• Communications for Statistical Applications and Methods
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• 제24권1호
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• pp.67-80
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• 2017

Over the last few decades, ensemble forecasts based on global climate models have become an important part of climate forecast due to the ability to reduce uncertainty in prediction. Moreover in ensemble forecast, assessing the prediction uncertainty is as important as estimating the optimal weights, and this is achieved through a probabilistic forecast which is based on the predictive distribution of future climate. The Bayesian model averaging has received much attention as a tool of probabilistic forecasting due to its simplicity and superior prediction. In this paper, we propose a new Bayesian model averaging method for probabilistic ensemble forecasting. The proposed method combines a deterministic ensemble forecast based on a multivariate regression approach with Bayesian model averaging. We demonstrate that the proposed method is better in prediction than the standard Bayesian model averaging approach by analyzing monthly average precipitations and temperatures for ten cities in Korea.

• Industrial Engineering and Management Systems
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• 제14권3호
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• pp.312-317
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• 2015

The dynamic system approach in time series has been used in many real problems. Based on Taken's embedding theorem, we can build the predictive function where input is the time delay coordinates vector which consists of the lagged values of the observed series and output is the future values of the observed series. Although the time delay coordinates vector from multivariate time series brings more information than the one from univariate time series, it can exhibit statistical redundancy which disturbs the performance of the prediction function. We apply dimension reduction techniques to solve this problem and analyze the effect of this approach for prediction. Our experiment uses delayed Lorenz series; least squares support vector regression approximates the predictive function. The result shows that linearly preserving projection improves the prediction performance.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 춘계정기학술대회
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• pp.399-408
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• 2001

This paper presents a simultaneous optimization approach of case-based reasoning (CBR) using a genetic algorithm(GA) for the prediction of stock price index. Prior research suggested many hybrid models of CBR and the GA for selecting a relevant feature subset or optimizing feature weights. Most studies, however, used the GA for improving only a part of architectural factors for the CBR system. However, the performance of CBR may be enhanced when these factors are simultaneously considered. In this study, the GA simultaneously optimizes multiple factors of the CBR system. Experimental results show that a GA approach to simultaneous optimization of CBR outperforms other conventional approaches for the prediction of stock price index.

• ETRI Journal
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• 제43권6호
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• pp.1058-1080
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• 2021

Crime is not a completely random event but rather shows a pattern in space and time. Capturing the dynamic nature of crime patterns is a challenging task. Crime prediction models that rely only on neighborhood influence and demographic features might not be able to capture the dynamics of crime patterns, as demographic data collection does not occur frequently and is static. This work proposes a novel approach for crime count and hotspot prediction to capture the dynamic nature of crime patterns using taxi data along with historical crime and demographic data. The proposed approach predicts crime events in spatial units and classifies each of them into a hotspot category based on the number of crime events. Four models are proposed, which consider different covariates to select a set of independent variables. The experimental results show that the proposed combined subset model (CSM), in which static and dynamic aspects of crime are combined by employing the taxi dataset, is more accurate than the other models presented in this study.

• Korean Circulation Journal
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• 제53권9호
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• pp.581-593
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• 2023

In the catheterization laboratory, the measurement of physiological indexes can help identify functionally significant lesions and has become one of the standard methods to guide treatment decision-making. Plaque vulnerability refers to a coronary plaque susceptible to rupture, enabling risk prediction before coronary events, and it can be detected by defining a certain type of plaque morphology on coronary imaging modalities. Although coronary physiology and plaque vulnerability have been considered different attributes of coronary artery disease, the underlying pathophysiological basis and clinical data indicate a strong correlation between coronary hemodynamic properties and vulnerable plaque. In prediction of coronary events, emerging data have suggested independent and additional implications of a physiology-based approach to a plaque-based approach. This review covers the fundamental interplay between coronary physiology and plaque morphology during disease progression with clinical data supporting this relationship and examines the clinical relevance of physiological indexes in prediction of clinical outcomes and therapeutic decision-making along with plaque vulnerability.

• 한국경영과학회지
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• 제23권1호
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• pp.67-95
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• 1998

This research addresses flow time prediction in the dynamic unbalanced job shop scheduling environment. The specific purpose of the research is to develop the job flow time prediction model in the dynamic unbalance djob shop. Such factors as job characteristics, job shop status, characteristics of the shop workload, shop dispatching rules, shop structure, etc, are considered in the prediction model. The regression prediction approach is analyzed within a dynamic, make-to-order job shop simulation model. Mean Absolute Lateness (MAL) and Mean Relative Error (MRE) are used to compare and evaluate alternative regression models devloped in this research.

• 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.

• Asian Journal of Atmospheric Environment
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• 제10권4호
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• pp.197-207
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• 2016

Large quantities of air pollutants are released into the atmosphere and hence, must be monitored and routinely assessed for their health implications. This paper proposes a stochastic technique to predict unobserved hazardous air pollutants (HAPs), especially Benzo[a]pyrene (BaP), which can have negative effects on human health. The proposed approach constructs a nearest-neighbor structure by incorporating the linkage between BaP and meteorology and meteorological effects. This approach is adopted in order to predict unobserved BaP concentrations based on observed (or forecasted) meteorological conditions, including temperature, precipitation, wind speed, and air quality. The effects of BaP on human health are examined by characterizing the cancer risk. The efficient prediction provides useful information relating to the optimal monitoring period and projections of future BaP concentrations for both industrial and residential areas within Korea.

• Bulletin of the Korean Chemical Society
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• 제34권4호
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• pp.1043-1046
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• 2013

Prediction of physicochemical properties of organic molecules is an important process in chemistry and chemical engineering. The MSEP approach developed in our lab calculates the molecular surface electrostatic potential (ESP) on van der Waals (vdW) surfaces of molecules. This approach includes geometry optimization and frequency calculation using hybrid density functional theory, B3LYP, at the 6-31G(d) basis set to find minima on the potential energy surface, and is known to give satisfactory QSPR results for various properties of organic molecules. However, this MSEP method is not applicable to screen large database because geometry optimization and frequency calculation require considerable computing time. To develop a fast but yet reliable approach, we have re-examined our previous work on organic molecules using two semi-empirical methods, AM1 and PM3. This new approach can be an efficient protocol in designing new molecules with improved properties.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제4권5호
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• pp.147-152
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• 2015

일반적으로 병렬 이벤트구동 시뮬레이션의 대표적 동기화 방법으로는 비관적 동기화 방식과 낙관적 동기화 방식이 있는데, 본 논문에서는 예측기반 병렬 이벤트구동 로직 시뮬레이션에서 이 두 가지 동기화 방식들을 혼용한 간헐적 동기화를 통한 시뮬레이션 성능 향상 기법을 제시한다. 제안되는 간헐적 동기화 방식은 예측기반 병렬 이벤트구동 로직 시뮬레이션에서 자주 일어나는 틀린 예측과 연관된 체크포인트 오버헤드 및 재실행 오버헤드를 최소화할 수 있어 시뮬레이션 성능 향상에 매우 효과적인데, 이를 다양한 실제 디자인들에 적용한 실험을 통하여 확인할 수 있었다.