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

Sieve Analysis and Grading Curves01:19

Sieve Analysis and Grading Curves

290
Sieve analysis is a method used to determine the particle size distribution of aggregate materials. This process involves the following steps:
290
Fineness Modulus01:19

Fineness Modulus

258
The fineness modulus (FM) of aggregate is a numerical index that measures the coarseness or fineness of the particles. It is calculated by adding the cumulative percentages of aggregate retained on each of a specified series of sieves and dividing the sum by 100.
Consider performing sieve analysis on sand through a set of ASTM sieves. The weight of aggregate retained in each sieve and pan placed at the bottom is recorded, as given in Column B of Table 1.
To determine the fineness modulus of...
258

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

Strength and Degradation Characteristics of Zein Biopolymer-Treated Sands Under Wetting-Drying Cycles.

Polymers·2026
Same author

Development and Experimental Validation of an Integrated Evaluation Framework for EMS Smartwear Electrodes.

Sensors (Basel, Switzerland)·2025
Same author

Soil Stabilization Using Gum Arabic Biopolymer.

Biopolymers·2025
Same author

Data-driven machine learning models for predicting engineering properties in deep-sea sediments.

Scientific reports·2025
Same author

Sporting a virtual future: exploring sports and virtual reality patents using deep learning-based analysis.

PeerJ. Computer science·2025
Same author

Longitudinal dispersivity and saturation of sand-clay mixtures: Impact of clay content, initial degree of saturation, and swelling potential.

Journal of contaminant hydrology·2025

相关实验视频

Updated: May 25, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

940

基于机器学习的Bender元素信号的模式识别,用于预测沙粒大小.

Yong-Hoon Byun1, Juik Son1, Jungmin Yun2

  • 1Department of Agricultural Civil Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 41566, Republic of Korea.

Scientific reports
|February 26, 2025
PubMed
概括
此摘要是机器生成的。

这项研究将器元件信号与卷积神经网络 (CNN) 集成在一起,以预测沙粒大小分布. 在CNN模型有效地分类沙子类型,使实时监测粒子大小变化.

关键词:
在Bender元件中使用Bender元件.卷积神经网络是一种卷积神经网络.截止频率的截止时间沙子颗粒的大小 沙子颗粒大小垂直应力是一种垂直应力.

更多相关视频

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
10:53

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

7.0K
Author Spotlight: Development of a Novel Finite Element Analysis Model for Improved Orthognathic Surgical Techniques
07:16

Author Spotlight: Development of a Novel Finite Element Analysis Model for Improved Orthognathic Surgical Techniques

Published on: October 20, 2023

1.2K

相关实验视频

Last Updated: May 25, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

940
Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
10:53

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

7.0K
Author Spotlight: Development of a Novel Finite Element Analysis Model for Improved Orthognathic Surgical Techniques
07:16

Author Spotlight: Development of a Novel Finite Element Analysis Model for Improved Orthognathic Surgical Techniques

Published on: October 20, 2023

1.2K

科学领域:

  • 地质技术工程 地质技术工程
  • 机器学习应用 机器学习应用
  • 信号处理 信号处理

背景情况:

  • 颗粒大小分布对于土壤的行为至关重要.
  • 曲元件测量土壤中的剪切波传播.
  • 卷积神经网络 (CNN) 在复杂数据中的模式识别方面表现出色.

研究的目的:

  • 为了研究曲元件信号和CNN的集成,用于预测沙粒大小分布.
  • 开发和优化CNN模型,根据杆元素数据分类不同类型的沙子.
  • 评估实时监测沙粒大小变化的可行性.

主要方法:

  • 对于四种沙子类型 (0.5-7毫米颗粒大小) 来说,利用了自元件的时间序列信号.
  • 应用了一个具有批量正常化和ReLU激活的单维CNN,通过贝叶斯技术进行优化.
  • 在不同的垂直应力 (10,50,150 kPa) 和切断频率 (10,50,100 kHz) 下进行了实验.

主要成果:

  • 更高的垂直应力增加了共振频率,减少了剪切波到达时间.
  • 美国有线电视新闻网 (CNN) 模型成功地将四种沙子类型分类为不同的压力和频率条件.
  • 每种沙类的独特信号模式都被CNN算法有效地捕获.

结论:

  • 曲元件信号与CNN相结合,为预测沙粒大小分布提供了一种可行的方法.
  • 开发的框架显示了实时监测土壤颗粒大小的潜力.
  • 在地质技术应用中,CNN可以有效地解释从杆元件的复杂信号模式.