• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.24 no.8
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• pp.627-635
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• 2012

This study aims at developing an artificial neural network(ANN)-based predictive and adaptive temperature control method to control the openings at internal and external skins, and heating systems used in a building with double skin envelope. Based on the predicted indoor temperature, the control logic determined opening conditions of air inlets and outlets, and the operation of the heating systems. The optimization process of the initial ANN model was conducted to determine the optimal structure and learning methods followed by the performance tests by the comparison with the actual data measured from the existing double skin envelope. The analysis proved the prediction accuracy and the adaptability of the ANN model in terms of Root Mean Square and Mean Square Errors. The analysis results implied that the proposed ANN-based temperature control logic had potentials to be applied for the temperature control in the double skin envelope buildings.

• Transactions of Materials Processing
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• v.27 no.6
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• pp.331-338
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• 2018

Electrohydraulic forming (EHF) process is a high speed forming process that utilizes the electric energy discharge in fluid-filled chamber to deform a sheet material. This process is completed in a very short time of less than 1ms. Therefore, finite element analysis is essential to observe the deformation mechanism of the material in detail. In addition, to perform the numerical simulation of EHF, the material properties obtained from the high-speed status, not quasi static conditions, should be applied. In this study, to obtain the parameters in the constitutive equation of Al 6061-T6 at high strain rate condition, a surrogate model using an artificial neural network (ANN) technique was employed. Using the results of the numerical simulation with free-bulging die in LS-DYNA, the surrogate model was constructed by ANN technique. By comparing the z-displacement with respect to the x-axis position in the experiment with the z-displacement in the ANN model, the parameters for the smallest error are obtained. Finally, the acquired parameters were validated by comparing the results of the finite element analysis, the ANN model and the experiment.

• Corrosion Science and Technology
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• v.22 no.5
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• pp.377-386
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• 2023

Aircraft washing is performed periodically for corrosion control. Currently, the aircraft washing interval is qualitatively set according to the geographical conditions of each base. We developed a washing interval determination algorithm based on atmospheric corrosion environment monitoring data at the Republic of Korea Air Force (ROKAF) bases and United States Air Force (USAF) bases to determine the optimal interval. The main factors of the washing interval decision algorithm were identified through hierarchical clustering, sensitivity analysis, and analysis of variance, and criteria were derived. To improve the classification accuracy, we developed a washing interval decision model based on an artificial neural network (ANN). The ANN model was calibrated and validated using the atmospheric corrosion environment monitoring data and washing intervals of the USAF bases. The new algorithm returned a three-level washing interval, depending on the corrosion rate of steel and the results of the ANN model. A new base-specific aircraft washing interval was proposed by inputting the atmospheric corrosion environment monitoring results of the ROKAF bases into the algorithm.

• 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.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.4
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• pp.73-81
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• 2012

In this study, the settlement data of 32 center cored rockfill dams (total 39 monitored data) were collected and analyzed to develop the method to predict the crest settlement of a CCRD after impounding by using the internal settlement data occurred during construction. An artificial neural network (ANN) modeling was used in developing the method, which was considered to be a more reliable approach since in the ANN model dam height, core width, and core type were all considered as input variables in deriving the crest settlement, whereas in conventional methods, such as Clements's method, only dam height is used as a variable. The ANN analysis results showed a good agreement with the measured data, compared to those by the conventional methods using regression analysis. In addition, a simple procedure to use the ANN model for engineers in practice was provided by proposing the equations used for given input values.

• Nuclear Engineering and Technology
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• v.56 no.7
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• pp.2835-2841
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• 2024

Real-time analysis of metallic mineral grade and slurry concentration is significant for improving flotation efficiency and product quality. This study proposes an online detection method of ore slurry combining the Prompt Gamma Neutron Activation Analysis (PGNAA) technology and artificial neural network (ANN), which can provide mineral information rapidly and accurately. Firstly, a PGNAA analyzer based on a D-T neutron generator and a BGO detector was used to obtain a gamma-ray spectrum dataset of ore slurry samples, which was used to construct and optimize the ANN model for adaptive analysis. The evaluation metrics calculated by leave-one-out cross-validation indicated that, compared with the weighted library least squares (WLLS) approach, ANN obtained more precise and stable results, with mean absolute percentage errors of 4.66% and 2.80% for Fe grade and slurry concentration, respectively, and the highest average standard deviation of only 0.0119. Meanwhile, the analytical errors of the samples most affected by matrix effects was reduced to 0.61 times and 0.56 times of the WLLS method, respectively.

• The Transactions of the Korean Institute of Power Electronics
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• v.11 no.1
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• pp.29-37
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• 2006

Precise control of interior permanent magnet synchronous motor(IPMSM) over wide speed range is an engineering challenge. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using learning mechanism-fuzzy neural network(LM-FNN) and ANN(artificial neural network) control. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility md numerical processing capability. Also, this paper proposes speed control of IPMSM using LM-FNN and estimation of speed using artificial neural network 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 back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. Analysis results to verify the effectiveness of the new hybrid intelligent control proposed in this paper.

• Wind and Structures
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• v.32 no.2
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• pp.161-167
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• 2021

In this study, the artificial neural network (ANN) method was used for estimating the monthly mean wind speed of Sivas, in the central part of Turkey. Eighteen years of wind speed data obtained from nine measurement stations during the period of 2000-2017 at 10 m height was used for ANN analysis. It was found that mean absolute percentage error (MAPE) ranged from 3.928 to 6.662, mean bias error (MBE) ranged from -0.089 to -0.003, while root mean square error (RMSE) ranged from 0.050 to 0.157 and R2 ranged from 0.86 to 0.966. ANN models provide a good approximation of the wind speed for all measurement stations, however, a tendency to underestimate is also obvious.

• Journal of Digital Convergence
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• v.18 no.8
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• pp.231-242
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• 2020

The thesis presents a system that continuously collects the human body's physiological vital information at rest with sensors and ICT information technology and predicts diabetes using the collected information. it shows the artificial neural network machine learning method and essential basic variable values. The study method analyzed the correlation between heart rate measurements of BCG and ECG sensors in 20 DM- and 15 DM+ subjects. Artificial Neural Network (ANN) machine learning program was used to predictability of diabetes. The input variables are time domain information of HRV, heart rate, heart rate variability, respiration rate, stroke volume, minimum blood pressure, highest blood pressure, age, and sex. ANN machine learning prediction accuracy is 99.53%. Thesis needs continuous research such as diabetic prediction model by BMI information, predicting cardiac dysfunction, and sleep disorder analysis model using ANN machine learning.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.270-278
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• 2000

A machine vision system using charge coupled device(CCD) camera for the weed detection in a radish farm was developed. Shape features were analyzed with the binary images obtained from color images of radish and weeds. Aspect, Elongation and PTB were selected as significant variables for discriminant models using the STEPDISC option. The selected variables were used in the DISCRIM procedure to compute a discriminant function for classifying images into one of the two classes. Using discriminant analysis, the successful recognition rate was 92% for radish and 98% for weeds. To recognize radish and weeds more effectively than the discriminant analysis, an artificial neural network(ANN) was used. The developed ANN model distinguished the radish from the weeds with 100%. The performance of ANNs was improved to prevent overfitting and to generalize well using a regularization method. The successful recognition rate in the farms was 93.3% for radish and 93.8% for weeds. As a whole, the machine vision system using CCD camera with the artificial neural network was useful to detect weeds in the radish farms.