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相关概念视频

Deformations in a Transverse Cross Section01:21

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When a material is subjected to uniaxial stress, it elongates or contracts in the direction of the applied force, and also undergoes changes in the perpendicular directions. This behavior is crucial for understanding how materials behave under stress and is governed by mechanical properties such as Poisson's ratio v, which measures the ratio of transverse strain to axial strain.
As the material stretches, it expands or contracts in orthogonal directions to the load. This phenomenon varies...
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Deformation in a Circular Shaft01:10

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One of the distinctive characteristics of circular shafts is their ability to maintain their cross-sectional integrity under torsion. In other words, each cross-section continues to exist as a flat, unaltered entity, simply rotating like a solid, rigid slab. To understand the distribution of shearing stress within such a shaft, consider a cylindrical section inside this circular shaft. This section has a length of L and a radius of R, with one end fixed. The radius of the cylindrical section is...
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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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When a rod is made of different materials or has various cross-sections, it must be divided into parts that meet the necessary conditions for determining the deformation. These parts are each characterized by their internal force, cross-sectional area, length, and modulus of elasticity. These parameters are then used to compute the deformation of the entire rod.
In the case of a member with a variable cross-section, the strain is not constant but depends on the position. The deformation of an...
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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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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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DeforT:用于视觉跟踪的可变形变压器

Kai Yang1, Qun Li2, Chunwei Tian3

  • 1School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan 430200, China; Hubei Luojia Laboratory, Wuhan 430200, China.

Neural networks : the official journal of the International Neural Network Society
|May 16, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种可变形的变压器,用于视觉对象跟踪,改善特征相关性和定位精度. 这种新的方法提高了对基准数据集的跟踪稳定性和性能.

关键词:
分类网络的分类网络.可变形变压器可以变形.回归网络的回归网络.视觉跟踪 视觉跟踪 视觉跟踪

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 传统的视觉追踪器通常依赖于分类和界限框回归.
  • 追踪器中的线性关联方法可能会失去语义特征,并导致局部最佳.
  • 现有的追踪器由于不可靠的分类分数和回归训练的交叉与联盟 (IoU) 损失而遭受性能恶化.

研究的目的:

  • 开发一种可变形的变压器模型,以实现更有效的视觉对象跟踪.
  • 为了解决当前跟踪方法中语义特征损失和局部最佳的局限性.
  • 在视觉跟踪中提高分类准确性和定位精度.

主要方法:

  • 使用可变形变压器计算训练和搜索集之间的相关性特征.
  • 为分类网络培训引入了质量意识焦点损失 (QAFL),以解决预测不一致的问题.
  • 在回归网络培训中使用一个α-Generalized Intersection over Union (α-GIoU) 损失来提高本地化准确性.
  • 集成的在线学习得分与变压器辅助框架和分类得分,用于强大的候选对象位置预测.

主要成果:

  • 可变形变压器模型有效计算相关性特征,克服线性方法的局限性.
  • 质量意识焦点损失 (QAFL) 提高了分类和定位质量预测之间的一致性.
  • 联盟 (α-GIoU) 上的α-通用交叉损失显著提高了定位准确性.
  • 拟议的方法在OTB-2015上获得了71.7%的成功分数,在NFS30上获得了67.3%的AUC分数,证明了卓越的性能.

结论:

  • 可变形变压器模型在视觉对象跟踪方面取得了重大进展.
  • 新的损失函数 (QAFL和α-GIoU) 有效地解决了跟踪精度和稳定性的关键挑战.
  • 拟议的方法在多个具有挑战性的基准数据集上展示了最先进的性能.