• Journal of Astronomy and Space Sciences
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• v.14 no.2
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• pp.381-385
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• 1997

A long-term prediction algorithm of geostationary orbit was developed using the analytical method. The perturbation force models include geopotential upto fifth order and degree and luni-solar gravitation, and solar radiation pressure. All of the perturbation effects were analyzed by secular variations, short-period variations, and long-period variations for equinoctial elements such as the semi-major axis, eccentricity vector, inclination vector, and mean longitude of the satellite. Result of the analytical orbit propagator was compared with that of the cowell orbit propagator for the KOREASAT. The comparison indicated that the analytical solution could predict the semi-major axis with an accuracy of better than $pm35$ meters over a period of 3 month.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.10 no.6
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• pp.959-967
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• 1999

In this paper, we propose a Linear Prediction Method(LPM) in conjunction with signal enhancement for solving the direction-of-arrival estimation problem of multiple incoherent plane waves incident on a uniform linear array. The basic idea of signal enhancement is that of finding the covariance matrix of given rank that lies closest to a given estimated matrix in Frobenius norm sense. It is well known that LPM has a high-resolution performance in general applications, while it provides a lower statistical performance in lower SNR environment. To solve this problem, the LPM combined with signal enhancement approach is herein proposed. Simulation results are illustrated to demonstrate the better performance of the proposed method than conventional LPM.

• The Journal of Information Technology
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• v.5 no.4
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• pp.173-183
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• 2002

In this paper, we propose an efficient forecasting methodology of the mid and long-term frequency demand in Korea. The methodology consists of the following three steps: classification of basic service group, calculation of effective traffic, and frequency forecasting. Based on the previous studies, we classify the services into wide area mobile, short range radio, fixed wireless access and digital video broadcasting in the step of the classification of basic service group. For the calculation of effective traffic, we use the measures of erlang and bps. The step of the calculation of effective traffic classifies the user and basic application, and evaluates the effective traffic. Finally, in the step of frequency forecasting, different methodology will be proposed for each service group.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.11
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• pp.1041-1045
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• 2010

Clusters can provide scalable and flexible architectures for parallel computing servers and data centers. Their performance prediction has been a very challenging issue. Existing performance measurement methodologies are able to measure the performance of servers already constructed. Thus they cannot provide a way to predict the overall system performance in advance when designing the system at the initial phase or adding more nodes for more capacity. Therefore, the performance modeling and prediction methodology for large-scale clusters is highly required. In this paper, we suggest a methodology to predict the performance of large-scale clusters, which consists of measurement, modeling and prediction steps. We apply the methodology to a real cluster server and show its usefulness.

• Journal of the Korea Society for Simulation
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• v.30 no.1
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• pp.75-85
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• 2021

In this paper, we present a deep neural network-based prediction model that processes and analyzes the corporate credit and personal credit information of individual business owners as a new method to predict the default rate of individual business more accurately. In modeling research in various fields, feature selection techniques have been actively studied as a method for improving performance, especially in predictive models including many features. In this paper, after statistical verification of macroeconomic indicators (macro variables) and credit information (micro variables), which are input variables used in the default rate prediction model, additionally, through the credit information feature selection method, the final feature set that improves prediction performance was identified. The proposed credit information feature selection method as an iterative & hybrid method that combines the filter-based and wrapper-based method builds submodels, constructs subsets by extracting important variables of the maximum performance submodels, and determines the final feature set through prediction performance analysis of the subset and the subset combined set.

• Journal of Internet of Things and Convergence
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• v.7 no.4
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• pp.9-14
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• 2021

In computing systems that require high reliability, the method of predicting the lifetime of a storage device is one of the important factors for system management because it can maximize usability as well as data protection. The life of a solid state drive (SSD) that has recently been used as a storage device in several storage systems is linked to the life of the NAND flash memory that constitutes it. Therefore, in a storage system configured using an SSD, a method of accurately and efficiently predicting the lifespan of a NAND flash memory is required. In this paper, a method for optimizing the lifetime prediction of a flash memory-based storage device using the frequency of NAND flash memory failure is proposed. For this, we design a cost matrix to collect the frequency of defects that occur when processing data in units of Drive Writes Per Day (DWPD). In addition, a method of predicting the remaining cost to the slope where the life-long finish occurs using the Gradient Descent method is proposed. Finally, we proved the excellence of the proposed idea when any defect occurs with simulation.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.6
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• pp.503-511
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• 2004

