• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.982-986
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• 2005

Digital Manufacturing is a technology to facilitate effective product developments and agile productions by digital environments representing the physical and logical schema and the behavior of real manufacturing system including manufacturing resources, processes and products. A digital virtual factory as a well-designed and integrated environment is essential for successful applications of this technology. In this research, we constructed a sophisticated digital virtual factory by measuring and 3-D CAD modeling using parametric methods. Specific parameters of each objects were decided by object-oriented schema of the digital factory. It is expected that this method is very useful for constructions of a digital factory, and helps to manage diverse information and re-use 3D models.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.12
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• pp.2061-2071
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• 2003

The macro-parametric approach, which is a method of CAD model exchange, has recently been proposed. CAD models can be exchanged in the form of a macro file, which is a sequence of modeling commands. As an event-driven commands set, the standard macro file can transfer design intents such as parameters, features and constraints. Moreover it is suitable for the network environment because the standard macro commands are open, explicit, and the data size is small. This paper introduces the concept of the macro-parametric method and proposes its representation using XML technology. Representing the macro-parametric data using XML allows managing vast amount of dynamic contents, Web-enabled distributed applications, and inherent characteristic of structure and validation.

• 한국IT서비스학회:학술대회논문집
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• 2005.11a
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• pp.382-390
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• 2005

소프트웨어 개발 프로젝트 사업의 성패는 사업의 첫 관문인 소프트웨어 견적의 결과에 좌우되는 경우가 많다. 특히, 확정가격 계약으로 수행되는 국내의 소프트웨어 사업 관행 하에서는 견적의 잘못이 회사의 존폐로 귀결되는 경우도 있다. 견적의 핵은 정확한 원가의 파악인데, 이를 위해서는 고객이 요구한 업무량과 개발자의 생산성을 정확히 아는 데서 출발해야 한다. 문제는 고객의 요구를 사업 초기에 정확히 파악하는 것이 쉽지 않을 뿐 아니라, 개발자 자신의 생산성을 잘 모른다는 것이다. 더욱이 정보의 부족으로 프로젝트의 특성 파악을 제대로 할 수 없어서, 해당 프로젝트에 적합한 생산성 보정계수의 적용이 어렵다는 점이다. 본 사례는 삼성SDS가 금년도에 종료된 수십 여 개의 프로젝트로부터 수집한 생산성 영향인자들이 생산성에 어떠한 영향을 어느 정도나 미치는 지를 분석한 것이다. 본 분석을 통하여 생산성에 영향을 미치는 주요 인자들을 식별할 수 있었고, 이들이 미치는 영향 정도를 바탕으로 견적에서 활용할 수 있는 다양한 파라메트릭 모델을 만들 수 있었다. 본 논문은 생산성 영향인자의 식별과 이들을 이용한 견적용 파라메트릭 모델의 개발 방법을 다루었다.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.157-158
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• 2021

Recently, there have been a growing number of cases using precast concrete construction methods to efficiently carry out construction projects. In order to efficiently carry out PC construction, it is necessary to establish a production plan of PC components that effectively reflect various design alternatives during the initial design stage. Because the production plan of PC components is based on productivity of PC members, the use of PC production simulations that can effectively predict productivity for design alternatives is necessary. Therefore, this paper propose a method to efficiently generate design alternatives which is necessary to perform to production simulations using parametric modeling techniques and BIM.

• Computational Structural Engineering
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• v.31 no.2
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• pp.4-8
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• 2018

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.4
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• pp.259-264
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• 2023

In this study, we devised a parametric analysis workflow for efficiently analyzing the material properties of 3D woven materials. The parametric model uses wire spacing in the woven materials as a design parameter; we generated 2,500 numerical models with various combinations of these design parameters. Using MATLAB and ANSYS software, we obtained various material properties, such as bulk modulus, thermal conductivity, and fluid permeability of the woven materials, through a parametric batch analysis. We then used this large dataset of material properties to perform a regression analysis to validate the relationship between design variables and material properties, as well as the accuracy of numerical analysis. Furthermore, we constructed an artificial neural network capable of predicting the material properties of 3D woven materials on the basis of the obtained material database. The trained network can accurately estimate the material properties of the woven materials with arbitrary design parameters, without the need for numerical analyses.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.28 no.4
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• pp.393-400
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• 2015

