Voronoi diagram and Monte-Carlo simulation based finite element optimization for cost-effective 3D printing

Zheng, Anzong; Bian, Shaojun; Chaudhry, Ehtzaz; Chang, Jian; Haron, Habibollah; You, Lihua; Zhang, Jian Jun

doi:10.1016/j.jocs.2021.101301

Cited by 8 publications

(2 citation statements)

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“…In recent years, several works have been published presenting novel methods in diverse fields such as computer graphics, pattern recognition, or robotics. For instance, in [ 51 ], a method to achieve cost-effective 3-D printing of stiffened thin-shell objects is proposed. For that, they use the finite element analysis to determine the regions of the object with high stress and use a given number of seeds to create a Voronoi diagram to distribute these seeds in the areas with higher stress.…”

Section: Related Workmentioning

confidence: 99%

Toward Accelerated Training of Parallel Support Vector Machines Based on Voronoi Diagrams

Alfaro

Gómez

Moguerza

et al. 2021

Entropy

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Typical applications of wireless sensor networks (WSN), such as in Industry 4.0 and smart cities, involves acquiring and processing large amounts of data in federated systems. Important challenges arise for machine learning algorithms in this scenario, such as reducing energy consumption and minimizing data exchange between devices in different zones. This paper introduces a novel method for accelerated training of parallel Support Vector Machines (pSVMs), based on ensembles, tailored to these kinds of problems. To achieve this, the training set is split into several Voronoi regions. These regions are small enough to permit faster parallel training of SVMs, reducing computational payload. Results from experiments comparing the proposed method with a single SVM and a standard ensemble of SVMs demonstrate that this approach can provide comparable performance while limiting the number of regions required to solve classification tasks. These advantages facilitate the development of energy-efficient policies in WSN.

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Section: Related Workmentioning

confidence: 99%

Toward Accelerated Training of Parallel Support Vector Machines Based on Voronoi Diagrams

Alfaro

Gómez

Moguerza

et al. 2021

Entropy

View full text Add to dashboard Cite

show abstract

“…Their design, however, presents challenges due to their inherent complexity and variability [24,25]. Techniques such as Voronoi tessellation and Delaunay triangulation are used for designing these intricate geometries [26,27].…”

Section: Introductionmentioning

confidence: 99%