• Proceedings of the KSR Conference
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• 2010.06a
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• pp.330-337
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• 2010

This paper shows an algorithm for identifying track irregularities using wavelet transfer function along the railway. An equivalent SISO wavelet transfer function is defined using continuous wavelet transform by the measured track geometry and acceleration at a bogie of a train. The estimated track geometry is made by inverse continuous wavelet transform from the regressed signals of measured acceleration signal and the pre-defined wavelet transfer function. The estimated rail irregularity geometry is evaluated by the coherence function and comparison of FRF(Frequency Response Function). As a result of evaluated outcome, This algorithm is regarded as appropriate for estimation of rail irregularity.

• Journal of the Korean Society of Marine Environment & Safety
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• v.20 no.5
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• pp.526-534
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• 2014

In this study, single-input transfer function model is applied to forecast monthly mean sea surface temperature(SST) in 2010 at Yeosu in Korean coastal waters. As input series, monthly mean air temperature series for ten years(2000-2009) at Yeosu in Korea is used, and Monthly mean SST at Yeosu station in Korean coastal waters is used as output series(the same period of input). To build transfer function model, first, input time series is prewhitened, and then cross-correlation functions between prewhitened input and output series are determined. The cross-correlation functions have just two significant values at time lag at 0 and 1. The lag between input and output series, the order of denominator and the order of numerator of transfer function, (b, r, s) are identified as (0, 1, 0). The selected transfer function model shows that there does not exist the lag between monthly mean air temperature and monthly mean SST, and that transfer function has a first-order autoregressive component for monthly mean SST, and that noise model was identified as $ARIMA(1,0,1)(2,0,0)_{12}$. The forecasted values by the selected transfer function model are generally $0.3-1.3^{\circ}C$ higher than actual SST in 2010 and have 6.4 % mean absolute percentage error(MAPE). The error is 2 % lower than MAPE by ARIMA model. This implies that transfer function model could be more available than ARIMA model in terms of forecasting performance of SST.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.1
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• pp.10-18
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• 2007

We synthesize and analyze the optical transfer function(OTF) of the modified triangular interferometer(MTI) using two-pupil synthesis method and we present the optimal MTI, which can obtain any bipolar function by combining a wave plate and a linear polarizer. Also, we analyze its potential phase error sources caused by polarization components.

• Journal of the Korean Society for Precision Engineering
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• v.4 no.3
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• pp.35-43
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• 1987

A new analytic method for the identification of process control system is presented. A second and a third order transfer function are considered as the estimated model function. For the second order transfer function, the new method is compared with the exisiting ones, simulation results show that the new method is superior to the existing ones. And also, In case of the third order transfer function which is difficult to analyze mathematically, system identification is tried.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.356-360
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• 2007

Fuel sloshing in a vehicle fuel tank generates a reluctant low frequency noise, called slosh noise. To reduce slosh noise, whilst many approaches have used the Computational Fluid Dynamics method to first identify fuel behavior in a fuel tank, this paper applies the Transfer Path Analysis method. It is to find contribution of each transfer path from noise transfer function, vibration transfer function and acceleration. Then the final goal is to attenuate slosh noise by controlling them. To this aim, two types of models are studied. One is the decoupled model in which some of connection points of the fuel tank with the vehicle underbody are separated. The other is the modified model which is created by changing noise transfer function and acceleration from the original model. The analysis and validation test results show that the transfer path analysis can be an approach to enhancing slosh noise.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.17 no.8
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• pp.766-770
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• 2007

Fuel sloshing in a vehicle fuel tank generates a reluctant low frequency noise, called slosh noise. To reduce slosh noise, whilst many approaches have used the Computational Fluid Dynamics method to first identify fuel behavior in a fuel tank, this paper applies the Transfer Path Analysis method. It is to find contribution of each transfer path from noise transfer function, vibration transfer function and acceleration. Then the final goal is to attenuate slosh noise by controlling them. To this aim, two types of models are studied. One is the decoupled model in which some of connection points of the fuel tank with the vehicle underbody are separated. The other is the modified model which is created by changing noise transfer function and acceleration from the original model. The analysis and validation test results show that the transfer path analysis can be an approach to enhancing slosh noise.

• 대한공업교육학회지
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• v.34 no.1
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• pp.238-251
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• 2009

The noise itself that affects the sensor array is defined as the noise which happens in the place where the system is installed and the circumference noise which comes from the ocean. The array structure for detecting acoustic signal in the underwater effected turbulent layer flow noise. In this paper to design the conformal array spectral density function was introduced and several cases of flow induced noise which affect transfer function were simulated. Modified Corcos wall pressure model was used as turbulent boundary layer flow noise. The effect of noise has been reduced as integrated sum of transfer function has been reduced by decreasing elastomer thickness and density when kx is in low wave number area. Also the characteristics of transfer function by Corcos wall pressure displayed the product of frequency density function. This simulation results can be applied to the conformal array design in unmmaned underwater vehicle in the near future.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2298-2303
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• 2003

In this paper, very simple methods based on the exponential analysis are presented by which transfer function models for processes can easily be obtained. These methods employ step responses or impulse responses of the processes. These can also give a more precise transfer function model compared to the well-known graphical methods. Transfer functions are determined based on Prony method, which is one of the oldest and the most representative methods in the exponential analysis. Here, the method is reformed and applied to obtain the so-called low-order transfer function with pure time delay from the data of the step response. The effectiveness of the proposed method is examined through several numerical examples and experiments of the 2-tank level control process.

• Journal of ILASS-Korea
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• v.21 no.2
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• pp.104-110
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• 2016

Main purpose of the current paper is to show results of time lag analysis using phase information of flame transfer function in order to predict combustion instabilities in a gas turbine combustor. The flame transfer function (FTF) is modeled using a commercial Computational Fluid Dynamics (CFD) code (Fluent). Comparisons of the modeled flame shapes with the measured ones were made using the optimized heat transfer conditions and combustion models. The FTF modeling results show a quite good agreement with the measurement data in predicting the phase delay (i.e. time lag). Time lag analysis results using the phase of FTF shows better combustion instability prediction accuracy than using time lag calculated from the steady state flame length.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.1
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• pp.16-21
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• 2004

This paper presents an approximation method for simplifying a high-order transfer function to a low-order transfer function. A model reduction is based on minimizing the error function weighted by the numerator polynomial of reduced systems. The proposed methods provide better low frequency fit and a computer aided algorithm that estimates the coefficients vector for the numerator and denominator polynomial on the simplified systems from an overdetermined linear system constructed by frequency responses of the original systems. Two examples are given to illustrate the feasibilities of the suggested schemes.