• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.5
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• pp.1-6
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• 2001

In this paper, we propose the initial optimized structure of the Radial Basis Function Network(RBFN) which is more simple in the part of the structure and converges more faster than Neural Network. For this, we use the analysis method using time frequency localization and we can decide the initial structure of the RBFN suitable for the given problem. When we compose the hidden nodes with the radial basis functions whose localization are similar with the target function in the plane of the time and frequency, we can make a good decision of the initial structure having an ability of approximation.

• Journal of the Korean Geotechnical Society
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• v.19 no.6
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• pp.343-350
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• 2003

The range of applications of artificial neural networks(Am) in many branches of geotechnical engineering is growing rapidly. This study was undertaken to develop an analysis model representing ultrasonically enhanced soil flushing by the use of ANN. Input data for the model-development were obtained by laboratory study, and used for training and verification. Analyses involved various ranges of momentum, loaming rate, activation function, hidden layer, and nodes. Results of the analyses were used to obtain the optimum conditions for establishing and verifying the model. The coefficient of correlation between the measured and the predicted data using the developed model was relatively high. It shows potential application of ANN to ultrasonically enhanced soil flushing which is not easy to build up a mathematical model.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.2
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• pp.393-406
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• 1993

In resistance spot welding process, size of molten nuggest have been utilized to assess the integrity of the weld quality. However real-time monitoring of the nugget size is an extremely difficult problem. This paper describes the design of an artificial neural networks(ANN) estimator to predict the nugget size for on-line use of weld quality monitoring. The main task of the ANN estimator is to realize the mapping characteristics from the sampled dynamic resistance signal to the actual negget size through training. The structure of the ANN estimator including the number of hidden layers and nodes in a layer is determined by an estimation error analysis. A series of welding experiments are performed to assess the performance of the ANN estimator. The results are quite promissing in that real-time estimation of the invisible nugget size can be achieved by analyzing the dynamic resistance signal without any conventional destructive testing of welds.

• Journal of Korean Society on Water Environment
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• v.21 no.1
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• pp.21-28
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• 2005

The training and prediction modeling using an artificial neural network was implemented to predict the turbidity of treated water as well as to estimate the optimized feed concentration of polyaluminium chloride (PACl) in a water treatment plant. The parameters used in the input layers were pH, temperature, turbidity and alkalinity, while those in output layers were PACl and turbidity of treated water. Levenberg-Marquadt method of feedforward back-propagation perceptron in the neural network toolbox of MATLAB program was used in this study. Correlation coefficients of the training data with the measured data were 0.9997 for PACl and 0.6850 for turbidity and those of the testing data with measured data were 0.9140 for PACl and 0.3828 for turbidity, when four parameters at input layer, 12-12 nodes each at both the first and the second hidden layers, and two parameters(PACl and turbidity) at output layer were used. Although the predictability of PACl was improved, compared to that of the previous studies to use the only coagulant dose as output layer, turbidity in treated water could not be predicted well. Acquisition of more data through several years obtained with the advanced on-line measuring system could make the artificial neural network useful and practical in actual water treatment plants.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.2
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• pp.155-163
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• 2008

This paper presents a novel framework for analysis of power system wide-area blackout based on so called fault cascading scenarios. For a given power system operating state, "triggering" faults or a "seed faults" are chosen based on the probabilities estimated from the hazard rates. The fault probabilities reflect both the load and the weather conditions. Effects of hidden failures in protection systems are also reflected in establishing the fault propagation scenarios since they are one of the major causes for the wide-area blackouts. A tree type data structure called a PS-BEST(Power System Blackout Event Scenario Tree) is proposed for construction of the fault cascading scenarios, in which nodes represent various power system operating states and the arcs are the events causing transitions between the states. Arcs can be either probabilistic or deterministic. For a given initial fault, the total probability of leading to wide-area blackout is estimated by aggregating the individual probability of each fault sequence route leading to wide-area blackout. A case study is performed on the IEEE RTS-79(24 bus) system based on the fault data presented by the North American Electrical Reliability Council(NERC). Test results demonstrate the potentials and the effectiveness of the proposed technique for the future wide-area blackout analysis.

