• ETRI Journal
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• v.35 no.4
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• pp.706-709
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• 2013

As an alternative to the traditional pilot-aided orthogonal frequency division multiplexing (OFDM), the time-domain pseudonoise (PN)-padded OFDM provides a higher spectral efficiency. However, the carrier frequency offset (CFO) attenuates peaks of the conventional PN correlation output, which limits the CFO estimation range of the OFDM synchronizer. An improved correlation is proposed in this letter to remove the CFO-induced amplitude attenuation of correlation peaks. For a synchronizer adopting the designed correlator, a larger range of CFO acquisition is obtained through using wider correlation windows with a smaller interval between them. The proposed method of CFO acquisition is verified in a digital terrestrial multimedia broadcast receiver, in which the synchronizer is able to acquire CFOs up to ${\pm}320$ kHz in the DVB-T F1 channel. Furthermore, the acquisition range can be expanded in more favorable channels.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.7
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• pp.615-620
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• 2015

DOB (Disturbance Observer) is an useful control method for estimating the disturbance applied to dynamic systems. Disturbance observer can be used to implement a robust control system to generate a control input for rejecting the disturbance, and it can be also used to estimate the disturbance to obtain information. The system that uses disturbance estimation is investigated for high performance control such as automatic door systems, walking robot and electric power steering system in vehicles. In this paper, a novel disturbance observer which is called disturbance and current observer for estimating load torque in the motor system is proposed. The difference between the DOB for disturbance rejection and DCOB is mathematically verified. Current and angular velocity are required for estimating the load torque of the motor in DOB. However, the DCOB can estimate load torque and current without current sensor. DCOB is designed based on modeling of the motor system. Appropriate Q-filter is selected and the applicability of DCOB is verified by simulation. The estimated disturbance and current of the electric motor can be verified without current sensor, as experiments of the actual motor system.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.10
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• pp.831-841
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• 1991

A combined model of probabilistic and MLP(multi layer perceptron) model is proposed for the pattern classification of EMG( electromyogram) signals. The MLP model has a problem of not guaranteeing the global minima of error and different quality of approximations to Bayesian probabilities. The probabilistic model is, however, closely related to the estimation error of model parameters and the fidelity of assumptions. A proper combination of these will reduce the effects of the problems and be robust to input variations. Proposed model is able to get the MAP(maximum a posteriori probability) in the probabilistic model by estimating a priori probability distribution using the MLP model adaptively. This method minimize the error probability of the probabilistic model as long as the realization of the MLP model is optimal, and this is a good combination of the probabilistic model and the MLP model for the usage of MLP model reliability. Simulation results show the benefit of the proposed model compared to use the Mlp and the probabilistic model seperately and the average calculation time fro classification is about 50ms in the case of combined motion using an IBM PC 25 MHz 386model.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.9
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• pp.66-75
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• 1999

Image segmentation is the process where an image is segmented into regions that are set of homogeneous pixels. The result has a ciritical effect on accuracy of image understanding. In this paper, an Markov random field (MRF) image segmentation is proposed using genetic algorithm(GA). We model an image using MRF which is resistant to noise and blurring. While MRF based methods are robust to degradation, these require accurate parameter estimation. So GA is used as a segmentation algorithm which is effective at dealing with combinatorial problems. The efficiency of the proposed method is shown by experimental results with real images and application to automatic vehicle extraction system.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.1
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• pp.18-27
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• 2002

This paper presents an implementation of digital high-performance speed sensorless control system of an induction motor drives with Direct Torque Control(DTC). The system consists of closed loop stator flux and torque observer, speed and torque estimators, two hysteresis controllers, an optimal switching look-up table, IGBT voltage source inverter, and TMS320C31 DSP controller board. The stator flux observer is based on the combined current and voltage model with stator flux feedback adaptive control for wide speed range. The speed estimator is using the model reference adaptive system(MRAS) with rotor flux linkages for speed turning signal estimation. In order to prove the suggested speed sensorless control algorithm, and to obtain a high-dynamic robust adaptive performance, we have some simulations and actual experiments at low(20rpm) and high(1000rpm) speed areas. The developed speed sensorless system are shown a good speed control response characteristic, and high performance features using 2.2[kW] general purposed induction motor.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.11
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• pp.894-901
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• 2017

In the existence of motion, azimuth phase error due to accuracy limitation of GPS/IMU and system delay is unavoidable and it is essential to apply autofocus to estimate and compensate the azimuth phase error. In this paper, autofocus algorithm using MWLS(Modified WLS) is proposed. It shows the robust performance compared with original WLS using new target selection/sorting metric and iterative azimuth phase estimation technique. SAR raw data obtained in a captive flight test is used to validate the performance of the proposed algorithm.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.11
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• pp.1647-1654
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• 2013

Detection performance of the vehicle on the road depends on weather conditions, the shadow by the movement of the sun, or illumination changes, etc. In this paper, a vehicle detection system in conjunction with a robust background estimate algorithm to environment change on the road in daytime is proposed. Gaussian Mixture Model is applied as background estimation algorithm, and also, Adaboost algorithm is applied to detect the vehicle for candidate region. Through the experiments with input videos obtained from a various weather conditions at the same actual road, the proposed algorithm were useful to detect vehicles in the road.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1016-1026
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• 2017

An adaptive extended Kalman filter based on the maximum likelihood (EKF-ML) is proposed for detecting voltage sag in this paper. Considering that the choice of the process and measurement error covariance matrices affects seriously the performance of the extended Kalman filter (EKF), the EKF-ML method uses the maximum likelihood method to adaptively optimize the error covariance matrices and the initial conditions. This can ensure that the EKF has better accuracy and faster convergence for estimating the voltage amplitude (states). Moreover, without more complexity, the EKF-ML algorithm is almost as simple as the conventional EKF, but it has better anti-disturbance performance and more accuracy in detection of the voltage sag. More importantly, the EKF-ML algorithm is capable of accurately estimating the noise parameters and is robust against various noise levels. Simulation results show that the proposed method performs with a fast dynamic and tracking response, when voltage signals contain harmonics or a pulse and are jointly embedded in an unknown measurement noise.

• Journal of Power Electronics
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• v.5 no.3
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• pp.173-179
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• 2005

In this paper, a very low cost and robust sensing method for the rotor position of a TSRM(Toroidal Switched Reluctance Motors) is described. Position information of the rotor is essential for SRM drives. The rotor position sensor such as an opto-interrupter or high performance encoder is generally used for the estimation of rotor position. However, these discrete position sensors not only add complexity and cost to the system but also tend to reduce the reliability of the drive system. In order to solve these problems, in the proposed method, rotor position detection is achieved using voltage waveforms induced by the time varying flux linkage in the search coils, and then the appropriate phases are excited to drive the SRM. But the search coil's EMF is generated only when the motor rotates. Therefore the rotor position sensing method using squared Euclidean distance at a standstill is also examined. The simulation and experimental results are presented to verify the performance of the proposed method in this paper.

• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.4
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• pp.323-330
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• 2005

In order to increase reliability and reduce system cost, this paper studies particularly applicable method for sensorless PM BLDCM drive system. The waveform of the motor internal voltages(or back emf) contains a fundamental and higher order frequency harmonics. Therefore the third harmonic component is extracted from the stator phase voltage. The resulting third harmonic signal keeps a constant phase relationship with the rotor flux for any motor speed and load condition, and is practically free of noise that can be introduced by the inverter switching, making this a robust sensing method. In addition, a simple starting method and a speed estimation approach are also proposed. Some experimental results are Provided to demonstrate the validity of the proposed control method.