• 국제학술발표논문집
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• The 1th International Conference on Construction Engineering and Project Management
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• pp.935-940
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• 2005

This paper presents the results of a statistical analysis using historical data of cost contingency. As a result, a model that predicts and estimates an accurate cost contingency value using the least squares estimation method was developed. Data such as original contract amounts, estimated contingency amounts set by maximum funding limits, and actual contingency amounts, were collected and used for model development. The more effective prediction model was selected from the two developed models based on its prediction capability. The model would help guide project managers making financial decisions when the determination of the cost contingency amounts for multiple projects is necessary.

• Journal of Electrical Engineering and Technology
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• 제4권4호
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• pp.438-442
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• 2009

Voltage instability is a phenomenon that could occur in power systems due to stressed conditions. The result would be an occurrence of voltage collapse leading to total blackout of the system. Therefore, voltage collapse prediction is an important part of power system planning and operation, and can help ensure that voltage collapse due to voltage instability is avoided. Line outages in power systems may also cause voltage collapse, thereby implying the contingency in the system. Contingency problems caused by line outages have been identified as one of the main causes of voltage instability in power systems. This paper presents a new technique for contingency ranking based on voltage stability conditions in power systems. A new line stability index was formulated and used to identify the critical line outages and sensitive lines in the system. Line outage contingency ranking was performed on several loading conditions in order to identify the effect of an increase in loading to critical line outages. Correlation studies on the results obtained from contingency ranking and voltage stability analysis were also conducted, and it was found that line outages in weak lines would cause voltage instability conditions in a system. Subsequently, using the results from the contingency ranking, weak areas in the system can be identified. The proposed contingency ranking technique was tested on the IEEE reliability test system.

• Architectural research
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• 제16권2호
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• pp.75-80
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• 2014

Risks, uncertainties, and associated cost overruns are critical problems for construction projects. Cost contingency is an important funding source for these unforeseen events and is included in the base estimate to help perform financially successful projects. In order to predict more accurate contingency, many empirical models using regression analysis and artificial neural network method have been proposed and showed its viability to minimize prediction errors. However, categorical factors on contingency cannot have been treated and thus considered in these empirical models since those models are able to treat only numerical factors. This paper identified potential factors on contingency in transportation construction projects and evaluated categorical factors using the one-way ANOVA statistical method. Among factors including project work type, delivery method type, contract agreement type, bid award type, letting type, and geographical location, two factors of project work type and contract agreement type were found to be statistically important on allocating cost contingency.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2017년도 춘계학술대회
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• pp.131-133
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• 2017

과거의 누적된 데이터는 미래를 예측하는데 있어서 기본 데이터를 제공한다. 우연성이론에 근거하여 많은 분야에서의 예측 방법들이 활용되고 있지만, 로또번호의 예측은 우연성이론에 근거하지 않는다. 본 연구에서는 누적된 데이터를 통하여 발생하는 예측력의 변화를 알아보는 방법을 제시하고자 한다.

• 한국건설관리학회논문집
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• 제19권5호
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• pp.81-89
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• 2018

본 연구의 목적은 플랜트 건설사업에서 모듈러 공법의 적용이 확대됨에 따라 모듈러 플랜트에 대한 업무특성을 고려하여 위험을 평가하고 위험에 대응하기 위한 예비비를 예측하는 것이다. 연구방법은 모듈러 플랜트의 업무특성을 고려하여 위험의 영향을 평가하기 위한 모델(방법)과 예비비를 예측하기 위한 모델(방법)을 제시한다. 그리고 제시된 모델을 기반으로 모듈러 플랜트 건설사업 1곳을 사례로 선정하여 위험요인의 영향을 평가하고 예비비를 예측한다. 상기와 같은 목적과 방법에 따라 도출된 결과는 다음과 같다. 위험요인의 발생확률과 영향점수를 평가하여 중요 위험요인 15개를 선정하였다. 그리고 모듈러 플랜트의 특성을 고려하기 위하여 설계(E), 구매(P), 제작(F), 운송(T), 시공(C)단계로 업무를 분류하여 예측된 예비비는 기초사업비(610,503,596 천원) 대비 약 6.739%이며, 설계(E) 2.850%, 구매(P) 6.225%, 제작(F) 6.211%, 운송(T) 4.165%, 시공(C) 8.168%로 도출되었다. 본 모델은 위험관리를 위한 의사결정 과정에서 정량적인 결과를 도출하는 방법으로 활용된다.

• The Plant Pathology Journal
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• 제36권1호
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• pp.54-66
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• 2020

This study was conducted to evaluate usefulness of numerical weather prediction data generated by the Unified Model (UM) for plant disease forecast. Using the UM06- and UM18-predicted weather data, which were released at 0600 and 1800 Universal Time Coordinated (UTC), respectively, by the Korea Meteorological Administration (KMA), disease forecast on bacterial grain rot (BGR) of rice was examined as compared with the model output based on the automated weather stations (AWS)-observed weather data. We analyzed performance of BGRcast based on the UM-predicted and the AWS-observed daily minimum temperature and average relative humidity in 2014 and 2015 from 29 locations representing major rice growing areas in Korea using regression analysis and two-way contingency table analysis. Temporal changes in weather conduciveness at two locations in 2014 were also analyzed with regard to daily weather conduciveness (C_i) and the 20-day and 7-day moving averages of C_i for the inoculum build-up phase (C_inc) prior to the panicle emergence of rice plants and the infection phase (C_inf) during the heading stage of rice plants, respectively. Based on C_inc and C_inf, we were able to obtain the same disease warnings at all locations regardless of the sources of weather data. In conclusion, the numerical weather prediction data from KMA could be reliable to apply as input data for plant disease forecast models. Weather prediction data would facilitate applications of weather-driven disease models for better disease management. Crop growers would have better options for disease control including both protective and curative measures when weather prediction data are used for disease warning.

