• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.559-561
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• 2004

본 논문에서는 다의어의 현실적인 의미 분포의 결정에 대해 이야기 하고자 한다. 수동으로 구축한 의미체계인 사전이나 시소러스들은 그 의미구분의 경개가 모호하고 비현실적인 부분이 많아서 언어처리 시스템의 적용에 문제점으로 지적되고 있다. 그러므로, 본 연구에서는 대용량 코퍼스에서 추출한 공기정보와 자동 군집화 방법들을 사용하여 실질적인 다의어의 의미 경계를 발견하는 방법을 제안하였다. 수동 구축된 사전과 코퍼스 기반 사전의 다의어 의미 분포와 비교해 본 결과, 본 논문에서 제안한 방법의 결과가 코퍼스 기반 사전의 의미 분포와 매우 유사한 결과를 보이는 것을 확인할 수 있었다.

• Asia Marketing Journal
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• v.11 no.4
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• pp.21-48
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• 2010

This article analyzes film performances in the Korean movie market utilizing three-stage models that incorporate available information in three different stages of the movie life cycle, i.e., at the time of its release, at the end of the first week, and at the end of its life cycle. Based on the premise that the performance of a movie is affected principally by factors of scale, evaluation, and competition, we attempted to ascertain the effects on these factors on performances, and how they differ in different stages. Also, by analyzing domestic and foreign movies released in Korea separately, we were able to compare the different effects of the three factors on the performances of the two categories of movies. Additionally, our movie performance models incorporated herding behavior among the customers. Our results demonstrate that herding is prominently observed after the first week only for domestic movies. In general, the scale factor has been shown to be most important for movie performances in all stages. For foreign films, it is particularly critical for the first week and total performances. Whereas the evaluation factor influences domestic film performance more strongly at the screen choice stage, it affects the performance of foreign films more strongly in the later stages of the life cycle. As compared to foreign films, domestic film performance appears to be more sensitive to the competition factor. We also discuss the effects of covariates such as genre and symbolicity on movie performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.224-229
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• 2009

When swarm robots exist in the same workspace, first we have to decide robots in order to accomplish some tasks. There have been a lot of works that research how to control robots in cooperation. The interest in using swarm robot systems is due to their unique characteristics such as increasing the adaptability and the flexibility of mission execution. When an invader is discovered, swarm robots have to enclose a invader through a variety of path, expecting invader's move, in order to effective enclose. In this paper, we propose an effective swarm robots enclosing and distributed moving algorithm in a two dimensional map.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.12
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• pp.2937-2943
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• 2013

This paper introduces a real-time flocking simulation framework through radial basis function(RBF). The proposed framework first divides the entire environment into a grid structure and then assign a vector per each cell. These vectors are automatically calculated by using RBF function, which is parameterized from user-input control lines. Once the construction of vector field is done, then, flocks determine their path by following the vector field flow. The collision with static obstacles are modeled as a repulsive vector field, which is ultimately over-layed on the existing vector field and the inter-individual collision is also handled through fast lattice-bin method.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.9
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• pp.965-970
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• 2014

In an effort to improve the convergence speed in k-means clustering, we introduce the notion of the birds' movement in a flock. Their motion is characterized by the observation that each bird runs after his nearest neighbor. We utilize this feature in clustering procedure. Once the class of a vector is determined, then a number of vectors in the vicinity of it are assigned to the same class. Experiments have shown that the required number of iterations for termination is significantly lower in the proposed method than in the conventional one. Furthermore, the time of calculation per iteration is more than 5% shorter in the proposed case. The quality of the clustering, as determined from the total accumulated distance between the vector and its centroid vector, was found to be practically the same. It might be phrased that we may acquire practically the same clustering result with shorter computational time.

• Journal of Digital Convergence
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• v.12 no.10
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• pp.243-252
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• 2014

Many researches about the effect on politics, economics and Sociocultural phenomenon using the social media are in progress. Authors utilized NAVER Trend most famous web browsing service in korea, NAVER Blog social media, NAVER Cafe service and Open Data(API) and also used temperature, humidity index data of Korea Meteorological Administration. This study analyzed a change of the public's emotion in korea using Cluster analysis of vocabulary of taste among its of feelings and senses. K-means clustering was followed by decision of the number of groups which was used Chi-square goodness of fit test and ward analysis. Eight groups was made and it represented sensitive vocabulary. By Discriminant analysis, eight groups decided by Cluster analysis has 98.9% accuracy. The change of the public's emotion has capability to predict people's activity so they can share sensibility and a bond of sympathy developed between them.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.6B
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• pp.888-894
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• 2010

For the indoor positioning, wireless fingerprinting is most favorable because fingerprinting is most accurate among the technique for wireless network based indoor positioning which does not require any special equipments dedicated for positioning. The deployment of a fingerprinting method consists of off-line phase and on-line phase and more efficient and accurate methods have been studied. This paper proposes a bayesian algorithm for wireless fingerprinting and indoor location determination using fuzzy clustering with bayesian learning as a statistical learning theory.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.1166-1168
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• 2019

본 논문은 엣지컴퓨팅 환경에서 딥러닝기반 추천모델을 이용한 지능형 디지털 사이니지 플랫폼을 제안한다. 제안하는 플랫폼은 서버와 엣지로 구성되어 있다. 서버는 데이터를 관리하고, 광고추천 모델을 학습시키며, 엣지는 학습된 광고추천 모델을 이용하여 실시간으로 광고될 상품을 결정한다. 광고추천 모델은 상품을 선별하는 단계와 구매확률을 예측하는 단계로 구성되어 있다. 선별단계에서는 DNN에 벡터화된 사용자 기본정보와 상품 메타데이터를 입력하여 구매할 만한 상품을 도출한다. 최종적으로 군집의 예측된 구매확률을 이용하여 가장 적합한 광고를 선정한다. 제안하는 시스템은 서버와 통신하지 않고 엣지에서 학습된 모델로 광고를 결정한다. 이를 다수의 사용자에게 즉각적인 반응을 필요로 하는 디지털 사이니지에 적용했다.

• Journal of the Korea Society of Computer and Information
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• v.12 no.6
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• pp.167-175
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• 2007

Fuzzy logic is used to represent qualitative knowledge and provides interpretability to a controlling system model in bioinformatics. This paper focuses on a bioinformatics data classification which is an important bioinformatics application. This paper reviews the two traditional controlling system models The sequence-based threshold controller have problems of optimal range decision for threshold readjustment and long processing time for optimal threshold induction. And the binary-based threshold controller does not guarantee for early system stability in the GPCR data classification for optimal threshold induction. To solve these problems, we proposes a fuzzy-based threshold controller for ART1 clustering in GPCR classification. We implement the proposed method and measure processing time by changing an induction recognition success rate and a classification threshold value. And, we compares the proposed method with the sequence-based threshold controller and the binary-based threshold controller The fuzzy-based threshold controller continuously readjusts threshold values with membership function of the previous recognition success rate. The fuzzy-based threshold controller keeps system stability and improves classification system efficiency in GPCR classification.

• The Journal of the Korea Contents Association
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• v.12 no.12
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• pp.733-741
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• 2012

In recent, many consumers visit the online shopping malls or price comparison sites to collect the information on the product category that they are interested in. However, the volumes of the data provided by such web sites are often too enormous, and significant number of consumers have trouble in making purchase decision based on the plethora of products and sellers. In this context, modern online shopping agents need to process the retrieved information in more intelligent way before providing them to the users. This paper proposes a novel approach for identifying the main price ranges hidden in a single product category. To this end, the price of an item in the category is represented as a row vector and k-means clustering analysis is applied to the price vectors to produce the clusters that consists of the product items with similar price vectors. Then, the main price ranges of the product category can be identified from the result of clustering analysis. In general, the price is one of the most important factors in the consumers' purchase decision, and the identified main price ranges will be helpful for the online shoppers to find appropriate items effectively.