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Updated: Jan 9, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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Improving Visible Light Positioning Accuracy Using Particle Swarm Optimization (PSO) for Deep Learning Hyperparameter

Chun-Ming Chang1, Yuan-Zeng Lin1, Chi-Wai Chow1

  • 1Department of Photonics & Graduate Institute of Electro-Optical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.

Sensors (Basel, Switzerland)
|December 11, 2025
PubMed
Summary

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This summary is machine-generated.

Automated hyperparameter tuning using particle swarm optimization (PSO) with convolutional neural networks (CNNs) significantly improves visible light positioning (VLP) accuracy. This approach enhances indoor positioning reliability in challenging, non-ideal lighting conditions.

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Visible light positioning (VLP) offers accurate and cost-effective indoor navigation but suffers from signal degradation due to environmental factors like reflections and light-deficient zones.
  • Non-uniform light distribution from LED luminaires and complex indoor optical channel characteristics challenge VLP accuracy.
  • Machine learning (ML), particularly Convolutional Neural Networks (CNNs), shows promise in addressing these challenges, but their performance is sensitive to hyperparameter settings.

Purpose of the Study:

  • To develop an automated system for optimizing CNN hyperparameters for VLP applications.
  • To enhance the accuracy, robustness, and reliability of VLP systems under non-ideal lighting conditions.
  • To investigate the effectiveness of integrating Received Signal Strength (RSS) pre-processing and Particle Swarm Optimization (PSO) with CNNs for VLP.
Keywords:
convolutional neural network (CNN)particle swarm optimization (PSO)received signal strength (RSS)visible light positioning (VLP)

Related Experiment Videos

Last Updated: Jan 9, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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991

Main Methods:

  • A VLP system incorporating RSS signal pre-processing was developed.
  • A CNN model was implemented and compared against Linear Regression (LR) and Artificial Neural Networks (ANN) across different height planes (200, 225, 250 cm).
  • Particle Swarm Optimization (PSO) was employed for automated hyperparameter tuning of the CNN model.

Main Results:

  • The CNN model with pre-processing reduced mean positioning error from 9.83 cm to 5.72 cm (41.81% improvement) at the 200 cm receiver plane.
  • Further optimization using CNN + pre-processing + PSO reduced the mean error to 4.93 cm.
  • The proposed integrated approach demonstrated significant enhancements in positioning accuracy and model robustness compared to baseline ML models.

Conclusions:

  • Automated hyperparameter tuning via PSO integration with CNNs and RSS pre-processing substantially improves VLP accuracy and reliability.
  • This method provides a scalable and effective solution for real-world indoor positioning in smart buildings and IoT environments.
  • The findings highlight the potential of adaptive ML techniques for overcoming limitations in optical wireless communication-based positioning systems.