• Management Science and Financial Engineering
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• v.14 no.2
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• pp.105-118
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• 2008

There are many debates on the topic of the relationship between oil prices and economic growth. Through the repeated processes of conformations and contractions on the subject, two main issues are developed; one is how to define and drive oil shocks from oil prices, and the other is how to specify an econometric model to reflect the asymmetric relations between oil prices and output growth. The study, thus, introduces the unobserved component model to pick up the oil shocks and a first-order Markov switching model to reflect the asymmetric features. We finally employ unique oil shock variables from the stochastic trend components of oil prices and adapt four lags of the mean growth Markov Switching model. The results indicate that oil shocks exert more impact to recessionary state than expansionary state and the supply-side oil shocks are more persistent and significant than the demand-side shocks.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.6
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• pp.1036-1041
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• 2008

Periodic disturbance is practically occurred in several engineering applications, especially in data storage systems. However, recently addressed controls for such problem were mostly dealt with its deterministic nature, which is rarely practical in real-time implementation. We present an adaptive control approach for DC motor systems with periodic stochastic disturbance whose frequency and magnitude are both random variables. We establish adaptive state feedback control which is linearly composed of nominal and corrective control parameter matrices. The former is derived from a nominal system model voiding disturbance and the latter is constructed from a disturbed system model by using Lyapunov stability theory. We carry out computer simulation to evaluate the proposed control methodology and compare to the recently addressed control method to demonstrate its superiority.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.2165-2170
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• 2007

At nanoscales, the Boltzmann transport equation (BTE) can best describe the behavior of phonons which are energy carriers in crystalline materials. Through this study, the phonon transport in some micro/nanoscale problems was simulated with the Monte Carlo method which is a kind of the stochastic approach to the BTE. In the Monte Carlo method, the superparticles of which the number is the weighted value to the actual number of phonons are allowed to drift and be scattered by other ones based on the scattering probability. Accounting for the phonon dispersion relation and polarizations, we have confirmed the one-dimensional transient phonon transport in ballistic and diffusion limits, respectively. The thermal conductivity for GaAs was also calculated from the kinetic theory by using the proposed model. Besides, we simulated the electrostatic discharge event in the NMOS transistor as a two-dimensional problem by applying the Monte Carlo method.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.228-229
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• 2008

This paper presents stochastic methodology based fault diction and diagnosis algorithm for induction motor systems. First, we construct probability distribution model from healthy motors and then probability distribution for faulty motors is recursively calculated by means of the proposed probability estimation. We measure motor current with hall sensors as system state. The estimated probability is compared to the model to generate a residue signal which is utilized for fault detection and diagnosis, that is, where a fault is occurred. We carry out real-time induction motor experiment to evaluate efficiency and reliability of the proposed approach.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.11
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• pp.1578-1583
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• 2013

In this paper, stochastic optimization algorithms of PSO (Particle Swarm Optimization) and APSO (Adaptive Particle Swam Optimization) are studied and compared. It is revealed that the APSO provides faster convergence and better search efficiency than the conventional PSO when they are adopted to find the global minimum of a two-dimensional function. The advantages of the APSO comes from the ability to control the inertia weight, and acceleration coefficients. To verify that the APSO is working better than the standard PSO, the design of a 10GHz microstrip patch as one of the elements of a high frequency array antenna is taken as a test-case and shows the optimized result with 5 iterations in the APSO and 28 iterations in th PSO.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.245-254
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• 2014

The traditional methods of simulating daily precipitation have paid little attention to the inherent dependence structure between the total precipitation amount and the precipitation frequency for a fixed period of time. To address this issue, we propose a new simulation algorithm using copula in order to incorporate the dependence into the traditional methods. The algorithm consists of two parts: First, while reflecting the observed dependence, we generate the total precipitation amount (S) and the frequency (N) during the period of interest; then we simulate the daily precipitation whose aggregation matches the pair of (N; S) generated in the first part. Our result shows that the proposed method substantially improves the traditional methods.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.686-689
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• 2004

Genetic algorithm is well known for stochastic searching method in imitating natural phenomena. In recent times, studies have been conducted in improving conventional evolutionary computation speed and promoting precision. This paper presents an approach to optimize PID controller gains with the application of modified Genetic Algorithm using Support Vector Machine (SVMGA). That is, we aim to explore optimum parameters of PID controller using SVMGA. Simulation results are given to compare to those of tuning methods, based on Simple Genetic Algorithm and Ziegler-Nicholas tuning method.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.7-12
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• 2003

In this paper, S(stochastic)-eigenvalue concept and its S-eigenvector for linear continuous-time systems with probabilistic uncertainties are proposed. The proposed concept is concerned with the perturbation of eigenvalues due to the probabilistic variable parameters in the dynamic model of a plant. S-eigenstructure assignment scheme via the Sylvester equation approach based on the S-eigenvalue concept is also proposed. The proposed design scheme is applied to the longitudinal dynamics of open-loop-unstable aircraft with possible uncertainties in aerodynamic and thrust effects as well as separate dynamic pressure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.495-513
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• 2020

The correlation identification of the subsurface is a novel electrical prospecting method which could suppress stochastic noise. This method is increasingly being utilized by geophysicists. It achieves the frequency response of the underground media through division of the cross spectrum of the input & output signal and the auto spectrum of the input signal. This is subject to the spectral leakage when the cross spectrum and the auto spectrum are computed from cross correlation and autocorrelation function by Discrete Fourier Transformation (DFT, "To obtain an accurate frequency response of the earth system, we propose an improved correlation identification method which uses all phase Fast Fourier Transform (APFFT) to acquire the cross spectrum and the auto spectrum. Simulation and engineering application results show that compared to existing correlation identification algorithm the new approach demonstrates more precise frequency response, especially the phase response of the system under identification.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.6
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• pp.685-692
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• 2004

A stochastic approach has been presented for superplastic deformation of Ti-6Al-4V alloy, and probability functions are used to model the heterogeneous phase distributions. The experimentally observed spatial correlation functions are developed, and microstructural evolutions together with superplastic deformation behavior have been investigated by means of the two-point and three-point probability functions. The results have shown that the probability varies approximately linearly with separation distance, and deformation enhanced probability changes during the process. The stress-strain behavior with the evolutions of probability function can be correctly predicted by the model. The finite element implementation using Monte Carlo simulation associated with reconstructed microstructures shows that better agreement with experimental data of failure strain on the test specimen.