• 국제학술발표논문집
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• The 5th International Conference on Construction Engineering and Project Management
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• pp.497-501
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• 2013

An important decision-making element for the success of housing redevelopment projects is a prediction of the profitability of redevelopment. Risk factors influencing profitability were deduced through a review of the literature about profitability and a risk analysis developed by a survey of maintenance projects. In addition, a profitability prediction depending on the analysis of risk factors is necessary to judge the business feasibility of a project in the planning stages. A profitability prediction model of management and disposal method, which is calculated by proportional rate and which helps estimate contributions to profitability, is proposed to prevent difficulties in business development. The proposed model has the potential to prevent interruptions, reduce the length of projects, generate cost savings, and enable rational decision-making during the project period by allowing a judgment of profitability at the planning stage.

• 교육녹색환경연구
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• 제11권3호
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• pp.46-54
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• 2012

The accuracy of cost estimation at an early stage in school building project is one of the critical factors for successful completion. So various of techniques are developed to predict the construction cost accurately and expeditely. Among the techniques, Support Vector Machine(SVM) has an excellent ability for generalization performance. Therefore, the purpose of this study is to construct the prediction model for construction cost of educational building project using support vector machine technique. And to verify the accuracy of prediction model for construction cost. The performance data used in this study are 217 school building project cost which have been completed from 2004 to 2007 in Gyeonggi-Do, Korea. The result shows that average error rate was 7.48% for SVM prediction model. So using SVM model on predicting construction cost of educational building project will be a considerably effective way at the early project stage.

• Asian-Australasian Journal of Animal Sciences
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• 제14권11호
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• pp.1511-1515
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• 2001

A prediction of 305 d milk production from early records using an empirical Bayes method (EBM) was performed. The EBM was compared with the best predicted estimation (BPE), test interval method (TIM), and the linearized Wood's model (LWM). Daily milk yields were obtained from 606 first lactation Japanese Holstein cows in three herds. From each file of 305 daily records, 10 random test day records with an interval of approximately one month were taken. The accuracies of these methods were compared using the absolute difference (AD) and the standard deviation (SD) of the differences between the actual and the estimated 305 d milk production. The results showed that in the early stage of the lactation, EBM was superior in obtaining the prediction with high accuracy. When all the herds were analyzed jointly, the AD during the first 5 test day records were on average 373, 590, 917 and 1,042 kg for EBM, BPE, TIM, and LWM, respectively. Corresponding SD for EBM, BPE, TIM, and LWM were on average 488, 733, 747 and 1,605 kg. When the herds were analyzed separately, the EBM predictions retained high accuracy. When more information on the actual lactation was added to the prediction, TIM and LWM gradually achieved better accuracies. Finally, in the last period of the lactation, the accuracy of both of the methods exceeded EBM and BPM. The AD for the last 2 samples analyzing all the herds jointly were on average 141, 142, 164, and 214 kg for LWM, TIM, EBM, and BPE, respectively. In the current practices of collecting monthly records, early prediction of future milk production may be more accurate using EBM. Alternatively, if enough information of the actual lactation is accumulated, TIM may obtain better accuracy in the latter stage of lactation.

• 한국군사과학기술학회지
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• 제22권6호
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• pp.824-831
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• 2019

For effective development in consideration of the maintainability of the weapon system, it is necessary to understand whether the maintainability design requirements are satisfied at the early phase of development. This requires the application of an early design phase maintainability prediction process to provide opportunities for improvement. By defining the ambiguity group definition, fault isolation level, fault isolation probability, and countermeasures for faults, it was possible to predict early phase development. The MTTR of the initial design phase applying Procedure V to the artillery system was 3.46H, which is about 16 % higher than 2.98H, the MTTR using Procedure II. This is a result of system design ambiguity that has not been specified in the early phase of development.

• International Journal of Contents
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• 제10권4호
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• pp.75-82
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• 2014

The purpose of this paper was to identify suitable variables for financial distress prediction models and to compare the accuracy of MDA and LA for early warning signals for wind energy companies in Korea. The research methods, discriminant analysis and logit analysis have been widely used. The data set consisted of 15 wind energy SMEs in KOSDAQ with financial statements in 2012 from KIS-Value. We found that five financial ratio variables were statistically significant and the accuracy of MDA was 86%, while that of LA is 100%. The importance of this study is that it demonstrates empirically that financial distress prediction models are applicable to the wind energy industry in Korea as an early warning signs of impending bankruptcy.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2010년도 춘계 학술대회 제22권1호
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• pp.443-444
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• 2010

본 연구에서는 공동주택 공기단축이 가능한 조강형 콘크리트의 양생이력 변동에 따른 조기강도 예측식을 이용하여, 실구조물에서의 거푸집 탈형시기 결정에 활용할 수 있음을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권3호
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• pp.1195-1211
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• 2016

