• ETRI Journal
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• v.14 no.3
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• pp.67-74
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• 1992

Discrete variable을 갖는 Mean Field Theory(MFT) neural network은 이미 많은 combinatorial optimization 문제에 적용되어져 왔다. 본 논문에서는 이를 확장하여 continuous variable을 갖는 mean field annealing을 제안하고, 이러한 network에서 integral로 표현되는 spin average를 mean field에 기초하여 어렵지 않게 구할 수 있는 one-variable stochastic simulated annealing을 제안하였다. 이런 방법으로 multi-body problem을 single-body problem으로 바꿀 수 있었다. 또한 이 방법을 이용한 응용으로서 통계학에서 잘 알려진 문제중의 하나인 quantification analysis 문제에 적용하여 타당성을 보였다.

• The Transactions of the Korea Information Processing Society
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• v.2 no.3
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• pp.417-424
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• 1995

This paper describes MFT(Mean Field Theory) neural network with continuous with continuous variables is applied to quantification analysis problem. A quantification analysis problem, one of the important problems in statistics, is NP complete and arises in the optimal location of objects in the design space according to the given similarities only. This paper presents a MFT neural network with continuous variables for the quantification problem. Starting with reformulation of the quantification problem to the penalty problem, this paper propose a "one-variable stochastic simulated annealing(one-variable SSA)" based on the mean field approximation. This makes it possible to evaluate of the spin average faster than real value calculating in the MFT neural network with continuous variables. Consequently, some experimental results show the feasibility of this approach to overcome the difficulties to evaluate the spin average value expressed by the integral in such models.ch models.

• Journal of the Korea Convergence Society
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• v.8 no.11
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• pp.103-108
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• 2017

In the ubiquitous environment, there has been a problem of transmitting data from various sensors at a long distance. Especially, in the process of integrating data arriving at different locations, data having different property values of data or having some loss in data had to be processed. This paper present a method to analyze such data. The core of this method is to define an objective function suitable for the problem and to develop an algorithm that can optimize this objective function. The objective function is used by modifying the OCS function. MFA (Mean Field Annealing), which was able to process only binary data, is extended to be applicable to fields with continuous values. It is called CMFA and used as an optimization algorithm.

• Journal of Korean Society for Atmospheric Environment
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• v.12 no.4
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• pp.459-472
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• 1996

An experimental investigation on the wind flow over smooth bell-shaped two-dimensional hills with hill slopes (the ratio of height to half width) of 0.3 and 0.5 is performed in an atmospheric boundary-layer wind tunnel. Two categories of the models are used in the present investigation; six two-dimensional single-hills, and four continuous double-hills. The measurements of the flow field and surface static-pressure distribution are carried out over the Reynolds number (based on the hill height) of 1.9 $\times 10^4, 3.3 \times 10^4, and 5.6 \times 10^4$. The velocity profiles and turbulence characteristics are measured by the pitot-tube and X-type hot-wire anemometer, respectively. The undisturbed boundary-layer profile on the bottom surface of the wind tunnel is reasonably consistent with the power-law profile with $\alpha = 7.0 (1/\alpha$ is the power-law exponent) and shows good spanwise uniformities. The profiles of turbulent intensity are found to be consistent along the centerline of the wind tunnel. The measured non-dimensional speed-up profiles at the hill crest show good agreements with the predictions of Jackson and Hunt's linear theory. The flow separation occurs in the hill slope of 0.5, and the oil-ink dot method is used to find the reattachment points in the leeside of the hill. The measured reattachment points are compared with the numerical predictions. Comparisons of the mean velocity profiles and surface pressure distributions between the numerical predictions and the experimental results show good agreements.