• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.1
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• pp.206-211
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• 2006

The quantification analysis problem is that how the m entities that have n characteristics can be linked to p-dimension space to reflect the similarity of each entity In this paper, the optimization approach for the quantification analysis problem using neural networks is suggested, and the performance is analyzed The computation of average variation volume by mean field theory that is analytical approximated mobility of a molecule system and the annealed mean field neural network approach are applied in this paper for solving the quantification analysis problem. As a result, the suggested approach by a mean field annealing neural network can obtain more optimal solution than the eigen value analysis approach in processing costs.

• 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.

• Composites Research
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• v.36 no.5
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• pp.329-334
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• 2023

The classical multiscale finite element (FE² ) method involves iterative calculations of micro-boundary value problems for representative volume elements at every integration point in macro scale, making it a computationally time and data storage space. To overcome this, we developed the data-driven multiscale analysis method based on the mean-field homogenization (MFH). Data-driven computational mechanics (DDCM) analysis is a model-free approach that directly utilizes strain-stress datasets. For performing multiscale analysis, we efficiently construct a strain-stress database for the microstructure of composite materials using mean-field homogenization and conduct data-driven computational mechanics simulations based on this database. In this paper, we apply the developed multiscale analysis framework to an example, confirming the results of data-driven computational mechanics simulations considering the microstructure of a hyperelastic composite material. Therefore, the application of data-driven computational mechanics approach in multiscale analysis can be applied to various materials and structures, opening up new possibilities for multiscale analysis research and applications.

• Journal of Magnetics
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• v.19 no.4
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• pp.315-322
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• 2014

In this work, Artificial Neural Network (ANN) was used to model the dynamic behavior of ferromagnetic hysteresis derived from performing the mean-field analysis on the Ising model. The effect of field parameters and system structure (via coordination number) on dynamic critical points was elucidated. The Ising magnetization equation was drawn from mean-field picture where the steady hysteresis loops were extracted, and series of the dynamic critical points for constructing dynamic phase-diagram were depicted. From the dynamic critical points, the field parameters and the coordination number were treated as inputs whereas the dynamic critical temperature was considered as the output of the ANN. The input-output datasets were divided into training, validating and testing datasets. The number of neurons in hidden layer was varied in structuring ANN network with highest accuracy. The network was then used to predict dynamic critical points of the untrained input. The predicted and the targeted outputs were found to match well over an extensive range even for systems with different structures and field parameters. This therefore confirms the ANN capabilities and indicates the ANN ability in modeling the ferromagnetic dynamic hysteresis behavior for establishing the dynamic-phase-diagram.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3630-3656
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• 2018

Degree distribution can provide basic information for structural characteristics and internal relationship in social network. It is a critical procedure for social network topology analysis. In this paper, based on the mean-field theory, we study a special type of social network with exponential distribution of time intervals. First of all, in order to improve the accuracy of analysis, we propose a spreading coefficient algorithm based on intimate relationship, which determines the number of the joined members through the intimacy among members. Then, simulation show that the degree distribution of follows the power-law distribution and has small-world characteristics. Finally, we compare the performance of our algorithm with the existing algorithms, and find that our algorithm improves the accuracy of degree distribution as well as reducing the time complexity significantly, which can complete 29.04% higher precision and 40.94% lower implementation time.

• Proceedings of the KIEE Conference
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• 1997.07e
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• pp.1824-1826
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• 1997

In this paper, in order to obtain statistical informations on lightning electromagnetic waveforms, electric field waveforms produced by lightning return strokes were measured and analyzed. The electric field measuring system consists of hemisphere antenna 30[cm] in diameter, integrator and data acquisition. system. The frequency bandwidth of the measuring system is 200[Hz] to 1.56[MHz], and the sensitivity is 0.96[mV/V/m]. The mean value of front time of electric field waveforms produced by positive lightning return strokes is 5.87[${\mu}s$], and that of negative is 4.12[${\mu}s$]. The mean values of zero-crossing time for positive or negative electric field waveforms are 35.00 and 26.61[${\mu}s$], respectively. The mean value of percentage dip-depth for positive electric field waveforms is 33.68[%], and that for negative is 28.36[%].

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.1
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• pp.185-192
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• 1997

We propose a method of paramete estimation using order-of-magnitude analysis for optimal boundary smoothing in Mean Field Annealing(MFA) technique in this paper. We previously proposed two boundary smoothing methods for consistent object representation in the previous paper, one is using a constratined regulaization(CR) method and the other is using a MFA method. The CR method causes unnecessary smoothing effects at corners. On the other hand, the MFA method method smooths our the noise without losing sharpness of corners. The MFA algorithm is influenced by several parameters such as standard deviation of the noise, the relativemagnitude of prior ter, initial temperature and final temperature. We propose a general parameter esimation method for optimal boundary smoothing using order-of-magnitude analysis to be used for consistent object representation in this paper. In addition, we prove the effectiveness of our parameter estimation and also show the temperature parameter sensitivities of the algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.2
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• pp.308-313
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• 2009

This paper presents a reliability analysis of S-bonds for AF track circuits, which detect train movement and transmit a speed control signal to the train. Field survey shows that S-bonds are exposed to very large vibrations transferred from rail, and suffer from frequent failures when they were installed on ballasted track. We collected the time-to-failure data of S-bonds from the maintenance field of Seoul metro line 2, and made a parametric approach to estimate the statistical distribution that fits the time-to-failure data. The analysis shows that S-bonds have time-to-failure characteristics described by Weibull distribution. The estimated shape parameter of Weibull distribution is 1.1, which means the distribution has constant failure rate characteristics like exponential distribution. The reliability function, hazard function, percentiles and mean lifetime are derived for maintenance support.

• Journal of IKEEE
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• v.2 no.1 s.2
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• pp.160-165
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• 1998

This paper describes a method for field analysis inside the flat-type brushless DC motor using 2-D field simulator. Rigorous field analysis entail 3-D analysis. However, this analysis is not often appropriate for system designs because of the time and cost involved. For field analysis in this study, the 3-D problem is reduced to a 2-D boundary value problem by introducing a cylindrical cutting plane at the mean radius of the magnets. Independent of sizes and shapes of systems, the exact 2-D field results can be obtained with reasonable predictability.