• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.340-345
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• 2001

Electric power market in Japan is now on the trend of deregulation and privatization just like in Europe and the United States. And various approaches for risk management have been investigated taking the electric power price fluctuation after the deregulation into account. The behavior of the investment in power generation plants has not, however, been studied in detail yet due to the complexity of the problem. The problem of the investment in the deregulated power market is that of autonomous decentralized decision-making system, which includes various kinds of decision-makers, that is, power producers called IPPS Each generator has its own criteria for plant investment. Therefore, the total behavior of the decentralized power market will be so complicated, and normative approach will not be applicable fur this analysis. We have developed a simulation-based system fur behavioral analysis and also the framework design of the decentralized power market.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.320-322
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• 1993

In this paper, the decentralized adaptive control scheme is applied to the control of drum type boiler system. Because the scheme requires the stability of interconnections, static feedback is used to satisfy the requirement of the scheme. From a priori knowledge of the system, system parameter estimates are bounded to prevent parameter drifting. Computer simulation shows that the decentralized adaptive control is suitable for the control of drum type boiler system and robust in the presence of disturbance.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.10a
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• pp.6-10
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• 1996

This paper presents an overview of research results obtained by the authors in a series of publications. Methods are developed both for time-varying and time-invariant for linear and nonlinear. for time domain and frequency domain . and for discrete-time and continuous-time systems. Among the topics presented are: 1. Learning control based on integral control concepts applied in the repetition domain. 2. New algorithms that give improved transient response of the indirect adaptive control ideas. 4. Direct model reference learning control. 5 . Learning control based frequency domain. 6. Use of neural networks in learning control. 7. Decentralized learning controllers. These learning algorithms apply to robot control. The decentralized learning control laws are important in such applications becaused of the usual robot decentralized controller structured.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.23-27
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• 2009

The joint dispatch of regional electricity markets can improve the overall economic efficiency of interconnected markets by increasing the combined social welfare of the interconnected markets. This paper presents a new decentralized optimization technique based on Augmented Lagrangian Relaxation (ALR) to perform the joint dispatch of interconnected electricity markets. The Block Coordinate Descent (BCD) technique is applied to decompose the inseparable quadratic term of the augmented Lagrangian equation into individual market optimization problems. The Interior Point/Cutting Plane (IP/CP) method is used to update the Lagrangian multiplier in the decomposed market optimization problem. The numerical example is presented to validate the effectiveness of the proposed decentralized method.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.414-417
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• 1997

We propose a decentralized $H_{\infty}$ state estimation method in the multisensor state estimation problem. The proposed method bounds the maximum energy gain from unknown external disturbances to the estimation errors in the suboptimal case. And we formulate the decentralized state estimation method in the general case of different global and local models using alternative gain equation of the $H_{\infty}$ state estimator which can calculate global state estimates from the the linear combination of local state estimates. In addition, the proposed update equation between global and local Riccati solutions can reduce unnecessary calculation burden efficiently.

• Bulletin of the Korean Space Science Society
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• 2007.10a
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• pp.135-138
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• 2007

The present work is to estimate the stability region of the State-Dependent Riccati Equation (SDRE) controlled system, which is used for a decentralized coordinated attitude control in satellite formation flying. In this research, currently emerging methods which estimate region of attraction for the SDRE controllers are introduced and the methods are applied to attitude control systems. The results guarantee the stability of the given decentralized coordinated attitude control system in satellite formation flying.

• Smart Structures and Systems
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• v.7 no.1
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• pp.59-82
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• 2011

In this study, an output-only modal identification approach is proposed for decentralized wireless sensor nodes used for linear structural systems. The following approaches are implemented to achieve the objective. Firstly, an output-only modal identification method is selected for decentralized wireless sensor networks. Secondly, the effect of time-unsynchronization is assessed with respect to the accuracy of modal identification analysis. Time-unsynchronized signals are analytically examined to quantify uncertainties and their corresponding errors in modal identification results. Thirdly, a modified approach using complex mode shapes is proposed to reduce the unsynchronization-induced errors in modal identification. In the new way, complex mode shapes are extracted from unsynchronized signals to deal both with modal amplitudes and with phase angles. Finally, the feasibility of the proposed approach is evaluated from numerical and experimental tests by comparing with the performance of existing approach using real mode shapes.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.657-660
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• 2003

Control of multi-link robot arms is a very difficult problem because of the highly nonlinear dynamics. Decentralized control scheme is developed for control of robot manipulators based on RBF(Radial Basis Function) Neural Networks. RBF Neural Networks is used to approximate the coupling forces among the joints, coriolis force, centrifugal force, gravitational force, and frictional force. The compensation controller is also proposed to estimate the bound of approximation error so that the chattering effect of the control effort can be reduced. The proposed scheme does not require an accurate manipulator dynamic, and it is proved that closed-loop system is asymptotic stable despite the gross robot parameter variations. Numerical simulations for two-link robot manipulator are included to show the effectiveness of controller.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1115-1126
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• 2021

In this paper, we developed a mobile student ID providing a self sovereignty identity (SSI) which replaces the conventional plastic-type student ID that includes private information of a student such as a name, a student number, a facial photo, etc. The implemented mobile student ID solves the problem of exposing student's identity due to a loss or a theft of a plastic-type student ID, and it has a structure and process of FRANCHISE model which is developed by a concept of a decentralized Identity(DID) of a Blockchain, in which specialized for convenience as an electronic student ID through an application on a smart phone device. In addition, it protects student's privacy by controlling personal information on oneself. By using a smartphone, not only it easily identifies the student but also it expands to several services such as participation in school events, online authentication, and a student's exchange program among colleges.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.469-471
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• 1999

In this paper, a dynamic output feedback controller design technique for robust decentralized stabilization of uncertain large-scale systems is presented. Based on the Lyapunov method, a sufficient condition for robust stability, is derived in terms of three linear matrix inequalities(LMIs). The solutions of the LMIs can be easily obtained using efficient convex optimization techniques.