• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.66-70
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• 1996

We investigate some of algebraic properties of T-generalized state machines, T-generalized transformation semigroups, coverings of T-generalized state machines and T-generalized transformation semigroups.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.4
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• pp.27-33
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• 1996

In this paper we introduce the notions of T-generalized state machines and primary submachines of T-generalized state machines and obtain a decomposition theorem for T-generalized state machine in terms of primary submachines.

• Journal of applied mathematics & informatics
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• v.6 no.2
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• pp.467-486
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• 1999

We introduce the concepts of a generalized state machine and a generalized transformation semigroup and discuss their algebraic properties.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.4
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• pp.39-48
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• 2009

Asynchronous sequential machines, or clockless logic circuits, have several advantages over synchronous machines such as fast operation speed, low power consumption, etc. In this paper, we propose a novel robust controller for input/output asynchronous sequential machines with uncertain state transitions. Due to model uncertainties or inner failures, the state transition function of the considered asynchronous machine is not completely known. In this study, we present a formulation to model this kind of asynchronous machines ana using generalized reachability matrices, we address the condition for the existence of an appropriate controller such that the closed-loop behavior matches that of a prescribed model. Based on the previous research results, we sketch design procedure of the proposed controller and analyze the stable-state operation of the closed-loop system.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1566-1577
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• 2018

This paper presents a stability analysis of AC-DC power system feeding a speed controlled DC motor in which this load behaves as a constant power load (CPL). A CPL can significantly degrade power system stability margin. Hence, the stability analysis is very important. The DQ and generalized state-space averaging methods are used to derive the mathematical model suitable for stability issues. The paper analyzes the stability of power systems for both speed control natural frequency and DC-link parameter variations and takes into account controlled speed motor dynamics. However, accurate DC-link filter and DC motor parameters are very important for the stability study of practical systems. According to the measurement errors and a large variation in a DC-link capacitor value, the system identification is needed to provide the accurate parameters. Therefore, the paper also presents the identification of system parameters using the adaptive Tabu search technique. The stability margins can be then predicted via the eigenvalue theorem with the resulting dynamic model. The intensive time-domain simulations and experimental results are used to support the theoretical results.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.496-499
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• 2003

Support Vector Machines (SVMs) is applied to a practical problem as one of standard tools for machine learning. The application of Reinforcement Learning (RL) and SVMs in action of mobile robot is investigated. A technique to decide the action of autonomous mobile robot in practice is explained in the paper, The proposed method is to find n basis for good action of the system under unknown environment. In multi-dimensional sensor input, the most reasonable action can be automatically decided in each state by RL. Using SVMs, not only optimal decision policy but also generalized state in unknown environment is obtained.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.279-289
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• 2006

The reconstruction of low dimension nonlinear behavior from the hydrologic time series has been an active area of research in the last decade. In this study, we present the applications of a powerful state space reconstruction methodology using the method of Support Vector Machines (SVM) to the Great Salt Lake (GSL) volume. SVMs are machine learning systems that use a hypothesis space of linear functions in a Kernel induced higher dimensional feature space. SVMs are optimized by minimizing a bound on a generalized error (risk) measure, rather than just the mean square error over a training set. The utility of this SVM regression approach is demonstrated through applications to the short term forecasts of the biweekly GSL volume. The SVM based reconstruction is used to develop time series forecasts for multiple lead times ranging from the period of two weeks to several months. The reliability of the algorithm in learning and forecasting the dynamics is tested using split sample sensitivity analyses, with a particular interest in forecasting extreme states. Unlike previously reported methodologies, SVMs are able to extract the dynamics using only a few past observed data points (Support Vectors, SV) out of the training examples. Considering statistical measures, the prediction model based on SVM demonstrated encouraging and promising results in a short-term prediction. Thus, the SVM method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.