結合仿生奈米結構與三維堆疊奈米纖維製作壓電靜電混能式自供電感測器與量化數據深度學習演算法建置應用(生醫工程：中風病患步態監控、妥瑞氏患者智慧口罩及人機介面智慧鍵盤);Hybridization of 3d Stacked Piezoelectric Nanofibers and Biomimetic-Textured Triboelectric Layers as Self-Powered Sensors for Developing the Large Scale Data Deep Learning Algorithms

NCUIR > Engineering College > Departmant of Mechanical Engineering > Research Project > Item 987654321/82452

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/82452

Title:	結合仿生奈米結構與三維堆疊奈米纖維製作壓電靜電混能式自供電感測器與量化數據深度學習演算法建置應用(生醫工程：中風病患步態監控、妥瑞氏患者智慧口罩及人機介面智慧鍵盤);Hybridization of 3d Stacked Piezoelectric Nanofibers and Biomimetic-Textured Triboelectric Layers as Self-Powered Sensors for Developing the Large Scale Data Deep Learning Algorithms
Authors:	傅尹坤
Contributors:	國立中央大學機械工程學系
Keywords:	仿生奈米結構;奈米纖維;壓電靜電混能式自供電感測器;深度學習演算法;biomimetic-textured;nanofibers;HYBRID self-powered sensors;deep learning algorithms
Date:	2020-01-13
Issue Date:	2020-01-13 14:54:24 (UTC+8)
Publisher:	科技部
Abstract:	此研究係延續105年度科技部計畫” 近場電紡織技術製作可撓與高透明之無電池式物聯網感測器，MOST 105-2221-E-008 -049 -MY3”之延續性研究，利用近場電紡織技術(Near-field electrospinning, NFES)沉積壓電奈米纖維，整合靜電發電機，並結合AI人工智慧深度學習，使用可撓性印刷電路板(printed circuit board，PCB)及奈米結構之混能式自供電感測器。預計以三年期間分別開發：第一年使用低價基板(PCB, PAPER, etc..)，以直寫方式做出具有三維高度之PVDF奈米纖維，並封裝製作成PBSS(paper based self-powered sound-sensing)奈米發電機/自供電式型變感測器。預計達成技術規格：壓電電壓/電流輸出可達10V/1μA，最高輸出功率約為100mW/m3，將此形變感測器作為步態感測器安裝在中風病患步態矯正機構上，記錄行走過程中電壓變化，收集復健者行走的即時步態。第二年開發新型壓電靜電混能 (HYBRID)，在彈性體膜表面上印刷奈米/微米圖案作為靜電功能層可增加電輸出。預計達成技術規格：靜電電壓/電流輸出可達15V/1μA，壓電與靜電混能式(HYBRID)電壓/電流輸出可達25V/1.5μA，最高輸出功率約為120mW/m3。製作具軟式貼片感測儀智慧口罩，以偵測妥瑞症患者抽動症狀時的電壓變化。第三年製作具有仿生圖案奈米形貌，達10~100nm之特徵尺寸，製作混能式仿生奈米結構發電感測器，利用收集外在震動機械能轉為電能輸出特性，設計個人化自供電感應鍵盤(intelligent keybaord , IKB)，收集使用者打字之原始電訊號，配合機器學習之長期短期記憶（long short term memory, LSTM）類神經網路，可以對不同的打字習慣、力道等做出偵辨，開發高階個人化辨識系統。 ;This project proposes a novel hybridization of 3D stacked piezoelectric nanofibers and biomimetic-textured triboelectric layers as self-powered sensors for developing the large scale data deep learning algorithms. Three specific applications include the biomedical engineering applications as the in-situ monitoring devices of stroke-patient rehabilitation and smart mask of Tourette’s patients. Additionally, the personalized human-machine interfact smart keyboard will also be applied based on the developed devices and algorithms. Respective aims and project goal can be summarized as follows:Year 1: In the first year study, the 3D piezoelectric PVDF fibers were sequentially constructed on the flexible substrates such as PCB or paper to fabricate the highly flexible and structurally durable piezoelectric sound-sensing elements with a simple processing and low cost strategy. In addition, the ultrathin intelligent self-powered sound-sensing elements can tightly cohere on the human moveable joint, can not only detected the vibration cause by sound but also detect the human motion without external energy source.Year 2:In the second year project, the smart patch was further integrated with the micro/nanotextured surfaces as the triboelectric friction layers to significantly enhanced the electrical output of seld-powered sensors. The developed sensors will be applied to the smart mask for the Tourette;s patients, as a mean to quantatively monitored the physical behaviors for the medical doctors to estimate the progress of symptoms.Year 3: The third year will continue the advancement of biomimeticnanotextured surfaces (the characteristic length will be 10-100 nm) as the triboelectric friction layers and therefore, the enhaced hybrid self-powered sensors can be effectively utilized as the basis for the large scale analysis in the era of artificial intelligence. he specific application will be the intelligent keybaord (IKB) such that indivisual typewriters’ keystroke dynamics can be identified via the deep-learning-based algorithm for increased keyboard-based information security. Multilayer long short term memory neural network (LSTM) will be established to mine the useful information from raw electronic signals as recorded from the developed hybrid sensors and output the keystroke dynamics identification result.
Relation:	財團法人國家實驗研究院科技政策研究與資訊中心
Appears in Collections:	[Departmant of Mechanical Engineering ] Research Project

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