Electrode Microstructure Reconstruction from FIB-SEM Datasets with Anisotropic Resolutions Article Swipe
Anna Ściążko
,
Yoskuke KOMATSU
,
Takaaki Shimura
,
Naoki Shikazono
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1299/jsmepes.2021.25.c212
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1299/jsmepes.2021.25.c212
A framework for automated SOFC microstructure reconstruction from large asymmetric-resolution FIB-SEM datasets is proposed. Machine learning techniques are used for super-resolving the in-depth direction, i.e. FIB slicing direction, and for automating the phase segmentation. Deep neural networks consisting of patch-VDSR residual network for the increasing slicing resolution of FIB-SEM data and patch-CNN in the encoder-decoder configuration for semantic segmentation are incorporated. The proposed algorithm can shorten the FIB-SEM measurement time or increase the size of microstructures maintaining high resolution.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1299/jsmepes.2021.25.c212
- OA Status
- diamond
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4210798904
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4210798904Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1299/jsmepes.2021.25.c212Digital Object Identifier
- Title
-
Electrode Microstructure Reconstruction from FIB-SEM Datasets with Anisotropic ResolutionsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-01-01Full publication date if available
- Authors
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Anna Ściążko, Yoskuke KOMATSU, Takaaki Shimura, Naoki ShikazonoList of authors in order
- Landing page
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https://doi.org/10.1299/jsmepes.2021.25.c212Publisher landing page
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1299/jsmepes.2021.25.c212Direct OA link when available
- Concepts
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Slicing, Microstructure, Materials science, Encoder, Segmentation, Focused ion beam, Computer science, Resolution (logic), Artificial intelligence, Artificial neural network, Image resolution, Residual, Algorithm, Composite material, Computer graphics (images), Ion, Physics, Operating system, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.time | 69 |
| abstract_inverted_index.used | 18 |
| abstract_inverted_index.large | 8 |
| abstract_inverted_index.phase | 32 |
| abstract_inverted_index.neural | 35 |
| abstract_inverted_index.FIB-SEM | 10, 48, 67 |
| abstract_inverted_index.Machine | 14 |
| abstract_inverted_index.network | 41 |
| abstract_inverted_index.shorten | 65 |
| abstract_inverted_index.slicing | 26, 45 |
| abstract_inverted_index.datasets | 11 |
| abstract_inverted_index.in-depth | 22 |
| abstract_inverted_index.increase | 71 |
| abstract_inverted_index.learning | 15 |
| abstract_inverted_index.networks | 36 |
| abstract_inverted_index.proposed | 62 |
| abstract_inverted_index.residual | 40 |
| abstract_inverted_index.semantic | 57 |
| abstract_inverted_index.algorithm | 63 |
| abstract_inverted_index.automated | 3 |
| abstract_inverted_index.framework | 1 |
| abstract_inverted_index.patch-CNN | 51 |
| abstract_inverted_index.proposed. | 13 |
| abstract_inverted_index.automating | 30 |
| abstract_inverted_index.consisting | 37 |
| abstract_inverted_index.direction, | 23, 27 |
| abstract_inverted_index.increasing | 44 |
| abstract_inverted_index.patch-VDSR | 39 |
| abstract_inverted_index.resolution | 46 |
| abstract_inverted_index.techniques | 16 |
| abstract_inverted_index.maintaining | 76 |
| abstract_inverted_index.measurement | 68 |
| abstract_inverted_index.resolution. | 78 |
| abstract_inverted_index.segmentation | 58 |
| abstract_inverted_index.configuration | 55 |
| abstract_inverted_index.incorporated. | 60 |
| abstract_inverted_index.segmentation. | 33 |
| abstract_inverted_index.microstructure | 5 |
| abstract_inverted_index.reconstruction | 6 |
| abstract_inverted_index.encoder-decoder | 54 |
| abstract_inverted_index.microstructures | 75 |
| abstract_inverted_index.super-resolving | 20 |
| abstract_inverted_index.asymmetric-resolution | 9 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.44417035 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |