• Journal of Communications and Networks
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• v.7 no.4
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• pp.438-449
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• 2005

In this paper, we describes the information-theoretic approaches to sensor selection and sensor placement in sensor net­works for target localization and tracking. We have developed a sensor selection heuristic to activate the most informative candidate sensor for collaborative target localization and tracking. The fusion of the observation by the selected sensor with the prior target location distribution yields nearly the greatest reduction of the entropy of the expected posterior target location distribution. Our sensor selection heuristic is computationally less complex and thus more suitable to sensor networks with moderate computing power than the mutual information sensor selection criteria. We have also developed a method to compute the posterior target location distribution with the minimum entropy that could be achieved by the fusion of observations of the sensor network with a given deployment geometry. We have found that the covariance matrix of the posterior target location distribution with the minimum entropy is consistent with the Cramer-Rao lower bound (CRB) of the target location estimate. Using the minimum entropy of the posterior target location distribution, we have characterized the effect of the sensor placement geometry on the localization accuracy.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.629-638
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• 2005

In this paper, we consider two issues in collaborative filtering, which are closely related with the number of ratings of a user. First issue is the relationship between the number of ratings of a user and the performance of collaborative filtering. The relationship is investigated with two datasets, EachMovie and Movielens datasets. The number of ratings of a user is critical when the number of ratings is small, but after the number is over a certain threshold, its influence on recommendation performance becomes smaller. We also provide an explanation on the relationship between the number of ratings of a user and the performance in terms of neighborhood formations in collaborative filtering. The second issue is how to select an initial product list for new users for gaining user responses. We suggest and analyze 14 selection strategies which include popularity, favorite, clustering, genre, and entropy methods. Popularity methods are adequate for getting higher number of ratings from users, and favorite methods are good for higher average preference ratings of users.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.4 s.310
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• pp.50-57
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• 2006

In Electronic Commerce, the latest most of the personalized recommender systems have applied to the collaborative filtering technique. This method calculates the weight of similarity among users who have a similar preference degree in order to predict and recommend the item which hits to propensity of users. In this case, we commonly use Pearson Correlation Coefficient. However, this method is feasible to calculate a correlation if only there are the items that two users evaluated a preference degree in common. Accordingly, the accuracy of prediction falls. The weight of similarity can affect not only the case which predicts the item which hits to propensity of users, but also the performance of the personalized recommender system. In this study, we verify the improvement of the prediction accuracy through an experiment after observing the rule of the weight of similarity applying Vector similarity, Entropy, Inverse user frequency, and Default voting of Information Retrieval field. The result shows that the method combining the weight of similarity using the Entropy with Default voting got the most efficient performance.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.6
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• pp.63-74
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• 2005

In Electronic Commerce, the latest most of the personalized recommender systems have applied to the collaborative filtering technique. This method calculates the weight of similarity among users who have a similar preference degree in order to predict and recommend the item which hits to propensity of users. In this case, we commonly use Pearson Correlation Coefficient. However, this method is feasible to calculate a correlation if only there are the items that two users evaluated a preference degree in common. Accordingly, the accuracy of prediction falls. The weight of similarity can affect not only the case which predicts the item which hits to propensity of users, but also the performance of the personalized recommender system. In this study, we verify the improvement of the prediction accuracy through an experiment after observing the rule of the weight of similarity applying Vector similarity, Entropy, Inverse user frequency, and Default voting of Information Retrieval field. The result shows that the method combining the weight of similarity using the Entropy with Default voting got the most efficient performance.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.2
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• pp.74-80
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• 2013

In this study, a novel and flexible recommender system was developed, based on product taxonomy and usage patterns of users. The proposed system consists of the following four steps : (i) estimation of the product-preference matrix, (ii) construction of the product-preference matrix, (iii) estimation of the popularity and similarity levels for sought-after products, and (iv) recommendation of a products for the user. The product-preference matrix for each user is estimated through a linear combination of clicks, basket placements, and purchase statuses. Then the preference matrix of a particular genre is constructed by computing the ratios of the number of clicks, basket placements, and purchases of a product with respect to the total. The popularity and similarity levels of a user's clicked product are estimated with an entropy index. Based on this information, collaborative and content-based filtering is used to recommend a product to the user. To assess the effectiveness of the proposed approach, an empirical study was conducted by constructing an experimental e-commerce site. Our results clearly showed that the proposed hybrid method is superior to conventional methods.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.115-117
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• 2001

