signal portion of the maximum local signal/noise ratio to evaluate the phase velocity to minimize the effects of noise. HWAW method determine detailed local $V_s$ profile because one experimental setup which consists of one pair of receivers with spacing of 1~3m is used to determine the dispersion curve of the whole depth. So, 2-D Vs imaging with relatively high resolution can be determined through a series of HWAW test. In order to estimate the applicability of HWAW method, field tests were performed in 4 sites. Through field applications and comparison with other test results, the good accuracy and applicability of the proposed method were verified.