Artificial Intelligence Meets Flexible Sensors: Emerging Smart Flexible Sensing Systems Driven by Machine Learning and Artificial Synapses Article Swipe
YOU?
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· 2023
· Open Access
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· DOI: https://doi.org/10.1007/s40820-023-01235-x
The recent wave of the artificial intelligence (AI) revolution has aroused unprecedented interest in the intelligentialize of human society. As an essential component that bridges the physical world and digital signals, flexible sensors are evolving from a single sensing element to a smarter system, which is capable of highly efficient acquisition, analysis, and even perception of vast, multifaceted data. While challenging from a manual perspective, the development of intelligent flexible sensing has been remarkably facilitated owing to the rapid advances of brain-inspired AI innovations from both the algorithm (machine learning) and the framework (artificial synapses) level. This review presents the recent progress of the emerging AI-driven, intelligent flexible sensing systems. The basic concept of machine learning and artificial synapses are introduced. The new enabling features induced by the fusion of AI and flexible sensing are comprehensively reviewed, which significantly advances the applications such as flexible sensory systems, soft/humanoid robotics, and human activity monitoring. As two of the most profound innovations in the twenty-first century, the deep incorporation of flexible sensing and AI technology holds tremendous potential for creating a smarter world for human beings.
Related Topics
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1007/s40820-023-01235-x
- https://link.springer.com/content/pdf/10.1007/s40820-023-01235-x.pdf
- OA Status
- diamond
- Cited By
- 231
- References
- 211
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388639388
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388639388Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s40820-023-01235-xDigital Object Identifier
- Title
-
Artificial Intelligence Meets Flexible Sensors: Emerging Smart Flexible Sensing Systems Driven by Machine Learning and Artificial SynapsesWork title
- Type
-
reviewOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-13Full publication date if available
- Authors
-
Tianming Sun, Bin Feng, Jinpeng Huo, Yu Xiao, Wengan Wang, Jin Peng, Zehua Li, Chengjie Du, Wenxian Wang, Guisheng Zou, Lei LiuList of authors in order
- Landing page
-
https://doi.org/10.1007/s40820-023-01235-xPublisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s40820-023-01235-x.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://link.springer.com/content/pdf/10.1007/s40820-023-01235-x.pdfDirect OA link when available
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Artificial intelligence, Computer science, Deep learning, Robotics, Applications of artificial intelligence, Component (thermodynamics), Human–computer interaction, Robot, Thermodynamics, PhysicsTop concepts (fields/topics) attached by OpenAlex
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231Total citation count in OpenAlex
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2025: 156, 2024: 75Per-year citation counts (last 5 years)
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211Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.multifaceted | 58 |
| abstract_inverted_index.perspective, | 65 |
| abstract_inverted_index.twenty-first | 163 |
| abstract_inverted_index.incorporation | 167 |
| abstract_inverted_index.significantly | 139 |
| abstract_inverted_index.soft/humanoid | 148 |
| abstract_inverted_index.unprecedented | 12 |
| abstract_inverted_index.brain-inspired | 82 |
| abstract_inverted_index.comprehensively | 136 |
| abstract_inverted_index.intelligentialize | 16 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 11 |
| citation_normalized_percentile.value | 0.99919509 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |