• 한국지반공학회:학술대회논문집
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• 한국지반공학회 1988년도 학술세미나 강연집
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• pp.3-67
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• 1988

Model studies on the response of homgeneous earth embankment dams subjected to strike-slip fault movement have been penomed via centrifuge and finite element analysis. The centrifuge model tests have shown that crack development in earth embankment experiences two major patters: shear failure deep inside the embankment and tension failure near the surface. The shear rupture zone develops from the base level and propagates upward continuously in the transverse direction but allows no open leakage chnnel. The open tensile cracks develop near the surface of the embankment, but they disappear deep in the embankment. The functional relationship has been developed based on the results of the centrifuge model tests incorporating tile variables of amount of fault movement, embankment geometry, and crack propagation extent in earth des. This set of information can be used as a guide line to evaluate a "transient" safety of the duaged embankment subjected to strike-slip fault movement. The finite element analysis has supplemented the additional expluations on crack development behavior identified from the results of the centrifuge model tests. The bounding surface time-independent plasticity soil model was employed in the numerical analysis. Due to the assumption of continuum in the current version of the 3-D FEM code, the prediction of the soil structure response beyond the failure condition was not quantitatively accurate. However, the fundamental mechanism of crack development was qualitatively evaluated based on the stress analysis for the deformed soil elements of the damaged earth embankment. The tensile failure zone is identified when the minor principal stress of the deformed soil elements less than zero. The shear failure zone is identified when the stress state of the deformed soil elements is at the point where the critical state line intersects the bounding surface.g surface.

• 한국콘텐츠학회논문지
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• 제8권7호
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• pp.66-74
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• 2008

소프트웨어 개발 프로세스의 초기 단계에서 결함경향성이 많은 모듈들을 예측하는 위험도 예측 모델은 프로젝트 자원할당에 도움을 주어 전체 시스템의 품질을 개선시키는 역할을 한다. 설계 복잡도 메트릭에 기반을 둔 여러 예측 모델들이 제안 되었지만 대부분 훈련 데이터 집합을 필요로 하는 모델들이었고 훈련 데이터 집합을 보유하고 있지 않은 대부분의 개발 집단들은 이들을 사용할 수 없다는 문제점이 있었다. 본 논문에서는 잘 알려진 감독형 학습 모델인 오류 역전파 신경망 모델에 SDL 시스템 명세를 정량화하여 적용한 예측 모델을 개발하였으며, 기존 학습 모델들의 문제점을 해결하기 위해 이 모델을 여러 제약조건을 가지고 만든 가상 훈련데이터집합으로 학습시켰다. 제안 모델의 사용가능성을 알아보기 위해 몇가지 모의실험을 수행 하였으며, 그 결과 제안 모델이 훈련 데이터 집합이 없는 개발 집단에서는 실제 데이터로 훈련된 예측 모델의 대안으로 사용될 수 있음을 보였다.

• Structural Engineering and Mechanics
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• 제68권5호
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• pp.575-589
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• 2018

It is well known that the incidence angle of seismic excitation has an influence on the structural response of buildings, and this effect can be more significant in the case of near-fault signals. However, current seismic codes do not include detailed requirements regarding the direction of application of the seismic action and they have only recently introduced specific provisions about near-fault earthquakes. Thus, engineers have the task of evaluating all the relevant directions or the most critical conditions case by case, in order to avoid underestimating structural demand. To facilitate the identification of the most critical incidence angle, this paper presents a procedure which makes use of a two-degree of freedom model for representing a building. The proposed procedure makes it possible to avoid the extensive computational effort of multiple dynamic analyses with varying angles of incidence of ground motion excitation, which is required if a spatial multi-degree of freedom model is used for representing a building. The procedure is validated through the analysis of two case studies consisting of an eight- and a six-storey reinforced concrete frame building, selected as representative of existing structures located in Italy. A set of 124 near-fault ground motion records oriented along 8 incidence angles, varying from 0 to 180 degrees, with increments of 22.5 degrees, is used to excite the structures. Comparisons between the results obtained with detailed models of the two structures and the proposed procedure are used to show the accuracy of the latter in the prediction of the most critical angle of seismic incidence.

