• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3007-3009
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• 2000

In this paper, we design a Multi-Fuzzy model by means of clustering method and genetic algorithms for a nonlinear system. In order to determine structure of the proposed Multi-Fuzzy model. HCM clustering method is used. The parameters of membership function of the Multi-Fuzzy are identified by genetic algorithms. We use simplified inference and linear inference as inference method of the proposed Multi-Fuzzy model and the standard least square method for estimating consequence parameters of the Multi-Fuzzy. Finally, we use some of numerical data to evaluate the proposed Multi-Fuzzy model and discuss about the usefulness.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.10 no.2
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• pp.38-45
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• 2011

The criteria for determining the elements are the minimum zone method(MZM) and the least squares method(LSM). The LSM is deterministic and simple but is limited at the measurements whose errors are significant compared with form errors. For the precise condition, minimum zone method(MZM) has been selected to determine the elements. The roundness is the fundamental problem in the evaluating form errors. In this paper, anew approach adapting the genius education concept is proposed to obtain an accurate results for the MZM and the LSM of the roundness. Its computational algorithm is studied on a set of measured sample data. To be of almost no account of the specification(the number and the standard deviation etc.) of the sanple data, the results shoqs excellent reliability and high accuracy in estimating the roundness.

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.3
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• pp.374-380
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• 2015

As semiconductor devices are being scaled down, random variation becomes a critical issue, especially in the case of static random access memory (SRAM). Thus, there is an urgent need for statistical methodologies to analyze the impact of random variations on the SRAM. In this paper, we propose a novel sampling method based on the concept of a confidence ellipse. Results show that the proposed method estimates the SRAM margin metrics in high-sigma regimes more efficiently than the standard Monte Carlo (MC) method.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2669-2671
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• 2001

In this paper, we propose design a Multi-Fuzzy Inference model structure. In order to determine structure of the proposed Multi-Fuzzy Inference model, HCM clustering method is used. The parameters of membership function of the Multi-Fuzzy are identified by genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. We use simplified inference and linear inference as inference method of the proposed Multi-Fuzzy model and the standard least square method for estimating consequence parameters of the Multi-Fuzzy. Finally, we use some of numerical data to evaluate the proposed Multi-Fuzzy model and discuss about the usefulness.

• Smart Structures and Systems
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• v.21 no.3
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• pp.335-347
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• 2018

The fast-growing number of mobile and wearable applications has driven several innovations in small-scale electret-based energy harvesting due to the compatibility with standard microfabrication processes and the ability to generate electrical energy from ambient vibrations. However, the current modeling methods used to design these small scale transducers or microgenerators are applicable only for constant-speed rotations and small sinusoidal translations, while in practice, large amplitude sinusoidal vibrations can happen. Therefore, in this paper, we formulate an analytical model for electret-based microgenerators under general sinusoidal excitations. The proposed model is validated using finite element modeling combined with numerical simulation approaches presented in the literature. The new model demonstrates a good agreement in estimating both the output voltage and power of the microgenerator. This new model provides useful insights into the microgenerator operating mechanism and design trade-offs, and therefore, can be utilized in the design and performance optimization of these small structures.

• International Journal of Automotive Technology
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• v.6 no.5
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• pp.475-481
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• 2005

This paper deals with the algorithm of estimating the longitudinal speed of a braking vehicle using measurements from an accelerometer and a standard wheel speed sensor. We evolve speed estimation algorithms of increasing complexity and accuracy on the basis of experimental tests. A final speed estimation algorithm based on a Kalman filtering is developed to reduce measurement noise of the wheel speed sensor, error of the tire radius, and accelerometer bias. This developed algorithm can give peak errors of less than 3 percent even when the accelerometer signal is significantly biased.

• Journal of the Korean Institute of Landscape Architecture
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• v.21 no.4
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• pp.69-82
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• 1994

The purpose of this study is to estimate the probable period of the planting work in consideration of weather factors. The impact degree of weather factors on the control of planting schedule was measured by the possible working days on the basis of weather condition. To establish the weather standard, the researcher analyzed the questionnaires on the manager of planting work and also the meteorological data for 10 years(1983-1992) in Seoul. The results are as follows; $\circled1$ The possible period of the planting work is from March 17 to May 18 Spring and from September 26 to December 15 in Autumn during a year. $\circled2$ The problem working days of the planting work(106-130) days per year) are less than the building construction days(174 days per year), because of handling the living material of plants, specially in summer and winter.

• Journal of the Korea institute for structural maintenance and inspection
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• v.6 no.1
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• pp.255-262
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• 2002

This paper presents a statistical analysis of empirical fragility curves for bridge. The empirical fragility curves are developed utilizing bridge damage data obtained from the 1995 Hyogoken Nanbu(Kobe) earthquake. Two-parameter lognormal distribution functions are used to represent the fragility curves with the parameters estimated by the maximum likelihood method. This paper also presents methods of testing the goodness of fit of the fragility curves and estimating the confidence intervals of the two parameters(median and log-standard deviation) of the distribution. An analytical interpretation of randomness and uncertainty associated with the median is provided.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.957-966
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• 1999

We evaluate the accuacy and precision of maximum likelihood estimation procedures for infectivity of HIV in partner studies. This is achieved by applying the oricedyre typothetical samples generated by computer. One hundred samples were generated with various combinations of parameters. The estimation procedure was found to be quite accurate. in addition it was found that the power of the test for equality of infectivities for two types of contact depends on sample size and length of observation period but not on the number of observations made on each subject. Tests based on a model for the infectivity had higher power than standard methods for comparing proportions.

• Journal of Korean Society for Quality Management
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• v.30 no.4
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• pp.68-77
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• 2002

EM algorithm has good convergence rate for numerical procedures which converges on very small step. In the case of proportion estimation in a mixed distribution which has very big incomplete data or of update of new data continuously, however, EM algorithm highly depends on a initial value with slow convergence ratio. There have been many studies to improve the convergence rate of EM algorithm in estimating the proportion parameter of a mixed data. Among them, dynamic EM algorithm by Hurray Jorgensen and Titterington algorithm by D. M. Titterington are proven to have better convergence rate than the standard EM algorithm, when a new data is continuously updated. In this paper we suggest dynamic EM algorithm and Titterington algorithm for the estimation of a mixed Poisson distribution and compare them in terms of convergence rate by using a simulation method.