• Korean Journal of Cognitive Science
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• v.23 no.3
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• pp.295-322
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• 2012

In this paper, we cluster similar word senses applying vector space model and HAL (Hyperspace Analog to Language). HAL measures corelation among words through a certain size of context (Lund and Burgess 1996). The similarity measurement between a word pair is cosine similarity based on the vector space model, which reduces distortion of space between high frequency words and low frequency words (Salton et al. 1975, Widdows 2004). We use PCA (Principal Component Analysis) and SVD (Singular Value Decomposition) to reduce a large amount of dimensions caused by similarity matrix. For sense similarity clustering, we adopt supervised and non-supervised learning methods. For non-supervised method, we use clustering. For supervised method, we use SVM (Support Vector Machine), Naive Bayes Classifier, and Maximum Entropy Method.

• Journal of Platform Technology
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• v.11 no.5
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• pp.23-36
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• 2023

This study aims to analyze the similarities and dissimilarities of various versions of <Heungbu-jeon> at both micro- and macro-levels using contents analysis techniques and the Hamming distance metrics. The 28 versions of <Heungbu-jeon> were segmented into 341 content units, and for each unit, the value of the content type was encoded. The dissimilarities between content types were compared among all versions by the content unit, respectively. The (dis-)similarities based on the content types of the 28 versions were aggregated and transformed into a distance matrix. The matrix was interpreted by multi-dimensional scaling, resulting into the two-dimensional coordinates. By visualizing the results by multi-dimensional scaling analysis, it was confirmed that the versions of <Heungbu-jeon> can be broadly divided into two groups. Hierarchical clustering and phylogenetic analysis were applied to analyze the clusters of the 28 versions, using the same distance matrix. The results showed that there are five clusters based on the micro-level analysis of (dis-)similarities within two major clusters. This study demonstrated the usefulness of applying digital humanities methods to encode the content of classical literary versions and analyze the data using clustering analysis techniques based on the (dis-)similarity of literary content.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.5-5
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• 2017

최근의 하천복원사업은 인위적으로 축소 또는 소실된 하천공간을 지형학적으로 되돌리기 위한 방향과 강이 가진 자연의 역동성의 복원에 주안점을 두고 있다. 최근 국내에서도 하천공간 확보라는 측면에서 구하천 구역을 복원하려는 시도가 이뤄지고 있고 대표적으로 청미천, 함평천, 황구지천 등에서 기존의 제방을 후퇴시키고 과거의 하천공간을 하천으로 되돌려 주었다. 그러나 이러한 시도는 구하도 또는 폐천부지 복원사업의 일환으로 시행되었으며, 복원된 공간규모가 크지 않아 사업의 효과를 크게 장담할 수 없는 현실적인 한계가 있다. 따라서 홍수에 안전하며 자연과 인간이 공존하는 터전으로의 공간복원, 그에 따른 하천생태계의 보전과 복원, 레저와 레크레이션이 가능한 친수공간 조성 등 하천기능의 강화와 융합을 종합적으로 고려한 다차원 하천공간 복원계획과 설계의 절차가 필요하다. 이를 위해서는 먼저 유역기반의 문제점과 그 원인에 대한 인식이 선행되어야 하고 그 해법으로 하천공간 확대의 필요성이 도출되어야 한다. 이후, 하천공간 복원 및 활용과 관련된 기본방향이 설정되며 세부 복원목표가 설정되어야 한다. 복원목적과 목표가 세워진 후, 복원대상지를 선정하고 공간 활용방안이 제시되면, 목표달성 여부 판단을 위한 효과분석이 필요하다. 만약, 기대효과를 달성할 수 없을 것으로 예상된다면 복원목표 재설정 및 복원대상지의 재선정이 필요할 것이다. 이와같이 하천공간 복원사업을 시행함에 있어 간결하고 단계적으로 접근하기 위해 본 연구에서는 다차원 하천공간 복원프레임워크를 개발하여 제시하였다. 제안된 하천공간 복원프레임워크를 통해 하천공간복원으로 인해 영향을 미칠 수 있고 또한 영향을 받을 수 있는 물리, 생물, 사회, 경제 등의 다양한 분야의 구조를 통합한 하나의 방법을 제시함으로서 임시방편 수준의 파괴된 하천복원사업에 변화를 주고 프레임워크를 통해 하천공간복원에 대한 응집력 있는 접근법을 적용하도록 촉진하여 복원사업에 참여하는 사람들 간에 공통의 유대감을 형성하는데 도움이 되고자 한다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.2
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• pp.103-110
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• 2011

