Wireless real-time monitoring based on triboelectric nanogenerator with artificial intelligence
A RepNet-based wireless self-powered sensor system is designed by just two components with deep learning algorithm, which has simple structure and high accuracy even without integrated circuit. Triboelectric nanogenerator (TENG) directly power the artificial intelligence sensor, and the algorithm ex...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-01-01
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| Series: | Internet of Things and Cyber-Physical Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667345223000482 |
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| author | Dexin Tang Yuankai Zhou Xin Cui Yan Zhang |
| author_facet | Dexin Tang Yuankai Zhou Xin Cui Yan Zhang |
| author_sort | Dexin Tang |
| collection | DOAJ |
| description | A RepNet-based wireless self-powered sensor system is designed by just two components with deep learning algorithm, which has simple structure and high accuracy even without integrated circuit. Triboelectric nanogenerator (TENG) directly power the artificial intelligence sensor, and the algorithm extracts and encodes the convolutional features and local temporal information from a video. To test this model, we assemble a test dataset of 192 videos, comprising 32 frequencies of TENG. We then show the real-time detection backend based on the RepNet. This deep-learning-based backend also works well and demonstrates great feasibility and potential in the applications such as counting the number of LED flashing, estimating the possibility of LED flashing and detecting the changes of frequency. It is a potential and novel approach for sensing and transmited information of TENG-based self-powered sensors. |
| format | Article |
| id | doaj-art-75a6d64047ee45d1a8252d6e615b1d70 |
| institution | Kabale University |
| issn | 2667-3452 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Internet of Things and Cyber-Physical Systems |
| spelling | doaj-art-75a6d64047ee45d1a8252d6e615b1d702025-01-27T04:22:33ZengKeAi Communications Co., Ltd.Internet of Things and Cyber-Physical Systems2667-34522024-01-0147781Wireless real-time monitoring based on triboelectric nanogenerator with artificial intelligenceDexin Tang0Yuankai Zhou1Xin Cui2Yan Zhang3School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, ChinaSchool of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, ChinaSchool of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China; Corresponding author.School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China; Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China; College of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China; Corresponding author. School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China.A RepNet-based wireless self-powered sensor system is designed by just two components with deep learning algorithm, which has simple structure and high accuracy even without integrated circuit. Triboelectric nanogenerator (TENG) directly power the artificial intelligence sensor, and the algorithm extracts and encodes the convolutional features and local temporal information from a video. To test this model, we assemble a test dataset of 192 videos, comprising 32 frequencies of TENG. We then show the real-time detection backend based on the RepNet. This deep-learning-based backend also works well and demonstrates great feasibility and potential in the applications such as counting the number of LED flashing, estimating the possibility of LED flashing and detecting the changes of frequency. It is a potential and novel approach for sensing and transmited information of TENG-based self-powered sensors.http://www.sciencedirect.com/science/article/pii/S2667345223000482Deep learningTriboelectric nanogeneratorSelf-powered systemRepNet |
| spellingShingle | Dexin Tang Yuankai Zhou Xin Cui Yan Zhang Wireless real-time monitoring based on triboelectric nanogenerator with artificial intelligence Internet of Things and Cyber-Physical Systems Deep learning Triboelectric nanogenerator Self-powered system RepNet |
| title | Wireless real-time monitoring based on triboelectric nanogenerator with artificial intelligence |
| title_full | Wireless real-time monitoring based on triboelectric nanogenerator with artificial intelligence |
| title_fullStr | Wireless real-time monitoring based on triboelectric nanogenerator with artificial intelligence |
| title_full_unstemmed | Wireless real-time monitoring based on triboelectric nanogenerator with artificial intelligence |
| title_short | Wireless real-time monitoring based on triboelectric nanogenerator with artificial intelligence |
| title_sort | wireless real time monitoring based on triboelectric nanogenerator with artificial intelligence |
| topic | Deep learning Triboelectric nanogenerator Self-powered system RepNet |
| url | http://www.sciencedirect.com/science/article/pii/S2667345223000482 |
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