• The Transactions of the Korean Institute of Electrical Engineers P
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• v.55 no.3
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• pp.117-122
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• 2006

The paper is proposed artificial neural network(ANN) sensorless control of induction motor drive with fuzzy learning control-fuzzy neural network(FLC-FNN) controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also this paper is proposed. speed control of induction motor using FLC-FNN and estimation of speed using ANN controller. The back Propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed so that the actual state variable will coincide with the desired one. The proposed control algorithm is applied to induction motor drive system controlled FLC-FNN and ANN controller, Also, this paper is proposed the analysis results to verify the effectiveness of the FLC-FNN and ANN controller.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.3
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• pp.49-62
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• 1999

Artificial Neural Networks(ANN) have been successfully applied to various kinds of business and engineering problems, especially those involved in pattern classification. However, because of the lack of design standard or guidelines, the structure of specific ANN depends on the designer's own experiments or choices. In other words, even though we could construct a better ANN, we often steeled down with just a satisfactory ANN. The purpose of this research is to apply the Genetic Algorithm(GA) to design a structure of ANN that yields better performance compared to the existing test results. For a bankruptcy prediction problem. an exiting research using ANN which consists of 22 input processing elements(PEs) for financial ratios and 5 hidden PEs showed 70% hit ratio. In our research, the input financial ratios and the number of hidden PEs are determined by GA. The best ANN, which consists of 8 input PEs and 6 hidden PEs, shows 78.03% hit ratio. In addition, we compare the performance of two types of reproduction schemes, i.e., generational reproduction and steady-state reproduction.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.99-102
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• 2001

We propose a PC cluster using general-purpose microprocessors and a high-speed network for simulating ANN (Artificial Neural Network) processes on Linux OS. We apply this cluster to intelligent information processing such as ANN simulation. The elapsed time for simulating ANNs can be reduced from 7,295 seconds by a PE (Processing Element) to 1,226 seconds by six PEs. The reliability of a pattern-classification using ANNs can be improved by the proposed ANN, Randomized-ANN. In order to generate a Randomized-ANN, we choose three ANNs and combine the output results from three huts by means of logical AND. Results are as follows: The mean correct answer rate is 94.4%, the mean wrong answer rate is only 0.1 %, and the mean unknown answer rate is 5.5 %. We make sure that Randomized-ANN approach reduces the mean wrong answer rate within a tenth part and improves the reliability of Japanese coin classification.

• Computers and Concrete
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• v.30 no.3
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• pp.209-224
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• 2022

In this paper, the artificial neural network (ANN) is employed to predict the flexural behavior of reinforced concrete (RC) beams retrofitted with carbon fiber/epoxy composites modified by carbon nanotubes (CNTs). Multiple techniques are used to improve the accuracy of the ANN prediction, as the data represents a multivalued function. These techniques include static ANN modeling, ANN modeling with load history, and ANN modeling with double load history. The developed ANN models are used to predict the load-displacement profiles of beams retrofitted with either CFRP or CNTs modified CFRP, flexural capacity, and maximum displacement of the beams. The results demonstrate that the ANN is able to predict the flexural behavior of the retrofitted RC beams as well as the effect of each parameter including the type of the used epoxy and the presence of the CNTs.

• Advances in Computational Design
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• v.8 no.1
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• pp.37-59
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• 2023

Squat reinforced concrete (SRC) shear walls are a critical part of the structure for both office/residential buildings and nuclear structures due to their significant role in withstanding seismic loads. Despite this, empirical formulae in current design standards and published studies demonstrate a considerable disparity in predicting SRC wall shear strength. The goal of this research is to develop and evaluate hybrid and ensemble artificial neural network (ANN) models. State-of-the-art population-based algorithms are used in this research for hybrid intelligence algorithms. Six models are developed, including Honey Badger Algorithm (HBA) with ANN (HBA-ANN), Hunger Games Search with ANN (HGS-ANN), fitness-distance balance coyote optimization algorithm (FDB-COA) with ANN (FDB-COA-ANN), Averaging Ensemble (AE) neural network, Snapshot Ensemble (SE) neural network, and Stacked Generalization (SG) ensemble neural network. A total of 434 test results of SRC walls is utilized to train and assess the models. The results reveal that the SG model not only minimizes prediction variance but also produces predictions (with R²= 0.99) that are superior to other models.

