• Journal of Hydrogen and New Energy
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• v.24 no.6
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• pp.487-494
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• 2013

In this study, solutions for a liquid pool spreading model with continuous and instantaneous release are discussed based on the model used in the FERC's report. The effects of the release time on the liquid pool volume and radius are investigated for the continuous release. For the continuous release with the frictional resistance force in the liquid pool spreading model, the vaporization time decreases as the release time increases. On the other hand, for the continuous release without the frictional resistance force in the liquid pool spreading model, the vaporization time increases as the release time increases. These phenomena are deeply related to the pool radius. In addition, the effects of the initial pool radius for the instantaneous release in the liquid pool spreading model are discussed. For the case with the frictional resistance force in the liquid pool spreading model, as reducing release time in the model with the frictional resistance force for the continuous release, the solution for a continuous release approaches to that for an instantaneous release. On the contrary to this, the pool volume and radius for the instantaneous release without the frictional resistance force are totally different from those for the continuous release without the frictional resistance force.

• Communications for Statistical Applications and Methods
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• v.18 no.2
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• pp.229-236
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• 2011

A continuous time stochastic volatility model for financial assets suggested by Barndorff-Nielsen and Shephard (2001) is considered, where the volatility process is modelled as an Ornstein-Uhlenbeck type process driven by a general L$\'{e}$vy process and the price process is then obtained by using an independent Brownian motion as the driving noise. The uniform ergodicity of the volatility process and exponential ${\alpha}$-mixing properties of the log price processes of given continuous time stochastic volatility models are obtained.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.10
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• pp.457-463
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• 2001

This paper presents a simple and new digital redesign algorithm for fuzzy-model-based controllers. In the first stage, a continuous-time TS fuzzy model is constructed for a given continuous-time nonlinear system and a corresponding continuous-time fuzzy-model-based controller is established based on the existing controller synthesis algorithms. In the second stage, the continuous-time fuzzy-model-based controller is converted to equivalent discrete-time fuzzy-model-based controller, aiming at maintaining the property of the analogue controlled system, which are called intelligent digital redesign. Finally, the proposed method is applied to the digital control of inverted pendulum system to shows the effectiveness and the effectiveness and the feasibility of the method.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.1
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• pp.76-81
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• 1998

This paper presents an on-line scheme for parameter estimation of continuous-time systems, based on the model adjustment technique and the genetic algorithm technique. To deal with the initialisation and unmeasurable signal problems in on-line parameter estimation of continuous-time systems, a discrete-time model is obtained for the linear differential equation model and approximations of unmeasurable states with the observable output and its time-delayed values are obtained for the nonlinear state space model. Noisy observations may affect these approximation processes and degrade the estimation performance. A digital prefilter is therefore incorporated to avoid direct approximations of system derivatives from possible noisy observations. The parameters of both the model and the designed filter are adjusted on-line by a genetic algorithm, A set of simulation works for linear and nonlinear systems is carried out to demonstrate the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2280-2282
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• 2000

This paper presents a simple and new digital redesign algorithm for fussy-model-based controllers. In the first stage, a continuous-time TS fuzzy model is constructed for a given continuous-time nonlinear system and a corresponding continuous-time fuzzy-model-based controller is established based on the existing controller synthesis algorithms. In the second stage, the continuous-time fuzzy-model-based controller is converted to equivalent discrete-time fuzzy-model-based controller, aiming at maintaining the property of the analogue controlled system, which are called intelligent digital redesign. Finally, the proposed method is applied to the digital control of inverted pendulum system to shows the effectiveness and the feasibility of the method.

• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.4
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• pp.303-310
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• 2020

Estimating the accurate internal state of lithium ion batteries to increase their safety and efficiency is crucial. Various algorithms are used to estimate the internal state of a lithium ion battery, such as the extended Kalman filter and sliding mode observer. A state-space model is essential in using algorithms to estimate the internal state of a battery. Two principal methods are used to express the state-space model, namely, continuous time and discrete time. In this work, the extended Kalman filter is employed to estimate the internal state of a battery. Moreover, this work presents and analyzes the estimation performance of algorithms consisting of a continuous time state-space model and a discrete time state-space model through static and dynamic profiles.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.157-160
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• 1996

Physical systems axe generally continuous-time in nature. However as the data measured from these systems is generally in the form of discrete samples, and most modern signal processing is performed in the discrete-time domain, discrete-time models are employed. This paper describes methods for estimating the coefficients of continuous-time system within a closed loop control system. The method employs a recursive estimation algorithm to identify the coefficients of a discrete-time bilinear-operator model. The coefficients of the discrete-time bilinear-operator model closely approximate those of the corresponding continuous-time Laplace transform transfer function.

• Communications for Statistical Applications and Methods
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• v.19 no.5
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• pp.639-646
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• 2012

The exponential continuous time GARCH(p,q) model for financial assets suggested by Haug and Czado (2007) is considered, where the log volatility process is driven by a general L$\acute{e}$vy process and the price process is then obtained by using the same L$\acute{e}$vy process as driving noise. Uniform ergodicity and ${\beta}$-mixing property of the log volatility process is obtained by adopting an extended generator and drift condition.

• The Transactions of the Korean Institute of Power Electronics
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• v.8 no.2
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• pp.137-142
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• 2003

Two existing continuous-time models for the current-mode control have presented noticeable differences in their small-signal predictions. As an attempt to clarify the origin of these disparities, this paper presents an alternative way of deriving a continuous-time model for the current-model control. The results of this paper would provide insights to comprehend the dissimilarity in the modeling method and final results of the earlier models of current-mode control models.

• Journal of Information Technology Services
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• v.10 no.3
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• pp.113-126
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• 2011

The smelting and the continuous casting of steel are important processes that determine the quality of steel products. Especially most of quality defects occur during solidification of the steel continuous casting process. Although quality control techniques such as six sigma, SQC, and TQM can be applied to the continuous casting process for improving quality of steel products, these techniques don't provide real-time analysis to identify the causes of defect occurrence. To solve problems, we have developed a detection model using decision tree which identified abnormal transactions to have a coarse grain structure. And we have compared the proposed model with models using neural network and logistic regression. Experiments on steel data showed that the performance of the proposed model was higher than those of neural network model and logistic regression model. Thus, we expect that the suggested model will be helpful to control the quality of steel products in real-time in the continuous casting process.