Comparison of drying kinetics, thermal and performance parameters during drying guava slices in natural and forced convection indirect solar dryers

Mugi, Vishnuvardhan Reddy; Chandramohan, V.P.

doi:10.1016/j.solener.2022.02.012

“…Potential applications of curve fitting are countless and encompass virtually all scientific disciplines. Examples include biosynthesis, 6 thermoluminescence, 7 solar energy, 8 materials science and technology, 9 agriculture, 10 cancer research, 11 kinetics, 12 thermal engineering, 13 transportation, 14 soil science, 15 remote sensing of ecosystems, 16 epidemiology, 17 power and energy engineering, 18 population growth 19 and spectroscopy, 20 to name just a few. The disagreement metrics to minimize during the fit depends on the properties of the noise and possibly on prior information on the parameters to fit.…”

Section: Mainmentioning

confidence: 99%

Correcting Systematic Errors by Hybrid 2D Correlation Loss Functions in nonlinear inverse modelling

Mayerhöfer

¹

,

Noda

²

,

Pahlow

³

et al. 2022

Preprint

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Recently a new family of loss functions called smart error sums has been suggested. These loss functions account for correlations within experimental data and force modeled data to obey these correlations. As a result, multiplicative systematic errors of experimental data can be revealed and corrected. The smart error sums are based on 2D correlation analysis which is a comparably recent methodology for analyzing spectroscopic data that has found broad application. In this contribution we mathematically generalize and break down this methodology and the smart error sums to uncover the mathematic roots and simplify it to craft a general tool beyond spectroscopic modelling. This reduction also allows a simplified discussion about limits and prospects of this new method including one of its potential future uses as a sophisticated loss function in deep learning. To support its deployment, the work includes computer code to allow reproduction of the basic results.

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“…Potential applications of curve fitting are countless and encompass virtually all scientific disciplines. Examples include biosynthesis [ 9 ], thermoluminescence [ 10 ], solar energy [ 11 ], materials science and technology [ 12 ], agriculture [ 13 ], cancer research [ 14 ], kinetics [ 15 ], thermal engineering [ 16 ], transportation [ 17 ], soil science [ 18 ], remote sensing of ecosystems [ 19 ], epidemiology [ 20 ], power and energy engineering [ 21 ], population growth [ 22 ] and spectroscopy [ 23 ], to name just a few. The disagreement metrics to minimize during the fit depends on the properties of the noise and possibly on prior information on the parameters to fit.…”

Section: Introductionmentioning

confidence: 99%

Correcting systematic errors by hybrid 2D correlation loss functions in nonlinear inverse modelling

Mayerhöfer

¹

,

Noda

²

,

Pahlow

³

et al. 2023

View full text Add to dashboard Cite

Recently a new family of loss functions called smart error sums has been suggested. These loss functions account for correlations within experimental data and force modeled data to obey these correlations. As a result, multiplicative systematic errors of experimental data can be revealed and corrected. The smart error sums are based on 2D correlation analysis which is a comparably recent methodology for analyzing spectroscopic data that has found broad application. In this contribution we mathematically generalize and break down this methodology and the smart error sums to uncover the mathematic roots and simplify it to craft a general tool beyond spectroscopic modelling. This reduction also allows a simplified discussion about limits and prospects of this new method including one of its potential future uses as a sophisticated loss function in deep learning. To support its deployment, the work includes computer code to allow reproduction of the basic results.

show abstract

A Comprehensive Review of Food Waste Dryers and Their Energy Supply Methods

Deymi-Dashtebayaz,

Hosseinzadeh,

Asadi

et al. 2024

Waste Biomass Valor

1

0

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Comparison of drying kinetics, thermal and performance parameters during drying guava slices in natural and forced convection indirect solar dryers

Cited by 27 publications

References 34 publications

Correcting Systematic Errors by Hybrid 2D Correlation Loss Functions in nonlinear inverse modelling

Correcting Systematic Errors by Hybrid 2D Correlation Loss Functions in nonlinear inverse modelling

Correcting systematic errors by hybrid 2D correlation loss functions in nonlinear inverse modelling

A Comprehensive Review of Food Waste Dryers and Their Energy Supply Methods

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