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Ultrathin crystalline-silicon-based strain gauges with deep learning algorithms for silent speech interfaces
Taemin Kim
, Yejee Shin
, Kyowon Kang
, Kiho Kim
, Gwanho Kim
, Yunsu Byeon
, Hwayeon Kim
, Yuyan Gao
, Jeong Ryong Lee
, Geonhui Son
, Taeseong Kim
, Yohan Jun
, Jihyun Kim
, Jinyoung Lee
, Seyun Um
, Yoohwan Kwon
, Byung Gwan Son
, Myeongki Cho
, Mingyu Sang
, Jongwoon Shin
Kyubeen Kim, Jungmin Suh, Heekyeong Choi, Seokjun Hong,
Huanyu Cheng
, Hong Goo Kang, Dosik Hwang, Ki Jun Yu
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Engineering Science and Mechanics
Materials Research Institute (MRI)
Research output
:
Contribution to journal
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Article
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peer-review
85
Scopus citations
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Keyphrases
Strain Gauge
100%
Silicon-based
100%
Deep Learning Algorithm
100%
Surface Electromyography (sEMG)
50%
Accuracy Rate
33%
Strain Sensor
33%
Signal-to-noise Ratio
16%
Biaxial Strain
16%
Crystalline Silicon
16%
System Reliability
16%
Performance Comparison
16%
Strain Data
16%
High Reliability
16%
Verbal Communication
16%
Vocalization
16%
Cell Dimension
16%
Signal Quality
16%
Quality-related
16%
Inter-electrode
16%
3D Convolution
16%
Engineering
Crystalline Silicon
100%
Strain Gage
100%
Deep Learning Method
100%
Signal-to-Noise Ratio
50%
System Reliability
50%
Signal Quality
50%
Strain Data
50%
Computer Science
strain sensor
66%