• Journal of Power Electronics
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• 제11권1호
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• pp.59-63
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• 2011

This paper presents the modeling of the sampling effect in the p-type average current mode control. The prediction of the high frequency components near half of the switching frequency in the current loop gain is given for the p-type average current mode control. By the proposed model, the prediction accuracy is improved when compared to that of conventional models. The proposed method is applied to a buck converter, and then the measurement results are analyzed.

• Advances in nano research
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• 제3권3호
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• pp.143-168
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• 2015

Initiation of crack and its growth simulation requires accurate model of traction - separation law. Accurate modeling of traction-separation law remains always a great challenge. Atomistic simulations based prediction has great potential in arriving at accurate traction-separation law. The present paper is aimed at establishing a method to address the above problem. A method for traction-separation law prediction via utilizing atomistic simulations data has been proposed. In this direction, firstly, a simpler approach of common neighbor analysis (CNA) for the prediction of crack growth has been proposed and results have been compared with previously used approach of threshold potential energy. Next, a scheme for prediction of crack speed has been demonstrated based on the stable crack growth criteria. Also, an algorithm has been proposed that utilizes a variable relaxation time period for the computation of crack growth, accurate stress behavior, and traction-separation atomistic law. An understanding has been established for the generation of smoother traction-separation law (including the effect of free surface) from a huge amount of raw atomistic data. A new curve fit has also been proposed for predicting traction-separation data generated from the molecular dynamics simulations. The proposed traction-separation law has also been compared with the polynomial and exponential model used earlier for the prediction of traction-separation law for the bulk materials.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.183-183
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• 2021

Deep learning methods and their application have become an essential part of prediction and modeling in water-related research areas, including hydrological processes, climate change, etc. It is known that application of deep learning leads to high availability of data sources in hydrology, which shows its usefulness in analysis of precipitation, runoff, groundwater level, evapotranspiration, and so on. However, there is still a limitation on microclimate analysis and prediction with deep learning methods because of deficiency of gauge-based data and shortcomings of existing technologies. In this study, a real-time rainfall prediction model was developed from a sky image data set with convolutional neural networks (CNNs). These daily image data were collected at Chung-Ang University and Korea University. For high accuracy of the proposed model, it considers data classification, image processing, ratio adjustment of no-rain data. Rainfall prediction data were compared with minutely rainfall data at rain gauge stations close to image sensors. It indicates that the proposed model could offer an interpolation of current rainfall observation system and have large potential to fill an observation gap. Information from small-scaled areas leads to advance in accurate weather forecasting and hydrological modeling at a micro scale.

• 대기
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• 제30권2호
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• pp.115-124
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• 2020

The Northern Hemisphere extratropical prediction skill of the Korea Meteorological Administration (KMA) Global Data Assimilation and Prediction System (GDAPS) is examined for January 2019. The real-time prediction skill, evaluated with mean squared skill score (MSSS) of 30-90°N geopotential height field at 500 hPa (Z500), is ~8 days in the troposphere. The MSSS of Z500 considerably decreases after 3 days mainly due to the increasing eddy errors. The eddy errors are largely explained by the eddy-phased errors with minor contribution of amplitude errors. In particular, planetary-scale eddy errors are considered as a main reason of rapidly increasing errors. It turns out that such errors are associated with the blocking highs over North Pacific (NP) and Euro-Atlantic (EA) regions. The model overestimates the blocking highs over NP and EA regions in time, showing dependence of blocking predictability on blocking initializations. This result suggests that the extratropical prediction skill could be improved by better representing blocking in the model.

• 한국산업정보학회논문지
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• 제27권2호
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• pp.35-44
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• 2022

본 논문은 깊이 영상의 부호화를 위해 이차 곡면 모델링 방법을 통한 화면 내 예측 방법을 제안한다. 깊이 영상 내 깊이 화소는 거리 정보를 통해 3차원 좌표로 변환한다. 화면 내 예측을 위한 참조 화소들에 대해 최소자승법을 통해 오차가 제일 작은 이차 곡면을 찾는다. 참조 화소로는 상단의 화소들 또는 좌단의 화소들 중 하나가 될 수 있다. 이차 곡면을 통한 화면 내 예측에서, 한 화소에 대해 두 개의 예측 값이 계산된다. 각각의 참조 화소에 대해 예측 값들과 참조 화소의 차아의 제곱합으로 두 오차 값을 계산한다. 계산된 총 4개의 오차 중 제일 작은 오차를 가지는 참조 화소 선택 방법과 예측 화소 선택 방법이 선택되고, 이를 통해 블록 내 화소를 예측한다. 실험 결과는 최신 영상 부호화 방법과 비교하여 왜곡과 비트율이 각각 최대 5.16%과 5.12% 개선됨을 보인다.

