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Related Concept Videos

Transformers in Distribution System01:27

Transformers in Distribution System

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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
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Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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Types Of Transformers01:16

Types Of Transformers

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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
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Energy Losses in Transformers01:21

Energy Losses in Transformers

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In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
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Energy-Efficient and Adversarially Resilient Underwater Object Detection via Adaptive Vision Transformers.

Leqi Li1, Gengpei Zhang1, Yongqian Zhou1

  • 1The School of Electronic Information and Electrical Engineering, East Campus, Yangtze University, Jingzhou 434100, China.

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Summary

This study introduces an Adaptive Vision Transformer (A-ViT) framework for robust underwater object detection, significantly improving image quality and detection accuracy while reducing latency and enhancing security against adversarial attacks.

Keywords:
adaptive vision transformeradversarial robustnessenergy efficiency optimizationimage enhancementsecurity defenseunderwater object detection

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Area of Science:

  • Computer Vision
  • Marine Technology
  • Artificial Intelligence

Background:

  • Underwater object detection faces challenges like optical degradation, high energy use, and adversarial threats.
  • Existing methods struggle with image quality and computational efficiency in diverse marine environments.

Purpose of the Study:

  • To develop an Adaptive Vision Transformer (A-ViT)-based framework for enhanced underwater object detection.
  • To improve image quality, detection accuracy, and system efficiency for marine applications.
  • To enhance system resilience against adversarial perturbations and ensure operational feasibility.

Main Methods:

  • Implemented a power-modeling and endurance-estimation scheme for hardware feasibility.
  • Utilized Hybrid Attention Transformer (HAT) for super-resolution and DICAM for staged image enhancement.
  • Employed an improved YOLOv11-Coordinate Attention-High-order Spatial Feature Pyramid Network (YOLOv11-CA_HSFPN) for detection.
  • Integrated an Adaptive Vision Transformer (A-ViT) with Region of Interest (ROI) pooling for efficiency.
  • Introduced an Image-stage Attack QuickCheck (IAQ) module for defense against adversarial attacks.

Main Results:

  • Achieved significant improvements in image quality metrics: PSNR (+74.8%), SSIM (+375.8%), UIQM (3.00 to 3.85), and UCIQE (0.550 to 0.673).
  • YOLOv11-CA_HSFPN reached 56.2% mAP@0.5, surpassing the baseline YOLOv11 by 1.5%.
  • A-ViT + ROI reduced inference latency by 27.3% and memory usage by 74.6% with YOLOv11-CA_HSFPN.
  • Demonstrated up to 48.9% latency reduction and 80.0% VRAM savings with other detectors.
  • The IAQ module reduced adversarial-attack-induced latency growth by 33-40%.

Conclusions:

  • The proposed A-ViT framework effectively addresses key challenges in underwater object detection.
  • The system demonstrates superior performance in image enhancement, detection accuracy, and computational efficiency.
  • The framework offers robust defense mechanisms against adversarial attacks, ensuring reliable operation in critical marine missions.