Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Transformers01:26

Transformers

1.2K
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...
1.2K
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

213
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...
213
Types Of Transformers01:16

Types Of Transformers

1.1K
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...
1.1K
Parallel Processing01:20

Parallel Processing

242
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
242
Multi-pass Transmembrane Proteins and β-barrels01:09

Multi-pass Transmembrane Proteins and β-barrels

5.6K
In multi-pass transmembrane proteins, the polypeptide chain crosses the membrane more than once. The transmembrane polypeptide chain either forms an α-helix or β-strand structure. α-Helix containing multi-pass transmembrane proteins are ubiquitous, whereas β-strand containing ones are mainly found in gram-negative bacteria, mitochondria, and chloroplasts.
α-Helix containing multi-pass transmembrane proteins
Multi-pass transmembrane proteins such as...
5.6K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

An improved grey wolf optimization algorithm for 3-D UWB indoor positioning.

PloS one·2026
Same author

Surufatinib plus toripalimab for patients with advanced solid tumors and disease progression after prior immunotherapy: an open-label multi-cohort phase 2 trial.

Cancer immunology, immunotherapy : CII·2026
Same author

Application of particle filter algorithm based on chaotic sequences and improved t-distribution in UWB indoor positioning.

Scientific reports·2026
Same author

Savolitinib in MET-amplified gastric or gastroesophageal junction adenocarcinoma: a phase 2 trial.

Nature medicine·2026
Same author

Correction: Differential association of the 5-factor modified frailty index with postoperative pulmonary complications: specific prediction of infection risk after pulmonary lobectomy.

Frontiers in medicine·2026
Same author

Preliminary study on the intraoperative application of the "dual-path" strategy for sentinel lymph node tracing in endometrial cancer.

Scientific reports·2026

相关实验视频

Updated: Sep 17, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

529

MAT-PointPillars:基于多尺度注意力机制和变压器的增强PointPillars算法.

Xinpeng Yao1, Peiyuan Liu2, Jingmei Zhou2

  • 1Shandong Key Laboratory of Smart Transportation (Preparation), Jinan, China.

PloS one
|June 27, 2025
PubMed
概括

MAT-PointPillars增强了对小型目标的3D物体检测,例如使用多尺度注意力和变压器的骑自行车者. 这种先进的算法提高了准确性,并保持了自动驾驶系统的实时性能.

更多相关视频

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.0K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

650

相关实验视频

Last Updated: Sep 17, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

529
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.0K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

650

科学领域:

  • 计算机视觉 计算机视觉
  • 机器人技术 机器人技术 机器人技术
  • 机器学习 机器学习

背景情况:

  • 现有的3D物体检测算法与小,不规则的目标 (如骑自行车者) 进行斗争.
  • 低检测准确度和识别错误阻碍了自主系统的可靠性.

研究的目的:

  • 开发一个改进的3D物体检测算法,MAT-PointPillars,专门解决检测小物体和不规则物体的挑战.
  • 为了提高3D目标检测的准确性和稳定性,用于诸如自动驾驶等应用.

主要方法:

  • MAT-PointPillars通过结合多尺度视觉转换器和注意力机制来扩展PointPillars算法.
  • 该方法使用支柱编码来编码语义点云,并集成注意力机制来完善骨干的上采样过程.
  • 引入了变压器编码器,以增强脊柱第三阶段的上采样结构.

主要成果:

  • 在KITTI数据集上,MAT-PointPillars在三个难度级别中实现了81.15%,62.02%和58.68%的3D平均检测精度 (AP3D).
  • 与基线模型相比,该算法在AP3D中显示了2.44%,1.19%和1.23%的改进.
  • 使用ROS构建的实时3D物体检测系统实现了每秒22.63的平均运行速度.

结论:

  • MAT-PointPillars在3D中显著提高了小型和不规则物体,特别是骑自行车者的检测精度.
  • 该算法保持了高检测速度,适合实时应用,超过传统LiDAR采样频率.
  • 增强的系统为在复杂的现实世界中运行的自主系统提供了更高的可靠性.