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

Classification and Mechanical Properties of Synthetic Polymers01:28

Classification and Mechanical Properties of Synthetic Polymers

Synthetic polymers are classified as elastomers, fibers, or plastics based on their crystallinity. Crystallinity, the degree of long-range order in the solid state, influences the mechanical properties (stretching or contracting) of elastomers. Elastomers are flexible polymers that can expand or contract easily upon the application of an external force. They have numerous crosslinks that pull them back into their original shape when stress is removed. Silicones, for instance, are highly elastic...

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

Spatiotemporal abundances and potential risks of microplastics in surface sediments of the inner Gulf of Thailand.

Environmental science and pollution research international·2026
Same author

Emerging plasticizer pollution in Thailand: Occurrence, distribution, and ecological risks of phthalate and non-phthalate esters.

Marine pollution bulletin·2026
Same author

Multi-Task Deep Learning Model for Automated Detection and Severity Grading of Lumbar Spinal Stenosis on MRI: Multi-Center External Validation.

Diseases (Basel, Switzerland)·2026
Same author

Non-Destructive Mangosteen Volume Estimation via Multi-View Instance Segmentation and Hybrid Geometric Modeling.

Journal of imaging·2026
Same author

Non-Destructive Volume Estimation of Oranges for Factory Quality Control Using Computer Vision and Ensemble Machine Learning.

Journal of imaging·2025
Same author

Strabismus Detection in Monocular Eye Images for Telemedicine Applications.

Journal of imaging·2024

相关实验视频

Updated: Jul 1, 2026

Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis
10:16

Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis

Published on: December 16, 2016

49.4K

使用双模态光谱和图像数据进行自动微塑料分类,以提高准确性.

Arsanchai Sukkuea1, Jakkaphong Inpun2, Phaothep Cherdsukjai3

  • 1School of Engineering and Technology, Walailak University, 222 Thaiburi, Thasala, Nakhon Si Thammarat 80160, Thailand; Research Center for Intelligent Technology and Integration, School of Engineering and Technology, Walailak University, Nakhon Si Thammarat 80160, Thailand.

Marine pollution bulletin
|February 17, 2025
PubMed
概括
此摘要是机器生成的。

一个自动化系统使用光谱数据对微塑料 (MP) 进行分类,克服了手工分析的局限性. 使用机器学习的双模式方法实现了超过99%的准确性,从而实现了高效的微塑料识别.

关键词:
分类系统的分类系统.双模式数据集的数据集.功能融合的特点是:微塑料是一种微塑料.μFTIR 频谱的使用

更多相关视频

Characterization of Aquatic Biofilms with Flow Cytometry
08:30

Characterization of Aquatic Biofilms with Flow Cytometry

Published on: June 6, 2018

9.0K
Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.1K

相关实验视频

Last Updated: Jul 1, 2026

Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis
10:16

Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis

Published on: December 16, 2016

49.4K
Characterization of Aquatic Biofilms with Flow Cytometry
08:30

Characterization of Aquatic Biofilms with Flow Cytometry

Published on: June 6, 2018

9.0K
Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.1K

科学领域:

  • 环境科学 环境科学
  • 分析化学 分析化学
  • 数据科学数据科学数据科学

背景情况:

  • 手动微塑料 (MPs) 光谱分析耗时且容易出现错误.
  • 准确和高效的MP识别对于了解和管理塑料污染至关重要.

研究的目的:

  • 利用光谱数据开发一个自动化的微塑料分类系统.
  • 为了比较各种机器学习模型用于MP识别的性能.
  • 为MP分类部署一个用户友好的Web应用程序.

主要方法:

  • 使用微里埃变换红外光谱 (μFTIR) 的双模数据集.
  • 使用深度学习模型提取的光谱特征:AlexNet,ResNet18和视觉转换器 (ViT).
  • 评估的机器学习分类器:决策树 (DT),极端随机树 (ET),支持向量分类器 (SVC) 和多类后勤回归 (LR).

主要成果:

  • 亚历克斯Net-LR模型实现了99.03%的验证率和99.99%的测试准确率.
  • ResNet18-LR显示了可比的准确性 (99%),培训和推断时间更快.
  • 开发的Web应用程序MPsSpecClassify有助于有效地识别MP.

结论:

  • 深度学习功能提取与机器学习分类器相结合,可以实现高度准确的自动化MP分类.
  • 由于其效率,ResNet18-LR为Web部署提供了一个实用的解决方案.
  • 该MPsSpecClassify应用程序支持改进的微塑料污染管理.