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학술지 Heterogeneous Structure Omnidirectional Strain Sensor Arrays With Cognitively Learned Neural Networks
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저자
이준호, 김성현, 허재상, 곽지영, 박찬우, 김인수, 이민혁, 박호현, 김용훈, 이수재, 박성규
발행일
202303
출처
Advanced Materials, v.35 no.13, pp.1-11
ISSN
0935-9648
출판사
Wiley
DOI
https://dx.doi.org/10.1002/adma.202208184
협약과제
23HB1800, 상시 근골격 모니터링 및 재활을 위한 무자각 온스킨 센서 디바이스 기술, 박찬우
초록
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 45째) 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?뱈ultioutput 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 its혻neural network algorithm exhibits overall strain intensity and direction accuracy around 98% 짹 2% over a strain range of ≈0??30% in various surface stimuli environments.
KSP 제안 키워드
Accurate Recognition, Classification algorithm, Coupled with, Gauge Factor, Heterogeneous structure, Heterogeneous surface, High accuracy, Highly accurate, Machine learning (ml), Network Algorithm, Neural Network Classification