• ETRI Journal
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• v.34 no.4
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• pp.641-644
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• 2012

The problem of sinusoidal parameter estimation at two channels with common frequency in white Gaussian noise is addressed. By making use of the linear prediction property, an iterative linear least squares (LLS) algorithm for accurate frequency estimation is devised. The remaining parameters are then determined according to the LLS fit with the use of the frequency estimate. It is proven that the variance of the frequency estimate achieves Cram$\acute{e}$r-Rao lower bound at sufficiently small noise conditions.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2538-2541
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• 2009

H.264/AVC High 4:4:4 Intra/Predictive profiles supports RGB 4:4:4 sequences for high fidelity video. RGB color planes rather than YCbCr color planes are preferred by high-fidelity video applications such as digital cinema, medical imaging, and UHDTV. Several RGB coding tools have therefore been developed to improve the coding efficiency of RGB video. In this paper, we propose a new method to extract more accurate correlation parameters for inter-plane prediction. We use a searching method to determine the matched macroblock (MB) that has a similar inter-color relation to the current MB. Using this block, we can infer more accurate correlation parameters to predict chroma MB from luma MB. Our proposed inter-plane prediction mode shows an average bits saving of 15.6% and a PSNR increase of 0.99 dB compared with H.264 high4:4:4 intra-profile RGB coding. Furthermore, extensive performance evaluation revealed that our proposed algorithm has better coding efficiency than existing algorithms..

• Journal of KSNVE
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• v.7 no.2
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• pp.293-300
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• 1997

Noise and vibration induced by subway operation is one of the major problem for the residents living nearby railway tracks. Many scientists and engineers have been working on proposing the more accurate prediction equation of noise and vibration to provide the better residential environment. Some predictiction equations were determined to compare the measurement value of a noise obtained from the inside of a residential area. It was observed that the condition of a soil type is one of the major parameter which should be considered to obtain the more accurate prediction value of a noise.

• Bulletin of the Korean Chemical Society
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• v.34 no.8
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• pp.2276-2280
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• 2013

The accurate prediction of vertical excitation energies is very important for the development of new materials in the dye and pigment industry. A time-dependent density functional theory (TD-DFT) approach coupled with 22 different exchange-correlation functionals was used for the prediction of vertical excitation energies in the halogenated copper phthalocyanine molecules in order to find the most appropriate functional and to determine the accuracy of the prediction of the absorption wavelength and observed spectral shifts. Among the tested functional, B3LYP functional provides much more accurate vertical excitation energies and UV-vis spectra. Our results clearly provide a benchmark calibration of the TD-DFT method for phthalocyanine based dyes and pigments used in industry.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1996.10a
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• pp.402-407
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• 1996

Noise and vibration induced by subway operation is one of the major problem for the residents living nearby railway tracks. Many scientists and engineers have been working on the more accurate prediction of noise and vibration to provide the better residential environment. Some prediction equations were determined to compare the measurement value of a noise obtained from the inside of a residential area. It was observed that the condition of a soil type is one of the major parameter which should be considered to obtain the more accurate prediction value of a noise.

• KEPCO Journal on Electric Power and Energy
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• v.5 no.3
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• pp.223-228
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• 2019

In order to integrate large amounts of variable generation resources such as wind and solar reliably into power grids, accurate renewable energy forecasting is necessary. Since renewable energy generation output is heavily influenced by environmental variables, accurate forecasting of power generation requires meteorological data at the point where the plant is located. Therefore, a spatial approach is required to predict the meteorological variables at the interesting points. In this paper, we propose the meteorological variable prediction model for enhancing renewable generation output forecasting model. The proposed model is implemented by three geostatistical techniques: Ordinary kriging, Universal kriging and Co-kriging.

• Wind and Structures
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• v.31 no.6
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• pp.549-560
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• 2020

Methods for stochastic simulation of non-Gaussian wind pressure have increasingly addressed the efficiency and accuracy contents to offer an accurate description of the extreme value estimation of the long-span and high-rise structures. This paper presents a linear prediction and z-transform (LPZ) based Cumulative distribution function (CDF) mapping algorithm for the simulation of multivariate non-Gaussian fluctuating wind pressure. The new algorithm generates realizations of non-Gaussian with prescribed marginal probability distribution function (PDF) and prescribed spectral density function (PSD). The inverse linear prediction and z-transform function (ILPZ) is deduced. LPZ is improved and applied to non-Gaussian wind pressure simulation for the first time. The new algorithm is demonstrated to be efficient, flexible, and more accurate in comparison with the FFT-based method and Hermite polynomial model method in two examples for transverse softening and longitudinal hardening non-Gaussian wind pressures.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.6
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• pp.1-11
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• 2004

Modern processors achieve high performance exploiting avaliable Instruction Level Parallelism(ILP) by using speculative technique such as branch prediction. Traditionally, branch direction can be predicted at very high accuracy by 2-level predictor, and branch target address is predicted by Branch Target Buffer(BTB). Except for indirect branch, each of the branch has the unique target, so its prediction is very accurate via BTB. But because indirect branch has dynamically polymorphic target, indirect branch target prediction is very difficult. In general, the technique of branch direction prediction is applied to indirect branch target prediction, and much better accuracy than traditional BTB is obtained for indirect branch. We present a new indirect branch target prediction scheme which combines a indirect branch instruction with its data dependent register of the instruction executed earlier than the branch. The result of SPEC benchmark simulation which are obtained on SimpleScalar simulator shows that the proposed predictor obtains the most perfect prediction accuracy than any other existing scheme.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.6
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• pp.1159-1166
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• 2010

To improve prediction quality of a nonlinear prediction system, the system's capability for uncertainty of nonlinear data should be satisfactory. This paper presents a TSK fuzzy prediction system that can consider and deal with the uncertainty of nonlinear data sufficiently. In the design procedures of the proposed system, HCBKA(Hierarchical Correlationship-Based K-means clustering Algorithm) was used to generate the accurate fuzzy rule base that can control output according to input efficiently, and the first-order difference method was applied to reflect various characteristics of the nonlinear data. Also, multiple prediction systems were designed to analyze the prediction tendencies of each difference data generated by the difference method. In addition, to enhance the prediction quality of the proposed system, an error compensation method was proposed and it compensated the prediction error of the systems suitably. Finally, the prediction performance of the proposed system was verified by simulating two typical time series examples.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.1-16
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• 2023

Accurate traffic information prediction is considered to be one of the most important aspects of intelligent transport systems(ITS), as it can be used to guide users of transportation facilities to avoid congested routes. Various deep learning models have been developed for accurate traffic prediction. Recently, ensemble techniques have been utilized to combine the strengths and weaknesses of various models in various ways to improve prediction accuracy and stability. Therefore, in this study, we developed and evaluated a traffic information prediction model using various deep learning models, and evaluated the performance of the developed deep learning models as a stacking ensemble. The individual models showed error rates within 10% for traffic volume prediction and 3% for speed prediction. The ensemble model showed higher accuracy compared to other models when no cross-validation was performed, and when cross-validation was performed, it showed a uniform error rate in long-term forecasting.