• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.4
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• pp.184-191
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• 2001

This paper proposes a prefilter type velocity compensating control system using modified chaotic neural networks for the trajectory control of robotic manipulator. Since the structure of modified chaotic neural networks(MCNN) and neurons have highly nonlinear dynamic characteristics, MCNN can show the robust characteristics for controlling highly nonlinear dynamics like robotic manipulators. For its application, the trajectory controller of the three-axis robot manipulator is designed by MCNN. The MCNN controller acts as the compensator of the PD controller. Simulation results show that learning error decrease drastically via on-line learning and the performance is excellent. The MCNN controller showed much better control performance and shorter calculation time compared to the RNN controller, Another advantage of the proposed controller could by attached to conventional robot controller without hardware changes.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.231-234
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• 1993

This paper deals with the design of an adaptive controller using neural network. We present RBFMLP Neural Network which consists of serial-connected two networks - Radial Basis Function Network and Multi Layer Perceptron, and then design a controller based on proposed networks with the adaptive control system structure, The plant and parameters of the controller are identified by the neural networks. We use the dynamic backpropagation algorithm for the learning of networks. Simulations represent the superiorities of the proposed network and the controller.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.21 no.3
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• pp.1-8
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• 2013

An application of adaptive flight controller is required for the non-linear and high uncertain system that configuration of tiltrotor aircraft is dramatically changed from rotary wing mode to fixed wing mode. In this paper, the applicable adaptive controller for the tiltrotor aircraft was designed using Neural Networks and DMI (Dynamic Model Inversion). The performance of the SCAS (Stability and Control Augmentation System) was simulated against manned military specification, using the fullscale model of 'Smart UAV(Unmanned Aerial Vehicle)' developed by Korea Aerospace Research Institute. And Neural Networks based adaptive controller was verified through its whole operating envelope using the established HQ (Handling Quality) criteria.

• International Journal of Aeronautical and Space Sciences
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• v.3 no.1
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• pp.50-60
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• 2002

A new co-evolutionary algorithm, of which the convergence speed is accelerated by neural networks, is proposed and verified in this paper. To reduce computational load required for co-evolutionary optimization processes, the cost function and constraint information is stored in the neural networks, and the extra offspring group, whose cost is computed by the neural networks, is generated. It increases the offspring population size without overloading computational effort; therefore, the convergence speed is accelerated. The proposed algorithm is applied to attitude control design of flexible satellites, and it is verified by computer simulations and experiments using a torque-free air bearing system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.171-178
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• 2017

This paper studies the state estimation problem for static neural networks with time-varying delay. Unlike other studies, the controller scheme, which involves time-varying sampling and uncertainties, is first employed to design the state estimator for delayed static neural networks. Based on Lyapunov functional approach and linear matrix inequality technique, the non-fragile sampled-data estimator is designed such that the resulting estimation error system is globally asymptotically stable with $H_{\infty}$ performance. Finally, the effectiveness of the developed results is demonstrated by a numerical example.

• Proceedings of the KIEE Conference
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• 1993.07b
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• pp.1004-1006
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• 1993

A new method for the optimal design of a single-sided linear induction motor(SLIM) is presented. The method utilizes the neural networks and finite element method for optimizing the design parameters of SLIM. The finite element analysis is used to produce a variety of neural networks training data and the neural networks is used for optimizing the design parameters by sequential unconstrained minimization technique(SUMT). As a result, it is known that the novel method is very efficient and accurate as an optimization technique.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.11a
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• pp.558-562
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• 2003

In this paper, neural networks is studied to apply as a PD source classification in XLPE power cable specimen. For treeing discharge sources in the specimen, three defected models are made. And these data making use of a computer-aided discharge analyser, statistical and other discharge parameters is calculated to discrimination between different models of discharge sources. And also these parameter is applied to classify PD sources by neural networks. Neural Networks has good recognition rate for three PD sources.

• Journal of IKEEE
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• v.23 no.3
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• pp.899-903
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• 2019

In networked control systems, time-varying delay of the transmitting signal is inevitable. If the transmission delay is longer than the fixed sampling time, the system will be unstable. To solve this problem, this paper proposes the method to predict the delay using logic-based fuzzy neural networks, and the predicted time delay will be used as a sampling time in the networked control systems. To verify the effectiveness of the proposed method, the delay data collected from the real system are used to train and test the logic-based fuzzy neural networks.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.63-68
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• 2022

In this study, we produced defect detection models using parallel convolution neural networks. If convolution neural networks are constructed parallel type, the model's detection accuracy will increase and detection time will decrease. We produced parallel-type defect detection models using 4 types of convolutional algorithms. The performance of models was evaluated using evaluation indicators. The model's performance is detection accuracy and detection time. We compared the performance of each parallel model. The detection accuracy of the model using AlexNet is 97 % and the detection time is 0.3 seconds. We confirmed that when AlexNet algorithm is constructed parallel type, the model has the highest performance.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.26 no.4
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• pp.263-279
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• 2022

This paper presents an automatic inspection of defects in semiconductor images. We devise a statistical method to find defects on homogeneous background from the observation that it has a log-normal distribution. If computer aided design (CAD) data is available, we use it to construct a signed distance function (SDF) and change the pixel values so that the average of pixel values along the level curve of the SDF is zero, so that the image has a homogeneous background. In the absence of CAD data, we devise a hybrid method consisting of a model-based algorithm and two neural networks. The model-based algorithm uses the first right singular vector to determine whether the image has a linear or complex structure. For an image with a linear structure, we remove the structure using the rank 1 approximation so that it has a homogeneous background. An image with a complex structure is inspected by two neural networks. We provide results of numerical experiments for the proposed methods.