• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.5
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• pp.1116-1125
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• 1997

In this paper, the modified UPC(Usage Parameter Control) algorithm is proposed. The proposed UPC algorithm is based on Leakey Bucket algorithm and adds the characteristics of the jumping window algorithm for monitoring the average bit rate. The proposed algorithm let a cell, which is tagged by Leaky Bucket algorithm, pass through the network, if the network does not violate the average bit rate. The measuring method of window mechanism like jumping window. This paper supposes On/Off traffic source model of rthe performance evaluation and analysis of the proposed algorithm. Therefore, as simulation results, the proposed algorithm acquires more reduced results of the cell loss rate and bucket size than the Leaky Bucket algorithm.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.115-125
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• 2021

Simulation-based hull form optimization is a typical HEB (high-dimensional, expensive computationally, black-box) problem. Conventional optimization algorithms easily fall into the "curse of dimensionality" when dealing with HEB problems. A recently proposed Cross-Entropy (CE) optimization algorithm is an advanced stochastic optimization algorithm based on a probability model, which has the potential to deal with high-dimensional optimization problems. Currently, the CE algorithm is still in the theoretical research stage and rarely applied to actual engineering optimization. One reason is that the Monte Carlo (MC) method is used to estimate the high-dimensional integrals in parameter update, leading to a large sample size. This paper proposes an improved CE algorithm based on quasi-Monte Carlo (QMC) estimation using high-dimensional truncated Sobol subsequence, referred to as the QMC-CE algorithm. The optimization performance of the proposed algorithm is better than that of the original CE algorithm. With a set of identical control parameters, the tests on six standard test functions and a hull form optimization problem show that the proposed algorithm not only has faster convergence but can also apply to complex simulation optimization problems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1437-1459
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• 2020

Wireless Sensor Networks (WSN) is a distributed Sensor network whose terminals are sensors that can sense and check the environment. Sensors are typically battery-powered and deployed in where the batteries are difficult to replace. Therefore, maximize the consumption of node energy and extend the network's life cycle are the problems that must to face. Low-energy adaptive clustering hierarchy (LEACH) protocol is an adaptive clustering topology algorithm, which can make the nodes in the network consume energy in a relatively balanced way and prolong the network lifetime. In this paper, the novel multi-objective LEACH protocol is proposed, in order to solve the proposed protocol, we design a multi-objective coupling algorithm based on bat algorithm (BA), glowworm swarm optimization algorithm (GSO) and bacterial foraging optimization algorithm (BFO). The advantages of BA, GSO and BFO are inherited in the multi-objective coupling algorithm (MBGF), which is tested on ZDT and SCH benchmarks, the results are shown the MBGF is superior. Then the multi-objective coupling algorithm is applied in the multi-objective LEACH protocol, experimental results show that the multi-objective LEACH protocol can greatly reduce the energy consumption of the node and prolong the network life cycle.

• The Journal of the Acoustical Society of Korea
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• v.33 no.6
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• pp.420-426
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• 2014

In this paper, we propose a new affine projection sign algorithm (APSA) to improve the convergence speed of the conventional APSA which has been proposed to enable the affine projection algorithm (APA) to operate robustly in impulsive noise environment. The conventional APSA has two advantages; it operates robustly against impulsive noise and does not need calculation for the inverse matrix. The proposed algorithm also has the conventional algorithm's advantages and furthermore, better convergence speed than the conventional algorithm. In the conventional algorithm, each input signal is normalized by $l_2$-norm of all input signals, but the proposed algorithm uses input signals normalized by their corresponding $l_2$-norm. We carried out a performance comparison of the proposed algorithm with the conventional algorithm using a system identification model. It is shown that the proposed algorithm has the faster convergence speed than the conventional algorithm.

• Journal of Industrial Technology
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• v.4
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• pp.3-10
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• 1984

A new FDCT algorithm was developed. It needed only half numbers of the discrete output signals and made use of the prime factor FFT algorithm. The FDST algorithm of the second kind of DST was developed by use of the FDCT algorithm.

• Journal of applied mathematics & informatics
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• v.3 no.2
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• pp.129-148
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• 1996

This paper propose the inference mechanism for handling linear polynomial constraints called consistency checking algorithm based on the feasibility checking algorithm borrowed from linear pro-gramming. in contrast with other approaches proposed algorithm can efficiently and coherented by linear polynomial forms. The developed algorithm is successfully applied to the symbolic simulation that offers a convenient means to conduct multiple simultaneous exploration of model behaviors.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.1051-1054
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• 1988

In this paper we present initial convergence properties of the Kalman filtering algorithm, we put an arbitrary small positive correlation matrix as an initial condition in the recursive algorithm. This arbitrary small initial condition perturbs the Kalman filtering algorithm and may lead to initial instability. We derive a condition which insures the stable operation of the Kalman filtering algorithm from the stochastic Lyapunov difference equation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.87-94
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• 2001

Silicon nitride films grown by plasma-enhanced chemical vapor deposition (PECVD) are useful for a variety of applications, including anti-reflecting coatings in solar cells, passivation layers, dielectric layers in metal/insulator structures, and diffusion masks. PECVD systems are controlled by many operating variables, including RF power, pressure, gas flow rate, reactant composition, and substrate temperature. The wide variety of processing conditions, as well as the complex nature of particle dynamics within a plasma, makes tailoring SiN film properties very challenging, since it is difficult to determine the exact relationship between desired film properties and controllable deposition conditions. In this study, SiN PECVD modeling using optimized neural networks has been investigated. The deposition of SiN was characterized via a central composite experimental design, and data from this experiment was used to train and optimize feed-forward neural networks using the back-propagation algorithm. From these neural process models, the effect of deposition conditions on film properties has been studied. A recipe synthesis (optimization) procedure was then performed using the optimized neural network models to generate the necessary deposition conditions to obtain several novel film qualities including high charge density and long lifetime. This optimization procedure utilized genetic algorithms, hybrid combinations of genetic algorithm and Powells algorithm, and hybrid combinations of genetic algorithm and simplex algorithm. Recipes predicted by these techniques were verified by experiment, and the performance of each optimization method are compared. It was found that the hybrid combinations of genetic algorithm and simplex algorithm generated recipes produced films of superior quality.

• Journal of Electrical Engineering and Technology
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• v.4 no.3
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• pp.360-364
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• 2009

This paper suggests the techniques in determining the values of the steady-state equivalent circuit parameters of a three-phase induction machine using genetic algorithm. The parameter estimation procedure is based on the steady-state phase current versus slip and input power versus slip characteristics. The propose estimation algorithm is of non-linear kind based on selection in genetic algorithm. The machine parameters are obtained as the solution of a minimization of objective function by genetic algorithm. Simulation shows good performance of the propose procedures.

• Journal of the Korean Data and Information Science Society
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• v.16 no.3
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• pp.549-556
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• 2005

A hard-type centroids in the conventional clustering algorithm such as k-modes algorithm cannot keep the uncertainty inherently in data sets as long as possible before actual clustering(decision) are made. Therefore, we propose the k-populations algorithm to extend clustering ability and to heed the data characteristics. This k-population algorithm as found to give markedly better clustering results through various experiments.