• 정보과학회 논문지
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• 제43권10호
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• pp.1124-1130
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• 2016

통제조직은 외부로 정보가 공개되지 않으므로 그 속성을 직접 확인할 수 없어 사회학적 접근 등 일반적인 분석방법으로는 내부 권력 구조와 그 변화를 분석하기 어렵다. 직접적인 접근이 어려운 통제조직을 SNA를 활용하여 분석하는 방법은 간접적인 정보를 이용하여 구성원 간의 관계를 분석, 연결망을 식별하여 중앙성이 높은 인물을 판별함으로써 내부적인 권력 구조를 추정할 수 있다. 본 논문에서는 구성원 개인의 영향력과 전반적인 권력 구조를 고려하는 위세 중앙성 분석을 통해 실질적인 권력 서열의 변화를 평가하였다. 실험 결과 개인의 활동량이나 구성원간의 친밀도 등으로 산출되는 다른 중앙성 척도에 비하여 보다 정확하게 권력 구조의 추정이 가능하였으며, 새로운 구성원의 등장이나 탈퇴 등의 변화하는 구성원간의 상황을 포함한 변화 추정이 가능하였다.

• 정보관리연구
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• 제37권2호
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• pp.33-50
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• 2006

본 연구에서는 하이퍼 링크 구조를 이용한 웹 검색 알고리즘에 대해 살펴 본 후 페이지 품질을 측정하기 위해 웹의 하이퍼 구조를 이용하고 있는 알고리즘인 HITS와 PageRank를 분석하였다. 이어서 이들 방법을 이용한 검색 엔진인 Google과 Ask.com을 검색 알고리즘의 특성을 기준으로 분석하였다. 이런 연구는 미래의 웹 문서의 중요도를 평가하는 데 기초자료로 활용할 수 있으며, 웹 정보검색의 검색성능을 향상시키는 시스템 개발에 도움이 될 수 있을 것이라 생각한다.

• 전자공학회논문지B
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• 제33B권11호
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• pp.72-82
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• 1996

In this paper, newly developed algorithm for enhancing images corrupted by white gaussian noise is proposed. In the method proposed here, image is subdivided into a number of subblocks, and each block is separated into cimponents corresponding to signal and noise subspaces, respectively through the signal subspace method. A clean signal is then estimated form the signal subspace by the adaptive wiener filtering. The decomposition of noisy signal into noise and signal subspaces in is implemented by eigendecomposition of covariance matrix for noisy image, and by performing blockwise KLT (karhunen loeve transformation) using eigenvector. To reduce the perceptual noise level and distortion, wiener filtering is implementd by adaptively adjusting noise level according to activity characteristics of given block. Simulation results show the effectiveness of proposed method. In particular, edge bluring effects are reduced compared to the previous methods.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제14권1호
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• pp.29-34
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• 2010

The QR method is one of the most common methods for calculating the eigenvalues of a square matrix, however its understanding would require complicated and sophisticated mathematical logics. In this article, we present a simple way to understand QR method only with a minimal mathematical knowledge. A deflation technique is introduced, and its combination with the power iteration leads to extracting all the eigenvectors. The orthogonal iteration is then shown to be compatible with the combination of deflation and power iteration. The connection of QR method to orthogonal iteration is then briefly reviewed. Our presentation is unique and easy to understand among many accounts for the QR method by introducing the orthogonal iteration in terms of deflation and power iteration.

• 대한음성학회지:말소리
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• 제66호
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• pp.105-116
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• 2008

In this research, we propose a speaker identification system using a kernel method which is expected to model the non-linearity of speech features well. We have been using principal component analysis (PCA) successfully, and extended to kernel PCA, which is used for many pattern recognition tasks such as face recognition. However, we cannot use kernel PCA for speaker identification directly because the storage required for the kernel matrix grows quadratically, and the computational cost grows linearly (computing eigenvector of $l{\times}l$ matrix) with the number of training vectors I. Therefore, we use greedy kernel PCA which can approximate kernel PCA with small representation error. In the experiments, we compare the accuracy of the greedy kernel PCA with the baseline Gaussian mixture models using MFCCs and PCA. As the results with limited enrollment data show, the greedy kernel PCA outperforms conventional methods.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1992년도 하계학술대회 논문집 A
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• pp.453-456
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• 1992

A Data-based Covariance Matrix(DCM) is introduced in the Eigenvector(EV) method, among subspace methods of estimating multiple sinusoidal frequencies from finite white noisy measurements. It is shown that the EV with the DCM can obtain the true. frequencies from finite noiseless data Some asymptotic results and further improvement on the DCM are also presented mathematically. Monte-carlo simulations are statistically conducted from the view-points of means and standard deviations in the EV's of DCM and Conventional Covariance Matrix(CCM). Simulations show a great promise for using the DCM, particularly for the cases of short data records, closely spaced frequencies and high signal-to-noise ratios.

