• Structural Engineering and Mechanics
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• 제36권6호
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• pp.729-744
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• 2010

There is world wide concern for safety of nuclear power installations after the terrorist attack on World Trade Center in 2001 and several other civilian structures in the last decade. The nuclear containment structure in many countries is a double shell structure (outer shell a RCC and inner a prestressed concrete). The outer reinforced concrete shell protects the inner shell and is designed for external loading like impact and blast. A comparative study of non-linear response of reinforced concrete nuclear containment cylindrical shell subjected to impact of an aircraft (Phantom) and explosion of different amounts of blast charges have been presented here. A material model which takes into account the strain rate sensitivity in dynamic loading situations, plastic and visco-plastic behavior in three dimensional stress state and cracking in tension has been developed earlier and implemented into a finite element code which has been validated with published literature. The analysis has been made using the developed software. Significant conclusions have been drawn for dissimilarity in response (deflections, stresses, cracks etc.) of the shell for impact and blast loading.

• 대기
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• 제16권4호
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• pp.303-318
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• 2006

A parameterization of gravity wave drag induced by cumulus convection (GWDC) proposed by Chun and Baik is implemented in the KMA operational global NWP model (GDAPS), and effects of the GWDC on the forecast for July 2005 by GDAPS are investigated. The forecast result is compared with NCEP final analyses data (FNL) and model's own analysis data. Cloud-top gravity wave stresses are concentrated in the tropical region, and the resultant forcing by the GWDC is strong in the tropical upper troposphere and lower stratosphere. Nevertheless, the effect of the GWDC is strong in the mid- to high latitudes of Southern Hemisphere and high latitudes of Northern Hemisphere. By examining the effect of the GWDC on the amplitude of the geopotential height perturbation with zonal wavenumbers 1-3, it is found that impact of the GWDC is extended to the high latitudes through the change of planetary wave activity, which is maximum in the winter hemisphere. The GWDC reduces the amplitude of zonal wavenumber 1 but increases wavenumber 2 in the winter hemisphere. This change alleviates model biases in the zonal wind not only in the lower stratosphere where the GWDC is imposed, but also in the whole troposphere, especially in the mid- to high latitudes of Southern Hemisphere. By examining root mean square error, it is found that the GWDC parameterization improves GDAPS forecast skill in the Southern Hemisphere before 7 days and partially in the Northern Hemisphere after about 5 days.

• 환경영향평가
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• 제19권2호
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• pp.117-125
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• 2010

Korea's researchers have recently studied the prediction of forest change, but they have not considered landuse/cover change compared to distribution of forest vegetation. The purpose of our study is to predict forest vegetation based on landuse/cover change on the Korean Peninsula in the 2090's. The methods of this study were Multi-layer perceptrom neural network for Landuse/cover (water, urban, barren, wetland, grass, forest, agriculture) change and Multinomial Logit Model for distribution prediction for forest vegetation (Pinus densiflora, Quercus Spp., Alpine Plants, Evergreen Broad-Leaved Plants). The classification accuracy of landuse/cover change on the Korean Peninsula was 71.3%. Urban areas expanded with large cities as the central, but forest and agriculture area contracted by 6%. The distribution model of forest vegetation has 63.6% prediction accuracy. Pinus densiflora and evergreen broad-leaved plants increased but Quercus Spp. and alpine plants decreased from the model. Finally, the results of forest vegetation based on landuse/cover change increased Pinus densiflora to 38.9% and evergreen broad-leaved plants to 70% when it is compared to the current climate. But Quercus Spp. decreased 10.2% and alpine plants disappeared almost completely for most of the Korean Peninsula. These results were difficult to make a distinction between the increase of Pinus densiflora and the decrease of Quercus Spp. because of they both inhabit a similar environment on the Korean Peninsula.

