• Journal of the Korean Society for Aviation and Aeronautics
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• v.20 no.4
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• pp.1-6
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• 2012

The accurate information of mass properties is required for the precise control of the spacecraft. The mass properties, mass and inertia, are changeable by some reasons such as consumption of propellant, deployment of solar panel, sloshing, environmental effect, etc. The gyro-based attitude data including noise and bias reduces the control accuracy so it needs to be compensated for improvement. This paper introduces a real-time inertia estimation method for the attitude determination of STSAT-3, Korea Science Technology Satellite. In this method we first filter the gyro noise with the Extended Kalman Filter(EKF), and then estimate the moment of inertia by using the filtered data from the EKF based on the Recursive Least Square(RLS).

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.4
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• pp.28-32
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• 2009

This paper compares two speech estimators under a generalized Gamma distribution for DFT-based single-microphone speech enhancement methods. For the speech enhancement, the noise estimation based on recursive averaging spectral values by spectral minimum noise is applied to two speech estimators based on the generalized Gamma distribution using $\kappa$=1 or $\kappa$=2. The performance of two speech enhancement algorithms is measured by recognition accuracy of automatic speech recognition(ASR) in car noisy environment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.11
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• pp.973-975
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• 2013

In this paper, the carrier-to-noise power ratio estimation performances for commercial GPS receiver are shown by simulation and are analyzed under weak signal reception, high sensitivity signal reception, and the matched spectrum jamming signal reception environments.

• Journal of KSNVE
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• v.10 no.5
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• pp.769-774
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• 2000

The purpose of the work is to present successful applications of a modified wavelet shrinkage method for the accurate and fast estimation of a transfer function. Although the experimental process of determining a transfer function introduces not only Gaussian but also non-Gaussian noises, most existing estimation methods are based only on a Gaussian noise model. To overcome this limitation, we propose to employ a modified wavelet shrinkage method in which L1 -based median filtering and L2 -based wavelet shrinkage are applied repeatedly. The underlying theory behind this approach is briefly explained and the superior performance of this modified wavelet shrinkage technique is demonstrated by a numerical example.

• Journal of the Korean Statistical Society
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• v.27 no.2
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• pp.165-187
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• 1998

Shin and Sarkar (1993, 1994) studied the problem of testing for a unit root in an AR(p) signal observed with MA(q) noise when the MA parameters are known. In this paper we consider the case when the MA parameters are unknown and to be estimated. Test statistics are defined using unit root parameter estimates based on three different estimation methods of Hannan and Rissanen (1982), Kohn (1979) and Shin and Sarkar (1995). An AR(p) process contaminated by MA(q) noise is a .estricted ARMA model, for which Shin and Sarkar (1995) derived an easy-to-compute Newton- Raphson estimator The two-stage estimation p.ocedu.e of Hannan and Rissanen (1982) is used to compute initial parameter estimates in implementing the iterative estimation methods of both Shin and Sarkar (1995) and Kohn (1979). In a simulation study we compare the relative performance of these unit root tests with respect to both size and power for p=q=1.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.301-302
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• 2008

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.979-980
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• 2008

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.404-406
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• 2021

In modern society, artificial intelligence and automation are being applied in various fields due to the development of the 4th industrial revolution and IoT technology. In particular, systems with a high proportion of image processing, such as automated processes, intelligent CCTV, medical industry, robots, and drones, are susceptible to external factors noise. In this paper, we propose a digital filter based on noise estimation and weights to reconstruct an image in a complex noise environment. The proposed algorithm classifies the types of noise using noise judgment, and determines the noise level of the filtering mask to switch the filtering process to obtain the final output. In order to verify the performance of the proposed algorithm, simulation was conducted, compared with the existing filter algorithm, and the results were analyzed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.518-523
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• 2017

This paper proposes a novel denoising method based on non-local(NL) means. The NL-means algorithm is effective for removing an additive Gaussian noise, but the denoising parameter should be controlled depending on the noise level for proper noise elimination. Therefore, the proposed method optimizes the denoising parameter according to the noise levels. The proposed method consists of two processes: off-line and on-line. In the off-line process, the relations between the noise level and the denoising parameter of the NL-means filter are analyzed. For a given noise level, the various denoising parameters are applied to the NL-means algorithm, and then the qualities of resulting images are quantified using a structural similarity index(SSIM). The parameter with the highest SSIM is chosen as the optimal denoising parameter for the given noise level. In the on-line process, we estimate the noise level for a given noisy image and select the optimal denoising parameter according to the estimated noise level. Finally, NL-means filtering is performed using the selected denoising parameter. As shown in the experimental results, the proposed method accurately estimated the noise level and effectively eliminated noise for various noise levels. The accuracy of noise estimation is 90.0% and the highest Peak Signal-to-noise ratio(PSNR), SSIM value.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.843-848
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• 2001

Railroad noise is one of the main causes of environmental impact. Whenever a new railroad line is planned or a housing project near an existing railroad is proposed, an estimate of the relevant noise levels is usually required. For this, it is necessary to quantify those parameters that affect the railroad noise. This paper deals with an estimation of railroad noise using soundproof tunnel in a high-rise apartment building