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Related Concept Videos

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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Discrete-time Fourier transform01:26

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The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
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Transformers in Distribution System01:27

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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.
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Types Of Transformers01:16

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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
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Equivalent Circuits for Practical Transformers01:28

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The practical equivalent circuits of single-phase two-winding transformers exhibit significant deviations from their idealized versions due to the inherent properties of winding resistance and finite core permeability. These properties result in real and reactive power losses, affecting the transformer's performance. Understanding these deviations is crucial for designing more efficient transformers.
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Difference from Background: Limit of Detection01:05

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
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A Frequency Domain-Enhanced Transformer for Nighttime Object Detection.

Yaru Li1, Li Shen2

  • 1Haide College, Ocean University of China, Qingdao 266100, China.

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

Night-Frequency Detection Transformer (NF-DETR) enhances nighttime object detection by using frequency domain information and physics-prior enhancement. This novel framework improves performance in low-light conditions for autonomous driving and surveillance.

Keywords:
frequency domainmathematical statisticsnighttime perceptionobject detectionwindow cross-attention fusion

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Nighttime object detection is hindered by low illumination, noise, and reduced contrast.
  • Standard detection models struggle in adverse low-light conditions, impacting safety-critical applications.

Purpose of the Study:

  • To introduce NF-DETR (Night-Frequency Detection Transformer), a novel framework for robust nighttime object detection.
  • To leverage frequency domain information and physics-prior enhancement to overcome low-light challenges.

Main Methods:

  • Integrated physics-prior enhancement for improved low-light visibility.
  • Frequency domain feature extraction to capture structural information.
  • Window cross-attention fusion for efficient complementary feature combination.

Main Results:

  • NF-DETR-L achieved significant improvements over baselines on BDD100K-Night and City-Night3K benchmarks.
  • Demonstrated up to +3.7% AP@50:95 improvement on BDD100K-Night.
  • Maintained competitive inference speeds while enhancing detection accuracy.

Conclusions:

  • NF-DETR effectively addresses challenges in nighttime object detection.
  • The framework offers a more robust solution for autonomous driving and surveillance systems.
  • Each component of NF-DETR contributes positively to overall detection performance.