It is necessary to accurate and efficient management for measured digital data from sensors in u-health system. It is not efficient that sensor network process input stream data of mass storage stored in database the same time. We propose to improve the processing performance of multidimensional stream data continuous incoming from multiple sensor. We propose process query based on sliding window for efficient input stream and found multiple query plan to Mjoin method and we reduce stored data using backpropagation algorithm. As a result, we obtained to efficient result about 18.3% reduction rate of database using 14,324 data sets.

• Journal of KIISE
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• v.42 no.2
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• pp.242-254
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• 2015

In this paper, we address the problem of improving the performance of multi-step k-NN search using multi-dimensional indexes. Due to information loss by lower-dimensional transformations, existing multi-step k-NN search solutions produce a large tolerance (i.e., a large search range), and thus, incur a large number of candidates, which are retrieved by a range query. Those many candidates lead to overwhelming I/O and CPU overheads in the postprocessing step. To overcome this problem, we propose two efficient solutions that improve the search performance by reducing the tolerance of a range query, and accordingly, reducing the number of candidates. First, we propose a tolerance reduction-based (approximate) solution that forcibly decreases the tolerance, which is determined by a k-NN query on the index, by the average ratio of high- and low-dimensional distances. Second, we propose a coefficient control-based (exact) solution that uses c k instead of k in a k-NN query to obtain a tigher tolerance and performs a range query using this tigher tolerance. Experimental results show that the proposed solutions significantly reduce the number of candidates, and accordingly, improve the search performance in comparison with the existing multi-step k-NN solution.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.244-244
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• 2011

기후변화의 영향으로 인한 자연재해의 대규모화는 정상성에 기반한 관측치를 대상으로 설계량을 결정하는 현행 방재기준의 한계를 보여주고 있다. 기후변화의 심각성과 기후변화 영향 고려 방법에 대한 사회적 요구를 충족하기 위하여 기후변화를 고려한 방재대책 수립 방안에 대한 연구가 필요하다. 따라서 본 연구에서는 미래 기후변화의 영향을 평가하고 수문사상의 발생 크기에 따라방재대책을 수립하는 방안을 제시하고자 한다. 우선, 대상유역인 굴포천 유역의 방재시설물을 조사하고 각 시설물의 한계능력을 평가한다. 그리고 기후변화 시나리오 및 기후모형 자료를 수집하여 대상유역으로 규모축소하고 도시유출모형인 SWMM 모형을 이용하여 미래 기후변화의 영향으로 발생 가능한 수문사상에 따른 홍수량을 산정한다. 또한 홍수피해규모를 다차원법으로 산정하고 이러한 피해를 줄이기 위한 방재시설물의 설치 및 개선에 의한 홍수피해 저감 편익을 비교하는 방식으로 경제성 분석에 기반한 방재대책을 수립하는 방안에 대하여 제시한다. 이러한 방법론은 향후 기후변화를 고려한 방재 대책을 마련하는데 있어 유용한 기초자료로 활용될 수 있을 것이다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.21-24
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• 2020

