• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.489-490
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• 2015

본 논문은 도서지역 전력수요 특성을 분석하고, 전력수요와 관련 있는 인자들과의 상관성 분석을 통한 도서지역의 최대 전력수요 예측 방안을 제시하였다. 과거 선행연구와의 예측 결과 비교를 통하여 예측 방안의 우수성을 검증하였고, 이를 바탕으로 도서지역 최대 전력수요 전망을 분석하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1012-1016
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• 2009

수자원 시스템 거동예측은 수문학적 지속성여부에 대한 판단이 선행 되어야 하며 가용한 시계열자료에 대한 추계학적 분석을 통하여 실시하여야 한다. 본 연구에서는 계절형 ARIMA모형을 통한 안동댐 유역의 강우량, 증발산량 및 유출량 시계열자료를 예측함에 있어 전형적인 Box-jenkins의 방법을 따랐고 모형의 식별, 추정, 검진의 3단계를 거쳐 모형화 하였다. 최적 수문시계열 예측 모형을 통하여 안동댐 유역의 강우량, 증발산량 및 유출량 시계열자료로 월별 수문시스템 거동을 예측하였으며, 예측된 결과를 토대로 TANK모형과 ARIMA+TANK결합모형에 의한 장기유출모의를 실시하였다. 분석결과 관측자료의 특성을 비교적 잘 반영 하였으며, 댐 유입량 예측을 위한 추계학적 결합모형의 적용가능성을 검토하였다. 이는 유출량자료의 보유년한이 짧은 대상유역에 월강우량과 증발산량자료 등의 수문시계열 인자 예측을 통한 유출을 모의함으로서 수자원의 중 장기 전략수립에 도움을 줄 것으로 사료된다.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.4
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• pp.116-127
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• 2011

This study conducted landslide susceptibility analysis using logistic regression. The performance of prediction model needs to be evaluated considering two aspects such as a goodness of fit and a prediction accuracy. Thus to gain more objective prediction results in this study, the prediction performance of the applied model was evaluated considering two such evaluation aspects. The selected study area is located between Inje-eup and Buk-myeon in the middle of Kwangwon. Landslides in the study area were caused by heavy rain in 2006. Landslide causal factors were extracted from topographic map, forest map and soil map. The evaluation of prediction model was assessed based on the area under the curve of the cumulative gain chart. From the results of experiments, 87.9% in the goodness of fit and 84.8% in the cross validation were evaluated, showing good prediction accuracies and not big difference between the results of the two evaluation methods. The results can be interpreted in terms of the use of environmental factors which are highly related to landslide occurrences and the accuracy of the prediction model.

• Journal of the Society of Disaster Information
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• v.16 no.2
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• pp.223-236
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• 2020

Purpose: In this study, a strong wind damage prediction function was developed in order to be used as a contingency during disaster management (preventive-preventive-response-recovery). Method: The predicted strong wind damage function proposed in this study took into account the re-enactment boy power, weather data and local characteristics at the time of damage. The meteorological data utilized the wind speed, temperature, and damage history observed by the Korea Meteorological Administration, the disaster year, and the recovery costs, population, vinyl house area, and farm water contained in the disaster report as factors to reflect the regional characteristics. Result: The function developed in this study reflected the predicted weather factors and local characteristics based on the history of strong wind damage in the past, and the extent of damage can be predicted in a short time. Conclusion: Strong wind damage prediction functions developed in this study are believed to be available for effective disaster management, such as decision making by policy-makers, deployment of emergency personnel and disaster prevention resources.

• International Journal of Highway Engineering
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• v.4 no.2 s.12
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• pp.9-18
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• 2002

The main distresses that influence pavement performance are rutting, fatigue cracking, and longitudinal roughness. Thus, it is important to analyze the factors that affect these three distresses, and to develop prediction models. In this paper, three distress prediction models were developed using DataPave program which stores data from a wide variety of pavement sections In the United States. Also, sensitivity studies were conducted to evaluate how the input variables impact on the distresses. The result of sensitivity study for the prediction model of rutting showed that asphalt content, air void, and optimum moisture content of subgrade were the major factors that affect rutting. The output of sensitivity study for the prediction model of fatigue cracking revealed that asphalt consistency, asphalt content, and air void were the most influential variables. The prediction model of longitudinal roughness indicated asphalt consistency, #200 passing percent of subgrade aggregate, and asphalt content were the factors that affect longitudinal roughness.

