• Journal of Institute of Control, Robotics and Systems
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• v.16 no.2
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• pp.134-139
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• 2010

In order to overcome some problems of existing anti-windup methods, this paper defines LQ (Linear Quadratic) observer and proposes a new anti-windup method using the LQ observer. LQ observer is derived by linear quadratic optimization in order to calculate controller states, which make the controller outputs equal to the plant inputs. And we propose an algorithm so that it can be implemented by a digital controller easily. The relationship between the design parameters and the anti-windup performance is shown via some numerical examples, which cover the cases with the anti-windup method using LQ observer designed and the case without it. Finally, the anti-windup performance of the proposed method is exemplified via comparison with the existing model-based conditioning scheme method[4].

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.54 no.12
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• pp.629-637
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• 2005

This paper proposes a new anti-windup strategy to suppress integrator windup for PI speed controller and to be implemented on the existing system with minimal modification. When the speed control mode is changed from P controller to PI controller. an appropriate initial value for integrator is assigned. This value restricts overshoot and high settling time. Also, the proposed method guarantees the designed performance independent on operating conditions, i.e. different set-point change and load torque. Simulation and experimental results for PMSM speed controller have shown its superior performance compared with the conditional integration and tracking back calculation.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.6
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• pp.1-10
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• 2019

This paper proposes a method to improve the detectable distance by reducing noise to perform a signal processing technique on the received signals. To increase the radar detection range, the noise component of the received signal has to be reduced. The proposed method reduces the noise component by employing two methods. First, the radar signals received with multiple pulses are accumulated. As the number of additions increases, the noise component gradually decreases due to noise randomness. On the other hand, the signal term gradually increases and thus signal to noise ratio increases. Secondly, after converting the accumulated signal into the frequency spectrum, a Least Mean Square (LMS) filter is applied. In the case of the radar received signal, desired signal exists in a specific part and most of the rest is a noise. Therefore, if the LMS filter is applied in the time domain, the noise increases. To prevent this, the LMS filter is applied after converting the received signal into the entire frequency spectrum. The LMS filter output is then transformed into the time domain and then range estimation algorithm is performed. Simulation results show that the proposed scheme reduces the noise component by about 25 dB. The experiment was conducted by comparing the proposed results with the conventional results of the radars held by the Korea Aerospace Research Institute for the international space station.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.12B
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• pp.2349-2358
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• 1999

Object-based stereoscopic video coding scheme is presented in this paper. In conventional BMA based stereoscopic video coding for low bit rate transmission, image prediction errors such as block artifacts and mosquito phenomena are occurred. In order to reduce these errors, object based coding scheme is adopted. The proposed scheme consists of preprocessing, object extraction, and object update procedures. The preprocessing procedure extracts non-object regions having low reliability for motion and disparity estimation. This procedure prohibits extracting inaccurate objects. For the better prediction of left channel image, the disparity information is added to the object extraction. And the proposed algorithm can reduce the accumulated error through the object update procedure that detects newly emerging objects, merges objects that have the same object-disparity and object motion, and splits object which has large image prediction error. The experimental results show that the proposed algorithms improve the quality of the prediction without block artifacts and mosquito phenomena.

• Journal of IKEEE
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• v.25 no.2
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• pp.243-251
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• 2021

In this paper, we propose a measurement technique of indoor location based on markerless applicable to AR. The proposed technique has the following originality. The first is to extract feature points and use them to generate local patches to enable faster computation by learning and using only local patches that are more useful than the surroundings without learning the entire image. Second, learning is performed through deep learning using the convolution neural network structure to improve accuracy by reducing the error rate. Third, unlike the existing feature point matching technique, it enables indoor location measurement including left and right movement. Fourth, since the indoor location is newly measured every frame, errors occurring in the front side during movement are prevented from accumulating. Therefore, it has the advantage that the error between the final arrival point and the predicted indoor location does not increase even if the moving distance increases. As a result of the experiment conducted to evaluate the time required and accuracy of the measurement technique of indoor location based on markerless applicable to AR proposed in this paper, the difference between the actual indoor location and the measured indoor location is an average of 12.8cm and a maximum of 21.2cm. As measured, the indoor location measurement accuracy was better than that of the existing IEEE paper. In addition, it was determined that it was possible to measure the user's indoor location in real time by displaying the measured result at 20 frames per second.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.12
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• pp.150-157
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• 2015

Pedestrian detection has largely been researched as one of the important technologies for autonomous driving vehicle and preventing accidents. There are two categories for pedestrian detection, camera-based and LIDAR-based. LIDAR-based methods have the advantage of the wide angle of view and insensitivity of illuminance change while camera-based methods have not. However, there are several problems with 3D LIDAR, such as insufficient resolution to detect distant pedestrians and decrease in detection rate in a complex situation due to segmentation error and occlusion. In this paper, two methods using GM-PHD filter are proposed to improve the poor rates of pedestrian detection algorithms based on 3D LIDAR. First one improves detection performance and resolution of object by automatic accumulation of points in previous frames onto current objects. Second one additionally enhances the detection results by applying the GM-PHD filter which is modified in order to handle the poor situation to classified multi target. A quantitative evaluation with autonomously acquired road environment data shows the proposed methods highly increase the performance of existing pedestrian detection algorithms.