• 한국환경과학회지
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• 제18권11호
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• pp.1271-1281
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• 2009

The heavy snowfall event over the eastern part of Seoul, Korea on Mar. 04, 2008 has been abruptly occurred after the frontal system with the heavy snowfall event had been past over the Korean peninsula on Mar. 03, 2008. Therefore, this heavy snowfall event couldn't be predicted well by any means of theoretical knowledges and models. After the cold front passed by, the cold air mass was flown over the peninsula immediately and became clear expectedly except the eastern part and southwestern part of peninsula with some large amount of snowfall. Even though the wide and intense massive cold anticyclone was expanded and enhanced by the lowest tropospheric baroclinicity over the Yellow Sea, but the intrusion and eastward movement of cold air to Seoul was too slow than normally predicted. Using the data of numerical model, satellite and radar images, three dimensional analysis Products(KLAPS : Korea Local Analysis and Prediction System) of the environmental conditions of this event such as temperature, equivalent potential temperature, wind, vertical circulation, divergence, moisture flux divergence and relative vorticity could be analyzed precisely. Through the analysis of this event, the formation and westward advection of lower cyclonic circulation with continuously horizontal movement of air into the eastern part of Seoul by the analyses of KLAPS fields have been affected by occurring the heavy snowfall event. As the predictability of abrupt snowfall event was very hard and dependent on not only the synoptic atmospheric circulation but also for mesoscale atmospheric circulation, the forecaster can be predicted well this event which may be occurred and developed within the very short time period using sequential satellite images and KLAPS products.

• 한국멀티미디어학회논문지
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• 제9권10호
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• pp.1322-1331
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• 2006

지역적으로 분산되어 있는 대규모 네트워크 게임은 다수의 참여자들에게 일관된 상호작용 성능을 통하여 필요한 정보를 공유해야한다. 동시성제어는 게임의 일관된 상태를 유지하도록 하기위한 중요한 요소이다. 동시성제어를 위해 전송하는 이벤트마다 재생 지연 시간을 설정해야하고, 수신된 이벤트에 대해서는 예정 재생시간까지 버퍼에 저장한 후 동시에 이벤트를 수행해야 한다. 그러나 다양한 이동속도를 가진 접속환경의 경우 참여자간에 동일한 속도로 이벤트를 수행시킨다는 것은 쉽지 않다. 이를 위해서는 다양한 이동속도를 지원하기 위한 확장성이 제공되어야 한다. 따라서 네트워크 게임엔진의 설계 시 게임의 성능에 중요한 요소는 대여폭과 지연에 대한 처리이다. 멀티플랫폼 게임을 위한 예측기반 동시성제어방식의 처리는 이벤트 손실율을 최소화시켜 게임의 상호작용 성능을 향상시킬 것이다. 그리고 데드러커닝 알고리즘으로 클라이언트 간의 신뢰성을 확보할 수 있을 것이다. 본 연구는 유 무선이 통합된 게임엔진설계에 따른 문제점을 분석하고 온라인 게임의 치명적인 장애요소가 될 수 있는 대여폭과 지연을 향상시키기 위한 처리 방안과 서버와 클라이언트간의 신뢰성확보를 위한 처리방법을 제시할 것이다.

• 한국물환경학회지
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• 제21권6호
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• pp.617-623
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• 2005

Forestry and agricultural land uses constitute 85% of Korea and these land uses are typically mixed in many watersheds. Biological Oxygen Demand (BOD) concentration is a primary factor for managing water qualities of the water resources in Korea. BOD loadings from diffuse sources, however, not well monitored yet. This study aims to assess BOD loadings from diffuse sources and their affecting factors to conserve quality of water resources. Event Mean Concentration (EMC) of BOD was calculated based on the monitoring data of forty rainfall events at four agricultural-forestry watersheds. Exceedence cumulative probability of BOD EMCs were plotted to show agricultural activities in a watershed impacts on the magnitude of EMCs. Prediction equation for each rainfall event was proposed to estimate BOD EMCs: $EMC_{BOD}(mg/L)=EXP(0.413+0.0000001157{\times}$(discharged runoff volume in $m^3$)+0.018${\times}$(ratio of agricultural land use to total watershed area).

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.232-240
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• 2022

To fulfill user expectations, the rapid evolution of software techniques and approaches has necessitated reliable and flawless software operations. Aging prediction in the software under operation is becoming a basic and unavoidable requirement for ensuring the systems' availability, reliability, and operations. In this paper, an improved evolutionary computing-driven extreme learning scheme (ECD-ELM) has been suggested for object-oriented software aging prediction. To perform aging prediction, we employed a variety of metrics, including program size, McCube complexity metrics, Halstead metrics, runtime failure event metrics, and some unique aging-related metrics (ARM). In our suggested paradigm, extracting OOP software metrics is done after pre-processing, which includes outlier detection and normalization. This technique improved our proposed system's ability to deal with instances with unbalanced biases and metrics. Further, different dimensional reduction and feature selection algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), and T-Test analysis have been applied. We have suggested a single hidden layer multi-feed forward neural network (SL-MFNN) based ELM, where an adaptive genetic algorithm (AGA) has been applied to estimate the weight and bias parameters for ELM learning. Unlike the traditional neural networks model, the implementation of GA-based ELM with LDA feature selection has outperformed other aging prediction approaches in terms of prediction accuracy, precision, recall, and F-measure. The results affirm that the implementation of outlier detection, normalization of imbalanced metrics, LDA-based feature selection, and GA-based ELM can be the reliable solution for object-oriented software aging prediction.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2000년도 추계학술대회 및 정기총회
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• pp.40-43
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• 2000

