• Journal of the Korean Society of Safety
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• v.25 no.3
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• pp.22-27
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• 2010

A prediction performance of Fire Dynamics Simulator(FDS) developed by NIST for the diffusion flame structure was validated with experimental results of a laminar slot jet diffusion flame. Two mixture fraction combustion models and two finite chemistry combustion models were used in the FDS simulation for the validation of the jet diffusion flame structure. In order to enhance the prediction performance of flame structure, DNS and radiation model was applied to the simulation. The reaction rates of the finite chemistry combustion models were appropriately adjusted to the diffusion flame. The mixture fraction combustion model predicted the diffusion flame structure reasonably. A 1-step finite chemistry combustion model cannot predict the flame structure well, but the simulation results of a 2-step model were in good agreement with those of experiment except $CO_2$ concentration. It was identified that the 2-step model can be used in the investigation of flame suppression limit with further adjustment of reaction rates

• Geomechanics and Engineering
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• v.22 no.1
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• pp.25-33
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• 2020

In metropolitan areas, the quantity and density of the underground structure increase rapidly in recent years. Even though most damage incidents of the underground structure were minor, there were still few incidents causing a great loss in lives and economy. Therefore, the safety evaluation of the underground structure becomes an important issue in the disaster prevention plan. Liquefaction induced uplift is one important factor damaging the underground structure. In order to perform a preliminary evaluation on the safety of the underground structure, simplified prediction equations were introduced to provide a first order estimation of the liquefaction induced uplift. From previous studies, the input motion is a major factor affecting the magnitude of the uplift. However, effects of the input motion were not studied and included in these equations in an appropriate and rational manner. In this article, a numerical simulation approach (FLAC program with UBCSAND model) is adopted to study effects of the input motion on the uplift. Numerical results show that the uplift and the Arias Intensity (Ia) are closely related. A simple modification procedure to include the input motion effects in the Sasaki and Tamura prediction equation is proposed in this article for engineering practices.

• Journal of the Korea Society for Simulation
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• v.25 no.4
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• pp.77-84
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• 2016

This paper proposes a methodology for creating a function based reliability prediction model. Although, there are various works for reliability prediction, one of the features of their research is that the research is based on hardware-centered reliability prediction. Reliability is often defined as the probability that a device will perform its intended function, under operating condition, for a specified period of time, there is a profound irony about reliability prediction problem. In this paper, we proposed four-phase modeling procedure for function-centered reliability prediction. The proposed modeling procedure consists of four models; 1) structure block model, 2) function block model, 3) device model, and 4) reliability prediction model. We performed function-centered reliability prediction for electronic ballast using the proposed modeling procedure and MIL-HDBK-217F which is the military handbook for reliability prediction of electronic equipment.

• Smart Structures and Systems
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• v.30 no.6
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• pp.601-612
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• 2022

The prediction of structural mechanical behaviors is vital important to early perceive the abnormal conditions and avoid the occurrence of disasters. Especially for underground engineering, complex geological conditions make the structure more prone to disasters. Aiming at solving the problems existing in previous studies, such as incomplete consideration factors and can only predict the continuous performance, the deep attention fused temporal convolution network (DATCN) is proposed in this paper to predict the spatial mechanical behaviors of structure, which integrates both the temporal effect and spatial effect and realize the cross-time prediction. The temporal convolution network (TCN) and self-attention mechanism are employed to learn the temporal correlation of each monitoring point and the spatial correlation among different points, respectively. Then, the predicted result obtained from DATCN is compared with that obtained from some classical baselines, including SVR, LR, MLP, and RNNs. Also, the parameters involved in DATCN are discussed to optimize the prediction ability. The prediction result demonstrates that the proposed DATCN model outperforms the state-of-the-art baselines. The prediction accuracy of DATCN model after 24 hours reaches 90 percent. Also, the performance in last 14 hours plays a domain role to predict the short-term behaviors of the structure. As a study case, the proposed model is applied in an underwater shield tunnel to predict the stress variation of concrete segments in space.

