• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.3-6
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• 2004

In this paper, the problem of identifying a time-varying nonlinear system in an adaptive way was considered, whereby the time-varying second-order Volterra series was employed to model the system and the filtered weighted least squares (FWLS) algorithm was utilized for the fast parameter tracking capability with low computational burden. Finally, the performance of the proposed approach was demonstrated by providing some computer simulation results.

• The Journal of the Acoustical Society of Korea
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• v.7 no.4
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• pp.98-109
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• 1988

This paper introduces an adaptive nonlinear filtering algorithm that uses the sequential regression(SER) method to update the second order Volterra filter coefficients in a recursive way. Conventionally, the SER method has been used to invert large matrices which result from direct application of Wiener filter theory to the Volterra filter. However, the algorithm proposed in this paper uses the SER approach to update the least squares solution which is derived for Gaussian input signals. In such an algorithm, the size of the matrix to be inverted is smaller than that of conventional approaches, and hence the proposed method is computationally simpler than conventional nonlinear system identification techniques. Simulation results are presented to demonstrate the performance of the proposed algorithm.

• Journal of the Korean Society of Fashion and Beauty
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• v.4 no.1 s.7
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• pp.60-64
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• 2006

에센셜 오일은 고대로부터 향을 즐기거나 직접 상처 부위에 바르는 등의 다양한 방법으로 사용해 왔다. 일반적으로 에센셜 오일을 얻을 수 있는 허브와 스파이스는 약 200종 정도이며 흔히 주변에서 볼 수 있는 것으로는 레몬이나 오렌지, 장미, 제라늄, 민트 등이 있다. 이들 식물의 꽃잎(장미), 잎사귀(유칼립투스), 뿌리(베티본), 고목(샌달우드), 송진(프랭킨센스), 줄기(버베나), 열매(베르가못) 등 여러 부분에서 독자적인 방법으로 추출할 수 있는 에센셜 오일은 식물의 강한 생명의 힘으로 종종 언급되었다. 이는 방향성, 비지방성의 100% 천연의 식물에서 추출한 물질이며, 저마다의 고유한 향과 각기 다른 독특한 생명력과 치유능력을 가지고 있다. 이러한 에센셜 오일을 사용함에 있어서 우리는 감정 치유와 신체 건강을 향상시킬 수 있다. 모든 에센셜 오일은 항-박테리아, 항-바이러스 또는 항균성을 지니고 있다. 연구 조사에 따르면 아로마(향)는 인간의 감정 센서에 직접적인 영향을 끼치는데 이는 우리 신체의 감각 중심의 뇌에 직접적으로 아로마 향이 투입되기 때문이다. 우리의 후각을 통해 뇌 시스템을 직접 통과하여 감정, 행동, 냄새를 판단하는 것을 총괄하게 된다. 항상 아름다운 냄새를 통해 즐거워 하고 우리의 감정 균형을 유지시키므로 에센셜 오일을 사용함에 있어 그 향기가 우리에게 즐거움을 주는 것은 중요한 일이다. 이 오일을 통해 우리의 감각신경이 균형을 유지하게 될 것이다. 이러한 에센셜 오일을 이용하여 마음의 건강, 정신의 건강, 신체의 건강을 향상시키며 유지할 수 있도록 유지, 관리하는 것이 아로마테라피이다. 아로마테라피는 적게는 담배로부터 향수까지 광범위하게 가장 화려한 글로 설명되어 왔다. 에센셜 오일을 사용함에 있어 우리 신체의 가장 좋은 투입 방법으로 마사지 법이 있으며 우리 피부는 에센셜 오일을 놀라울 만큼 빠르게 흡수해 낸다. 이러한 효능의 에센셜 오일을 피부관리시에 적용하여 신체의 건강뿐만 아니라, 정신의 건강을 향상시키며 유지 할 필요가 있다고 본다.