In this paper, we investigate how the average survival time of listed companies in the Korea Stock Exchange (KSE) are affected by changes in macro-economic environment and covariate vectors which show peculiar financial characteristics of each company. We also apply the survival analysis approach to the dichotomous firm failure prediction and the results show a similar pattern of forecasting performance using the existing dichotomous prediction techniques. These findings suggest that, when we consider a bankruptcy model under a certain economic event, the survival approach can be a useful alternative to the existing dichotomous prediction methods since the approach provides estimation of average survival time as well as simple binary prediction.

• 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.

• 한국수소및신에너지학회논문집
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• 제31권1호
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• pp.73-81
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• 2020

For the fault tree analysis (FTA) analysis of the packaged hydrogen filling station, the composition of the charging station was analyzed and the fault tree (FT) diagram was prepared. FT diagrams were created by dividing the causes of events into external factors and internal factors with the hydrogen event as the top event. The external factors include the effects of major disasters caused by natural disasters and external factors as OR gates. Internal factors are divided into tube tailer, compressor & storage tank, and dispenser, which are composed of mistakes in operation process and causes of accidents caused by parts leakage. In this study, the purpose was to improve the hydrogen station. The subjects of this study were domestic packaged hydrogen stations and FTA study was conducted based on the previous studies, failure mode & effect analysis (FMEA) and hazard & operability study (HAZOP). Top event as a hydrogen leaking event and constructed the flow of events based on the previous study. Refer to "Off shore and onshore reliability data 6th edition", "European Industry Reliability Data Bank", technique for human error rate prediction (THERP) for reliability data. We hope that this study will help to improve the safety and activation of the hydrogen station.

• Genomics & Informatics
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• 제17권4호
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• pp.41.1-41.12
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• 2019

Survival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains "censored" data, in which the time to event cannot be completely observed, but instead represents the lower bound of the time to event. Only the occurrence of either time to event or censoring time is observed. Many traditional statistical methods have been effectively used for analyzing survival data with censored observations. However, with the development of high-throughput technologies for producing "omics" data, more advanced statistical methods, such as regularization, should be required to construct the predictive survival model with high-dimensional genomic data. Furthermore, machine learning approaches have been adapted for survival analysis, to fit nonlinear and complex interaction effects between predictors, and achieve more accurate prediction of individual survival probability. Presently, since most clinicians and medical researchers can easily assess statistical programs for analyzing survival data, a review article is helpful for understanding statistical methods used in survival analysis. We review traditional survival methods and regularization methods, with various penalty functions, for the analysis of high-dimensional genomics, and describe machine learning techniques that have been adapted to survival analysis.

• 한국농공학회논문집
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• 제59권5호
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• pp.101-108
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• 2017

Recently, the occurrence of landslides has been increasing over the years due to the extreme weather event. Developments of landslides monitoring technology that reduce damage caused by landslide are urgently needed. Therefore, in this study, a strain ratio sensor was developed to predict the ground behavior during the slope failure, and the change in surface ground displacement was observed as slope failed on the field model experiment. As a result, in the slope failure, the ground displacement process increases the risk of collapse as the inverse displacement approaches zero. It is closely related to the prediction of precursor. In all cases, increase in displacement and reverse speed of inverse displacement with time was observed during the slope failure, and it is very important event for monitoring collapse phenomenon of risky slopes. In the future, it can be used as disaster prevention technology to contribute in reduction of landslide damage and activation of measurement industry.

• 한국수자원학회논문집
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• 제54권9호
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• pp.667-680
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• 2021

이 연구의 목적은 서리 발생일과 무상일 기간의 특성을 분석하고 로지스틱 회귀, 랜덤 포레스트, Long-short Term Memory (LSTM) 기법을 활용하여 서리발생 예측모델을 개발하고 평가하는데 있다. 수원, 청주, 광주 지점에서 봄철과 가을철 서리발생 예측모델 개발을 위한 기상변수들을 수집하였으며, 수집기간은 1973년부터 2019년까지이다. 프리시전(precision), 리콜(Recall), f-1 스코어와, AUC 및 Reliability Diagram과 같은 그래피컬 평가기법을 이용해 서리발생 예측모델을 평가하였다. 봄철과 가을철 모두 서리발생일이 줄어드는 경향성(유의수준: 0.01)을 보였다. 0.9 이상의 높은 AUC 값에도 불구하고, 신뢰도는 일정한 값을 보여주지는 않았다. 서리발생일 측뿐만 아니라, 초상일과 종상일을 정확히 예측할 수 있도록 모형 개선이 필요해 보이며, 다른 지역의 더 많은 지점에서 동일한 기법을 적용해 보는 연구가 필요해 보인다.