• Journal of Industrial Technology
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• v.25 no.B
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• pp.157-164
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• 2005

Two methods, the bin averaging method and least square method, are often used in calibrating wind turbine test sites. The objective of this work was to determine a better method to predict the wind speed at wind turbine installing point. The calibration was done at the test site on a complex terrain located in Daegwallyeong, Korea. It was performed for two different cases based on the IEC 61400-12 power performance measurement standard. The wind speeds averaged for 10 minutes ranged between 4 m/s and 16 m/s. The wind-direction bins of each meteorological mast were 10 degrees apart, and only the bins having data measured for more than 24 hours were employed for the test site calibration. For both cases, the two methods were found to yield almost same results which estimated real wind speed very closely.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1995.04a
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• pp.110-115
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• 1995

In this paper, the problem of non-linear torsional oscillation of a system incorporating a Hooke's joint is studied. Classical perturbation methods including higher order averaging and bifurcation theory are adopted for analysis. The equation of motion derived by Porter[1] is presented and the type of the system is identified. It has been found that two important cases deserve extensive study. Method of higher order averaging which is a main research tool in this study is introduced briefly. The averaged equations are studied analyticallyand numerically and the method of averaging has been found to be effective to study complex non-linear system.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2315-2325
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• 2015

Induction motors are widely used in industrial processes since they offer a very high degree of reliability. But like any other machine, they are vulnerable to faults, which if left unmonitored, might lead to an unexpected interruption at the industrial plant. Therefore, the condition monitoring of the induction motors have been a challenging topic for many electrical machine researchers. Indeed, the effectiveness of the fault diagnosis and prognosis techniques depends very much on the quality of the fault features selection. However, in induction-motor drives, rotor defects are the most complex in terms of detection since they interact with the supply frequency within a restricted band around this frequency, especially in the no-loaded case. To overcome this drawback, this paper deals with an efficient and new method to diagnose the induction-motor rotor fault based on the digital implementation of the monitoring algorithm based on the association of the Time Synchronous Averaging technique and Discrete Wavelet Transform. Experimental results are presented in order to show the effectiveness of the proposed method. The obtained results are largely satisfactory, indicating a promising industrial application of the combined “Time Synchronous Averaging – Discrete Wavelet Transform” approach.

• Advances in nano research
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• v.9 no.4
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• pp.277-294
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• 2020

In current paper, nonlocal (NLT), nonlocal strain gradient (NSGT) and Gurtin-Murdoch surface/interface (GMSIT) theories with classical theory (CT) are utilized to investigate vibration and stability analysis of Double Walled Piezoelectric Nanosensor (DWPENS) based on cylindrical nanoshell. DWPENS simultaneously subjected to direct electrostatic voltage DC and harmonic excitations, structural damping, two piezoelectric layers and also nonlinear van der Waals force. For this purpose, Hamilton's principle, Galerkin technique, complex averaging and with arc-length continuation methods are used to analyze nonlinear behavior of DWPENS. For this work, three nonclassical theories compared with classical theory CT to investigate Dimensionless Natural Frequency (DNF), pull-in voltage, nonlinear frequency response and stability analysis of the DWPENS considering the nonlocal, material length scale, surface/interface (S/I) effects, electrostatic and harmonic excitation.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.262-265
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• 2012

In this paper we propose a new deinterlacing method with block classification and motion vector smoothing. The proposed method classifies a block, then depending on the region it belongs to, the motion estimation or line averaging is applied. To classify a block its variance is calculated. Then, for those blocks that belong to simple non-texture region the line averaging is done while motion estimation is applied to complex region. The motion vector field is smoothed using median filter what yields more accurate interpolation. In the experiments for the subjective evaluation, the proposed method bas shown satisfying results in terms of computation time consumption and peak signal-to-noise ratio. Due to the simplicity of the algorithm computation time was drastically decreased.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.1
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• pp.83-92
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• 2000

Sound intensity method is well known as a visualization technique of sound field or sound propagation in noise control. Sound intensity or energy flux is a vector quantity which describes the amount and the direction of net flow of acoustic energy at a given position. Especially two dimensional sound intensity method is very useful in evaluating periodic characteristics and acoustic propagation mode of noise source. In this paper, we have studied the noise source Identification, acoustic sound field analysis, and characteristics of noise source of rotary compressor and scroll compressor for air conditioner using complex sound intensity method. Also we proposed a now method of time domain analysis which is used in evaluating of position of noise source in rotary and scroll compressor in this paper This paper presents the advantage, simplicity and economical efficiency of this method by analysing the characteristics of noise source with two dimensional complex sound intensity simultaneously.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.446-449
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• 1988

A spectral inverse technique, which was developed by applying the pulse basis moment method procedure on the direct scattering problem in the reverse sequence for the reconstruction of complex permittivity profiles inside inhomogeneous dielectric objects, is modified to be applicable to the moment method with series-expanded basis. By performing numerical simulations for various type of dielectric objects, it is demonstrated that this inverse technique provides close reconstruction of permittivity profiles. Futhermore, compared to the previous scheme of the pulse basis, the presented method is shown to reduce the computation cost, relative error of reconstructed permittivity profiles by averaging in each cell, and the ill-posedness inherent to this inverse scattering problem.

• Journal of Mechanical Science and Technology
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• v.14 no.6
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• pp.622-632
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• 2000

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. The cutting forces are difficult to predict because they are very complex and time variant. In order to predict the cutting forces of end-milling processes for various cutting conditions, their mathematical model is important and the 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 forces 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 the learning stage as the omitted number of experimental data increase the average errors increase as well.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1406-1416
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• 2017

In complex and large-scale industries, properly designed fault detection and identification (FDI) systems considerably improve safety, reliability and availability of target processes. In thermal power plants (TPPs), generating units operate under very dangerous conditions; system failures can cause severe loss of life and property. In this paper, we propose a bagged auto-associative kernel regression (AAKR)-based FDI approach for steam boilers in TPPs. AAKR estimates new query vectors by online local modeling, and is suitable for TPPs operating under various load levels. By combining the bagging method, more stable and reliable estimations can be achieved, since the effects of random fluctuations decrease because of ensemble averaging. To validate performance, the proposed method and comparison methods (i.e., a clustering-based method and principal component analysis) are applied to failure data due to water wall tube leakage gathered from a 250 MW coal-fired TPP. Experimental results show that the proposed method fulfills reasonable false alarm rates and, at the same time, achieves better fault detection performance than the comparison methods. After performing fault detection, contribution analysis is carried out to identify fault variables; this helps operators to confirm the types of faults and efficiently take preventive actions.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.62 no.4
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• pp.204-209
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• 2013

FFT based fingerprinting to conceal more information has developed for video copyright protection. More complex information of video is necessary to prove an ownership and legal distributions in invisible form. This paper describes a method to insert more information and to detect them. $3{\times}3$ points structure is used to present information. The possible ways to show are 8bit, $2^8$ = 256 where one point of 9 is always turn on. The points are marked in frequency domain that both real and imaginary party numbers are modified. The five successive frames of same scenes are used to mark because the same scene has very similar shape in FFT result. However, the detail values of coefficients are totally different each other to recognize the marked points. This paper also describes a method to detect the marked points by averaging and correlation algorithm. The PSNRs of marked images by our method had 51.138[dB] to 51.143[dB]. And we could get the correlation values from 0.79 to 0.87.