• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.318-321
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• 2007

Since human listening is very sensitive to sound, a subjective index of a sound quality is required. Therefore, in the analysis for each situation, the sound evaluation is composed with sound quality factor. Many researchers spends their effort to make a more reliable and more accurate of sound in term of sound quality index for various system noise. The previous methods to evaluation of the SQ about vehicle interior noise are linear regression analysis of subjective SQ metrics by statistics and the estimation of the subjective SQ values by neural network. But these are so depended on jury test very much that they result in many difficulties. So, to reduce jury test weight, we suggested a new method using Mahalanobis distance for SQ evaluation. Threrefore, in this study Mahalanobis distance for the vehicle interior noise was derived using the objective SQ except jury test. Finnaly, the results of the SQ evaluation was analyzed discrimination between reference and abnormal group.

• Journal of Environmental Impact Assessment
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• v.21 no.2
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• pp.289-296
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• 2012

The shock vibration such as machine vibration, blasting vibration etc. has effect on nearby structure as well as human body. The purpose of this study is to predict allowable limit distance of vibration about human body. First of all, vibration velocity such as PPV, PVS was measured by shock vibration experiment, and vibration level was calculated by conversion formula of vibration velocity. And then, allowable limit distance was analyzed by converted vibration level. The results are as follows : Firstly, the correlation coefficient of converted vibration level was over R=0.94, and vibration level caused by PVS was usually represented to high curve line. Secondly, the cross point of vibration level between ground and concrete condition was represented to 66.68dB(V), and allowable limit distance was represented to difference over three times when vibration regulation was raised from 65dB(V) to 80dB(V).

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.1
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• pp.57-60
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• 2008

Since human listening is very sensitive to sound, for evaluating of a sound quality is required. Therefore, in the analysis for each situation, the sound evaluation is composed with sound quality factor. My researchers spends their effort to make a more reliable and more accurate of sound in term of sound quality index for various system noise. The previous methods to evaluation of the SQ about vehicle interior noise are linear regression analysis of subjective SQ metrics by statistics and the estimation of the subjective SQ values by neural network. But these are highly dependent on jury test and have many difficulties due to various environmental factors. So, to reduce jury test weight. we suggested a new method using Mahalanobis distance for SQ evaluation. Threrefore, in this study Mahalanobis distance for the vehicle interior noise was derived using the objective SQ except jury test. Finnaly, the results of the SQ evaluation was analyzed discrimination between reference and abnormal group.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.3
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• pp.250-255
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• 2012

Face recognition has wide applications in security and surveillance systems as well as in robot vision and machine interfaces. Conventional challenges in face recognition include pose, illumination, and expression, and face recognition at a distance involves additional challenges because long-distance images are often degraded due to poor focusing and motion blurring. This study investigates the effectiveness of applying photon-counting linear discriminant analysis (Pc-LDA) to face recognition in harsh environments. A related technique, Fisher linear discriminant analysis, has been found to be optimal, but it often suffers from the singularity problem because the number of available training images is generally much smaller than the number of pixels. Pc-LDA, on the other hand, realizes the Fisher criterion in high-dimensional space without any dimensionality reduction. Therefore, it provides more invariant solutions to image recognition under distortion and degradation. Two decision rules are employed: one is based on Euclidean distance; the other, on normalized correlation. In the experiments, the asymptotic equivalence of the photon-counting method to the Fisher method is verified with simulated data. Degraded facial images are employed to demonstrate the robustness of the photon-counting classifier in harsh environments. Four types of blurring point spread functions are applied to the test images in order to simulate long-distance acquisition. The results are compared with those of conventional Eigen face and Fisher face methods. The results indicate that Pc-LDA is better than conventional facial recognition techniques.

