• Journal of Electrical Engineering and Technology
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• v.9 no.5
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• pp.1544-1550
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• 2014

In this paper, an adaptive optimization strategy utilizing Kriging model and genetic algorithm is proposed for the optimal design of electromagnetic devices. The ordinary Kriging assisted by the spherical covariance model is used to construct surrogate models. In order to improve the computational efficiency, the adaptive uniform sampling strategy is applied to generate sampling points in design space. Through several iterations and gradual refinement process, the global optimal point can be found by genetic algorithm. The proposed algorithm is validated by application to the optimal design of a switched reluctance motor, where the stator pole face and shape of pole shoe attached to the lateral face of the rotor pole are optimized to reduce the torque ripple.

• Journal of Environmental Health Sciences
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• v.37 no.1
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• pp.73-79
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• 2011

The growing interest in nanotechnology has resulted in increasing concern and a number of published environmental and workplace measurements for assessing occupational exposure to engineered nanomaterials. However, the amount of previous exposure data remains limited. Furthermore the data available was collected with extensive variation in terms of exposure measurement strategy, which limits the ability to pool the data in the future. In response, this paper reviewed several pertinent issues related to exposure measurement strategy to suggest a harmonized measurement strategy which would make exposure data more useful in the future, e.g. correlation between exposure metrics, relationship between activity and exposure, task-based or shift-based assessment, background concentration, limitation of personal exposure monitoring and other determinants of exposure/modeling. An improved sampling strategy for nanomaterial exposure assessment should be considered in order to maximize the use of the data from various real time monitoring instruments.

• Proceedings of the KIPE Conference
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• 2010.07a
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• pp.448-449
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• 2010

This paper proposes a sensorless control strategy for the Interior Permanent Magnet Synchronous Motor (IPMSM) by using the parallel reduced-order Extended Kalman Filter. The sensorless control strategy is composed with two EKFs alternately computed every sampling period with a new model. The new model is based on the extended electromotive force (EEMF) which has a simple structure, making position estimation possible without approximation. The proposed strategy can save computation time and estimate rotor speed and position. To verify the merit of the proposed strategy, simulation and experimental results validate the theoretical analysis and show the feasibility of the proposed control strategy.

• Communications for Statistical Applications and Methods
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• v.22 no.5
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• pp.445-462
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• 2015

This article deals with the problem of estimation of the population mean in presence of multi-auxiliary information in two occasion rotation sampling. A multivariate exponential ratio type estimator has been proposed to estimate population mean at current (second) occasion using information on p-additional auxiliary variates which are positively correlated to study variates. The theoretical properties of the proposed estimator are investigated along with the discussion of optimum replacement strategies. The worthiness of proposed estimator has been justified by comparing it to well-known recent estimators that exist in the literature of rotation sampling. Theoretical results are justified through empirical investigations and a detailed study has been done by taking different choices of the correlation coefficients. A simulation study has been conducted to show the practicability of the proposed estimator.

• Communications for Statistical Applications and Methods
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• v.28 no.6
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• pp.643-653
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• 2021

Few studies are found in literature on estimation of population quantiles using the method of ranked set sampling (RSS). The optimal RSS strategy is to select observations with at most two fixed rank order statistics from different ranked sets. In this paper, a near optimal unbalanced RSS model for estimating p^th(0 < p < 1) population quantile is proposed. Main advantage of this model is to use each rank order statistics and is distributionfree. The asymptotic relative efficiency (ARE) for balanced RSS, unbalanced optimal and proposed near-optimal methods are computed for different values of p. We also compared these AREs with respect to simple random sampling. The results show that proposed unbalanced RSS performs uniformly better than balanced RSS for all set sizes and is very close to the optimal RSS for large set sizes. For the practical utility, the near optimal unbalanced RSS is recommended for estimating the quantiles.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.414-422
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• 2022

Presented in this paper is a methodology for combining a Bayesian statistical approach with Data Quality Objectives (a structured decision-making method) to provide increased levels of confidence in analytical data when approaching a waste boundary. Development of sampling and analysis plans for the characterisation of radioactive waste often use a simple, one pass statistical approach as underpinning for the sampling schedule. Using a Bayesian statistical approach introduces the concept of Prior information giving an adaptive sample strategy based on previous knowledge. This aligns more closely with the iterative approach demanded of the most commonly used structured decision-making tool in this area (Data Quality Objectives) and the potential to provide a more fully underpinned justification than the more traditional statistical approach. The approach described has been developed in a UK regulatory context but is translated to a waste stream from the Fukushima Daiichi Nuclear Power Station to demonstrate how the methodology can be applied in this context to support decision making regarding the ultimate disposal option for radioactive waste in a more global context.

• Proceedings of the Korea Society of Environmental Biology Conference
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• 2002.05a
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• pp.71-71
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• 2002

• Korean Journal of Computational Design and Engineering
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• v.17 no.5
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• pp.375-386
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• 2012

Most engineering design problems require analyses or simulations to evaluate objective functions. However, a single simulation can take many hours or even days to finish for many real world problems. As a result, design optimization becomes impossible since they require hundreds or thousands of simulation evaluations. The surrogate-based optimization (SBO) strategy became a remedy for such computationally expensive analyses and simulations. A surrogate-based optimization strategy has been developed in this study in order to improve global optimization performance. The strategy is a heuristic algorithm and it exploits not only multiple surrogates, but also multiple optimizers. Multiple optimizations of multiple surrogate models yield multiple candidate design points of optima. During the sequential sampling process, the algorithm ranks candidate design points, selects the points as many as specified, and builds the improved surrogate model. Various mathematical functions with different numbers of design variables are chosen to compare the proposed method with the other most recent algorithm, MSEGO. The proposed method shows superior performance to the other method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.5
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• pp.897-903
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• 2010

This paper presents a response surface method(RSM) with Latin Hypercube Sampling strategy, which is employed to optimize a magnet pole shape of large scale BLDC motor to minimize the cogging torque. The proposed LHS algorithm consists of the multi-objective Pareto optimization and (1+1) evolution strategy. The algorithm is compared with the uniform sampling point method in view points of computing time and convergence. In order to verify the developed algorithm, a 6 MW BLDC motor is simulated with 4 design parameters (arc length and 3 variables for magnet) and 4 constraints for minimizing of the cogging torque. The optimization procedure has two stages; the fist is to optimize the arc length of the PM and the second is to optimize the magnet pole shape by using the proposed hybrid algorithm. At the 3rd iteration, an optimal point is obtained, and the cogging torque of the optimized shape is converged to about 14% of the initial one. It means that 3 iterations aregood enough to obtain the optimal design parameters in the program.

• Communications for Statistical Applications and Methods
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• v.11 no.2
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• pp.413-418
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• 2004

Statistically illiterate people seem to believe that there are some strategies for choosing winning numbers in lottery. One seemingly plausible strategy is to select the hot numbers which most frequently appeared in the past. In this article we investigate the existence of hot numbers in the Korean national lottery called Lotto. A numerical method is proposed to estimate the exact sampling distribution of test statistic for checking the existence of hot numbers among 45 possible numbers of choice.