• Proceedings of the Korea Contents Association Conference
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• 2010.05a
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• pp.13-15
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• 2010

이동객체의 공간 데이터를 색인하기 위해 검색성능이 뛰어난 R-tree구조가 많이 활용된다. 최근 R-tree를 B+-tree처럼 인접한 단말노드 간의 연결을 통해 질의 처리를 수행하는 ISR-tree와 ISG-index가 제안되었다. 이 기법들은 MBR (Minimum Boundary Rectangle) 간의 인접한 이웃 노드를 결정하기 위해 보르노이 다이어그램(Voronoi Diagram)을 이용한다. MBR을 대상으로 하는 보르노이 다이어그램은 매우 복잡한 연산과정을 거친다. 본 논문에서는 점을 대상으로 하는 보르노이 다이어그램 연산을 활용한 인접한 이웃 MBR을 연산하는 기법을 제안한다. 각 MBR의 꼭지점들을 기준으로 보르노이 다이어그램을 만들 경우, 인접한 MBR의 꼭지점들의 보르노이 셀이 항상 인접한 것을 알아내었고, 이를 활용한다. 제안하는 기법의 우수성을 증명하기 위해 기존의 기법과 비교하여 성능평가를 수행하였다.

• KIISE Transactions on Computing Practices
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• v.21 no.4
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• pp.327-332
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• 2015

Constructing a k-nearest neighbor (k-NN) graph is a primitive operation in the field of recommender systems, information retrieval, data mining and machine learning. Although there have been many algorithms proposed for constructing a k-NN graph, either the existing approaches cannot be used for various types of similarity measures, or the performance of the approaches is decreased as the number of nodes or dimensions increases. In this paper, we present a novel algorithm for k-NN graph construction based on "balanced" canopy clustering. The experimental results show that irrespective of the number of nodes or dimensions, our algorithm is at least five times faster than the brute-force approach while retaining an accuracy of approximately 92%.

• Journal of Korea Multimedia Society
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• v.14 no.11
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• pp.1467-1477
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• 2011

This paper proposes a HPPS(Hybrid Preference Prediction System) design using the analysis of user profile and of the similarity among users precisely to predict the preference for custom-tailored service. Contrary to the existing NBCFA(Neighborhood Based Collaborative Filtering Algorithm), this paper is designed using these following rules. First, if there is no neighbor's commodity rating value in a preference prediction formula, this formula uses the rating average value for a commodity. Second, this formula reflects the weighting value through the analysis of a user's characteristics. Finally, when the nearest neighbor is selected, we consider the similarity, the commodity rating, and the rating frequency. Therefore, the first and second preference prediction formula made HPPS improve the precision by 97.24%, and the nearest neighbor selection method made HPPS improve the precision by 75%, compared with the existing NBCFA.

• Journal of the Korea Computer Graphics Society
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• v.6 no.3
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• pp.25-34
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• 2000

본 논문에서는 점진적 메쉬의 보다 엄밀한 의미에서의 선택적 세분화 방법을 제안한다. 기존의 선택적 세분화 방법은 정점분할 및 에지붕괴 연산이 수행되기 위해서는 현재의 1-고리 이웃 상황이 점진적 메쉬 분석 단계에 기억해 놓은 1-고리 이웃과 같을 때만 올바로 동작하도록 되어 있는 증가적 방법이다. 이러한 증가적 방법은 메쉬의 부분적 해상도 변경을 하게 되면 인접한 부분의 해상도가 그 부분의 해상도를 좇아가게 되는 단점을 가지고 있다. 본 논문에서 제안하는 방법은 점진적 메쉬 표현이 가지는 정점의 계층적 구획화 성질에 기반한 것으로, 원하는 메쉬의 부분에 대해 해상도를 변경할 때, 인접 부분의 정점분할 및 에지붕괴 연산을 초래하지 않아 보다 엄밀한 의미에서의 점진적 메쉬의 선택적 세분화를 수행할 수 있다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.4
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• pp.817-827
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• 2015

