• ETRI Journal
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• v.37 no.5
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• pp.917-921
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• 2015

We consider a multiserver system with two classes of customers with preemption, which is a widely used system in the analysis of cognitive radio networks. It is known that the optimal admission control for this system is of threshold type. We express the expected total discounted profit using the total number of customers, thus reducing the stochastic optimization problem with a two-dimensional state space to a problem with a one-dimensional birth-and-death structure. An efficient algorithm is proposed for the calculation of the expected total discounted profit.

• Proceedings of the Korea Society for Simulation Conference
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• 2000.11a
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• pp.165-173
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• 2000

Analytic models have been developed to solve integrated production-distribution problems in supply chain management (SCM). As one of major constraints in analytic models, capacity, which is the total operation time in this paper has mostly been known or disregarded assuming infinite capacity. Also, as major factors, machine processing time to fabricate or assemble a part or product at a certain machine center in production system and vehicle processing time to deliver a product to a customer by a certain vehicle in distribution system have been fixed and regarded as a static factor, But in the real systems significant differences exit between capacity and the required time to achieve the production-distribution plan and between processing time and consumed time to process a part or product. In this paper, capacity and processing times in the analytic model are considered as dynamic factors and adjusted by the results from independently developed simulation model, which includes general production-distribution characteristics. Through experiments, we obtain the more realistic solutions reflecting stochastic natures by performing the iterative analytic-simulation procedure.

• Journal of Communications and Networks
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• v.7 no.1
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• pp.54-64
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• 2005

This paper presents an advanced architecture for a traffic generator capable of producing ATM traffic streams according to fully general semi-Markovian stochastic models. The architecture employs a basic traffic generator platform and enhances it by adding facilities for 'driving' the cell generation process through high-level specifications. Several kinds of optimization are employed for enhancing the software's speed to match the hardware's potential and for ensuring that traffic streams corresponding to models with a wide range of parameters can be generated efficiently and reliably. The proposed traffic generation procedure is highly modular. Thus, although this paper deals with ATM traffic, the main elements of the architecture can be used equally well for generating traffic loads on other networking technologies, IP-based networks being a notable example.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.430-437
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• 2018

Ultrasonic attenuation is an important parameter in Quantitative Ultrasound and many algorithms have been proposed to improve estimation accuracy and repeatability for multiple independent estimates. In this work, we propose an improved algorithm for estimating ultrasonic attenuation utilizing the optimal frequency compounding technique based on stochastic noise model. We formulate mathematical compounding equations in the AWGN channel model and solve optimization problems to maximize the signal-to-noise ratio for multiple frequency components. Individual estimates are calculated by the reference phantom method which provides very stable results in uniformly attenuating regions. We also propose the guideline to select frequency ranges of reflected RF signals. Simulation results using numerical phantoms show that the proposed optimal frequency compounding method provides improved accuracy while minimizing estimation bias. The estimation variance is reduced by only 16% for the un-compounding case, whereas it is reduced by 68% for the uniformly compounding case. The frequency range corresponding to the half-power for reflected signals also provides robust and efficient estimation performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.8
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• pp.1305-1310
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• 2008

Traditional maintenance planning is based on a constant maintenance interval for equipment life. In order to consider economic aspect for time based preventive maintenance, preventive maintenance is desirable to be scheduled by RCM(Reliability-Centered Maintenance) evaluation. The main objective of RCM is to reduce the maintenance cost, by focusing on the most important functions of the system and avoiding or removing maintenance actions that are not strictly necessary. 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 the stepwise Markov model in detail. Also, because the system is not continuously monitored, the inspection is considered. In case study, simulation results about RCM will be shown using the real historical data of combustion turbine generating unit in Korean power systems.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.697-699
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• 2004

Despite many successful spectral clustering algorithm (based on the spectral decomposition of Laplacian(1) or stochastic matrix(2) ) there are several unsolved problems. Most spectral clustering Problems are based on the normalized of algorithm(3) . are close to the classical graph paritioning problem which is NP-hard problem. To get good solution in polynomial time. it needs to establish its convex form by using relaxation. In this paper, we apply a novel optimization technique. semidefinite programming(SDP). to the unsupervised clustering Problem. and present a new multiple Partitioning method. Experimental results confirm that the Proposed method improves the clustering performance. especially in the Problem of being mixed with non-compact clusters compared to the previous multiple spectral clustering methods.

• Journal of Korean Society of Water and Wastewater
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• v.11 no.3
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• pp.87-95
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• 1997

A river water quality management model was made by Dynamic programming. This model optimizes the wastewater treatment cost of the application area, and computed water quality with it must meet the water quality standard. And this model takes into consideration tributary input, wastewater treatment plant effluent, withdrawls for several purposes. Modified Streeter-Phelps equation was used to calculate BOD and DO. Optimization problem was solved with particular exceedance probability flow, and the water quality of each point was calculated with the decided treatment efficiencies. At that time, the probability satisfying the water quality standard of constraints to the exceedance probability of the flow. The developed model was applied to the lower part of the Han-River. The reliability to meet the water quality standard is 70 % when 4 wastewater treatment plants of Seoul City are operated by activated sludge system at autumn of the year 2001. Treatment cost of this case is 121.288 billion won per year.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.1-11
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• 2021

Estimation of distribution algorithm (EDA) is a popular stochastic metaheuristic algorithm. EDA has been widely utilized in various optimization problems. However, it has been shown that the diversity of the population gradually decreases during the iterations, which makes EDA easily lead to premature convergence. This article introduces a hybrid estimation of distribution algorithm (EDA) with differential evolution (DE) based on self-adaptive strategy, namely HEDADE-SA. Firstly, an alternative probability model is used in sampling to improve population diversity. Secondly, the proposed algorithm is combined with DE, and a self-adaptive strategy is adopted to improve the convergence speed of the algorithm. Finally, twenty-five benchmark problems are conducted to verify the performance of HEDADE-SA. Experimental results indicate that HEDADE-SA is a feasible and effective algorithm.

• Journal of Integrative Natural Science
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• v.11 no.4
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• pp.204-208
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• 2018

Comparison of different optimizer performance in photovoltaic power modeling using artificial neural deep learning techniques is described in this paper. Six different deep learning optimizers are tested for Long-Short-Term Memory networks in this study. The optimizers are namely Adam, Stochastic Gradient Descent, Root Mean Square Propagation, Adaptive Gradient, and some variants such as Adamax and Nadam. For comparing the optimization techniques, high and low fluctuated photovoltaic power output are examined and the power output is real data obtained from the site at Mokpo university. Using Python Keras version, we have developed the prediction program for the performance evaluation of the optimizations. The prediction error results of each optimizer in both high and low power cases shows that the Adam has better performance compared to the other optimizers.

• Earthquakes and Structures
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• v.22 no.4
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• pp.421-430
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• 2022

This paper addresses the stochastic control problem of robots within the framework of parameter uncertainty and uncertain noise covariance. First of all, an open circle deterministic trajectory optimization issue is explained without knowing the unequivocal type of the dynamical framework. Then, a Linear Quadratic Gaussian (LQG) controller is intended for the ostensible trajectory-dependent linearized framework, to such an extent that robust hereditary NN robotic controller made out of the Kalman filter and the fuzzy controller is blended to ensure the asymptotic stability of the non-continuous controlled frameworks. Applicability and performance of the proposed algorithm shown through simulation results in the complex systems which are demonstrate the feasible to improve the performance by the proposed approach.