• 웰빙융합연구
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• 제6권4호
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• pp.27-31
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• 2023

Purpose: This study analyzed papers identified by entering the two keywords 'unification education' and 'university' during research from 2013 to 2022 in order to identify trends and key concepts in unification education research at domestic universities. Research design, data, and methodology: The study analyzed 224 papers, excluding those on primary, middle, and high school unification education, as well as unrelated and duplicate papers. The analysis included developing a co-occurrence network of keywords, utilizing topic modeling to categorize research types, and confirming visualizations such as word clouds and sociograms. Results: In the final analysis, the research identified 1,500 keywords, with notable ones like 'Korea,' 'education,' 'unification.' Centrality analysis, measuring influence through connected keywords, revealed that 'Korea,' 'education,' 'north,' and 'unification' held significant positions. Keywords with high centrality compared to their frequency included 'learning,' 'development,' 'training,' 'peace,' and 'language,' in that order. Conclusions: This study investigated trends and structures in university-level unification education by analyzing papers identified with the keywords 'unification education' and 'university.' The use of keyword network analysis aimed to elucidate patterns and structures in university-level unification education. The significance of the study lies in offering foundational data for future research directions in the field of unification education at universities.

• 대한인간공학회지
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• 제25권2호
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• pp.161-172
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• 2006

This study aims to compare and contrast the Kansei modeling methods for building a luxuriousness model that people feel about appearance of mobile phones. For the evaluation based on Kansei engineering approaches, 15 participants were employed to evaluate 18 mobile phones using a questionnaire. The results of evaluation were analyzed to build luxuriousness models through quantification I method, neural network, and decision tree method, respectively. The performance of Kansei modeling methods was compared and contrasted in terms of accuracy and predictability. The result of comparison of modeling methods indicated that model accuracy and predictability was closely related to the number of variables and data size. It was also revealed that quantification I method was the best in terms of model accuracy while decision tree method was the best modeling method with small variance in terms of predictability. However, it was empirically found that quantification I method showed extremely unstable predictability with small number of data. Consequently, it is expected that the research findings of this study might be utilized as a guideline for selecting proper Kansei modeling method.

• 한국시뮬레이션학회논문지
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• 제15권4호
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• pp.1-10
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• 2006

컴퓨터 바이러스의 모델링 및 시뮬레이션에 관한 연구는 주로 네트워크 취약성 분석에 초점이 맞추어져 있었다. 그러나 컴퓨터 바이러스는 생물학적인 관점에서 분석되어 질 수 있다고 생각하여, 인공생명 기술을 이용하여 컴퓨터 바이러스를 분석하였다. 이 연구를 통해 컴퓨터 바이러스로 인해 네트워크에 미칠 영향과 행동 메커니즘을 이해할 수 있을 것이다. 본 논문에서는 인공생명을 이용한 컴퓨터 바이러스의 모델링 및 시뮬레이션 방법론을 제안한다. 이를 통해 컴퓨터 바이러스 백신의 연구에도 영향을 줄 수 있다.

• 한국시뮬레이션학회논문지
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• 제18권4호
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• pp.127-136
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• 2009

MANET(Mobile Ad-hoc NETwork)에 있어 모델링 및 시뮬레이션은 실제 시스템 환경을 구축하기 어려운 여건에서 가상의 환경을 통한 분석연구를 위한 가장 효과적이고 유용한 방법이다. MANET의 연구에 있어서 네트워크 전체의 동작특성을 관찰하기 위해서는 노드간 전달과정과 관련한 상태 및 특성변화의 추적이 중요하며 이산 사건 시스템의 특징을 가진다. Zeigler's DEVS(Discrete Event System Specification) 형식론은 계층적이고 모듈라한 기법으로 이산사건 시스템을 명세할 수 있는 수학적 형식론이며, 이에 기반한 DEVSim++는 모델링의 무결성을 제공하며 객체지향형기법에 의한 계층적 최적화 모델링 및 시뮬레이션 환경을 제공한다. 그러나 네트워크를 구성하는 노드의 이동 특성으로 인해 네트워크의 연결 상태가 지속적으로 변하는 MANET을 모델링하기에는 어려운 부분이 있다. 본 논문에서는 DEVS방법론을 도입하여 MANET을 모델링하는 과정에서 노드의 이동 특성을 고려한, 네트워크의 변화특성에 따른 네트워크 상태의 변화를 관찰하기 위하여 네트워크 특성을 중심으로 MANET을 표현하는 방법을 제안하고 MANET DEVS 모델을 제시한다. 또한 제시한 모델을 DEVSim++시뮬레이션 엔진에 적용하여 시뮬레이션 함으로써 모델의 동작을 실증하였다.

