• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.65-68
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• 1996

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. 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. The TMS32OC31 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.

• 전기의세계
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• v.23 no.1
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• pp.73-78
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• 1974

Digital simulation algorithms and program for multimachine dynamic stability have been developed which represent the effects of machines much more complety than have been available previously. Emphasis is given to the savings of the memory spaces required, thus making it possible to use a small computer with limited capacity of core storage (without auxiliary storage). Both d- and q- aris quantities are fully represented, and the speed-governing and voltage-regulating system available are ertensive, thus allowing a very close approximation to any physical system. Facilities for dynamic and nonlinear loads are also included. The computational algorithms and program developed have been shown to be extensive and complete, and are very desirable features minimizing memory spaces for stability calculations. The capabilities have been demonstrated by several case studies for an actual power system of 44 generators, 22 loads and 33 buses. About 13-K words of memory spaces have been required for the case studies on the basis of two words per real variable and a word per integer variable.

• Journal of Ubiquitous Convergence Technology
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• v.2 no.1
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• pp.51-55
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• 2008

A digital home is a ubiquitous environment that is expected to be realized in the near future. All information appliances in a house are connected to each other through wired and/or wireless home networks. Authenticated user can access the various services provided by the digital home, and the access is not restricted by equipment, time, or location. The technologies that form the digital home can be grouped into two categories: wired networking technologies and wireless networking technologies. For the purpose of ubiquitous environments, wireless-networks offer suitable and seamless high-quality services. A wireless LAN can be created simply by equipping a single access point. For that reason, the cost of establishing such a network is low and using it is easy. Of course, there is an exciting new wireless technology. It is the Ultra Wide Band (UWB). However, it is not enough to bring wireless convenience, although offering a broad range of high-speed data transfer capabilities, Because of unstable. Thus, of the wireless-networking systems, we focus on the wireless LAN. We quantitatively analyze its capabilities. The dynamic and adaptive wireless LAN provides a foundation for the evolution toward the next generation of wireless and adaptive networks. The difference between wired LAN and wireless LAN in upload and download rates is small. Although the wireless LAN experiences a greater loss rate than the wired LAN, the difference is minimal. We conclude that a wireless LAN is suitable for use in an apartment environment.

• 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.

• Journal of the Semiconductor & Display Technology
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• v.20 no.2
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• pp.137-140
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• 2021

High dynamic range (HDR) technology has been drawing attention in the field of imaging and consumer entertainment. As tools for capturing and creating HDR contents, encoding, and transmission evolve to support HDR formats, various display capabilities are being developed and increased. Hence, there is need for remapping native HDR imagery for display on lower quality legacy standard dynamic range (SDR) displays. This operation is referred to as tone mapping. In this paper, we present a sectional tone mapping method by Lenzen, and expand upon a tone mapping approach to improve temporal stability while maintaining picture quality. Compared to the existing block-based sectional tone mapping, our method uses the edge awareness-based tone mapping. We estimate the performance of the objective metric on temporal flickering. The experimental result shows that the algorithm maintains a smoother relationship between the output luminance values, and this reveals success in reducing halos and improving temporal stability with adopted edge aware filtering.

• 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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• 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 Precision Engineering Conference
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• 1996.11a
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• pp.759-763
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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 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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• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.69.2-69
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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.