• Journal of the Society of Naval Architects of Korea
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• v.54 no.3
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• pp.227-241
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• 2017

In this study, a Volterra system for the variations of metacentric height (GM) in waves is employed to simulate the parametric roll phenomena of ships in head sea condition. Using the present Volterra system, the transfer function of each harmonic component in the GM variation is computed for different ship models, including mathematical models and a real containership, and the results are validated through the comparison with the values obtained using the direct calculations based on a weakly nonlinear time-domain method. Then, a semi-analytic approach employing a 1-degree of freedom equation for roll motion is developed to simulate the parametric roll motions in irregular waves. In the derived approach, the nonlinear and time-varying restoring forces in the waves are approximated using the Volterra system. Through simulations of the parametric roll for different sea states, the effects of the 1st and 2nd-order harmonic components of the variations in the occurrence and amplitude of the parametric roll motions are investigated. Because of the strong nonlinearities in the phenomena, a stochastic analysis is conducted to examine the statistical properties of the roll motions in consideration of the sensitivities and uncertainties in the computations.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.5
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• pp.415-419
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• 2001

This paper proposes and adaptive nonlinear equalizer based on Volterra Series along with M-band wavelet transform(M-DWT). The proposed wavelet transform-domain approach leads to diagonalization of the input vector auto-correlation matrix, which yields clustering its eigenvalue spread around one, and improving the convergence rate of the corresponding transform-domain LMS algorithm. In particular, the proposed adaptive Volterra equalizer is employed to compensate for the output distortion produced by a weakly nonlinear system. Finally, some simulation results obtained by using a TWT amplifier model are provide to demonstrated the converging performance of the proposed approach.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.12
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• pp.827-833
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• 2004

In active noise control (ANC) systems, the error-reduction performance of the conventional Filtered-X Least Mean Square (FXLMS) algorithm may be affected by nonlinear distortions in the secondary path such as in the power amplifiers, loudspeakers and transducers. In this paper, a nonlinear FXLMS algorithm with high error-reduction performance is proposed to compensate for undesirable nonlinearities in the secondary-path of ANC systems by employing the inverse Volterra filtering approach. In particular, the proposed approach is based on the utilization of the conventional P-th order inverse approach to nonlinearity compensation in the secondary path of ANC systems. Finally, the simulation results showed that the proposed approach yields a better nonlinearity compensation performance for the ANC systems with a nonlinear secondary path than the conventional FXLMS.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.238-239
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• 2008

In this paper, active noise control (ANC) of a Volterra system with a nonlinear secondary path is proposed in the presence of a linear acoustic feedback, whereby the conventional ANC of a linear system with online acoustic feedback-path modeling is further extended to ANC of a Volterra system with a linear acoustic feedback path. In particular, the proposed ANC system consists of two adaptive Volterra filters (for nonlinear noise control and nonlinear adaptive noise cancellation) and one feedback-path modeling filter. Simulation results show that the proposed approach yields more effective reduction of disturbances arising from the acoustic feedback, in addition to high nonlinear ANC performance.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.553-556
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• 2000

This paper proposes an adaptive linearization method of Volterra nonlinear systems using DWT(Discrete Wavelet Transform)and an LMS-type predistorter. In particular, the proposed wavelet transform-domain lineatization method leads to diagonalization of the input vector auto-correlation matrix which yields improvement of the convergence rate of the corresponding transform-domain LMS algorithm. Furthermore, the adaptive Volterra predistorter followed by a corresponding weakly Volterra nonlinear system(here. a TWT amplifier model in a satellite communication system) is utilized to compensate for the distortion in the output. Also,12-PSK and 4-QAM are applied as the input to the nonlinear system to be tested. Some simulation results show that the proposed linearization approach has better performance than DCT-based or conventional normalized LMS algorithms do.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.70-72
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• 2009

In this paper, nonlinear active noise control(ANC) of a 3rd-order Volterra system, which is robust to background noise variation, is proposed along with a variable step-size Gauss-Seidel pseudo affine projection(VSSGS-PAP) algorithm. Simulation results demonstrate that the proposed approach yields better nonlinear ANC performance in a background noise variation environment, compared with the conventional methods for the nonlinear ANC.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.395-397
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• 2007

In this paper, a Gauss-Seidel fast affine projection (GS-FAP) algorithm developed for the linear active noise control (ANC) is further utilized for nonlinear ANC of a 2nd-order Volterra systems with a nonlinear primary path and a noisy secondary path. The simulation results, obtained by applying adaptive Volterra filtering, show that the proposed approach yields more stable and faster nonlinear AN.C, compared with the conventional methods for the nonlinear ANC in case of noisy plant models.