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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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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
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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.
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Kernelized convolutional transformer network based driver behavior estimation for conflict resolution at unsignalized

Omveer Sharma1, N C Sahoo1, Niladri B Puhan1

  • 1School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, Odisha, India.

ISA Transactions
|July 25, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new Kernelized Convolutional Transformer Network (KCTN) for predicting driver behavior. The KCTN enhances safety in autonomous driving by improving behavior estimation accuracy at complex intersections.

Keywords:
Attention mechanismConvolutional neural networkDeep learningDriver behaviorIntelligent vehicleRoundabout

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Robotics

Background:

  • Driver behavior modeling is vital for Advanced Driver Assistance Systems (ADAS).
  • Accurate behavior estimation of surrounding vehicles is critical for autonomous vehicle navigation, especially in unsignalized intersections.
  • Unsignalized three-way roundabouts present complex driving scenarios requiring sophisticated behavior prediction.

Purpose of the Study:

  • To propose a novel Kernelized Convolutional Transformer Network (KCTN) for driver behavior estimation.
  • To enhance the model's capacity to capture higher-order feature interactions in non-linear spaces.
  • To improve the accuracy and lead time of behavior prediction in challenging driving environments.

Main Methods:

  • Developed a Kernelized Convolutional Transformer Network (KCTN) incorporating a multi-head attention (MHA) mechanism.
  • Introduced a kervolution operation, generalizing convolution into non-linear space using a Gaussian kernel function.
  • Validated the proposed model using the real-world ACFR dataset.

Main Results:

  • The KCTN model demonstrated superior performance compared to current state-of-the-art methods.
  • Achieved higher accuracy in driver behavior prediction.
  • Provided a significant lead time for identifying potential conflict situations.

Conclusions:

  • The proposed KCTN with kervolution and MHA is effective for driver behavior estimation at unsignalized roundabouts.
  • The model enhances the safety and reliability of autonomous navigation systems.
  • This approach offers a significant advancement in predicting complex driver behaviors for ADAS and autonomous vehicles.