• International Journal of Control, Automation, and Systems
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• 제6권2호
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• pp.282-287
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• 2008

In distributed mobile robot systems, autonomous robots accomplish complicated tasks through intelligent cooperation with each other. This paper presents behavior learning and online distributed evolution for cooperative behavior of a group of autonomous robots. Learning and evolution capabilities are essential for a group of autonomous robots to adapt to unstructured environments. Behavior learning finds an optimal state-action mapping of a robot for a given operating condition. In behavior learning, a Q-learning algorithm is modified to handle delayed rewards in the distributed robot systems. A group of robots implements cooperative behaviors through communication with other robots. Individual robots improve the state-action mapping through online evolution with the crossover operator based on the Q-values and their update frequencies. A cooperative material search problem demonstrated the effectiveness of the proposed behavior learning and online distributed evolution method for implementing cooperative behavior of a group of autonomous mobile robots.

• 대한전기학회논문지:시스템및제어부문D
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• 제50권11호
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• pp.522-531
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• 2001

Recently conventional artificial intelligence(AI) approaches have been employed to build action selectors for the autonomous mobile robot(AMR). However, in these approaches, the decision making process to choose an action from multiple competence modules is still an open question. Many researches have been focused on the reactive planning systems such as the biological immune system. In this paper, we attempt to construct an action selector for an AMR based on the artificial immune network and internet. The information from vision sensors is used for antibody. We propose a learning method for artificial immune network using evolutionary algorithm to produce antibody automatically. The internet environment for an AMR action selector shows the usefulness of the proposed learning artificial immune network application.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.2953-2955
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• 1999

In this paper, the concept of evolvable hardware and evolutionary robotics are introduced and constructing the mobile robot controller without human operator is suggested. The robot controller is evolved to avoid obstacles by genetic learning which determines the weights between sensor inputs and motor outputs. Genetic algorithms which is executed in a computer(PC) searches the best weights by interacting with robot performance under it's environment. The experiment is done by real mobile robot Khepera and a simple GA.

• 한국지능시스템학회논문지
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• 제25권3호
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• pp.248-253
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• 2015

휴머노이드 로봇이 인간의 생활환경에 적응하여 미션을 수행하기 위해서는 최소 아동과 비슷한 키를 가져야 한다. 본 논문에서는 아동과 비슷한 키의 1m 이상 휴머노이드 로봇의 설계에 대하여 다루고 있다. 구체적으로는 휴머노이드 로봇의 기구학, 3차원 모델 설계, 메커니즘 개발, 그리고 서보모터와 소형 PC를 이용한 하드웨어 구조를 제시하였다. 이 과정을 통하여 1m 10cm, 무게 8.16kg의 아동형 휴머노이드 로봇 'CHARLES(Cognitive Humanoid Autonomous Robot with Learning and Evolutionary Systems)' 를 설계하고 제작하였다. 로봇의 보행을 위해 ZMP 기반 보행기법을 적용하고, 보행패턴 생성 알고리즘을 적용하였다. 그리고 보행 실험을 통하여 보행패턴 파리미터의 설정에 따른 보행패턴의 생성 및 변화를 분석하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2007년도 하계종합학술대회 논문집
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• pp.449-450
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• 2007

Evolvable hardware(EHW) is a dynamic field that brings together reconfigurable hardware, artificial intelligence, fault tolerance and autonomous systems. This paper gives an introduction to the field. The features that can be used to identify and classify evolvable hardware are the evolutionary algorithm, the implementation and the genotype representation. Evolvable hardware (EHW) is hardware that can change its own circuit structure by genetic learning to achieve maximum adaptation to the environment. In conventional EHW, the learning is executed by software on a computer.

• 전자통신동향분석
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• 제36권1호
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• pp.81-88
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• 2021

Post COVID-19, the medical legacy system will be transformed for utilizing medical resources efficiently, minimizing medical service imbalance, activating remote medical care, and strengthening private-public medical cooperation. This can be realized by achieving an entire medical paradigm shift and not simply via the application of advanced technologies such as AI. We propose a medical system configuration named "Medical AI Hub" that can realize the shift of the existing paradigm. The development stage of this configuration is categorized into "AI Cooperation Hospital," "AI Base Hospital," and "AI Hub Hospital." In the "AI Hub Hospital" stage, the medical intelligence in charge of individual patients cooperates and communicates autonomously with various medical intelligences, thereby achieving synchronous evolution. Thus, this medical intelligence supports doctors in optimally treating patients. The core technologies required during configuration development and their current R&D trends are described in this paper. The realization of the central configuration of medical AI through the development of these core technologies will induce a paradigm shift in the new medical system by innovating all medical fields with influences at the individual, society, industry, and public levels and by making the existing medical system more efficient and intelligent.