• Korean journal of food and cookery science
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• v.22 no.6 s.96
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• pp.840-848
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• 2006

To analyse the impact of recognition for local food on frequency of visiting for local food restaurants, we surveyed 333 residents in the Kyungsangdo areas. The findings are summarized as follows. On interest of native foods, 'much' scored 40.6% and 'taste' scored 32.9%, in requirement of development. The criteria of selection of local foods was 62.3% in 'taste'. 'Institute(municipal government)' scored 31.3% as the main responsible body for the succession of local foods. The most significant criterion for tourism product of local foods was 'taste'(34.5%). The most effective way to popularize the local foods was to 'hold various kinds of cultural events'(27.5%). The necessity score on tourism product of local foods was 3.55 points. The highest recognition on native local foods was 'succession to next generation'(3.96 points). The most influential variable affecting the visit frequency toward local food restaurants was 'health factor'.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.447-448
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• 2006

In this paper, we propose tracking algorithm using multi resolution in wavelet transform domain. This algorithm consists of two steps. The first step is feature extraction that is select feature-points using 1-level wavelet transform in ROI (Region of Interest). The other step is feature tracking. Based on multi resolution of wavelet transform, we estimate a displacement between current frame and next frame on the basis of selected feature-points. Experimental results show that the proposed algorithm confirmed a better performance than a centroid tracking and correlation tracking.

• The KIPS Transactions:PartA
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• v.8A no.1
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• pp.56-62
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• 2001

This paper presents a neural network based variable structure control scheme for nonlinear systems. In this scheme, a set of local variable structure control laws are designed on the basis of the linear models about preselected representative points which cover the range of the system operation of interest. From the combination of the set of local variable structure control laws, neural networks infer the approximate control input in between the operating points. The neural network based variable structure control alleviates the effects of model uncertainties, which cannot be compensated by the control techniques using feedback linearization. It also relaxes the discontinuity in the system’s behavior that appears when the control schemes based on the family of the linear models are applied to nonlinear systems. Simulation results of a ball and beam system, to which feedback linearization cannot be applied, demonstrate the feasibility of the proposed method.

• 한국신재생에너지학회:학술대회논문집
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• 2006.06a
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• pp.304-307
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• 2006

Recently, as the global warming by fossil fuels and the steep rise of the oil price become social issues, the interest for renewable energy producing system is increasing rapidly. Among these, the wind turbine is most highlighted because of its economic competitiveness. The tower is one of the main components of wind turbine, which occupying about 20% of overall turbine costs. The tower access door located to base part of the tower, is used to enter the tower. This is the main structural weak points because of door hole, weldment, etc. And so are the weldments between the cans and the flanges. In this study, for the top flange part of the tower, by FEM using ANSYS, we retrieved the maximum von Mises stress on that and carried out fatigue analysis using stresses at such weak points.

• International Journal of Contents
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• v.2 no.3
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• pp.27-32
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• 2006

We present an effective algorithm for automatic tracing of retinal vessel structure and vascular landmark extraction of bifurcations and ending points. In this paper we deal with vascular patterns from RGB images for personal identification. Vessel tracing algorithms are of interest in a variety of biometric and medical application such as personal identification, biometrics, and ophthalmic disorders like vessel change detection. However eye surface vasculature tracing in RGB images has many problems which are subject to improper illumination, glare, fade-out, shadow and artifacts arising from reflection, refraction, and dispersion. The proposed algorithm on vascular tracing employs multi-stage processing of ten-layers as followings: Image Acquisition, Image Enhancement by gray scale retinal image enhancement, reducing background artifact and illuminations and removing interlacing minute characteristics of vessels, Vascular Structure Extraction by connecting broken vessels, extracting vascular structure using eight directional information, and extracting retinal vascular structure, and Vascular Landmark Extraction by extracting bifurcations and ending points. The results of automatic retinal vessel extraction using jive different thresholds applied 34 eye images are presented. The results of vasculature tracing algorithm shows that the suggested algorithm can obtain not only robust and accurate vessel tracing but also vascular landmarks according to thresholds.

