• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.138-144
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• 2011

L(2, 1)-labeling problem of a graph G = (V, E) is a problem to find an efficient way to distribute radio frequencies to various wireless equipments in wireless networks. In this work, we suggest a Simulated Annealing algorithm that can be applied to the L(2, 1)-labeling problem. By applying the suggested algorithm to various graphs we will try to show the efficiency of our algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.999-1004
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• 1989

This paper discusses a new method of recognizing patterns employing consistent labeling. A consistent labeling problem is a generalized expression of constraint satisfaction problems. When a pattern is recognized by pattern matching, the matching between a reference pattern and an acquired pattern resolves itself into finding correspondence between the pixels on the former and those on the latter. This can be expressed as a consistent labeling problem. Pattern association, a variation of pattern recognition, is also described employing consistent labeling. The proposed technique is supported by experimental results, yet further studies need to be done before its practical use.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.131-136
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• 2008

L(2,1)-labeling of a graph G is a function f: V(G) $\rightarrow$ {0, 1, 2, ...} such that $|f(u)\;-\;f(\upsilon)|\;{\geq}\;2$ when d(u, v) = 1 and $|f(u)\;-\;f(\upsilon)|\;{\geq}\;1$ when d(u, $\upsilon$) = 2. L(2,1)-labeling number of G, denoted by ${\lambda}(G)$, is the smallest number m such that G has an L(2,1)-labeling with no label greater than m. Since this problem has been proved to be NP-complete, in this article, we develop genetic algorithms for L(2,1)-labeling problem and show that the suggested genetic algorithm peforms very efficiently by applying the algorithms to the class of graphs with known optimum values.

• Proceedings of the IEEK Conference
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• 2004.06a
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• pp.167-170
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• 2004

We present a novel graph labeling problem called S-edge labeling. The constraint in this labeling is placed on the allowable edge label which is the difference between the labels of endvertices of an edge. Each edge label should be ${ a_n / a_n = 4 a_{n-l}+l,\;a_{n-1}=0}$. We show that every binomial tree is possible S-edge labeling by giving labeling schems to them. The labelings on the binomial trees are applied to their embedings into interconnection networks.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.731-734
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• 2000

We present a novel graph labeling problem called Fibonacci edge labeling. The constraint in this labeling is placed on the allowable edge label which is the difference between the labels of endvertices of an edge. Each edge label should be (3m+2)-th Fibonacci numbers. We show that every Fibonacci tree can be labeled Fibonacci edge labeling. The labelings on the Fibonacci trees are applied to their embeddings into Fibonacci Circulants.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.4
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• pp.248-254
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• 1993

This paper proposes the labeling method for solving bin-picking problem in robot vision. It has the processing steps such as image thresholding, region labeling, and moment computation. To determine a target object from object, the modified labeling method is used to. The moment concept applied to determine the position and orientation of target object. Finally, some experiment result are illustrated and compared with the results of conventional shrinking algorithm and collision fronts algorithm. The proposed labeling method has reduced processing time.

• Journal of KIISE
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• v.44 no.1
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• pp.36-43
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• 2017

Syntactic information represents the dependency relation between predicates and arguments, and it is helpful for improving the performance of Semantic Role Labeling systems. However, syntax analysis can cause computational overhead and inherit incorrect syntactic information. To solve this problem, we exclude syntactic information and use only morpheme information to construct Semantic Role Labeling systems. In this study, we propose an end-to-end SRL system that only uses morpheme information with Stacked Bidirectional LSTM-CRFs model by extending the LSTM RNN that is suitable for sequence labeling problem. Our experimental results show that our proposed model has better performance, as compare to other models.

• Communications of the Korean Mathematical Society
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• v.32 no.2
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• pp.435-445
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• 2017

Let D be a directed graph with p vertices and q arcs. A vertex out-magic total labeling is a bijection f from $V(D){\cup}A(D){\rightarrow}\{1,2,{\ldots},p+q\}$ with the property that for every $v{\in}V(D)$, $f(v)+\sum_{u{\in}O(v)}f((v,u))=k$, for some constant k. Such a labeling is called a V-super vertex out-magic total labeling (V-SVOMT labeling) if $f(V(D))=\{1,2,3,{\ldots},p\}$. A digraph D is called a V-super vertex out-magic total digraph (V-SVOMT digraph) if D admits a V-SVOMT labeling. In this paper, we provide a method to find the most vital nodes in a network by introducing the above labeling and we study the basic properties of such labelings for digraphs. In particular, we completely solve the problem of finding V-SVOMT labeling of generalized de Bruijn digraphs which are used in the interconnection network topologies.

• Journal of KIISE
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• v.42 no.2
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• pp.220-226
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• 2015

Semantic role labeling (SRL) systems determine the semantic role labels of the arguments of predicates in natural language text. An SRL system usually needs to perform four tasks in sequence: Predicate Identification (PI), Predicate Classification (PC), Argument Identification (AI), and Argument Classification (AC). In this paper, we use the Korean Propbank to develop our Korean semantic role labeling system. We describe our Korean semantic role labeling system that uses sequence labeling with structured Support Vector Machine (SVM). The results of our experiments on the Korean Propbank dataset reveal that our method obtains a 97.13% F1 score on Predicate Identification and Classification (PIC), and a 76.96% F1 score on Argument Identification and Classification (AIC).

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.12
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• pp.1817-1824
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• 1990

This paper describes a shape matching algorithm for occluded or distorted two-dimensional objects. In our approach, the shape matchin is viewed as a segment matching problem. A shape matching algorithm, based on both the stochastic labeling technique and the hypothesis generate-test paradigm, is proposed, and a simple technique which performs the stochastic labeling process in accordance with the definition of consisten labeling assignment without requiring an iterative updating process of probability valiues is also proposed. Several simulation results show that the proposed algorithm is very effective when occlusion, scaling or change of orientation has occurred in the object.