• Journal of applied mathematics & informatics
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• v.6 no.3
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• pp.727-734
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• 1999

Let G be a connected graph of n vertices. The problem of finding a depth-first spanning tree of G is to find a connected subgraph of G with the n vertices and n-1 edges by depth-first-search. in this paper we propose an O(n) time algorithm to solve this problem on permutation graphs.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.15 no.2
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• pp.83-96
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• 2011

This work proposes a binomial method for pricing the cliquet options, which provide a guaranteed minimum annual return. The proposed binomial tree algorithm simplifies the standard binomial approach, which is problematic for cliquet options in the computational point of view, or other recent methods, which may be of intricate algorithm or require pre- or post-processing computations. Our method is simple, efficient and reliable in a Black-Scholes framework with constant interest rates and volatilities.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.233-241
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• 2014

This paper suggests a fast minimum spanning tree algorithm which simplify the original graph to 2-edge connected graph, and using the cycling property. Borůvka algorithm firstly gets the partial spanning tree using cycle property for one-edge connected graph that selects the only one minimum weighted edge (e) per vertex (v). Additionally, that selects minimum weighted edge between partial spanning trees using cut property. Kruskal algorithm uses cut property for ascending ordered of all edges. Reverse-delete algorithm uses cycle property for descending ordered of all edges. Borůvka and Kruskal algorithms always perform |e| times for all edges. The proposed algorithm obtains 2-edge connected graph that selects 2 minimum weighted edges for each vertex firstly. Secondly, we use cycle property for 2-edges connected graph, and stop the algorithm until |e|=|v|-1 For actual 10 benchmark data, The proposed algorithm can be get the minimum spanning trees. Also, this algorithm reduces 60% of the trial number than Borůvka, Kruskal and Reverse-delete algorithms.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.6
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• pp.102-110
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• 1996

A new design scheme of a binary decision tree is proposed. In this scheme a binary decision tree is constructed by using genetic algorithm and FCM algorithm. At each node optimal or near-optimal feature subset is selected which optimizes fitness function in genetic algorithm. The fitness function is inversely proportional to classification error, balance between cluster, number of feature used. The binary strings in genetic algorithm determine the feature subset and classification results - error, balance - form fuzzy partition matrix affect reproduction of next genratin. The proposed design scheme is applied to the tire tread patterns and handwriteen alphabetic characters. Experimental results show the usefulness of the proposed scheme.

• Journal of Korean Society of Transportation
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• v.23 no.8 s.86
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• pp.113-128
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• 2005

We propose a modified version of 'a Lazy Version of Eppstein's k shortest paths Algorithm(LVEA)' which can find the k shortest paths in total time O(m+ n log n+ K log K) in the worst-case. The algorithm we propose, since the Link repeated paths are all eliminated when enumerating k shortest paths, is No link repeated paths algorithm that is suitable in a transportation network.

• Journal of the Korean Statistical Society
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• v.21 no.2
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• pp.201-207
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• 1992

The algorithm for obtaining the isotonic regression in simple tree order, the most basic and simplest model next to the simple order, is considered. We propose to call it "Pool-the-Maximum-Violators" algorithm (PMVA) in conjunction with the "Pool-Adjacent-Violators" algorithm (PAVA) in the simple order. The dual problem of obtaining the isotonic regression in simple tree order is our main concern. An intuitively appealing relation between the primal and the dual problems is demonstrated. The interesting difference is that in simple order the required number of pooling is at least the number of initial violating pairs and any path leads to the solution, whereas in the simple tree order it is at most the number of initial violators and there is only one advisable path although there may be some others leading to the same solution.o the same solution.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.11
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• pp.1027-1033
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• 2012

Semantic-based information retrieval techniques understand the meanings of the concepts that users specify in their queries, but the traditional semantic matching methods based on the ontology tree have three weaknesses which may lead to many false matches, causing the falling precision. In order to improve the matching precision and the recall of the information retrieval, this paper proposes a multi-agent improved semantic similarity matching algorithm based on the ontology tree, which can avoid the considerable computation redundancies and mismatching during the entire matching process. The results of the experiments performed on our algorithm show improvements in precision and recall compared with the information retrieval techniques based on the traditional semantic similarity matching methods.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.1
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• pp.25-33
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• 1998

This paepr presents an algorithm for searching an object in a fast way which contains a continuous moving object in multi-dimensional spatical databases. This algorithm improves the search method of R-tree for the case that a target object is continuously moving in a spatial database. It starts the searching from the current node instead of the root of R-tree. Thus, the algorithm will find the target object from the entries of current node or sibling nodes in the most cases. The performance analysis shows that it is more efficient than the existing algorithm for R-tree when search windows or target objects are continuously moving.

• Speech Sciences
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• v.10 no.1
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• pp.71-84
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• 2003

In the acoustic modeling for large vocabulary speech recognition, a sparse data problem caused by a huge number of context-dependent (CD) models usually leads the estimated models to being unreliable. In this paper, we develop a new clustering method based on the C45 decision-tree learning algorithm that effectively encapsulates the CD modeling. The proposed scheme essentially constructs a supervised decision rule and applies over the pre-clustered triphones using the C45 algorithm, which is known to effectively search through the attributes of the training instances and extract the attribute that best separates the given examples. In particular, the data driven method is used as a clustering algorithm while its result is used as the learning target of the C45 algorithm. This scheme has been shown to be effective particularly over the database of low unknown-context ratio in terms of recognition performance. For speaker-independent, task-independent continuous speech recognition task, the proposed method reduced the percent accuracy WER by 3.93% compared to the existing rule-based methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.12
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• pp.756-763
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• 2014

In this paper, we introduce a new sparse signal recovery algorithm referred to as the matching pursuit with greedy tree search (GTMP). The tree search in our proposed method is implemented to minimize the cost function to improve the recovery performance of sparse signals. In addition, a pruning strategy is employed to each node of the tree for efficient implementation. In our performance guarantee analysis, we provide the condition that ensures the exact identification of the nonzero locations. Through empirical simulations, we show that GTMP is effective for sparse signal reconstruction and outperforms conventional sparse recovery algorithms.