• Journal of Power Electronics
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• v.14 no.6
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• pp.1303-1313
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• 2014

Dynamic behavior of the harmonic detection part of an active power filter (APF) has an essential role in filter compensation performances during transient conditions. Instantaneous power (p-q) theory is extensively used to design harmonic detectors for active filters. Large overshoot of p-q theory method deteriorates filter response at a large and rapid load change. In this study the harmonic estimation of an APF during transient conditions for balanced three-phase nonlinear loads is conducted. A novel fuzzy instantaneous power (FIP) theory is proposed to improve conventional p-q theory dynamic performances during transient conditions to adapt automatically to any random and rapid nonlinear load change. Adding fuzzy rules in p-q theory improves the decomposition of the alternating current components of active and reactive power signals and develops correct reference during rapid and random current variation. Modifying p-q theory internal high-pass filter performance using fuzzy rules without any drawback is a prospect. In the simulated system using MATLAB/SIMULINK, the shunt active filter is connected to a rapidly time-varying nonlinear load. The harmonic detection parts of the shunt active filter are developed for FIP theory-based and p-q theory-based algorithms. The harmonic detector hardware is also developed using the TMS320F28335 digital signal processor and connected to a laboratory nonlinear load. The software is developed for FIP theory-based and p-q theory-based algorithms. The simulation and experimental tests results verify the ability of the new technique in harmonic detection of rapid changing nonlinear loads.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.464-469
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• 1987

The Kalman filter is an optimal linear estimator that has been an active research topic for the past three decades. The scheme has become the milestone of modern filtering, and it is applied to many areas including navigations and controls of free vehicle. The Kalman filter technique is matured. But some problems are still remained to be resolved. The prevention of divergence induced by digital implementation, nonoptimal application for nonlinear system, and application to non-Gaussian processes are some of the problems. This paper surveys the problems. The square root filtering is suggested to prevent the divergence. The extended Kalman filter is used for nonlinear systems. And, many other approaches to Kalman-like optimal estimators are also investigated.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.16-19
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• 1996

The objective of this paper is to present a new adaptive nonlinear compensation method, which is based upon the Pth-order inverse theory and can be implemented in a systematic way, for weakly nonlinear systems that can be modeled by a Volterra series. In particular, employment of the proposed approach for the linearization of a given nonlinear system leads to the effective elimination of (up to a required order) nonlinearities in the overall system output. To demonstrate the feasibility of the proposed method, simulation results using a satellite communication system model are also provided.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.3
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• pp.263-273
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• 2008

The precision of sensors' position is particularly important in the application of road extraction or digital map generation. In general, the various ranging solution systems such as GPS, Total Station, and Laser Ranger have been employed for the position of the sensor. Basically, the ranging solution system has problems that the signal may be blocked or degraded by various environmental circumstances and has low temporal resolution. To overcome those limitations a IMU/range integrated system could be introduced. In this paper, after pointing out the limitation of extended Kalman filter which has been used for workhorse in navigation and geodetic community, the two sampling based nonlinear filters which are sigma point Kalman filter using nonlinear transformation and carefully chosen sigma points and particle filter using the non-gaussian assumption are implemented and compared with extended Kalman filter in a simulation test. For the ranging solution system, the GPS and Total station was selected and the three levels of IMUs(IMU400C, HG1700, LN100) are chosen for the simulation. For all ranging solution system and IMUs the sampling based nonlinear filter yield improved position result and it is more noticeable that the superiority of nonlinear filter in low temporal resolution such as 5 sec. Therefore, it is recommended to apply non-linear filter to determine the sensor's position with low degree position sensors.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.12
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• pp.205-209
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• 2013

A conventional acoustic echo suppressor (AES) considering only room impulse response between a loudspeaker and a microphone eliminates acoustic echo from the microphone input. However, in a nonlinear acoustic echo environment, the conventional AES degraded because of a nonlinearity of the loudspeaker. In this paper, we adopt AES based on the frequency-domain second-order Volterra filter using Least Square method. For comparing performances, we conduct objective tests including Echo Return Loss Enhancement (ERLE) and Speech Attenuation (SA). The proposed algorithm shows better performance than the conventional in both linear and nonlinear acoustic echo environments.

