• The Transactions of the Korean Institute of Power Electronics
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• v.9 no.3
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• pp.246-252
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• 2004

This paper presents a position sensorless control system of Switched Reluctance Motor (SRM) using neural network. The control of SRM depends on the commutation of the stator phases in synchronism with the rotor position. The position sensing requirement increases the overall cost and complexity. In this paper, the current-flux-rotor position lookup table based position sensorless operation of SRM is presented. Neural network is used to construct the current-flux-rotor position lookup table, and is trained by sufficient experimental data. Experimental results for a 1-hp SRM is presented for the verification of the proposed sensorless algorithm.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.2
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• pp.80-87
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• 1999

This paper proposed a high impedance fault detection method using a neural network on distribution lines. The $\upsilon-i$ characteristic curve was obtained by high impedance fault data tested in various soil conditions. High impedance fault was simulated using EMTP. The pattern of High Impedance Fault on high density pebbles was taken as the learning model, and the neural network was evaluated on various soil conditions. The average values after analyzing fault current by FFT of even.odd harmonics and fundamental rms were used for the neural network input. Test results were verified the validity of the proposed method .ethod .

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.2
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• pp.63-70
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• 1999

This paper is proposed a new method to deal with the optimized auto-tuning for the Pill controller which is used to the process-control in various fields. First of all, in this method, 1st order delay system with dead time which is modelled from the unit step response of the system is Pade-approximated, then initial values are determined by the Ziegler-Nichols method. So we can find the parameters of Pill controller so as to minimize the fuzzy criterion function which includes the maximum overshoot, damping ratio, rising time and settling time. Finally, after studying the parameters of Pill controller by Backpropagation of Neural-Network, when we give new K, L, T values to Neural-Network, the optimized parameter of Pill controller is found by Neural-Network Program.rogram.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.4
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• pp.727-732
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• 2012

This paper presents an approach to classify normal and arrhythmia from the MIT-BIH Arrhythmia Database using Discrete Cosine Transform(DCT), Discrete Wavelet Transform(DWT) and neural network. In the first step, Discrete Cosine Transform is used to obtain the representative 15 coefficients for input features of neural network. In the second step, Discrete Wavelet Transform are used to extract maximum value, minimum value, mean value, variance, and standard deviation of detail coefficients. Neural network classifies normal and arrhythmia beats using 55 numbers of input features, and then the accuracy rate is 98.8%.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.4
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• pp.997-1006
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• 2015

This study analyzed urban infrastructure risk to flooding based on the possibility map of flooding calculated by neural network model focusing on Seoul. This study found that Gangnam-gu, Songpa-gu, Seocho-gu and Seodaemun-gu contained relatively large high-risk areas to flooding. Over $4.17km^2$ of transportation facilities were located in high-risk area to flooding and Gangnam-gu included over $0.85km^2$ of infrastructures exposed to high inundation risk. This study is meaningful in that it first applied the neural network modeling to flooding risk assesment and results of risk assessment can be incorporated into various planning process.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.23 no.6 s.165
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• pp.979-987
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• 1999

In a high precision vertical machining center, the estimation of cutting forces is important for many reasons such as prediction of chatter vibration, surface roughness and so on, and cutting forces are difficult to predict because they are very complex and time variant. In order to predict the cutting forces of end-milling process for various cutting conditions, a mathematical model is important and this model is based on chip load, cutting geometry, and the relationship between cutting forces and chip loads. Specific cutting force coefficients of the model have been obtained as interpolation function types by averaging farces of cutting tests. In this paper, the coefficients are obtained by neural network and the results of the conventional method and those of the proposed method are compared. The results show that the neural network method gives more correct values than the function type and that in teaming stage as the omitted numbers of experimental data increases the average errors increase.

• The Journal of the Korea Contents Association
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• v.6 no.9
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• pp.50-59
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• 2006

This paper proposes an automatic video monitoring system and its application to emergency detection by analyzing human behavior using neural network. The object area is identified by subtracting the statistically constructed background image from the input image. The identified object area then is transformed to the feature vector. Neural network has been adapted for analyzing the human behavior using the feature vector, and is designed to classify the behavior in rather simple numerical calculation. The system proposed in this paper is able to classify the three human behavior: stand, faint, and squat. Experiment results shows that the proposed algorithm is very efficient and useful in detecting the emergency situation.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.25-29
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• 2001

There are so many researches about the search method for the most compatible dispatching rule to a manufacturing system state. Most of researches select the dispatching rule using simulation results. This paper touches upon two research topics: the clustering method for manufacturing system states using simulation, and the search method for the most compatible dispatching rule to a manufacturing system state. The manufacturing system state variables are given to ART II neural network as input. The ART II neural network is trained to cluster the system state. After being trained, the ART II neural network classifies any system state as one state of some clustered states. The simulation results using clustered system state information and those of various dispatching rules are compared and the most compatible dispatching rule to the system state is defined. Finally there are made two knowledge bases. The simulation experiments are given to compare the proposed methods with other scheduling methods. The result shows the superiority of the proposed knowledge base.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.12
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• pp.5097-5106
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• 2010

Two perspectives on developing better decision capabilities for a warranty system can be identified: one involving the inclusion of a 'learning' module and the other the inclusion of a 'prioritization' capability. This paper demonstrates how a warning process can be included in a warranty system by coupling with a neural network's learning capabilities. In addition to the neural network, a method is employed for assigning priorities to warning criteria by using the analytic hierarchy process (AHP). Thus, it is possible to construct an integrated system with three components: the warranty system, the AHP module, and the neural network system. A case study is provided to enhance the accuracy of warning/detection judgment in a warranty system for automobile companies, having many factors related to the warranty system.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.37-39
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• 2000

The purpose of this paper is to present a neural network model based on change-point detection for the prediction of the U.S. Treasury Securities. Interest rates have been studied by a number of researchers since they strongly affect other economic and financial parameters. Contrary to other chaotic financial data, the movement of interest rates has a series of change points due to the monetary policy of the U.S. government. The basic concept of this proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in interest rates forecasting. The proposed model consists of three stages. The first stage is to detect successive change points in the interest rates dataset. The second stage is to forecast the change-point group with the backpropagation neural network (BPN). The final stage is to forecast the output with BPN. This study then examines the predictability of the integrated neural network model for interest rates forecasting using change-point detection.