• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.14-19
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• 2021

Aim of this research was to investigate the effects of Digital Elevation Model (DEM) for sensitivity analysis with two types of DEMs: 1 to 24,000 and 1 to 250,000 DEM. Another emphasis was given to the development of methodology for processing DEMs to create ArcGIS Pro and GRASS layers. This was done while developing water quality system modeling using DEMs which were used to model hydrological processes and SWAT model. Sensitivity analysis with DEMs resulted in different runoff volumes in the model simulation. Runoff volume was higher for the 1:24,000 DEM than 1:250,000 DEM, probably due to the finer resolution and slope which increased the estimated runoff from the watershed. Certainly the DEMs were factors in precision of the simulations and it was obvious during sensitivity analysis that DEMs had significant effect on runoff volumes. We suggest, however, that additional comparative research could be conducted involving more parameters such as soil and hydrologic parameters to provide insight into the overall physical system which the SWAT model represents.

• The Journal of Information Systems
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• v.30 no.4
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• pp.227-245
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• 2021

Purpose The purpose of this study is to examine the variables affecting user satisfaction through previous studies and to examine the differences between apps. Differences are based on factors that determine the quality of real estate service apps and derived by the topic modeling results. Design/methodology/approach This study conducts topic modeling to find factors affecting user satisfaction of real estate service apps using user reviews. Sentiment analysis is additionally conduct on the derived topics to examine the user responses. Findings Users give high sentiment scores for services that can manage factors such as usefulness of information, false sales, and hype. In addition, managing the basic services of app is an important factor influencing user satisfaction.

• Transactions of Materials Processing
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• v.31 no.4
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• pp.194-199
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• 2022

In finite element method (FEM) simulations, constitutive models are widely used and developed to represent a wide range of true stress-strain curves using a small number of modeling parameters. Nevertheless, many studies has been conducted to find a suitable constitutive model and optimal modeling parameters to represent experimentally obtained true stress-strain curves. Therefore, in this study, a new constitutive modeling approach using the combined Swift and Voce model is suggested, and confirmed through comparisons of the experimental results with the FEM simulation results.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.7
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• pp.1351-1362
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• 1992

Qualitative models in model-based expert system needs modeling paradigm which provides intelligent control of modeling assumptions and extracts robust inferences without quantitative information about the system to be modeled. Qualitative reasoning methodologies has proved the property of the completeness but not the soundness to the corresponding quantitative model. We propose new methodology of qualitative reasoning by introducing the concept of Centroid-Oriented Composite Interval to improve the soundness problem. Arithmetic operations and equivalence classes were composed using this definition. Qualitative simulation results were compared to Kuipers's results and the improvements in the soundness problem is verified.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2888-2890
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• 2005

In this paper, we introduce a new structure of fuzzy-neural networks Fuzzy Set-based Polynomial Neural Networks (FSPNN). The two underlying design mechanisms of such networks involve genetic optimization and information granulation. The resulting constructs are Fuzzy Polynomial Neural Networks (FPNN) with fuzzy set-based polynomial neurons (FSPNs) regarded as their generic processing elements. First, we introduce a comprehensive design methodology (viz. a genetic optimization using Genetic Algorithms) to determine the optimal structure of the FSPNNs. This methodology hinges on the extended Group Method of Data Handling (GMDH) and fuzzy set-based rules. It concerns FSPNN-related parameters such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of a specific subset of input variables realized through the mechanism of genetic optimization. Second, the fuzzy rules used in the networks exploit the notion of information granules defined over systems variables and formed through the process of information granulation. This granulation is realized with the aid of the hard C- Means clustering (HCM). The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.3-5
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• 2005

In this paper. we introduce a new structure of fuzzy-neural networks Fuzzy Set-based Polynomial Neural Networks (FSPNN). The two underlying design mechanisms of such networks involve genetic optimization and information granulation. The resulting constructs are Fuzzy Polynomial Neural Networks (FPNN) with fuzzy set-based polynomial neurons (FSPNs) regarded as their generic processing elements. First, we introduce a comprehensive design methodology (viz. a genetic optimization using Genetic Algorithms) to determine the optimal structure of the FSPNNs. This methodology hinges on the extended Group Method of Data Handling (GMDH) and fuzzy set-based rules. It concerns FSPNN-related parameters such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of a specific subset of input variables realized through the mechanism of genetic optimization. Second, the fuzzy rules used in the networks exploit the notion of information granules defined over systems variables and formed through the process of information granulation. This granulation is realized with the aid of the hard C-Means clustering (HCM). The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.

