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차보남(Bo-Nam Cha),노춘수(Chun-Su Roh),강성기(Sung-Ki Kang),김원일(Won-il Kim) 한국산업융합학회 2014 한국산업융합학회 논문집 Vol.17 No.2
This paper describes a new technology to develop the character recognition technology based on pattern recognition for non-contacting inspection optical lens slant or precision parts, and including external form state of lens or electronic parts for the performance verification, this development can achieve badness finding. And, establish to existing reflex data because inputting surface badness degree of scratch"s standard specification condition directly, and error designed to distinguish from product more than schedule error to badness product by normalcy product within schedule extent after calculate the error comparing actuality measurement reflex data and standard reflex data mutually. Developed system to smallest 1 pixel unit though measuring is possible 1 pixel as 37㎛ ×37㎛ (0.1369×10-4㎟) the accuracy to 1.5×10-4㎜ minutely measuring is possible performance verification and trust ability through an experiment prove.
디지털 시그널 프로세서를 이용한 스카라 로봇의 적응-신경제어기 설계
한성현,차보남 한국공작기계학회 1997 한국생산제조학회지 Vol.6 No.1
During the past decade, there were many well-established theories for the adaptive control of linear systems, but there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of industrial robot control. Neural network computing methods provide one approach to the development of adaptive and learning behavior in robotic system for manufacturing. Computational neural networks have been demonstrated which exhibit capabilities for supervised learning. matching, and generalization for problems on an experimental scale. supervised learning could improve the efficiency of training an development of robotic systems. In this paper, a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator using digital signal processors is proposed. Digital signal processors, DSPs, are micro-processors that are developed particularly for fast numerical computations involving sums and products of variables. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. The proposed adaptive-neuro control scheme is illustrated to be an efficient control scheme for implementation of real-time control for SCARA robot with four-axes by experiment.