기존의 인터넷 웹사이트에서는 사용자의 만족을 극대화시키기 위하여 사용자별로 개인화 된 서비스를 제공하는 협력적 필터링 방식을 적용하고 있다. 협력적 필터링 기술은 사용자의 취향에 맞는 아이템을 예측하여 추천하며, 비슷한 선호도를 가진 다른 사용자들과의 상관관계를 구하기 위하여 일반적으로 피어슨 상관계수를 많이 이용한다. 그러나, 피어슨 상관계수를 이용한 방법은 사용자가 평가를 한 아이템이 있을 때에만 상관관계를 구할 수 있다는 단점과 예측의 정확성이 떨어진다는 단점을 가지고 있다. 따라서, 본 논문에서는 피어슨 상관관계 기반 예측 기법을 보완하여 보다 정확한 사용자 유사도를 구하는 방법을 제안한다. 제안된 방법에서는 사용자들을 대상으로 사용자가 평가를 한 아이템의 선호도를 사용해서 엔트로피를 적용하였고, 사용자가 선호도를 표시하지 않은 상품에 대해서는 Default Voting 방법을 이용하여 보다 정확한 헙력적 필터링 방식을 구현하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.31-34
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• 2009

유비쿼터스 환경에서 상황인지 능력을 가진 지능적 컴퓨팅 개체들 중 사람에게 의존적이지 않고 독립적으로 행동할 수 있는 개체는 유비쿼터스 로봇으로 볼 수 있다. 이러한 로봇은 최근 상호협력 함으로서 보다 최적화된 서비스를 제공하는 연구가 진행되고 있으며, 또한 다수의 로봇이 포함된 환경일 때는 특정한 작업을 수행하기 위하여 특정 로봇의 선별에 관한 연구가 진행 중이다. 본 논문에서는 유비쿼터스 환경에서 서로 다른 기능과 구조를 가지고 있는 지능형 로봇들이 협력하여 특수한 상황이나 임무를 그룹으로 대처할 수 있는 로봇 그룹핑을 설계하고 이를 구현한 결과에 대하여 기술한다. 다수의 로봇 중에서 특정 임무수행을 위한 로봇의 선별 알고리즘은 Entropy를 이용하여 결정 트리를 생성하였다. 또한 Grouping을 위한 Group Layer를 설계하여 구현하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3414-3434
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• 2016

Classical key generation is complicated to update and key distribution generally requires fixed infrastructures. In order to eliminate these restrictions researchers have focused much attention on physical-layer (PHY-layer) based key generation methods. In this paper, we present a PHY-layer fingerprints based fuzzy key generation scheme, which works to prevent primary user emulation (PUE) attacks and spectrum sensing data falsification (SSDF) attacks, with multi-node collaborative defense strategies. We also propose two algorithms, the EA algorithm and the TA algorithm, to defend against eavesdropping attacks and tampering attacks in mobile cognitive radio networks (CRNs). We give security analyses of these algorithms in both the spatial and temporal domains, and prove the upper bound of the entropy loss in theory. We present a simulation result based on a MIMO-OFDM communication system which shows that the channel response characteristics received by legitimates tend to be consistent and phase characteristics are much more robust for key generation in mobile CRNs. In addition, NIST statistical tests show that the generated key in our proposed approach is secure and reliable.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.607-609
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• 2005

온라인 및 오프라인 상에서 추천시스템에 대한 요구가 커지고 있으며 이에 관련해 않은 연구가 이루어지고 있다. 추천시스템은 마케팅 활용의 관점에서 목표 상품에 대한 반응 가능성이 높은 고객군을 추천하는 타겟마케팅 추천시스템과 고객 개인별로 구매 가능성이 높은 상품을 추천하는 개인화 추천시스템으로 구분할 수 있다. 지금까지의 추천시스템에 관한 연구는 대부분 개인화 추천시스템의 효율 향상에 목표를 두고 있다. 그러나 기업의 타겟마케팅에 대한 요구를 적절히 지원하지 못하고 있어 타겟마케팅에 대한 연구가 필요하다. 본 연구에서는 상품별 구매 패턴을 이용하는 프로파일 기반 추천 방법을 제안하고 이 방법과 기존의 협력적 추천 방법을 결합하여 특정 상품에 반응 가능성이 높은 고객을 추천하는 방법을 제안한다. 프로파일 기반 추천에서는 카이스퀘어 검정을 사용하여 상품별로 구매 패턴에 영향을 미치는 요인을 추출하고 이를 이용하여 특징 고객군을 선별하여 전체 고객군과 특징 고객과의 엔트로피(Entropy)의 변이 정도를 예측값으로 사용한다. 실험결과, 프로파일 기반 추천과 협력적 추천을 결합하여 추천하는 방법은 한 가지 방법을 사용할 때 보다 좋은 추천 정확도를 나타내었다.