• Smart Media Journal
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• v.1 no.2
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• pp.47-53
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• 2012

Wind energy is currently the fastest growing source of renewable energy used for electrical generation around world. Wind farms are adding a significant amount of electrical generation capacity. The increase in the number of wind farms has led to the need for more effective operation and maintenance procedures. Condition Monitoring System(CMS) can be used to aid plant owners in achieving these goals. In this work, systematic design procedure for artificial neural network based normal behavior model which can be applied for fault detection of various devices is proposed. Furthermore, to verify the design method SCADA(Supervisor Control and Data Acquisition) data from 850kW wind turbine system installed in Beaung port were utilized.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.5
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• pp.603-609
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• 2012

Wind energy is currently the fastest growing source of renewable energy used for electrical generation around world. Wind farms are adding a significant amount of electrical generation capacity. The increase in the number of wind farms has led to the need for more effective operation and maintenance procedures. Condition Monitoring System(CMS) can be used to aid plant owners in achieving these goals. Its aim is to provide operators with information regarding the health of their machines, which in turn, can help them improve operational efficiency. In this work, systematic design procedure for artificial neural network based normal behavior model which can be applied for fault detection of various devices is proposed. Furthermore, to verify the design method SCADA(Supervisor Control and Data Acquisition) data from 850KW wind turbine system installed in Beaung port were utilized.

• IE interfaces
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• v.23 no.4
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• pp.311-318
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• 2010

In the daily multi-reservoir operating problem, monthly storage targets can be used as principal operational guidelines. In this study, we tested the use of a simple back-propagation Artificial Neural Network (ANN) model to derive monthly storage guideline for daily Coordinated Multi-reservoir Operating Model (CoMOM) of the Han-River basin. This approach is based on the belief that the optimum solution of the daily CoMOM has a good performance, and the ANN model trained with the results of daily CoMOM would produce effective monthly operating guidelines. The optimum results of daily CoMOM is used as the training set for the back-propagation ANN model, which is designed to derive monthly reservoir storage targets in the basin. For the input patterns of the ANN model, we adopted the ratios of initial storage of each dam to the storage of Paldang dam, ratios of monthly expected inflow of each dam to the total inflow of the whole basin, ratios of monthly demand at each dam to the total demand of the whole basin, ratio of total storage of the whole basin to the active storage of Paldang dam, and the ratio of total inflow of the whole basin to the active storage of the whole basin. And the output pattern of ANN model is the optimal final storages that are generated by the daily CoMOM. Then, we analyzed the performance of the ANN model by using a real-time simulation procedure for the multi-reservoir system of the Han-river basin, assuming that historical inflows from October 1st, 2004 to June 30th, 2007 (except July, August, September) were occurred. The simulation results showed that by utilizing the monthly storage target provided by the ANN model, we could reduce the spillages, increase hydropower generation, and secure more water at the end of the planning horizon compared to the historical records.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1201-1208
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• 2019

The government has operated financing fund under the National Finance Act for the smooth conduct of national policy. But, It is exposed to problems such as the possibility of abuse of fund and the lack of after-loan management. In this paper, It uses fintech such as the blockchain and machine learning to solve these problems. The fund operation procedure is designed as a consortium blockchain, and it suggests the application of PBFT negotiation algorithm and the smart contract. In case of the fund management, it suggests utilizing multilayer artificial neural network model of machine learning and a module of result interpretation. The introduction of this research approach will improve the transparency and efficiency of the financing fund, ensure the credibility and also contribute to the improvement of the fund management and the establishment of the fund policy.