• Korean Journal of Computational Design and Engineering
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• v.16 no.5
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• pp.351-360
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• 2011

Product systems are quickly and frequently changed because Product Life Cycle is continuously reduced and adopting new product is steadily fast. Thus, various studies are progressed using simulation which is one of digital manufacturing. The research that is concerning simulation of control verification for shorten the commissioning which has a lot of trial and error is in progress. Also, simulation of control verification has strength that it can catch the errors in advance. However, a control program in simulation needs virtual factory for representation of control information. For this reason, excessive time and energy is put into controlling the virtual factory. So, in this paper, we construct library which is using exist data, in order to overcome limitation of these problems. Furthermore, we suggest methodology which can model and verify the process more speedy using library. Especially, we give body to the BB/BR Line process which has many altering equipment and need high technology effectively using physical and logical modeling. We can set up a control simulation environment very rapidly, as well as cut process time down using our suggestion.

• The Journal of the Korea Contents Association
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• v.6 no.1
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• pp.54-63
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• 2006

In this paper we will propose a domain analysis methodology that uses an extended workflow mechanism based on dynamic modeling to solve problems of a traditional domain analysis on legacy systems. This methodology is called WODA(Workflow Oriented Domain Analysis). Following procedures on WODA, we can identify common/uncommon component, and also extract the cluster of components. It will be effectively reusable on developing new systems with these components. With our proposed component testing metrics, we can determine highly reusable component/scenario on identifying possible scenarios of the particular system. We can also recognize most critical/most frequent reusable components and prioritize possible component scenarios of the system. This paper contains one application of UPS that illustrates our autonomous modeling tool, WODA.

• Journal of Korean Society for Quality Management
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• v.46 no.1
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• pp.39-74
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• 2018

Purpose: For more than 30 years, robust parameter design (RPD), which attempts to minimize the process bias (i.e., deviation between the mean and the target) and its variability simultaneously, has received consistent attention from researchers in academia and industry. Based on Taguchi's philosophy, a number of RPD methodologies have been developed to improve the quality of products and processes. The primary purpose of this paper is to review and discuss existing RPD methodologies in terms of the three sequential RPD procedures of experimental design, parameter estimation, and optimization. Methods: This literature study composes three review aspects including experimental design, estimation modeling, and optimization methods. Results: To analyze the benefits and weaknesses of conventional RPD methods and investigate the requirements of future research, we first analyze a variety of experimental formats associated with input control and noise factors, output responses and replication, and estimation approaches. Secondly, existing estimation methods are categorized according to their implementation of least-squares, maximum likelihood estimation, generalized linear models, Bayesian techniques, or the response surface methodology. Thirdly, optimization models for single and multiple responses problems are analyzed within their historical and functional framework. Conclusion: This study identifies the current RPD foundations and unresolved problems, including ample discussion of further directions of study.

• Food Science of Animal Resources
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• v.32 no.3
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• pp.274-284
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• 2012

In this study, the effects of physical parameters (30-270 MPa of pressure, 3-57 min of time, and 1-$49^{\circ}C$ of temperature) on pork quality were investigated. Response surface methodology was used in order to monitor and model the changes in pork quality under varied pressure conditions. As quality characteristics, shear force, water holding capacity (WHC) and the CIE color of pork were measured, and optimum pressure conditions were evaluated by statistical modeling. Pressure improved the WHC of pork at relatively low temperature ($<25^{\circ}C$); however, the opposite occurred with increasing temperature. Although pressure and temperature affected the tenderness of the meat, interaction effects among variations were not observed. At pressure levels higher than 200 MPa, the color of pork differed markedly from that of the untreated controls. In particular, differential scanning calorimetry (DSC) revealed marked evidence of myosin denaturation. The present study demonstrates that pork quality varies depending on pressure conditions.