• Communications for Statistical Applications and Methods
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• v.25 no.6
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• pp.647-658
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• 2018

In this paper, we study statistical inferences on the maximum likelihood estimation of a normal distribution when data are randomly censored. Likelihood equations are derived assuming that the censoring distribution does not involve any parameters of interest. The maximum likelihood estimators (MLEs) of the censored normal distribution do not have an explicit form, and it should be solved in an iterative way. We consider a simple method to derive an explicit form of the approximate MLEs with no iterations by expanding the nonlinear parts of the likelihood equations in Taylor series around some suitable points. The points are closely related to Kaplan-Meier estimators. By using the same method, the observed Fisher information is also approximated to obtain asymptotic variances of the estimators. An illustrative example is presented, and a simulation study is conducted to compare the performances of the estimators. In addition to their explicit form, the approximate MLEs are as efficient as the MLEs in terms of variances.

• Communications for Statistical Applications and Methods
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• v.28 no.5
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• pp.511-520
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• 2021

Over the last decade, Bitcoin has attracted a great deal of public interest and Bitcoin market has grown rapidly. One of the main characteristics of the market is that it often undergoes some events or incidents that cause outlying observations. To obtain reliable results in the statistical analysis of Bitcoin data, these outlying observations need to be carefully treated. In this study, we are interested in change point analysis for Bitcoin return series having such outlying observations. Since these outlying observations can affect change point analysis undesirably, we use a robust test for parameter change to locate change points. We report some significant change points that are not detected by the existing tests and demonstrate that the model allowing for parameter changes is better fitted to the data. Finally, we show that the model with parameter change can improve the forecasting performance of Value-at-Risk.

• Journal of Korea Game Society
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• v.12 no.1
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• pp.79-90
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• 2012

This study suggests effects and procedure of G-Learning math class which had implemented toward a class of K-5 for 6 weeks in La Ballona elementary school located in Culver City, LA in USA. For designing G-Learning math class, developing the G-Learning contents, constructing teaching and learning model, publishing the teacher and student's book and conducting teacher training were carried out. As for the results, the achievement score of G-Learning class rose 12 points which marked higher improvement than the compare class. Also in G-Learning class, the score of 1/3 lower achievement group increased 22 points and 1/3 higher achievement group rose 9 points with statistical significance. Moreover, after G-Learning math class, interest and awareness to effectiveness toward G-Learning math was positively increased.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.11 s.188
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• pp.93-102
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• 2006

In this study an advanced domain decomposition method is suggested in order to construct surface models from very large amount of points. In this method the spatial domain of interest that is occupied by the input set of points is divided in repetitive manner. First, the space is divided into smaller domains where the problem can be solved independently. Then each subdomain is again divided into much smaller domains where the problem can be solved locally. These local solutions of subdivided domains are blended together to obtain a solution of each subdomain using partition of unity function. Then the solutions of subdomains are merged together in order to construct whole surface model. The suggested methods are conceptually very simple and easy to implement. Since RDDM(Repetitive Domain Decomposition Method) is effective in the computation time and memory consumption, the present study is capable of providing a fast and accurate reconstructions of complex shapes from large amount of point data containing millions of points. The effectiveness and validity of the suggested methods are demonstrated by performing numerical experiments for the various types of point data.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.2
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• pp.212-218
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• 2011

The curriculum of programming education including algorithm has been recognized as a very important subject to many students majoring in natural sciences and engineering including electronic engineering and computer related departments. This study analyzed usability of the learning system of programming languages using basic algorithms so as for students to easily learn basic algorithm among the entire programming curriculum. The results show that the grade of learning achievement of experimental group using the learning system is 15 points higher than that of non-experimental group and the grade of interest, concentration, effectiveness, understanding, convenience, suitability, and attending a lecture in the next semester are 4 points or more of 5 points criterion.