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

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dc.contributor.advisorJo, Sungho-
dc.contributor.advisor조성호-
dc.contributor.authorHan, Seunghyun-
dc.date.accessioned2019-09-04T02:47:43Z-
dc.date.available2019-09-04T02:47:43Z-
dc.date.issued2018-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=734208&flag=dissertationen_US
dc.identifier.urihttp://hdl.handle.net/10203/267092-
dc.description학위논문(석사) - 한국과학기술원 : 전산학부, 2018.2,[iii, 22 p. :]-
dc.description.abstractSoft 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.-
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectSoft Robotics▼aSoft Sensor▼aDeep Learning in Robotics▼aForce Sensing-
dc.subject소프트 로보틱스▼a소프트 센서▼a딥러닝▼a압력 센싱-
dc.titleUse of deep learning for characterization of microfluidic soft sensors = 딥러닝을 활용한 마이크로플루이딕 소프트 센서의 특성 분석-
dc.typeThesis(Master)-
dc.identifier.CNRN325007-
dc.description.department한국과학기술원 :전산학부,-
dc.contributor.alternativeauthor한승현-
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