Data · dataset · 2022
Deep learning assisted far-field multi-beam pointing measurement
Listed in ScienceDB
In this work, we present a deep learning approach to synchronously measure the multi-beam pointing error.
Description
This approach uses only one detector to identify the pointing change of the far-field spot by the deep convolutional neural network algorithm. It can be well applied to multi-beam coherent combination for high-power laser systems.Figure1.tif describes the simulated two-beam far-field interference pattern.
Figure2.tif describes the training and measurement process of DCNN. Figure3.tif describes the experimental sample acquisition setup. Figure4.tif describes the experimental far-field interference pattern and the experimental results.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.06837 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Computer Science & AI · Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Machine learning 71%
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.06837 | 8 d ago | JSON v1 |
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|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].anzsrc:group:4611 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (71%) |
| concepts[field].local:field:computer-science-ai | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
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| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |