具仿生結構之混能式壓力/風能傳感器與深度學習姿態辨識之應用;Biomimetic fiber-based hybrid sensor for Multifunctional Pressure Sensing and human gesture identification via Deep Learning Method

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題名:	具仿生結構之混能式壓力/風能傳感器與深度學習姿態辨識之應用;Biomimetic fiber-based hybrid sensor for Multifunctional Pressure Sensing and human gesture identification via Deep Learning Method
作者:	徐妙華;Syu, Miao-hua
貢獻者:	機械工程學系
關鍵詞:	近場電紡織技術;PVDF;P(VDF-TrFE);可撓性印刷電路板;仿生混能式自供電感測器;Long Short-Term Memory (LSTM)
日期:	2019-07-18
上傳時間:	2019-09-03 16:33:13 (UTC+8)
出版者:	國立中央大學
摘要:	本論文利用近場電紡織技術(near-field electrospinning，NFES)製作具有壓電奈米纖維之奈米發電機(nanogenetator，NG)，將具有高度壓電性能的高分子材料聚偏氟乙烯(polyvinylidene fluoride，PVDF)及其共聚物poly(vinylidene fluorideco-trifluoroethylene，P(VDF-TrFE))精確的沉積在可撓性基板上，以製作成具奈微米纖維(nano/micro fibers，NMFs)壓電奈米發電機。其一以可撓性印刷電路板(printed circuit board，PCB)沉積PVDF壓電纖維，並透過結合了之聚二甲基矽氧烷(polydimethylsiloxane，PDMS)翻印具仿生奈米表面結構來提高靜電效應輸出，靜電發電機與壓電發電機製成仿生混能式自供電感測器(Biomimetic Hybrid self-powered sensors，BHSS)。將具有仿生奈米表面結構之混能式感測器用以量測人體因不同姿態動作所產生不同的電壓輸出，並將其輸出訊號應用在感測人體動作，藉由產生的訊號使用機器學習長短期記憶(Long Short Term Memory，LSTM)演算法來分辨不同的動作，可以達到82.3％的準確率。其二，本論文亦進一步研究利用P(VDF-TrFE)製成之奈米發電器應用於風能收集的可能性，利用奈米發電機柔軟、可撓的特性，製成風能發電器，進行了風速與輸出電壓相關之研究、戶外實驗收集環境風能，實驗表明此風能發電器即使在低風速(~3.5 m / s)下也能持續發電(~2V)。;Within this paper, Near-field electrospinning (NFES) technological employed to deposit your nano/micro fibers for the different starting, and a new nanogenerator (NG)/deformation sensor ended up being fabricated. Within this study, polyvinylidene fluoride (PVDF), a polymer product with substantial piezoelectric components, was lodged and properly arranged with a flexible substrate by direct-write process using near-field electrospinning technological and XY detail motion stage as being a piezoelectric nano-generator. One of the research use of flexible printed circuit board (PCB) to deposit piezoelectric fibers, in order to make the generator more efficient to collect mechanical energy, we applied Mytilidae nano-structured patterns on the surface of PDMS film via the soft transfer molding technique as electrostatic generator. We combined the and piezoelectric generator and biomimetic triboelectric generator as biomimetic hybrid self-powered sensors (BHSS). Furthermore, an intelligent glove and the force sensor with are successively confirmed that the developed BHSS has promising applications in wearable self-power sensor technology. The machine learning algorithm of Long Short-Term Memory (LSTM) in the context of gesture recognition was used and effectively distinguish five human actions satisfactorily. LSTM based real-time electrical signals of five gestures dataset with varying duration and complexity can achieve an overall classification rate of 82.3%. This paper also further studies the possibility of using nanogenerators made of P(VDF-TrFE) for wind energy collection, using the soft and flexible characteristics of nanogenerators to make wind energy generators and wind speed. Research related to output voltage, outdoor experiments and collection of ambient wind energy to assess the potential of wind energy generators and their electrical output. This wind power generator can continue to generate electricity (~2V) even at low wind speeds (~3.5 m / s).
顯示於類別:	[機械工程研究所] 博碩士論文

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