• Structural Engineering and Mechanics
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• 제69권3호
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• pp.243-255
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• 2019

Utilization of fiber beam-column element has gained considerable attention in recent years due mainly to its ability to model distributed plasticity over the length of the element through a number of integration points. However, the relatively high sensitivity of the method to modeling parameters as well as material behavior models can pose a significant challenge. Residual drift is one of the seismic demands which is highly sensitive to modeling parameters and material behavior models. Permanent deformations play a prominent role in the post-earthquake evaluation of serviceability of bridges affected by a near-fault ground shaking. In this research, the influence of distributed plasticity modeling parameters using both force-based and displacement-based fiber elements in the prediction of internal forces obtained from the nonlinear static analysis is studied. Having chosen suitable type and size of elements and number of integration points, the authors take the next step by investigating the influence of material behavioral model employed for the prediction of permanent deformations in the nonlinear dynamic analysis. The result shows that the choice of element type and size, number of integration points, modification of cyclic concrete behavior model and reloading strain of concrete significantly influence the fidelity of fiber element method for the prediction of permanent deformations.

• 터널과지하공간
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• 제28권6호
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• pp.670-691
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• 2018

본 논문에서는 국제공동연구 DECOVALEX-2019 프로젝트의 일환으로 수행된 Task B Benchmark Model Test(BMT)의 연구 결과를 소개하였다. Task B는 'Fault slip modelling'을 연구주제로 하며, 유체의 주입으로 인해 발생하는 단층의 재활성과 수리역학적 연계거동을 예측할 수 있는 해석기법을 개발하는 데에 목적이 있다. BMT 시나리오 해석은 각 참가팀들의 수치모델이 단층의 수리역학적 연동거동을 적절히 모사할 수 있는지 교차검증함으로써 각 해석코드의 완성도를 높이기 위하여 수행되었으며, 주입압 적용 조건, 단층 물성, 수리역학적 연동해석 조건 등에 따라 7개의 해석 모델로 이루어져 있다. 본 연구에서는 TOUGH-FLAC 연동해석 기법을 이용하여, 역학적 변형으로 야기되는 단층의 수리적 물성 변화와 간극의 기하학적 변화를 동시에 반영할 수 있는 수리역학적 커플링 모듈을 개발하였다. BMT 시나리오 해석을 위하여 Task B 1단계(Step 1) 연구에서 개발된 수치모델을 일부 수정하였고, 단층의 변형에 따른 압축률과 투수계수, 단층의 해석 메쉬의 변화가 해석에 반영될 수 있도록 하였다. 단층의 투수량계수와 저류계수가 단층 내 압력 분포, 주입수량, 변위, 응력 등 수리역학적 거동에 미치는 영향을 검토하였으며, 수정된 수치모델을 기수행된 1단계 연구에 적용하여 해석결과를 업데이트하였다. 해석 결과, 본 연구에서 개발한 해석기법이 물 주입으로 인한 단층의 거동을 합리적인 수준에서 재현할 수 있는 것으로 판단할 수 있었다. 본 연구의 해석모델은 Task B에 참여하는 국외 연구팀들과의 의견 교류와 워크숍을 통해 지속적으로 개선하는 한편, 향후 연구의 현장시험에 적용하여 타당성을 검증할 예정이다.

• Journal of Power Electronics
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• 제16권1호
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• pp.319-328
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• 2016

In this study, we first briefly introduce the effect of circulating current control on the modulation signal of a modular multilevel converter (MMC). The maximum modulation index is also theoretically derived. According to the optimal modulation index analysis and the model in the continuous domain, different DC-side output impedance equivalent models of MMC with/without compensating component are derived. The DC-side impedance of MMC inverter station can be regarded as a series xR + yL + zC branch in both cases. The compensating component of the maximum modulation index is also related to the DC equivalent impedance with circulating current control. The frequency characteristic of impedance for MMC, which is observed from its DC side, is analyzed. Finally, this study investigates the prediction of the DC voltage ripple transfer between two-terminal MMC high-voltage direct current systems under unbalanced conditions. The rationality and accuracy of the impedance model are verified through MATLAB/Simulink simulations and experimental results.

