• Journal of Korean Institute of Industrial Engineers
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• v.29 no.2
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• pp.157-164
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• 2003

The amount of information increases rapidly when working in a distributed environment where multiple collaborative partners work together on a complex product. Today's PDM (product data management) systems provide good capabilities regarding the management of product data within a single company. However, taking into account the variety of systems used at partner sites in an engineering environment one can easily imagine problems regarding the interoperability and the data consistency. This paper presents a concept to improve the workflow management using the parameters network. It shows a parameter driven engineering workflow that is able to manage engineering task across company boarders. We introduce a mechanism of workflow management based on the engineering parameters and an architecture of the distributed workspace to apply it within a PDM system. For a parameter mapping between CAD and PDM system we developed an XML-based CATIA data interface module using CAA.

• Advances in Computational Design
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• v.2 no.3
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• pp.241-256
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• 2017

This paper introduces the process for Multi-objective Optimization Framework (MOF) which mediates multiple conflicting design targets. Even though the extensive researches have shown the benefits of optimization in engineering and design disciplines, most optimizations have been limited to the performance-related targets or the single-objective optimization which seek optimum solution within one design parameter. In design practice, however, designers should consider the multiple parameters whose resultant purposes are conflicting. The MOF is a BIM-integrated and simulation-based parametric workflow capable of optimizing the configuration of building components by using performance and non-performance driven measure to satisfy requirements including build programs, climate-based daylighting, occupant's experience, construction cost and etc. The MOF will generate, evaluate all different possible configurations within the predefined each parameter, present the most optimized set of solution, and then feed BIM environment to minimize data loss across software platform. This paper illustrates how Multi-objective optimization methodology can be utilized in design practice by integrating advanced simulation, optimization algorithm and BIM.

• Mass Spectrometry Letters
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• v.11 no.1
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• pp.1-5
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• 2020

In this study, some of the recently reported data processing strategies were evaluated and modified based on their capabilities and a brief workflow for data mining was redefined for Q-TOF LC-MS based untargeted metabolomics. Commercial pooled human plasma samples were used for this purpose. An ultrafiltration procedure was applied on sample preparation. Sample set was analyzed through Q-TOF LC/MS. A C18 column (Agilent Zorbax 1.8 µM, 50 × 2.1 mm) was used for chromatographic separation. Raw chromatograms were processed using XCMS - R programming language edition and Isotopologue Parameter Optimization (IPO) was used to optimize XCMS parameters. The raw XCMS table was processed using MS Excel to find reliable and reproducible peaks. Totally 1650 reliable and reproducible potential metabolite peaks were found based on the data processing procedures given in this paper. The redefined dataset was upload into MetaboAnalyst platform and the identified metabolites were matched with 86 metabolic pathways. Thus, two list were obtained and presented in this study as supplement files. The first list is to present the retention times and m/z values of detected metabolite peaks. The second list is the metabolic pathways related with the identified metabolites. The briefly described data processing strategies and dataset presented in this study could be beneficial for the researchers working on untargeted metabolomics for processing their data and validating their results.

• Journal of Digital Convergence
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• v.15 no.8
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• pp.145-160
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• 2017

This paper aims to investigate the effects of Smart-Work IT resources on job performance. The survey was conducted amongst the business workers who are currently engaged in domestic major companies as well as small businesses, and analyzed based on the frequency, factor and reliability. Using PROCESS Macro, we conducted an analysis of the relationship between the IT resources and job performance, treating collective intelligence as a mediating parameter. The results of the study are as follows. First, the IT resources of the companies were found to have a meaningful effect on job performance. Second, the IT resources were found to have no effect on employee productivity except through their effect on the collective intelligence. Finally, while openness and cooperation were found to be the aspects of collective intelligence which mediated between IT resources and workflow improvement, participation, openness and cooperation were found to be the aspects of collective intelligence which mediated between IT resources and workflow coordination, In the future, it is necessary to compare quantitatively the financial performance of Smart-Work adopters and others in the same industry or to compare the financial performance before and after introducing Smart-Work dynamically within the same company.

• 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.