Use of deep learning for characterization of microfluidic soft sensors = 딥러닝을 활용한 마이크로플루이딕 소프트 센서의 특성 분석

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Soft sensors made of highly deformable materials are one of the enabling technologies to various soft robotic systems, such as soft mobile robots, soft wearable robots, and soft grippers. However, major drawbacks of soft sensors compared with traditional sensors are their nonlinearity and hysteresis in response, which are common especially in microfluidic soft sensors. In this research, we propose to address the above issues of soft sensors by taking advantage of deep learning. We implemented a hierarchical recurrent sensing network, a type of recur- rent neural network model, to the calibration of soft sensors for estimating the magnitude and the location of a contact pressure simultaneously. The proposed approach in this paper were not only able to model the nonlinear characteristic with hysteresis of the pressure response, but also find the location of the pressure.
Advisors
Jo, Sunghoresearcher조성호researcher
Description
한국과학기술원 :전산학부,
Publisher
한국과학기술원
Issue Date
2018
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전산학부, 2018.2,[iii, 22 p. :]

Keywords

Soft Robotics▼aSoft Sensor▼aDeep Learning in Robotics▼aForce Sensing; 소프트 로보틱스▼a소프트 센서▼a딥러닝▼a압력 센싱

URI
http://hdl.handle.net/10203/267092
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=734208&flag=dissertation
Appears in Collection
CS-Theses_Master(석사논문)
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