• Journal of KIISE:Software and Applications
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• v.36 no.3
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• pp.208-216
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• 2009

A number of estimation of distribution algorithms have been proposed that do not use explicitly crossover and mutation of traditional genetic algorithms, but estimate the distribution of population for more efficient search. But because it is not easy to discover higher-order correlations of variables, lower-order correlations are estimated most cases under various constraints. In this paper, we propose a new estimation of distribution algorithm that represents higher-order correlations of the data and finds global optimum more efficiently. The proposed algorithm represents the higher-order correlations among variables by building random hypergraph model composed of hyperedges consisting of variables which are expected to be correlated, and generates the next population by Bayesian sampling algorithm Experimental results show that the proposed algorithm can find global optimum and outperforms the simple genetic algorithm and BOA(Bayesian Optimization Algorithm) on decomposable functions with deceptive building blocks.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.5
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• pp.3434-3439
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• 2015

As a criterion of information theoretic learning, the Euclidean distance (ED) of two error probability distribution functions (minimum ED of error, MEDE) has been adopted in nonlinear (decision feedback, DF) supervised equalizer algorithms and has shown significantly improved performance in severe channel distortion and impulsive noise environments. However, the MEDE-DF algorithm has the problem of heavy computational complexity. In this paper, the recursive ED for MEDE-DF algorithm is derived first, and then the feed-forward and feedback section gradients for weight update are estimated recursively. To prove the effectiveness of the recursive gradient estimation for the MEDE-DF algorithm, the number of multiplications are compared and MSE performance in impulsive noise and underwater communication environments is compared through computer simulation. The ratio of the number of multiplications between the proposed DF and the conventional MEDE-DF algorithm is revealed to be $2(9N+4):2(3N^2+3N)$ for the sample size N with the same MSE learning performance in the impulsive noise and underwater channel environment.

• Journal of Communications and Networks
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• v.5 no.2
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• pp.157-166
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• 2003

This paper deals with the problem of channel identification for Single Input Multiple Output (SIMO) slow fading channels using clustering algorithms. Due to the intrinsic memory of the discrete-time model of the channel, over short observation periods, the received data vectors of the SIMO model are spread in clusters because of the AWGN noise. Each cluster is practically centered around the ideal channel output labels without noise and the noisy received vectors are distributed according to a multivariate Gaussian distribution. Starting from the Markov SIMO channel model, simultaneous maximum ikelihood estimation of the input vector and the channel coefficients reduce to one of obtaining the values of this pair that minimizes the sum of the Euclidean norms between the received and the estimated output vectors. Viterbi algorithm can be used for this purpose provided the trellis diagram of the Markov model can be labeled with the noiseless channel outputs. The problem of identification of the ideal channel outputs, which is the focus of this paper, is then equivalent to designing a Vector Quantizer (VQ) from a training set corresponding to the observed noisy channel outputs. The Linde-Buzo-Gray (LBG)-type clustering algorithms [1] could be used to obtain the noiseless channel output labels from the noisy received vectors. One problem with the use of such algorithms for blind time-varying channel identification is the codebook initialization. This paper looks at two critical issues with regards to the use of VQ for channel identification. The first has to deal with the applicability of this technique in general; we present theoretical results for the conditions under which the technique may be applicable. The second aims at overcoming the codebook initialization problem by proposing a novel approach which attempts to make the first phase of the channel estimation faster than the classical codebook initialization methods. Sample simulation results are provided confirming the effectiveness of the proposed initialization technique.

• The Transactions of the Korea Information Processing Society
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• v.1 no.1
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• pp.14-22
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• 1994

This paper proposes two Bayes estimators and their evaluation algorithms of the software reliability at the end testing stage in the Smith's Bayesian software reliability growth model under the data prior distribution BE(a, b), which is more general than uniform distribution, as a class of prior information. We consider both a squared-error loss function and the Harris loss function in the Bayesian estimation procedures. We also compare the MSE performances of the Bayes estimators and their algorithms of software reliability using computer simulations. And we conclude that the Bayes estimator of software reliability under the Harris loss function is more efficient than other estimators in terms of the MSE performances as a is larger and b is smaller, and that the Bayes estimators using the beta prior distribution as a conjugate prior is better than the Bayes estimators under the uniform prior distribution as a noninformative prior when a＞b.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1635-1638
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• 1997

