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相关概念视频

Transformers01:26

Transformers

1.1K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.1K
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

180
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...
180
Types Of Transformers01:16

Types Of Transformers

1.0K
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...
1.0K
Transformers in Distribution System01:27

Transformers in Distribution System

125
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...
125
Position Vectors01:29

Position Vectors

957
A position vector is a fundamental concept in mathematics that helps determine the position of one point with respect to another point in space. It is a vector that describes the direction and distance between two points. Position vectors are highly useful in the field of math and science, as they help represent spatial relationships and make calculations easier.
For instance, we want to locate a point P(x, y, z) relative to the origin of coordinates O. In that case, we can define a position...
957
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

489
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...
489

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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在鸟眼视图中使用变压器预测周围环境的表现.

Jiahui Yu1, Wenli Zheng2, Yongquan Chen1

  • 1Shenzhen Institute of Artificial Intelligence and Robotics for Society, and the SSE/IRIM, The Chinese University of Hong Kong, Shenzhen, Guangdong, China.

Frontiers in neuroscience
|July 20, 2023
PubMed
概括

本研究介绍了基于变压器的新型神经网络,用于鸟眼视图 (BEV) 语义映射,在精度和效率上优于卷积神经网络 (CNN),用于复杂的环境感知.

关键词:
在 BEV 地图上,可以看到 BEV 地图.关注注意力注意力注意力注意力自动驾驶自动驾驶的自动驾驶.深度学习是一种深度学习.变压器 变压器 变压器

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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
  • 机器人技术 机器人技术 机器人技术

背景情况:

  • 鸟眼视图 (BEV) 地图对于自主系统至关重要,它代表环境传感数据.
  • 生成语义BEV地图涉及对象检测和细分,这些任务通常受到当前卷积神经网络 (CNN) 能力的限制.
  • 现有的CNN与多规模和多类元素作斗争,阻碍了准确的表示预测.

研究的目的:

  • 开发先进的神经网络,用于准确的鸟眼视图 (BEV) 语义表示预测.
  • 克服CNN在感知微妙的环境细微差别和处理复杂的场景元素方面的局限性.
  • 提出一种新的,端到端的预测方法,用于使用变压器生成BEV地图.

主要方法:

  • 拟议的新型神经网络仅使用变压器,避免卷积层.
  • 为BEV地图开发了一种新的变压器驱动的像素生成方法.
  • 介绍了一种用于图像到BEV转换的新算法,以及用于图像特征提取的基于注意力的网络.

主要成果:

  • 拟议的基于变压器的模型与最先进的CNN相比,表现出更高的性能.
  • 在NuScenes数据集上实现了2.4%的相对改善,在Argoverse 3D数据集上达到5.2%.
  • 通过端到端预测方式成功生成了每类概率的BEV地图.

结论:

  • 新的变压器架构为BEV语义表示预测提供了更有效,更准确的解决方案.
  • 这种方法有效地解决了CNN在处理多规模和多类环境数据方面的局限性.
  • 拟议的方法代表了用于自动驾驶等应用的语义BEV地图生成的重大进步.