The emerging video coding standard High Efficiency Video Coding (HEVC) has shown almost 40% bit-rate reduction over the state-of-the-art Advanced Video Coding (AVC) standard but at about 40% computational complexity overhead. The main reason for HEVC computational complexity is the inter prediction that accounts for 60%-70% of the whole encoding time. In this paper, we propose several fast coding unit (CU) encoding schemes based on the Merge mode and motion estimation information to reduce the computational complexity caused by the HEVC inter prediction. Firstly, an early Merge mode decision method based on motion estimation (EMD) is proposed for each CU size. Then, a Merge mode based early termination method (MET) is developed to determine the CU size at an early stage. To provide a better balance between computational complexity and coding efficiency, several fast CU encoding schemes are surveyed according to the rate-distortion-complexity characteristics of EMD and MET methods as a function of CU sizes. These fast CU encoding schemes can be seamlessly incorporated in the existing control structures of the HEVC encoder without limiting its potential parallelization and hardware acceleration. Experimental results demonstrate that the proposed schemes achieve 19%-46% computational complexity reduction over the HEVC test model reference software, HM 16.4, at a cost of 0.2%-2.4% bit-rate increases under the random access coding configuration. The respective values under the low-delay B coding configuration are 17%-43% and 0.1%-1.2%.

• 한국시뮬레이션학회논문지
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• 제30권2호
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• pp.41-48
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• 2021

농촌 인구의 감소와 고령화가 지속되면서 농업 생상성 향상의 중요성이 높아지고 있는 가운데, 농작물 품질에 대한 조기 예측은 농업 생산성 및 수익성 향상에 중요한 역할을 할 수 있다. 최근 CNN 기반의 딥러닝 기술 및 전이 학습을 활용하여 농작물의 질병을 분류하거나 수확량을 예측하는 연구가 활발하게 진행되고 있지만, 수확 후 농작물의 품질을 식재단계에서 조기에 예측하는 연구는 찾아보기 힘들다. 본 연구에서는 건강 기능성 식품으로 주목받고 있는 새싹삼을 대상으로, 수확 후 새싹삼의 품질을 식재단계에서 조기에 예측하는 모델을 제안한다. 이를 위하여 묘삼의 이미지를 촬영한 후 수경재배를 통해 새싹삼을 재배하였고, 수확 후 새싹삼의 품질을 분류하여 실험 데이터를 수집하였다. 다수의 CNN 기반의 사전 학습된 모델을 활용하여 새싹삼 조기 품질 예측 모델을 구축하고, 수집된 데이터를 이용하여 각 모델의 학습 및 예측 성능을 비교 분석하였다. 분석 결과 모든 예측 모델에서 80% 이상의 예측 정확도를 보였으며, 특히 ResNet152V2 기반의 예측 모델에서 가장 높은 정확도를 보였다. 본 연구를 통해 인력에 의존하던 기존의 묘삼 선별 작업을 자동화하여 새싹삼의 품질을 높이고 생산량을 증대시켜 농가의 수익창출에 기여할 수 있을 것으로 기대된다.

• 방송공학회논문지
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• 제20권1호
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• pp.171-182
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• 2015

HEVC는 H.264/AVC에 비해 압축 성능을 크게 개선시킬 수 있지만 부호화기와 복호화기 모두 복잡도가 크게 증가한다. 본 논문에서는 HEVC의 화면 간 예측 모드 결정 과정을 분석하고, 이 결과로부터 부호화기 및 복호화기의 복잡도를 효과적으로 감소시키기 위한 방법을 제안하였다. 제안하는 방법은 단방향 예측 모드의 결과로부터 양방향 예측 모드를 수행하지 않아도 되는 조건을 찾고, 이 조건을 만족하는 경우 미리 종료시킴으로써 부호화 복잡도를 감소시킨다. 실험 결과 압축률 하락 폭이 각각 0.6%, 1.0%, 1.5%인 경우 부호화 복잡도를 12.0%, 14.2%, 17.2% 감소시킬 수 있었으며, 이 때, 양방향 예측 모드의 비율을 각각 6.3%, 11.8%, 16.6% 감소시킴으로써 복호화기의 복잡도도 함께 감소시킬 수 있었다. 마지막으로, 제안한 방법이 HEVC 참조 소프트웨어에 기 적용되어 있는 고속화 알고리즘과 함께 사용되는 경우에도 유사한 효과를 낼 수 있음을 검증하였다.

• Journal of Animal Science and Technology
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• 제45권2호
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• pp.327-334
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• 2003

본 시험은 초음파를 이용하여 한우의 도체형질을 조기에 예측하기 위하여 거세한우 66두를 대상으로 18, 21 및 24개월령에 도체형질을 측정하고, 중회귀 분석 및 의사결정나무 분석을 이용하여 24개월령 출하시 도체형질을 예측하였다. 그 결과를 요약하면 다음과 같다. 도체육량등급에 따라 군을 분류하고 성장에 따른 도체형질의 변화를 관찰한 결과, 등지방 두께는 전기간에 걸쳐 각 도체육량등급간에 유의적(p<0.05)인 차이를 보이며 A, B, C등급 순으로 얇게 나타났다. 중회귀 분석에 의한 도체육량등급의 예측율은 18, 21 및 24개월령에서 각각 78.8%, 86.4% 및 90.9%를 나타냈으며, 의사결정나무 분석에 의한 도체육량등급 예측율은 각 개월령에 따라 78.8%, 89.4% 및 89.4%를 나타냈다.