This study proposes a method to create and combine a superstructure module by parametric modeling, in order to improve the production efficiency of information model for modular steel bridge superstructure that can be used in planning, design and construction phase. Compound classification was performed in order to derive elements to apply the parametric modeling, and according to assembly condition, the classified elements were grouped into 13 types. In addition, three assembly conditions were derived for production of stable superstructure through combination of superstructure module, which is a production unit for modular steel bridge factory. Parameter that reflects assembly condition in compound shape when producing superstructure module through parametric modeling was deducted. Superstructure module compounds were produced according to type and parameter using interface generation based on Building Information Model(BIM) software that was developed in this study. The superstructure module produced reflects information to combine into a superstructure. To verify this, information model based on Industry Foundation Classes(IFC) was built and confirmed the application in production of superstructure by identifying the reflected property information.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2009.04a
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• pp.348-351
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• 2009

본 논문에서는 구조해석을 위한 PSC 박스 거더교의 객체 정보 모델에 관한 연구를 수행하였다. 대상 교량의 객체 정보 모델을 생성하기 위해서는 수많은 형상 및 치수에 관한 파라미터를 필요로 하게 된다. 따라서 본 연구에서는 이 교량의 설계 목적에 맞는 파라미터를 분류하였고, 파라미터들 사이의 계층구조(Structure)와 상관관계를 정의하였다. 또한 본 연구에서 적용된 인터페이스 프로그램은 3차원 객체 모델에서 출력된 파라미터를 변환하여 구조해석을 위한 입력값으로 변환시켜, 해석 결과값을 구조계산서에 출력시킴으로써 엔지니어가 설계 타당성과 모델변경 요구를 용이하게 할 수 있게 하였다. 그리고 대상 모델에 대한 설계변경은 구조물의 특징에 맞는 상관파라메트릭 방법을 적용하여 신속하게 할 수 있도록 유도하였다. 이 연구를 통해 건설구조물의 설계를 3D 모델로 하기위한 가능성을 확인하였다.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.616-625
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• 2021

Deep neural networks are an approximation method that approximates an arbitrary function to a linear model and then repeats additional approximation using a nonlinear active function. In this process, the method of evaluating the performance of approximation uses the loss function. Existing in-depth learning methods implement approximation that takes into account loss functions in the linear approximation process, but non-linear approximation phases that use active functions use non-linear transformation that is not related to reduction of loss functions of loss. This study proposes parametric activation functions that introduce scale parameters that can change the scale of activation functions and location parameters that can change the location of activation functions. By introducing parametric activation functions based on scale and location parameters, the performance of nonlinear approximation using activation functions can be improved. The scale and location parameters in each hidden layer can improve the performance of the deep neural network by determining parameters that minimize the loss function value through the learning process using the primary differential coefficient of the loss function for the parameters in the backpropagation. Through MNIST classification problems and XOR problems, parametric activation functions have been found to have superior performance over existing activation functions.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.6
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• pp.341-348
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• 2019

The purpose of this study is to model and analyze retractable large spatial structures by applying parametric modeling techniques. The modeling of wind loads in the analysis of typical structures including curved surfaces can be error-prone, and the processing time increases dramatically when there are many types of variables. However, the method based on StrAuto that was developed in previous research, facilitates the efficacious assignment of wind loads to structures and the rapid arrival of conclusions. As a result, it is possible to compare alternatives with various loads, including wind loads, to determine an optimal alternative much faster than the existing process. Further, it is almost impossible to directly input the wind load by calculating the area of an irregularly curved surface. However, the proposed method automatically assigns the wind load, which allows for automatic optimization in a structural analysis system. The approach was applied and optimized using several models, and the results are presented.