• Proceedings of the IEEK Conference
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• 2002.06e
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• pp.25-28
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• 2002

The path planning of autonomous mobile robot use two method. One is global path planning and another is local path planning. In this paper, We study the local path planning of autonomous mobile robot move in unknown enviroment. This local path planning is based on neural network using the direction indicator rules learning. also the system is made up of sensor system. The motion control system for real-time execution. The experimental results show that the developed direction indicator system operates properly and strongly at circumstance.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.431-431
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• 2000

The modeling of 5-bar linkage robot manipulator dynamics by means of a mathematical and neural architecture is presented. Such a model is applicable to the design of a feedforward controller or adjustment of controller parameters. The inverse model consists of two parts: a mathematical part and a compensation part. In the mathematical part, the subsystems of a 5-bar linkage robot manipulator are constructed by applying Kawato's Feedback-Error-Learning method, and trained by given training data. In the compensation part, MLP backpropagation algorithm is used to compensate the unmodeled dynamics. The forward model is realized from the inverse model using the inverse of inertia matrix and the compensation torque is decoupled in the input torque of the forward model. This scheme can use tile mathematical knowledge of the robot manipulator and analogize the robot characteristics. It is shown that the model is reasonable to be used for design and initial gain tuning of a controller.

• The Transactions of the Korea Information Processing Society
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• v.2 no.5
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• pp.626-633
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• 1995

It is vary hard to identify the fuzzy rules and tune the membership functions of the fuzzy inference in fuzzy systems modeling, We propose a fuzzy neural network model which can automatically identify the fuzzy rules and tune the membership functions of fuzzy inference simultaneously using artificial neural networks, and modify backpropagation algorithm for improving the convergence. The proposed method is verified by the simulation for a robot manipulator.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.855-858
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• 1995

A neural network has very simple construction (input, output and connection weight) and then it can be robusted against some disturbance. In this paper, we proposed a neuro-controller using a Multi-Layered neural network which is combined with PD controller. The proposed neuro-controller is learned by backpropagation learning rule with momentum and neuro-controller adjusts connection weight in neural network to make approximate dynamic model of DC Servo motor. Computer Simulation results show that the proposed neuro-controller's performance is better than that of origianl PD controller.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.55.2-55
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• 2001

The statistical analysis of the feature extraction and the neural networks are proposed to recognize a human face. In the preprocessing step, the normalized skin color map with Gaussian functions is employed to extract the region efface candidate. The feature information in the region of face candidate is used to detect a face region. In the recognition step, as a tested, the 360 images of 30 persons are trained by the backpropagation algorithm. The images of each person are obtained from the various direction, pose, and facial expression, Input variables of the neural networks are the feature information that comes from the eigenface spaces. The simulation results of 30 persons show that the proposed method yields high recognition rates.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.2
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• pp.63-70
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• 1999

This paper is proposed a new method to deal with the optimized auto-tuning for the Pill controller which is used to the process-control in various fields. First of all, in this method, 1st order delay system with dead time which is modelled from the unit step response of the system is Pade-approximated, then initial values are determined by the Ziegler-Nichols method. So we can find the parameters of Pill controller so as to minimize the fuzzy criterion function which includes the maximum overshoot, damping ratio, rising time and settling time. Finally, after studying the parameters of Pill controller by Backpropagation of Neural-Network, when we give new K, L, T values to Neural-Network, the optimized parameter of Pill controller is found by Neural-Network Program.rogram.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2414-2433
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• 2021

This article focuses on providing remedial solutions for COVID disease through the data collection process. Recently, In India, sudden human losses are happening due to the spread of infectious viruses. All people are not able to differentiate the number of affected people and their locations. Therefore, the proposed method integrates robotic technology for monitoring the health condition of different people. If any individual is affected by infectious disease, then data will be collected and within a short span of time, it will be reported to the control center. Once, the information is collected, then all individuals can access the same using an application platform. The application platform will be developed based on certain parametric values, where the location of each individual will be retained. For precise application development, the parametric values related to the identification process such as sub-interval points and intensity of detection should be established. Therefore, to check the effectiveness of the proposed robotic technology, an online monitoring system is employed where the output is realized using MATLAB. From simulated values, it is observed that the proposed method outperforms the existing method in terms of data quality with an observed percentage of 82.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.2
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• pp.88-95
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• 1996

It is inevitable for local systems to have deviations which represent interactions and modeling errors originated from the decomposition process of a large scale system. This paper presents a decentralized control scheme for interconnected systems using local linear models and a neuro-coordinator. In the proposed method, the local system is composed of a linear model and unknown deviations caused by linearizing the subsystems around operating points or by estimating parameters of the subsystems. Because the local system has unmeasurable deviations we define a local reference model which consists of a local linear model and a neural network to estimate the deviations indirectly. The reference model is reformed into a linear model which has no deviations through a transformation of input variables and we obtain an optimum feedback control law which minimizes a local performance index. Finally, we derive a decentralized feedback control law which consists of local linear states and neural network outputs. In the decentralized control, the neuro-coordinator generates a corrective control signal to cancel the effect of deviations through backpropagation learning with the errors obtained from the differences of the local system outputs and reference model outputs. Also, the stability of local system is proved by the degree of learning of the neural network under an assumption on a neural network learning index. It is shown by computer simulations that the proposed control scheme can be applied successfully to the control of a biased two-link planar robot manipulator.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.11a
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• pp.573-577
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• 2002

A new estimated neural networks are proposed in order to measure nonlinear road environments in realtime. This new neural networks is Error Estimated Neural Networks. The structure of it is similar to recurrent neural networks; a delayed output as the input and a delayed error between the output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models. To show the performance of this one, we control 7 degree simulation, this controller and driver were proved to be effective to drive a car in the environments of nonlinear road systems.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1B
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• pp.39-50
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• 2006

The flood control offices of main rivers have used a storage function model to forecast flood stage in Korea and studies of flood forecasting actively have been done even now. On this account, the storage function model, which is used in flood control office, regression models and artificial neural network model are applied into flood forecasting of study watershed in this paper. The result obtained by each method are analyzed for the comparative study. In case of storage function model, this paper uses the representative parameters of the flood control offices and the optimized parameters. Regression coefficients are obtained by regression analysis and neural network is trained by backpropagation algorithm after selecting four events between 1995 to 2001. As a result of this study, it is shown that the optimized parameters are superior to the representative parameters for flood forecasting. The results obtained by multiple, robust, stepwise regression analysis, one of the regression methods, show very good forecasts. Although the artificial neural network model shows less exact results than the regression model, it can be efficient way to produce a good forecasts.