• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.263-268
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• 2017

A hot forming process is required for Mg alloys to enhance the formability and plastic workability due to the insufficient formability at room temperature. Mg alloy undergoes dynamic recrystallization (DRX) during the hot working process, which is a restoration or softening mechanism that reduces the dislocation density and releases the accumulated energy to facilitate plastic deformation. The flow stress curve shows three stages of complicated strain hardening and softening phenomena. As the strain increases, the stress also increases due to work hardening, and it abruptly decreases work softening by dynamic recrystallization. It then maintains a steady-state region due to the equilibrium between the work hardening and softening. In this paper, an efficient optimization process is proposed for the material model of the dynamic recrystallization to improve the accuracy of the flow curve. A total of 18 variables of the constitutive equation of AZ80 alloy were systematically optimized at an elevated forming temperature($300^{\circ}C$) with various strain rates(0.001, 0.1, 1, 10/sec). The proposed method was validated by applying it to the constitutive equation of AZ61 alloy.

• Communications for Statistical Applications and Methods
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• v.24 no.4
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• pp.383-396
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• 2017

The model in our approach assumes that computer responses are a realization of a Gaussian processes superimposed on a regression model called a Gaussian process regression model (GPRM). Selecting a subset of variables or building a good reduced model in classical regression is an important process to identify variables influential to responses and for further analysis such as prediction or classification. One reason to select some variables in the prediction aspect is to prevent the over-fitting or under-fitting to data. The same reasoning and approach can be applicable to GPRM. However, only a few works on the variable selection in GPRM were done. In this paper, we propose a new algorithm to build a good prediction model among some GPRMs. It is a post-work of the algorithm that includes the Welch method suggested by previous researchers. The proposed algorithms select some non-zero regression coefficients (${\beta}^{\prime}s$) using forward and backward methods along with the Lasso guided approach. During this process, the fixed were covariance parameters (${\theta}^{\prime}s$) that were pre-selected by the Welch algorithm. We illustrated the superiority of our proposed models over the Welch method and non-selection models using four test functions and one real data example. Future extensions are also discussed.

• Journal of the Korea Institute of Building Construction
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• v.12 no.3
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• pp.257-265
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• 2012

Many construction companies are simultaneously carrying out numerous projects in the housing construction industry. It is essential to accurately forecast the cash flow of a project through optimal process management and resource input in order to manage funds rationally and enhance the competitiveness of a company. Current cash flow forecasting methods offer lower accuracy due to a large gap between the revenue and expenditure element. Expenditure elements depends on the real-time changing actual cost for work performed. This research survey was conducted on the actual state of construction management of K company to investigate the problems of cash flow forecasting. To achieve this, the work process and construction management system were integrated to improve the cost management system of K company. To accurately forecast the cash flow of a project, revenue and expenditure elements were displayed in the total cash flow forecast window. This research is expected to assist in the implementation of a system of cash flow forecasting on housing construction by excluding negative elements of revenue and expenditure.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.891-898
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• 2022

Purpose: There is not much time left for mandatory BIM implementation for all sectors and stages of the construction industry. Therefore, it is necessary to find a way to secure technology to substantially improve the productivity of BIM work. In the research, we proposed a method to automatically verify related construction standards for major objects produced by BIM modeling procedures so that engineers can verify construction standards in the BIM-based design process. Method: We defined a modeling work procedure for BIM-based road design work and prepared a method for constructing related design standards in a database. In addition, a process map for developing a BIM-based design basis review automation system was also presented. Result: A BIM-based design standard review automation module was developed using Civil3D and Dynamo. And it was confirmed by the test application that it is possible to quickly judge whether the BIM object manufactured in the design process conforms to the construction design standard. Conclusion: BIM-based design standard review automation technology can improve the productivity of BIM model production work and secure the quality of the BIM model.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.10 no.6
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• pp.140-145
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• 2011

An anisotropic beam model is proposed by employing an asymptotic expansion method for thermo-mechanical multiphysics environment. An asymptotic method based on virtual work is introduced first, and then the variables of mechanical displacement and temperature rise are asymptotically expanded by taking advantage of geometrical slenderness of elastic bodies. Subsequently substituting these expansions into the virtual work principle allows us to asymptotically expand the virtual work. This will yield a set of recursive virtual works from which two-dimensional microscopic and one-dimensional macroscopic equations are systematically derived at each order. In this way, homogenized stiffnesses and thermomechanical coupling coefficients are derived. To demonstrate the validity and efficiency of the proposed approach, composite beams are taken as a test-bed example. The results obtained herein are compared to those of three-dimensional finite element analysis.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2013.05a
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• pp.210-212
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• 2013

