• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2003.11a
• /
• pp.60-65
• /
• 2003

In this study, for estimating responses of a controlled structure and determining the maximum control force of velocity-dependent damping devices, three estimation models such as Fourier envelope convex model, probability model, and Newmark design spectrum are used. For this purpose, a procedure proposed by Gupta (1990) for estimating spectral velocity using pseudo-spectral velocity which is given by the estimation models is used and modified to consider the effects of increased damping ratio by the damping device. Time history results indicate that Newmark design spectrum gives the best estimation of maximum control force for over all period structures.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.1
• /
• pp.430-437
• /
• 2018

Ultrasonic attenuation is an important parameter in Quantitative Ultrasound and many algorithms have been proposed to improve estimation accuracy and repeatability for multiple independent estimates. In this work, we propose an improved algorithm for estimating ultrasonic attenuation utilizing the optimal frequency compounding technique based on stochastic noise model. We formulate mathematical compounding equations in the AWGN channel model and solve optimization problems to maximize the signal-to-noise ratio for multiple frequency components. Individual estimates are calculated by the reference phantom method which provides very stable results in uniformly attenuating regions. We also propose the guideline to select frequency ranges of reflected RF signals. Simulation results using numerical phantoms show that the proposed optimal frequency compounding method provides improved accuracy while minimizing estimation bias. The estimation variance is reduced by only 16% for the un-compounding case, whereas it is reduced by 68% for the uniformly compounding case. The frequency range corresponding to the half-power for reflected signals also provides robust and efficient estimation performance.

• Advances in aircraft and spacecraft science
• /
• v.4 no.3
• /
• pp.335-351
• /
• 2017

Kalman filter based spacecraft attitude estimation has been used in many space missions and has been widely discussed in literature. While some models in spacecraft attitude estimation include spacecraft dynamics, most do not. To our best knowledge, there is no comparison on which model is a better choice. In this paper, we discuss the reasons why spacecraft dynamics should be considered in the Kalman filter based spacecraft attitude estimation problem. We also propose a reduced quaternion spacecraft dynamics model which admits additive noise. Geometry of the reduced quaternion model and the additive noise are discussed. This treatment is easier in computation than the one with full quaternion. Simulations are conducted to verify our claims.

• Journal of Korea Multimedia Society
• /
• v.18 no.4
• /
• pp.437-442
• /
• 2015

Voice activity detection is very important process that voice activity separated form noisy speech signal for speech enhance. Over the past few years, many studies have been made on voice activity detection, but it has poor performance in low signal to noise ratio environment or fickle noise such as car noise. In this paper, it proposed new voice activity detection algorithm using ensemble variance based on wavelet band entropy and soft thresholding method. We conduct a survey in a lot of signal to noise ratio environment of car noise to evaluate performance of the proposed algorithm and confirmed performance of the proposed algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.26 no.12A
• /
• pp.2050-2058
• /
• 2001

In this paper, we propose the filtered-x least mean fourth (LMF) algorithm where the error raised to the power of four is minimized and analyze its convergence behavior for a multiple sinusoidal acoustic noise and Gaussian measurement noise. Application of the filtered-x LMF adaptive filter to active noise cancellation (ANC) requires estimating of the transfer characteristic of the acoustic path between the output and error signal of the adaptive controller. The results of 7he convergence analysis of the filtered-x LMF algorithm indicates that the effects of the parameter estimation inaccuracy on the convergence behavior of the algorithm are characterized by two distinct components : Phase estimation error and estimated gain. In particular, the convergence is shown to be strongly affected by the accuracy of the phase response estimate. Also, we newly show that convergence behavior can differ depending on the relative sizes of the Gaussian measurement noise and convergence constant.

