• Journal of Korean Society of Water and Wastewater
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• v.19 no.4
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• pp.395-403
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• 2005

It is very important to forecast water supply for reasonal operation and management of water utilities. In this paper, water supply forecasting models using artificial intelligence are developed. Artificial intelligence models shows better results by using Temperature(t), water supply discharge (t-1) and water supply discharge (t-2), which are expressed by neural network(LMNNWS; Levenberg-Marquardt Neural Network for Water Supply, MDNNWS; MoDular Neural Network for Water Supply) and neuro fuzzy(ANASWS; Adaptive Neuro-Fuzzy Inference Systems for Water Supply). ANFISWS model which is applied for water supply forecasting shows stable application to the variable water supply data. As results, MDNNWS model shows the highest overall accuracy among proposed water supply forecasting models and the lowest estimation error with the order of ANFISWS, LMNNWS model.

• Korean Journal of Organic Agriculture
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• v.25 no.3
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• pp.555-567
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• 2017

This study suggests the yield forecast model for chilli pepper using artificial neural network. For this, we select the most suitable network models for chilli pepper's yield and compare the predictive power with adaptive expectation model and panel model. The results show that the predictive power of artificial neural network with 5 weather input variables (temperature, precipitation, temperature range, humidity, sunshine amount) is higher than the alternative models. Implications for forecasting of yields are suggested at the end of this study.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1054-1058
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• 1995

There have been many research works for the indirect cutting force measurement in machining process, which deal with the case of one-axis cutting process. In multi-axis cutting process, the main difficulties to estimate the cutting forces occur when the feed direction is reversed. This paper presents the indirect cutting force measurement method in contour NC milling processes by using current signals of servo motors. An artificial neural network (ANN) system are suggested. An artificial neural network(ANN) system is also implemented with a training set of experimental cutting data to measure cutting force indirectly. The input variables of the ANN system are the motor currents and the feedrates of x and y-axis servo motors, and output variable is the cutting force of each axis. A series of experimental works on the circular interpolated contour milling process with the path of a complete circle has been performed. It is concluded that by comparing the ANN system with a dynamometer measuring cutting force directil, the ANN system has a good performance.

• Structural Engineering and Mechanics
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• v.28 no.2
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• pp.153-166
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• 2008

This paper suggests the use of wavelet multiresolution analysis (WMRA) and neural network for generation of artificial earthquake accelerograms from target spectrum. This procedure uses the learning capabilities of radial basis function (RBF) neural network to expand the knowledge of the inverse mapping from response spectrum to earthquake accelerogram. In the first step, WMRA is used to decompose earthquake accelerograms to several levels that each level covers a special range of frequencies, and then for every level a RBF neural network is trained to learn to relate the response spectrum to wavelet coefficients. Finally the generated accelerogram using inverse discrete wavelet transform is obtained. An example is presented to demonstrate the effectiveness of the method.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.5
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• pp.261-268
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• 2020

Genetic algorithms find the optimal solution by mimicking the evolution of natural organisms. In this study, the genetic algorithm was used to enable Pac-Man's reinforcement learning, and a simulator to observe the evolutionary process was implemented. The purpose of this paper is to reinforce the learning of the Pacman AI of the simulator, and utilize genetic algorithm and artificial neural network as the method. In particular, by building a low-power artificial neural network and applying it to a genetic algorithm, it was intended to increase the possibility of implementation in a low-power embedded system.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.3
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• pp.14-21
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• 1998

The in-process detection of the state of cutting tool is one of the most important technical problem in Intelligent Machining System. This paper presents a method of detecting the state of cutting tool in turning process, by using Artificial Neural Network. In order to sense the state of cutting tool. the sensor fusion of an acoustic emission sensor and a force sensor is applied in this paper. It is shown that AErms and three directional dynamic mean cutting forces are sensitive to the tool wear. Therefore the six pattern features that is, the four sensory signal features and two cutting conditions are selected for the monitoring system with Artificial Neural Network. The proposed monitoring system shows a good recogniton rate for the different cutting conditions.

• Journal of the Korean Society for Heat Treatment
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• v.31 no.1
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• pp.1-5
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• 2018

This work demonstrated the application of an artificial neural network model for predicting the Jominy hardness curve by considering 13 alloying elements in low alloy steels. End-quench Jominy tests were carried out according to ASTM A255 standard method for 1197 samples. The hardness values of Jominy sample were measured at different points from the quenched end. The developed artificial neural network model predicted the Jominy curve with high accuracy ($R^2=0.9969$ for training and $R^2=0.9956$ for verification). In addition, the model was used to investigate the average sensitivity of input variables to hardness change.

• Journal of the Korean Geotechnical Society
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• v.20 no.3
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• pp.53-65
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• 2004

This paper presents the prediction of deep excavation-induced ground surface movements using artificial neural network(ANN) technique, which is of prime importance in the damage assessment of adjacent buildings. A finite element model, which can realistically replicate deep excavation-induced ground movements, was employed to perform a parametric study on deep excavations with emphasis on ground movements. The result of the finite element analysis formed a basis for the Artificial Neural Network(ANN) system development. It was shown that the developed ANN system can be effective for a first-order prediction of ground movements associated with deep-excavation.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.4
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• pp.220-227
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• 2005

This paper presents hybrid artificial intelligent(HAI) controller based on the vector controlled IPMSM drive system. And it is based on artificial technologies that adaptive neural network fuzzy(A-NNF) is to speed control and artificial neural network(ANN) is to speed estimation. The salient feature of this technique is the HAI controller The hybrid action tolerates any inaccuracies in the fuzzy logic assignment rules or in the neural network stationary weights. Speed estimators using feedforward multilayer and artificial neural network(ANN) are compared. The back-propagation algorithm is easy to derived the estimated speed tracks precisely the actual motor speed. This paper presents the theoretical analysis as well as the simulation results to verify the effectiveness of the new hybrid intelligent control.

• Journal of Environmental Science International
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• v.24 no.8
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• pp.1023-1036
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• 2015

A reliable streamflow forecasting is essential for flood disaster prevention, reservoir operation, water supply and water resources management. This study proposes a hybrid model for river stage forecasting and investigates its accuracy. The proposed model is the wavelet packet-based artificial neural network(WPANN). Wavelet packet transform(WPT) module in WPANN model is employed to decompose an input time series into approximation and detail components. The decomposed time series are then used as inputs of artificial neural network(ANN) module in WPANN model. Based on model performance indexes, WPANN models are found to produce better efficiency than ANN model. WPANN-sym10 model yields the best performance among all other models. It is found that WPT improves the accuracy of ANN model. The results obtained from this study indicate that the conjunction of WPT and ANN can improve the efficiency of ANN model and can be a potential tool for forecasting river stage more accurately.