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深度学习技术将粗糙的压阻式碳纳米管-生态弹性体复合片转换为智能、便携式、一次性和超灵活的键盘。

Deep-Learning Technique To Convert a Crude Piezoresistive Carbon Nanotube-Ecoflex Composite Sheet into a Smart, Portable, Disposable, and Extremely Flexible Keypad.

机构信息

Faculty of Nanotechnology and Advanced Materials Engineering , Sejong University , Seoul 143-747 , Republic of Korea.

Laboratory of Big-Data Applications for Public Sector , Chung-Ang University , 221, Heukseok-dong, Dongjak-gu , Seoul 156-756 , Republic of Korea.

出版信息

ACS Appl Mater Interfaces. 2018 Jun 20;10(24):20862-20868. doi: 10.1021/acsami.8b04914. Epub 2018 Jun 11.

Abstract

An extremely simple bulk sheet made of a piezoresistive carbon nanotube (CNT)-Ecoflex composite can act as a smart keypad that is portable, disposable, and flexible enough to be carried crushed inside the pocket of a pair of trousers. Both a rigid-button-imbedded, rollable (or foldable) pad and a patterned flexible pad have been introduced for use as portable keyboards. Herein, we suggest a bare, bulk, macroscale piezoresistive sheet as a replacement for these complex devices that are achievable only through high-cost fabrication processes such as patterning-based coating, printing, deposition, and mounting. A deep-learning technique based on deep neural networks (DNN) enables this extremely simple bulk sheet to play the role of a smart keypad without the use of complicated fabrication processes. To develop this keypad, instantaneous electrical resistance change was recorded at several locations on the edge of the sheet along with the exact information on the touch position and pressure for a huge number of random touches. The recorded data were used for training a DNN model that could eventually act as a brain for a simple sheet-type keypad. This simple sheet-type keypad worked perfectly and outperformed all of the existing portable keypads in terms of functionality, flexibility, disposability, and cost.

摘要

一个由压阻式碳纳米管(CNT)-Ecoflex 复合材料制成的极其简单的块状薄片可以作为一个智能键盘,它便携、一次性、足够灵活,可以压碎放在一条裤子的口袋里。已经引入了一种刚性按钮嵌入的、可滚动(或可折叠)的垫和一种图案化的柔性垫作为便携式键盘。在这里,我们提出了一个裸露的、块状的、宏观的压阻薄片作为这些复杂设备的替代品,这些复杂设备只能通过昂贵的制造工艺来实现,如基于图案的涂层、印刷、沉积和安装。一种基于深度神经网络(DNN)的深度学习技术使得这种极其简单的块状薄片可以在不使用复杂制造工艺的情况下充当智能键盘。为了开发这个键盘,在薄片边缘的几个位置记录了瞬时电阻变化,并记录了大量随机触摸时的触摸位置和压力的准确信息。记录的数据用于训练一个 DNN 模型,该模型最终可以作为一个简单的薄片式键盘的大脑。这个简单的薄片式键盘工作得非常好,在功能、灵活性、一次性和成本方面都超过了所有现有的便携式键盘。

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