• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.1059-1065
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• 2014

Wind turbine system can obtain the maximum wind energy using torque control under the rated wind speed, and wind turbine power is controlled as the rated power using pitch control over the rated wind speed. In this paper, we present a method for wind turbine pitch controller using neural networks. The purpose of the pitch control is to control generator speed and power in the above rated wind speed. To improve the neural network pitch controller, the difference between a rated and current speed of generator has been used for another input of neural networks as well as wind speed. Error back-propagation algorithm is used for training the neural network pitch controller and simulation and Matlab/Simulink is used for verifying that this system is controlled well.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4A
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• pp.639-645
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• 2006

FRP (Fiber Reinforced Polymer) is an excellent new constructional material in resistibility to corrosion, high intensity, resistibility to fatigue, and plasticity. FBG (Fiber Bragg Grating) sensor is widely used at present as a smart sensor due to lots of advantages such as electric resistance, small-sized material, and high durability. However, with insufficiency of measuring displacement, FBG sensor is used only as a sensor measuring physical properties like strain or temperature. In this study, FRP and FBG sensors are to be hybridized, which could lead to the development of a smart FRP rod. Moreover, developing the estimated model for deflection with neural network method, with the data measured through FBG sensor, could make conquest of a disadvantage of FBG sensor - uniquely used for sensing strain. Artificial neural network is MLP (Multi-layer perceptron), trained within error rate of 0.001. Nonlinear object function and back-propagation algorithm is applied to training and this model is verified with the measured axial displacement through UTM and the estimated numerical values.

• The Journal of Engineering Geology
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• v.15 no.1
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• pp.67-76
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• 2005

This paper presents the application of a neural network for prediction of the unconfined compressive strength from physical properties and schmidt hardness number on rock samples. To investigate the suitability of this approach, the results of analysis using a neural network are compared to predictions obtained by statistical relations. The data sets containing 55 rock sample records which are composed of sandstone and shale were assembled in Daegu area. They were used to learn the neural network model with the back-propagation teaming algorithm. The rock characteristics as the teaming input of the neural network are: schmidt hardness number, specific gravity, absorption, porosity, p-wave velocity and S-wave velocity, while the corresponding unconfined compressive strength value functions as the teaming output of the neural network. A data set containing 45 test results was used to train the networks with the back-propagation teaming algorithm. Another data set of 10 test results was used to validate the generalization and prediction capabilities of the neural network.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.917-920
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• 1998

In this paper, we propose a modified elman neural network structure for nonlinear system identification. The proposed structure is that all of network output feed back into hidden units and output units. Learning algorithm is standard back-propagation algorithm. The simulation showed the effectiveness of using the modified elman neural network structure in the nonlinear system identification.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.4
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• pp.1796-1802
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• 2011

In this paper, the radio propagation path loss prediction simulator in tunnel was developed. It used a image theory method for analysing precise radio propagation path. And it can predict radio propagation path loss in straight and curved tunnels. The simulator can plot realtime radio propagation paths using various parameters which was input by user. And it can simulate from changing transmitter and receiver positions. The predicted path loss of simulator was compared with the measurements in Chunhyun tunnel and confirmed the validity.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.607-608
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• 2012

• Journal of KSNVE
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• v.4 no.1
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• pp.59-69
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• 1994

A new LMS algorithm titled constrained LMS' is proposed for problems with constrained structure. The conventional LMS algorithm can not be used because it destroys the constrained structures of the weights or parameters. Proposed method uses error-back propagation, which is popular in training neural networks, for error minimization. The illustrative examplesare shown to demonstrate the applicability of the proposed algorithm.

• Electrical & Electronic Materials
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• v.8 no.4
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• pp.509-517
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• 1995

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.52-57
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• 1993

Conventional active control algorithm for duct system is developed without considering problems of constrained structure. Therefore it destroys the constrained structures of the weights or parameters. A new LMS algorithm, which does keep the constraints, is proposed for systems with known constrained structure. It is based on error-back propagation. The stability analysis and simulation example are also included.

• Proceedings of the Acoustical Society of Korea Conference
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• 1991.06a
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• pp.73-77
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• 1991

As the basic analysis to solve the stop consonants in phoneme based speech recognition using Back Propagation learning algorithm, changes in hidden units, training set and iteration. Also we propose an efficient processing method of separation between consonants and vowels.