• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.192-194
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• 2008

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.04a
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• pp.267-268
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• 2014

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.04a
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• pp.510-511
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• 2011

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.813-821
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• 2020

Recently, research using unstructured data such as images and videos has been actively conducted in various fields. Edge detection is one of the most useful image enhancement techniques to improve the quality of the image process. However, it is very difficult to perform edge detection in noise images because the edges and noise having high frequency components. This paper uses a linear model and standard deviation as an effective edge detection method for noise images. The edge is detected by the difference between the standard deviation of the pixels included in the pixel block and the standard deviation of the residual obtained by fitting the linear model. The results of edge detection are compared with the results of the Sobel edge detector. In the original image, the Sobel edge detection result and the proposed edge detection result are similar. Proposed method was confirmed that the edge with reduced noise was detected in the various levels of noise images.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.2
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• pp.185-190
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• 2015

The purpose of this study is developing the quantify the seat belt pulling Noise index and evaluation method. This paper presents the objective method to evaluate the emotional feeling about the pulling Noise of the seat belt. The physical quantification is required to objectively evaluate the emotional feeling of the pulling Noise. This is called the "Noise metric." The Noise metric is should correlated to the subjective rating of the pulling Noise. The pulling Noise index is developed throughout the linear regression of the Noise metric and the subjective rating. The developed index is used for the objective evaluation of the emotional feeling about the pulling Noise of a seat belt throughout the modification of seat belt components.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1587-1590
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• 1987

A non-linear filtering for the noise cancelling of signals degraded by random impulsive noise is proposed. The non-linear algorithm is based on a criterion for the overall smoothness of the signal. The smoothness criterion is optimized by a dynamic programming strategy. It performs considerably better than a LDNF(low-distortion nonlinear filter), although being comparable in computing time.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.704-707
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• 2000

In this experimental study, noise level measurements were conducted on physical LBB(Linear Ball Bush) samples in a semi-anechoic chamber. Evaluation of the measured data demonstrates that LBB's noise level is proportional to the logarithmic of its speed multiplied by the ball diameter. Furthermore details of implementations and process for measuring noise level and operating speed of LBB are also presented.

• ETRI Journal
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• v.16 no.1
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• pp.35-43
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• 1994

In this paper, the robustness of the artificial neural networks to noise is demonstrated with a multilayer perceptron, and the reason of robustness is due to the statistical orthogonality among hidden nodes and its hierarchical information extraction capability. Also, the misclassification probability of a well-trained multilayer perceptron is derived without any linear approximations when the inputs are contaminated with random noises. The misclassification probability for a noisy pattern is shown to be a function of the input pattern, noise variances, the weight matrices, and the nonlinear transformations. The result is verified with a handwritten digit recognition problem, which shows better result than that using linear approximations.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.788_789
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• 2009

The cogging force of a permanent-magnet linear motor is a major component of the detent force, but unfortunately makes a ripple in the thrust force and induces undesired vibration and acoustic noise. In this paper, Coupling Particles Swarm Optimization is applied to optimization the shape of permanent magnet linear motor by minimizing the undesired vibration and acoustic noise in the thrust force and also considering the maximum thrust force. The result shows that the 9-pole 10-slot PMLM removes almost of the cogging force while giving a big thrust force.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1998.04a
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• pp.392-398
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• 1998

In order to check the validity of nonlinear normal modes of continuous, systems by means of the energy-based formulation, we consider a beam with a nonlinear boundary condition. The initial and boundary e c6nsl of a linear partial differential equation and a nonlinear boundary condition is reduced to a linear boundary value problem consisting of an 8th order ordinary differential equations and linear boundary conditions. After obtaining the asymptotic solution corresponding to each normal mode, we compare this with numerical results by the finite element method.