Analysis of reliability, survivability and telemetry data of on-board equipment of small satellites

Skobtsov, Vadim; Lapitskaja, Natalia; Kim, Dmitriy; Novoselova, Natalia; Saksonov, Roman; Nikolaenya, Eugene

doi:10.1109/cadsm.2019.8779289

Cited by 2 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These samples are projected onto a map generated from the Kohonen map, and marked with B and C for rectangular and hexagonal grids, respectively. For regime 2, samples from data (5,6,7,8,9,10,14,15,16,17,18) and data 2 (P8, P9, P10, P11, P12, P13, P14) are listed and projected onto a map obtained from the Kohonen map, and marked with C and E for rectangular and hexagonal grids, respectively.…”

Section: Parametersmentioning

confidence: 99%

See 1 more Smart Citation

Decision support system based on a hybrid genetic algorithm–Kohonen map for combined mode conduction–radiation heat transfer in a porous medium: A comparative assessment of three variations of the Kohonen map

Ansari,

Mishra,

Sahu

et al. 2024

Heat Trans

View full text Add to dashboard Cite

A hybrid genetic algorithm (GA)–Kohonen map, with its three variants, is explored for the first time for the decision‐making system in a porous ceramic matrix (PCM)‐based burner through determination of the regime of operation. Four different attributes of PCMs such as convective coupling (P2), extinction coefficient (β), downstream porosity (ϕ2), and scattering albedo (ω) are selected for determining the regime of operation of a PCM‐based burner. Changes in any of these attributes of a PCM lead to significant changes in the temperature profiles of the gas and solid phases. Temperature profiles of the gas and solid phases are computed by developing a numerical model. Various samples corresponding to different regimes are generated and used in a hybrid GA–Kohonen map. The best architectural details such as the neuron number and training epochs are obtained from GA as output. The best Kohonen map is trained with the input data, and regimes of operation for new temperature profiles are predicted. A supervised Kohonen map is able to provide the highest average class prediction of more than 40%. All the variants are assessed under two different types of neuron grids: hexagonal and rectangular. Comparative assessments of the three different variants of Kohonen maps, in terms of CPU time and average class prediction, are carried out.

show abstract

Section: Parametersmentioning

confidence: 99%

“…This has led to more alterations in terms of developing supervised learning based on Kohonen maps, such as supervised Kohonen maps and XY-fused networks. 5,6 Such types of Kohonen map-based networks are widely used for solving problems from different fields such as medical, 7,8 space, 9 agriculture, 10,11 and so forth.…”

Section: Introductionmentioning

confidence: 99%