• Journal of Power System Engineering
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• v.6 no.3
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• pp.57-63
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• 2002

This paper describes the acoustic analysis of mid duty truck. The focus of the analysis is on structure borne engine noise with major contributions of 2nd order. It has been previously recognized that the noise contribution of each transfer path of structure borne noise can be varied with the charateristics of each mounts and vibro acoustic sensitivity of car body. The structure of car body will be split up into three major sub components, which are modeled separately, the engine, the frame and the cab. The acoustic performance is evaluated on three levels: engine to frame transfer, frame to cab transfer, and panel contribution from cab to driver. In order to perform these analyses, analytical models are created for the engine, frame, cab and acoustic cavity. The models are linked through a coupled fluid structure calculation, and through FRF Based Substructuring for the structural couplings. Based on the structural coupling calculations, a transfer path analysis is performed to identify the most important transfer paths. These paths are then the focussing points for applying modifications to the structure or the mount system. Finally, a number of modification are proposed and their effect is quantified.

• Journal of the Korean Statistical Society
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• v.19 no.1
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• pp.1-14
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• 1990

This paper proposes an approach to the identification of trnasfer function models. A strategy for the identification of the model structure is based on R and S arrays constructed by the impulse response function of the model. Theoretical patterns of the arrays associated with the model are investigated, and the practical implementation method of the suggested approach is also discussed. Finally two published samples are employed to demonstrate the practicability of the approach.

• Journal of KSNVE
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• v.10 no.6
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• pp.941-948
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• 2000

Active noise control is an approach to reduce the noise by utilizing a secondary noise source that destructively interferes with the unwanted noise. In general, active noise control systems rely on multiple sensors to measure the unwanted noise field and the effect of the cancellation. This paper develops an approach that utilizes a single sensor. The noise field is modeled as a stochastic process, and an adaptive algorithm is used to adaptively estimate the parameters of the process. Based on these parameter estimates, a canceling signal is generated. Oppenheim assumed that transfer function characteristics from the canceling source to the error sensor is only a propagation delay. This paper proposes a modified Oppenheim algorithm by considering transfer characteristics of speaker-path-sensor This transfer characteristics is adaptively cancelled by the proposed adaptive modeling technique. Feasibility of the proposed method is proved by computer simulations with artificially generated random noises and sine wave noise. The details of the proposed architecture. and theoretical simulation of the noise cancellation system for three dimension enclosure are presented in the Paper.

• Journal of Internet of Things and Convergence
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• v.8 no.2
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• pp.79-84
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• 2022

This paper presents an enhancement method of document restoration capability which is robust for security, loss, and contamination, It is based on two methods, that is, encryption and DnCNN(DeNoise Convolution Neural Network). In order to implement this encryption method, a mathematical model is applied as a spatial frequency transfer function used in optics of 2D image information. Then a method is proposed with optical interference patterns as encryption using spatial frequency transfer functions and using mathematical variables of spatial frequency transfer functions as ciphers. In addition, by applying the DnCNN method which is bsed on deep learning technique, the restoration capability is enhanced by removing noise. With an experimental evaluation, with 65% information loss, by applying Pre-Training DnCNN Deep Learning, the peak signal-to-noise ratio (PSNR) shows 11% or more superior in compared to that of the spatial frequency transfer function only. In addition, it is confirmed that the characteristic of CC(Correlation Coefficient) is enhanced by 16% or more.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.11 no.4
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• pp.5-12
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• 2001

This paper presents the simulation methodology of the interior noise of vehicle using the frequency response function based hybrid modeling of the system which consists of multi-subsystem models obtained by the test or analysis. The complex systems such as a trimmed body of high modal density and a powertrain were modeled by using experimental data, and a sub-frame of a vehicle of low modal density was modeled by finite element data. Modeling of the whole system was executed and validated in the two stages. The first stave is combining the trimmed body and the sub-frame, and the second stage is attaching the powertrain, which is a exciting source, to the combined model of the first stage. The input force to the system was modeled as an equivalent force in the virtual space, which was obtained from impedance method using the FRFs of the powertrain and the responses. The interior noise predicted by the proposed method was very close to the direct measurement, which showed feasibility of the proposed modeling procedure. Since the methodology is easily applied to both the transfer path analysis of structure-borne noise and the analysis of noise contribution of a sub-system, it is expected to be a strong tool for design change of a vehicle in the earlier stare.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.1
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• pp.42-49
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• 2009

In this paper, an alternative method was introduced to conduct a transfer path analysis for airborne noise. The method used the transfer function matrix composed of acoustic transfer functions that are referenced by the input voltage of a calibration source. A calibration factor which is converting a virtual voltage to source strength was deduced by vibro-acoustical reciprocity theorem. The calibration factor is then multiplied to the virtual input voltage to estimate the operational source strength. Three loudspeakers were used to noise sources of acrylic half car model. The method was applied to airborne noise transfer path analysis of the half car. The estimated source strength by transfer path analysis was compared the deduced source strength by vibro-acoustical reciprocity to verify the method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.2
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• pp.107-111
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• 2003

In the power-line communication systems, there are many factors of noise and attenuation in the power-line channels, because they were designed for not the communication but the power transmission. Also the transfer function of the channels is highly changed with the topology and the load of the power-lines. To cope with these poor channel situation, channel modeling, one of the many studies in progress, is being studied hard. Channel modeling is essential to apply the active schemes to overcome the bad channel(e.g. modulation technique, channel coding, signal coupling & filtering, etc.) to the power-line communications. In this paper, we have realized the statistical model(this model is suggested as the channel modeling method for the power-line channels) that is combined the transfer function with the various noises. And we have compared and examined the results with the measured data. Also we have studied the plan which can effectively establish the channel data base for the channel information consisted of the parameters that are derived from this modeling, and we have studied the plan to utilize the data base.

• 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.

• Structural Engineering and Mechanics
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• v.68 no.1
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• pp.39-51
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• 2018

In this study, a frequency domain model updating method is presented using power spectral density (PSD) data. It uses the sensitivity of PSD function with respect to the unknown structural parameters through a decomposed form of transfer function. The stiffness parameters are captured with high accuracy through solving the sensitivity equations utilizing the least square approach. Using numerically noise polluted data, the model updating results of a truss model prove robustness of the method against measurement and mass modelling errors. Results prove the capabilities of the method for parameter estimation using highly noise polluted data of low ranges of excitation frequency.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.920-927
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• 2008

This study deals with modeling of Head-Related Transfer Functions (HRTFs) using Principal Components Analysis (PCA) in the time and frequency domains. Four PCA models based on Head-Related Impulse Responses (HRIRs), complex-valued HRTFs, augmented HRTFs, and log-magnitudes of HRTFs are investigated. The objective of this study is to compare modeling performances of the PCA models in the least-squares sense and to show the theoretical relationship between the PCA models. In terms of the number of principal components needed for modeling, the PCA model based on HRIR or augmented HRTFs showed more efficient modeling performance than the PCA model based on complex-valued HRTFs. The PCA model based on HRIRs in the time domain and that based on augmented HRTFs in the frequency domain are shown to be theoretically equivalent. Modeling performance of the PCA model based on log-magnitudes of HRTFs cannot be compared with that of other PCA models because the PCA model deals with log-scaled magnitude components only, whereas the other PCA models consider both magnitude and phase components in linear scale.