• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.515-524
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• 2007

This paper deals with local linear estimation of fuzzy regression models based on Diamond(1998) as a new class of non-linear fuzzy regression. The purpose of this paper is to introduce a use of smoothing in testing for lack of fit of parametric fuzzy regression models.

• Proceedings of the IEEK Conference
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• 2000.11c
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• pp.109-112
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• 2000

Many applications in computer graphics require highly detailed complex models. However, the level of detail may vary considerably according to applications. It is often desirable to use approximations in place of excessively detailed models. We have developed a surface simplification algorithm which uses iterative contractions of edges to simplify models and maintains surface error approximations using a quadric metric. In this paper, we present an improved quadric error metric for simplifying meshes. The new metric, based on subdivided edge classification, results in more accurate simplified meshes. We show that a subdivided edge classification captures discontinuities efficiently. The new scheme is demonstrated on a variety of meshes.

• Journal of the Korea Society for Simulation
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• v.6 no.1
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• pp.61-69
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• 1997

In 3D computer graphics, fractal techniques have been applied to terrain models. Even though fractal models are convenient way to get the data of terrain models, it is not easy to gain the final results by manipulating the data of terrain model. However, by using the object oriented programming techniques, we could reduce the effort of programming job to find the final result. In this paper, a set of classes made by object oriented programming technique is presented. To show the results, the data of a terrain model were made by a fractal technique, namely, the midpoint displacement methods with square lattices of points.

• The Transactions of the Korea Information Processing Society
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• v.6 no.11
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• pp.2965-2980
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• 1999

There are several models and the corresponding diagrams to express software system in many kinds of viewpoints, but these are supposed to be integrated and implemented into only one system. Therefore, the software modelers should have the models ensuring the consistency between information in software development life cycle. To support the robust models for modelers using OO modeling methods, i.e. UML, and CASE tools, the meta models of the software architecture and the consistency rules between the models are suggested in this thesis. Finally, the rules are implemented in the OO CASE tool, DEBUTO(Design By UML Tool). It supports UML1.1 notations and has visual modeling editors that enable users make their own software model.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1995.04a
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• pp.73-81
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• 1995

In Korea, the pilot construction of the first high-speed railroad on the Seoul-Pusan has already started 2 years ago. In the thesis, an attempt is made to develop reliability-based integrity-assessment models for the computer-aided control and maintenance of high-speed railroad bridges. The strength limit state models for PC railroad bridges encompass the bending and shear strengths as well as the strength interaction equations which simultaneously take into the element and system reliablities of the proposed limit states and reliability models. Then, the actual load carrying capacity and the realistic safety of bridges are evaluated using the system reliability-based equivalent strength, and the results are compared with those of the element reliability-based or conventional methods. Various parametric studies are performed for the proposed reliability-based safety and integrity-assessment models using the actual PC box girder bridges used in the pilot construction. And the sensitivity analyses are performed for the basic random variables included in strength limit state models. It is concluded that proposed models may be practically applied for the rational assessment of safety and integrity of high speed railroad bridges.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.1
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• pp.96-107
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• 2009

Worm attacks can greatly distort network performance, and countering infections can exact a heavy toll on economic and technical resources. Worm modeling helps us to better understand the spread and propagation of worms through a network, and combining effective types of mitigation techniques helps prevent and mitigate the effects of worm attacks. In this paper, we propose a mathematical model which combines both dynamic quarantine and passive benign worms. This Passive Worm Dynamic Quarantine (PWDQ) model departs from previous models in that infected hosts will be recovered either by passive benign worms or quarantine measure. Computer simulation shows that the performance of our proposed model is significantly better than existing models, in terms of decreasing the number of infectious hosts and reducing the worm propagation speed.

• Journal of Integrative Natural Science
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• v.7 no.3
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• pp.193-199
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• 2014

As the requirement on the quality of the system has increased, the reliability is very important part in terms of enhance stability and to provide high quality services to customers. Many statistical models have been developed in the past years for the estimation of software reliability. We consider the functions for NHPP software reliability model and time series model in software failure data. We estimate parameters for the proposed models from three data sets. The values of SSE and MSE is presented from three data sets. We compare the predicted number of faults with the actual three data sets using the NHPP software reliability model and time series model.

• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.671-675
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• 2011

DVC (Distributed Video Coding) and FRUC (Frame Rate Up Conversion) techniques need to have an efficient motion compensated frame interpolation algorithms. Conventional works of these applications have mainly focused on the performance improvement of overall system. But, in some applications, it is necessary to evaluate how well the MCI (Motion Compensated Interpolation) frame matches the original frame. For this aim, this paper deals with the modeling methods for evaluating the block-based matching cost. First, several matching criteria, which have already been dealt with the motion compensated frame interpolation, are introduced and then combined to make estimate models for the size of MSE (Mean Square Error) noise of the MCI frame to original one. Through computer simulations, it is shown that the block-based matching criteria are evaluated and the proposed model can be effectively used for estimating the MSE noise.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.339-344
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• 2001

In recent engineering, the designer has become more and more dependent on the computer simulations such as FEM (Finite Element Method) and BEM (Boundary Element Method). In order to optimize such implicit models more efficiently and reliably, the meta-modeling technique has been developed for solving such a complex problems combined with the DACE (Design and Analysis of Computer Experiments). It is widely used for exploring the engineer's design space and for building meta-models in order to facilitate an effective solution of multi-objective and multi-disciplinary optimization problems. Optimization of a train suspension is performed according to the minimization of forty-six responses that represent ten ride comforts, twelve derailment quotients, twelve unloading ratios, and twelve stabilities by using the Kriging meta-model of a train suspension. After each Kriging meta-model is constructed, multi-objective optimal solutions are achieved by using a nonlinear programming method called SQP (Sequential Quadratic Programming).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1796-1816
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• 2020

The health and safety of elderly and disabled patients who cannot live alone is an important issue. Timely detection of sudden events is necessary to protect these people, and anomaly detection in smart homes is an efficient approach to extracting such information. In the real world, there is a causal relationship between an occupant's behaviour and the order in which appliances are used in the home. Bayesian networks are appropriate tools for assessing the probability of an effect due to the occurrence of its causes, and vice versa. This paper defines different subsets of random variables on the basis of sensory data from a smart home, and it presents an anomaly detection system based on various models of Bayesian networks and drawing upon these variables. We examine different models to obtain the best network, one that has higher assessment scores and a smaller size. Experimental evaluations of real datasets show the effectiveness of the proposed method.