• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2008년도 추계 학술발표회
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• pp.1712-1716
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• 2008

Performance of ground improvement project using prefabricated vertical drains of condition, in which approximately 10m dredged fill overlies original soft foundation layer in the coastal area has been conducted. From field monitoring results, excessive ground settlement compared to predicted settlement in design stage developed during the following one year. In order to predict the final consolidation behavior, recalculation of consolidation settlements and back analysis using observed settlements were conducted. Field monitoring results of surface settlements were evaluated, and then corrected because large shear deformation was occurred by construction events in the early stages of consolidation. To predict the consolidation behavior, material functions and in-situ conditions from laboratory consolidation test were re-analyzed. Using these results, height of additional embankment is estimated to satisfy residual settlement limit and maintain an adequate ground elevation. The recalculated time-settlement curve has been compared to field monitoring results after additional surcharge was applied.

• 대한조선학회논문집
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• 제44권5호
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• pp.467-473
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• 2007

Cavitation phenomena appearing on ship propellers have long been interested and recent theoretical analysises give good results comparing with model tests. In accordance with a continuous rise in heavy powered and high speed ships, hull forms have been changed and loads acting on the propeller surface have also been increased, and they result in various and particular cavitations. In some cases, cavitation appears not only on the back but also on the face of the propeller and it causes additive pressure fluctuations and erosion of the propeller and reduces propulsion efficiency of the ship. In this study, we predict the face cavity inception using unsteady propeller analysis based on the panel method and compare the results with experimental observations.

• 한국지반공학회지:지반
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• 제13권1호
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• pp.47-58
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• 1997

지반계수와 지반형상에 포함된 불확실성 뿐만아니라 근사적인 수치모델링에 기인하여 현장 연약지반에 대하여 예측된 거동과 실제로 계측된 거동은 매우 상이한 경우가 많다. 이러한 예측 결과를 개선하기 위하여 본 논문에서는 다음사항을 고려하였다. 계측치로부터 현장지반의 물성치를 보다 적절히 추정하기 위하여 최적화 기법이 적용되었으며, 3차원 거동효과를 효과적으로 고려하기 위하여 등가의 모델이 적용되었다. 지반의 압밀과정에 영향을 주는 수정 Carnflay모델의 지반계수값을 현장에서 계측된 침하량과 간극수압을 바탕으로 BFGS기법을 적용하여 최적화하였으며, 최적화 기법은 일반적인 압밀 해석 프로그램인 SPINED에 적용되었다. 제안된 프로그램을 사용하여 연약지반의 시간의존적인 압밀거동을 적절히 예측할 수 있다.

• IMMUNE NETWORK
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• 제11권6호
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• pp.324-335
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• 2011

The human leukocyte antigen (HLA), the major histocompatibility complex (MHC) in humans has been known to reside on chromosome 6 and encodes cell-surface antigen-presenting proteins and many other proteins related to immune system function. The HLA is highly polymorphic and the most genetically variable coding loci in humans. In addition to a critical role in transplantation medicine, HLA and disease associations have been widely studied across the populations worldwide and are found to be important in prediction of disease susceptibility, resistance and of evolutionary maintenance of genetic diversity. Because recently developed molecular based HLA typing has several advantages like improved specimen stability and increased resolution of HLA types, the association between HLA alleles and a given disease could be more accurately quantified. Here, in this review, we have collected HLA association data on some autoimmune diseases, infectious diseases, cancers, drug responsiveness and other diseases with unknown etiology in Koreans and attempt to summarize some remarkable HLA alleles related with specific diseases.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제48권8호
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• pp.400-407
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• 1999

This paper treated the design, manufacture and the performance characteristics under each mode of high speed motor/generator for an electro-mechanical battery(EMB). This machine is employed as an integral part of a flywheel energy storage system(FESS), i.e., a modular flywheel system to be used as a device for storing electrical or mechanical energy. In this machine, the magnetic field system is constructed by using special magnet array, dipole Halbach array with 16 permanent magnet segments and the armature is composed of a plastic bobbin and multi-phase windings with Litz wire. The magnet array produces a highly uniform dipole field without back iron. The motor/generator is 3-phase machine in which the dipole Halbach array surrounding the winding is rotating. Since there are no iron laminations, this field system offers some unique advantages for the simplicity of the design and the theoretical prediction of characteristics of a high speed electric machine. This paper describes the results obtained when EMB system was tested in the laboratory.

