• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.9
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• pp.164-171
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• 2013

XRF spectrum data of a material include a lot of background signals which are not related to its components. Since an XRF analyzer analyzes components and concentrations of an analyte using the locations and magnitudes of gaussian-shaped peaks extracted from a spectrum, its background signals need to be removed completely from the spectrum for the accurate analysis. Morphology-based method, SNIP-based method and thresholding-based method have been used to remove background signals. In the paper, a background removal method, an improved version of an interval-thresholding-based method, is proposed. The proposed method consists of interval partitioning, interval classifying, and background estimation. Experimental results show that the proposed method has better performance on background removal from the spectrum than the existing methods, morphology-based method and SNIP-based method.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.5
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• pp.363-371
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• 2020

The parameters of the modified BM and JONSWAP spectra are estimated using spectral data set off Namhangjin, located in the east coast of Korea, collected during high wave events. The parameters of the modified BM spectrum were estimated to be 1.04 and 0.27, which were similar to the conventional values of 1.098 and 0.30, but showed significant differences in statistical terms. On the other hand, the peak enhancement factor of JONSWAP spectrum was estimated to be 1.4, which was substantially small compared to the conventional value of 3.3. The RMSE differences from the fitted results of the two spectra were small, approximately 0.2. In the frequency range greater than the peak frequency, however, the spectral energy density showed relatively mild decrease with increase of the frequency, compared to the standard forms of the modified BM and JONSWAP spectra.

• Earthquakes and Structures
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• v.16 no.2
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• pp.197-208
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• 2019

The objective of this study is to propose new seismic intensity parameters based on the Hilbert spectrum and to associate them with the seismic damage potential. In recent years the assessment of even more seismic features derived from the seismic acceleration time-histories was associated with the structural damage. For a better insight into the complex seismic acceleration time-history, Hilbert-Huang Transform (HHT) analysis is utilized for its processing, and the Hilbert spectrum is obtained. New proposed seismic intensity parameters based on the Hilbert spectrum are derived. The aim is to achieve a significant estimation of the seismic damage potential on structures from the proposed new intensity parameters confirmed by statistical methods. Park-Ang overall structural damage index is used to describe the postseismic damage status of structures. Thus, a set of recorded seismic accelerograms from all over the word is applied on a reinforced concrete frame structure, and the Park-Ang indices through nonlinear dynamic analysis are provided and considered subsequently as reference numerical values. Conventional seismic parameters, with well-known seismic structural damage interrelation, are evaluated for the same set of excitations. Statistical procedures, namely correlation study and multilinear regression analysis, are applied on the set of the conventional parameters and the set of proposed new parameters separately, to confirm their interrelation with the seismic structural damage. The regression models are used for the evaluation of the structural damage indices for every set of parameters, respectively. The predicted numerical values of the structural damage indices evaluated from the two sets of seismic intensity parameters are inter-compared with the reference values. The numerical results confirm the ability of the proposed Hilbert spectrum based new seismic intensity parameters to approximate the postseismic structural damage with a smaller Standard Error of Estimation than this accomplished of the conventional ones.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.255-258
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• 2005

A blind-mode integer frequency offset estimation algorithm is proposed for an OFDM system. Imperfect integer frequency offset estimation causes ambiguity in the data sub-carrier position. Morelli's blind integer frequency offiet estimation algorithm exploits the likelihood function by comparing the power in sub-carriers. It, however, shows performance degradation when there is the fractional frequency offset. The proposed algorithm solves this by using interpolation in the frequency domain. In this algorithm, it is exploited that the effect of the frequency offset is shown as a shift of power spectrum. By calculating the covariance of over-sampled samples, most approximate samples to integer point are obtained. It enables integer frequency offset estimation less affected by fractional frequency offset.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.10
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• pp.858-864
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• 2012

In South Korea, to effective and systematic response to disasters, the government will build wireless communication network for PPDR(Public Protection and Disaster Relief) on a national level by 2015. To do it, as MOPAS(Ministry of Public Administration and Security) requested the new frequency assignment for PPDR, KCC(Korea Communications Commission) has been examined new frequency allocations about TETRA and WiBro technologies. In this paper, we were defined the technical parameters, were estimated the spectrum of TETRA-TRS requirements for Korean wireless communication of PPDR based on M.2033 methodology recommended by ITU-R.

