• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.365-373
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• 1999

Recently, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as a model construction process. Irrespective of the efficiency of a learning procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network models. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables for neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.141-148
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• 2016

In this paper, we propose a topic network analysis approach which integrates topic modeling and social network analysis. We collected 2,039 scientific papers from five top journals in the field of data mining published from 1996 to 2015, and analyzed them with the proposed approach. To identify topic trends, time-series analysis of topic network is performed based on 4 intervals. Our experimental results show centralization of the topic network has the highest score from 1996 to 2000, and decreases for next 5 years and increases again. For last 5 years, centralization of the degree centrality increases, while centralization of the betweenness centrality and closeness centrality decreases again. Also, clustering is identified as the most interrelated topic among other topics. Topics with the highest degree centrality evolves clustering, web applications, clustering and dimensionality reduction according to time. Our approach extracts the interrelationships of topics, which cannot be detected with conventional topic modeling approaches, and provides topical trends of data mining research fields.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.8
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• pp.727-731
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• 2000

This paper presents a new practical autopilot design approach to acceleration control for tail-controlled STT(Skid-to-Turn) missiles. The approach is novel in that the proposed parametric affine missile model adopts acceleration as th controlled output and considers the couplings between the forces as well as the moments and control fin deflections. The aerodynamic coefficients in the proposed model are expressed in a closed form with fittable parameters over the whole operating range. The parameters are fitted from aerodynamic coefficient look-up tables by the function approximation technique which is based on the combination of local parametric models through curve fitting using the corresponding influence functions. In this paper in order to employ the results of parametric affine modeling in the autopilot controller design we derived a parametric affine missile model and designed a feedback linearizing controller for the obtained model. Stability analysis for the overall closed loop sys-tem is provided considering the uncertainties arising from approximation errors. the validity of the proposed modeling and control approach is demonstrated through simulations for an STT missile.

• IE interfaces
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• v.17 no.3
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• pp.330-337
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• 2004

Since XML (eXtensible Markup Language) was highlighted as an information interchange format, there is an increasing demand for incorporating XML with databases. Most of the approaches are focused on RDB (Relational Databases) because of legacy systems. But these approaches depend on the database system. Countless researches are being focused on DTD (Document Type Definition). However XML Schema is more comprehensive and efficient in many perspectives. We propose a meta-model for XML Schema that is independent of the database. There are three processes to build our meta-model: DOM (Document Object Model) tree analysis, object modeling and storing object into a fixed DB schema using model mapping approach. We propose four mapping rules for object modeling, which conform to the ODMG (Object Data Management Group) 3.0 standard. We expect that the model will be especially useful in building XML-based e-business applications.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.83-93
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• 2001

Prediction of corporate failure using past financial data is well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks (NNs) can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. Although numerous theoretical and experimental studies reported the usefulness or neural networks in classification studies, there exists a major drawback in building and using the model. That is, the user can not readily comprehend the final rules that the neural network models acquire. We propose a genetic algorithms (GAs) approach in this study and illustrate how GAs can be applied to corporate failure prediction modeling. An advantage of GAs approach offers is that it is capable of extracting rules that are easy to understand for users like expert systems. The preliminary results show that rule extraction approach using GAs for bankruptcy prediction modeling is promising.

• Korean Journal of Computational Design and Engineering
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• v.16 no.3
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• pp.178-186
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• 2011

Static CAD models are the CAD models that do not have feature information and modeling history. These static models are generated by translating CAD models in a specific CAD system into neutral formats such as STEP and IGES. When a CAD model is translated into a neutral format, its precious feature information such as feature parameters and modeling history is lost. Once the feature information is lost, the advantage of feature based modeling is not valid any longer, and modification for the model is purely dependent on geometric and topological manipulations. However, the capabilities of the existing methods to modify static CAD models are limited, Direct modification methods such as tweaking can only handle the modifications that do not involve topological changes. There was also an approach to modify static CAD model by using volume decomposition. However, this approach was also limited to modifications of protrusion features. To address this problem, we extend the volume decomposition approach to handle not only protrusion features but also depression features in a static CAD model. This method first generates the model that contains the volume of depression feature using the bounding box of a static CAD model. The difference between the model and the bounding box is selectively decomposed into so called the feature volume and the base volume. A modification of depression feature is achieved by manipulating the feature volume of the static CAD model.

