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Reducing Line Loss01:18

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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.
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Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round...
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Boundary Conditions: Lossless Lines01:21

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Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
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Updated: Jan 16, 2026

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
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Improved Perceptual Loss for Sketch Image Domain.

Chang Wook Seo1

  • 1Anigma Technologies, Seoul 06241, Republic of Korea.

Journal of Imaging
|September 26, 2025
PubMed
Summary

This study introduces a new perceptual loss function specifically for sketch images, improving generation and retrieval accuracy by over 10%. The enhanced model bridges the gap between photographic and sketch domains for better computer vision applications.

Keywords:
computer visiongenerative modelssketch image

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Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Perceptual loss functions, typically for photographic images, underperform on sketch data due to domain differences.
  • Existing methods lack domain-specific adaptations for sketch image analysis.

Purpose of the Study:

  • To develop an improved perceptual loss function tailored for sketch images.
  • To enhance performance in sketch generation, retrieval, and recognition tasks.

Main Methods:

  • Fine-tuned a pre-trained VGG-16 model on the ImageNet-Sketch dataset.
  • Incorporated spatial and channel attention mechanisms by replacing max-pooling layers.

Main Results:

  • Achieved superior sketch generation quality.
  • Improved sketch retrieval accuracy by over 10%.
  • Demonstrated a 6-fold increase in class separability, indicating better feature organization.

Conclusions:

  • The domain-specific perceptual loss effectively bridges the gap between photographic and sketch domains.
  • Both model fine-tuning and attention mechanisms are crucial for performance enhancement.
  • The proposed method offers significant improvements for sketch-related computer vision applications.