• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.4
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• pp.755-760
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• 2013

The dynamics of the winder roller of a roll-to-roll printing system for printed electronics is a time-varying system because of the variation of the winder roller radius owing to rewinding or unwinding of the web. Therefore, an adaptive control method considering the time-variant characteristics is required for precise tension control. In this study, the variable PID gain method is applied to the actual roll-to-roll system and verified by experiments for unwinder tension control. The required value of the winder roller radius for the application of the variable PID gain is estimated from the measurement of the winder tension and winder motor torque. The simulation results as well as experimental results show that the fixed PID gain control cannot stabilize the tension of the winder roller with varying winder roller radius. On the other hand, the variable PID gain method can control the tension of the winder roller regardless of the winder roller radius.

• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.1-6
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• 2016

The biased zero-error probability and its related algorithms require heavy computational burden related with some summation operations at each iteration time. In this paper, a recursive approach to the biased zero-error probability and related algorithms are proposed, and compared in the simulation environment of shallow water communication channels with ambient noise of biased Gaussian and impulsive noise. The proposed recursive method has significantly reduced computational burden regardless of sample size, contrast to the original MBZEP algorithm with computational complexity proportional to sample size. With this computational efficiency the proposed algorithm, compared with the block-processing method, shows the equivalent robustness to multipath fading, biased Gaussian and impulsive noise.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.710-726
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• 2018

This paper presents an extended particle filter to increase the accuracy and decrease the computation load of vehicle tracking. Particle filter has been the subject of extensive interest in video-based tracking which is capable of solving nonlinear and non-Gaussian problems. However, there still exist problems such as preventing unnecessary particle consumption, reducing the computational burden, and increasing the accuracy. We aim to increase the accuracy without an increase in computation load. In proposed method, we calculate the direction angle of the target vehicle. The angular difference between the direction of the target vehicle and each particle of the particle filter is observed. Particles are filtered and weighted, based on their angular difference. Particles with angular difference greater than a threshold is eliminated and the remaining are stored with greater weights in order to increase their probability for state estimation. Threshold value is very critical for performance. Thus, instead of having a constant threshold value, proposed algorithm updates it online. The first advantage of our algorithm is that it prevents the system from failures caused by insufficient amount of particles. Second advantage is to reduce the risk of using unnecessary number of particles in tracking which causes computation load. Proposed algorithm is compared against camshift, direction-based particle filter and condensation algorithms. Results show that the proposed algorithm outperforms the other methods in terms of accuracy, tracking duration and particle consumption.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1113-1114
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• 2011

This paper is proposed design of high performance controller of SynRM drive using FNN and NNC. Also, This paper is proposed of designing fuzzy neural network controller(FNNC) which adopts the fuzzy logic to the artificial neural network(ANN). FNNC combines the capability of fuzzy reasoning in handling uncertain information and the capability of neural network in learning from processes. This controller is controlled speed using FNNC and model reference adaptive fuzzy control(MFC), and estimation of speed using ANN. The performance of proposed controller was demonstrated through response results. The results confirm that the proposed controller is high performance and robust under the variation of load torque and parameters.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.727-728
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• 2006

This paper is proposed robust control based on the vector controlled induction motor drive with adaptive fuzzy learning control(AFLC). The fuzzy logic principle is first utilized for the control rotor speed. AFLC scheme is then proposed in which the adaptation mechanism is executed using fuzzy logic. Also, this paper is proposed estimation of speed of induction motor using ANN Controller. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. This paper is proposed the analysis results to verify the effectiveness of the new method.

• The Transactions of the Korean Institute of Power Electronics
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• v.4 no.1
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• pp.1-12
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• 1999

A design and DSP-based implementation of robust nonlinear speed control of a permanent magnet synchronous motor(PMSM) under the unknown parameter variations and speed measurement error is presented. The model reference adaptive system(MRAS) based adaptation mechanisms for the estimation of slowly varying parameters are derived using the MIT rule. For the disturbances or quickly varying parameters, a quasilinearized and decoupled model which includes the influence of parameter variations and speed measurement error on the nonlinear speed control of a PMSM is derived. Based on this model, a boundary layer integral sliding mode controller to improve the robustness and performance of the nonlinear speed control of a PMSM is designed and compared with the conventional controller which employs Proportional plus Derivative(PD) control. To show the validity of the proposed scheme, simulations and DSP-based experimental works are carried out and compared with the conventional control scheme.

• Journal of KIISE
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• v.43 no.3
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• pp.327-335
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• 2016

In this paper, we propose a buffer management scheme suitable for interactive multimedia services. We consider a typical delay optimization environment so that receiver buffer lengths vary according to the round trip time estimation. In this environment, we propose an optimization technique for minimizing the loss of information that may occur when a reduced buffer length forces I/P/B frames in the buffer to drop. We modeled our problem as a Knapsack Problem for which we used dynamic programing in order to find an approximate solution. The proposed technique is compared with the existing buffer management techniques. Through simulation studies, we found that our approach could increase PSNR, which is important to video quality.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.3
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• pp.249-256
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• 2009

The widely used control theory based design of PI family controllers fails to perform satisfactorily under parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of learning through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive learning mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control (FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN, FLC and ANN controller.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.10
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• pp.975-979
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• 2010

Existing quality adaptation schemes are useful for improving network stability in a congested network, but these schemes do not guarantee the quality of experience on video streaming due to the frequent variations in playback quality for a streaming service. In this paper, we propose a network-adaptive quality adaptation scheme to guarantee the quality of experience on SVC(Scalable Video Coding) video streaming. The proposed scheme estimates the available bandwidth by bandwidth measurement scheme using media characteristic, and then smoothly adjusts the quality level of SVC video stream according to the estimated bandwidth. Through the simulation, we prove that our scheme guarantees the quality of experience for video streaming by minimizing quality variation.

• International Journal of Aeronautical and Space Sciences
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• v.12 no.1
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• pp.43-56
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• 2011

An integrated system composed of guidance, navigation and control (GNC) system for autonomous proximity operations and the docking of two spacecraft was developed. The position maneuvers were determined through the integration of the state-dependent Riccati equation formulated from nonlinear relative motion dynamics and relative navigation using rendezvous laser vision (Lidar) and a vision sensor system. In the vision sensor system, a switch between sensors was made along the approach phase in order to provide continuously effective navigation. As an extension of the rendezvous laser vision system, an automated terminal guidance scheme based on the Clohessy-Wiltshire state transition matrix was used to formulate a "V-bar hopping approach" reference trajectory. A proximity operations strategy was then adapted from the approach strategy used with the automated transfer vehicle. The attitude maneuvers, determined from a linear quadratic Gaussian-type control including quaternion based attitude estimation using star trackers or a vision sensor system, provided precise attitude control and robustness under uncertainties in the moments of inertia and external disturbances. These functions were then integrated into an autonomous GNC system that can perform proximity operations and meet all conditions for successful docking. A six-degree of freedom simulation was used to demonstrate the effectiveness of the integrated system.