• 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.

• Journal of the Korea Computer Industry Society
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• v.6 no.3
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• pp.421-428
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• 2005

Insufficiency of special knowledge and construction cost makes it difficult in build a customized-feed service system in the agricultural and livestock industry In this paper, customized-feed service system was developed in the internet service environment by using digital information and technology as a grafted system. This system has a couple of advantages like economic feed supply and effective management of feed production cost which make it possible to establish a customized-feed database suitable for the working environment in the agricultural and livestock industry. And this system is expected as a solution for 21st century digitalized agricultural and livestock industry which make it certain in real time by constructing a network environment and digitalizing the equipment and component related to the farm production.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.917-920
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• 1998

In this paper, we propose a modified elman neural network structure for nonlinear system identification. The proposed structure is that all of network output feed back into hidden units and output units. Learning algorithm is standard back-propagation algorithm. The simulation showed the effectiveness of using the modified elman neural network structure in the nonlinear system identification.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.272-277
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• 2003

In order to predict vibration during end-milling process, the cutting dynamics was modelled by using neural network and combined with structural dynamics by considering dynamic cutting states. Specific cutting constants of the cutting dynamics model were obtained by averaging cutting forces and tool diameter, cutting speed, feed, axial depth radial depth were considered as machining factors. Cutting farces by test and by neural network simulation were compared and the vibration during end-milling was simulated.

• Smart Structures and Systems
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• v.18 no.6
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• pp.1233-1250
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• 2016

Cracks in plate-like structures are some of the main reasons for destruction of the entire structure. In this study, a novel two-stage methodology is proposed for damage detection of flexural plates using an optimized artificial neural network. In the first stage, location of damages in plates is investigated using curvature-moment and curvature-moment derivative concepts. After detecting the damaged areas, the equations for damage severity detection are solved via Bat Algorithm (BA). In the second stage, in order to efficiently reduce the computational cost of model updating during the optimization process of damage severity detection, multiple damage location assurance criterion index based on the frequency change vector of structures are evaluated using properly trained cascade feed-forward neural network (CFNN) as a surrogate model. In order to achieve the most generalized neural network as a surrogate model, its structure is optimized using binary version of BA. To validate this proposed solution method, two examples are presented. The results indicate that after determining the damage location based on curvature-moment derivative concept, the proposed solution method for damage severity detection leads to significant reduction of computational time compared with direct finite element method. Furthermore, integrating BA with the efficient approximation mechanism of finite element model, maintains the acceptable accuracy of damage severity detection.

• Journal of Electrical Engineering and Technology
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• v.10 no.2
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• pp.634-640
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• 2015

This paper deals with the design of a microstrip line feed toppled T shaped microstrip patch antenna that gives dualband characteristics at 4 GHz and 6.73 GHz respectively. The simulation of proposed antenna geometry has been performed using method of moment based IE3D simulation software. A radial basis function neural network (RBFNN) is used for the estimation of bandwidth for dualband at 4 GHz and 6.73 GHz respectively. In RBFNN model, antenna parameters such as dielectric constant, height of substrate, and width are used as input and bandwidth of first and second band is considered as output of the network. To validate the RBFNN output, an antenna has been physically fabricated on glass epoxy substrate. The fabricated antenna can be utilized in S and C bands applications. RBFNN results are found in close agreement with simulated and experimental results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.9
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• pp.1700-1707
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• 2011

Interior permanent magnet synchronous motor(IPMSM) adjustable speed drives offer significant advantages over induction motor drives in a wide variety of industrial applications such as high power density, high efficiency, improved dynamic performance and reliability. Since the fuzzy neural network(FNN) is recognized general approximate method to control non-linearities and uncertainties, the development of FNN control systems have also grown rapidly. The FNN controller is compounded of fuzzy and neural network. It has an advantage that is the robustness of fuzzy control and the ability to adapt of neural network. However, the FNN has static problem due to their feed-forward network structure. This paper proposes high performance speed control of IPMSM drive using the recurrent FNN(RFNN) which improved conventional FNN controller. The RFNN has excellent dynamic response characteristics because of it has internally feed-back structure. Also, this paper proposes speed estimation of IPMSM drive using ANN. The proposed method is analyzed and compared to conventional FNN controller in various operating condition such as parameter variation, steady and transient states etc.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.663-670
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• 2019

Corrective thermal performance analysis is required for power plants' turbine cycles to determine the performance status of the cycle and improve the economic operation of the power plant. We developed a sectional classification method for the main feed-water flow to make precise corrections for the performance analysis based on the Performance Test Code (PTC) of the American Society of Mechanical Engineers (ASME). The method was developed for the estimation of the turbine cycle performance in a classified section. The classification is based on feature identification of the correlation status of the main feed-water flow measurements. We also developed predictive algorithms for the corrected main feed-water through a Kernel Regression (KR) model for each classified feature area. The method was compared with estimation using an Artificial Neural Network (ANN). The feature classification and predictive model provided more practical and reliable methods for the corrective thermal performance analysis of a turbine cycle.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.381-386
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• 2004

A flexible disk grinding system process has been introduced that utilized varying disk orientation with respect to workpiece along with the applied feed speed. A known process model methodologies has been used to fomulate processed surface profiles. Various process conditions including cutting speed, maximum feed speed and orientation angles could applied to observe process outcomes. Even though continuous and constant feed speed has been applied to the process, the results from the trapezoidal input velocity profiles would be observed and compared. Based on the control strategies including neural network methodologies, several output results were compared to find the optimum process condition.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.309-314
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• 2004

A flexible disk grinding system process has been introduced that utilized varying disk orientation with respect to workpiece along with the applied feed speed. Various process conditions including cutting speed, maximum feed speed and orientation angles could applied to observe process outcomes. Even though continuous and constant feed speed has been applied to the process, the results from the trapezoidal input velocity profiles would be observed and compared. Based on the control strategies including neural network methodologies, several output results were compared to find the optimum process condition. Two axis control results were displayed showing better performance with higher trajectory error for larger training epoch.