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Previous methods for the evaluation of the sound quality in vehicle interiors focused on the linear regression analysis of subjective sound quality metrics using statistics and estimations of subjective sound quality values by neural networks.Recently, sound quality evaluation using subjective measures has focused on identifying sound quality metrics which can predict subjective responses. It has been used to study a variety of subjective measures such as the four parameters used by Zwicker, but it is difficult to identify highly correlated sound quality metrics with the jury test. The Mahalanobis distance is a useful method to reduce the number of dimensions and to develop measures based on the correlation between the various variables. In particular, the Mahalanobis distance can be used as a new sound quality metric because it can convert the sound quality that is represented by several measures to a single value. In this study, a new sound quality metric is suggested which employs the four parameters used by Zwicker and is based on the Mahalanobis distance in order to predict subjective responses in sound quality evaluation. In addition, in order to calculate the Mahalanobis distance more accurately, after using data from a number of vehicles, sound quality metrics were reselected to remove those that do not require correlation analyses between each metric. Finally, we verified that the logarithmic Mahalanobis distance can be used not only as a new sound quality metric through correlation analysis with a jury test but also as a criterion to determine the vehicle quality. In order to verify the reliability of the regression equation, arbitrary vehicle data are applied to the regression equation. The regression equation using the logarithmic Mahalanobis distance is validated by the listening results, and the regression results after applying arbitrary data are similar.

The reduction of the Vehicle interior noise has been the main interest of NVH engineers. The driver’s perception on the vehicle noise is affected largely by psychoacoustic characteristic of the noise as well as the SPL. In particular, the HVAC sound among the vehicle interior noise has been reflected sensitively in the side of psychology. In previous study, we have developed to verify identification of source for the vehicle HVAC system through multiple-dimensional spectral analysis. Also we carried out objective assessments on the vehicle HVAC noises and subjective assessments have been already performed with 30 subjects. In this study, the linear regression models were obtained for the subjective evaluation and the sound quality metrics. The regression procedure also allows you to produce diagnostic statistics to evaluate the regression estimates including appropriation and accuracy. Appropriation of regression model is necessary to R2 value and F-value. And testing for regression model is necessary to Independence, Homoscedesticity and Normality. To enhance sound quality, we applied active noise control (ANC) which is effective in the low-frequency bandwidth. Primary noise of the HVAC system is less than 500Hz. As a result of ANC application, sound quality is improved by more quiet, powerful, expensive, smooth.

Abstract. In this paper, we investigate L ∞ -error estimates for the convex optimal control problem governed by nonlinear elliptic equations using interpolation coefficients mixed finite element methods. By using the interpolation coefficient thought to process the nonlinear term of equations, we present the mixed finite element approximation with interpolated coefficients for nonlinear optimal control problem. We derive L ∞ -error estimates for the interpolation coefficients mixed finite element approximation of nonlinear optimal control problem. Finally some numerical examples are given to confirm our theoretical results.Mathematics subject classification (2010): 49J20, 65N30.