• 대한기계학회논문집B
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• 제32권4호
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• pp.317-326
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• 2008

The main purpose of the present study is to perform shape optimizations of transonic compressor blade in order to enhance its performance. In this study, the Latin hypercube sampling of design of experiments and the weighted average surrogate model with the help of a gradient based optimization algorithm are used within design space by the lower and upper limits of each design variable and for finding optimum designs, respectively. 3-D Reynolds-averaged Navier-Stokes solver is used to evaluate the objective functions of adiabatic efficiency and pressure ratio. Six variables from lean and airfoil thickness profile are selected as design variables. The results show that the adiabatic efficiency is enhanced by 1.43% by efficiency optimization while the pressure ratio is increased very small, and pressure ratio is increased by 0.24% by pressure ratio optimization.

• Computers and Concrete
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• 제32권4호
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• pp.373-381
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• 2023

Fly ash, granulated blast furnace slag, marble waste powder, etc. are just some of the by-products of other sectors that the construction industry is looking to include into the many types of concrete they produce. This research seeks to use surrogate machine learning methods to forecast the compressive strength of self-compacting concrete. The surrogate models were developed using Gradient Boosting Machine (GBM), Support Vector Machine (SVM), Random Forest (RF), and Gaussian Process Regression (GPR) techniques. Compressive strength is used as the output variable, with nano silica content, cement content, coarse aggregate content, fine aggregate content, superplasticizer, curing duration, and water-binder ratio as input variables. Of the four models, GBM had the highest accuracy in determining the compressive strength of SCC. The concrete's compressive strength is worst predicted by GPR. Compressive strength of SCC with nano silica is found to be most affected by curing time and least by fine aggregate.

• International Journal of Aeronautical and Space Sciences
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• 제17권1호
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• pp.29-36
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• 2016

Tensairity girder is a light weight inflatable fabric structural concept which can be used in road emergency transportation. It uses low pressure air to stabilize compression elements against buckling. With the purpose of obtaining the comprehensive target of minimum deflection and weight under ultimate load, the cross-section and the inner pressure of tensairity girder was optimized in this paper. The Variable Complexity Modeling (VCM) method was used in this paper combining the Kriging approximate method with the Finite Element Analysis (FEA) method, which was implemented by ABAQUS. In the Kriging method, the sample points of the surrogate model were outlined by Design of Experiment (DOE) technique based on Optimal Latin Hypercube. The optimization framework was constructed in iSIGHT with a global optimization method, Multi-Island Genetic Algorithm (MIGA), followed by a local optimization method, Sequential Quadratic Program (SQP). The result of the optimization gives a prominent conceptual design of the tensairity girder, which approves the solution architecture of VCM is feasible and efficient. Furthermore, a useful trend of sensitivity between optimization variables and responses was performed to guide future design. It was proved that the inner pressure is the key parameter to balance the maximum Von Mises stress and deflection on tensairity girder, and the parameters of cross section impact the mass of tensairity girder obviously.

• 품질경영학회지
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• 제27권1호
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• pp.35-45
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• 1999

A process monitoring procedure using a correlated variable is presented when a lower specification limit is given on the performance variable. Every item is inspected with a variable correlated with the performance variable. When an item is rejected in the screening inspection, the process is checked for change using the mean and variance of measurements of the correlated variable for n preceding items including the rejected one. The performance variable is assumed to be normally distributed. A linear relationship between the performance and surrogate variables is assumed with normally distributed error term. The monitoring procedure is designed so that the prespecified outgoing quality can be attained.

• Computers and Concrete
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• 제24권2호
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• pp.137-150
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• 2019

In this paper, surrogate models such as multivariate adaptive regression splines (MARS) and M5P model tree (M5P MT) methods have been investigated in order to propose a new formulation for the 28-days compressive strength of self-compacting concrete (SCC) incorporating metakaolin as a supplementary cementitious materials. A database comprising experimental data has been assembled from several published papers in the literature and the data have been used for training and testing. In particular, the data are arranged in a format of seven input parameters covering contents of cement, coarse aggregate to fine aggregate ratio, water, metakaolin, super plasticizer, largest maximum size and binder as well as one output parameter, which is the 28-days compressive strength. The efficiency of the proposed techniques has been demonstrated by means of certain statistical criteria. The findings have been compared to experimental results and their comparisons shows that the MARS and M5P MT approaches predict the compressive strength of SCC incorporating metakaolin with great precision. The performed sensitivity analysis to assign effective parameters on 28-days compressive strength indicates that cementitious binder content is the most effective variable in the mixture.

