• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.4
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• pp.84-90
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• 2011

In active noise control filter, LMS algorithms which used for control filter, assure the convergence property, and computational burden of these algorithms are proportionate to the filter taps. The convergence speed of LMS algorithms is mainly determined by value of the convergence coefficient, so optimal selection of the value of convergence coefficient is very important. In this paper, We proposed novel adaptive fuzzy logic LMS algorithms with FIR filter structure which has better convergence speed and less computational burden than conventional LMS algorithms, for single channel active noise control with ill conditioned signal case. Computer simulations were performed to show the effectiveness of a proposed algorithms.

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.4
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• pp.67-80
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• 2000

Symbiotic evolutionary algorithms, also called cooperative coevolutionary algorithms, are stochastic search algorithms that imitate the biological coevolution process through symbiotic interactions. In the algorithms, the fitness evaluation of an individual required first selecting symbiotic partners of the individual. Several partner selection strategies are provided. The goal of this study is to analyze how much partnering strategies can influence the performance of the algorithms. With two types of test-bed problems: the NKC model and the binary string covering problem, extensive experiments are carried out to compare the performance of partnering strategies, using the analysis of variance. The experimental results indicate that there does not exist statistically significant difference in their performance.

• Journal of Ocean Engineering and Technology
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• v.21 no.2 s.75
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• pp.75-80
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• 2007

The design variables for structural systems, in most practical designs, are chosen from a list of discrete values, which are commercially available sizing. This paper presents the application of Genetic Algorithms for determining the optimum design for two-dimensional structures with discrete and pseudocontinuous design variables. Genetic Algorithms are heuristic search algorithms and are effective tools for finding global solutions for discrete optimization. In this paper, Genetic Algorithms are used as the method of Elitism and penalty parameters, in order to improve fitness in the reproduction process. Examples in this paper include: 10 bar planar truss and 1 bay 8-story frame. Truss with discrete and pseudoucontinuous design variables and steel frame with W-sections are used for the design of discrete optimization.

• The KIPS Transactions:PartC
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• v.12C no.4 s.100
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• pp.525-534
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• 2005

We present several distributed algorithms for localizing nodes of a wireless sensor network. Our algorithms determine locations of nodes based on the connectivity between nodes. The basic idea behind our algorithms is to estimate distances between nearby nodes by counting their common neighbors. We analyze the performance of our algorithms experimentally. The results of experiments show that our algorithms achieve performance improvements upon the existing algorithms

• International Journal of Contents
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• v.11 no.4
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• pp.1-6
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• 2015

In practice, the most important performance parameters for music information retrieval (MIR) service are robustness of fingerprint in real noise environments and recognition accuracy when the obtained query clips are matched with the an entry in the database. To satisfy these conditions, we proposed a conjoined fingerprint algorithm for use in massive MIR service. The conjoined fingerprint scheme uses interhash and intrahash algorithms to produce a robust fingerprint scheme in real noise environments. Because the interhash and intrahash algorithms are masked in the predominant pitch estimation, a compact fingerprint can be produced through their relationship. Experimental performance comparison results showed that our algorithms were superior to existing algorithms, i.e., the sub-mask and Philips algorithms, in real noise environments.

• Journal of Korean Academic Society of Home Health Care Nursing
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• v.4
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• pp.65-75
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• 1997

The needs for the home care of postpartum mothers and their infants are increasing, but the quality control of home care nurses is not developed yet. The objective of this study is to develop assessment - intervention algorithms for the home care of postpartum mothers and their infants. We can use these algorithms when we assess the client's condition, and find appropriate nursing interventions. Also, these algorithms can offer guidelines for home care nurses, so that standardization of home care can be attained. Common problems for postpartum mothers are postpartum hemorrhage, abnormal vaginal discharge(endometritis), episiotomy pain, breast problems, breastfeeding difficulty, edema, urinary dysfunction and defecation difficulties. Also, commom problems for infants are abnormal body temperature, tarchycardia, respiratory problem, neonatal jaundice, cord problem, abnormal stool, breast feeding, and bathing. These algorithms can be used as a basis for the development of computerized infomation system for the home health care.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.21-28
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• 2005

This paper proposes a new method for accelerating the search speed of genetic algorithms by taking derivative evaluation and conditional random selection into account in their evolution process. Derivative evaluation makes genetic algorithms focus on the individuals whose fitness is rapidly increased. This accelerates the search speed of genetic algorithms by enhancing exploitation like steepest descent methods but also increases the possibility of a premature convergence that means most individuals after a few generations approach to local optima. On the other hand, derivative evaluation under a premature convergence helps genetic algorithms escape the local optima by enhancing exploration. If GAs fall into a premature convergence, random selection is used in order to help escaping local optimum, but its effects are not large. We experimented our method with one combinatorial problem and five complex function optimization problems. Experimental results showed that our method was superior to the simple genetic algorithm especially when the search space is large.

• Journal of Ocean Engineering and Technology
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• v.16 no.4
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• pp.25-31
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• 2002

This paper is to find optimum design of plane framed structures with discrete variables. Global search algorithms for this problem are Genetic Algorithms(GAs), Simulated Annealing(SA) and Shuffled Complex Evolution(SCE), and hybrid methods (GAs-SA, GAs-SCE). GAs and SA are heuristic search algorithms and effective tools which is finding global solution for discrete optimization. In particular, GAs is known as the search method to find global optimum or near global optimum. In this paper, reinforced concrete plane frames with rectangular section and steel plane frames with W-sections are used for the design of discrete optimization. These structures are designed for stress constraints. The robust and effectiveness of Genetic Algorithms are demonstrated through several examples.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.769-780
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• 2005

Recently, the discretization algorithms for continuous data have been actively studied. But there are few articles to compare the efficiency of these algorithms. In this paper we introduce the principles of some binary discretization algorithms including C4.5, CART and QUEST and investigate the efficiency of these algorithms through numerical study. For various underlying distribution, we compare these algorithms in view of misclassification rate and MSE. Real data examples are also included.

• Journal of Korean Society for Quality Management
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• v.17 no.1
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• pp.35-47
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• 1989

This paper is concerned with a development and evaluation of heuristic algorithms for the n-job, M-stage flowshop with sequence dependent setup times. Three heuristic algorithms, CAIDAN, DANNEN and PETROV, are proposed. The makespan is taken as a performance measure for the algorithms. The experiment for each algorithm is designed for a $4{\times}3{\times}3$ factorial design with 360 observations. The experimental factors are PS (ratio of processing times to setup times), M (number of machines), and N (number of jobs). The makespan of the proposed heuristic algorithms is compared with the optimal makespan obtained by the complete enumeration method. The result of comparision of performance measure is called a relative error. The mean relative errors of CAIDAN, DANNEN and PETROV algorithms are 4.488%. 6.712% and 7.282%, respectively. The computational results are analysed using SPSS. The experimental results show that the three factors are statistically signiticant at 5% level.