• 한국구조물진단유지관리공학회 논문집
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• 제24권1호
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• pp.125-132
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• 2020

콘크리트의 수축현상은 체적 변화를 발생시키며 균열의 원인이 되어 구조물 내구성 및 안정성에 영향을 미친다. 콘크리트의 수축에 영향을 미치는 요인은 매우 다양하며, 특히 골재는 시멘트 페이스트의 변형을 구속하여 수축 발생을 억제하기 때문에 골재의 특성은 수축 현상에서 중요하게 고려하여야 하는 부분이다. 한편, 골재 부족 현상으로 인해 천연 골재 대체재 개발 및 적용에 대한 연구가 다방면으로 진행되고 있으며 콘크리트용 골재로 사용 되는 재료도 점차 다양해지고 있다. 따라서 본 연구에서는 전기로 산화 슬래그를 굵은 골재로 사용한 콘크리트의 수축 특성을 평가하기 위해 수축 실험을 진행하였으며, 실험 결과와 수축 예측 모델을 비교하여 기존 예측 모델 의 적용성을 검토하였다. 실험 결과, 전기로 산화 슬래그를 굵은 골재로 사용함에 따라 수축량이 감소하는 결과가 나타났으며, 특히 자기수축 저감 효과가 크게 나타났다. 예측 모델과의 비교 시 건조수축과 자기수축 각각 GL2000 모델과 Tazawa 모델이 가장 유사한 예측값을 나타냈으나, 보다 정확한 예측을 위해서는 골재 및 혼화재의 물성을 고려할 수 있도록 보완이 필요한 것으로 판단된다.

• 한국ITS학회 논문지
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• 제20권4호
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• pp.46-56
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• 2021

돌발상황으로 인한 비반복정체로 발생하는 높은 교통비용과 혼잡을 효과적으로 해소하기 위해서 돌발상황 처리시간을 예측하는 것은 중요하다. 본 연구에서는 인공신경망을 활용한 예측모델 개발을 위해 국내 도로상황에 적합한 돌발상황 처리시간 영향요인을 분석하고, 이를 학습데이터로 생성하였다. 기존 연구에서 장시간 소요되는 돌발상황 처리시간에 대한 과소 예측 문제가 발생하여 이에 대한 해결방안으로 본 연구에서는 SMOGN기법을 적용한 오버샘플링 학습데이터를 생성하여 이를 모델에 적용하였다. 그 결과 SMOGN기법을 적용한 DNN모델이 MAE 18.3분으로 연구 과정에서 구축된 모델 중 가장 높은 정확도로 돌발상황 처리시간을 예측하여, 기존에 개발된 예측모델의 한계점을 보완할 수 있을 것으로 기대한다.

• 대한원격탐사학회지
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• 제23권4호
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• pp.287-295
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• 2007

광물자원 분포도는 아직 발견되지 않은 광상의 부존 가능성을 공간적 분포로 나타내는 일종의 예측 주제도에 해당된다. 이러한 예측 주제도는 예측 가능성이 높은 지역의 공간적 위치뿐만 아니라 예측 능력에 대한 검증 정보가 함께 제시되어야 주제도의 신뢰성을 판단할 수 있다 이 연구의 목적은 미래의 광상 발견과 관련된 예측 확률을 추정하기 위해 교차 검증을 통해 얻어지는 예측비율곡선을 응용하는데 있다. 지화학 자료를 이용한 열수 맥상 형태의 Au-Ag 광상을 예측도 작성 사례 연구를 통해, 예측 확률 추정 과정과 결과의 해석을 예시하였다. 사례연구 수행 결과, 예측 주제도의 해석을 위해서는 검증을 통한 정량적 근거가 함께 제시되어야 함을 확인할 수 있었다. 이 연구를 통해 얻어지는 정량적 검증 자료는 추후 광상 개발 관련비용 분석과 환경 영향 추정에도 이용될 수 있을 것으로 기대된다.