• 사회복지연구
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• 제43권2호
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• pp.87-111
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• 2012

본 연구에서는 '청소년의 부모와의 애착관계는 낙관성을 매개로 심리적응과 사회적응에 영향을 미친다'는 이론적 모형을 그룹홈 청소년에게 적용하여 검증하였다. 그룹홈 청소년은 여러 가지 이유로 친부모와 함께 살지 못하고, 대신 부모의 역할을 대행하는 그룹홈 생활교사(대리양육자)와 함께 살기 때문에 '대리양육자와의 애착관계'가 낙관성을 매개로 심리적응과 사회적응에 미치는 영향을 알아보았다. 분석 결과, 심리적응 모형과 사회적응 모형 모두에서 낙관성은 애착관계와 심리적응 및 사회적응 사이에서 완전 매개변인으로 작동하는 것으로 나타났다. 이러한 결과는 그동안 사회복지 연구에서 많은 관심을 받지 못했던 '낙관성'의 효과에 관심을 갖게 하며, 이에 대한 연구가 활발히 이루어질 필요성을 시사한다.

• 한국소음진동공학회논문집
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• 제12권6호
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• pp.461-468
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• 2002

Computer simulation is essential to design the suspension elements of railway vehicle. By computer simulation, engineers can assess the feasibility of the given design factors and change them to get a better design. But if one wishes to perform complex analysis on the simulation, such as railway vehicle dynamic, the computational time can become overwhelming. Therefore, many researchers have used a surrogate model that has a regression model performed on a data sampling of the simulation. In general, metamodels(surrogate model) take the form y($\chi$)＝f($\chi$)＋$\varepsilon$, where y($\chi$) is the true output, f($\chi$) is the metamodel output, and is the error. In this paper, a second order polynomial equation is used as the RSM(response surface model) for high speed train that have twenty-nine design variables and forty-six responses. After the RSM is constructed, multi-objective optimal solutions are achieved by using a nonlinear programming method called VMM(variable matric method) This paper shows that the RSM is a very efficient model to solve the complex optimization problem.

• 한국안전학회지
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• 제28권1호
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• pp.158-163
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• 2013

While job stress evaluations are reported in the recent surveys upon the nuclear power plants(NPPs), any significant advance in the types of questionnaires is not currently found. There are limitations to their usefulness as analytic tools for the management of safety resources in NPPs. Data mining(DM) has emerged as one of the key features for data computing and analysis to conduct a survey analysis. There are still limitations to its capability such as dimensionality associated with many survey questions and quality of information. Even though some survey methods may have significant advantages, often these methods do not provide enough evidence of causal relationships and the statistical inferences among a large number of input factors and responses. In order to address these limitations on the data computing and analysis capabilities, we propose an advanced procedure of survey analysis incorporating the DM method into a statistical analysis. The DM method can reduce dimensionality of risk factors, but DM method may not discuss the robustness of solutions, either by considering data preprocesses for outliers and missing values, or by considering uncontrollable noise factors. We propose three steps to address these limitations. The first step shows data mining with response surface method(RSM), to deal with specific situations by creating a new method called response surface data mining(RSDM). The second step follows the RSDM with detailed statistical relationships between the risk factors and the response of interest, and shows the demonstration the proposed RSDM can effectively find significant physical, psycho-social, and environmental risk factors by reducing the dimensionality with the process providing detailed statistical inferences. The final step suggest a robust stress management system which effectively manage job stress of the workers in NPPs as a part of a safety resource management using the surrogate variable concept.

• 한국경영정보학회:학술대회논문집
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• 한국경영정보학회 2007년도 추계학술대회
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• pp.117-122
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• 2007

Recently, as the management environments are changing rapidly and the uncertainty is becoming larger, the needs of internal control for management and IS become stronger. In order to construct a new internal control system for IS, it is necessary to evaluate the former research of the system. This study emphasizes the importance of effective internal control system, presents a conceptual framework for the preceding factors to consider, and verifies empirically the framework. This study sets the organization citizenship behavior, IS innovation resistance, and IT capability from the viewpoint of Socio-Technical system as the preceding factors for the effectiveness of internal control system. A research model, affecting the above factors on IS capability as a mediating variable of the internal control effectiveness for the use of IS, is set up. PLS-Graph 3.0 is used to verify the model. We found that the internal control effectiveness have affirmative effect on information capability, a surrogate variable of the IS effectiveness and a mediation effect is meaningful.

• 품질경영학회지
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• 제40권2호
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• pp.186-198
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• 2012

Since Duncan(1956) first proposed an economic design of $\bar{X}$-control charts, most of the succeeding works on economic design of control charts assumed the exponential failure model like Duncan. Hu(1984), however, assumed a more versatile Weibull failure model to develop an economic design of $\bar{X}$-control charts and Banerjee and Rahim(1988) further improved Hu's design by changing the assumption of fixed-length sampling intervals to variable-length ones. In this article we follow the approach of Banerjee and Rahim(1988) but include a pair of warning limits inside the control limits in order to search for a failure without stopping the process when the sample mean falls between warning and control limits. The computational results indicate that the proposed model gives a lower cost than Banerjee and Rahim's model unless the early failure probability of a Weibull distribution is relatively large. The reduction in cost is shown to become larger as the cost of production loss outweighs the cost of searches for a failure.