Optimization of multilayer electromagnetic shields: A genetic algorithm approach

Sagalianov, Igor Yu.; Вовченко, Л. Л.; Matzui, L. Yu.; Lazarenko, Oleksandra; Oliynyk, V. V.; Lozitsky, O. V.; Ritter, Uwe

doi:10.1002/mawe.201600483

Cited by 15 publications

(7 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different types of the carbon nanoparticles are well known and promising candidates as filler in polymer composites. Extraordinarily, electrical properties and specific shape with high aspect ratio (AR), relatively small volume concentrations of carbon nanotubes (CNT) or graphite nanoplatelets (GNP) required for percolation threshold achievement, allow us to produce composites with excellent percolation threshold, responsible for mechanical, thermal, and electrical properties of a composite [ 1 – 8 ].…”

Section: Introductionmentioning

confidence: 99%

Synergistic Enhancement of the Percolation Threshold in Hybrid Polymeric Nanocomposites Based on Carbon Nanotubes and Graphite Nanoplatelets

et al. 2017

View full text Add to dashboard Cite

Synergistic effect causes significant decreasing of the percolation threshold in the ternary polymer composites filled with carbon nanotubes (CNT) and graphite nanoplatelets (GNP) in comparison with binary ones. Enhancement of the percolation threshold strongly depends only on the relative aspect ratios of the filler particles due to the formation of the bridges between puddles of the different filler components. Conditions of both appearance and fading away of the synergistic effect are investigated depending on the relative morphology of CNT or GNP components of the filler. Different lateral sizes, aspect ratios, and volume concentrations of both CNT and GNP in the selected ternary composites were considered. Conditions of the effective substitution of GNP with CNT and vice versa in equal volume concentrations without enlarging of the percolation threshold were established. The results are obtained numerically using the Monte Carlo simulation of the percolation threshold of the different ternary composites.

show abstract

Section: Introductionmentioning

confidence: 99%

Synergistic Enhancement of the Percolation Threshold in Hybrid Polymeric Nanocomposites Based on Carbon Nanotubes and Graphite Nanoplatelets

et al. 2017

View full text Add to dashboard Cite

show abstract

“…For the measurements in rectangular waveguide, the frequency dependencies of EMR transmission SE T ( f ) and reflection RL ( f ) indexes within TL method are described by the following relations [ 44 ]: …”

Section: Resultsmentioning

confidence: 99%

Microwave Properties of One-dimensional Photonic Structures Based on Composite Layers Filled with Nanocarbon

et al. 2017

View full text Add to dashboard Cite

This work presents the results of computer modeling and experimental measurements of microwave transmission properties for one-dimensional periodic multi-layered photonic structures (PCs), composed of epoxy layers and composite layers filled with nanocarbon particles—multi-walled carbon nanotubes and graphite nanoplatelets. The results show that the characteristics of observed photonic band gaps in transmission spectra of PC can be controlled by varying the parameters of layers, namely, the complex permittivity and the layer thickness. It was found that the insertion of the defects (for instance, magnetic layer) into photonic structure can change the EMR transmission spectrum. The comparative analysis of EMR transmission spectra for investigated photonic structures has showed good agreement between the experimental and simulated data. It was found that EMR absorption in composite layers of photonic structures shifts the transmission spectra to the smaller values of EMR transmission index and reduces the sharpness of photonic band gaps. Thus, by changing the parameters of composite layers in photonic structure, we can obtain the tunable photonic band gaps, necessary for technological applications in devices, capable of storing, guiding, and filtering microwaves.

show abstract

“…Loosli et al proposed a Tennessee Eastman process fault diagnosis method based on support vector machine and verified the effectiveness of the method. Thereafter, Sagalianov et al used genetic algorithm (GA) to optimize the Tennessee Eastman process fault diagnosis method. Zhang et al proposed an improved method based on fuzzy theory and maximum inter‐cluster variance.…”

Section: Introductionmentioning

confidence: 99%

Chemical process fault diagnosis based on enchanted machine‐learning approach

et al. 2019

View full text Add to dashboard Cite

In the chemical industry, fault diagnosis is a challenging task due to the complexity of chemical equipment. This paper proposes a machine learning‐based approach to achieve the goal of fault diagnosis. First, in order to reduce the impact of redundant features, support vector machine recursive feature elimination (SVMRFE) is used to select important features. The trained probabilistic neural network (PNN) is then used for fault diagnosis. Considering that the diagnostic performance is affected by its hidden layer element smoothing factor (σ), the modified bat algorithm (MBA) is used to optimize the PNN to obtain optimal global parameter values. The MBA adopts a better optimization mechanism than the basic algorithm and achieves excellent global convergence. It can globally optimize the smoothing factor, which effectively improves the fault diagnosis ability of the PNN. During the testing of the Tennessee Eastman (TE) process data set, we evaluate the performance of the proposed model by comparing the F1‐score and accuracy of the different methods. The charts provided describe the fault diagnostic results and classification for the different models. The results indicate that the MBA has a better optimization ability than other traditional optimization algorithms. At the same time, the combination method proposed in this paper is also superior to others and can significantly improve the accuracy of TE process fault diagnosis.

show abstract

Optimization of multilayer electromagnetic shields: A genetic algorithm approach

Cited by 15 publications

References 15 publications

Synergistic Enhancement of the Percolation Threshold in Hybrid Polymeric Nanocomposites Based on Carbon Nanotubes and Graphite Nanoplatelets

Synergistic Enhancement of the Percolation Threshold in Hybrid Polymeric Nanocomposites Based on Carbon Nanotubes and Graphite Nanoplatelets

Microwave Properties of One-dimensional Photonic Structures Based on Composite Layers Filled with Nanocarbon

Chemical process fault diagnosis based on enchanted machine‐learning approach

Contact Info

Product

Resources

About