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Related Concept Videos

Transformers with Off-Nominal Turns Ratios01:25

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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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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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Updated: Aug 22, 2025

Automated Delivery of Microfabricated Targets for Intense Laser Irradiation Experiments
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Transformer-Based Maneuvering Target Tracking.

Guanghui Zhao1, Zelin Wang1, Yixiong Huang1

  • 1School of Artificial Intelligence, Xidian University, Xi'an 710071, China.

Sensors (Basel, Switzerland)
|November 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a transformer-based network (TBN) for maneuvering target tracking, improving global motion state analysis. The TBN outperforms long short-term memory networks by enhancing trajectory generalization and reducing training complexity.

Keywords:
attention mechanismmaneuvering target trackingrecurrent neural networktransformer-based network

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Area of Science:

  • * Computer Vision and Machine Learning
  • * Signal Processing and Tracking Systems

Background:

  • * Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, are commonly used for tracking maneuvering targets by analyzing sequential motion states.
  • * LSTMs exhibit limitations in capturing global motion patterns due to their stepwise feature extraction and struggle with trajectory datasets generated within fixed distance ranges, leading to training complexity and reduced generalization.
  • * Initial position uncertainty and fixed dataset ranges complicate network training and limit adaptability to diverse trajectory scenarios.

Purpose of the Study:

  • * To propose a novel transformer-based network (TBN) for enhanced maneuvering target tracking.
  • * To address the limitations of existing LSTM-based methods in global motion modeling and generalization.
  • * To introduce a center-max normalization technique to improve network training efficiency and robustness.

Main Methods:

  • * Development of a transformer-based network (TBN) comprising an encoder (transformer layers) and a decoder (1D convolutional layers).
  • * Leveraging the attention mechanism within the transformer network to capture long and short-term dependencies in target states from a global viewpoint.
  • * Implementation of center-max normalization to mitigate training complexity and enhance the generalization capabilities of the TBN.

Main Results:

  • * The proposed TBN effectively captures long-term and short-term dependencies in target motion states globally.
  • * Center-max normalization successfully reduces the complexity associated with TBN training.
  • * Experimental evaluations demonstrate that the TBN significantly outperforms traditional LSTM-based tracking networks.

Conclusions:

  • * The transformer-based network (TBN) offers a superior approach to maneuvering target tracking compared to LSTM-based methods.
  • * The TBN's global perspective and enhanced generalization capabilities make it more effective for diverse tracking scenarios.
  • * The proposed center-max normalization is a valuable technique for improving the training and performance of target tracking networks.