• Architectural research
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• v.2 no.1
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• pp.41-46
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• 2000

The sawtooth skylight is an excellent daylighting concept for the uniform interior illuminance over large working areas. In computer simulation, it is difficult for an architect to get accurate daylight illuminances for the spaces where sawtooth apertures are applied. In this study, daylight prediction algorithms for sawtooth apertures are developed. The flux transfer method is applied for this study to predict daylight illuminances. The simplified equations from this study can be used effectively for preliminary prediction of daylight in sawtooth spaces.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1379-1382
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• 1987

IN ADAPTIVE LINEAR PREDICTION, AN ADAPTIVE CAPABILITY IS BUILT INTO THE PROCESSOR SUCH THAT AS THE IMAGE STATISTICS CHANGE, THE PREDICTION FILTER COEFFICIENTS THEMSELVES CHANGE, PRODUCING A NEW FILTER MORE CLOSELY OPTIMIZED TO THE NEW SET OF IMAGES STATISTICS. THE LMS ALGORITHM MAY BE USED TO ADAPT THE COEFFICIENT OF AN ADAPTIVE PREDICTION FILTER FOR IMAGE SOURCE ENCODING. IN THIS PAPER, TWO CODING SYSTEMS USING DPCM AND LMS ALGORITHMS RESPECTIVELY FOR OBTAINING THE FIRST TRANSFORMED COEFFICIENT IN HYBRID CODING ARE COMPARED.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.85-88
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• 1996

We develop an efficient technique of controlling chaos using M-step ahead prediction with the OGY method. It has smaller transient time than the OGY method, and prevents burst phenomena that occur in noisy environment. This technique is very simple and needs small memory compared with targeting algorithms. Numerical examples show that the proposed algorithm has good performance, especially in noisy environment.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.999-1012
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• 2005

We compare two popular algorithms in current machine learning and statistical learning areas, boosting method represented by AdaBoost and kernel based SVM (Support Vector Machine) using 13 real data sets. This comparative study shows that boosting method has smaller prediction error in data with heavy noise, whereas SVM has smaller prediction error in the data with little noise.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1463-1466
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• 2002

This paper suggests the effective channel assignment scheme for mobility prediction handover. For maintaining required quality of service (QoS) during handover, there are handover algorithms these reserve the channel where the movement is predicted. But channel assignment schemes these have been studied are not considered mobility prediction handover. This paper suggests the channel assignment scheme that considers mobility predicted handover. The suggested algorithm maintains dropping probability of handover calls, decreases blocking probability of new calls and increases channel utilization.

• Journal of KIISE
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• v.42 no.2
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• pp.145-154
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• 2015

This paper proposes an enhanced projection candidate selection algorithm to improve the performance of the existing path prediction algorithm. Various user path prediction algorithms have previously been developed, but those algorithms are inappropriate for a real-time and close user path prediction environment. To resolve this issue, a new prediction algorithm has been proposed, but several problems still remain. In particular, this algorithm should be enhanced to provide much faster processing performance. The major cause of the high processing time of the previous path prediction algorithm is the high time complexity of its projection candidate selection. Therefore, this paper proposes a new path fragment management structure and an improved projection candidate selection algorithm to improve the processing speed of the existing projection candidate selection algorithm. This paper also shows the effectiveness of the algorithm herein proposed through a comparative performance evaluation.

• Journal of Communications and Networks
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• v.16 no.4
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• pp.436-446
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• 2014

The high intensity of research and modeling in fields of mathematics, physics, biology and chemistry requires new computing resources. For the big computational complexity of such tasks computing time is large and costly. The most efficient way to increase efficiency is to adopt parallel principles. Purpose of this paper is to present the issue of parallel computing with emphasis on the analysis of parallel systems, the impact of communication delays on their efficiency and on overall execution time. Paper focuses is on finite algorithms for solving systems of linear equations, namely the matrix manipulation (Gauss elimination method, GEM). Algorithms are designed for architectures with shared memory (open multiprocessing, openMP), distributed-memory (message passing interface, MPI) and for their combination (MPI + openMP). The properties of the algorithms were analytically determined and they were experimentally verified. The conclusions are drawn for theory and practice.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.12
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• pp.1522-1531
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• 1988

The purpose of this paper is to explain eigenstructure based spatial spectrum estimation algorithms computing better estimates than the other approaches. Also, as an approach to overcome performance degradations in a multipath environments, the notion of forward and backwark spatial smoothing is discussed. Intensive simulation results,which include the comparisons of the eigenbased spatial spectral estimation algorithms in the situations of faulty estimation of the number of signals, are presented. The simulation results have shown that overestimation of the number of signals is more desirable than underestimation in using EV (Eigen Vector) and MUSIC (Multiple Signal Classification) algorithms and that underestimation of the number of signals is better strategy than overestimation in using eigenstructure based LP(Linear Prediction) algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.8A
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• pp.753-762
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• 2002

Blind equalization of transmission channel is important in communication areas and signal processing applications because it does not need training sequences, nor dose it require a priori channel information. In this paper, an adaptive blind MMSE channel equalization technique based on second-order statistics in investigated. We present an adaptive blind MMSE channel equalization using multichannel linear prediction error method for estimating cross-correlation vector. They can be implemented as RLS or LMS algorithms to recursively update the cross-correlation vector. Once cross-correlation vector is available, it can be used for MMSE channel equalization. Unlike many known subspace methods, our proposed algorithms do not require channel order estimation. Therefore, our algorithms are robust to channel order mismatch. Performance of our algorithms and comparisons with existing algorithms are shown for real measured digital microwave channel.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1195-1205
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• 2022

Recent developments on spectral diffusion algorithms, i.e., algorithms which exploit the projection of the solution on the eigenfunctions of the Laplacian operator, demonstrated their effective applicability in fast transient conditions. Nevertheless, the numerical error introduced by these algorithms, together with the uncertainties associated with model parameters, may impact the reliability of the predictions on short-lived volatile fission product release from nuclear fuel. In this work, we provide an upper bound on the numerical error introduced by the presented spectral diffusion algorithm, in both constant and time-varying conditions, depending on the number of modes and on the time discretization. The definition of this upper bound allows introducing a methodology to a priori bound the numerical error on short-lived volatile fission product retention.