• The Journal of the Acoustical Society of Korea
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• v.32 no.4
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• pp.329-334
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• 2013

Order spectrum analysis is widely used to grasp the features of noises due to powertrain system including engine and intake/exhaust system. It is known from many previous researches that order components related to the first and second firing frequencies of engine considerably affect the noise of car interior. The purpose of this paper is to find out the difference in sound quality: Pleasantness of car interior noise according to the change of its order spectrum. For this, car interior noises of 6-cylinder and 4-cylinder engines are recorded and their order spectrum levels are modified by applying adaptive digital filters. After subjective listening test employing paired comparison method is conducted, it is investigated that the level change of half-order components is a noticeable factor to improve Pleasantness of the car interior noises whereas level decrease of firing order does not always give the positive effect on its sound quality.

• ETRI Journal
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• v.41 no.2
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• pp.176-183
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• 2019

Spectrum sensing plays an important role in spectrum sharing. Energy detection is generally used because it does not require a priori knowledge of primary user (PU) signals; however, it is sensitive to noise uncertainty. An order statistics (OS) detector provides inherent protection against nonhomogeneous background signals. However, no analysis has been conducted yet to apply OS detection to spectrum sensing in a wireless channel to solve noise uncertainty. In this paper, we propose a robust spectrum sensing scheme based on generalized order statistics (GOS) and analyze the exact false alarm and detection probabilities under noise uncertainty. From the equation of the exact false alarm probability, the threshold value is calculated to maintain a constant false alarm rate. The detection probability is obtained from the calculated threshold under noise uncertainty. As a fusion rule for cooperative spectrum sensing, we adopt an OR rule, that is, a 1-out-of-N rule, and we call the proposed scheme GOS-OR. The analytical results show that the GOS-OR scheme can achieve optimum performance and maintain the desired false alarm rates if the coefficients of the GOS-OR detector can be correctly selected.

• Journal of Biomedical Engineering Research
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• v.9 no.1
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• pp.101-108
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• 1988

In this paper the power spectrum of background EEG is estimated by the LMS PHD based on least mean square. At the power spectrum estimatiom, the stocastic process of background EEG is assumed to consist of the nonharmonic sinusoid and the white noise. In the LMS PHD the model parameters are obtained by the least mean square at optimal order which is obtained from the fact that the eigenvalue's fluctuation of autocorrelation matrix of the normal back-ground EEG is smaller at some order than at other order when the power spectrum of background EEG is esitmated by PHD. The optimal order of this model is the 6-th order when the eigenvalue's fluctuation of autocorrelation matrix of background EEG is considered. The estimation results are with compared the results from the Maximum Entropy Spectral Estimation and Pisarenko Harmonic Decomposition. From the comparison results. The LMS PHD is possible to estimate the power spectrum of background EEG.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.1
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• pp.19-26
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• 2010

In this paper, we propose an efficient decision rule in order to get better chance to detect the unused spectrum assigned to a licensed user and improve reliability of spectrum sensing performance. Each secondary user receives the signals from the licensed user. And the resulting signals input to an energy detector. Then, each sensing result is combined and used to make a decision whether the primary user is present at the licensed spectrum band or not. In order to make the reliable decision, we apply an efficient decision rule that is called as a majority rule in this paper. The simulation results show that spectrum sensing performance with the proposed decision rule is more reasonable and efficient than that with conventional decision rules.

• Kyungpook Mathematical Journal
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• v.60 no.2
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• pp.297-305
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• 2020

We show that selfadjoint operator extensions of minimal second order difference operators have only discrete spectrum when the odd order coefficient is unbounded but grows or decays according to specific conditions. Selfadjoint operator extensions of minimal differential operator under similar growth and decay conditions on the coefficients have a absolutely continuous spectrum of multiplicity one.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.1 s.178
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• pp.105-112
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• 2006

The nonlinear damping force model is made to identify the properties of the ER (electro-rheological) fluid suspension damper. The instrumentation is carried out to measure the damping force of the ER damper. The higher order spectral analysis method is used to investigate the nonlinear frequency coupling phenomena with the damping force signal according to the sinusoidal excitation of the damper. The distinctive higher order nonlinear characteristics are observed. The nonlinear damping force model, which has the higher order velocity terms, is proposed with the result of higher order spectrum analysis. The higher order terms coefficients, which vary according to the strength of the electric field, are calculated using the least square method.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.106-109
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• 2000

There have been proposed two types of low bit rate vocoder upto now : One is MBE type using the spectrum modeling and another is CELP type using the hybrid coding method. CELP type vocoder has mainly studied between them. Specially, much of intensity is concentrated in CELP vocoder due to the emergence of Internet Phone and PCS in a domestic. In order to improve the speech quality in CELP vocoder, in this paper, we proposed a new spectrum analysis algorithm with variable window, In CELP vocoder, the spectrum of the synthesised speech signal is distorted because the fixed size windows is used for spectrum analysis. So we have measured the spectral leakage and in order to minimize the spectral leakage have adjusted the window size. Applying this method G.723.1 ACELP, we can get SD(Spectral Distortion) reduction 0.084(dB), residual energy reduction 6.3% and MOS(Mean Opinion Score) improvement 0.1.

• Journal of electromagnetic engineering and science
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• v.12 no.3
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• pp.203-209
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• 2012

In order to improve sensing performance when the noise variance is not known, this paper considers a so-called blind spectrum sensing technique that is based on eigenvalue models. In this paper, we employed the spiked population models in order to identify the miss detection probability. At first, we try to estimate the unknown noise variance based on the blind measurements at a secondary location. We then investigate the performance of detection, in terms of both theoretical and empirical aspects, after applying this estimated noise variance result. In addition, we study the effects of the number of SUs and the number of samples on the spectrum sensing performance.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.4
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• pp.85-92
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• 2000

The pulsating pressure waves are composed of fundamental frequency and higher order harmonics in exhaust pipe of engines. The nonlinearity in exhaust pipe is caused by their interactions. The error which is between prediction and measurement is induced by the nonlinearity. We can not explain this phenomenon using linear acoustic theory which is existing theory. So power spectrum which was used in linear theory is not useful. Bispectrum and bicoherence functions which are a higher order spectrum are applicable to explain this phenomenon. This paper proposes a nonlinear effect of pulsating pressure waves. The phenomenon proposed here is identified by using of higher order spectrum density functions.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.359-362
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• 2004

In multi-dimensional image, ICA-based feature extraction algorithm, which is proposed in this paper, is for the purpose of detecting target feature about pixel assumed as a linear mixed spectrum sphere, which is consisted of each different type of material object (target feature and background feature) in spectrum sphere of reflectance of each pixel. Landsat ETM+ satellite image is consisted of multi-dimensional data structure and, there is target feature, which is purposed to extract and various background image is mixed. In this paper, in order to eliminate background features (tidal flat, seawater and etc) around target feature (laver farm) effectively, pixel spectrum sphere of target feature is projected onto the orthogonal spectrum sphere of background feature. The rest amount of spectrum sphere of target feature in the pixel can be presumed to remove spectrum sphere of background feature. In order to make sure the excellence of feature extraction method based on ICA, which is proposed in this paper, laver farm feature extraction from Landsat ETM+ satellite image is applied. Also, In the side of feature extraction accuracy and the noise level, which is still remaining not to remove after feature extraction, we have conducted a comparing test with traditionally most popular method, maximum-likelihood. As a consequence, the proposed method from this paper can effectively eliminate background features around mixed spectrum sphere to extract target feature. So, we found that it had excellent detection efficiency.