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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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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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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
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Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
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Voxel Transformer with Density-Aware Deformable Attention for 3D Object Detection.

Taeho Kim1, Joohee Kim1

  • 1Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA.

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|August 26, 2023
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Summary

The Voxel Transformer with Density-Aware Deformable Attention (VoTr-DADA) enhances 3D object detection by improving the receptive field adaptability. This novel approach achieves superior performance on benchmark datasets while maintaining fast inference speeds.

Keywords:
3D object detectiondeep learningdeformable attentiondensitytransformer

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • The Voxel Transformer (VoTr) is a key model for 3D object detection, utilizing self-attention for voxel relationships.
  • VoTr's fixed receptive field limits its flexibility in capturing complex spatial information.

Purpose of the Study:

  • To introduce a novel 3D object detection model, VoTr-DADA, that addresses the limitations of VoTr's fixed receptive field.
  • To enhance adaptability and feature extraction in 3D object detection through a new attention mechanism.

Main Methods:

  • Developed the Density-Aware Deformable Attention (DADA) module for adaptive receptive fields.
  • Integrated DADA into the Voxel Transformer architecture, creating VoTr-DADA.
  • Utilized density features to guide attention to crucial input areas.

Main Results:

  • VoTr-DADA demonstrated superior performance compared to the baseline VoTr model in 3D object detection.
  • The proposed method achieved state-of-the-art results on the KITTI and Waymo Open datasets.
  • VoTr-DADA maintained fast inference speeds, indicating computational efficiency.

Conclusions:

  • The Density-Aware Deformable Attention module significantly improves 3D object detection accuracy and flexibility.
  • VoTr-DADA offers a promising advancement in transformer-based 3D object detection, balancing performance and speed.