• Journal of Aerospace System Engineering
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• v.3 no.2
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• pp.12-19
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• 2009

The research for the fatigue analysis is regarded greatly as important in aerospace field. Moreover, a study on the fatigue characteristic is very actively progressing. In this study, the fatigue life estimation was performed for Flaperon Joint which has FCL(fatigue critical location) of tilt-rotor UAV. The Flaperon Joint should be taken the various loads by several missions profiles of UAV. The fatigue load spectrum of Flaperon Joint is generated by the standard mission segment for the tilt-rotor UAV, and this spectrum is used for the fatigue test and analysis. The in-house fatigue analysis program is applied to calculate the fatigue life based on Stress-Life(S-N) method. The S-N curve is generated from the S-N data of Mil-Handbook by second order polynomial regression method. Moreover, the coefficient of determination is used to ensure how accuracy it has. In addition, the Goodman equation is used to consider the mean stress effect for evaluating more accurate fatigue life. Finally, the result of fatigue analysis is verified by comparing with the fatigue test result for the Flaperon Joint.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.1
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• pp.1-8
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• 2014

This paper proposes an emotion classifier from EEG signals based on Bayes' theorem and a machine learning using a perceptron convergence algorithm. The emotions are represented on the valence and arousal dimensions. The fast Fourier transform spectrum analysis is used to extract features from the EEG signals. To verify the proposed method, we use an open database for emotion analysis using physiological signal (DEAP) and compare it with C-SVC which is one of the support vector machines. An emotion is defined as two-level class and three-level class in both valence and arousal dimensions. For the two-level class case, the accuracy of the valence and arousal estimation is 67% and 66%, respectively. For the three-level class case, the accuracy is 53% and 51%, respectively. Compared with the best case of the C-SVC, the proposed classifier gave 4% and 8% more accurate estimations of valence and arousal for the two-level class. In estimation of three-level class, the proposed method showed a similar performance to the best case of the C-SVC.

• The Journal of the Acoustical Society of Korea
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• v.25 no.2E
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• pp.69-73
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• 2006

In this paper, we propose a robust music audio fingerprinting system for automatic music retrieval. The fingerprint feature is extracted from the long-term dynamic modulation spectrum (LDMS) estimation in the perceptual compressed domain. The major advantage of this feature is its significant robustness against severe background noise from the street and cars. Further the fast searching is performed by looking up hash table with 32-bit hash values. The hash value bits are quantized from the logarithmic scale modulation frequency coefficients. Experiments illustrate that the LDMS fingerprint has advantages of high scalability, robustness and small fingerprint size. Moreover, the performance is improved remarkably under the severe recording-noise conditions compared with other power spectrum-based robust fingerprints.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.3A
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• pp.205-210
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• 2002

In a Doppler weather radar, high resolution windspeed profile measurements are needed to provide the reliable detection of hazardous weather conditions. For this purpose, the pulse pair method is generally considered to be the most efficient estimator, However, this estimator has some bias errors due to asymmetric spectra and may yield meaningless results in the case of a multimodal return spectrum. Although the poly-pulse pair method can reduce the bias errors of skewed weather spectra, the modes of spectrum may provide more reliable information than the statistical mean for the case of a multimodal or seriously skewed spectrum. Therefore, the idea of relatively simple mode estimator for a weather radar is developed in this paper, Performance simulations show promising results in the detection of hazardous weather conditions.

• Publications of The Korean Astronomical Society
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• v.7 no.1
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• pp.9-17
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• 1992

The power spectrum of the galaxy distribution is accurately measured up to wavelengths over $100\;h^{-1}$ Mpc from the CfA 1 and 2 catalogs. We find that our results agree with power spectra calculated by others from smaller samples of optical, radio and infrared galaxies. The power spectrum of an open CDM model (${\Omega}h$ = 0.2 and ${\delta}_8$ = 1; see below for definitions) best approximates the observed power spectrum. The power spectrum of the standard COM model (${\Omega}h$ = 0.5 and ${\delta}_8$ = 1) is inconsistent with the observed one at the 99% confidence level. Our best estimation of the corresponding correlation function in real space is ${\xi}(r)\;=\;(r/6.2h^{-1}Mpc)^{-1.8}$ for r < $20h^{-1}$ Mpc.