• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1997.10a
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• pp.184-189
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• 1997

This paper introduces a new combined method by genetic algorithm and random tabu search method as optimization algorithm. Genetic algorithm can search the global optimum and tabu search method is very fast in speed. The optimizing ability of new combined method is identified by comparing other optimizing algorithm and used for optimum design of damping plate.

• Bulletin of the Korean Mathematical Society
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• v.34 no.4
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• pp.603-615
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• 1997

In this paper, we introduce and study a new class of random completely generalized nonlinear variational inclusions with non-compact valued random mappings and construct some new iterative algorithms. We prove the existence of random solutions for this class of random variational inclusions and the convergence of random iterative sequences generated by the algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.140-140
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• 2000

Traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on any particular domain. Hybridizing a genetic algorithm with other algorithms can produce better performance than both the genetic algorithm and the other algorithms. In this paper, we use random process and simulated annealing instead of mutation operator in order to get well tuned fuzzy rules. The key of this approach is to adjust both the width and the center of membership functions so that the tuned rule-based fuzzy controller can generate the desired performance. The effectiveness of the proposed algorithm is verified by computer simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.2
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• pp.131-137
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• 2001

Traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on only particular domian. Hybridizing a genetic algorithm with other algorithms can produce better performance than both the genetic algorithm and the other algorithms. This paper describes the application of random signal-based learning to a genetic algorithm in order to get well tuned fuzzy rules. The key of tis approach is to adjust both the width and the center of membership functions so that the tuned rule-based fuzzy controller can generate the desired performance. The effectiveness of the proposed algorithm is verified by computer simulation.

• Journal of Communications and Networks
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• v.3 no.4
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• pp.334-341
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• 2001

The provision of QoS control is a key of the successful deployment of multimedia networks. Buffer management plays an important role in QoS control. Therefore, this paper proposes a novel QoS buffer management algorithm named GREEN (Global Random Early Estimation for Nipping), which extends the concepts of ERD (early random drop) and RED (random early detection). Specifically, GREEN enhances the concept of "Random" to "Global Random" by globally considering the random probability function. It also enhances the concept of "Early" to "Early Esti mation" by early estimating the network status. For performance evaluation, except compared with RED, extensive simulation cases are performed to probe the characteristics of GREEN.

• The KIPS Transactions:PartC
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• v.16C no.5
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• pp.563-574
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• 2009

The merits of random substitutions are various applicability and security guarantee on the view point of privacy breach. However there is no research to improve the accuracy of random substitutions. In this paper we propose an algorithm for improving the accuracy of random substitutions by an advanced theoretical analysis about the standard errors. We examine that random substitutions have an unpractical accuracy level and our improved algorithm meets the theoretical results by some experiments for data sets having uniform and normal distributions. By our proposed algorithm, it is possible to upgrade the accuracy level under the same security level as the original method. The additional cost of computation for our algorithm is still acceptable and practical.

• Journal of the Korea Society for Simulation
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• v.32 no.1
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• pp.23-34
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• 2023

The purpose of this paper is to review global stochastic optimization algorithms(GSOA) in case binary response experimentation is used and to compare the performances of them. GSOAs utilise estimator of probability of success $\^p$ instead of population probability of success p, since p is unknown and only known by its estimator which has stochastic characteristics. Hill climbing algorithm algorithm, simple random search, random search with random restart, random optimization, simulated annealing and particle swarm algorithm as a population based algorithm are considered as global stochastic optimization algorithms. For the purpose of comparing the algorithms, two types of test functions(one is simple uni-modal the other is complex multi-modal) are proposed and Monte Carlo simulation study is done to measure the performances of the algorithms. All algorithms show similar performances for simple test function. Less greedy algorithms such as Random optimization with Random Restart and Simulated Annealing, Particle Swarm Optimization(PSO) based on population show much better performances for complex multi-modal function.

• Journal of Biomedical Engineering Research
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• v.39 no.2
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• pp.69-79
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• 2018

The present study proposes an algorithm that can discriminate between normal subjects and paroxysmal atrial fibrillation (PAF) patients, which is conducted using electrocardiogram (ECG) without PAF events. For this, time-domain features and random forest classifier are used. Time-domain features are obtained from Poincare plot, Lorenz plot of ${\delta}RR$ interval, and morphology analysis. Afterward, three features are selected in total through feature selection. PAF patients and normal subjects are classified using random forest. The classification result showed that sensitivity and specificity were 81.82% and 95.24% respectively, the positive predictive value and negative predictive value were 96.43% and 76.92% respectively, and accuracy was 87.04%. The proposed algorithm had an advantage in terms of the computation requirement compared to existing algorithm, so it has suggested applicability in the more efficient prediction of PAF.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.405-406
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• 2006

This paper proposes the new algorithm for the extraction of the corresponding points. Our algorithm is based on RANSAC(Random Sample Consensus) with EM(Expectation-Maximization). In the procedure of RANSAC, N-points are selected by the result of EM instead of the random selection. EM+SAC algorithm is applied to the correspondence for the mosaicing.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.41-48
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• 2013

An efficient encryption algorithm based on the Data Encryption Standard (DES) for personal cloud computing environments is presented. The proposed algorithm improves data privacy, security and also encryption speed, compared with the triple DES. The improvement of the proposed algorithm stems from enhanced privacy inherent from the use of spread random interleaver in the place of the known substitution table for initial and final permutations in the DES algorithm. The simulation results demonstrate that the interleaver based DES (I-DES) is found to run faster than the triple DES algorithm and also offer improved security. The proposed algorithm also offers encryption for variable-length data using the Cipher Block Chaining (CBC).