• East Asian mathematical journal
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• v.25 no.2
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• pp.215-220
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• 2009

In this paper, we investigate the generating strings and the number of 2-(edge)connected bipartite Steinhaus graphs.

• Journal of KIISE
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• v.43 no.11
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• pp.1275-1280
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• 2016

Existing techniques for string similarity search first generate a set of candidate strings and then verify the candidates. The efficiency of string similarity search is highly dependent on candidate generation methods. State of the art techniques select fixed length q-grams from a query string and generate candidates using inverted lists of the selected q-grams. In this paper, we propose a technique to generate candidates using variable length grams of a query string and develop a dynamic programming algorithm that selects an optimal combination of variable length grams from a query string. Experimental results show that the proposed technique improves the performance of string similarity search compared with the existing techniques.

• The Journal of Korea Robotics Society
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• v.5 no.4
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• pp.318-325
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• 2010

This paper proposes a new string controller for puppet which is optimized in terms of the number of motors and its size. To optimize the number of motors needed for generating the essential motions of puppet, the motion of bending a leg is implemented by one string and the walking motion by two legs is implemented by one motor. To minimize the space needed for the controller when generating the essential motions of puppet, cylindrical and articulated joints are used in the controller. The proposed controller is actually implemented to perform various puppet shows and it has been proved that the size of the controller is small enough for two puppets to stand close to shake hands and it is fast enough to simulate fast dance motions.

• Kyungpook Mathematical Journal
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• v.43 no.4
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• pp.567-577
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• 2003

In this paper, we classify the Steinhaus graphs with minimum degree two.

• Genomics & Informatics
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• v.10 no.4
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• pp.266-270
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• 2012

Grammatical inference methods are expected to find grammatical structures hidden in biological sequences. One hopes that studies of grammar serve as an appropriate tool for theory formation. Thus, we have developed JSequitur for automatically generating the grammatical structure of biological sequences in an inference framework of string compression algorithms. Our original motivation was to find any grammatical traits of several cancer genes that can be detected by string compression algorithms. Through this research, we could not find any meaningful unique traits of the cancer genes yet, but we could observe some interesting traits in regards to the relationship among gene length, similarity of sequences, the patterns of the generated grammar, and compression rate.

• East Asian mathematical journal
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• v.26 no.3
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• pp.423-427
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• 2010

In this paper, we give an upper bound for dominations of Steinhaus graphs, and the domination numbers of the bipartite Steinhaus graphs. Also, we give an upper bound for Nordhaus-Gaddum type result for the bipartite Steinhaus graphs.

• Kyungpook Mathematical Journal
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• v.48 no.2
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• pp.331-335
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• 2008

In this paper, we investigate the number of ones in rows of Pascal's Rectangle. Using these results, we determine the existence of regular bipartite Steinhaus graphs. Also, we give an upper bound for the minimum degree of bipartite Steinhaus graphs.

• The Transactions of the Korea Information Processing Society
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• v.3 no.7
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• pp.1659-1668
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• 1996

Graph grammars generate graph languages while string grammars generate string languages which are the subset of graph languages. One of the most successful graph grammars models is the NLC grammars, which gen-erate graphs by replacing a node by a node by a graph through node labels. For grammars generating hypergraphs which are the superset of graphs, there are CFHG grammars, which replace a hyperedge by a hypergraph through their preidentified gluing points, an extension of CFHG grammars called HH grammars, which replace a handle by a hypergraph through the rewriting mechanism that can also duplicate or delete the hyperedges surrounding the replaced handle, and finally HRNCE grammars, which replace a handle by a hypergraph through an eNCE way of rewriting, In this paper, we compare the language-generating power of HRNCE grammars with that a graph grammars mentioned above by comparing graph langrages generated by them, respecti vely.

• Speech Sciences
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• v.15 no.4
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• pp.67-81
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• 2008

The accuracy rate of P2G (Phoneme-to-Grapheme) is one of the important factors determining the quality of unlimited voice recognition (VR) systems. Few studies were, however, conducted to reduce ambiguities of a phoneme string which can be segmented into a variety of different linguistic units (i.e. morphemes, words, eo-jeols), thus be transformed into more than one grapheme string. This paper is a preliminary research for building a large knowledge base of those homonymic & heterographic units(HHUs), which will provide unlimited Korean VR systems with more accurate P2G information. This paper analyzes 2 main factors generating HHUs: (1) boundary determination of the prosodic unit; (2) its segmentation into linguistic units. In this paper, linguistic characteristics determining variable boundaries of a prosodic unit are investigated, and the ambiguity types of HHUs are classified in accordance with their morphological and syntactic structures as well as with the phonological rules governing them.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.5
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• pp.277-286
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• 2019

In this paper, we propose an efficient hyperplane generation technique to classify human activity from combination of events and sequence information obtained from multiple-event sensors. By generating hyperplane efficiently, our machine learning algorithm classify with less memory and run time than the LSVM (Linear Support Vector Machine) for embedded system. Because the fact that light weight and high speed algorithm is one of the most critical issue in the IoT, the study can be applied to smart home to predict human activity and provide related services. Our approach is based on reducing numbers of hyperplanes and utilizing robust string comparing algorithm. The proposed method results in reduction of memory consumption compared to the conventional ML (Machine Learning) algorithms; 252 times to LSVM and 34,033 times to LSTM (Long Short-Term Memory), although accuracy is decreased slightly. Thus our method showed outstanding performance on accuracy per hyperplane; 240 times to LSVM and 30,520 times to LSTM. The binarized image is then divided into groups, where each groups are converted to binary number, in order to reduce the number of comparison done in runtime process. The binary numbers are then converted to string. The test data is evaluated by converting to string and measuring similarity between hyperplanes using Levenshtein algorithm, which is a robust dynamic string comparing algorithm. This technique reduces runtime and enables the proposed algorithm to become 27% faster than LSVM, and 90% faster than LSTM.