• The Transactions of the Korea Information Processing Society
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• v.7 no.6
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• pp.1861-1866
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• 2000

This paper describes a multi-constrained university timetabling problem that is a one of the field of artificial intelligent research area. For this problem, we propose the 2type edge graph that is can be represented time-conflict and day-conflict constraints simultaneously. The genetic algorithms are devised and considered for it. And we describe a method of local search in traditional random operator for its search efficiency. In computational experiments, the solutions of proposed method are average 71% costs that ware compared with solutions of random method in 10,000 iterations.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.77-80
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• 2011

그래프는 전산학의 주요 주제 중 하나이며 World Wide Web과 Social Network의 중요성이 커지면서 더욱 주목을 받고 있다. 그래프와 관련하여 그래프 데이터베이스에 대한 질의 모델에 관한 연구도 중요하게 다투어져 왔다. 하지만 이들 연구는 패턴 매칭을 통한 질의를 주로 다루었다. 하지만 그래프 데이터를 추천이나 검색 등의 응용하기 위해서는 PageRank 등 그래프 내의 연결 구조를 양으로 분석해내는 작업이 요구된다. 또한 SimRank 및 Random Walk with Restart 등 다양한 양적 분석 측도가 제안되고 있다. 이에 따라 본 연구에서는 Random Walk를 기반으로 하는 그래프에 대한 유연한 양적 분석을 지원하는 질의 언어를 제시한다. 또한 기존의 양적 분석 측도들이 본 질의 모델을 통하여 어떻게 표현되는지를 통하여 본 질의 모델의 유용성 및 확장성을 보인다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.923-929
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• 2002

We discuss scheduling analysis for a discrete event system with time windows of which firing or holding time delays are subject to random variation within some finite range. To do this, we propose a modified p-lime Petri net, named p+-time Petri net. We develop a condition for which a synchronized transition does not have a dead token, that is, the firing epochs do not violate the time window constraints. We propose a method of computing the feasible range of the token sojourn time at each place based on a time difference graph. We also discuss an application for analyzing wafer residency times within the process chambers for a dual-armed cluster tool for chemical vapor deposition.

• Journal for History of Mathematics
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• v.18 no.4
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• pp.101-112
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• 2005

In this article, we introduce a generous but eccentric genius in mathematics, Paul Erdos. He invented probabilistic methods, pioneered in their applications to discrete mathematics, and estabilshed new theories, which are regarded as the greatest among his contributions to mathematical world. Here we introduce the probabilistic methods and random graph theory developed by Erdos and look at his life in glance with great respect for him.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.179-186
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• 2021

We have been developed the smart tourism app and the Instagram and YouTube contents to provide personalized tourism information and travel product information to individual tourists. In this paper, we develop a rule-based smart tourism chatbot with the khaiii (Kakao Hangul Analyzer III) morphological analyzer and Neo4J graph database. In the proposed chatbot system, we use a morpheme analyzer, a proper noun dictionary including tourist destination names, and a general noun dictionary including containing frequently used words in tourist information search to understand the intention of the user's question. The tourism knowledge base built using the Neo4J graph database provides adequate answers to tourists' questions. In this paper, the nodes of Neo4J are Area based on tourist destination address, Contents with property of tourist information, and Service including service attribute data frequently used for search. A Neo4J query is created based on the result of analyzing the intention of a tourist's question with the property of nodes and relationships in Neo4J database. An answer to the question is made by searching in the tourism knowledge base. In this paper, we create the tourism knowledge base using more than 1300 Jeju tourism information used in the smart tourism app. We plan to develop a multilingual smart tour chatbot using the named entity recognition (NER), intention classification using conditional random field(CRF), and transfer learning using the pretrained language models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4864-4882
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• 2016

In this work, we consider the optimization problem of minimizing energy consumption for real-time multicast over wireless multi-hop networks. Previously, a distributed primal-dual subgradient algorithm was used for finding a solution to the optimization problem. However, the traditional subgradient algorithms have drawbacks in terms of i) sensitivity to iteration parameters; ii) need for saving previous iteration results for computing the optimization results at the current iteration. To overcome these drawbacks, using a joint network coding and scheduling optimization framework, we propose a novel distributed primal-dual Random Deflected Subgradient (RDS) algorithm for solving the optimization problem. Furthermore, we derive the corresponding recursive formulas for the proposed RDS algorithm, which are useful for practical applications. In comparison with the traditional subgradient algorithms, the illustrated performance results show that the proposed RDS algorithm can achieve an improved optimal solution. Moreover, the proposed algorithm is stable and robust against the choice of parameter values used in the algorithm.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.403-415
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• 2017

In this paper, we investigate cosponsorship networks found in the 17th National Assembly of Republic of Korea. New legislation should be sponsored by at least 10 legislators including one main sponsor. Cosponsorship networks can be constructed, using directional links from cosponsors of legislation to its main sponsor; subsequently, these networks indicate the social relationships among the legislators. We apply Exponential Random Graph Model (ERGM) for valued networks to capture structural properties and the covariate effects of networks. We find the effect of the same party has the greatest influence on the composition of the network. Mutuality also plays an important role in the cosponsorship network; in addition, the effect of the number of elections won by a legislator has a small but significant influence.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.527-541
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• 2022

A company is a social group of many individuals that work together to obtain better results, and it is an organization that pursues common goals such as profit. As a result, forming networks among members, as well as individual communication abilities, is critical. The purpose of this research was to determine what factors influence the creation of employee advice relationships. Using the ERGM(Exponential Random Graph Model) approach, we looked at the network data of 44 individuals from consulting firms with offices in the United States and Europe. The significance of structural network factors like connectivity was first discovered. Second, the gender factor had the most significant main influence on the likelihood of adopting each other's advice. Third, geographical homogeneity resulted in higher link probabilities than major impacts of gender. This research looked at ways to make a company's network more efficient and active.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.7B
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• pp.417-426
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• 2005

Routing and wavelength assignment problem is one of the most important issues in optical transport networks based on wavelength division multiplexing(WDM) technique. In this paper, we propose a novel approach using path conflict graphs and an algorithm for finding all edge disjoint paths. And then we compare the performance of the proposed algorithm with that of bounded greedy approach for EDP(BGAforEDP). The proposed one outperforms up to about 20$\%$ in the fixed traditional topology(NSFNET) and about 32$\%$ in random topologies over the BGA for EDP algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.364-380
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• 2016

The most popular approach in object classification is based on the bag of visual-words model, which has several fundamental problems that restricting the performance of this method, such as low time efficiency, the synonym and polysemy of visual words, and the lack of spatial information between visual words. In view of this, an object classification based on weakly supervised E2LSH and saliency map weighting is proposed. Firstly, E2LSH (Exact Euclidean Locality Sensitive Hashing) is employed to generate a group of weakly randomized visual dictionary by clustering SIFT features of the training dataset, and the selecting process of hash functions is effectively supervised inspired by the random forest ideas to reduce the randomcity of E2LSH. Secondly, graph-based visual saliency (GBVS) algorithm is applied to detect the saliency map of different images and weight the visual words according to the saliency prior. Finally, saliency map weighted visual language model is carried out to accomplish object classification. Experimental results datasets of Pascal 2007 and Caltech-256 indicate that the distinguishability of objects is effectively improved and our method is superior to the state-of-the-art object classification methods.