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A Triboelectric Sensor with Double Bubble Structure Applied in a High Security Double Lock System.

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This study introduces a novel high-security intelligent lock system using a self-powered triboelectric nanogenerator (TENG) sensor and deep learning. The system accurately identifies users via respiratory patterns and facial recognition for enhanced security.

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Area of Science:

  • Materials Science
  • Electrical Engineering
  • Computer Science

Background:

  • Growing concerns about personal privacy necessitate advanced security solutions.
  • Existing intelligent lock systems often lack robust, multi-factor authentication methods.
  • The integration of self-powered sensors and AI offers a promising avenue for next-generation security.

Purpose of the Study:

  • To develop a novel high-security intelligent lock system.
  • To integrate triboelectric nanogenerators (TENGs) with a double bubble structure (DB-TENG) as a self-powered respiratory sensor.
  • To utilize deep learning models for accurate user identification based on respiratory patterns and facial recognition.

Main Methods:

  • Fabrication of a DB-TENG sensor using silicone rubber and copper foil, optimizing its structure for high sensitivity (19.08 V/kPa).
  • Development of a smart belt to capture respiratory behaviors for generating a unique respiratory code.
  • Implementation of a Long Short-Term Memory (LSTM) network for classifying respiratory signals with high accuracy (97.00%).
  • Deployment of the system on a Raspberry Pi for real-time authentication combining respiratory code and facial image analysis.

Main Results:

  • The DB-TENG demonstrated high sensitivity and effectiveness as a self-powered respiratory sensor.
  • The LSTM network achieved an average accuracy of 97.00% in identifying four distinct respiratory signals.
  • The integrated system successfully authenticated users by cross-referencing respiratory codes and facial images.
  • The system allows for discreet alarm signaling by manipulating respiratory patterns.

Conclusions:

  • The proposed DB-TENG and deep learning-based intelligent lock system offers a high-security solution.
  • The system provides a novel, non-invasive, and multi-factor authentication method.
  • This technology holds significant potential for applications in security-demanding environments, enhancing personal privacy and defense.