• Journal of Biosystems Engineering
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• 제17권1호
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• pp.65-78
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• 1992

When using a computer vision system for a measurement, the geometrically distorted input image usually restricts the site and size of the measuring window. A geometrically distorted image caused by the image sensing and processing hardware degrades the accuracy of the visual measurement and prohibits the arbitrary selection of the measuring scope. Therefore, an image calibration is inevitable to improve the measuring accuracy. A calibration process is usually done via four steps such as measurement, modeling, parameter estimation, and compensation. In this paper, the efficient error calibration technique of a geometrically distorted input image was developed using a neural network. After calibrating a unit pixel, the distorted image was compensated by training CMLAN(Cerebellar Model Linear Associator Network) without modeling the behavior of any system element. The input/output training pairs for the network was obtained by processing the image of the devised sampled pattern. The generalization property of the network successfully compensates the distortion errors of the untrained arbitrary pixel points on the image space. The error convergence of the trained network with respect to the network control parameters were also presented. The compensated image through the network was then post processed using a simple DDA(Digital Differential Analyzer) to avoid the pixel disconnectivity. The compensation effect was verified using known sized geometric primitives. A way to extract directly a real scaled geometric quantity of the object from the 8-directional chain coding was also devised and coded. Since the developed calibration algorithm does not require any knowledge of modeling system elements and estimating parameters, it can be applied simply to any image processing system. Furthermore, it efficiently enhances the measurement accuracy and allows the arbitrary sizing and locating of the measuring window. The applied and developed algorithms were coded as a menu driven way using MS-C language Ver. 6.0, PC VISION PLUS library functions, and VGA graphic functions.

• Industrial Engineering and Management Systems
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• 제15권4호
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• pp.396-402
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• 2016

In this study, hydraulic analysis of water-supply networks in Marivan was performed by modeling. WATERGEMS was used for modeling and it was calibrated using existing rules and regulations. The purpose of this research is modeling urban water network and its analysis based on hydraulic criteria and meeting pressure conditions at the nodes and complying the economic speed. To achieve this goal, first the pipelines of city streets was designed in AutoCAD on a map of the city. It should be mentioned that it was tried to prevent from creating additional loops in the network and the optimal network was designed by a combination of annular and branch loops. In the next step, the pipes were called in WATERGEMS and then we continue the operation by the allocation of elevation digits to the pipes. Since the topography of this city is very specific and unique, the number of pressure zones was increased. Three zones created only covers about 20% of the population in the city. In this dissertation, the design was performed on the city's main zone with the largest density in the Figures 1,320-1,340. In the next step, the network triangulation was conducted. Finally, the Debiw as allocated based on the triangulation conducted and considering the density of the city for year of horizon. Ultimately, the network of Marivan was designed and calibrated according to hydraulic criteria and pressure zoning. The output of this model can be used in water-supply projects, improvement and reform of the existing net-work in the city, and various other studies. Numerous and various graphs obtained in different parts of a network modelled can be used in the analysis of critical situation, leakage.

• 대한산업공학회지
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• 제20권4호
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• pp.113-134
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• 1994

A radial basis hybrid neural network (RHNN) is presented for an on-line detection of machine condition change. Two-phase modeling by RHNN is designed for describing a machine condition process and for predicting future signal. A moving block procedure is also designed for detecting a process change. A fast on-line learning algorithm, the recursive least square estimation, is introduced. Experimental results showed the RHNN could be utilized efficiently for on-line machine condition monitoring.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.200-200
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• 2000

Highly nonlinear dynamical systems are easily identified using neural networks. When disturbances are included in the learning data set Int system modeling, modeling process will be poorly performed. Since the radial basis functions in the radial basis function network(RBFN) are centered at the points specified by the weights, RBF networks are robust for approximating the process including the narrow-band disturbances deviating significantly from the regular signals. To exclude(filter) these disturbances, a robust algorithm for system identification, based on the RBFN, is proposed. The performance of system identification excluding disturbances is investigated and compared with the one including disturbances.

• 대한기계학회논문집A
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• 제27권7호
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• pp.1110-1119
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• 2003

This paper deals with the synthesis of the 3-dimensional grasp planning for unknown objects. Previous studies have many problems, which the estimation time for finding the grasping points is much long and the analysis used the not-perfect 3-dimensional modeling. To overcome these limitations in this paper new algorithm is proposed, which algorithm is achieved by two steps. First step is to find the whole 3-dimensional geometrical modeling for unknown objects by using stereo matching. Second step is to find the optimal grasping points for unknown objects by using the neural network trained by the result of optimization using genetic algorithm. The algorithm is verified by computer simulation, comparing the result between neural network and optimization.

• 한국산업융합학회 논문집
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• 제14권3호
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• pp.95-100
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• 2011

The modern society longing for the convenience of up-to-date technology, there are attempts of miniaturization and high reliance of power equipments in the effectiveness aspect of urban area's usage of space while requiring more electrical energy than now. Consequently, paper used to the Neral Network for a forcasting conservation system. A neral network is powerful asta modeling tool that is able to capture and represent complex input/output relationships. The true power and advantage of neral networks lies in their ability to learn these relationships directly from the data being modeled. Traditional linear models are simply inadequate when it comes to modeling data that contains non-linear characteristics. Form results of this study, the Neral Network is will play an important role for insulation diagnosis system of real site GIS and power eqipment using $SF_6$ gas.