• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.133-139
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• 1997

This paper presents a new approach to the dynamic control technique for track vehicle system using neural network-fuzzy control method. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is propored a learning controller consisting of two neural network-fuzzy based on independent resoning and a connection net with fixed weights to simply the neural network-fuzzy. The performance of the proposed controller is shown by simulation for trajectory tracking of the speed and azimuth of a track vehicle

• 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 Ocean Engineering and Technology
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• v.11 no.1
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• pp.84-95
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• 1997

This paper presents a new approach to the design speed and azimuth control of a mobile robot with 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 frmework 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 simple 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.

• 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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• 1999.10a
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• pp.142-147
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• 1999

This paper presents a new approach to the dynamic control technique for track vehicle system using neural network-fuzzy control method. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, 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 simulation for trajectory tracking of the speed and azimuth of a track vehicle.

• Proceedings of the KIPE Conference
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• 1998.07a
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• pp.384-387
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• 1998

This paper presents a newly developed speed sensorless drive using RLS(Recursive Least Squares) based on Neural Network Training Algorithm. The proposed algorithm based on the RLS has just the time-varying learning rate, while the well-known back-propagation (or generalized delta rule) algorithm based on gradient descent has a constant learning rate. The number of iterations required by the new algorithm to converge is less than that of the back-propagation algorithm. The RLS based on NN is used to adjust the motor speed so that the neural model output follows the desired trajectory. This mechanism forces the estimated speed to follow precisely the actual motor speed. In this paper, a flux estimation strategy using filter concept is discussed. The theoretical analysis and experimental results to verify the effectiveness of the proposed analysis and the proposed control strategy are described.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.4
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• pp.75-86
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• 1996

In this paper, we propose a new fuzzy-neural network control scheme for the speed and azimuth control of a mobile robot. 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 frame-work of the specialized learning architecture. It is proposed a learning controller consisting of two fuzzy-neural networks based on independent reasoning and a connection net woth fixed weights to simply the fuzzy-neural network. The effectiveness of the proposed controller is illustrated by performing the computer simulation for a circular trajectory tracking of a mobile robot driven by two independent wheels.

• Journal of Korean Society for Atmospheric Environment
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• v.31 no.5
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• pp.472-481
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• 2015

Black carbon (BC) aerosols were monitored at the KIST site ($37.603^{\circ}N$, $127.046^{\circ}E$) and Cheonan-KOREATECH site ($36.766^{\circ}N$, $127.281^{\circ}E$) during the pre KORea-US Air Quality Study (KORUS-AQ) campaign using a couple of Muliti Angle Absorption Photometers (MAAP). BC mass concentrations were presented as $2.14{\pm}1.06{\mu}g/m^3$ and $0.94{\pm}0.60{\mu}g/m^3$ at KIST site (Seoul) and KOREATECH site (Cheonan), respectively. BC mass concentrations measured at KIST and KOREATECH sites from 22:00 on May 22 to 12:00 on May 23, 2015 showed 80% and 72% higher than average BC mass concentrations measured during campaign period, respectively. It indicates both sites could be influenced by a remote source. Similar patterns of BC concentrations between two sites from 20:00 to 24:00 on June 6, 2015 implies that the BC could be transported into both sites and then be stagnant inside the Korean Peninsula. Diurnal variation of BC in weekdays and weekends were also presented for the KIST and KOREATECH sites. Morning rush hour peak was observed at KIST site located in metropolitan area though no distinct morning rush hour peak was not observed at KOREATECH site located in a suburban area. This study revealed transport pathways of BC near the Korean Peninsula using back-trajectory analysis of BC measured both in a metropolitan area and in a suburban area.

• Bulletin of the Korean Chemical Society
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• v.35 no.4
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• pp.1149-1153
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• 2014

Real-time monitoring of hourly atmospheric radon (Rn-222) concentration was performed throughout 2011 at Gosan station, Jeju Island, one of the least polluted regions in Korea, in order to characterize the background levels, and temporal variations on diurnal to seasonal time-scales. The annual mean radon concentration for 2011 was $2527{\pm}1356$ mBq $m^{-3}$, and the seasonal cycle was characterized by a broad winter maximum, and narrow summer minimum. Mean monthly radon concentrations, in descending order of magnitude, were Oct > Sep > Feb > Nov > Jan > Dec > Mar > Aug > Apr > Jun > May > Jul. The maximum monthly mean value (3595 mBq $m^{-3}$, October), exceeded the minimum value (1243 mBq $m^{-3}$, July), by almost a factor of three. Diurnal composite hourly concentrations increased throughout the night to reach their maximum (2956 mBq $m^{-3}$) at around 7 a.m., after which they decreased to their minimum value (2259 mBq $m^{-3}$) at around 3 p.m. Back trajectory analyses indicated that the highest radon events typically exhibited long-term continental fetch over Asia before arriving at Jeju. In contrast, low radon events were generally correlated with air mass fetch over the North Pacific Ocean. Radon concentrations typical of predominantly continental, and predominantly oceanic fetch, differed by a factor of 3.8.