• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.11a
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• pp.289-292
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• 2006

In this paper, the hybrid algorithm of Support Vector Machine md Artificial Neural Network is used for the defect diagnostics algorithm for the aircraft turbo-shaft engine. The results of learning of ANN, especially, accuracy or speed of convergence are sensitive to the number of data, so a comparison between design point and off-design area, especially, Mach number and fuel flow variable area, is essential research. From application results for diagnostics of gas turbine engine, it was confirmed that the hybrid algorithm could detect well in the of-design area as well as design point.

• Journal of the Korean GEO-environmental Society
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• v.10 no.5
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• pp.49-57
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• 2009

In this study, one of the complicated geotechnical problem, compression index was predicted by a artificial neural network method of Levenberg-Marquardt (LM) algorithm. Predicted values were compared and evaluated by the results of the Back Propagation (BP) method, which is used extensively in geotechnical engineering. Also two different results were compared with experimental values estimated by verified experimental methods in order to evaluate the accuracy of each method. The results from experimental method generally showed higher error than the results of both artificial neural network method. The predicted compression index by LM algorithm showed better comprehensive results than BP algorithm in terms of convergence, but accuracy was similar each other.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.11a
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• pp.23-27
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• 2008

In this study, Real Coded Genetic Algorithm(RCGA) and Artificial Neural Network(ANN) are used for developing the defect diagnostics of the aircraft turbo-shaft engine. ANN accompanied with large amount data has a most serious problem to fall in the local minima. Because of this weak point, it becomes very difficult to obtain good convergence ratio and high accuracy. To solve this problem, GA based ANN has been suggested. GA is able to search the global minima better than ANN. GA based ANN has shown the RMS defect error of 5% less in single and dual defect cases.

• Journal of the Korean Geotechnical Society
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• v.16 no.1
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• pp.83-97
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• 2000

In the present study, artificial neural network based on the multi-layer perceptron is used and an optimum model is chosen through the process of efficiency evaluation in order to develop a system predicting maximum displacements of the earth retaining walls at various excavation stages. By analyzing the measured field data collected at various urban excavation sites in Korea, factors influencing on the behaviors of the excavation wall are examined. Among the measured data collected, reliable data are further selected on the basis of the performance ratio and are used as a data base. Data-based measurements are also utilized for both teaming and verifying the artificial neural network model. The learning is carried out by using the back-propagation algorithm based on the steepest descent method. Finally, to verify a validity of the formulated artificial neural network system, both the magnitude and the occurring position of the maximum horizontal displacement are predicted and compared with measured data at real excavation sites not included in the teaming process.

• Journal of Navigation and Port Research
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• v.44 no.3
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• pp.187-194
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• 2020

It is very important to forecast freight volume accurately to establish major port policies and future operation plans. Thus, related studies are being conducted because of this importance. In this paper, stepwise regression analysis and artificial neural network model were analyzed to compare the predictive power of each model on Gwangyang Port, the largest domestic port for coal and iron ore transportation. Data of a total of 121 months J anuary 2009-J anuary 2019 were used. Factors affecting coal and iron ore trade volume were selected and classified into supply-related factors and market/economy-related factors. In the stepwise regression analysis, the tonnage of ships entering the port, coal price, and dollar exchange rate were selected as the final variables in case of the Gwangyang Port coal volume forecasting model. In the iron ore volume forecasting model, the tonnage of ships entering the port and the price of iron ore were selected as the final variables. In the analysis using the artificial neural network model, trial-and-error method that various Hyper-parameters affecting the performance of the model were selected to identify the most optimal model used. The analysis results showed that the artificial neural network model had better predictive performance than the stepwise regression analysis. The model which showed the most excellent performance was the Gwangyang Port Coal Volume Forecasting Artificial Neural Network Model. In comparing forecasted values by various predictive models and actually measured values, the artificial neural network model showed closer values to the actual highest point and the lowest point than the stepwise regression analysis.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1399-1414
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• 2018

