• Journal of Electrical Engineering and Technology
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• v.4 no.1
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• pp.28-34
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• 2009

In a Gas Insulated Substation (GIS), Very Fast Transients (VFTs) are generated mainly due to switching operations. These transients may cause internal faults, i.e., layer-to-layer faults in a capacitively graded bushing as it is one of the most important terminal equipment for GIS. The healthiness of the bushing is generally verified by measuring its leakage current. However, the change in current magnitude/pattern is only marginal for different types of fault conditions. Leakage current monitoring (LCM) systems generate large amounts of data and computer aided interpretation of defects may be of great assistance when analyzing this data. In view of the above, ANN techniques have been used in this study for identification of these minor faults. A single layer perceptron network, a two layer feed-forward back propagation network and cascade correlation (CC) network models are used to identify interlayer faults in the bushing. The effectiveness of the CC network over perceptron and back propagation networks in identification of a fault has been analysed as part of the paper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1078-1084
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• 2010

This paper presents a speech enhancement system which restores the amplitude components and phase components by discrete Fourier transform (DFT), using neural network training by back-propagation algorithm. First, a neural network is trained using DFT amplitude components and phase components of noisy speech signal, then the proposed system enhances speech signals that are degraded by white noise using a neural network. Experimental results demonstrate that speech signals degraded by white noise are enhanced by the proposed system using the neural network, whose inputs are DFT amplitude components and phase components. Based on measuring spectral distortion measurement, experiments confirm that the proposed system is effective for white noise.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.6
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• pp.1889-1893
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• 2008

This paper explores the classification performance of applying to support vector machines (SVMs) for the image classification problems. In this study, we extract the color, texture and shape features of natural images and compare the performance of image classification using each individual feature and integrated features. The experiment results show that classification accuracy on the basis of color feature is better than that based on texture and shape features and the results of the integrating features also provides a better and more robust performance than individual feature. In additions, we show that the proposed classifier of SVM based approach outperforms BPNN to corporate the image classification problems.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.8-14
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• 1999

This paper presents the methods of short-term load forecasting Kohonen neural networks and back-propagation neural networks. First, historical load data is divided into 5 patterns for the each seasonal data using Kohonen neural networks and using these results, load forecasting neural network is used for next day hourly load forecasting. Next day hourly load of weekdays and weekend except holidays are forecasted. For load forecasting in summer, max-temperature and min-temperature data as well as historical hourly load date are used as inputs of load forecasting neural networks for a better forecasting accuracy. To show the possibility of the proposed method, it was tested with hourly load data of Korea Electric Power Corporation(1994-95).

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.2
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• pp.138-142
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• 2004

This paper proposes the design of the model following control system using the PID control structure. PID control system became model following control by inserting new pre-compensator in order to improve control performance in discrete-time region. Gain of the PID controller needs to be readjusted when response of system changes due to disturbance or load fluctuation. Performance of control system improves by joining neural network to PID control system because performance of control system depends largely on each PID gain in PID control system. And the games of the PID controller in the proposed control system are automatically adjusted by back-propagation algorithm of the neural network. Angular position of DC servo motor is selected as a plant in order to verify control performance in model following control. After it is applied to the position control system, it's performance is verified through computer experiment.

• Journal of the Korean Institute of Intelligent Systems
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• v.2 no.1
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• pp.17-32
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• 1992

The multi-layer neural network that has broadly been utilized in designing the controller of robot manipulator possesses the desirable characteristics of learning capacity, by which the uncertain variation of the dynamic parameters of robot can be handled adaptively, and parallel distributed processing that makes it possible to control on real-time. However the error back propagation algorithm that has been utilized popularly in the learning of the multi-layer neural network has the problem of its slow convergence speed. In this paper, an approach to improve the convergence speed is proposed using the fuzzy logic that can effectively handle the uncertain and fuzzy informations by linguistic level. The effectiveness of the proposed algorithm is demonstrated by computer simulation of PUMA 560 robot manupulator.

• Journal of Sensor Science and Technology
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• v.13 no.1
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• pp.41-46
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• 2004

There is currently much interest in the development of instruments that emulate the senses of humans. Increasingly, there is demand for mimicking the human sense of smell, which is a sophisticated chemosensory system. An electronic nose system is applicable to a large area of industries including environmental monitoring. We have designed a protable electronic nose system using an array of commercial chemical gas sensors for recognizing and analyzing the various odours. In this paper, we have implemented a portable electronic nose system using an array of gas sensors for recognizing and analyzing VOCs (Volatile Organic Compounds) in the field. The accuracy of a portable electronic nose system may be lower than an instrument such as GC/MS (Gas Chromatography/Mass Spectrometer). However, a portable electronic nose system could be used on the field and showed fast response to pollutants in the field. Several different algorithms for odours recognition were used such as BP (Back-Propagation) or LM-BP (Levenberq-Marquardt Back-Propagation). We applied RBF (Radial Basis Function) Network for recognition and quantifying of odours, which has simpler and faster compared to the previously used algorithms such as BP and LM-BP.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.26-31
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• 2014

In this paper, we present performance analysis of video smoke detection based on BPN-Network that is using multi-smoke feature, and Neural Network. Conventional smoke detection method consist of simple or mixed functions using color, temporal, spatial characteristics. However, most of all, they don't consider the early fire conditions. In this paper, we analysis the smoke color and motion characteristics, and revised distinguish the candidate smoke region. Smoke diffusion, transparency and shape features are used for detection stage. Then it apply the BPN-Network (Back-Propagation Neural Network). The simulation results showed 91.31% accuracy and 2.62% of false detection rate.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.7
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• pp.2913-2918
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• 2011

This paper proposed the friction model using Preisach algorithm with neural network based on experimental results. In order to apply the neural network algorithm, the back propagation update rule was used and the updated weighting factor of neural network was applied to distribute function of Preisach model. In order to implement the proposed algorithm, the LabView software was used to apply to the precision control of mechanical system. The evaluation of the proposed friction model was executed through experiments.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.703-715
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• 2017

Mammogram images are sensitive in nature and even a minor change in the environment affects the quality of the images. Due to the lack of expert radiologists, it is difficult to interpret the mammogram images. In this paper an algorithm is proposed for a computer-aided diagnosis system, which is based on the wavelet based adaptive sigmoid function. The cascade feed-forward back propagation technique has been used for training and testing purposes. Due to the poor contrast in digital mammogram images it is difficult to process the images directly. Thus, the images were first processed using the wavelet based adaptive sigmoid function and then the suspicious regions were selected to extract the features. A combination of texture features and gray-level co-occurrence matrix features were extracted and used for training and testing purposes. The system was trained with 150 images, while a total 100 mammogram images were used for testing. A classification accuracy of more than 95% was obtained with our proposed method.