• Nonlinear Functional Analysis and Applications
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• v.28 no.2
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• pp.365-382
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• 2023

In this paper we prove the existence and uniqueness of solution of stochastic fractional neutral differential equations with Gaussian noise or Lévy noise by using the Picard-Lindelöf successive approximation scheme. Further stability results of nonlinear stochastic fractional dynamical system with Gaussian and Lévy noises are established. Examples are provided to illustrate the theoretical results.

• Journal of the military operations research society of Korea
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• v.18 no.2
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• pp.96-113
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• 1992

A time-varying nonhomogeneous poisson process approximation of the nonexponential stochastic Lanchester model is defined and evaluated over a range of combat parameters including initial force sizes. breakpoints. and interkilling random variables. The proposed approximation is far excellent and takes much less CPU time than the existing models. The sensitivity analysis was peformed to evaluate the efficiency of the proposed model and three recommended factors are suggested to guide the combat operators.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.6
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• pp.89-96
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• 1989

An improved method on texture coding, which is a part of the region growing based image coding, is presented in this paper. An image is segmented into stochastic regions which can be described as a stochastic random field, and non-stochastic ones in order to efficiently represent texture. In the texture coding and reconstruction, an autoregressive model is used for the stochastic regions, while a two-dimensional polynomial approximation is used for the non-stochastic ones. This proposed method leads to a better subjective quality, relatively higher compression ratio and shorter processing time for coding and reconstructing than the conventional method which uses only two-dimensional polynomial approximation.

• Journal of Applied and Pure Mathematics
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• v.4 no.5_6
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• pp.233-247
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• 2022

This manuscript addressed, the existence and uniqueness result for random impulsive stochastic functional differential equations with finite time delays. The study of random impulsive stochastic system is a new area of research. We interpret the meaning of a stochastic derivative and how it differs from the classical derivative. We prove the existence and uniqueness of mild solutions to the equations by using the successive approximation method. We conclude the article with some interesting future extension. This work extends the work of [18, 12, 20]. Finally, an example is given to illustrate the theoretical result.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.14-19
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• 2008

The paper presents an intelligent time series model to predict uncertain electricity market price in the deregulated industry environment. Since the price of electricity in a deregulated market is very volatile, it is difficult to estimate an accurate market price using historically observed data. The parameter of an intelligent time series model is obtained based on the simultaneous perturbation stochastic approximation (SPSA). The SPSA is flexible to use in high dimensional systems. Since prediction models have their modeling error, an error compensator is developed as compensation. The SPSA based intelligent model is applied to predict the electricity market price in the Pennsylvania-New Jersey-Maryland (PJM) electricity market.

• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.837-846
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• 2012

Monte Carlo methods have been used in exact inference for contingency tables for a long time; however, they suffer from ergodicity and the ability to achieve a desired proportion of valid tables. In this paper, we apply the stochastic approximation Monte Carlo(SAMC; Liang et al., 2007) algorithm, as an adaptive Markov chain Monte Carlo, to the exact test of mutual independence in a multiway contingency table. The performance of SAMC has been investigated on real datasets compared to with existing Markov chain Monte Carlo methods. The numerical results are in favor of the new method in terms of the quality of estimates.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.624-627
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• 2005

This paper presents a new data classification method using the Robbins Monro stochastic approximation algorithm k-nearest neighbor and distribution analysis. To cluster the data set, we decide the centroid of the test data set using k-nearest neighbor algorithm and the local area of data set. To decide each class of the data, the Robbins Monro stochastic approximation algorithm is applied to the decided local area of the data set. To evaluate the performance, the proposed classification method is compared to the conventional fuzzy c-mean method and k-nn algorithm. The simulation results show that the proposed method is more accurate than fuzzy c-mean method, k-nn algorithm and discriminant analysis algorithm.

• Fisheries and Aquatic Sciences
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• v.27 no.4
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• pp.225-230
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• 2024

We examined the dB difference in target strength at multiple frequencies (ΔTS) for the identification of Antarctic krill (Euphausia superba) and ice krill (Euphausia crystallorophias) using a stochastic distorted-wave Born approximation model. Our investigation focused on ΔTS patterns at multiple frequencies in relation to size, along with key acoustic properties influencing TS, including density and sound speed contrast, fatness, and orientation. The findings revealed that the orientation and fatness significantly affect the ΔTS patterns. The results provide insight into the importance of the multi-frequency technique for estimating krill biomass and their ecological interactions with environmental features in the Southern Ocean.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.501-515
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• 2022

Despite the stylized statistical features of returns of financial returns such as fat-tailed distribution and leverage effect, no stochastic volatility models that can explicitly capture these features have been presented in the existing frequentist approach. we propose an approximate parameterization of stochastic volatility models that can explicitly capture the fat-tailed distribution and leverage effect of financial returns and a maximum likelihood estimation of the model using Langrock et al. (2012)'s hidden Markov model approximation in a frequentist approach. Through extensive simulation experiments and an empirical analysis, we present the statistical evidences validating the efficacy and accuracy of proposed parameterization.

• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.506-514
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• 2008

This paper presents an intelligent model; named as free model, approach for a closed-loop system identification using input and output data and its application to design a power system stabilizer (PSS). The free model concept is introduced as an alternative intelligent system technique to design a controller for such dynamic system, which is complex, difficult to know, or unknown, with input and output data only, and it does not require the detail knowledge of mathematical model for the system. In the free model, the data used has incremental forms using backward difference operators. The parameters of the free model can be obtained by simultaneous perturbation stochastic approximation (SPSA) method. A linear transformation is introduced to convert the free model into a linear model so that a conventional linear controller design method can be applied. In this paper, the feasibility of the proposed method is demonstrated in a one-machine infinite bus power system. The linear quadratic regulator (LQR) method is applied to the free model to design a PSS for the system, and compared with the conventional PSS. The proposed SPSA-based LQR controller is robust in different loading conditions and system failures such as the outage of a major transmission line or a three phase to ground fault which causes the change of the system structure.