• 대기
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• 제28권4호
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• pp.509-520
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• 2018

The performance of the newly designed Pusan National University Coupled General Circulation Model (PNU CGCM) Ensemble Forecast System which produce 40 ensemble members for 12-month lead prediction is evaluated and analyzed in terms of boreal winter temperature over South Korea (S. Korea). The influence of ensemble size on prediction skill is examined with 40 ensemble members and the result shows that spreads of predictability are larger when the size of ensemble member is smaller. Moreover, it is suggested that more than 20 ensemble members are required for better prediction of statistically significant inter-annual variability of wintertime temperature over S. Korea. As for the ensemble average (ENS), it shows superior forecast skill compared to each ensemble member and has significant temporal correlation with Automated Surface Observing System (ASOS) temperature at 99% confidence level. In addition to forecast skill for inter-annual variability of wintertime temperature over S. Korea, winter climatology around East Asia and synoptic characteristics of warm (above normal) and cold (below normal) winters are reasonably captured by PNU CGCM. For the categorical forecast with $3{\times}3$ contingency table, the deterministic forecast generally shows better performance than probabilistic forecast except for warm winter (hit rate of probabilistic forecast: 71%). It is also found that, in case of concentrated distribution of 40 ensemble members to one category out of the three, the probabilistic forecast tends to have relatively high predictability. Meanwhile, in the case when the ensemble members distribute evenly throughout the categories, the predictability becomes lower in the probabilistic forecast.

• 한국방재안전학회논문집
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• 제9권2호
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• pp.23-32
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• 2016

오늘날 주민 생활공간과 인접해 있는 산업단지의 노후화 문제는 지역사회 안전을 위협하는 원인으로 지적되고 있다. 본 연구에서는 현행의 위험도 분석 및 지역사회 고지범위를 제고하기 위해 대안의 사고시나리오의 영향범위와 계절별 기상 최빈값을 활용하여 모사된 영향범위를 비교 분석하였고, 시간대별로 관측된 풍향을 계절별로 검토하여 독성물질 누출 시 피해 가능성이 높은 지역을 예측하였다. 또한 인명피해를 최소화하기 위한 한계 대피시간 및 최소 이격거리를 제시하였으며, 비상대응에 활용되고 있는 위해관리계획서를 고찰하여 인근 근로자 및 주민에 대해 한층 더 합리적인 안전대책이 이루어질 수 있도록 제언하였다.

• 지능정보연구
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• 제19권2호
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• pp.55-71
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• 2013

부도예측을 위한 지식기반시스템에서 모델은 실적에 영향을 끼치는 주요한 요인이다. 예측 모형의 개발에 있어 초기 연구들은 통계기법 및 인공지능기법들을 이용하여 최고 실적을 가지는 단일 모델을 만드는데 주력하였다. 1980년대 중반 이후에는 다수 기술의 통합(하이브리드), 더 나아가, 다수 모델의 결과의 결합(앙상블) 기법이 수많은 실험에서 개별 모델들보다 더 나은 결과를 보여왔다. 다수 모델들의 출력값들을 결합하여 한 개의 최종 예측값을 산출하는 앙상블 모델링에서 결합기법은 앙상블의 예측 정확도에 영향을 끼치는 중요한 이슈이다. 본 논문은 부도예측을 위한 앙상블 결합기법으로서 앙상블 멤버들이 다른 유형의 연속형 수치 출력값들을 산출하더라도 통일된 확신을 측정할 수 있는 확신 기반의 선택 접근법을 제안하고 이에 대한 앙상블 멤버 사이즈의 영향을 연구하였다. 실험 결과는 앙상블 멤버들의 생성 타입에 따라 결합하는 모델 개수를 변화시켰을 때 가장 많은 기본 모델들을 가지는 앙상블에서의 제안 결합기법이 부도예측에 가장 자주 사용되는 다른 방법들에 비해서도 가장 높은 실적을 가진다는 것을 보였다.

• 천문학회지
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• 제40권4호
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• pp.99-106
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• 2007

Statistical analyses were performed to investigate the relative success and accuracy of daily maximum X-ray flux (MXF) predictions, using both multilinear regression and autoregressive time-series prediction methods. As input data for this work, we used 14 solar activity parameters recorded over the prior 2 year period (1989-1990) during the solar maximum of cycle 22. We applied the multilinear regression method to the following three groups: all 14 variables (G1), the 2 so-called 'cause' variables (sunspot complexity and sunspot group area) showing the highest correlations with MXF (G2), and the 2 'effect' variables (previous day MXF and the number of flares stronger than C4 class) showing the highest correlations with MXF (G3). For the advanced three days forecast, we applied the autoregressive timeseries method to the MXF data (GT). We compared the statistical results of these groups for 1991 data, using several statistical measures obtained from a $2{\times}2$ contingency table for forecasted versus observed events. As a result, we found that the statistical results of G1 and G3 are nearly the same each other and the 'effect' variables (G3) are more reliable predictors than the 'cause' variables. It is also found that while the statistical results of GT are a little worse than those of G1 for relatively weak flares, they are comparable to each other for strong flares. In general, all statistical measures show good predictions from all groups, provided that the flares are weaker than about M5 class; stronger flares rapidly become difficult to predict well, which is probably due to statistical inaccuracies arising from their rarity. Our statistical results of all flares except for the X-class flares were confirmed by Yates' $X^2$ statistical significance tests, at the 99% confidence level. Based on our model testing, we recommend a practical strategy for solar X-ray flare predictions.