• 약학회지
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• 제59권1호
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• pp.29-39
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• 2015

In recent years, physiologically based pharmacokinetic (PBPK) modeling has been widely used in pharmaceutical industries as well as regulatory health authorities for drug discovery and development. Several application areas of PBPK have been introduced so far including drug-drug interaction prediction, transporter-mediated interaction prediction, and pediatric PK prediction. The purpose of this review is to introduce PBPK and illustrates one of its application areas, particularly pediatric PK prediction by utilizing existing adult PK data and in vitro data. The evaluation of the initial PBPK for adult was done by comparing with experimental PK profiles and the scaling from adult to pediatric was conducted using age-related changes in size such as tissue compartments, and protein binding etc. Sotalol and lorazepam were selected in this review as model drugs for this purpose and were re-evaluated using the PBPK models by GastroPlus$^{(R)}$. The challenges and strategies of PBPK models using adult PK data as well as appropriate in vitro assay data for extrapolating pediatric PK at various ages were also discussed in this paper.

• 터널과지하공간
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• 제17권6호
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• pp.484-492
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• 2007

국내에서는 주로 환산거리 진동예측식에 의한 발파진동 예측 방법이 사용되고 있다. 그러나 이러한 환산거리방식은 실규모의 발파가 시행되어져야 할 필요성이 있다. 최근 국내에서는 터널 등의 공사 시행 전 사전 조사단계에서 발파진동의 영향권을 예측하려는 시도로서 지질 조사용 시추공 등에 장약 발파하여 지반진동을 측정하고 본 발파의 발파 진동을 예측하는 방법이 사용되고 있다. 그러나 이러한 발파진동 예측 방법은 본 발파시의 진동의 전달 특성을 완전하게 반영하지는 못한다. 이러한 발파진동 예측방법의 결점을 보완하기 위하여 본 연구에서는 사전 조사 단계의 단일공 파형 중첩 모델링을 통하여 발파진동을 예측하는 방법을 개발하였다.

• 융합보안논문지
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• 제14권4호
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• pp.19-26
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• 2014

모바일 악성코드는 웜에 의한 전파가 대표적이며, 웜의 확산 특징을 분석하기 위한 모델링 기법들이 제시되었지만 거시적인 분석만 가능하였고 특정 바이러스, 악성코드에 대해 예측하기는 한계점이 있다. 따라서 본 논문에서는 과거의 악성코드 데이터를 활용하여 미래의 악성코드의 발생을 예측 할 수 있는 마코프 체인을 기반으로 한 예측 방법을 제시하였다. 마코프 체인 예측 모델링에 적용할 악성코드 평균값은 전체 평균값, 최근 1년 평균값, 최근 평균값(6개월)의 세 가지 범위로 분류하여 적용하였고, 적용하여 얻어진 예측 값을 비교하여 최근 평균 값(6개월)을 적용하는 것이 악성코드 예측 확률을 높일 수 있음을 확인하였다.

• 한국시뮬레이션학회논문지
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• 제14권3호
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• pp.91-100
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• 2005

Grid computing is widely applicable to various fields of industry including process control and manufacturing, military command and control, transportation management, and so on. In a viewpoint of application area, grid computing can be classified to three aspects that are computational grid, data grid and access grid. This paper focuses on computational grid which handles complex and large-scale computing problems. Computational grid is characterized by system dynamics which handles a variety of processors and jobs on continuous time. To solve problems of system complexity and reliability due to complex system dynamics, computational grid needs scheduling policies that allocate various jobs to proper processors and decide processing orders of allocated jobs. This paper proposes a service prediction-based job scheduling model and present its scheduling algorithm that is applicable for computational grid. The service prediction-based job scheduling model can minimize overall system execution time since the model predicts the next processing time of each processing component and distributes a job to a processing component with minimum processing time. This paper implements the job scheduling model on the DEVS modeling and simulation environment and evaluates its efficiency and reliability. Empirical results, which are compared to conventional scheduling policies, show the usefulness of service prediction-based job scheduling.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 춘계학술대회 학술발표 논문집
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• pp.191-194
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• 2001

The ozone forecasting systems have many problems because the mechanism of the ozone concentration is highly complex, nonlinear, and nonstationary. Also, the results of prediction are not a good performance so far, especially in the high-level ozone concentration. This paper describes the modeling method of the ozone prediction system using neuro-fuzzy approaches and fuzzy clustering. The dynamic polynomial neural network (DPNN) based upon a typical algorithm of GMDH (group method of data handling) is a useful method for data analysis, identification of nonlinear complex system, and prediction of a dynamical system.