In this modern society, people are having a close relationship with smartphone. This makes easier for hackers to gain the user's information by installing the malware in the user's smartphone without the user's authority. This kind of action are threats to the user's privacy. The malware characteristics are different to the general applications. It requires the user's authority. In this paper, we proposed a new classification method of user requirements method by each application using the Principle Component Analysis(PCA) and Probabilistic K-Nearest Neighbor(PKNN) methods. The combination of those method outputs the improved result to classify between malware and general applications. By using the K-fold Cross Validation, the measurement precision of PKNN is improved compare to the previous K-Nearest Neighbor(KNN). The classification which difficult to solve by KNN also can be solve by PKNN with optimizing the discovering the parameter k and ${\beta}$. Also the sample that has being use in this experiment is based on the Contagio.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.209-216
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• 2005

In wireless sensor networks, an estimation method is proposed for distances between nodes within two hops. The method uses only proximity information of nodes without physiccal distance measurements. It drastically improves the performance of localization algorithms based on Proximity information. In addition, it is the first method that estimates distances between nodes exactly in two hops. The distances are estimated from the number of common neighbors under an assumption that the number of common neighbors is proportional to the intersection of two unit disks centered at the two nodes. Simulation analysis shows that the estimation error is roughly from 10 to 20 percent of real distances. Meanwhile, the number of messages required by a distributed algorithm realizing this method is only two times the number of nodes.

• The KIPS Transactions:PartB
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• v.13B no.6 s.109
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• pp.625-632
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• 2006

Collaborative filtering is a technology that aims at teaming predictive models of user preferences. Collaborative filtering systems have succeeded in Ecommerce market but they have shortcomings of high dimensionality and sparsity. In this paper we propose the nearest neighbor collaborative filtering method using non-negative matrix factorization(NNMF). We replace the missing values in the user-item matrix by using the user variance coefficient method as preprocessing for matrix decomposition and apply non-negative factorization to the matrix. The positive decomposition method using the non-negative decomposition represents users as semantic vectors and classifies the users into groups based on semantic relations. We compute the similarity between users by using vector similarity and selects the nearest neighbors based on the similarity. We predict the missing values of items that didn't rate by a new user based on the values that the nearest neighbors rated items.

• The KIPS Transactions:PartB
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• v.12B no.1 s.97
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• pp.31-38
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• 2005

This paper proposes an object-based image retrieval scheme using color adjacency and clustering method. Color adjacency features in boundary regions are utilized to extract candidate blocks of interest from image database and a clustering method is used to extract the regions of interest(ROI) from candidate blocks of interest. To measure the similarity between the query and database images, the histogram intersection technique is used. The color pair information used in the proposed method is robust against translation, rotation, and scaling. Consequently, experimental results have shown that the proposed scheme is superior to existing methods in terms of ANMRR.

• Journal of Digital Convergence
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• v.12 no.7
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• pp.247-252
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• 2014

Traffic network is composed of passing vehicls, delayed vehicles, traffic situation which is traffic incomes of traffic interfacing system. Traffic green time light is concluded by inside input factor, that is green light cycle, yellow light cycle, led light cycle, which light cycle is sensor inputs. That light cycle is converted to traffic phase composed of passing peoples and delayed vehicles, whose intervals is concluding of traffic network factors composed of consumptiom power factors, delayed time situation, occupying sensor nodes. This is very important sector,because of much poor traffic situation.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.373-375
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• 2005

본 논문은 무선 센서 네트워크상에서 클러스터 2중 분할 알고리즘을 제안한다. 본 알고리즘은 센서 네트워크에서 클러스터 방식 프로토콜이 데이터를 헤드에서 수집하고 집약하여 전송한다는 특성과 이웃한 노드간 유사한 데이터를 가진다는 특성을 이용한다. 인접한 이웃노드가 쌍을 형성하여 교대로 센싱하는 논리적인 클러스터 2중 분할을 하고 헤드도 2개가 존재하여 교대로 데이터 전송을 함으로써 에너지 효율을 높인다.