• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.6
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• pp.347-357
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• 2013

Cyber-Physical system(CPS) is characterized by collaborating computational elements controlling physical entities. In CPS, human desire to acquire useful information and control devices anytime and anywhere automatically has increased the necessity of a high reliable system. However, the physical world where CPS is deployed has management complexity and maintenance cost of 'CPS', so that it is impossible to make reliable systems. Thus, this paper presents an 'Autonomic Control System towards High-reliable Cyber-Physical Systems' that comprise 8-steps including 'fault analysis', 'fault event analysis', 'fault modeling', 'fault state interpretation', 'fault strategy decision', 'fault detection', 'diagnosis&reasoning' and 'maneuver execution'. Through these activities, we fascinate to design and implement 'Autonomic control system' than before. As a proof of the approach, we used a ISR(Intelligent Service Robot) for case study. The experimental results show that it achieves to detect a fault event for autonomic control of 'CPS'.

• International Journal of Control, Automation, and Systems
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• v.3 no.3
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• pp.411-418
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• 2005

This paper discusses an output-tracking control design method for Takagi-Sugeno fuzzy systems with parametric uncertainties. We first represent the concerned system as a set of uncertain linear systems. The tracking problem is then converted into a stabilization problem thereby leading to a more feasible control design procedure. A sufficient condition for robust practical output tracking is derived in terms of a set of linear matrix inequalities. A numerical example for a flexible-joint robot-arm model has been demonstrated, to convincingly show effectiveness of the proposed system modeling and control design.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2711-2713
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• 2005

This paper presents the tracking control method of robotic systems with uncertainties using self recurrent wavelet neural network (SRWNN) via the backstepping design technique. The SRWNN is used as the uncertainty observer of the robotic systems. The adaptation laws for weights of the robotic systems are induced from the Lyapunov stability theorem, which are used for on-line controlling robotic systems. Computer simulations of a three-link robot manipulator with uncertainties verify the validity of the proposed SRWNN controller.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1990-1993
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• 2005

Stereovision is known to be one of the most important tools for robot vision systems. Previously, 2D active contour model has been applied to stereovision by defining the contour on the 3D space instead of image plane. However, the proposed model is still that of curve so that some complex shapes such as surfaces with high curvature can not be properly estimated because of occlusion phenomena. In this paper, the authors extend the curve model to the surface model. The surface is approximated by polygons and new energy function and its optimization method for surface estimation is proposed. Its effectiveness is examined by experiments with real stereo images.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.413-416
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• 1997

Recently, dynamic recurrent neural networks(DRNN) for identification of nonlinear dynamic systems have been researched extensively. In general, dynamic backpropagation was used to adjust the weights of neural networks. But, this method requires many complex calculations and has the possibility of falling into a local minimum. So, we propose a new approach to identify nonlinear dynamic systems using DRNN. In order to adjust the weights of neurons, we use evolution strategies, which is a method used to solve an optimal problem having many local minimums. DRNN trained by evolution strategies with mutation as the main operator can act as a plant emulator. And the fitness function of evolution strategies is based on the difference of the plant's outputs and DRNN's outputs. Thus, this new approach at identifying nonlinear dynamic system, when applied to the simulation of a two-link robot manipulator, demonstrates the performance and efficiency of this proposed approach.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1297-1300
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• 1993

Machine learning in an uncertain or unknown environment is of vital interest to those working with intelligent systems. The ability to garner new information, process it, and increase the understanding/ capability of the machine is crucial to the performance of autonomous systems. The field of artificial intelligence provides two major approaches to the problem of knowledge engineering-expert systems and neural networks. Harnessing the power of these two techniques in a hybrid, cooperating system holds great promise.

• Annual Conference of KIPS
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• 2011.11a
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• pp.91-93
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• 2011

본 논문은 영상처리 및 센서를 이용하여 주유할 차량으로 이동, 주유구 인식 및 개폐, 주유까지의 과정을 처리할 수 있는 무인 주유 로봇의 구현 과정을 기술하였다. 캐터펄트는 자동차가 주차된 곳에 정확히 멈추게 되고, 로봇 팔이 영상 처리를 이용하여 주유구 위치를 찾아낸 뒤 주유를 시작한다. 로봇은 현 주유 시스템의 불편한 점을 사람 없이 전 과정을 처리하는 역할을 함으로써 주유소를 무인화하여 인건비를 최소화 시키고, 악천후 상황에서 주유의 편리성을 기대할 수 있다.

• Annual Conference of KIPS
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• 2011.11a
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• pp.87-90
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• 2011

현재 의료기술의 발달로 실버 인구가 늘어남에 따라 주변의 도움이 필요한 일들이 많아지고 있다. 특히 무거운 물체를 옮기는데 도움이 필요한 경우가 많은 것이 현실이다. 그래서 사용자를 따라다니며 도움을 주는 로봇의 필요성이 인식되면서 추적로봇이 만들어지고 있다. 기존방식은 초음파를 이용하는데 이와 같은 방식은 송신기를 휴대해야 한다는 단점이 있다. 본 논문에서는 그것을 보완하기 위한방법으로 카메라를 이용하여 영상처리를 통한 방식을 제안한다. 표적을 부착함으로써 사용자를 인식 및 추적이 가능하게 됨으로써 생활에 편리한 제품으로 자리 잡을 수 있을 것으로 기대된다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.433-434
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• 2021

본 논문에서는 RPA 도구 개발 과정 중 자동화 및 데이터 전처리 기능을 이용한 양질의 데이터 추출 과정을 기술한다. 개발된 RPA 도구에서는 녹화 기능으로 자동화 기능을 향상시켰으며, 비대칭 데이터 변환 기능과 이상치 처리 기능을 통해 업무 생산효율 증가 및 휴먼에러 방지를 제공한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.318-323
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• 2005

In this paper, we propose a systematic method of a fuzzy-model-based controller for continuous-time nonlinear dynamical systems which may contain uncertainties. The continuous-time uncertain TS fuzzy model is first constructed to represent the uncertain nonlinear system. A parallel distributed compensation (PDC) technique is then used to design a fuzzy model based controller for both stabilization and tracking. Finally, the designed continuous-time controller is converted to an equivalent discrete-time controller by using an intelligent digital redesign method. This new design technique provides a systematic and effective framework for integration of the fuzzy model based control theory and the advanced digital redesign technique for nonlinear dynamical systems with uncertainties. Finally, the single link flexible-joint robot arm is used as an illustrative example to show the effectiveness and the feasibility of the developed design method.