Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

222
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...
222
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

559
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
559
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

457
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
457
Types Of Transformers01:16

Types Of Transformers

1.1K
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...
1.1K
The Ideal Transformer01:26

The Ideal Transformer

942
In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's...
942
Transformers in Distribution System01:27

Transformers in Distribution System

170
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...
170

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

ScriptManager: a platform for scalable and reproducible high-resolution analysis of genomics datasets.

bioRxiv : the preprint server for biology·2026
Same author

MGDR-YOLO: An Efficient Multi-Backbone YOLOv11 Framework for X-Ray Weld Defect Inspection.

Sensors (Basel, Switzerland)·2026
Same author

Distinct vulnerable plaque phenotypes underlie perforator and hypoperfusion strokes: multimodal high resolution vessel wall imaging and optical coherence tomography study.

Journal of neurointerventional surgery·2026
Same author

Altered glucose metabolic pattern in basal ganglia: <sup>18</sup>F-FDG PET/CT semi-quantitative analysis of metabolic signatures in neonatal bilirubin encephalopathy.

Experimental neurology·2026
Same author

Does tranexamic acid fix all bleeding in surgery?

Journal of the National Medical Association·2026
Same author

Plaque features for the prediction of in-stent restenosis in symptomatic intracranial atherosclerotic stenosis from a retrospective single-center study.

European radiology·2025

Related Experiment Video

Updated: Sep 27, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.0K

RODFormer: High-Precision Design for Rotating Object Detection with Transformers.

Yaonan Dai1, Jiuyang Yu1, Dean Zhang1

  • 1Hubei Provincial Engineering Technology Research Center of Green Chemical Equipment, School of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan 430205, China.

Sensors (Basel, Switzerland)
|April 12, 2022
PubMed
Summary
This summary is machine-generated.

RODFormer, a novel Transformer-based model, enhances rotating object detection by improving feature collection and addressing boundary loss. It achieves superior accuracy on the DOTA dataset compared to existing methods.

Keywords:
RODFormerrotating object detectionspatial-FFNstructured transformers

More Related Videos

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

662
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

537

Related Experiment Videos

Last Updated: Sep 27, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.0K
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

662
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

537

Area of Science:

  • Computer Vision
  • Deep Learning
  • Object Detection

Background:

  • Transformers in object detection often lack local spatial information.
  • Discontinuous boundary loss is a challenge in rotating object detection.

Purpose of the Study:

  • To propose a Transformer-based model, RODFormer, for high-precision rotating object detection.
  • To enhance local spatial modeling and mitigate boundary loss issues.

Main Methods:

  • Utilized a structured Transformer architecture for multi-resolution feature collection.
  • Introduced spatial-FFN, fusing local 3x3 depthwise separable convolutions with MLP global channel features.
  • Developed a detection head with CIOU-smooth L1 loss to reduce rotating frame loss discontinuity.

Main Results:

  • Ablation studies confirmed the effectiveness of the Transformer module, spatial-FFN, and CIOU-smooth L1 loss.
  • RODFormer achieved the highest average detection accuracy (75.60%) on the DOTA dataset.
  • Demonstrated superior performance compared to 12 other rotating object detection models.

Conclusions:

  • RODFormer effectively addresses limitations of standard Transformers in rotating object detection.
  • The proposed spatial-FFN and loss function significantly improve detection accuracy.
  • RODFormer offers a competitive and accurate solution for rotating object detection tasks.