• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.12 no.3
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• pp.124-131
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• 1998

This paper proposed the alarm processing system to improve the efficiency of monitoring method by applying the neural network and troubleshooting knowledge base for IADAPS(Intelligent Alarm Diagnosis And Processing System) method in an receiving and distributing board of Building complex. This IADAPS is abased on the cumulative generalized delta rule of backpropagation in neural network. It was used to infer the minimum alarms among multi-fired alarms, and the inferred alarm can be displayed maintenance information of facility by using a pre-defined troubleshoot knowledge base. For validating the proposed monitoring method, he method of simulation used to the five case of virtual scenario. As comparison results, a proposed method in this paper could be proved the applied possibility of an neural network and utilized in fields of facilities maintenance, if needed, be operated by non-expertise.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.12
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• pp.2418-2425
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• 1994

Although the error backpropagation(EBP) algorithm based on the gradient descent method is a widely-used learning algorithm of neural networks, learning sometimes takes a long time to acquire accuracy. This paper develops a novel learning method to alleviate the problems of EBP algorithm such as local minima, slow speed, and size of structure and thus to improve performance by adopting other new networks. Gaussian Potential Function networks(GPFN), in parallel with multilayer neural networks. Empirical simulations show the efficacy of the proposed algorithm in function approximation, which enables us to train networks faster with the better generalization capabilities.

• The Journal of the Acoustical Society of Korea
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• v.13 no.1
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• pp.5-13
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• 1994

This paper presents a study on Neural Networks for Phoneme Recognition and performs the Phoneme Recognition using TDNN (Time Delay Neural Network). Also, this paper proposes training algorithm for speech recognition using neural nets that is a proper to large scale TDNN. Because Phoneme Recognition is indispensable for continuous speech recognition, this paper uses TDNN to get accurate recognition result of phonemes. And this paper proposes new training algorithm that can converge TDNN to an optimal state regardless of the number of phonemes to be recognized. The recognition experiment was performed with new training algorithm for TDNN that combines backpropagation and Cauchy algorithm using stochastic approach. The results of the recognition experiment for three phoneme classes for two speakers show the recognition rates of $98.1\%$. And this paper yielded that the proposed algorithm is an efficient method for higher performance recognition and more reduced convergence time than TDNN.

• Journal of Sensor Science and Technology
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• v.8 no.1
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• pp.45-52
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• 1999

In this raper, an enhanced learning method is proposed for improving the learning speed of the error back propagation learning algorithm. In order to cope with the premature saturation phenomenon at the initial learning stage, a variation scheme of active functions is introduced by using higher order functions, which does not need much increase of computation load. It naturally changes the learning rate of inter-connection weights to a large value as the derivative of sigmoid function abnormally decrease to a small value during the learning epoch. Also, we suggest the hybrid learning method incorporated the proposed method with the momentum training algorithm. Computer simulation results show that the proposed learning algorithm outperforms the conventional methods such as momentum and delta-bar-delta algorithms.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.3 s.174
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• pp.634-644
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• 2000

Fatigue crack growth and life is estimated by various fracture mechanical parameters but affected by load, material and environment. Fatigue character of component without surface notch cannot be e valuated by above-mentioned parameters due to microstructure of in-service material. Single fracture mechanical parameter or nondestructive parameter cannot predict fatigue damage in arbitrary boundary condition but multiple fracture mechanical parameters or nondestructive parameters can Fatigue crack growth modelling with three point representation scheme uses this merit but has limit on real-time monitoring. Therefore, this study shows fatigue damage model using backpropagatior. neural networks on the basis of X-ray half breadth ratio B/$B_o$ fractal dimension $D_f$ and fracture mechanical parameters can predict fatigue crack growth rate da/dN and cycle ratioN/$N_f$ at the same time within engineering estimated mean error(5%).

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.3
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• pp.41-48
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• 2003

This paper presents an indirect adaptive neuro controller using modified chaotic neural networks(MCNN) for nonlinear dynamic system. A modified chaotic neural networks model is presented for simplifying the traditional chaotic neural networks and enforcing dynamic characteristics. A new Dynamic Backpropagation learning method is also developed. The proposed MCNN paradigm is applied to the system identification of a MIMO system and the indirect adaptive neuro controller. The simulation results show good performances, since the MCNN has robust adaptability to nonlinear dynamic system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.11
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• pp.2264-2275
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• 1994

In this paper, image compression based on neural networks is presented with block classification and coding. Multilayer neural networks with error back-propagation learning algorithm are used to transform the normalized image date into the compressed hidden values by reducing spatial redundancies. Image compression can basically be achieved with smaller number of hidden neurons than the numbers of input and output neurons. Additionally, the image blocks can be grouped for adaptive compression rates depending on the characteristics of the complexity of the blocks in accordance with the sensitivity of the human visual system(HVS). The quantized output of the hidden neuron can also be entropy coded for an efficient transmission. In computer simulation, this approach lie in the good performances even with images outside the training set and about 25:1 compression rate was achieved using the entropy coding without much degradation of the reconstructed images.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.3
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• pp.144-151
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• 2005

This paper presents a forecasting technique of the short-term marginal price (SMP) using an Artificial Neural Network (ANN). The SW forecasting is a very important element in an electricity market for the optimal biddings of market participants as well as for market stabilization of regulatory bodies. Input data are organized in two different approaches, time-axis and day-axis approaches, and the resulting patterns are used to train the ANN. Performances of the two approaches are compared and the better estimate is selected by a composition rule to forecast the SMP. By combining the two approaches, the proposed composition technique reflects the characteristics of hourly, daily and seasonal variations, as well as the condition of sudden changes in the spot market, and thus improves the accuracy of forecasting. The proposed method is applied to the historical real-world data from the Korea Power Exchange (KPX) to verify the effectiveness of the technique.

• 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.

• Journal of Welding and Joining
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• v.17 no.5
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• pp.98-106
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• 1999

In GMAW, the spatters are generated according to the variation of the arc. Of the arc is stable, Few spatters are generated. But if unstable, too many spatters are generated. So, this means the spatters are dependent on the arc state. The aim of this study is to accurately estimate the arc state. To do this, the generated spatters were captured under the some welding conditions, and the waveforms of the arc voltage and welding current were collected. From the collected signals, the waveform factors and their standard deviations were extracted. Using these factors as input parameters of multi-layer artificial neural network, the learning for the weight of the generated spatters is performed and the estimation results to the real spatter are assessed. Obtained results are as follow: the linear correlation coefficient between the estimated result and the real spatters was 0.9986. And although the average convergence error was set 0.002, the estimated error to the real spatter was within 0.1 gr/min at each welding condition. In the estimation for the weight generated spatters, the result with multi-layer neural network was far better than with multiple regression analysis. Especially, even though under the welding condition which the arc state is unstable (the spatter is generated much more), very excellent estimation performance was shown.