Deep learning analysis on microscopic imaging in materials science (Record no. 55374)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02028nam a2200265Ia 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | MX-MdCICY |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250625164352.0 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | CICY |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) | |
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) | B-21307 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250602s9999 xx |||||s2 |||| ||und|d |
| 245 10 - TITLE STATEMENT | |
| Title | Deep learning analysis on microscopic imaging in materials science |
| 490 0# - SERIES STATEMENT | |
| Series statement | Materials Today Nano. 11, 100087, 2020, DOI: 10.1016/j.mtnano.2020.100087 |
| 520 3# - SUMMARY, ETC. | |
| Summary, etc. | Microscopic imaging providing the real-space information of matter, plays an important role for understanding the correlations between structure and properties in the field of materials science. For the microscopic images of different kinds of objects at different scales, it is a time-consuming task to retrieve useful information on morphology, size, distribution, intensity etc. Alternatively, deep learning has shown great potential in the applications on complicated systems for its ability of extracting useful information automatically. Recently, researchers have utilized deep learning methods on imaging analysis to identify structures and retrieve the linkage between microstructure and performance. In this review, we summarize the recent progresses of the applications of deep learning analysis on microscopic imaging, including scanning electron microscopy (SEM), transmission electron microscopy (TEM), and scanning probe microscopy (SPM). We present sequentially the basic concepts of deep learning methods, the review of the applications on imaging analysis, and our perspective on the future development. Based on the published results, a general workflow of deep learning analysis is put forward. © 2020 |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | IMAGE ANALYSIS |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MATERIALS INFORMATICS |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | SEM |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | SPM |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | TEM |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Ge M. |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Su F. |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Zhao Z. |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Su D. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://drive.google.com/file/d/11p1WkWuOO4eb-AOh4A8tcELnu_nrNskL/view?usp=drivesdk">https://drive.google.com/file/d/11p1WkWuOO4eb-AOh4A8tcELnu_nrNskL/view?usp=drivesdk</a> |
| Public note | Para ver el documento ingresa a Google con tu cuenta @cicy.edu.mx |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Clasificación local |
| Koha item type | Documentos solicitados |
| Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection | Home library | Current library | Shelving location | Date acquired | Total checkouts | Full call number | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Clasificación local | Ref1 | CICY | CICY | Documento préstamo interbibliotecario | 25.06.2025 | B-21307 | 25.06.2025 | 25.06.2025 | Documentos solicitados |