• Nuclear Engineering and Technology
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• 제49권1호
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• pp.17-28
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• 2017

The modified power method has been studied by many researchers to calculate the higher eigenmodes and accelerate the convergence of the fundamental mode. Its application to multidimensional problems may be unstable due to degenerated or near-degenerated eigenmodes. Complex eigenmode solutions are occasionally encountered in such cases, and the shapes of the corresponding eigenvectors may change during the simulation. These issues must be addressed for the successful implementation of the modified power method. Complex components are examined and an approximation method to eliminate the usage of the complex numbers is provided. A technique to fix the eigenvector shapes is also provided. The performance of the methods for dealing with those aforementioned problems is demonstrated with two dimensional one group and three dimensional one group homogeneous diffusion problems.

• 보건행정학회지
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• 제29권2호
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• pp.138-145
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• 2019

Background: The purpose of this study is supposed to figure out the keyword network from 2009 to 2018 with social network analysis and provide the research data that can help the Korea government's policy making on smoking cessation. Methods: First, frequency analysis on the keyword was performed. After, in this study, I applied three classic centrality measures (degree centrality, betweenness centrality, and eigenvector centrality) with R 3.5.1. Moreover, I visualized the results as the word cloud and keyword network. Results: As a result of network analysis, 'smoking' and 'smoking cessation' were key words with high frequency, high degree centrality, and betweenness centrality. As a result of looking at trends in keyword, many study had been done on the keyword 'secondhand smoke' and 'adolescent' from 2009 to 2013, and 'cigarette graphic warning' and 'electronic cigarette' from 2014 to 2018. Conclusion: This study contributes to understand trends on smoking cessation study and seek further study with the keyword network analysis.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.370-373
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• 2005

We have developed Hybrid algorithm for yellow sand detection. Hybrid algorithm is composed of three methods using infrared bands. The first method used the differential absorption in brightness temperature difference between $11\mu m\;and\;12\mu m$ (BID _1), through which help distinguish the yellow sand from various meteorological clouds. The second method uses the brightness temperature difference between $3.7\mu m\;and\;11\mu m$ (BID_2). The technique would be most sensitive to dust loading during the day when the BID _2 is enhanced by reflection of $3.7\mu m$ solar radiation. The third one is a newly developed algorithm from our research, the so-called surface temperature variation method (STY). We have applied the three methods to MODIS for derivation of the yellow sand dust and in conjunction with the Principle Component Analysis (PCA), a form of eigenvector statistical analysis. PCI shows better results for yellow sand detection in comparison with the results from individual method. The comparison between PCI and MODIS aerosols optical depth (AOD) shows remarkable good correlations during daytime and relatively good correlations over the land.

• 대한원격탐사학회지
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• 제22권5호
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• pp.403-406
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• 2006

An algorithm for detection of yellow sand aerosols has been developed with infrared bands. This algorithm is a hybrid algorithm that has used two methods combined. The first method used the differential absorption in brightness temperature difference between $11{\mu}m\;and\;12{\mu}m\;(BTD1)$. The radiation at $11{\mu}m$ is absorbed more than at $12{\mu}m$ when yellow sand is loaded in the atmosphere, whereas it will be the other way around when cloud is present. The second method uses the brightness temperature difference between $3.7{\mu}m\;and\;11{\mu}m(BTD2)$. This technique is sensitive to dust loading, which the BTD2 is enhanced by reflection of $3.7{\mu}m$ solar radiation. First the Principle Component Analysis (PCA), a form of eigenvector statistical analysis from the two methods, is performed and the aerosol pixel with the lowest 10% of the eigenvalue is eliminated. Then the aerosol index (AI) from the combination of BTD 1 and 2 is derived. We applied this method to Multi-functional Transport Satellite-l Replacement (MTSAT-1R) data and obtained that the derived AI showed remarkably good agreements with Ozone Mapping Instrument (OMI) AI and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth.