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Related Concept Videos

Reducing Line Loss01:18

Reducing Line Loss

In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...

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Adaptive Nonlinear Bernstein-Guided Parrot Optimizer for Mural Image Segmentation.

Jianfeng Wang1, Jiawei Fan2, Xiaoyan Zhang1

  • 1College of Design, Hanyang University, Ansan 15588, Republic of Korea.

Biomimetics (Basel, Switzerland)
|August 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces the Adaptive Nonlinear Bernstein-guided Parrot Optimizer (ANBPO) for improved mural image segmentation, enhancing cultural heritage preservation. ANBPO significantly boosts segmentation accuracy and preserves original feature information.

Keywords:
adaptive learning strategymural image segmentationnonlinear factorparrot optimizerthird-order Bernstein-guided strategy

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Cultural Heritage Conservation

Background:

  • Mural degradation threatens world cultural heritage, necessitating advanced restoration techniques.
  • Current image segmentation methods for mural conservation exhibit suboptimal performance.
  • Effective image segmentation is crucial for accurate mural restoration and protection.

Purpose of the Study:

  • To propose an efficient mural image segmentation method to overcome limitations of existing techniques.
  • To enhance the Parrot Optimizer (PO) algorithm for superior mural image segmentation quality.
  • To improve the preservation of original feature information in mural images.

Main Methods:

  • Developed the Adaptive Nonlinear Bernstein-guided Parrot Optimizer (ANBPO) by integrating adaptive learning, a nonlinear factor, and a third-order Bernstein-guided strategy into the Parrot Optimizer (PO).
  • Adaptive learning strategy enhances global exploration by considering individual information disparities.
  • Nonlinear factor and third-order Bernstein-guided strategy improve local exploitation and escape local optima.

Main Results:

  • ANBPO achieved a 91.6% win rate in fitness function values against competing algorithms.
  • ANBPO demonstrated superior performance with improvements of 67.6% (PSNR), 69.4% (SSIM), and 69.7% (FSIM).
  • The algorithm effectively segmented twelve mural images, preserving original feature information.

Conclusions:

  • The ANBPO algorithm offers an efficient and effective solution for mural image segmentation.
  • ANBPO significantly enhances segmentation accuracy, crucial for cultural heritage conservation.
  • The proposed method preserves vital feature information, aiding in mural restoration efforts.