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Related Concept Videos

Quality Assurance01:19

Quality Assurance

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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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Artificial Intelligence-Assisted RFID Tag-Integrated Multi-Sensor for Quality Assessment and Sensing.

Chenyang Song1, Zhipeng Wu1

  • 1Department of Electrical and Electronic Engineering, University of Manchester, Manchester M13 9PL, UK.

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|March 28, 2024
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Summary

This study introduces an AI-assisted RFID multi-sensing technology for the Industrial Internet of Things (IIoT). It integrates machine learning with RFID data for accurate food quality assessment, improving manufacturing and supply chains.

Keywords:
RF energy harvestingRFID sensingUHF RFIDproduct quality assessment and sensing

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Food Science

Background:

  • Radio Frequency Identification (RFID) is crucial for the Industrial Internet of Things (IIoT) but faces challenges in pervasive monitoring and item-level data recording.
  • Existing IIoT systems often require complex, additional sensor networks, increasing costs and complexity.

Purpose of the Study:

  • To develop an Artificial Intelligence (AI)-assisted RFID-based multi-sensing technology to overcome limitations in current IIoT monitoring.
  • To integrate passive and semi-passive RFID tag-integrated multi-sensors for enhanced sensing capabilities.
  • To apply machine learning algorithms for food product quality assessment and sensing (QAS) using RFID data.

Main Methods:

  • Development of UHF RFID tag-integrated multi-sensors with boosted charge pumps for improved RF sensitivity and operational range.
  • Hardware design optimization, including antenna and energy harvester.
  • Integration and demonstration of the NARX (autoregressive model with exogenous input) neural network for analyzing RFID sensing data.

Main Results:

  • A novel UHF RFID tag-integrated multi-sensor was designed and tested, showing high RF sensitivity and extended operational range.
  • The NARX neural network model achieved high accuracy in ham product quality assessment and sensing (QAS), with an RMSE of 0.007 and R-squared of 0.99.
  • Successful demonstration of AI-assisted RFID sensing for real-world food product quality assessment.

Conclusions:

  • The proposed AI-assisted RFID multi-sensing technology offers a low-cost, timely, and flexible solution for product quality assessment in manufacturing.
  • This technology enhances product quality, optimizes manufacturing lines, and improves supply chain management.
  • The integration of machine learning with RFID data represents a novel approach for advanced sensing applications in the IIoT.