张维强/刁东风等再次在JCR1区Nano Energy发表论文阅读次数 [37] 发布时间 :2020-08-03 11:06:04
近日，INSE联合佐治亚理工学院王中林院士课题组及其他课题组在国际顶级期刊Nano Energy（影响因子：16.6）上发表题为“Multilanguage-handwriting Self-powered Recognition Based on Triboelectric Nanogenerator Enabled Machine Learning”的研究论文。该文通过制备系列特色微纳米结构表面，开发了基于微/纳织构化表面的接触-分离模式摩擦纳米发电机的笔迹信息采集器件，可以实现不同个体书写笔迹信息的全面采集。利用小波包分解技术处理采集到的书写笔迹信号，并通过机器学习方法识别不同个体的书写笔迹。试验结果表明，设计的智能识别系统能够实现对中文词组、英文单词、阿拉伯数字、英文句子和中文语句的高精度识别。此工作展示了基于TENG的自驱动器件在个人信息采集和隐私保护上的潜在应用。
Multilanguage-handwriting self-powered recognition based on triboelectric nanogenerator enabled machine learning
Nano Energy77(2020)105174 (PDF-File)
Weiqiang Zhang, Linfeng Deng, Lei Yang, Ping Yang, Dongfeng Diao*, Pengfei Wang**, Zhong Lin Wang***
Handwriting signature is widely used and the main challenge for handwriting recognition is how to obtain comprehensive handwriting information. Triboelectric nanogenerator is sensitive to external triggering force and can be used to record personal handwriting signals and associated characteristics. In this work, micro/nano structure textured TENG acting as a smart self-powered handwriting pad is developed and its effectiveness for handwriting recognition is demonstrated. Three individuals’ handwriting signals of English words, Chinese characters and Arabic numerals are acquired by leaf-inspired TENG, and the other three people’s handwriting signals of English sentences and the corresponding Chinese sentences are obtained by cylindrical microstructured PDMS based TENG, and these signals exhibit unique features. Combined with the machine learning method, the people’s handwriting was successfully identified. The classification accuracies of 99.66%, 93.63%, 91.36%, 99.05%, and 97.73% were reached for English words, Arabic numerals, Chinese characters, English sentences, and the corresponding Chinese sentences, respectively. The results strongly suggested that the textured TENG exhibited great potential in personal handwriting signature identification, security defense, and private information protection applications.
