With more attention on personal privacy and the need for a security defense, it is necessary to design an intelligent lock system with a higher security performance. Here, a novel high security double lock system integrating triboelectric nanogenerators (TENGs) with a double bubble structure (DB-TENG) and deep learning models is proposed. The TENG as a self-powered sensor is developed using silicone rubber and copper foil. By optimizing the thickness of the top layer film, surface microstructure, the size of the air bubble, and design of the double bubble structure, the sensitivity of the DB-TENG reaches 19.08 V/kPa. For the feasibility study, the sensor is fabricated to a smart belt to collect respiratory behaviors as a respiratory code. A Long Short-Term Memory network is adopted to identify four typical respiratory signals with an average accuracy of 97.00%. The system is deployed on a Raspberry Pi to determine whether the user is permitted through both the collected respiratory code and the related face image and will send an alarm message if one of the two does not match. It is worth mentioning that users can send alarm signals undiscovered by controlling their respiratory signals. Therefore, the proposed system has superb potential in security demanding environments.

中文翻译：

---

双泡结构摩擦电传感器在高安全性双锁系统中的应用


随着人们对个人隐私的重视和安全防御的需要，需要设计一种具有更高安全性能的智能锁系统。在此，提出了一种新型高安全性双锁系统，将摩擦纳米发电机（TENG）与双气泡结构（DB-TENG）和深度学习模型相结合。 TENG 作为一种自供电传感器，是使用硅橡胶和铜箔开发的。通过优化顶层薄膜的厚度、表面微观结构、气泡尺寸以及双气泡结构的设计，DB-TENG的灵敏度达到19.08 V/kPa。在可行性研究中，传感器被安装在智能腰带上，以收集呼吸行为作为呼吸代码。采用长短期记忆网络识别四种典型呼吸信号，平均准确率为97.00%。该系统部署在Raspberry Pi上，通过收集的呼吸代码和相关的人脸图像来确定用户是否被允许，如果两者之一不匹配，将发送警报消息。值得一提的是，用户可以通过控制呼吸信号，在未被发现的情况下发出报警信号。因此，所提出的系统在安全要求较高的环境中具有巨大的潜力。