With the extensive application of active magnetic bearings (AMBs), the robust multiobjective optimization of the structure seems to be a priority. However, it is a challenge due to the high dimension and huge computat...
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With the extensive application of active magnetic bearings (AMBs), the robust multiobjective optimization of the structure seems to be a priority. However, it is a challenge due to the high dimension and huge computational cost of finite-element analysis. In this article, a robust multi-objective optimization method is proposed to pursue good performance for a three-pole AMB. To increase the efficiency of the optimization process, the Kendall correlation coefficient is applied to assist in determining the sensitivity. The parameters are divided into three layers, and a three-level multiobjective optimization structure is established. Meanwhile, Kriging model is employed to improve the optimization efficiency. The selection of the final solution in Pareto curves is always an issue. The proposed optimization structure can only ensure the performance of the AMB, rather than robustness. Thus, a robust solution selection method is proposed based on the climbing algorithm. The robustness can be easily shown in the Pareto curve obtained through the optimization structure. The final solution is selected with good robustness in terms of suspension force and force ripple. The experimental results based on a prototype are provided to verify the effectiveness of the proposed optimization method.
In real-time imaging applications, the auto-focus algorithm and hardware structure must be adapted to the limited resources for fast and accurate real-time auto-focusing. In this paper, to obtain more precision, a new...
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ISBN:
(纸本)9780769548968
In real-time imaging applications, the auto-focus algorithm and hardware structure must be adapted to the limited resources for fast and accurate real-time auto-focusing. In this paper, to obtain more precision, a new clarity evaluation function base on image's edge energy is proposed. Then, we present the corresponding FPGA hardware implementation which has simple structure. Additionally, utilizing the prior knowledge during the climbing process, a new climbing algorithm for the auto-focus which has the random start point is proposed. The experiment results show the proposed auto-focus algorithm can reduce the steps during the climbing and accelerate the auto-focus speed simultaneously.
The paper presents a passive auto-focusing technology by combining the discrete cosine transform(DCT) algorithm and small univalve segment assimilating nucleus(SUSAN) algorithm with evaluation function. The new method...
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The paper presents a passive auto-focusing technology by combining the discrete cosine transform(DCT) algorithm and small univalve segment assimilating nucleus(SUSAN) algorithm with evaluation function. The new method has the advantage of unimodal, local extremum and sensitivity of the focus curve. For the focusing search algorithm, the improved mountain climbing method which combined the large step and small step has been proposed, it makes the micro-vision system could find the focal plane more quickly and accurately. (C) 2017 Elsevier GmbH. All rights reserved.
Wind energy has been regarded as an environmentally friendly, logistically feasible and economically responsible alternative energy resource. In order to produce as much power as possible in variable speed wind turbin...
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ISBN:
(纸本)9789075815153
Wind energy has been regarded as an environmentally friendly, logistically feasible and economically responsible alternative energy resource. In order to produce as much power as possible in variable speed wind turbine generators, the maximum power point tracking (MPPT) becomes a hotspot of research in this field. In this paper, a simple control method for maximum power point tracking (MPPT) in a variable speed wind turbine by using a boost converter without mechanical sensors is proposed. A climbing algorithm is used to achieve MPPT. Simulation and experimental results are presented to validate the proposed technique.
In order to adapt to changes in the distributed database system, the need for appropriate data redistribution strategies to reduce the cost of data redistribution. Given Two dynamic data redistribution algorithm: part...
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In order to adapt to changes in the distributed database system, the need for appropriate data redistribution strategies to reduce the cost of data redistribution. Given Two dynamic data redistribution algorithm: part of the redistribution (Partial Reallocate) and full redistribution (Full Reallocate) algorithm. The two algorithms with With linear complexity, can be used in a variety of different sizes distributed database system. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CEIS 2011]
In order to adapt to changes in the distributed database system, the need for appropriate data redistribution strategies to reduce the cost of data redistribution. Given Two dynamic data redistribution algorithm: part...
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In order to adapt to changes in the distributed database system, the need for appropriate data redistribution strategies to reduce the cost of data redistribution. Given Two dynamic data redistribution algorithm: part of the redistribution (Partial Reallocate) and full redistribution (Full Reallocate) algorithm. The two algorithms with With linear complexity, can be used in a variety of different sizes distributed database system.
In this article, no probability density and no prior probability are assumed for the good and defective classes, except possibly two means. A sequence of unidentified input lifetimes is used to estimate the percentage...
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In this article, no probability density and no prior probability are assumed for the good and defective classes, except possibly two means. A sequence of unidentified input lifetimes is used to estimate the percentages of two classes. After the classifier system is put into use, the new coming unidentified input lifetime can improve the percentages. A Monte Carlo simulation study with exponential or Weibull or gamma distribution on life is presented to demonstrate the classification. The study can be widely used in quality control.
An orthogonal hill-climbing algorithm for dynamic optimization problems with continuous variables (labeled ODHC is proposed in present paper. The local peak climber is not a solution x, but rather a "niche",...
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ISBN:
(纸本)9780780394872
An orthogonal hill-climbing algorithm for dynamic optimization problems with continuous variables (labeled ODHC is proposed in present paper. The local peak climber is not a solution x, but rather a "niche", a small hyperrectangle. An orthogonal design method is employed on the niches for the niche to climb a potentially peak fast. An archive is used to store the latest found higher peaks for the ODHC algorithm learning from the past search. The randomly creating niches implement the global search. Numerical experiments show that the ODHC algorithm performs a lot better than the SOS (Self Organizing Scouts) algorithm [1].
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