• Proceedings of the KAIS Fall Conference
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• 2008.05a
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• pp.354-357
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• 2008

In this paper, we develop business models for the robot industry that facilitates to explore new business opportunities in the convergence age. Considering the trend of convergence society, we analyze the market drivers. Analyzing market dynamics and value chain, we design a set of the business model for the robot industry focused on u-health robots. In addition, we describe the evolution path of the proposed business model in terms of technology development and market. Finally, we develop a framework for evaluating the effectiveness of the business model.

• Bulletin of the Korean Mathematical Society
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• v.56 no.4
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• pp.899-910
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• 2019

In this paper, we enumerate the cardinalities for the set of all vertices of outdegree ${\geq}k$ at level ${\geq}{\ell}$ among all rooted ordered d-trees with n edges. Our results unite and generalize several previous works in the literature.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.114-116
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• 2015

This paper presents a Stochastic Time-Cost Tradeoff analysis system (STCT) that identifies optimal construction methods for activities, hence reducing the project completion time and cost simultaneously. It makes use of schedule information obtained from critical path method (CPM), applies alternative construction methods data obtained from estimators to respective activities, computes an optimal set of genetic algorithm (GA) parameters, executes simulation based GA experiments, and identifies near optimal solution(s). A test case verifies the usability of STCT.

• Korean Journal of Cognitive Science
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• v.3 no.2
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• pp.225-248
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• 1992

In this paper, word sense disambiguation methods utilizing a knowledge base based on a semantic network are introduced. The basic idea is to keep track of a set of paths in the knowledge base which correspond to the inctemental semantic interpretation of a input sentence. These paths are called the semantic paths. when the parser reads a word, the senses of this word which are not involved in any of the semantic paths are removed. Then the removal operation is propagated through the knowledge base to invoke the removal of the senses of other words that have been read before. This removal operation is called recusively as long as senses can be removed. This is called the recursive word sense removal. Concretion of a vague word's concept is one of the important word sense disambiguation methods. We introduce a method called the path adjustment that extends the conctetion operation. How to use semantic association or syntactic processing in coorporation with the above methods is also considered.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.89-100
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• 2008

In an urban network controlled by traffic signals, there is an interaction between the signal timing and the routes chosen by individual road users. This study develops a bi level programming model for traffic signal optimization in networks with path based traffic assignment. In the bi level programming model, genetic algorithm approach has been proposed to solve upper level problem for a signalized road network. Path based traffic assignment using column generation technique which is proposed by M.H. Xu, is applied at the lower-level. Genetic Algorithm provieds a feasible set of signal timings within specified lower and upper bounds signal timing variables and feeds into lower level problem. The performance of this model is investigated in numerical experiment in a sample network. In result, optimal signal settings and user equilibrium flows are made.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5464-5488
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• 2019

The multi-population genetic algorithm in multi-target paths coverage has become a top choice for many test engineers. Also, information sharing strategy can improve the efficiency of multi-population genetic algorithm to generate multi-target test data; however, there is still space for some improvements in several aspects, which will affect the effectiveness of covering the target path set. Therefore, a multi-target paths coverage strategy is proposed by improving multi-population genetic algorithm based on individual information sharing among populations. It primarily contains three aspects. Firstly, the behavior of the sub-population covering corresponding target path is improved, so that it can continue to try to cover other sub-paths after covering the current target path, so as to take full advantage of population resources; Secondly, the populations initialized are prioritized according to the matching process, so that those sub-populations with better path coverage rate are executed firstly. Thirdly, for difficultly-covered paths, the individual chromosome features which can cover the difficultly-covered paths are extracted by utilizing the data generated, so as to screen those individuals who can cover the difficultly-covered paths. In the experiments, several benchmark programs were employed to verify the accuracy of the method from different aspects and also compare with similar methods. The experimental results show that it takes less time to cover target paths by our approach than the similar ones, and achieves more efficient test case generation process. Finally, a plug-in prototype is given to implement the approach proposed.

• Journal of KIISE:Databases
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• v.36 no.5
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• pp.405-410
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• 2009

This paper proposes an efficient path finding scheme capable of searching the paths to k static objects from a given query point, aiming at both improving the legacy k-nearest neighbor search and making it easily applicable to the road network environment. To the end of improving the speed of finding one-to-many paths, the modified A* obviates the duplicated part of node scans involved in the multiple executions of a one-to-one path finding algorithm. Additionally, the cost to the each object found in this step makes it possible to finalize the k objects according to the network distance from the candidate set as well as to order them by the path cost. Experiment results show that the proposed scheme has the accuracy of around 100% and improves the search speed by $1.3{\sim}3.0$ times of k-nearest neighbor searches, compared with INE, post-Dijkstra, and $na{\ddot{i}}ve$ method.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.5
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• pp.233-240
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• 2015

About 500 Bizcool High School will be designated by the year of 2017. The policy of designating Bizcool will promote startup club activity among Bizcool high school Student. In particular, the startup club activities will help Bizcool high school student fixing their career path after graduation by enhancing their creativity, leadership, team spirit, and problem solving ability through understanding the concept of entrepreneurship and embodying the startup process taking real startup experience. Hence, this paper set and test hypothesis of assuming the differences with respect to entrepreneurship will and maturing career path whether they have startup club activity as presenting Bizcool high school or not. The empirical test have bee done over 274 Bizcool high school students. The result have shown that significant level of differences(Determinants and Confidence) are found between the group of having startup club activity and those of not having.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.19-24
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• 2011

This paper presents a method about amorphous obstacles avoidance for local path planning in the two-dimensional sensor environment. In particular, the proposed method is extended from some of the recent studies about a point obstacle avoidance. In the paper, repulsive forces of two types are proposed in order that the robot avoids from the amorphous obstacle with various size and form. A judgment of curvatures in the proposed method simplifies the recognition of obstacles to make the path-planning efficient. In addition, the line of sight(LOS) and the range of recognition are considered in the environment. By simulation results, the proposed method for amorphous obstacle avoidance shows better performance than the related existing method and we confirmed advantages of proposed method.

• Journal of the Korean Society of Hazard Mitigation
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• v.11 no.3
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• pp.49-56
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• 2011

This paper deals with a methodology for searching optimal route of hazard material (hazmat) vehicles. When we make a decision of hazmat optimal paths, there is a conflict between the public aspect which wants to minimize risk and the private aspect which has a goal of minimizing travel time. This paper presents Efficient Vector Labeling algorithm as a methodology for searching optimal path of hazmat transportation, which is intrinsically one of the multi-criteria decision making problems. The output of the presented algorithm is a set of Pareto optimal paths considering both risk and travel time at a time. Also, the proposed algorithm is able to identify non-dominated paths which are significantly different from each other in terms of links used. The proposed Efficient Vector Labeling algorithm are applied to test bed network and compared with the existing k-shortest path algorithm. Analysis of result shows that the proposed algorithm is more efficient and advantageous in searching reasonable alternative routes than the existing one.