The construction cost estimates during the design phase becomes the standard to judge profitability and validity, and is very important in various decision-makings by project owner. However, since approximate costs are quoted when many parts are undecided in the early stage of project, differences are bound to occur between the construction cost calculated through approximate quotation and that put into construction actually. Also, since in existing quotation works, quantity calculations have been dependent on the staff's manual work, involving error potential, and thus differences are likely in quantity calculation depending on the quotation staff's method of calculation. In this study, the process of creating space model to deduce 3D geometry information for approximate quotation in association with knowledge information and the expression for calculation of finishing area were proposed.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.19-29
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• 2021

This paper presents the validation of the MCS code for critical safety analysis with burnup credit for the spent fuel casks. The validation process in this work considers five critical benchmark problem sets, which consist of total 80 critical experiments having MOX fuels from the International Criticality Safety Benchmark Evaluation Project (ICSBEP). The similarity analysis with the use of sensitivity and uncertainty tool TSUNAMI in SCALE was used to determine the applicable benchmark experiments corresponding to each spent fuel cask model and then the Upper Safety Limits (USLs) except for the isotopic validation were evaluated following the guidance from NUREG/CR-6698. The validation process in this work was also performed with the MCNP6 for comparison with the results using MCS calculations. The results of this work showed the consistence between MCS and MCNP6 for the MOX fueled criticality benchmarks, thus proving the reliability of the MCS calculations.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.17 no.2
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• pp.47-53
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• 2018

Nowadays, additive manufacturing (AM) technology is a promising process to fabricate complex shaped devices applied in medical and dental services. Among the AM processes, a DLP (digital light processing) type 3D printing process has some advantages, such as high precision, relatively low cost, etc. In this work, we propose a simple method to fabricate precise dental models using a DLP 3D printer. After 3D printing, a part is commonly post-cured using secondary UV-curing equipment for complete polymerization. However, some shrinkage occurs during the post-curing process, so we adaptively control the UV-exposure time on each layer for over- or under-curing to change the local shape-size of a part in the DLP process. From the results, the shrinkage amounts in the post-curing process vary due to the UV-dose in 3D printing. We believe that the proposed method can be utilized to fabricate dental models precisely, even with a change of the 3D CAD model.

• Journal of Korean Society of Water and Wastewater
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• v.35 no.1
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• pp.93-100
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• 2021

Forward osmosis (FO) process is a chemical potential driven process, where highly concentrated draw solution (DS) is used to take water through semi-permeable membrane from feed solution (FS) with lower concentration. Recently, commercial FO membrane modules have been developed so that full-scale FO process can be applied to seawater desalination or water reuse. In order to design a real-scale FO plant, the performance prediction of FO membrane modules installed in the plant is essential. Especially, the flux prediction is the most important task because the amount of diluted draw solution and concentrate solution flowing out of FO modules can be expected from the flux. Through a previous study, a theoretical based FO module model to predict flux was developed. However it needs an intensive numerical calculation work and a fitting process to reflect a complex module geometry. The idea of this work is to introduce deep learning to predict flux of FO membrane modules using 116 experimental data set, which include six input variables (flow rate, pressure, and ion concentration of DS and FS) and one output variable (flux). The procedure of optimizing a deep learning model to minimize prediction error and overfitting problem was developed and tested. The optimized deep learning model (error of 3.87%) was found to predict flux better than the theoretical based FO module model (error of 10.13%) in the data set which were not used in machine learning.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.8 no.2
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• pp.299-306
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• 1996

This work presents an analytical model, so called modified LMTD method, to predict the thermal performance of finned-tube heat exchanger under frosting conditions. In this model, the total heat transfer coefficient and effective thermal conductivity of the frost layer were defined as a function of frost surface temperature. The surface temperature of the frost layer formed on the heat exchanger was calculated through the analysis of the heat and mass transfer process in the air and frost layer. To examine the validity of this analytical model, the computed results from the present model, such as heat transfer rate, frost mass and thickness of frost, were compared with the ones of the expermental work and LMED method.