다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Vehicle system dynamics integration encompasses interdisciplinary challenges innovations in various aspects related to vehicle system/subsystems/components dynamic characteristics, modeling and validation, vehicle dynamics state measurement and estimation, vehicle/chassis control systems, coordination of power management and dynamics/stability control, etc [1-4]. These are becoming more critical due to the increasing concern and rapid development in electric and hybrid vehicles, which tend to exhibit different vehicle dynamics/stability and control characteristics when compared to conventional vehicles.

Mechanical systems and signal processing

Review: Advances in active noise control: A survey, with emphasis on recent nonlinear techniques

Two-stage fractional least mean square identification algorithm for parameter estimation of CARMA systems

Practical multi-objective control for automotive semi-active suspension system with nonlinear hydraulic adjustable damper

In this paper, particle swarm optimization (PSO) algorithm, which is a nongradient but simple evolutionary computing-type algorithm, is proposed for developing an efficient active noise control (ANC) system. The ANC is conventionally used to control low-frequency acoustic noise by employing a gradient-optimization-based filtered-X least mean square (FXLMS) algorithm. Hence, there is a possibility that the performance of the ANC may be trapped by local minima problem. In addition, the conventional FXLMS algorithm needs prior identification of the secondary path. The proposed PSO-based ANC algorithm does not require the estimation of secondary path transfer function unlike FXLMS algorithm and, hence, is immune to time-varying nature of the secondary path. In this investigation, a small modification is incorporated in the conventional PSO algorithm to develop a conditional reinitialized PSO algorithm to suit to the time-varying plants of the ANC system. Systematic computer simulation studies are carried out to evaluate the performance of the new PSO-based ANC algorithm.

Particle Swarm Optimization Based Active Noise Control Algorithm Without Secondary Path Identification

Feedback active noise control has been used for tonal noise only and it is impractical for broadband noise. In this paper, it has been proposed that the feedback ANC algorithm can be applied to a broadband noise if the noise characteristic is chaotic in nature. Chaotic noise is neither tonal nor random; it is broadband and nonlinearly predictable. It is generated from dynamic sources such as fans, airfoils, etc. Therefore, a nonlinear controller using a functional link artificial neural network is proposed in a feedback configuration to control chaotic noise. A series of synthetic chaotic noise is generated for performance evaluation of the algorithm. It is shown that the proposed nonlinear controller is capable to control the broadband chaotic noise using feedback ANC which uses only one microphone whereas the conventional filtered-X least mean square (FXLMS) algorithm is incapable for controlling this type of noise.

Nonlinear feedback active noise control for broadband chaotic noise

Saturation nonlinearity effect on the ANC performance is addressed.PSO based nonlinear structure ANC algorithm is proposed.Filter bank based FLANN structure is tuned with PSO.The proposed ANC algorithm does not require secondary path estimate.Computer simulations are carried out for performance evaluation.Sensitivity analysis is done for PSO parameter tuning. In this paper, a method is proposed to overcome the saturation non-linearity linked to the microphones and loudspeakers of active noise control (ANC) system. The reference microphone gets saturated when the acoustic noise at the source increases beyond the dynamic limits of the microphone. When the controller tries to drive the loudspeaker system beyond its dynamic limits, the saturation nonlinearity is also introduced into the system. The secondary path which is generally estimated with a low level auxiliary noise by a linear transfer function does not model such saturation nonlinearity. Therefore, the filtered-x least mean square (FXLMS) algorithm fails to perform when the noise level is increased. For alleviating the saturation nonlinearity effect a nonlinear functional expansion based ANC algorithm is proposed where the particle swarm optimization (PSO) algorithm is suitably applied to tune the parameters of a filter bank based functional link artificial neural network (FLANN) structure, named as PSO based nonlinear structure (PSO-NLS) algorithm. The proposed algorithm does not require any computation of secondary path estimate filtering unlike other conventional gradient based algorithms and hence has got computational advantage. The computer simulation experiments show its superior performance compared to the FXLMS, filtered-s LMS and genetic algorithms under saturation present at both at secondary and reference paths. The paper also includes a sensitivity analysis to study the effect of different parameters on ANC performance.

Particle swarm optimization based nonlinear active noise control under saturation nonlinearity

In the paper, an echo canceller is discussed which is based on system identification approach. This canceller uses some adaptive algorithms such as Least Mean Square Algorithm & an Improved LMS algorithm, which also called as the Fractional Least Mean square algorithm. The algorithms use FIR filters with taps, which are chosen to minimize the error signal coming from the system, where minimization of the error based on the stochastic gradient method. Both algorithms such as LMS & FLMS are discussed & simulated in Mat lab. The echo is generated in Mat lab by adding several delayed and attenuated replica of speech. The comparison in between the algorithms was discussed on the basis of their convergence rate.

Nirmal Kumar Rout

Papers

Particle Swarm Optimization Based Active Noise Control Algorithm Without Secondary Path Identification

Nonlinear feedback active noise control for broadband chaotic noise

Particle swarm optimization based nonlinear active noise control under saturation nonlinearity

FLMS algorithm for acoustic echo cancellation and its comparison with LMS

PSO based adaptive narrowband ANC algorithm without the use of synchronization signal and secondary path estimate