• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2011년도 춘계학술대회 논문집
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• pp.534-537
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• 2011

This Paper present prediction Vehicle Interior Noise using ATF(Acoustic Transfer Function) and engine radiated sound power. This is useful tool to qualifying the effectiveness of Air-borne noise Path. Furthermore This method provide acoustic package performance of the vehicle and able to prepare frequency band to same segment or benchmarking vehicle.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권1호
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• pp.17-25
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• 2005

This paper provides sufficient conditions for the robustness of Affine linear TFM(Transfer Function Matrix) MIMO (Multi-Input Multi-Output) uncertain systems based on Rosenbrock's DNA (Direct Nyquist Array). The parametric uncertainty is modeled through a Affine TFM MIMO description, and the unstructured uncertainty through a bounded perturbation of Affine polynomials. Gershgorin's theorem and concepts of diagonal dominance and GB(Gershgorin Bands) are extended to include model uncertainty. For this type of parametric robust performance we show robustness of the Affine TFM systems using Nyquist diagram and GB, DNA(Direct Nyquist Array). Multiloop PI/PB controllers can be tuned by using a modified version of the Ziegler-Nickels (ZN) relations. Simulation examples show the performance and efficiency of the proposed multiloop design method.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 춘계학술대회논문집
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• pp.501-506
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• 2007

Structure-borne noise is the interior noise that results from the low frequency vibrational energy transmitted through those body and joint parts. The relation between the excitation of powertrain and resultant interior sound must be analyzed in order to identify and predict the structure borne noise. The method of acoustic source excitation is preferred than the method of mechanical force excitation to measure the NTF(noise transfer function). Because acoustical method is more convenient and reliable. In this paper, to analysis and identify vehicle interior noise by powertrain is performed, and the vibro-acoustic transfer function is extracted from experimental measurement. These are important step of TPA(transfer path analysis) to identify effect of interior noise resulted from powertrain running excitation.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 춘계학술대회논문집
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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.

• 한국소음진동공학회논문집
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• 제17권8호
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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.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2002년도 추계학술대회논문집
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• pp.517-522
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• 2002

The frequency response function(FRF) of each substructure is used for the transfer function synthesis method(TFS). The dynamic characteristics of the full system are obtained by synthesizing FRFs of each substructure. The validation of TFS depends on accuracy for FRF of each substructure. Impact hammer testing is widely used to obtain the modal characteristics of structures. However, the FRF obtained from impact hammer testing contains several errors, such as finite record length error and leakage error of which characteristic depends on data acquisition time which we call record length. In this paper, a method to remove these errors is proposed so as to enhance results of TFS. Numerical examples show that the FRF of full structure can be predicted exactly by the method proposed in this paper.

• Journal of the Korean Wood Science and Technology
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• 제46권4호
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• pp.362-367
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• 2018

This study estimates the sound absorption capability of rice straw particle mats by two methods: the transfer function method and the reverberation room method. In the reverberation method, the central frequency was set to the one-third octave band in the 100-5000 Hz range; in the transfer function method, the frequency range was 500-6400 Hz. Both methods yielded similar noise reduction coefficients of the rice straw mats. The noise reduction coefficient was approximately 0.8, indicating a high sound-absorption property of the mats. Therefore, rice straw matting is a suitable candidate material for sound-barrier walls against highway noise.

• Earthquakes and Structures
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• 제1권3호
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• pp.289-306
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• 2010

A gradient-based evolutionary optimization methodology is presented for finding the optimal design of viscous dampers to minimize an objective function defined for a linear multi-storey structure. The maximum value along height of the transfer function amplitudes for the interstorey drifts is taken as the objective function. Since the ground motion includes various uncertainties, the optimal damper placement may be different depending on the ground motion used for design. Furthermore, the transfer function treated as the objective function depends on the properties of structural parameters and added dampers. This implies that a more robust damper design is desired. A reliable and robust damping design system against any unpredictable ground motions can be provided by minimizing the maximum transfer function. Such design system is proposed in this paper.

• 한국환경과학회지
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• 제8권3호
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• pp.349-354
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• 1999

The transfer function was introduced to establish the prediction method for the DO concentration at the intaking point of Kongju Water Works System. In the mose cases we analyze a single time series without explicitly using information contained in the related time series. In many forecasting situations, other events will systematically influence the series to be forecasted(the dependent variables), and therefore, there is need to go beyond a univariate forecasting model. Thus, we must bulid a forecasting model that incorporates more than one time series and introduces explicitly the dynamic characteristics of the system. Such a model is called a multiple time series model or transfer function model. The purpose of this study is to develop the stochastic stream water quality model for the intaking station of Kongju city waterworks in Keum river system. The performance of the multiplicative ARIMA model and the transfer function noise model were examined through comparisons between the historical and generated monthly dissolved oxygen series. The result reveal that the transfer function noise model lead to the improved accuracy.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.129-134
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• 1998

In this paper, a simple method is presented to synthesize a transfer function from experimentally obtained gain and phase data. The method we offer here is based on the previous method given by M.Hassul etc. [1], where they proposed relevant formulas in a straightforward manner so that undergraduate students could follow the development more easily. This method, however, inevitably is accompanied by a significant difference between the real and identified model especially in the low frequency region. We solve this problem by introducing a new weighting function that can be determined by using the additive uncertainty of the Identified transfer function.