Natural forests are un-manned forests where the artificial forces of people are not applied to the formation of forests. On the other hand, artificial forests are managed by people for their own purposes such as producing wood, preventing natural disasters, and protecting wind. The artificial forests enable us to enhance economical benefits of producing more wood per unit area because it is well-maintained with the purpose of the production of wood. The distinction surveys have been performed due to different management methods according to forests. The distinction survey between natural forests and artificial forests is traditionally performed via airborne remote sensing or in-situ surveys. In this study, we suggest a classification method of forest types using satellite imagery to reduce the time and cost of in-situ surveying. A classification map of natural forest and artificial forest were generated using KOMPSAT-3, 3A, 5 data by employing artificial neural network (ANN). And in order to validate the accuracy of classification, we utilized reference data from 1/5,000 stock map. As a result of the study on the classification of natural forest and plantation forest using artificial neural network, the overall accuracy of classification of learning result is 77.03% when compared with 1/5,000 stock map. It was confirmed that the acquisition time of the image and other factors such as needleleaf trees and broadleaf trees affect the distinction between artificial and natural forests using artificial neural networks.

• Journal of the Korean Geotechnical Society
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• v.18 no.5
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• pp.37-42
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• 2002

Artificial neural networks are efficient computing techniques that are widely used to solve complex problems in many fields. In this paper a liquefaction potential was estimated by using a back propagation neural network model applicated to cyclic triaxial test data, soil parameters and site investigation data. Training and testing of the network were based on a database of 43 cyclic triaxial test data from 00 sites. The neural networks are trained by modifying the weights of the neurons in response to the errors between the actual output values and the target output value. Training was done iteratively until the average sum squared errors over all the training patterns were minimized. This generally occurred after about 15,000 cycles of training. The accuracy from 72% to 98% was shown for the model equipped with two hidden layers and ten input variables. Important effective input variables have been identified as the NOC,$D_10$ and (N$_1$)$_60$. The study showed that the neural network model predicted a CSR(Cyclic shear stress Ratio) of silty-sand reasonably well. Analyzed results indicate that the neural-network model is more reliable than simplified method using N value of SPT.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12A
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• pp.1181-1187
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• 2010

This paper proposes a mobile AI (Artificial Intelligence) conducting decision-making in the game through education for intelligent character on the basis of Neural Network. Neural Network is learned through the input/output value of the algorithm which defines the game rule and the problem solving method. The learned character is able to perceive the circumstances and make proper action. In this paper, the mobile AI using Neural Network has been step-by-step designed, and a simple game has been materialized for its functional experiment. In this game, the goal, the character, and obstacles exist on regular 2D space, and the character, evading obstacles, has to move where the goal is. The mobile AI can achieve its goals in changing environment by learning the solution to several problems through the algorithm defined in each experiment. The defined algorithm and Neural Network are designed to make the input/output system the same. As the experimental results, the suggested mobile AI showed that it could perceive the circumstances to conduct action and to complete its mission. If mobile AI learns the defined algorithm even in the game of complex structure, its Neural Network will be able to show proper results even in the changing environment.

• Journal of Energy Engineering
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• v.6 no.1
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• pp.68-76
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• 1997

This paper presents a new neural network training algorithm which reduces the required training time considerably and overcomes many of the shortcomings presented by the conventional back-propagation algorithm. The algorithm uses a modified form of the back-propagation algorithm to minimize the mean squared error between the desired and actual outputs with respect to the inputs to the nonlinearities. Artificial Neural Network (ANN) model using the new algorithm is applied to forecast the short-term electric load. Inputs to the ANN are past loads and the output of the ANN is the hourly load forecast for a given day.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6C
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• pp.421-429
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• 2006

This study was undertaken to develop an analysis model representing a reliable estimation of rigidity of Korean soft clay using an artificial neural network (ANN). Data for the model development were obtained through a laboratory study, and were used for training and verification. The coefficient of correlation between the measured and predicted data using the developed model was relatively high. It demonstrates the potential application of ANN for the reliable estimation of Korean soft clay rigidity while past attempts at building such a mathematical model have proved difficult.