• International conference on construction engineering and project management
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• 2005.10a
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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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• v.4 no.4
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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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• v.16 no.2
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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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.131-133
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• 2017

To predict the future, the accumulated data can be fundamental basic. While many prediction methods based on contingency theory have been used, the prediction of LOTTO number can not be based on the contingency theory. But, this research attempts to suggest the method to predict LOTTO numbers through using the change of the prediction capability on accumulated data.

• Korean Journal of Construction Engineering and Management
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• v.19 no.5
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• pp.81-89
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• 2018

The purpose of this study is to assess the risk and predict the contingency for modular plant construction projects. Considering the work characteristics of the modular plant, The adapted research method is that suggest models for assessment impact of risk and predict the contingency considering risk. Based on the proposed models, It is selected one modular plant construction project and assessment impact of risk factors and predicted the contingency. The results of this study are as follows: Assessment the probability of occurrence of risk factors and intensity of impact, and extract 15 important risk factors. These are classified as Engineering, Procurement, Fabrication, Transportation, Construction phases to consider the work characteristics of the modular plant. The predicted contingency is that 6.739%(Engineering 2.850%, Procurement 6.225%, Fabrication 6.211%, Transportation 4.165%, Construction 8.168%) to prepare the basic business expense. The model is used as a way to derive quantitative results in the decision-making process for risk management in construction projects.

• The Plant Pathology Journal
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• v.36 no.1
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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.

• Atmosphere
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• v.28 no.4
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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.

• Journal of Korean Society of Disaster and Security
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• v.9 no.2
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• pp.23-32
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• 2016

Today the issue of deterioration of industrial complexes that are located close to life space of residents has been raised as a cause of threats to the safety of local communities. In this study, in order to improve the current risk analysis and scope of community notification, simulated threat zones were comparatively analyzed by utilizing the threat zones of alternative accident scenarios and modes of seasonal weather, and the area with a high probability of damage upon the leakage of toxic substances was predicted by examining wind directions observed at each time slot for each season. In addition, limit evacuation time and minimum separation distance to minimize casualties were suggested, and a proposal to enable more reasonable safety measures for on-site workers and nearby residents made by reviewing the risk management plan currently utilized for emergency response.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.55-71
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• 2013

The prediction model is the main factor affecting the performance of a knowledge-based system for bankruptcy prediction. Earlier studies on prediction modeling have focused on the building of a single best model using statistical and artificial intelligence techniques. However, since the mid-1980s, integration of multiple techniques (hybrid techniques) and, by extension, combinations of the outputs of several models (ensemble techniques) have, according to the experimental results, generally outperformed individual models. An ensemble is a technique that constructs a set of multiple models, combines their outputs, and produces one final prediction. The way in which the outputs of ensemble members are combined is one of the important issues affecting prediction accuracy. A variety of combination schemes have been proposed in order to improve prediction performance in ensembles. Each combination scheme has advantages and limitations, and can be influenced by domain and circumstance. Accordingly, decisions on the most appropriate combination scheme in a given domain and contingency are very difficult. This paper proposes a confidence-based selection approach as part of an ensemble bankruptcy-prediction scheme that can measure unified confidence, even if ensemble members produce different types of continuous-valued outputs. The present experimental results show that when varying the number of models to combine, according to the creation type of ensemble members, the proposed combination method offers the best performance in the ensemble having the largest number of models, even when compared with the methods most often employed in bankruptcy prediction.

• Journal of The Korean Astronomical Society
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• v.40 no.4
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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.