• Proceedings of the KIEE Conference
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2167-2170
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• 2002

In this paper, model-based tuning rules of the PID controller are proposed incorporating with genetic algorithms. Three sets of optimal PID parameters for set-point tracking are obtained based on the first-order time delay model and a genetic algorithm as a optimization tool which minimizes performance indices(IAE, ISE and ITAE). Then tuning rules are derived using the tuned parameter sets, potential rule models and a genetic algorithm. Simulation is carried out to verify the effectiveness of the proposed rules.

• Proceedings of the KIEE Conference
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• 대한전기학회 1998년도 하계학술대회 논문집 C
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• pp.1147-1149
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• 1998

This paper presents the result of an analysis on effects of phase compensation on power system stability in the PSS parameter tuning. Synchronizing and damping coefficients are induced from lineal model for generator with PSS. Synchronizing and damping coefficients corresponding to time constants of phase compensation control block are calculated on a single machine, infinite bus test system. The Parameter tuning concepts, basic function, structural elements and performance criteria of PSS are introduced.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.663-668
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• 1990

This paper deals with the application of a Generalized Predictive Control (CPC) to a Pressurized Water Reactor (P.W.R) Nuclear Power Plant. Generalized Predictive Control is a sort of Explicit Self-Tuning Control. Current self-tuning algorithms lack robustness to prior choices of either dead-time (input time delay of a plant) or model order. GPC is shown by simulation studies to be superior to accepted self-tuning techniques such as minimum variance and pole-placement from the viewpoint that it is robust to prior choices of dead-time or model order. In this paper a GPC controller is designed to control the P.W.R. nuclear power rlant with varying dead-time and through the designing procedure the designer is free from the constraint of knowing the exact dead-time. The controller is constructed based on the 2nd order linear model approximated in the vicinity of operating point. To ensure that this low-order model describes the complex real dynamics well enough for control purposes, model parameters are updated on-line with a Recursive Least Squares algorithm. Simulation results are successful and show the possibilities of the GPC control application to actual plants with varying or unknown dead-time.

• Journal of KIISE:Databases
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• 제32권4호
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• pp.416-428
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• 2005

In the paper we propose an index method for XML streaming services, where a server broadcasts XML data to a lot of clients in wireless information systems. In order to control the access and tuning time of mobile clients, the proposed method constructs the XML stream through replicating partial index intermixed with parts of data. For this purpose, we propose a two-level tree structure for separating index and XML data into two parts: replicated vs. non-replicated. We analyze the performance of the proposed method with respect to access time and tuning time. With the analysis result, we derive the optimal level value.

• Journal of IKEEE
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• 제27권3호
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• pp.313-318
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• 2023

This paper proposes a neural network applying fine-tuning as a way to improve the performance of Video Classification based on Vision Transformer. Recently, the need for real-time video image analysis based on deep learning has emerged. Due to the characteristics of the existing CNN model used in Image Classification, it is difficult to analyze the association of consecutive frames. We want to find and solve the optimal model by comparing and analyzing the Vision Transformer and Non-local neural network models with the Attention mechanism. In addition, we propose an optimal fine-tuning neural network model by applying various methods of fine-tuning as a transfer learning method. The experiment trained the model with the UCF101 dataset and then verified the performance of the model by applying a transfer learning method to the UTA-RLDD dataset.

• The Journal of Korean Institute of Communications and Information Sciences
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• 제38A권12호
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• pp.991-997
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• 2013

A new frequency tuning scheme and a transconductor with a wide tuning range and low harmonic distortion is presented. This frequency tuning technique is based on the relationship between the time-constant and the elapsed times in charging a capacitor up to a certain level. Its structure is as simple as that of a conventional tuning scheme using a VCF(Voltage-Controlled Filter) and it does not need a pure sine wave but uses a CLK(Clock) pulse as a reference signal, which is easily obtained from on-chip system clocks or external X-tal oscillators. When a certain reference CLK is given, without complex capacitor arrays the pole frequency of the filter can be controlled continuously in the frequency domain. Simulation results are presented to confirm the operation of the proposed approach.

• Journal of Satellite, Information and Communications
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• 제11권4호
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• pp.39-43
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• 2016

In this paper, we propose PID gain auto tuning method in steering type mobile robot. PID controller gain select method are various methods. Ziegler-Nichols step tuning method is one method tuning in PID controller. Use step tuning method find a the first gain and did experiment in steering mobile robot. and Make a new the second gains from the first gains. After appling the second gain in PID controller, Where perform observe for convergence time and stabilization error. Experiments result the second gain are useful in real steering mobile robot system.

• Journal of the Korean Institute of Telematics and Electronics S
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• 제36S권11호
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• pp.22-33
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• 1999

This paper presents the method for the automatic tuning of a design weighting polynomial parameters of a generalized minimum-variance stochastic ultivariable self-tuning controller which adapts to changes in the higher order nonminimum phase system parameters with time delays and noises. The self-tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing the design weighting polynomial parameters of the controller. The proposed multivariable self-tuning method is simple and effective compared with pole restriction method. The computer simulation results are presented to adapt the higher order multivariable system with nonminimum phase and with changeable system parameters.

• Journal of the Institute of Electronics Engineers of Korea SC
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• 제40권4호
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• pp.264-274
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• 2003

This paper presents a direct generalized minimum-variance self tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior, noises and time delays. The self-tuning controller with a PID structure is a combination of the simple structure of a PID controller and the characteristics of a self-tuning controller that can adapt to changes in the environment. The self-tuning control effect is achieved through the RLS (recursive least square) algorithm at the parameter estimation stage as well as through the Robbins-Monro algorithm at the stage of optimizing the design parameter of the controller. The neural network control effect which compensates for nonlinear factor is obtained from the learning algorithm which the learning error between the filtered reference and the auxiliary output of plant becomes zero. Computer simulation has shown that the proposed method works effectively on the nonlinear nonminimum phase system with time delays and changed system parameter.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1986년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 17-18 Oct. 1986
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• pp.456-458
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• 1986

A method for on-line tuning of the PID-controller parameters for a discrete-time multivariable process system is presented. And it is based on a step change in the controller set point. The system is presumed to be a linear, open loop stable and known one. The controller parameters are determined by the performance criterion and Fletcher-Powell methods.