• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10a
• /
• pp.699-703
• /
• 1993

When the error path can be modeled as a pure delay, an adaptive algorithm for slowly time varying system is proposed to minimize the sound pressure level. This algorithm makes it possible to use the fittered-x LMS algorithm with on-line delay modeling of the error path. Another simple adaptive algorithm for pure tone noise is proposed which eliminates the cross term in the multiple error filtered-x LMS algorithm.

• Journal of the korean Society of Automotive Engineers
• /
• v.14 no.5
• /
• pp.95-106
• /
• 1992

Active control of acoustic noise is an application area of adaptive digital signal processing with increasingly interest along the last year. This work studies the implementation of the multichannel LMS filter and the application of this algorithm for the reduction of the noise inside a vechicle cabin using a number of 'secondary sources' drived by adaptive filtering of a reference noise source. Firstly, we propose the use of an adaptive method for the time-varient optimal convergence factor. Secondly, we propose the use of adaptive delayed inverse model to estimate the elastic-acoustic transfer function presented in vechicle cabin. The original, primary source is often periodic, with a known fundamental frequency. A suitably filtered reference signal can thus be used to drive the secondary sources. An algorithm is presented for adapting the coefficients of an FIR filter feeding such a secondary source in such a way as to minimize the output of a suitably placed microphone. In this algorithm, the coefficients of adaptive filter driving an array of secondary sources can be adapted to minimize the sum of the squares of the outputs of a number of error microphones. The multichannel LMS algorithm displays that such an algorithm is considered suitable to used for the global suppression of noise in vehicle cabin.

• Journal of Navigation and Port Research
• /
• v.30 no.2
• /
• pp.119-124
• /
• 2006

In Part I(theoretical study) of the paper, a new adaptive autopilot for ships based on Adaptive Neural Networks was proposed. The ANNAI autopilot was designed for course-keeping, turning and track-keeping control for ships. In this part of the paper, to show the effectiveness and feasibility of the ANNAI autopilot and automatic selection algorithm for learning rate and number of iterations, computer simulations of course-keeping and track-keeping tasks with and without the effects of measurement noise and external disturbances are presented. Additionally, the results of the previous studies using Adaptive Neural Network by backpropagation algorithm are also showed for comparison.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2005.10a
• /
• pp.23-28
• /
• 2005

In Part I (theoretical study) of the paper, a new adaptive autopilot for ships based on Adaptive Neural Networks was proposed. The ANNAI autopilot was designed for course-keeping, turning and track-keeping control for ships. In this part of the paper, to show the effectiveness and feasibility of the ANNAI autopilot, computer simulations of course-keeping and track-keeping tasks with and without the effects of measurement noise and external disturbances are presented. Additionally, the results of the previous studies using Adaptive Neural Network by backpropagation algorithm are also showed for comparison.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.05a
• /
• pp.409-411
• /
• 2016

Salt and pepper noise is caused by various causes such as camera malfunction, storage media memory error, and transmission channel error. Representative filters to remove salt and pepper noise include SMF(standard median filter), CWMF(center weighted median filter), and AMF(adaptive median filter). However previous filters have inadequate noise removal characteristics in high density salt-and-pepper noise environment. Therefore the study suggested a composite filter which, through noise evaluation, preserves original pixels when the central pixel is non-noise, and uses spatial weighted value mask and median when there is noise.

• Proceedings of the KIPE Conference
• /
• 2002.07a
• /
• pp.730-733
• /
• 2002

In this paper, implementation of active noise barriers using active noise control techniques is presented. Multi-channel FX-LMS algorithms and Leaky LMS algorithms are used for adaptive filters to attenuate noise which is propagated from the outside of experimental enclosures. Experiments have done to show the effectivene a proposed active noise barriers.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.14 no.8
• /
• pp.1759-1766
• /
• 2010

The performance of noise attenuation of the adaptive active noise control algorithm is improved using the recurrent neural network. The FXLMS that has been frequently used in the active noise control is simple and has low computational load, but this method is weak to nonlinearity of the main or secondary path since it is based on the FIR linear filter method. In this paper, the recurrent neural network filter has been developed and applied to improvement of the active noise attenuation by simulation.

• Journal of KSNVE
• /
• v.5 no.3
• /
• pp.385-394
• /
• 1995

Recently, since robot manipulator becomes faster and lighter, its link is no longer regarded as rigid body, and robot controller which only controls robot position cannot reduce vibration of the flexible link. Therefore vibration control is needed in robot manipulator control in addition to position control. In the case that tip mass changes when robot manipulator in working, it is clear that the efficiency of the vibration/position controller designed for the fixed system goes down. In this paper, the system with time varying parameters, adaptive control theory is adopted which estimates parameters changed by the variation of the tip mass and re-calculates the gain of the controller. Validify of the proposed adaptive controller and capability of the estimator are evaluated by computer simulations and experiments. Comparison results of the optimal controller for the fixed system and proposed adaptive controller and carried out.

• 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.