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역할 모델의 적응적 전환을 통한 협업 채집 무리 로봇의 에너지 효율 향상
이종현(Jong-Hyun Lee),안진웅(Jinung An),안창욱(Chang Wook Ahn) 제어로봇시스템학회 2012 제어·로봇·시스템학회 논문지 Vol.18 No.1
We can efficiently collect crops or minerals by operating multi-robot foraging. As foraging spaces become wider, control algorithms demand scalability and reliability. Swarm robotics is a state-of-the-art algorithm on wide foraging spaces due to its advantages, such as self-organization, robustness, and flexibility. However, high initial and operating costs are main barriers in performing multi-robot foraging system. In this paper, we propose a novel method to improve the energy efficiency of the system to reduce operating costs. The idea is to employ a new behavior model regarding role division in concert with the search space division.
데이터 그룹화를 이용한 상호진화연산 기반의 추천 시스템
김현태(Hyun-Tae Kim),안창욱(Chang Wook Ahn),안진웅(Jinung An) 제어로봇시스템학회 2011 제어·로봇·시스템학회 논문지 Vol.17 No.8
Recently, recommender systems have been widely applied in E-commerce websites to help their customers find the items what they want. A recommender system should be able to provide users with useful information regarding their interests. The ability to immediately respond to the changes in user’s preference is a valuable asset of recommender systems. This paper proposes a novel recommender system which aims to effectively adapt and respond to the immediate changes in user’s preference. The proposed system combines IEC (Interactive Evolutionary Computation) with a content-based filtering method and also employs data grouping in order to improve time efficiency. Experiments show that the proposed system makes acceptable recommendations while ensuring quality and speed. From a comparative experimental study with an existing recommender system which uses the content-based filtering, it is revealed that the proposed system produces more reliable recommendations and adaptively responds to the changes in any given condition. It denotes that the proposed approach can be an alternative to resolve limitations (e.g., over-specialization and sparse problems) of the existing methods.