• Journal of the Korea Organic Resources Recycling Association
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• v.15 no.4
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• pp.138-146
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• 2007

There are still lots of areas where are covered with combined sewer pipes in Seoul. All buildings within those areas are equipping septic tanks which take part in separating solids from flushing water of chamber pots. Septic tanks legally demand emptying and cleaning the those inner bodies, resulting the generation of sludge which should be purified using the specified treatment plants, as one of environmental infrastructures. Scale of treatment plants for septic tank sludge are affected by sludge volume generated from cleaning, which give emphasis to adequate estimation of sludge volume in the future. This study aimed to define prediction tools for sludge volume. Among various parameters, floor area of building is most reasonable one to estimate the quantity of cleaning sludge, showing increasing gradually up to 13,149kL a day in 2020. Using same parameter also are able to assess the amount of BOD in the cleaning sludge.

• Korean Journal of Food Science and Technology
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• v.22 no.3
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• pp.325-331
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• 1990

The most important factors in the cooking process which is a main process in the sausage manufacture are cooking temperature and relative humidity. In order to design energy efficient processes in cooking, accurate data for the process parameters are necessary. Therefore, texture profiles were analysed and weight losses were measured at different process conditions of the forementioned factors and at different sizes of sausage, The prediction model for the sausage cooking time was then developed by the SPSS computer program The models were developed as a function of cooking temperature, relative humidity and the diameter of sausage by analyszing the scattergram. Then the model obtained could predict the values within 2.5% error. The higher temperature and relative humidity are the less changes of weight during sausage cooking. As the results of measuring physical properties, the values of hardness and cohesiveness at different temperatures and humidities were so much changed, while the values of elasticity and chewiness had little differences.

• Journal of Aerospace System Engineering
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• v.18 no.2
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• pp.21-28
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• 2024

In light of the rapid development of the space industry, there has been increased attention on small satellites. These satellites rely on components that are considered to have lower reliability compared to larger-scale satellites. As a result, predicting reliability becomes even more crucial in this context. Therefore, this study aims to compare three reliability prediction techniques: MIL-HDBK-217F, RiAC-HDBK-217Plus, and FIDES. The goal is to determine a suitable reliability standard specifically for nano-satellites. Furthermore, we have refined the quality assurance factors of the manufacturing company. These factors have been adjusted to be applicable across various industrial sectors, with a particular focus on the selected FIDES prediction standard. This approach ensures that the scoring system accurately reflects the suitability for the aerospace industry. Finally, by implementing this refined system, we confirm the impact of the manufacturer's quality assurance level on the total failure rate.

• Pediatric Infection and Vaccine
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• v.21 no.1
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• pp.29-36
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• 2014

Purpose: The objective of this study was to find the predictors and generate a prediction scoring model of nonresponse to intravenous immunoglobulin in patients with Kawasaki disease. Methods: We examined 573 children diagnosed with KD at the Severance Children's Hospital between January 2009 and december 2012. We retrospectively reviewed their medical records. These patients were divided into 2 groups; the experimental group (N=433) and the validation group (N=140). Each group were divided into 2 groups the intravenous immunoglobulin nonresponders and the responders. Multivariate logistic regression analysis identified predictive factors of intravenous immunoglobulin nonresponders which make predictive scoring model. We practice internal validation and external validation. Results: Multivariate logistic regression analysis identified male, cervical lymphadenopathy, changes of the extremities, platelet, total bilirubin, alkaline phophatase, lactate dehydrogenase, C-reactive protein as significant predictors for nonresponse to intravenous immunoglobulin. We generated prediction score assigning 1 point for (1) male, (2) cervical lymphadenopathy, (3) changes of the extremities, (4) platelet (${\leq}368,000/mm^3$), (5) total bilirubin (${\geq}0.4mg/dL$), (6) alkaline phophatase (${\geq}227IU/L$), (7) lactate dehydrogenase (${\geq}268IU/L$), (8) C-reactive protein (>77.1 mg/dL). Using a cut-off point of 4 and more with this prediction score, we could identify the intravenous immunoglobulin nonresponder group. Sensitivity and specificity were 52.5% and 82.4% in experimental group and 37.8% and 81.8% in validation group, respectively. Conclusion: Our predictive scoring models had high specificity and low sensitivity in Korean patients. Therefore it is useful in predicting nonresponse to intravenous immunoglobulin with Kawasaki disease.

• The Journal of Engineering Geology
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• v.14 no.4 s.41
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• pp.443-450
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• 2004

This study developed a prediction model of debris flow to predict a landslide probability on natural terrain composed of the Tertiary sedimentary and volcanic rocks using a logistic regression analysis. The landslides data were collected around Pohang, Gyeongbuk province where more than 100 landslides were occurred in 1998. Considered with basic characteristics of the logistic regression analysis, field survey and laboratory soil tests were performed for both slided points and not-slided points. The final iufluential factors on landslides were selected as six factors by the logistic regression analysis. The six factors are composed of two topographic factors and four geologic factors. The developed landslide prediction model has more than $90\%$ of prediction accuracy. Therefore, it is possible to make probabilistic and quantitative prediction of landslide occurrence using the developed model in this study area as well as the previously developed model for metamorphic and granitic rocks.