• Journal of the Korean Society of Environmental Restoration Technology
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• v.11 no.2
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• pp.1-9
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• 2008

The objective of the study is 10 know the relation of landslide occurrence with using TPI (Topographic Position Index) in the Pyungchang County. Total 659 landslide scars were detected from aerial photographs. To analyze TPI, 100m SN (Small-Neighborhood) TPI map, 500m LN (Large-Neighborhood) TPI map, and slope map were generated from the DEM (Digital Elevation Model) data which are made from 1 : 5,000 digital topographic map. 10 classes clustered by regular condition after overlapping each TPI maps and slope map. Through this process, we could make landform classification map. Because it is only to classify landform, 7 classes were finally regrouped by the slope angle information of landslide occurrence detected from aerial photography analysis. The accuracy of reclassified map is about 46%.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.432-436
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• 2010

Finding communities from network data including social networks can be done by clustering the nodes of the network as densely interconnected groups, where keeping interconnection between groups sparse. To exploit a clustering algorithm for community detection task, we need a well-defined similarity measure between network nodes. In this paper, we propose a new similarity measure named "Common Neighborhood Sub-graph density" and combine the similarity with affinity propagation, which is a recently devised clustering algorithm.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.1
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• pp.64-72
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• 1993

We propose "algorithm MA" which can detect PSF that is the functional fault in RAM. This algorithm based on the restricted PSF(or neighborhood PSF) and can detect not only conventional stuck-at and transition faults but also SNPSF, PNPSF and partially ANPSF. The time complexity of "algorithm MA" has 1536xP(P=no. of partition). We propose total BIST(built-in self test) scheme which implement this algorithm in memory chip.ithm in memory chip.

• The KIPS Transactions:PartB
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• v.8B no.4
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• pp.389-394
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• 2001

본 논문에서는 독립 성분 해석(Independent Component Analysis, ICA) 기법과 인근 평균 및 정규화를 이용한 영상 분류 방법을 제안하였다. ICA에 잡음을 주어 영상을 분류하였을 때, 잡음에 대한 강인성을 증가시키기 위하여, 제안된 인근 평균 및 정규화를 전처리로 적용하였다. 제안된 방법은 전처리 없이 ICA에 주성분 해석(Principal Component Analysis, PCA)을 이용한 것에 비해 잡음에 대한 강인성을 증가시키는 것을 모의 실험을 통하여 확인하였다.

• Proceeding of Spring/Autumn Annual Conference of KHA
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• 2003.11a
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• pp.121-126
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• 2003

The purpose of this study is to propose design guidelines for vitalizing neighborhood traditional markets within the city of Ulsan. To do practical study, we surveyed 9 markets in Ulsan. we analyzed results of the problems through investigating physical characteristics, its conditions and collected user's information by interview.

• Communications for Statistical Applications and Methods
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• v.18 no.2
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• pp.213-217
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• 2011

For the social network of n nodes, one might be interested in finding k nodes to disseminate the information as quickly as possible or to identify key nodes of high "local centrality". I propose two algorithms for determining k "local centers" of the network and work on a real case.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.287-296
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• 2003

There has been much research focused on collaborative filtering technique in Recommender System. However, these studies have shown the First-Rater Problem and the Sparsity Problem. The main purpose of this Paper is to solve these Problems. In this Paper, we suggest the user's predicting preference method using Bayesian estimated value and the associative user clustering for the recalculation of preference. In addition to this method, to complement a shortcoming, which doesn't regard the attribution of item, we use Representative Attribute-Neighborhood method that is used for the prediction when we find the similar neighborhood through extracting the representative attribution, which most affect the preference. We improved the efficiency by using the associative user's clustering analysis in order to calculate the preference of specific item within the cluster item vector to the collaborative filtering algorithm. Besides, for the problem of the Sparsity and First-Rater, through using Association Rule Hypergraph Partitioning algorithm associative users are clustered according to the genre. New users are classified into one of these genres by Naive Bayes classifier. In addition, in order to get the similarity value between users belonged to the classified genre and new users, and this paper allows the different estimated value to item which user evaluated through Naive Bayes learning. As applying the preference granted the estimated value to Pearson correlation coefficient, it can make the higher accuracy because the errors that cause the missing value come less. We evaluate our method on a large collaborative filtering database of user rating and it significantly outperforms previous proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3572-3590
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• 2014

Cellular automata (CA) based cryptosystem has been studied for almost three decades, yet most of previously reported researches focus on the symmetric key encryption schemes. Up to now, few CA based public key encryption scheme has been proposed. To fill the gap, in this paper, we propose a new public key encryption scheme based on layered cellular automata (LCA). Specifically, in the proposed scheme, based on the T-shaped neighborhood structure, we combine four one-dimensional reversible CAs (set as the private key) to form the transition rules of a two-dimension CA, where the two-dimension CA is set as the corresponding public key. Based on the hardness assumption of the Decisional Dependent CA problem in LCA, we formally prove the proposed scheme is indistinguishably secure against the chosen-plaintext attack (IND-CPA). In addition, we also use a numeric example to demonstrate its feasibility. Finally, analysis of key space and time efficiency are also carried out along with RSA-1024, and the simulation results demonstrate that our proposed scheme is more efficient.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.8
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• pp.656-661
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• 2013

In Guided Scrambling (GS) encoding for the holographic storage, after scrambling augmented source word into codeword, the best codeword satisfying modulation constraint is determined. Modulation constraints considered in this paper are strength which is the minimum number of transition between '0' and '1' in each row and column of codeword array and the symbol balancedness of codeword array. In this paper, we show that GS encoding procedure can be formulated as an integer programming model and develop a fast neighborhood search heuristic for fast computation of control bits. In the simulation, we compared the performance of heuristic algorithm with the integer programming model for various array and control bit size combinations.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.4
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• pp.61-66
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• 2009

This Paper describes a new program called DEM Generator need to process DEM from LiDAR data or digital map data. It is difficult to generate raster DEM from LiDAR mass point data sets and digital maps too large to fit into memory. The DEM Generator was designed to process DEM and shaded relief image of GeoTiff format in order of streaming meshes; I/O minimize tag, delaunay triangle, natural neighborhood or TIN, temporary files and grid. It is expected that we can be improved the precision of DEM and solved the time consuming problem of DEM generating of a wider area.