• Proceedings of the KIEE Conference
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• 2009.04b
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• pp.32-34
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• 2009

The field oriented control of induction motors is widely used in high performance applications. However, detuning caused by parameter disturbance still limits the performance of these drives. In order to accomplish variable speed operation, conventional PI-like controllers are commonly used. These controllers provide limited good Performance over a wide range of operation, even under ideal field oriented conditions. This paper is proposed model reference adaptive fuzzy control(MFC) and artificial neural network(ANN) based on the vector controlled induction motor drive system. Also, this paper is proposed control of speed and current using fuzzy adaption mechanism(FAM), MFC and estimation of speed using ANN. The proposed control algorithm is applied to induction motor drive system using FAM, MFC and ANN controller. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• The Journal of the Korea Contents Association
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• v.10 no.4
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• pp.84-90
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• 2010

DBMS is used for a range of applications from data warehousing through on-line transaction processing. As a result of this demand, DBMS has continued to grow in terms of its size. This growth invokes the most important issue of manually tuning the performance of DBMS. The DBMS tuning should be adaptive to the type of the workload put upon it. But, identifying workloads in mixed database applications might be quite difficult. Therefore, a method is necessary for identifying workloads in the mixed database environment. In this paper, we propose a SVM workload classifier to automatically identify a DBMS workload. Database workloads are collected in TPC-C and TPC-W benchmark while changing the resource parameters. Parameters for SVM workload classifier, C and kernel parameter, were chosen experimentally. The experiments revealed that the accuracy of the proposed SVM workload classifier is about 9% higher than that of Decision tree, Naive Bayes, Multilayer perceptron and K-NN classifier.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.191-202
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• 2013

A control structure that makes possible the integration of a kinematic controller and a fuzzy logic (FL) deadzone compensator for mobile robots is presented. A tuning algorithm is given for the fuzzy logic parameters, so that the deadzone compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded parameter estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The fuzzy logic deadzone compensator is implemented on a mobile robot to show its efficacy.

• Proceedings of the KIEE Conference
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• 2004.10a
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• pp.182-184
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• 2004

This paper presents a PI controller tuning method for high performance permanent magnet synchronous motor (PMSM) drives under load variations using clustering fuzzy algorithm. In many speed tracking control systems PI controller has been used due to its simple structure and easy of design. PI controller, however, suffers from the electrical machine parameter variations and disturbances. In order to improve the tracking control performance under load variations, the PI controller parameters are modified during operation by clustering fuzzy method. This method based on optimal fuzzy logic system has simple structure and computational simplicity. It needs only sample data which is obtained by optimal controller off-line. As the sample data implemented in the adaptive fuzzy system can be modified or extended, a flexible control system can be obtained Simulation results show the usefulness of the proposed controller.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.102.4-102
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• 2002

A deadzone compensator is designed for a positioning system using fuzzy logic. The classification property of fuzzy logic systems make them a natural candidate for the rejection of errors induced by the deadzone, which has regions in which it behaves differently. A tuning algorithm is given for the fuzzy logic parameters, so that the deadzone compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded parameter estimates, formal nonlinear stability proofs are given to show that the tracking error is small. The fuzzy logic deadzone compensator is implemented on a positioning system to show its efficacy. 1. Deadzone Compansation 2. XY positioning table 3. Fuzzy Logic 4. Actuator nonlinearity

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.2
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• pp.17-28
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• 2004

A deadzone compensator is designed for a XY positioning table using fuzzy logic. The classification property of fuzzy logic systems makes them a natural candidate for the rejection of errors induced by the deadzone, which has regions in which it behaves differently. A tuning algorithm is given for the fuzzy logic parameters, so that the deadzone compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded parameter estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The fuzzy logic deadzone compensator is implemented on a XY positioning table to show its efficacy.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.11a
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• pp.12-15
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• 2013