In this study, for estimating responses of a controlled structure and determining the maximum control force of velocity-dependent damping devices, three estimation models such as Fourier envelope convex model, probability model, and Newmark design spectrum are used. For this purpose, a procedure is proposed for estimating actual velocity using pseudo-velocity and this procedure considers the effects of damping ratio increased by the damping device. Time history results indicate that actual velocity should be used for estimating accurate maximum control force of damping device and Newmark design spectrum modified by the proposed equation gives the best estimation results for over all period structures.

• Proceedings of the KWS Conference
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• 2009.11a
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• pp.5-5
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• 2009

용접에 의한 이면부 도장의 Burn damage는 관리하기 힘든 고질적인 품질문제이다. 도장면의 Burn damage 품질문제 발생시 재작업 등으로 인하여 많은 비용이 발생한다. 이런 경우 기존에 보유한 실험자료 및 적절한 이론자료 부족으로 인하여 일회적인 실험 혹은 해석적 방법을 사용하여 용접 이면부의 최고온도 등을 예측하고 회피할 수 있는 방법론을 제공하였다. 그러나 각 경우에 대해 해석 및 실험을 진행하게 되면 시간 및 비용에서 많은 문제점을 일으키게 된다. 따라서 체계적이고 효율적인 Burn damage 예측방법의 필요성이 대두되었다. 본 연구의 목적은 실험적/해석적 방법을 통해 용접이면부 최고 온도를 예측하고 이를 통해 일반화된 용접이면부 최고도달온도 예측식을 유도하는 것에 있다. 이를 위해 다양한 조건에서의 실험과 해석을 실시하였으며 이를 통해 일반화된 용접 이면부 최고도달온도 예측식을 유도하였다.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.9
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• pp.355-360
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• 2014

Forecasting of emerging technology plays important roles in business strategy and R&D investment. There are various ways for technology forecasting including patent analysis. Qualitative analysis methods through experts' evaluations and opinions have been mainly used for technology forecasting using patents. However qualitative methods do not assure objectivity of analysis results and requires high cost and long time. To make up for the weaknesses, we are able to analyze patent data quantitatively and statistically by using text mining technique. In this paper, we suggest a new method of technology forecasting using text mining and ARIMA analysis.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.135-139
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• 2012

하천에서의 정확한 유량 산정은 하천의 설계 및 운영 유지에 매우 중요한 요소이다. 현재 하천의 유량 생산은 수위-유량관계곡선을 통하여 이루어지고 있다. 수위-유량관계곡선법은 측정된 수위와 유량자료의 관계를 바탕으로 홍수기 때의 유량을 회귀 추정법으로 예측하여 사용하는 방법이다. 비교적 간편하게, 특히 측정이 어려운 홍수기 때의 유량을 예측하여 사용할 수 있다는 장점을 가지고 있지만 수위와 유량만의 관계를 사용하므로 하천의 수리학적 특성을 반영하기 곤란하기 때문에 기본적으로 개선되어야 할 사항이 있다. 따라서, 본 연구에서는 하천유량을 예측하는 새로운 방법론의 하나로 KSCE에 기 게재된 Choo 등(2011)의 방법에 따라, 개수로에서 널리 사용되어 오고 있는 Manning식과 Chezy식을 활용하여 하천의 전체적인 특성을 잘 반영하는 특성조도계수 n과 특성Chezy계수 C를 사용하여 하천의 유량을 예측하였다. 실험실 직선수로에서 측정된 정류 자료와 객관성 있는 해외 하천 유량측정 자료를 사용하여 증명하였고 결정계수 0.8 정도 수준의 높은 정확성을 보여주는 성과를 나타내었다. 따라서 본 연구결과를 통해 하천의 수리학적 특성을 반영하면서도 간단하게 유량을 예측할 수 있는 방법으로 실무에서 간편하게 활용될 수 있을 것으로 기대한다.