• Communications of the Korean Mathematical Society
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• v.27 no.1
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• pp.23-36
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• 2012

Dual monoidal category $\mathcal{C}^*$ of a monoidal functor F : $\mathcal{C}\;{\rightarrow}\;\mathcal{V}$ has been constructed by S. Majid. In this paper, we extend the construction of dual structures for an Ann-functor F : $\mathcal{B}\;{\rightarrow}\;\mathcal{A}$. In particular, when F = $id_{\mathcal{A}}$, then the dual category $\mathcal{A}^*$ is indeed the center of $\mathcal{A}$ an this is a braided Ann-category.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.12 no.11
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• pp.1038-1047
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• 2000

The objective of this study is to develop an optimized Artificial Neural Network(ANN) model to predict the descending time of room air temperature. For this, program for predicting room air temperature and ANN program using generalized delta rule were collected through simulation for predicting room air temperature. ANN was trained and the ANN model having the optimized values-learning rate, moment, bias, number of hidden layer, and number of neuron of hidden layer was presented.

• Journal of Korea Water Resources Association
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• v.53 no.11
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• pp.929-938
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• 2020

In this study, we estimated missing evapotranspiration (ET) data at a eddy-covariance flux tower in the Cheongmicheon farmland site using the Artificial Neural Network (ANN). The ANN showed excellent performance in numerical analysis and is expanding in various fields. To evaluate the performance the ANN-based gap-filling, ET was calculated using the existing gap-filling methods of Mean Diagnostic Variation (MDV) and Food and Aggregation Organization Penman-Monteith (FAO-PM). Then ET was evaluated by time series method and statistical analysis (coefficient of determination, index of agreement (IOA), root mean squared error (RMSE) and mean absolute error (MAE). For the validation of each gap-filling model, we used 30 minutes of data in 2015. Of the 121 missing values, the ANN method showed the best performance by supplementing 70, 53 and 84 missing values, respectively, in the order of MDV, FAO-PM, and ANN methods. Analysis of the coefficient of determination (MDV, FAO-PM, and ANN methods followed by 0.673, 0.784, and 0.841, respectively.) and the IOA (The MDV, FAO-PM, and ANN methods followed by 0.899, 0.890, and 0.951 respectively.) indicated that, all three methods were highly correlated and considered to be fully utilized, and among them, ANN models showed the highest performance and suitability. Based on this study, it could be used more appropriately in the study of gap-filling method of flux tower data using machine learning method.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.1
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• pp.57-64
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• 2006

This paper is proposed for a artificial neural network(ANN) sensorless control based on the vector controlled induction motor drive, or proposes a adaptive fuzzy teaming control(AFLC). The fuzzy logic principle is first utilized for the control rotor speed. AFLC scheme is then proposed in which the adaptation mechanism is executed using fuzzy logic. Also, this paper is proposed for a method of the estimation of speed of induction motor using ANN Controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the analysis results to verify the effectiveness of the new method.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.4
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• pp.55-70
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• 2001

Artificial neural network(ANN) models have been widely used for the classification problems in business such as bankruptcy prediction, credit evaluation, etc. Although the application of ANN to classification of consumers＇ choice behavior is a promising research area, there have been only a few researches. In general, most of the researches have reported that the classification performance of the ANN models were better than conventional statistical model Because the survey data on consumer behavior may include much noise and missing data, ANN model will be more robust than conventional statistical models welch need various assumptions. The purpose of this paper is to study the potential of the ANN model for forecasting consumers＇ choice behavior based on survey data. The data was collected by questionnaires to the shoppers of department stores and discount stores. Then the correct classification rates of the ANN models for the training and test sample with that of multiple discriminant analysis(MDA) and logistic regression(Logit) model. The performance of the ANN models were betted than the performance of the MDA and Logit model with respect to correct classification rate. By using input variables identified as significant in the stepwise MDA, the performance of the ANN models were improved.