• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.53.3-53.3
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• 2021

Adaptive Optics (AO) was first studied in the field of astronomy, and its applications have been extended to the field of laser, microscopy, bio, medical, and free space laser communication. AO modelling and simulation are required throughout the system development process. It is necessary not only for proper design but also for performance verification after the final system is built. In KASI, we are trying to develop the AO Python Package for AO modelling and simulation. It includes modelling classes of atmosphere, telescope, Shack-Hartmann wavefront sensor, deformable mirror, which are the components for an AO system. It also includes the ability to simulate the entire AO system over time. It is being developed in the Super Eye Bridge project to develop a segmented mirror, an adaptive optics, and an emersion grating spectrograph, which are future telescope technologies. And it is planned to be used as a performance analysis system for several telescope projects in Korea.

• Environmental Engineering Research
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• v.23 no.1
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• pp.107-113
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• 2018

Air quality and pollution have recently become a major concern; vehicle emissions significantly pollute the air, especially in large and crowded cities. There are various factors that affect vehicle emissions; this research aims to find the most influential factors affecting $CO_2$ and $NO_x$ emissions using Adaptive Neural Fuzzy Inference System (ANFIS) as well as a systematic approach. The modified ANFIS (MANFIS) was developed to enhance modelling and Root Mean Square Error was used to evaluate the model performance. The results show that percentages of $CO_2$ from trucks represent the best input combination to model. While for $NO_x$ modelling, the best pair combination is the vehicle delay and percentage of heavy trucks. However, the final MANFIS structure involves two inputs, three membership functions and nine rules. For $CO_2$ modelling the triangular membership function is the best, while for $NO_x$ the membership function is two-sided Gaussian.

• Journal of IKEEE
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• v.13 no.1
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• pp.36-40
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• 2009

In control of time delay systems, if the informations about the system model and the disturbance can be estimated exactly, the ideal response can be achieved by using Smith predictor controller. Therefore, in this paper, an adaptive algorithm is proposed to control time delay systems existing modelling errors and disturbance. An adaptive observer to estimate disturbance and system model is designed and adaptive laws adjusting the observer are proposed. The new Smith predictor controller is designed using the proposed adaptive observer. As a result, the proposed controller can eliminate the effects of the disturbance and the modelling error. The effectiveness and the improved performance of the proposed system are verified by computer simulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.144-147
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• 2001

This paper presents adaptive fuzzy controller which is uncertainty or unknown variation in different parameters with nonlinear system of helicopter. The proposed adaptive fuzzy controller applied TSK(Takagi-Sugeno-Kang) fuzzy system which is not only low number of fuzzy rule, and a linear input-output equation with a constant term, but also can represent a large class of nonlinear system with good accuracy. The adaptive law was designed by using Lyapunov stability theory. The adaptive fuzzy controller is a model reference adaptive controller which can adjust the parameter $\theta$ so that the plant output tracks the reference model output. First of all, system of helicopter was considered as stopping state, and design of controller was simulated from dynamics equation with stopping state. Results show that it is controlled more successfully with a model reference adaptive controller than with a non-adaptive fuzzy controller when there is a modelling error between system and model or a continuous added noise in such unstable system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.415-418
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• 2003

Fuzzy modelling has the approximation property far the given input-output relationship. Especially, Takagi-Sugeno fuzzy models are widely used because they show very good performance in the nonlinear function approximation problem. But generally there is not the systematic method incorporating the human expert's knowledge or experience in fuzzy rules and it is not easy to End the membership function of fuzzy rule to minimize the output error as well. The ANFIS (Adaptive Network-based Fuzzy Inference Systems) is one of the neural network based fuzzy modelling methods that can be used with various type of fuzzy rules. But in this model, it is the problem to End the optimum number of fuzzy rules in fuzzy model. In this paper, a new fuzzy modelling method based on the ANFIS and pruning techniques with the measure named impact factor is proposed and the performance of proposed method is evaluated with several simulation results.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.3
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• pp.65-71
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• 1997

With the emergence of neural network, there is a revived interest in identification of nonlinear systems. So in this paper, to identify unknown nonlinear systems dynamically we propose DPNN(Dynamic Polynomial Neural Network) using GMDH (Group Method of Data Handling) algorithm. The dynamic system identification using GMDH consists of applying a set of inputloutput data to train the network by dynamically computing the necessary coeffici1:nt sets. Then, MRAC(Mode1 Reference Adaptive Control) is designed to control nonlinear systems using DPNN. In the result, we can see that the modelling and control using DPNN work well by computer simulation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.653-657
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• 1996

This paper presents adaptive cutting force control in milling process using indirect cutting force measurement. The cutting forces in X, Y, and Z axes are measured indirectly from the sensing current of the feed-drive servo motor. After modelling the feed-drive system of a horizontal machining center, the relation between the cutting force and the servo motor current is analyzed. The pulsating milling forces are measured from the sensing current within the bandwidth of the servo. It is shown that indirect cutting farce measurement can be used in adaptive cutting force control. The adaptive control scheme which is globally convergent and stable is attached to a commercial CNC machining center. Cutting experiments on end milling are performed for diagonal cutting.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1962-1964
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• 2006

In this paper, the dynamic models of SOFC are suggested. It consists of electrochemical model, thermal model, voltage equation and several loss equations. Control problems on tracking steady voltage by air flow is discussed and an adaptive controller is designed to withstand to the variation of stack current. Simulation is done to prove the solution of control algorithms.

• Proceedings of the IEEK Conference
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• 2002.06e
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• pp.39-42
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• 2002

A new adaptive PRML architecture, considered not only DVD-ROM but also DVD-Multi including DVD-RAM as well, is presented to demonstrate its superiority over the conventional analog channel in a DVD system. For this new architecture, channel adaptation algorithm using gain controlled type of FIR filter, and asymmetry compensation algorithm using expected level adaptation of viterbi decoder are presented. In addition, a method of modelling the disk tilt and asymmetrical read-back signal are discussed.

• Proceedings of the Acoustical Society of Korea Conference
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• 1992.06a
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• pp.23-28
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• 1992

The purpose of this paper is to develop a new adaptive echo canceller improving convergence speed and near-end-talker detection performance of the conventional echo canceller. In a conventional adaptive echo canceller, an adaptive digital filter with TDL(Tapped-Delay Line) structure modelling the echo path uses the LMS(Least Mean Square) algorithm to cote the coefficients, and NET detector using energy comparison method prevents the adaptive digital filter to update the coefficients during the periods of the NET signal presence. The convergence speed of the LMS algorithm depends on the eigenvalue spread ratio of the reference signal and NET detector using the energy comparison method yields poor detection performance if the magnitude of the NET signal is small. This paper presents a new adaptive echo canceller which uses the pre-whitening filter to improve the convergence speed of the LMS algorithm. The pre-whitening filter is realized by using a low-order lattice predictor. Also, a new NET signal detection algorithm is presented, where the start point of the NET signal is detected by computing the cross-correlation coefficient between the primary input and the ADF(Adaptive Digital Filter) output while the end point is detected by using the energy comparison method. The simulation results show that the convergence speed of the proposed adaptive echo canceller is faster than that of the conventional echo canceller and the cross-correlation coefficient yield more accurate detection of the start point of the NET signal.