• 한국물환경학회지
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• 제24권1호
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• pp.91-98
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• 2008

This work was performed to develop a model possible to predict the influent flow and influent components, which are one of main disturbances causing process problems at the operation of municipal wastewater treatment plant. In this study, artificial neural network (ANN) was used in order to develop a model that was able to predict the influent flow, $COD_{Mn}$, SS, TN 1 day-ahead, 2day-ahead and 3 day ahead. Multi-layer feed-forward back-propagation network was chosen as neural network type, and tanh-sigmoid function was used as activation function to transport signal at the neural network. And Levenberg-Marquart (LM) algorithm was used as learning algorithm to train neural network. Among 420 data sets except missing data, which were collected between 2005 and 2006 at field plant, 210 data sets were used for training, and other 210 data sets were used for validation. As result of it, ANN model for predicting the influent flow and components 1-3day ahead could be developed successfully. It is expected that this developed model can be practically used as follows: Detecting the fault related to effluent concentration that can be happened in the future by combining with other models to predict process performance in advance, and minimization of the process fault through the establishment of various control strategies based on the detection result.

• ETRI Journal
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• 제21권3호
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• pp.6-15
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• 1999

Many studies in the software reliability have attempted to develop a model for predicting the faults of a software module because the application of good prediction models provides the optimal resource allocation during the development period. In this paper, we consider the change request data collected from the field test of the software module that incorporate a functional relation between the faults and some software metrics. To this end, we discuss the general aspect if regression method, the problem of multicollinearity and the measures of model evaluation. We consider four possible regression models including two stepwise regression models and two nonlinear models. Four developed models are evaluated with respect to the predictive quality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권9호
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• pp.4087-4107
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• 2016

The current cloud computing paradigm is still vulnerable to a significant number of system failures. The increasing demand for fault tolerance and resilience in a cost-effective and device-independent manner is a primary reason for creating an effective means to address system dependability and availability concerns. This paper focuses on online failure prediction for cloud computing systems using system runtime data, which is different from traditional tolerance techniques that require an in-depth knowledge of underlying mechanisms. A 'failure prediction' approach, based on Cloud Theory (CT) and the Hidden Markov Model (HMM), is proposed that extends the HMM by training with CT. In the approach, the parameter ω is defined as the correlations between various indices and failures, taking into account multiple runtime indices in cloud computing systems. Furthermore, the approach uses multiple dimensions to describe failure prediction in detail by extending parameters of the HMM. The likelihood and membership degree computing algorithms in the CT are used, instead of traditional algorithms in HMM, to reduce computing overhead in the model training phase. Finally, the results from simulations show that the proposed approach provides very accurate results at low computational cost. It can obtain an optimal tradeoff between 'failure prediction' performance and computing overhead.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2015년도 제49회 하계 정기학술대회 초록집
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• pp.132-132
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• 2015

Virtual metrology (VM) model based on plasma information (PI) parameter for C4F8 plasma-assisted oxide etching processes is developed to predict and monitor the process results such as an etching rate with improved performance. To apply fault detection and classification (FDC) or advanced process control (APC) models on to the real mass production lines efficiently, high performance VM model is certainly required and principal component regression (PCR) is preferred technique for VM modeling despite this method requires many number of data set to obtain statistically guaranteed accuracy. In this study, as an effective method to include the 'good information' representing parameter into the VM model, PI parameters are introduced and applied for the etch rate prediction. By the adoption of PI parameters of b-, q-factors and surface passivation parameters as PCs into the PCR based VM model, information about the reactions in the plasma volume, surface, and sheath regions can be efficiently included into the VM model; thus, the performance of VM is secured even for insufficient data set provided cases. For mass production data of 350 wafers, developed PI based VM (PI-VM) model was satisfied required prediction accuracy of industry in C4F8 plasma-assisted oxide etching process.