A measurement of conduction veloctiy of the action potentials along the muscle fibres has been applied to various diagnosis. When we measure muscle fiber conduction velocity, it occurs that not only change of conduction velocity but alos inclusion of mipulse component by physiological and experimental reason. So, robuster time delay estimation algorithm than general methods[1] is needed to find correct time delay form these signals. In this paper we, propose new time delay estimation algorithms, robust in impulsive noise, by using characteristic of .alpha.-stable distribution whcih defines impulsive noise well. Then we apply proposed algorthms to measure muscle fiber conduction velocity and compare them with other studies.

• Journal of KIISE:Databases
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• v.34 no.2
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• pp.141-152
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• 2007

Selectivity estimation has been a crucial component in query optimization in relational databases. While extensive researches have been done on this topic for the predicates of numerical data, only little work has been done for substring predicates. We propose novel suffix tree transform algorithms for this problem. Unlike previous approaches where a full suffix tree is pruned and then an estimation algorithm is employed, we transform a suffix tree into a suffix graph systematically. In our approach, nodes with similar counts are merged while structural information in the original suffix tree is preserved in a controlled manner. We present both an error-bound algorithm and a space-bound algorithm. Experimental results with real life data sets show that our algorithms have lower average relative error than that of the previous works as well as good error distribution characteristics.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.9
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• pp.1499-1507
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• 2003

Physical importance of cutting temperatures has long been recognized. Cutting temperatures have strongly influenced both the tool life and the metallurgical state of machined surfaces. Temperatures in drilling processes are particularly important, because chips remain in contact with the tool for a relatively long time in a hole. Tool temperatures tend to be higher in drilling processes than in other in machining processes. This paper concerns with modeling of thermal behaviors in drilling processes as well as estimation of the cutting temperature distribution based on remote temperature measurements. One- and two-dimensional estimation problems are proposed to analyze drilling temperatures. The proposed thermal models are compared with solutions of finite element methods. Observer algorithms are developed to solve inverse heat conduction problems. In order to apply the estimation of cutting temperatures, approximation methods are proposed by using the solution of the finite element method. In two-dimensional analysis, a moving heat source according to feedrate of the drilling process is regarded as a fixed heat source with respect to the drilling location. Simulation results confirm the application of the proposed methods.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.28 no.6
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• pp.705-712
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• 2004

An inverse boundary analysis of surface radiation in an axisymmetric cylindrical enclosure has been conducted in this study. Net energy exchange method was used to calculate the radiative heat flux on each surface, and a hybrid genetic algorithm was adopted to minimize an objective function, which is expressed by sum of square errors between estimated and measured or desired heat fluxes on the design surface. We have examined the effects of the measurement error as well as the number of measurement points on the estimation accuracy. Furthermore, the effect of a variation in one boundary condition on the other boundary conditions was also investigated to get the same desired heat flux and temperature distribution on the design surface.

• Korean Management Science Review
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• v.34 no.1
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• pp.1-13
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• 2017

The percentage of 3PL (Third-party Logistics), which uses third party businesses to outsource elements of the company's distribution and fulfillment services, is increasing steadily. To provide 3PL service to the customers, it is needed to estimate the total transportation cost and propose the unit cost to the customers. In this paper, we develop a decision support system for estimation of transportation cost of 3PL provider considering various transportation services, such as direct transportation, multi point visiting transportation, and cross docking. The system supports route planning of vehicles by using algorithms based on tabu search and dynamic programming.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.1
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• pp.64-72
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• 2016

The coverage area of a GNSS regional ionospheric delay model is mainly determined by the distribution of GNSS ground monitoring stations. Extrapolation of the ionospheric model data can extend the coverage area. An extrapolation algorithm, which combines observed ionospheric delay with the environmental parameters, is proposed. Neural network and least square regression algorithms are developed to utilize the combined input data. The bi-harmonic spline method is also tested for comparison. The IGS ionosphere map data is used to simulate the delays and to compute the extrapolation error statistics. The neural network method outperforms the other methods and demonstrates a high extrapolation accuracy. In order to determine the directional characteristics, the estimation error is classified into four direction components. The South extrapolation area yields the largest estimation error followed by North area, which yields the second-largest error.