• Journal of Ocean Engineering and Technology
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• v.36 no.5
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• pp.313-325
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• 2022

Recently around the world, coastal erosion is paying attention as a social issue. Various constructions using low-crested and submerged structures are being performed to deal with the problems. In addition, a prediction study was researched using machine learning techniques to determine the wave attenuation characteristics of low crested structure to develop prediction matrix for wave attenuation coefficient prediction matrix consisting of weights and biases for ease access of engineers. In this study, a deep neural network model was constructed to predict the wave height transmission rate of low crested structures using Tensor flow, an open source platform. The neural network model shows a reliable prediction performance and is expected to be applied to a wide range of practical application in the field of coastal engineering. As a result of predicting the wave height transmission coefficient of the low crested structure depends on various input variable combinations, the combination of 5 condition showed relatively high accuracy with a small number of input variables defined as 0.961. In terms of the time cost of the model, it is considered that the method using the combination 5 conditions can be a good alternative. As a result of predicting the wave transmission rate of the trained deep neural network model, MSE was 1.3×10^-3, I was 0.995, SI was 0.078, and I was 0.979, which have very good prediction accuracy. It is judged that the proposed model can be used as a design tool by engineers and scientists to predict the wave transmission coefficient behind the low crested structure.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.1
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• pp.67-95
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• 1998

This research addresses flow time prediction in the dynamic unbalanced job shop scheduling environment. The specific purpose of the research is to develop the job flow time prediction model in the dynamic unbalance djob shop. Such factors as job characteristics, job shop status, characteristics of the shop workload, shop dispatching rules, shop structure, etc, are considered in the prediction model. The regression prediction approach is analyzed within a dynamic, make-to-order job shop simulation model. Mean Absolute Lateness (MAL) and Mean Relative Error (MRE) are used to compare and evaluate alternative regression models devloped in this research.

• BMB Reports
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• v.42 no.11
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• pp.697-704
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• 2009

Membrane proteins play important roles in the biology of the cell, including intercellular communication and molecular transport. Their well-established importance notwithstanding, the high-resolution structures of membrane proteins remain elusive due to difficulties in protein expression, purification and crystallization. Thus, accurate prediction of membrane protein topology can increase the understanding of membrane protein function. Here, we provide a brief review of the diverse computational methods for predicting membrane protein structure and function, including recent progress and essential bioinformatics tools. Our hope is that this review will be instructive to users studying membrane protein biology in their choice of appropriate bioinformatics methods.

• Journal of the Korea Concrete Institute
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• v.12 no.1
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• pp.33-44
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• 2000

Since the mechanism of chloride diffusion and its ratio in concrete depend on structural conditions and concrete as a micro-structure, if these are analyzed quantitatively, the long-term ageing of structures can be predicted. Although, a quantitative analysis of concrete micro-structure, in which the results are affected by various parameters, is very difficult, this can be done indirectly by the durability test of concrete. In this study, the compressive strength, void ratio and air permeability of concrete. In this study, the compressive strength, void ratio and air permeability of concrete are chosen as the parameters in concrete durability test, and these effects on test results are analysed according to changes of mixing properties. The relationships between parameters and chloride diffusion velocity is used for prediction models of chloride diffusion. The developed prediction models for the chloride diffusion according to mixing and physical properties, can be used to estimate the service life and corrosion initiation of reinforcing bars in marine structures.

• Journal of the Korean Chemical Society
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• v.58 no.6
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• pp.543-552
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• 2014

A quantitative structure activity relationship (QSAR) study is performed for modeling and prediction of oral bioavailabilities of 216 diverse set of drugs. After calculation and screening of molecular descriptors, linear and nonlinear models were developed by using multiple linear regression (MLR), artificial neural network (ANN), support vector machine (SVM) and random forest (RF) techniques. Comparison between statistical parameters of these models indicates the suitability of SVM over other models. The root mean square errors of SVM model were 5.933 and 4.934 for training and test sets, respectively. Robustness and reliability of the developed SVM model was evaluated by performing of leave many out cross validation test, which produces the statistic of $Q^2_{SVM}=0.603$ and SPRESS = 7.902. Moreover, the chemical applicability domains of model were determined via leverage approach. The results of this study revealed the applicability of QSAR approach by using SVM in prediction of oral bioavailability of drugs.

• Magazine of the Korea Concrete Institute
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• v.9 no.4
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• pp.155-165
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• 1997

Since the biggest time-dependent prestress loss of reactor containment structure is due to creep of concrete. the creep is one of important structural factors to be considered for the safety maintenance in the containment structure during design. construction and main enance. This paper is about the creep charactoristies of concrete for the reactor containment structure. In this paper, creep test was performed to show the creep characteristics of reactor containment concrete structure made of the type-V cement. Then, in order to evaluate the applicability of creep prediction equations of recently revised Korean Concrete Standard Specification(KSCE-96) and Japanes Concrete Standard Specification. ACI-209. CEB/FIP-90. and HANSEN, creep test results were compared with prediction results obtained from he equations. From the comparisons, it was shown that the equation of th KSCE-96 predicts creep for younger concrete than 1 year, better than the other equations and that all of the equations predicts creep, for older concrete than 1 year, smaller than test. From regression analysis. a creep prediction equation which effectively predicts creep of concrete due to loading after 1year was proposed.