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相关概念视频

Upsampling01:22

Upsampling

682
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
682
Transformers in Distribution System01:27

Transformers in Distribution System

553
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...
553

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相关实验视频

Updated: Mar 15, 2026

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

852

一个新的CNN-ViT模型与级联抽样,以实现高效的裂细分.

Ahmed Tibermacine1, Imad Eddine Tibermacine2, Zineddine S Kahhoul3

  • 1LESIA Laboratory, Department of Computer Science, University of Biskra, Biskra 07000, Algeria.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
概括

本研究提出了一种高效的混合人工智能模型,用于在基础设施图像中准确的裂细分. 新型架构提高了缺陷检测,同时降低了实用实时检查的计算成本.

关键词:
卷积神经网络是一种卷积神经网络.裂纹细分 裂纹细分 裂纹细分深度学习是一种深度学习.边缘计算是一种边缘计算.光学数字成像技术的应用.结构健康监测 结构健康监测视觉变压器 视觉变压器

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

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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
  • 土木工程 土木工程是指土木工程.

背景情况:

  • 由于低对比度,不连续的缺陷和环境噪音,基础设施中精确的裂纹细分很难.
  • 现有的变压器模型提供了全球背景,但具有很高的计算开销,限制了实际使用.

研究的目的:

  • 开发一个高效的混合细分架构,以改善民用基础设施的裂检测.
  • 解决现有模型在实际应用中的计算和内存效率方面的局限性.

主要方法:

  • 一种混合架构,结合了卷积编码器 (本地特征) 和轻量级变压器瓶 (全球上下文).
  • 一个渐进式解码器,具有多级别的跳过功能融合,以恢复空间分辨率.
  • 培训包括一个复合的子-二进制交叉的目标和一个专门的代币化策略,以保护细裂纹.

主要成果:

  • 在四个公共基准上,与卷积式,基于变压器和混合基线相比,性能得到了持续的改善.
  • 废弃性研究证实了单个设计组件的有效性.
  • 有利的运行时配置,显示低延迟和内存使用率,用于实时部署.

结论:

  • 提出的高效混合模型显著提高了裂细分的准确性和效率.
  • 该架构适用于资源有限的环境和实时检查平台.
  • 这项工作为自动化基础设施健康监测提供了实际解决方案.