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Summary

Edge AI enhances industrial mobile terminals by using deep learning to precisely control defrosting operations, significantly reducing energy waste. This intelligent approach improves operational efficiency and extends battery life in challenging environments.

Keywords:
classificationconvolutional neural network (CNN)deep learningenergy savingfrostindustrial mobile terminalscanner window

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Area of Science:

  • Industrial Automation
  • Artificial Intelligence
  • Computer Vision

Background:

  • Mobile terminals in logistics face operational challenges due to environmental variations.
  • Frosting on scanner windows in cold storage environments leads to inefficient energy use for defrosting.

Purpose of the Study:

  • To propose an energy-efficient defrosting method for industrial mobile terminals using edge AI.
  • To reduce energy waste caused by conventional defrosting rules.

Main Methods:

  • Developed an AI-based approach combining temperature sensing with a convolutional neural network (CNN) classifier.
  • Embedded the CNN classifier on the device for real-time frost detection and defrost control.
  • Compared energy consumption with existing systems on a test terminal.

Main Results:

  • The edge AI system reduced heating film energy consumption by a factor of 14:1 compared to the existing system.
  • The AI system increased operating hours by 86% (over 6 hours) accounting for current dissipation.

Conclusions:

  • Edge AI offers a significant improvement in energy efficiency for industrial mobile terminals.
  • Precise defrosting control through AI prevents unnecessary energy expenditure and enhances device usability.