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Multi-Sensor Data Fusion with a Reconfigurable Module and Its Application to Unmanned Storage Boxes.

Sung-Kyu Lee1, Seung-Hyun Hong1, Won-Ho Jun1

  • 1Department of Computer Science and Engineering, Incheon National University, Incheon 22012, Korea.

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|July 27, 2022
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Summary
This summary is machine-generated.

This study introduces a reconfigurable module (RM) for multi-sensor data fusion. The RM enhances reliability in systems using low-cost sensors, demonstrated with an unmanned storage box application.

Keywords:
an unmanned storage boxdata refinementmulti-sensor fusionreconfigurable modulethreshold

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Area of Science:

  • Engineering
  • Computer Science
  • Sensor Technology

Background:

  • Multi-sensor data fusion is crucial for enhancing system reliability and accuracy.
  • Existing fusion models may lack adaptability for diverse applications.
  • The COVID-19 pandemic highlighted the need for reliable monitoring systems, such as for unmanned storage.

Purpose of the Study:

  • To propose a novel reconfigurable module (RM) for adaptable multi-sensor data fusion.
  • To demonstrate the RM's effectiveness in improving reliability using low-cost sensors.
  • To validate the RM's application in a real-world scenario, such as monitoring unmanned storage boxes.

Main Methods:

  • Developed a three-layer fusion model: data, feature, and decision layers.
  • Implemented data refinement based on sensor characteristics and conversion to logical values.
  • Configured a fusion tree with adjustable logical operations for feature extraction and decision making.

Main Results:

  • Successfully reconstructed a reconfigurable module (RM) adaptable for specific applications.
  • Implemented a prototype system for monitoring unmanned storage boxes using the RM.
  • Confirmed enhanced reliability in state identification using low-cost sensors through multi-sensor data fusion.

Conclusions:

  • The proposed reconfigurable module (RM) offers an adaptable approach to multi-sensor data fusion.
  • The RM enables reliable state identification even with low-cost sensor systems.
  • This method provides a flexible framework for various sensor fusion applications.