• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.12
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• pp.3016-3022
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• 2013

In this paper, we propose a novel design and implementation method to detect musical plagiarism which can provide human experts evidences to decide plagiarism using cutting-edge information technologies and thereby can solve exhaustive disputes on cases of musical plagiarism when the cases are decided by human experts' emotional preferences. We first search digital music elements to analyze music source and examine how to use these in plagiarism analysis using IT techniques. Therefore we designed music plagiarism analysis system by using MusicString which is supported in JFugue and construct AST to manipulate music plagiarism analysis efficiently.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.2
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• pp.91-100
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• 2011

The Triple Data Encryption Algorithm (Triple DES) is an international standard of block cipher, which composed of two encryption processes and one decryption process of DES to increase security level. In this paper, we proposed a Differential Fault Analysis (DFA) attack to retrieve secret keys using reduction of last round execution for each DES process in the Triple DES by fault injections. From the simulation result for the proposed attack method, we could extract three 56-bit secret keys using exhaustive search attack for $2^{24}$ candidate keys which are refined from about 9 faulty-correct cipher text pairs. Using laser fault injection experiment, we also verified that the proposed DFA attack could be applied to a pure microprocessor ATmega 128 chip in which the Triple DES algorithm was implemented.

• Journal of Broadcast Engineering
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• v.13 no.6
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• pp.828-837
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• 2008

We propose a method to select representative views of single objects and classes of objects for 3D object retrieval and classification. Our method is based on projected 2D shapes, or views, of the 3D objects, where the representative views are selected by applying affinity propagation to cluster uniformly sampled views. Affinity propagation assigns prototypes to each cluster during the clustering process, thereby providing a natural criterion to select views. We recursively apply affinity propagation to the selected views of objects classified as single classes to obtain representative views of classes of objects. By enabling classification as well as retrieval, effective management of large scale databases for retrieval can be enhanced, since we can avoid exhaustive search over all objects by first classifying the object. We demonstrate the effectiveness of the proposed method for both retrieval and classification by experimental results based on the Princeton benchmark database [16].

• KIISE Transactions on Computing Practices
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• v.21 no.2
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• pp.126-131
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• 2015

As flash-based storage systems have been actively employed in large-scale servers and data centers, reliability has become an indispensable element. One promising technique for enhancing reliability is static wear-leveling, which distributes erase operations evenly among blocks so that the lifespan of storage systems can be prolonged. However, increasing the capacity makes the processing overhead of this technique non-trivial, mainly due to searching for blocks whose erase count would be minimum (or maximum) among all blocks. To reduce this overhead, we introduce a new randomized block selection method in static wear-leveling. Specifically, without exhaustive search, it chooses n blocks randomly and selects the maximal/minimal erased blocks among the chosen set. Our experimental results revealed that, when n is 2, the wear-leveling effects can be obtained, while for n beyond 4, the effect is close to that obtained from traditional static wear-leveling. For quantitative evaluation of the processing overhead, the scheme was actually implemented on an FPGA board, and overhead reduction of more than 3 times was observed. This implies that the proposed scheme performs as effectively as the traditional static wear-leveling while reducing overhead.