• 한국정보처리학회논문지
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• 제7권10호
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• pp.3195-3200
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• 2000

본 논문은 ATM망의 통화유랑 제어중 최적한 폭주제어의 실현을 위해 필수적인 다중매체 통화유량 예측에 관한 논문으로서 ATM망에 유입 될 다중매체 통화유량의 특성이 시대의 발전에 따라 서서히 변화될 것이 예상되므로 모의실험에 사용 될 다중매체 통화유랑을 단위시간당 접속호수는 프아송분포, 각 호당 요구전송속도는 감마분포, 각 호의 유지시간은 지수분포를 기준으로 하여 각각의 분포특성을 변화시켜 통화유량 특성변화를 유도하여 발생시킨 후 이를 신경망과 실시간 처리를 위해 제안된 3중신경망 모델[3]로 추정하여 비교함으로써 제안된 모델이 ATM망의 통화유량 예측에 이용될 수 있음을 보인다.

• 한국산학기술학회논문지
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• 제12권4호
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• pp.1796-1802
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• 2011

본 논문에서는 지하 터널환경에서 전파의 경로 손실 특성을 예측할 수 있는 시뮬레이터를 개발하였다. 전파의 경로를 정확히 분석하기 위하여 image theory 방법을 이용하였으며, 직선 터널 구간뿐만 아니라 곡선 터널 구간에서도 전파 손실을 예측할 수 있도록 구현하였다. 시뮬레이터는 다양한 변수들을 입력받아 실시간으로 전파 경로를 도식화하여 결과를 보여줄 수 있으며, 송신부와 수신부의 위치를 변경하며 결과를 예측할 수도 있다. 개발된 시뮬레이터의 결과는 충훈 터널에서 실측한 전파 경로 손실 데이터와 비교 분석하여 타당성을 확인하였다.

• Smart Structures and Systems
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• 제24권6호
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Nuclear Engineering and Technology
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• 제35권2호
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• pp.154-164
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• 2003

The explicit consideration of different pressurization rates in estimating the probabilities of containment failure modes has a profound effect on the confidence of containment performance evaluation that is so critical for risk assessment of nuclear power plants. Except for the sophisticated NUREG-1150 study, many of the recent containment performance analyses (through Level 2 PSAs or IPE back-end analyses) did not take into account an explicit distinction between slow and fast pressurization in their analyses. A careful investigation of both approaches shows that many of the approaches adopted in the recent containment performance analyses exactly correspond to the NUREG-1150 approach for the prediction of containment failure mode probabilities in the presence of fast pressurization. As a result, it was expected that the existing containment performance analysis results would be subjected to greater or less conservatism in light of the ultimate failure mode of the containment. The main purpose of this paper is to assess potential conservatism of a plant-specific containment performance analysis result in light of containment failure mode probabilities.

• Computers and Concrete
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• 제20권6호
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• pp.627-634
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• 2017

Predicting the compressive strength of concrete is important to assess the load-carrying capacity of a structure. However, the use of blended cements to accrue the technical, economic and environmental benefits has increased the complexity of prediction models. Artificial Neural Networks (ANNs) have been used for predicting the compressive strength of ordinary Portland cement concrete, i.e., concrete produced without the addition of supplementary cementing materials. In this study, models to predict the compressive strength of blended cement concrete prepared with a natural pozzolan were developed using regression models and single- and 2-phase learning ANNs. Back-propagation (BP), Levenberg-Marquardt (LM) and Conjugate Gradient Descent (CGD) methods were used for training the ANNs. A 2-phase learning algorithm is proposed for the first time in this study for predictive modeling of the compressive strength of blended cement concrete. The output of these predictive models indicates that the use of a 2-phase learning algorithm will provide better results than the linear regression model or the traditional single-phase ANN models.