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

Aggregates Classification01:29

Aggregates Classification

329
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
329
Recycling Endosomes and Transcytosis00:58

Recycling Endosomes and Transcytosis

2.7K
The recycling endosome, also known as the endosomal recycling compartment (ERC), is a part of the slow-recycling process of the endocytic pathway. Molecules internalized through receptor-mediated endocytosis are either degraded in the lysosomes or are recycled to the plasma membrane through the fast- or slow-recycling route.
The recycling endosome is not a single organelle but an extensively tubulated network of recycling pathways. It functions in storing molecules or transporting them across...
2.7K
Deleterious Substances in Aggregate01:25

Deleterious Substances in Aggregate

174
Deleterious substances in aggregates can be detrimental to the quality and durability of concrete. These substances include organic impurities like loam, which interfere with cement hydration and are usually present in the sand. These prevent a good bond between aggregate and cement paste. Organic impurities can be detected using the colorimetric test, where the darkness of a solution after agitation indicates the level of organic content.
Another type of impurity is clay and fine material that...
174

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

Comparative analysis of whole spine and sacroiliac joint magnetic resonance SPARCC scores in patients with SAPHO syndrome and ankylosing spondylitis.

Frontiers in immunology·2026
Same author

SemanticST: Semantics-enhanced Spatio-Temporal Modeling for Ejection Fraction Estimation in Echocardiography.

IEEE journal of biomedical and health informatics·2026
Same author

The Role of Multimodal Generative AI in Older Adults' Health Management: Systematic Scoping Review.

JMIR AI·2026
Same author

Shock-induced magnetic reconnection in the Venusian magnetotail.

Nature communications·2026
Same author

How do digital technologies improve the physical health of higher education students? A systematic scoping review.

BMC public health·2026
Same author

Educating older adults to use technologies for health management in social settings: perspectives of older adults and community workers.

BMC geriatrics·2026
Same journal

ZACP: enhancing skin lesion classification ability using zero attention and complete perception.

Visual computing for industry, biomedicine, and art·2026
Same journal

CRR-Net: a correlation reconstruction and refinement network for deformable medical image registration.

Visual computing for industry, biomedicine, and art·2026
Same journal

Foundation model for screening severe mitral regurgitation and severe aortic stenosis from coronary angiograms.

Visual computing for industry, biomedicine, and art·2026
Same journal

Multiscale feature fusion for few-shot medical image learning with fisher information-driven layer selection.

Visual computing for industry, biomedicine, and art·2026
Same journal

MEDI-SLATE: medical imaging slide-lecture aligned teaching ensemble.

Visual computing for industry, biomedicine, and art·2026
Same journal

Construction of complex non-uniform rational B-spline volume parametric models with G<sup>1</sup> continuity.

Visual computing for industry, biomedicine, and art·2026
查看所有相关文章

相关实验视频

Updated: Jul 13, 2025

The Effect of Construction and Demolition Waste Plastic Fractions on Wood-Polymer Composite Properties
09:06

The Effect of Construction and Demolition Waste Plastic Fractions on Wood-Polymer Composite Properties

Published on: June 7, 2020

8.1K

焦点-RCNet:基于焦点和知识蒸的轻量级可回收废物分类算法.

Dashun Zheng1, Rongsheng Wang1, Yaofei Duan1

  • 1Faculty of Applied Sciences, Macao Polytechnic University, Rua de Luís Gonzaga Gomes, Macao, 999078, China.

Visual computing for industry, biomedicine, and art
|October 11, 2023
PubMed
概括
此摘要是机器生成的。

一个新的轻量级AI模型,Focus-RCNet,有效地对可回收废物图像进行分类. 该模型实现了高精度,适用于实时嵌入式应用程序,解决了复杂的深度学习网络的局限性.

关键词:
注意力 注意力 注意力 注意力知识的蒸知识的蒸.轻量化 轻量化 轻量化 轻量化 轻量化废物分类废物的分类.废物回收利用废物回收利用

更多相关视频

Author Spotlight: Effective Reuse of Polycarbonate Tubes for Extracellular Vesicle Isolation
02:36

Author Spotlight: Effective Reuse of Polycarbonate Tubes for Extracellular Vesicle Isolation

Published on: March 8, 2024

1.0K
Depolymerizable Olefinic Polymers Based on Fused-Ring Cyclooctene Monomers
08:12

Depolymerizable Olefinic Polymers Based on Fused-Ring Cyclooctene Monomers

Published on: December 16, 2022

3.3K

相关实验视频

Last Updated: Jul 13, 2025

The Effect of Construction and Demolition Waste Plastic Fractions on Wood-Polymer Composite Properties
09:06

The Effect of Construction and Demolition Waste Plastic Fractions on Wood-Polymer Composite Properties

Published on: June 7, 2020

8.1K
Author Spotlight: Effective Reuse of Polycarbonate Tubes for Extracellular Vesicle Isolation
02:36

Author Spotlight: Effective Reuse of Polycarbonate Tubes for Extracellular Vesicle Isolation

Published on: March 8, 2024

1.0K
Depolymerizable Olefinic Polymers Based on Fused-Ring Cyclooctene Monomers
08:12

Depolymerizable Olefinic Polymers Based on Fused-Ring Cyclooctene Monomers

Published on: December 16, 2022

3.3K

科学领域:

  • 环境科学 环境科学
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 由于生活水平和消费模式的改善,全球废物污染正在不断升级.
  • 使用人工智能的自动废物分类提供了一个有希望的解决方案.
  • 现有的卷积神经网络通常对实时嵌入式系统来说过于计算密集.

研究的目的:

  • 为高效准确的可回收废物图像分类开发一个轻量级的网络架构.
  • 在嵌入式应用中克服传统深度学习模型的计算局限性.

主要方法:

  • 提出了Focus-RCNet,这是一个以MobileNetV2的结构为灵感的轻量级网络,利用深层可分离卷积.
  • 集成了一个Focus模块,用于减少特征维度,同时保留关键信息.
  • 采用SimAM注意力机制,以最小的参数增强特征焦点.
  • 应用知识蒸以进一步压缩模型参数.

主要成果:

  • 在TrashNet数据集上,Focus-RCNet模型实现了92%的准确性.
  • 展示了高度的部署流动性,使其适合实时应用.
  • 与传统网络相比,成功降低了模型复杂性和参数数量.

结论:

  • 焦点RCNet提供了一个有效和高效的解决方案,用于自动化可回收废物分类.
  • 该模型的轻量级设计和高精度使其非常适合实时嵌入式系统.
  • 这种方法有助于通过人工智能创新解决废物管理日益增长的挑战.