대용량의 데이터를 시각적 요소를 활용하여 눈으로 볼 수 있도록 하는 데이터 시각화에 대한 관심이 꾸준히 증가하고 있다. 데이터 시각화는 데이터의 전처리를 거쳐 차원 축소를 하여 데이터의 분포를 시각적으로 확인할 수 있다. 공개된 데이터 셋은 캐글(kaggle), 아마존 AWS 데이터셋(Amazon AWS datasets), UC 얼바인 머신러닝 저장소(UC irvine machine learning repository)등 다양하다. 본 논문에서는 UCI의 화학 가스의 데이터셋을 이용하여 딥러닝을 이용하여 다양한 환경 및 조건에서의 학습을 통한 데이터분석 및 학습 결과가 좋을 경우와 그렇지 않을 경우의 마지막 레이어의 특징 벡터를 시각화하여 직관적인 결과를 확인 가능 하도록 하였다. 또한 다차원 입력 데이터를 시각화 함으로써 시각화 된 결과가 딥러닝의 학습결과와 연관이 있는지를 확인 한다.

• Korean Journal of Cognitive Science
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• v.15 no.2
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• pp.23-31
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• 2004

Latent Semantic Analysis (LSA Landauer ＆ Dumais, 1997) is a technique to represent the meanings of words using co-occurrence information of words appearing in he same context, which is usually a sentence or a document. In LSA, a word is represented as a point in multidimensional space where each axis represents a context, and a word's meaning is determined by its frequency in each context. The space is reduced by singular value decomposition (SVD). The present study elaborates upon LSA for use of representation of ambiguous words. The proposed LSA applies rotation of axes in the document space which makes possible to interpret the meaning of un. A simulation study was conducted to illustrate the performance of LSA in representation of ambiguous words. In the simulation, first, the texts which contain an ambiguous word were extracted and LSA with rotation was performed. By comparing loading matrix, we categorized the texts according to meanings. The first meaning of an ambiguous wold was represented by LSA with the matrix excluding the vectors for the other meaning. The other meanings were also represented in the same way. The simulation showed that this way of representation of an ambiguous word can identify the meanings of the word. This result suggest that LSA with axis rotation can be applied to representation of ambiguous words. We discussed that the use of rotation makes it possible to represent multiple meanings of ambiguous words, and this technique can be applied in the area of web searching.

• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.105-114
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• 2006

In ubiquitous environment where various kinds of computers are embedded in persons, objects and environment and they are interconnected and can be used in my place as necessary, different types of data need to be exchanged between heterogeneous machines through home network. In the environment, the efficient processing, transmission and monitoring of image data are essential technologies. We need to make research not only on traditional image processing such as spatial and visual resolution, color expression and methods of measuring image quality but also on transmission rate on home network that has a limited bandwidth. The present study proposes a new motion vector estimation algorithm for transmitting, processing and controlling image data, which is the core part of contents in home network situation and, using algorithm, implements a real time monitoring system of multi dimensional images transmitted from multiple cameras. Image data of stereo cameras to be transmitted in different environment in angle, distance, etc. are preprocessed through reduction, magnification, shift or correction, and compressed and sent using the proposed metamorphosis hierarchical motion vector estimation algorithm for the correction of motion. The proposed algorithm adopts advantages and complements disadvantages of existing motion vector estimation algorithms such as whole range search, three stage search and hierarchical search, and estimates efficiently the motion of images with high variation of brightness using an atypical small size macro block. The proposed metamorphosis hierarchical motion vector estimation algorithm and implemented image systems can be utilized in various ways in ubiquitous environment.

• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.135-140
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• 2007

Feature selection is a preprocessing technique commonly used on high dimensional data. Feature selection studies how to select a subset or list of attributes that are used to construct models describing data. Feature selection methods attempt to explore data's intrinsic properties by employing statistics or information theory. The recent developments have involved approaches like correlation method, dimensionality reduction and mutual information technique. This feature selection have become the focus of much research in areas of applications with massive and complex data sets. In this paper, we provide a feature selection method considering data characteristics and generalization capability. It provides a computational approach for feature selection based on fuzzy cluster analysis of its attribute values and its performance measures. And we apply it to the system for classifying computer virus and compared with heuristic method using the contrast concept. Experimental result shows the proposed approach can give a feature ranking, select the features, and improve the system performance.