최근 UHD TV 출시와 HD급 영상의 보편화로 영상에 대한 해상도 변경 기술의 중요성이 높아지고 있다. 본 논문에서는 기존의 cubic convolution 기법을 응용하여, 영역별 특성에 따라 적응적인 가변 커널 길이의 cubic convolution으로 화면 해상도를 변환하는 기법을 제안한다. 제안하는 기법은 영상의 화질을 개선시키면서도 상황에 따라 하드웨어의 line memory를 절약할 수 있도록 설계를 하여 화질 개선뿐만이 아니라 하드웨어적으로도 더 효율적으로 사용이 가능하다. 또 tuning parameter 를 최적화 하는 방법을 통해 기존의 cubic convolution 기법보다 더 좋은 화질의 영상을 얻을 수 있다.

• Nuclear Engineering and Technology
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• v.27 no.3
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• pp.336-343
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• 1995

The Self Tuning Regulator(STR) method which is an approach of adaptive control theory, is ap-plied to design the fully automatic power controller of the nonlinear reactor model. The adaptive control represent a proper approach to design the suboptimal controller for nonlinear, time-varying stochastic systems. The control system is based on a third­order linear model with unknown, time-varying parameters. The updating of the parameter estimates is achieved by the recursive extended least square method with a variable forgetting factor. Based on the estimated parameters, the output (average coolant temperature) is predicted one-step ahead. And then, a weighted one-step ahead controller is designed so that the difference between the output and the desired output is minimized and the variation of the control rod position is small. Also, an integral action is added in order to remove the steady­state error. A nonlinear M plant model was used to simulate the proposed controller of reactor power which covers a wide operating range. From the simulation result, the performances of this controller for ramp input (increase or decrease) are proved to be successful. However, for step input this controller leaves something to be desired.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.2
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• pp.247-268
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• 2011

Due to the application-specific nature of wireless sensor networks, the sensitivity to such a requirement as data reporting interval varies according to the type of application. Such considerations require an application-specific, parameter tuning paradigm allowing us to maximize energy conservation prolonging the operational network lifetime. In this paper, we propose a reporting interval adaptive, sensor control platform for energy-saving data gathering in wireless sensor networks. The ultimate goal is to extend the network lifetime by providing sensors with high adaptability to application-dependent or time-varying, reporting interval requirements. The proposed sensor control platform is based upon a two phase clustering (TPC) scheme which constructs two types of links within each cluster - namely, direct link and relay link. The direct links are used for control and time-critical, sensed data forwarding while the relay links are used only for multi-hop data reporting. Sensors opportunistically use the energy-saving relay link depending on the user reporting, interval constraint. We present factors that should be considered in deciding the total number of relay links and how sensors are scheduled for sensed data forwarding within a cluster for a given reporting interval and link quality. Simulation and implementation studies demonstrate that the proposed sensor control platform can help individual sensors save a significant amount of energy in reporting data, particularly in dense sensor networks. Such saving can be realized by the adaptability of the sensor to the reporting interval requirements.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.6
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• pp.301-308
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• 2018

This paper presents an optimal design method of a hybrid structural control system considering multi-hazard. Unlike a typical structural control system in which one system is designed for one specific type of hazard, a simultaneous optimal design method for both active and passive control systems is proposed for the mitigation of seismic and wind induced vibration responses of structures. As a numerical example, an optimal design problem is illustrated for a hybrid mass damper(HMD) and 30 viscous dampers which are installed on a 30 story building structure. In order to solve the optimization problem, a self-adaptive Harmony Search(HS) algorithm is adopted. Harmony Search algorithm is one of the meta-heuristic evolutionary methods for the global optimization, which mimics the human player's tuning process of musical instruments. A self-adaptive, dynamic parameter adjustment algorithm is also utilized for the purpose of broad search and fast convergence. The optimization results shows that the performance and effectiveness of the proposed system is superior with respect to a reference hybrid system in which the active and passive systems are independently optimized.