• 대기
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• 제29권1호
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• pp.21-39
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• 2019

The conventional observations of the Korea Meteorological Administration (KMA) and National Centers for Environmental Prediction (NCEP) are compared in the numerical weather forecast system at the Korea Institute of Atmospheric Prediction Systems (KIAPS). The weather forecasting system used in this study is consists of Korea Integrated Model (KIM) as a global numerical weather prediction model, three-dimensional variational method as a data assimilation system, and KIAPS Package for Observation Processing (KPOP) as an observation pre-processing system. As a result, the forecast performance of NCEP observation was better while the number of observation is similar to the KMA observation. In addition, the sensitivity of forecast performance was investigated for each SONDE, SURFACE and AIRCRAFT observations. The differences in AIRCRAFT observation were not sensitive to forecast, but the use of NCEP SONDE and SURFACE observations have shown better forecast performance. It is found that the NCEP observations have more wind observations of the SONDE in the upper atmosphere and more surface pressure observations of the SURFACE in the ocean. The results suggest that evenly distributed observations can lead to improved forecast performance.

• IEIE Transactions on Smart Processing and Computing
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• 제5권6호
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• pp.430-439
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• 2016

Estimation and analysis of traffic jams plays a vital role in an intelligent transportation system and advances safety in the transportation system as well as mobility and optimization of environmental impact. For these reasons, many researchers currently mainly focus on the brilliant machine learning-based prediction approaches for traffic prediction systems. This paper primarily addresses the analysis and comparison of prediction accuracy between two machine learning algorithms: Naïve Bayes and K-Nearest Neighbor (K-NN). Based on the fact that optimized estimation accuracy of these methods mainly depends on a large amount of recounted data and that they require much time to compute the same function heuristically for each action, we propose an approach that applies multi-threading to these heuristic methods. It is obvious that the greater the amount of historical data, the more processing time is necessary. For a real-time system, operational response time is vital, and the proposed system also focuses on the time complexity cost as well as computational complexity. It is experimentally confirmed that K-NN does much better than Naïve Bayes, not only in prediction accuracy but also in processing time. Multi-threading-based K-NN could compute four times faster than classical K-NN, whereas multi-threading-based Naïve Bayes could process only twice as fast as classical Bayes.

• 한국소음진동공학회논문집
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• 제19권4호
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• pp.425-432
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• 2009

Recently, reduction of road traffic noise in residential buildings has become one of the most important subjects. To reduce the road traffic noise, noise impact assessment by the road traffic prediction model is required before building construction. For reasonable road traffic noise prediction, it is required to analysis of various factors in road traffic prediction models. This paper was studied the road traffic noise propagation factors such as distance from road to building, receiver height, alignment angle of building and reflection coefficient of the building facade by two calculation models, RLS-90 and CRTN. The result showed that noise reduction was generally higher at bottom stories by ground absorption effect. The reflection coefficient of the building facade was affect of additional sound pressure level by facade reflecting. And alignment angle of building at $90^{\circ}$ was performed effective noise reduction better than $0^{\circ}$.

• Journal of Electrical Engineering and Technology
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• 제9권5호
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• pp.1739-1745
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• 2014

Prediction of fault-prone modules continues to attract researcher's interest due to its significant impact on software development cost. The most important goal of such techniques is to correctly identify the modules where faults are most likely to present in early phases of software development lifecycle. Various software metrics related to modules level fault data have been successfully used for prediction of fault-prone modules. Goal of this research is to predict the faulty modules at design phase using design metrics of modules and faults related to modules. We have analyzed the effect of pre-processing and different machine learning schemes on eleven projects from NASA Metrics Data Program which offers design metrics and its related faults. Using seven machine learning and four preprocessing techniques we confirmed that models built from design metrics are surprisingly good at fault proneness prediction. The result shows that we should choose Naïve Bayes or Voting feature intervals with discretization for different data sets as they outperformed out of 28 schemes. Naive Bayes and Voting feature intervals has performed AUC > 0.7 on average of eleven projects. Our proposed framework is effective and can predict an acceptable level of fault at design phases.