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유석종(Seok-jong Yoo),이승열(Seung-yeol Lee),최종호(Jong-ho Choe),이용석(Yong-seok Lee),길명수(Myeong-su Gil),이상호(Sang-ho Lee),이상헌(Sang-heon Lee),한창수(Chang-soo Han) 대한기계학회 2006 대한기계학회 춘추학술대회 Vol.2006 No.6
The objective of the study was to propose a multipurpose field robot in hazardous operation environments. This system combines a basic system composed of a multi-DOF manipulator and a mobile platform with an additional module for construction, national defense and emergency-rescue. According to an additional module type combined with a basic system, it can be used in a various fields. In this study, we describe a construction robot which helps a human operator handle easily heavy materials in case of using the cooperation system on construction site. This study introduces an additional module for construction and a robot control algorithm for a HRC(Human-Robot Cooperation). In addition, it proposes a novel construction method to install heavy materials with robot on the construction site.
길명수(Myeong-su Gil),이승열(Seung-Yeol Lee),유석종(Seok-jong Yoo),최종호(Jong-Ho Choe),심형준(Hyung-joon Sim),한창수(Chang-soo Han) 대한기계학회 2006 대한기계학회 춘추학술대회 Vol.2006 No.6
This paper presents a study on the automation of steel frame building. The hooks used with tower crane are passive, and the linking and unlinking operations between hooks and steel objects are done manually. These operations are laborious as well as dangerous. In particular, the unlinking operations which must be accomplished by workers at the tip of steel columns on the top of building are very dangerous. This study describes invention of a mechanism and a control system for safety and user interface. The design of the mechanism and the control algorithm was evaluated in the construction site.
Spark 환경에서 대용량 그래프 유사 서브 그래프 매칭 기법
임종태(Jongtae Lim),최도진(Dojin Choi),서동민(Dongmin Seo),유석종(Seok Jong Yu),복경수(Kyoungsoo Bok),유재수(Jaesoo Yoo) 한국정보과학회 2018 정보과학회 컴퓨팅의 실제 논문지 Vol.24 No.9
최근 각종 실험 장비의 발전에 따라 과학데이터가 급격히 증가하고 있다. 특히 그래프 데이터를 활용한 유사 서브 그래프 매칭 기법은 다양한 분야의 응용 및 연구에서 중요하게 활용되고 있다. 하지만 기존의 유사 서브 그래프 매칭 기법들은 단일 서버 환경에서 동작하도록 설계되어 있기 때문에 대용량 그래프의 처리에 한계가 존재한다. 따라서 본 논문에서는 Spark 환경에서 대용량 그래프 유사 서브 그래프 매칭 기법을 제안한다. 제안하는 기법은 분산 컴퓨팅 환경을 고려하여 대용량 그래프에 대한 처리를 수행한다. 또한 보다 효율적인 가지치기, 유사도 계산, 그리고 결과 반환 기법을 이용하여 유사 서브 그래프 매칭의 가지치기 효율 및 속도를 향상시킨다. With the development of various experiment tools, the amount of science data generated for fields such as astronomy, cosmology, biology, and humanities has increased rapidly. Among these science data, graph data occupies a very high proportion. Approximate sub-graph matching is the analytic technique that searches for the similar subgraphs with a query graph in target graph. However, the existing approximate subgraph matching schemes have limits to process large scale network data because they do not consider the distributed computing environments. In this paper, we propose an approximate subgraph matching scheme for large-scale graph data in distributed computing environments. The proposed scheme uses big data processing platform to process the large-scale graph data. And the proposed scheme improves the performance of the query processing using efficiently pruning algorithm and similarity calculate algorithm.