• Journal of Broadcast Engineering
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• v.27 no.4
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• pp.477-486
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• 2022

In this paper, we propose a novel intra-prediction method using convolutional neural network (CNN) to improve a quality of a predicted block in VVC. The proposed algorithm goes through a two-step procedure. First, an input prediction block is generated using one of the VVC intra-prediction modes. Second, the prediction block is further refined through a CNN model, by inputting the prediction block itself and reconstructed reference samples in the boundary. The proposed algorithm outputs a refined block to reduce residual signals and enhance coding efficiency, which is enabled by a CU-level flag. Experimental results demonstrate that the proposed method achieves improved rate-distortion performance as compared a VVC reference software, I.e., VTM version 10.0.

• Journal of Electrical Engineering and Technology
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• v.4 no.3
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• pp.389-395
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• 2009

Industrial motors are subject to incipient faults which, if undetected, can lead to motor failure. The necessity of incipient fault detection can be justified by safety and economical reasons. The technology of artificial neural networks has been successfully used to solve the motor incipient fault detection problem. This paper develops inexpensive, reliable, and noninvasive NN based incipient fault detection scheme for small and medium sized induction motors. Detailed design procedure for achieving the optimal NN model and Principal Component Analysis for dimensionality reduction is proposed. Overall thirteen statistical parameters are used as feature space to achieve the desired classification. GFFD NN model is designed and verified for optimal performance in fault identification on experimental data set of custom designed 2 HP, three phase 50 Hz induction motor.

• Structural Engineering and Mechanics
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• v.15 no.6
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• pp.609-628
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• 2003

Mechanical anchorage devices are generally tested in the laboratory and may be analyzed using the finite element method. These devices are composed of many components interacting through diverse contact interfaces. Generally, a Coulomb friction law is sufficient to take into account friction between smooth surfaces. However, in the case of mechanical anchorages, a gripping system, named herein the wedge-tendon system, is used to anchor the prestressing tendon. The wedge inner surface is made of a series of triangular notches designed to grip the tendon. In this particular case, the Coulomb law is not adapted to simulate the contact interface. The present paper deals with a new constitutive contact/gripping law to simulate the gripping effect. A parameter identification procedure, based on experimental results as well as on a finite element/neural network approach, is presented. It is demonstrated that all parameters have been selected in a satisfactory way and that the proposed constitutive law is well adapted to simulate the wedge gripping effect taking place in a mechanical anchorage device.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1030-1032
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• 1998

In this paper, an efficient feature vector extraction method and MLP neural network are utilized to automatically detect and classify power quality disturbances, where the proposed classification procedure consists of the following three parts: i.e., (i) PQ disturbance detection using discrete wavelet transform. (ii) feature vector extraction from the detected disturbance. using several methods, such as FFT, DWT, Fisher's criterion. etc.. and (iii) classification of the corresponding type of each PQ disturbance by recognizing the pattern of the extracted feature vector. To demonstrate the performance and, applicability of the proposed classification algorithm. some test results obtained by analyzing 10-class PQ disturbances are also provided.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1155-1157
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• 1996

In this paper, the direct adaptive control using neural networks is presented for the control of chaotic nonlinear systems. The direct adaptive control method has an advantage that the additional system identification procedure is not necessary. Two direct adaptive control methods are applied to a Duffing's equation and the simulation results show the effectiveness of the controllers.

• Journal of Sensor Science and Technology
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• v.13 no.1
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• pp.27-34
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• 2004

Camera calibration is an important and fundamental procedure for the application of a vision sensor to 3D problems. Recently many camera calibration methods have been proposed particularly in the area of robot vision. However, the reliability of data used in calibration has been seldomly considered in spite of its importance. In addition, a camera model can not guarantee good results consistently in various conditions. This paper proposes methods to overcome such uncertainty problems of data and camera models as we often encounter them in practical camera calibration steps. By the use of the RANSAC (Random Sample Consensus) algorithm, few data having excessive magnitudes of errors are excluded. Artificial neural networks combined in a two-step structure are trained to compensate for the result by a calibration method of a particular model in a given condition. The proposed methods are useful because they can be employed additionally to most existing camera calibration techniques if needed. We applied them to a linear camera calibration method and could get improved results.