• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.04a
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• pp.125-130
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• 1997

In this paper, it is presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C3x is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, andsuitable for implementation of robust control.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.3
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• pp.21-24
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• 1993

인간을 학술용어로는 호모 사피엔스(Homo Sapiens:지성인) 또는 호모 파베르(Homo Faber:공작인)로 표현하는 것에서도 알 수 있듯이 우리 인간은 그 이유야 어쨌든 본질적으로 도구를 사용하고 물건을 만드는 능력을 가지고 있다. 그리고 물건만들기(생산) 기술은 수렵사회, 농경사회, 공업사회, 정보화사회로 사회가 바뀌는 동안 끈임없이 변화 발전해 왔다. 특히 40년대말 50년대 초에 걸쳐 개발된 컴퓨터, NC(수치제어)기 술과 70년대에 개발된 마이크로프로세서 기술은 그후의 생산기술을 획기적으로 변화시키고 말았다. 즉 설계, 가공, 조립, 검사, 보관 등 생산에 필요한 요소가능을 자동화시키고, 나아가 이들을 연결 통합하여 CAC/CAM, FMC, FMS와 같은 고도의 자동화 생산시스템이 가능하게 하였다. 그 결과 인간은 재이없고 지루한 단순 반복 작업에서 해방되어, 자동화 시스템을 프로그래밍 또는 관리. 통제하는 지적작업에 전념하게 되었고, 고품질에 싼 제품이 생산되게 되었다. 더나아서 최근에는 생산 시스템에 지능을 부여한 IMS(Intelligent Manufacturing System:지능형 생산 시스템)를 실현하고자 하는 국제 공동연구가 추진되기에 이르렀다. 이 글에서는 IMS의 모습을 잠시 살펴본 뒤 그 핵심기술의 하나인 지능형 CAM의 모습과 실현방법에 대해서 알아 보도록 한다.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.255-260
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• 2001

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.10a
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• pp.133-137
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• 1996

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have bevome increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking arre indispensable capabilities for their versatile application. the need to meet demanding control requirement in increasingly complex dynamical control systems under sygnificant uncertainties leads toward design of implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme the ntworks intrduced are neural nets with dynamic neurouns whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure fast in computation and suitable for implementation of real-time control, Performance of the neural controller is illustrated by simulation and experimental results for a SCAEA robot.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.5
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• pp.162-165
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• 2007

TFT-LCD process has two advantages as compared with the semiconductor-process. It is that cycle time is short and number of the final products are small. But it needs complicated inspection / assembly line to be treated manually and much higher labor costs in the TFT-LCD process. Also, It is necessary to build PICS(Production Information and Control System) which is automated and intelligent. In this paper, an automated process of LCD module assembly line that can increase productivity and reduce the cost of production to strengthen the competitiveness corresponding with global market is planned in comparison with its manual/semi-auto. It is noted that The automated line for COG$\sim$FOG process replacing with the existing facilities had the following effects; the productivity is increased to about 1.5 times and labor cost reduced 85%. In addition, whole assembly line can be short and simple.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.193-194
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• 2008

IEC 61850 기반의 변전소자동화시스템이 세계적으로 많은 관심을 가지면서 IEC 61850 프로토콜 기반의 IED(Intelligent Electronic Device) 개발을 위해 전 세계적으로 많은 노력이 진행되고 있다. 본 연구에서는 IEC 61850 표준규격에서 제안하는 객체 모델링 및 통신프로토콜을 이용하여 2권선 변압기 보호용 IED를 개발하고 그 구현방법을 설명하였다. 구현을 위해 리눅스 OS(Operation System)가 포팅된 통신보드에 MMS(Manufacturing Message Specification), GOOSE(Generic object oriented substation event) 통신모듈을 탑재하였고 데모 시스템을 구성하여 성능을 검증하였다.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.113-118
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• 1999

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important n the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.439-444
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• 2004

A new efficient intelligent machining strategy named the Steepest Directed Tree method is presented in this study, which makes surface model discrete with triangulation meshes and the cutter paths track along the tree directions. In order to formulate these algorithms practically, it is deduced the multi-stage machining approach of uncut volume caused by cutter gouging in the course of milling using flat end mill. It is systematized the checking process the cutter interference by grouping the 6 kinds of gouging types, which yields the environment of connectivity data lists including CL-data, and then the multi-stage machining strategy, that minimizes uncut area by continuously sequencing the generative subsequent CL-paths, is shamed to determine the second tool path for the next uncut area and to compose the operating multi-stage cutting processes. The completed machining system of curved surfaces is evaluated by testing the practical machining experiments which have various kinds of shape conditions.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.207-212
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• 2004

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network, and back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.5
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• pp.96-103
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• 2001

The quality requirements for thickness accuracy in cold rolling continue to become more stringent, particularly in response to exacting design specification from automotive customers. One of the major impacts from the tighter tolerance level is more unusable product on the head end and tail end of tandem mill coils when the mill is in transition to or from steady state rolling condition. A strip thickness control system for a tandem cold steel rolling mills is composed with blocked non-interacting controller and controllers for strip thickness and tension control of each rolling stands. An intelligent mathematical model included an elastic deformation of strip has been developed and applied to the field in order to predict the rolling force. The simulated results showed that the effect of elastic recovery should be included the model, even if the effect of elastic compression was not important.