• Journal of KIISE:Software and Applications
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• v.33 no.12
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• pp.1090-1102
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• 2006

This paper suggests a new algorithm for detecting the plagiarism among a set of source codes, constrained to be functionally equivalent, such are submitted for a programming assignment or for a programming contest problem. The typical algorithms largely exploited up to now are based on Greedy-String Tiling, which seeks for a perfect match of substrings, and analysis of similarity between strings based on the local alignment of the two strings. This paper introduces a new method for detecting the similar interval of the given programs based on an adaptive similarity matrix, each entry of which is the logarithm of the probabilities of the keywords based on the frequencies of them in the given set of programs. We experimented this method using a set of programs submitted for more than 10 real programming contests. According to the experimental results, we can find several advantages of this method compared to the previous one which uses fixed similarity matrix(+1 for match, -1 for mismatch, -2 for gap) and also can find that the adaptive similarity matrix can be used for detecting various plagiarism cases.

• Journal of KIISE:Software and Applications
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• v.31 no.11
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• pp.1519-1533
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• 2004

When Web-based special retrieval systems for scientific field extremely restrict the expression of user's information request, the process of the information content analysis and that of the information acquisition become inconsistent. In this paper, we apply the fuzzy retrieval model to solve the high time complexity of the retrieval system by constructing a reduced term set for the term's relatively importance degree. Furthermore, we perform a cluster retrieval to reflect the user's Query exactly through the similarity relation matrix satisfying the characteristics of the fuzzy compatibility relation. We have proven the performance of a proposed re-ranking model based on the similarity union of the fuzzy retrieval model and the document cluster retrieval model.

• Journal of Advanced Navigation Technology
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• v.13 no.1
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• pp.151-155
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• 2009

We proposes a new method using the non-negative matrix factorization (NMF) and clustering method to extract the sentences for personalized document summarization. The proposed method uses clustering method for retrieving documents to extract sentences which are well reflected topics and sub-topics in document. Beside it can extract sentences with respect to query which are well reflected user interesting by using the inherent semantic features in document by NMF. The experimental results shows that the proposed method achieves better performance than other methods use the similarity and the NMF.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.9 no.6
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• pp.735-745
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• 1998

The method of moments has been widely used in the analysis of TM scattering problems. Recently, significant advances in the development of fast and efficient techniques for solving large problems have been reported. In such methods, iterative matrix solvers are preferred by virtue of their speed and low memory requirements. But for near resonant and strong multiple scattering problems, e.g., involving an aircraft engine inlet, a large number of iterations is required for convergence. In this paper, an efficient approximate inverse based preconditioner is used to reduce this number of iterations. By using the matrix partitioning method, the computational is used to reduce this number of iterations. By using the matrix partitioning method, the computational cost for obtaining the approximate inverse is reduced to O(N). We apply this preconditioner to an O(NlogN) algorithm, the multilevel fast multipole algorithm, for the aircraft engine inlet problem. The numerical results show the efficiency of this preconditioner.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2603-2608
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• 2009

Document clustering is an important method for document analysis and is used in many different information retrieval applications. This paper proposes a new document clustering model using the clustering method based NMF(non-negative matrix factorization) and refinement of documents in cluster by using coherence of cluster. The proposed method can improve the quality of document clustering because the re-assigned documents in cluster by using coherence of cluster based similarity between documents, the semantic feature matrix and the semantic variable matrix, which is used in document clustering, can represent an inherent structure of document set more well. The experimental results demonstrate appling the proposed method to document clustering methods achieves better performance than documents clustering methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.362-364
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• 2012

컬러 정보는 이미지 처리 시스템에서 이미지에 대한 중요한 특징값을 제공한다. 그러나 조명이나 객체의 물리적 특성으로 인하여 녹취된 이미지의 컬러값을 활용하기에는 많은 문제점이 따르게 된다. 이러한 문제점을 해결하기 위하여 컬러불변치에 대한 많은 연구가 있어 왔다. 이 연구에서는 컬러불변치와 병발행렬 특징값과의 관계에 대한 기초 데이터를 제공함으로써 위치기반 어플리케이션에서 이미지 유사도를 측정하는데 활용하고자 한다.

• The Journal of the Korea Contents Association
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• v.16 no.7
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• pp.667-680
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• 2016

As distributed computing platforms like Hadoop MapReduce have been developed, it is necessary to perform the conventional query processing techniques, which have been executed in a single computing machine, in distributed computing environments efficiently. Especially, studies on similarity join query processing in distributed computing environments have been done where similarity join means retrieving all data pairs with high similarity between given two data sets. But the existing similarity join query processing schemes for distributed computing environments have a problem of skewed computing load balance between clusters because they consider only the data transmission cost. In this paper, we propose Matrix-based Load-balancing Algorithm for efficient similarity join query processing in distributed computing environment. In order to uniform load balancing of clusters, the proposed algorithm estimates expected computing cost by using matrix and generates partitions based on the estimated cost. In addition, it can reduce computing loads by filtering out data which are not used in query processing in clusters. Finally, it is shown from our performance evaluation that the proposed algorithm is better on query processing performance than the existing one.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.4C
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• pp.440-446
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• 2007

This paper addresses a new representation of DFT matrix via the Jacket transform based on the element inverse processing. We simply represent the inverse of the DFT matrix following on the factorization way of the Jacket transform, and the results show that the inverse of DFT matrix is only simply related to its sparse matrix and the permutations. The decomposed DFT matrix via Jacket matrix has a strong geometric structure that exhibits a block modulating property. This means that the DFT matrix decomposed via the Jacket matrix can be interpreted as a block modulating process.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.3
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• pp.1330-1336
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• 2012

The Image retrieval has been study different approaches which are text-based, contents-based, area-based method and sub-image finding. The sub-image retrieval is to find a query image in the target one. In this paper, we propose a novel sub-image retrieval algorithm by Dot-Matrix method to be used in the bioinformatics. Dot-Matrix is a method to evaluate similarity between two sequences and we redefine the problem for retrieval of sub-image to the finding similarity of two images. For the approach, the 2 dimensional array of image converts a the vector which has gray-scale value. The 2 converted images align by dot-matrix and the result shows candidate sub-images. We used 10 images as target and 5 queries: duplicated, small scaled, and large scaled images included x-axes and y-axes scaled one for experiment.

• Journal of the Korea Society of Computer and Information
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• v.19 no.5
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• pp.61-69
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• 2014

Collaborative filtering recommendation creates similar item subset or similar user subset based on user preference about items and predict user preference to particular item by using them. Thus, if preference matrix has low density, reliability of recommendation will be sharply decreased. To solve these problems we suggest Hybrid Preference Prediction Technique Using Weighting based Data Reliability. Preference prediction is carried out by creating similar item subset and similar user subset and predicting user preference by each subset and merging each predictive value by weighting point applying model condition. According to this technique, we can increase accuracy of user preference prediction and implement recommendation system which can provide highly reliable recommendation when density of preference matrix is low. Efficiency of this system is verified by Mean Absolute Error. Proposed technique shows average 21.7% improvement than Hao Ji's technique when preference matrix sparsity is more than 84% through experiment.