2020
DOI: 10.1515/hf-2019-0131
|View full text |Cite
|
Sign up to set email alerts
|

Wood grain angles variations inEucalyptusand their relationships to physical-mechanical properties

Abstract: AbstractThe relationship between grain angle and wood properties has not been focus of researches in wood industry. The aim of this study was to establish grain angle variations in commercial Eucalyptus logs and their effects on physical-mechanical wood properties. Wood maximum angular deviation (MAD) was correlated with density, volumetric shrinkage, compressive strength parallel to grain, flexural strength and stiffness as determined by bending and … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
4
1

Relationship

0
5

Authors

Journals

citations
Cited by 7 publications
(1 citation statement)
references
References 29 publications
0
1
0
Order By: Relevance
“…The learning rate is related to the length of time required for model training, and a good learning rate setting can even avoid training "falling into a dead zone". According to the actual situation of model training, the setting of the model learning rate should not be static, often requiring a larger learning rate in the early stages, while as training continues to deepen, a smaller learning rate is expected in order to make the model converge better [11] .…”
Section: Deep Belief Network Optimization Modelmentioning
confidence: 99%
“…The learning rate is related to the length of time required for model training, and a good learning rate setting can even avoid training "falling into a dead zone". According to the actual situation of model training, the setting of the model learning rate should not be static, often requiring a larger learning rate in the early stages, while as training continues to deepen, a smaller learning rate is expected in order to make the model converge better [11] .…”
Section: Deep Belief Network Optimization Modelmentioning
confidence: 99%