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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1002/adma.202208184
|2 doi
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|a pubmed24n1170.xml
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|a (DE-627)NLM351131337
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|a (NLM)36601963
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
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|a Lee, Jun Ho
|e verfasserin
|4 aut
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| 245 |
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|a Heterogeneous Structure Omnidirectional Strain Sensor Arrays With Cognitively Learned Neural Networks
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|c 2023
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Completed 29.03.2023
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|a Date Revised 29.03.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2023 Wiley-VCH GmbH.
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|a Mechanically stretchable strain sensors gain tremendous attention for bioinspired skin sensation systems and artificially intelligent tactile sensors. However, high-accuracy detection of both strain intensity and direction with simple device/array structures is still insufficient. To overcome this limitation, an omnidirectional strain perception platform utilizing a stretchable strain sensor array with triangular-sensor-assembly (three sensors tilted by 45°) coupled with machine learning (ML) -based neural network classification algorithm, is proposed. The strain sensor, which is constructed with strain-insensitive electrode regions and strain-sensitive channel region, can minimize the undesirable electrical intrusion from the electrodes by strain, leading to a heterogeneous surface structure for more reliable strain sensing characteristics. The strain sensor exhibits decent sensitivity with gauge factor (GF) of ≈8, a moderate sensing range (≈0-35%), and relatively good reliability (3000 stretching cycles). More importantly, by employing a multiclass-multioutput behavior-learned cognition algorithm, the stretchable sensor array with triangular-sensor-assembly exhibits highly accurate recognition of both direction and intensity of an arbitrary strain by interpretating the correlated signals from the three-unit sensors. The omnidirectional strain perception platform with its neural network algorithm exhibits overall strain intensity and direction accuracy around 98% ± 2% over a strain range of ≈0-30% in various surface stimuli environments
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|a Journal Article
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|a direction recognition
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|a machine learned strain sensors
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|a omnidirectional strain sensors
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|a strain sensor
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|a stretchable electronics
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|a Kim, Seong Hyun
|e verfasserin
|4 aut
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|a Heo, Jae Sang
|e verfasserin
|4 aut
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|a Kwak, Jee Young
|e verfasserin
|4 aut
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|a Park, Chan Woo
|e verfasserin
|4 aut
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|a Kim, Insoo
|e verfasserin
|4 aut
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| 700 |
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|a Lee, Minhyeok
|e verfasserin
|4 aut
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| 700 |
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|a Park, Ho-Hyun
|e verfasserin
|4 aut
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| 700 |
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|a Kim, Yong-Hoon
|e verfasserin
|4 aut
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| 700 |
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|a Lee, Su Jae
|e verfasserin
|4 aut
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|a Park, Sung Kyu
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g 35(2023), 13 vom: 15. März, Seite e2208184
|w (DE-627)NLM098206397
|x 1521-4095
|7 nnns
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| 773 |
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|g volume:35
|g year:2023
|g number:13
|g day:15
|g month:03
|g pages:e2208184
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|u http://dx.doi.org/10.1002/adma.202208184
|3 Volltext
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