• Structural Engineering and Mechanics
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• v.63 no.2
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• pp.237-249
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• 2017

The Self-Organization Feature Map as an unsupervised network is very widely used these days in engineering science. The applied network in this paper is the Self Organization Feature Map with constant weights which includes Kohonen Network. In this research, Reinforced Concrete Shear Wall buildings with different stories and heights are analyzed and a database consisting of measured fundamental periods and characteristics of 78 RC SW buildings is created. The input parameters of these buildings include number of stories, height, length, width, whereas the output parameter is the fundamental period. In addition, using Genetic Algorithm, the structure of the Self-Organization Feature Map algorithm is optimized with respect to the numbers of layers, numbers of nodes in hidden layers, type of transfer function and learning. Evaluation of the SOFM model was performed by comparing the obtained values to the measured values and values calculated by expressions given in building codes. Results show that the Self-Organization Feature Map, which is optimized by using Genetic Algorithm, has a higher capacity, flexibility and accuracy in predicting the fundamental period.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.4 s.193
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• pp.93-101
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• 2007

Aluminum alloy which is one of the light materials has been tried to apply to light weight vehicle body. In order to do that, welding technology is very important. In case of the aluminum laser welding, the strength of welded part is reduced due to porosity, underfill, and magnesium loss. To overcome these problems, laser welding of aluminum with filler wire was suggested. In this study, experiment about laser welding of AA5182 aluminum alloy with AA5356 filler wire was performed according to process parameters such as laser power, welding speed and wire feed rate. The tensile strength was measured to find the weldability of laser welding with filler wire. The models to estimate tensile strength were suggested using three regression models and one neural network model. For regression models, one was the multiple linear regression model, another was the second order polynomial regression model, and the other was the multiple nonlinear regression model. Neural network model with 2 hidden layers which had 5 and 3 nodes respectively was investigated to find the most suitable model for the system. Estimation performance was evaluated for each model using the average error rate. Among the three regression models, the second order polynomial regression model had the best estimation performance. For all models, neural network model has the best estimation performance.

• Journal of Korean Society on Water Environment
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• v.22 no.5
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• pp.952-957
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• 2006

The present study applied wavelet transform and artificial neural networks (ANNs) for the prediction of daily TOC data. TOC data were transformed into denoised data by the wavelet transform and the noise-reduced data were used for the prediction model by artificial neural networks. For the application of wavelet transform, Daubechies wavelet of order 10 ('db10') was used as a basis function and decomposed the TOC data up to fifth level with five detail components and one approximation component. ANNs were calibrated with the input data of the segregated TOC data corresponding to the details from second to fifth level and the approximation. Consequently, the ANNs model for the prediction of daily TOC data showed the best result when it had seventeen hidden nodes in its layer.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2565-2571
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• 2020

Background: Understanding the behaviour of nuclear fuel claddings by conducting burst test on single cladding tube under simulated loss-of-coolant accident conditions and developing theoretical cum empirical predictive computer codes have been the focus of several investigations. The developed burst criterion (a) assumes symmetrical deformation of cladding tube in contrast to experimental observation (b) interpolates the properties of Zircaloy-4 cladding in mixed α+β phase (c) does not account for azimuthal temperature variations. In order to overcome all these drawbacks of burst criterion, it is reasoned that artificial intelligence technique may be a better option to predict the burst parameters. Methods: Artificial neural network models based on feedforward backpropagation algorithm with logsig transfer function are developed. Results: Neural network architecture of 2-4-4-3, that is model with two hidden layers having four nodes in each layer is found to be the most suitable. The mean, maximum, and minimum prediction errors for this optimised model are 0.82%, 19.62%, and 0.004%, respectively. Conclusion: The burst stress, burst temperature, and burst strain obtained from burst criterion have average deviation of 19%, 12%, and 53% respectively whereas the developed neural network model predicted these parameters with average deviation of 6%, 2%, and 8%, respectively.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.4
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• pp.67-75
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• 1998

In general, Evoluationary Algorithm(EAs) are refered to as methods of population-based optimization. And EAs are considered as very efficient methods of optimal sytem design because they can provice much opportunity for obtaining the global optimal solution. This paper presents a co-evolution scheme of artifical neural networks, which has two different, still cooperatively working, populations, called as a host popuation and a parasite population, respectively. Using the conventional generatic algorithm the host population is evolved in the given environment, and the parastie population composed of schemata is evolved to find useful schema for the host population. the structure of artificial neural network is a diagonal recurrent neural netork which has self-feedback loops only in its hidden nodes. To find optimal neural networks we should take into account the structure of the neural network as well as the adaptive parameters, weight of neurons. So we use the genetic algorithm that searches the structure of the neural network by the co-evolution mechanism, and for the weights learning we adopted the evolutionary stategies. As a results of co-evolution we will find the optimal structure of the neural network in a short time with a small population. The validity and effectiveness of the proposed method are inspected by applying it to the stabilization and position control of the invered-pendulum system. And we will show that the result of co-evolution is better than that of the conventioal genetic algorithm.