• Structural Engineering and Mechanics
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• v.78 no.2
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• pp.219-230
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• 2021

International seismic codes stipulate that adjacent buildings should be separated by a specified minimum distance, otherwise the pounding effect should be considered in the design. Recent researches proposed an alternative method (Double Difference Combination Rule) to estimate seismic gap between structures, as this method considers the cross relation of adjacent buildings behavior during earthquakes. Four different criteria were used to calculate the minimum separation distance using this method and results are compared to the international codes for five separation cases. These cases used four case study buildings classified by different heights, lateral load resisting systems and fundamental periods of vibrations to assess the consistency in results for the alternative methods. Non-linear analysis was performed to calculate the inelastic displacements of the four buildings, and the results were used to evaluate the relation between elastic and inelastic displacements due to the ductility of structural elements resisting seismic loads. A verification analysis was conducted to guarantee that the separation distance calculated is sufficient to avoid pounding. Results shows that the use of two out of the four studied methods yields separation distances smaller than that calculated by the code specified equations without under-estimating the minimum separation distance required to avoid pounding.

• Communications for Statistical Applications and Methods
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• v.11 no.2
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• pp.369-380
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• 2004

Clustering algorithms with different similarity measures are commonly used to find an optimal clustering or close to original clustering. The recovery level of using Euclidean distance and distances transformed from correlation coefficients is evaluated and compared using Rand's (1971) C statistic. The C values present how the resultant clustering is close to the original clustering. In simulation study, the recovery level is improved by applying the correlation coefficients between objects. Using the data set from Spellman et al. (1998), the recovery levels with different similarity measures are also presented. In general, the recovery level of true clusters was increased by using the correlation coefficients.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.2
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• pp.123-129
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• 2010

The purpose of study is to investigate the correlation between the Lyapunov exponent (LE) and the displacement of the center of gravity (DCG) for clarifying walking stability on the treadmill. From fifteen young healthy subjects volunteered, lower extremity joint angles were recorded using a three-dimensional motion capture system with reflective markers. The anteroposterior DCG and the LE were calculated by a commercial software. A linear correlation between LE and DCG (p<0.05) showed that LEs compensated for walking distance on the treadmill walking. However, LEs were found to be independent of self-selected walking speeds by a negligible correlation between LE and the Froude number (p>0.05).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.3
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• pp.498-504
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• 2015

This paper proposed an Spread Spectrum Time Domain Reflectometry (SSTDR) using time-frequency correlation analysis in order to have more accurate fault determination and location detection than classical SSTDR despite increased signal attenuation due to the long distance to cable fault location. The proposed method was validated through comparison with classical SSTDR methods in open- and short-circuit fault detection experiments of low-voltage power cables. The experimental results showed that the proposed method can detect correlation coefficients at fault locations accurately despite reflected signal attenuation so that cable faults can be detected more accurately and clearly in comparison to existing methods.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.32 no.5
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• pp.374-381
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• 2008

The correlation coefficient method, which was proposed by our research group, is applied to digital particle holography to locate the focal plane of particles. It uses the fact that the correlation coefficient is maximum at the focal plane. The factors influencing this method are discussed with a numerical simulation of holograms. For real holograms, the Wiener filter was proposed to process both recorded holograms and reconstructed images. The application results using the dot array target showed that the Wiener filter is a very effective tool for processing holography-related images. The effects of the dot size and the object distance on the errors in the determination of the focal plane by the correlation coefficient method were investigated by using the calibration target.

• The Korean Journal of Applied Statistics
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• v.37 no.1
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• pp.1-12
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• 2024

In many real-world data, multiple response variables are often dependent on the same set of explanatory variables. In particular, if several response variables are correlated with each other, simultaneous estimation considering the correlation between response variables might be more effective way than individual analysis by each response variable. In this multivariate regression analysis, least distance estimator (LDE) can estimate the regression coefficients simultaneously to minimize the distance between each training data and the estimates in a multidimensional Euclidean space. It provides a robustness for the outliers as well. In this paper, we examine the least distance estimation method in multivariate linear regression analysis, and furthermore, we present the penalized least distance estimator (PLDE) for efficient variable selection. The LDE technique applied with the adaptive group LASSO penalty term (AGLDE) is proposed in this study which can reflect the correlation between response variables in the model and can efficiently select variables according to the importance of explanatory variables. The validity of the proposed method was confirmed through simulations and real data analysis.