• Smart Structures and Systems
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• v.4 no.3
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• pp.319-342
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• 2008

Identification of the nonlinear hysteretic behavior of a reinforced concrete (RC) bridge pier subjected to earthquake loads is carried out based on acceleration measurements of the earthquake motion and bridge responses. The modified Takeda model is used to describe the hysteretic behavior of the RC pier with a small number of parameters, in which the nonlinear behavior is described in logical forms rather than analytical expressions. Hence, the modified extended Kalman filter is employed to construct the state transition matrix using a finite difference scheme. The sequential modified extended Kalman filter algorithm is proposed to identify the unknown parameters and the state vector separately in two steps, so that the size of the problem for each identification procedure may be reduced and possible numerical problems may be avoided. Mode superposition with a modal sorting technique is also proposed to reduce the size of the identification problem for the nonlinear dynamic system with multi-degrees of freedom. Example analysis is carried out for a continuous bridge with a RC pier subjected to earthquake loads in the longitudinal and transverse directions.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.7
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• pp.468-474
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• 2005

Brain-machine interface (BMI) based on neuronal spike trains is regarded as one of the most promising means to restore basic body functions of severely paralyzed patients. The spike train decoding algorithm, which extracts underlying information of neuronal signals, is essential for the BMI. Previous studies report that a linear filter is effective for this purpose and there is no noteworthy gain from the use of nonlinear mapping algorithms, in spite of the fact that neuronal encoding process is obviously nonlinear. We designed several decoding algorithms based on the linear filter, and two nonlinear mapping algorithms using multilayer perceptron (MLP) and support vector machine regression (SVR), and show that the nonlinear algorithms are superior in general. The MLP often showed unsatisfactory performance especially when it is carelessly trained. The nonlinear SVR showed the highest performance. This may be due to the superiority of the SVR in training and generalization. The advantage of using nonlinear algorithms were more profound for the cases when there are false-positive/negative errors in spike trains.

• Journal of Aerospace System Engineering
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• v.10 no.1
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• pp.15-20
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• 2016

The optimal estimation of a general continuous-discrete system can be achieved through the solution of the Fokker-Planck equation and the Bayesian update. Due the high nonlinearity of the equation of motion of the system and the measurement model, it is necessary to linearize the both equation. To avoid linearization, the filter based on Fokker-Planck equation is designed. with the unscented transformation update mechanism, in which the associated Fokker-Planck equation was solved efficiently and accurately via discrete quadrature and the measurement update was done through the unscented transformation update mechanism. This filter based on the Direct Quadrature Moment of Method(DQMOM) and the unscented transformation update is applied to the bearing only target tracking problem. The proposed filter can still provide more accurate estimation of the state than those of the extended Kalman filter especially when measurements are sparse. Simulation results indicate that the advantages of the proposed filter based on the DQMOM and the unscented transformation update make it a promising alternative to the extended Kalman filter.

• Journal of the Optical Society of Korea
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• v.19 no.2
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• pp.130-135
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• 2015

Based on the extended nonlinear Schr$\ddot{o}$dinger equation, the influences of the filter effect on pulse splitting in a passively mode-locked erbium-doped fiber laser with positive dispersion cavity are investigated theoretically. Numerical results show that, as the bandwidth of the spectral filter decreases, the nonlinear chirp appended to the pulse increases under the combined action of the filter effect of the super-Gaussian spectral filter and the self-phase modulation effect. On further decreasing the bandwidth, the wave breaking of the pulse takes place. In addition, by varying the pump power of the laser or the profile of the spectral filter, the influences of the filter effect on pulse splitting also change accordingly.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.7
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• pp.1003-1009
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• 1995

In This Paper, Linearization problem is discussed to reduce distortion of a nonlinear system based on Schetzen's pth-orfer inverse theorem. We propose a nonlinear adaptive prefiltering algorithm which can reduse nonlinear distortion up to pth order by tandemly connecting a pth-order Volterra filter before the nonlinear system under the consideration and by adjusting the filter coefficients adaptively. The feasibility of applying the proposed algorithm to a nonlinear system is conformed via computer simulation by observing significant reduction of total nonlinear distortion for the case of random input and sinusoidal input excitation.