• Nuclear Engineering and Technology
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• v.44 no.8
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• pp.889-896
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• 2012

Recent progress in the computational fluid dynamics methods of two- and multiphase phase flows has already started opening up new exciting possibilities for using complete multidimensional models to simulate boiling systems. Combining this new theoretical and computational approach with novel experimental methods should dramatically improve both our understanding of the physics of boiling and the predictive capabilities of models at various scale levels. However, for the multidimensional modeling framework to become an effective predictive tool, it must be complemented with accurate mechanistic closure laws of local boiling mechanisms. Boiling heat transfer has been studied quite extensively before. However, it turns out that the prevailing approach to the analysis of experimental data for both pool boiling and forced-convection boiling has been associated with formulating correlations which normally included several adjustable coefficients rather than based on first principle models of the underlying physical phenomena. One reason for this has been the tendency (driven by practical applications and industrial needs) to formulate single expressions which encompass a broad range of conditions and fluids. This, in turn, makes it difficult to identify various specific factors which can be independently modeled for different situations. The objective of this paper is to present a mechanistic modeling concept for both pool boiling and forced-convection boiling. The proposed approach is based on theoretical first-principle concepts, and uses a minimal number of coefficients which require calibration against experimental data. The proposed models have been validated against experimental data for water and parametrically tested. Model predictions are shown for a broad range of conditions.

• Asian Journal of Atmospheric Environment
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• v.8 no.4
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• pp.184-191
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• 2014

Air quality in a subway tunnel has been crucial in most of the subway environments where IAQ could be affected by many factors such as the number of passengers, the amount and types of ventilation, train operation factors and other facilities. A modeling approach has been introduced to manage the general IAQ in a subway station. Field surveys and $CO_2$ measurements were initially conducted to analyze and understand the relationship between indoor and outdoor air quality while considering internal pollution sources, such as passengers and subway trains, etc. The measurement data were then employed for the model development with other statistical information. For the model development, the algorithm of simple continuity was set up and applied to model the subway IAQ concerned, while considering the major air transport through staircases and tunnels. Monitored $CO_2$ concentration on the concourse and platform were correlated with modeling results where the correlation values for the concourse and platform were $R^2=0.96$ and $R^2=0.75$, respectively. It implies that the box modeling approach introduced in this study would be beneficial to predict and control the indoor air quality in subway environments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.7
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• pp.1391-1396
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• 2012

It is introduced an approach to model for numerical analysis of a sound absorbing material that has different absorbing coefficient according to frequency. For modeling of a sound absorbing material, we tried to model by a traditional modeling method. But it had large differences on frequency domain, especially a capacitance component due to increasing of frequency. We approach to model a sound absorbing material by the Debye polarization technique with non-linear least square method. At first, we estimated parameters form a polyurethane with thickness 25 mm, then we could model a polyurethane with thickness 50 mm using same parameters. Therefor, we could find that the Debye polarization is an useful way to model sound absorbing materials.

• Environmental Engineering Research
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• v.25 no.3
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• pp.366-373
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• 2020

The autonomous vehicles (AVs) could make a positive or negative impact on reducing mobile emissions. This study investigated the changes of mobile emissions that could be caused by large-scale adoption of AVs. The factors of road capacity increase and speed limit increase impacts were simulated using a conceptual modeling approach that combines a hypothetical speed-emission function and a traffic demand model using a virtual transportation network. The simulation results show that road capacity increase impact is significant in decreasing mobile emissions until the market share of AVs is less than 80%. If the road capacity increases by 100%, the mobile emissions will decrease by about 30%. On the other hand, driving speed limit increase impact is significant in increasing mobile emissions, and the environmentally desirable speed limit was found at around 95 km/h. If the speed limit increases to 140 km/h, the mobile emissions will increase by about 25%. This is because some vehicles begin to bypass the congested routes at high speeds as speed limit increases. Based on the simulation results, it is clear that the vehicle platooning technology implemented at reasonable speed limit is one of the AV technologies that are encouraging from the environmental point of view.