• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.357-359
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• 2006

Traditional time based preventive maintenance is used to constant maintenance interval for equipment life. In order to consider economic aspect for time based preventive maintenance, preventive maintenance is scheduled by RCM(Reliability-Centered Maintenance) evaluation. So, Markov state model is utilized considering stochastic state in RCM. In this paper, a Markov state model which can be used for scheduling and optimization of maintenance is presented. The deterioration process of system condition is modeled by a Markov model. In case study, simulation results about RCM are used to the real historical data of combustion turbine generating units in Korean power systems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.2
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• pp.62-67
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• 2003

This paper deals with a First-Come, First-Served queueing model to analyze the behavior of heterogeneous finite source system with a single server Each sources and the processor are assumed to operate in independently Markovian environments, respectively. Each request is characterized by its own exponentially distributed source and service time with parameter depending on the state of the corresponding environment, that is, the arrival and service rates are subject to random fluctuations. Our aim is to get the usual stationary performance measures of the system, such as, utilizations, mean number of requests staying at the server, mean queue lengths, average waiting and sojourn times. In the case of fast arrivals or fast service asymptotic methods can be applied. In the intermediate situations stochastic simulation Is used. As applications of this model some problems in the field of telecommunications are treated.

• Water Engineering Research
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• v.5 no.2
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• pp.81-99
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• 2004

Long term streamflow regime under virtual climate change scenario was examined. Rainfall forecast simulation of the Canadian Global Coupled Model (CGCM2) of the Canadian Climate Center for modeling and analysis for the IPCC SRES B2 scenario was used for analysis. The B2 scenario envisions slower population growth (10.4 billion by 2010) with a more rapidly evolving economy and more emphasis on environmental protection. The relatively large scale of GCM hinders the accurate computation of the important streamflow characteristics such as the peak flow rate and lag time, etc. The GCM rainfall with more than 100km scale was downscaled to 2km-scale using the space-time stochastic random cascade model. The HEC-HMS was used for distributed hydrologic model which can take the grid rainfall as input data. The result illustrates that the annual variation of the total runoff and the peak flow can be much greater than rainfall variation, which means actual impact of rainfall variation for the available water resources can be much greater than the extent of the rainfall variation.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.931-934
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• 2005

This paper describes an adaptive recommendation system that provides real-time personalized trading advice to the investors based on their profiles and trading information environment. A proposed system integrates Stochastic technical analysis and artificial neural network that incorporates an adaptive user modeling. The user model is constructed and updated based on initial user profile and recorded user interactions with the system. The information presented to each individual user is also tailor-made to fit the user's behavior and preference. A system prototype was implemented in JAVA. Experiments used to evaluate the system's performance were done on both human subjects and synthetic users. The results show our proposed system is able to rapidly learn to provide appropriate advice to different types of users.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.777-782
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• 2011

Neural network technique is widely employed in the fields of signal processing, control systems, pattern recognition, etc. Learning of neural networks is an important procedure to accomplish dynamic system modeling. This paper presents a novel learning approach for differential neural network models based on the Kalman-Bucy filter theory. We construct an augmented state vector including original neural state and parameter vectors and derive a state estimation rule avoiding gradient function terms which involve to the conventional neural learning methods such as a back-propagation approach. We carry out numerical simulation to evaluate the proposed learning approach in nonlinear system modeling. By comparing to the well-known back-propagation approach and Kalman-Bucy filtering, its superiority is additionally proved under stochastic system environments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.6A
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• pp.881-887
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• 1999

In this paper, were introduce two types of equalizers, called equalizer-a and equalizer-b, applying to wireless communications having unknown channel characteristics. The equalizer-a, which has the single sample detector with equalizer system, is developed while the equalizer-b has the partition detectors with the same system used in equalizer-a. The methodologiy we adopt for designing the equalizers is that the sample space is partitioned into finite number of regions by using quantiles, which are estimated by robbins-monro stochastic approximation (RMSA) algorithm, and the coefficients of equalizers are calculated based on nonlinear minimum mean, square error (MMSE) algorithm. Through the computer simulation, the equalizers show much better performance in equiprobably partitioned sample subspaces of observations than the single sample detector and the detector, which has the conventional equalizer, in unquantized observation space under various noise environments.

• 유체기계공업학회:학술대회논문집
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• 2006.08a
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• pp.437-440
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• 2006

Particle suspension is frequently observed in many natural flows such as in the atmosphere and the ocean as well as in various engineering flows. Recently, airborne micro or nano-scale particles in atmosphere attract much attention from environmental society since small particle cause serious environmental problems in the industrialized areas. Also, the characteristics of such heavy particles' behavior is quite different from its fluid particles because the inertia force and buoyance force acting on the heavy particles are different than those acting on fluid particles. Therefore, our studies is to investigate the characteristics of the behavior of heavy particles considering the inertia effect with or without gravity effect, but do not consider modification of turbulence by the particles, that is one-way interaction. We carried out direct numerical simulation of isotropic turbulence with particles under the Stokes drag assumption for a spherical particle. These results can be used in the development of a stochastic model for predicting particle's behavior.

• Transactions of the Korean Society of Mechanical Engineers
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• v.14 no.2
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• pp.331-338
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• 1990

In adaptive control, the lack of persistent and rich excitation causes the estimated parameters to drift, which degrade the performance of the system and may introduces instability to the system in a stochastic environment. To solve the problem of the parameter drift, the concept of single parameter adaptation is presented. For the parameter identification, a priori error is directly used for adaptation error. The structure of the controller is based upon the minimum variance control technique. The stability and robustness analysis is carried out by the sector stability theorem for the second order system. The computer simulation is performed to justify the theoretical analysis for the various cases.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.28 no.1
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• pp.89-98
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• 2004

In this paper, we develope a dispersion model based on the Generalized Langevin Model. Thomson's well-mixed condition is the well known criterion to determine particle dispersion. But, it has 'non-uniqueness problem'. To resolve this, we adopt a turbulent model which is a new approach in this field of study. Our model was greatly simplified under the self-similarity condition, leaving model only two model constants $C_{0}$ and ${\gamma}$$_{5}$ that control the dispersion and spin which measures rotational property of the Lagrangian particle trajectory. We investigated the sign of spin as well as magnitude by using the Direct Numerical Simulation. Model calculations were performed on the neutrally stable boundary layer flow. We found that spin has weak effect on the particle dispersion but it shows the significant effect on the horizontal flux compared to the zero-spin model.

• Journal of Korea Foundry Society
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• v.16 no.6
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• pp.550-555
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• 1996

A stochastic model, based on the coupling of the finite volume(FV) method for macroscopic heat flow calculation and a two-dimensional cellular automaton(CA) model for treating microstructural evolution was applied-for the prediction of microstructural evolution in squeeze casting. The interfacial heat transfer coefficient at the casting/die interface was evaluated as a function of time using an inverse problem method in order to provide a quantitative simulation of solidification sequences under high pressure. The effects of casting process variables on the formation of solidification grain structures and on the columnar to equiaxed transition of an Al-4.5wt%Cu alloy in squeeze casting were investigated. The calculated solidification grain structures were in good agreement with those obtained experimentally.