• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1028-1033
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• 1991

This paper describes the design of a neural network estimator to estimate weld pool sizes for on-line use of quality monitoring and control in GMA welding processes. The estimator utilizes surface temperatures measured at various points on the top surface of the weldment as its input. The main task of the neural net is to realize the mapping characteristics from the point temperatures to the weld pool sizes through training, A series of bead-on plate welding experiments were performed to assess the performance of the neural estimator.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2001.10a
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• pp.91-96
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• 2001

This paper presents a neural net based nonlinear adaptive controller for an autonomous underwater vehicle (AUV). AUV's dynamics are highly nonlinear and their hydrodynamic coefficients vary with different operational conditions, so it is necessary for the high performance control system of an AUV to have the capacities of learning and adapting to the change of the AUV's dynamics. In this paper a linearly parameterized neural network is used to approximate the uncertainties of the AUV's dynamics, and a sliding mode control is introduced to attenuate the effects of the neural network's reconstruction errors and the disturbances of AUV's dynamics. The presented controller is consist of three parallel schemes; linear feedback control, sliding mode control and neural network. Lyapunov theory is used to guarantee the asymptotic convergence of trajectory tracking errors and the neural network's weights errors. Numerical simulations for motion control of an AUV are performed to illustrate to effectiveness of the proposed techniques.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.872-877
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• 1991

This paper presents the composite control method using fuzzy and neural network theory. Fuzzy theory is applied to make control rules and neural net is used to learn them and to generate proper control signals. The electric furnace is controlled to maintain the desired temperature and to minimize the fluctuation of the temperatures in various locations inside the furnace. This controller consists of three neural nets which deal with the average of the temperatures, variances of them and the temperature stabilizing mechanism. Experiments are performed with the target temperatures of 70.deg. C and 80.deg. C. Test results show that this simple method is very effective.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.143-143
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• 2000

A neural net based generalized predictive control(NNGPC) is presented for a hydraulic servo position control system. The proposed scheme employs generalized predictive control, where the future output being generated from the output of artificial neural networks. The proposed NNGPC does not require an accurate mathematical model for the nonlinear hydraulic system and takes less calculation time than GPC algorithm if the teaming of neural network is done. Simulation studies have been conducted on the position control of a hydraulic motor to validate and illustrate the proposed method.

• Journal of Ocean Engineering and Technology
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• v.16 no.1
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• pp.8-15
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• 2002

This paper presents a neural net based nonlinear adaptive controller for an autonomous underwater vehicle (AUV). AUV's dynamics are highly nonlinear and their hydrodynamic coefficients vary with different operational conditions, so it is necessary for the high performance control system of an AUV to have the capacities of learning and adapting to the change of the AUV's dynamics. In this paper a linearly parameterized neural network is used to approximate the uncertainties of the AUV's dynamic, and the basis function vector of network is constructed according to th AUV's physical properties. A sliding mode control scheme is introduced to attenuate the effect of the neural network's reconstruction errors and the disturbances in AUV's dynamics. Using Lyapunov theory, the stability of the presented control system is guaranteed as well as the uniformly boundedness of tracking errors and neural network's weights estimation errors. Finally, numerical simulations for motion control of an AUV are performed to illustrate the effectiveness of the proposed techniques.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.330-333
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• 1992

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.243-247
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• 1992

This paper describes a neural network based control scheme with MRAC. The system consists of two neural network; one is for identifier and the other is for controller. Identification is firstly performed to learn the behavior of the nonlinear plant. Neural net controller is next trained by backpropagating the error at the output of plant through the identifier. Also the training method used in this paper repeatedly updates weights of neural network to track the reference model.

• Journal of Korean Society for Quality Management
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• v.49 no.4
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• pp.539-550
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• 2021

Purpose: The purpose of this study is to propose a multivariate CUSUM control chart that can detect the out-of-control state fast while monitoring the cross- and auto- correlated multivariate time series data. Methods: We first build models to estimate the observation data and calculate the corresponding residuals. After then, a multivariate CUSUM chart is applied to monitor the residuals instead of the original raw observation data. Vector Autoregression and Artificial Neural Net are selected for the modelling, and Separated-MCUSUM chart is selected for the monitoring. The suggested methods are tested under a number of experimental settings and the performances are compared with those of other existing methods. Results: We find that Artificial Neural Net is more appropriate than Vector Autoregression for the modelling and show the combination of Separated-MCUSUM with Artificial Neural Net outperforms the other alternatives considered in this paper. Conclusion: The suggested chart has many advantages. It can monitor the complicated multivariate data with cross- and auto- correlation, and detects the out-of-control state fast. Unlike other CUSUM charts finding their control limits by trial and error simulation, the suggested chart saves lots of time and effort by approximating its control limit mathematically. We expect that the suggested chart performs not only effectively but also efficiently for monitoring the process with complicated correlations and frequently-changed parameters.

• KIEE International Transaction on Systems and Control
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• v.2D no.2
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• pp.108-114
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• 2002

This paper describes the design of a robust adaptive controller for a nonlinear dynamical system with unknown nonlinearities. These unknown nonlinearities are approximated by multilayered neural networks (MNNs) whose parameters are adjusted on-line, according to some adaptive laws far controlling the output of the nonlinear system, to track a given trajectory. The main contribution of this paper is a method for considering input constraint with a rigorous stability proof. The Lyapunov synthesis approach is used to develop a state-feedback adaptive control algorithm based on the adaptive MNN model. An overall control system guarantees that the tracking error converges at about zero and that all signals involved are uniformly bounded even in the presence of input saturation. Theoretical results are illustrated through a simulation example.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.640-642
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• 1998

When resampling an image to a new set of coordinates, there is often a noticeable loss in image quality. The interpolation kernel determines the quality of interpolation. In this paper, We think two interpolation methods: cubic-spline method, neural net method, at first study given interpolation method using spline and then present new interpolation methon using SOFM and LAM(neural net method), finally compare the performance of several interpolation methods including replication, bilinear, spline and new methods.