• The Journal of the Acoustical Society of Korea
• /
• v.15 no.3E
• /
• pp.66-73
• /
• 1996

In this paper, we propose the filtered-x least mean fourth (LMF) algorithm where the error raised to the power of four is minimized and analyze its convergence behavior for a multiple sinusoidal acoustic noise and Gaussian measurement noise. Application of the filtered-x LMF adaptive filter to active noise control(ANC) requires estimating of the transfer characteristic of the acoustic path between the output and error signal of the adaptive controller. The results of the convergence analysis of the filtered-x LMF algorithm indicates that the effects of the parameter estimation inaccuracy on the convergence behavior of the algorithm are characterized by two distinct components : Phase estimation error and estimated gain. In particular, the convergence is shown to be strongly affected by the accuracy of the phase response estimate. Also, we newly show that convergence behavior can differ depending on the relative sizes of the Gaussian measurement noise and convergence constant.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.32B no.11
• /
• pp.1506-1516
• /
• 1995

In this paper, we propose the filtered-x least mean fourth (FXLMF) algorithm where the error raised to the power of four is minimized and analyze its convergence behavior for a multiple sinusoidal acoustic noise and Gaussian measurement noise. Application of the FXLMF adaptive filter to active noise control requires to estimate the transfer characteristics of the acoustic path between the output and the error signal of the adaptive controller. The results of the convergence analysis of the FXLMF algorithm indicate that the effects of the parameter estimation inaccuracy on the convergence behavior of the algorithm are characterized by two distinct components : Phase estimation error and estimated gain. In particular, the convergence is shown to be strongly affected by the accuracy of the phase response estimate. Also, we newly show that the convergence behavior can differ depending on the relative sizes of the Gaussian noise and the convergence constant.

• Proceedings of the Acoustical Society of Korea Conference
• /
• 1998.06d
• /
• pp.80-83
• /
• 1998

In this paper, we propose a new filtered-x least mean fouth (LMF) algorithm where the error raised to the power of four is minimized and analyze its convergence behavior or a multiple sinusoidal acoustic noise and Gaussian measurement noise. Application of the filtered-x LMF adaptive filter to active noise cancellation (ANC) requires estimating of the transfer characteristic of the acoustic path between the ouput and error signal of the adaptive canceller. The results of the convergence analysis of the filtered-x LMF algorithm indicates that the effects of the parameter estimation inaccuracy on the convergence behavior of the algorithm are characterized by two distinct component . Phase estimation error and estimated again. In particular , the convergence is shown to be strongly affected by the accuracy of the phase response estimate. Also, we newly show that convergence behavior can differ depending on the relative sizes of the Gaussian measurement noise and convergence constant.

• Proceedings of the KIPE Conference
• /
• 2007.07a
• /
• pp.369-371
• /
• 2007

Due to the measurement noise or system noise, the performance of photovoltaic power generation system can be degraded. If this noise is contained in the solar array voltage measurement signal, the correct operation of the maximum power point tracker can not be guaranteed. The application of the extended Kalman filter to the photovoltaic system can obtain enhanced states estimation result. The Kalman filter provides a recursive solution to optimally estimate from random noise signals. Additionally, as a consequence of Kalman filter, the unmeasurable state such as inductor current can be estimated without current sensor. The methods for system modeling and extended Kalman filter design are presented and the experimental results verify the validity of the proposed system.

• Journal of the Korean Society for Precision Engineering
• /
• v.13 no.5
• /
• pp.38-43
• /
• 1996

Windowing routines have as their purpose the reduction of the sidelobes of a spectral output of the FFT or DFT routines. Windowing routines accomplish this by forcing the beginning and end of any sequence to approach each other in value. Since they must work with any sequence they force the beginning and ending samples near zero. To make up for this reduction in power, windowing routines give extra weight to the values near the middle of the sequence. The difference between windows is the way in which they transition from the low weights near the edges to the higher weights neqr the middle of the sequence. Signal-to-noise ratio(SNR) can be determined by the ratio of the output noisy signal variance to the input noisy signal variance of a window. Standard deviation of noise is reduced by windowing. Thus, the windowing operation improved the SNR of the noisy signal. This paper shows a performance estimation of windowing on a single tone with added Gaussian noise and uniform noise.