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

Force Classification01:22

Force Classification

1.6K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.6K
Classification of Systems-I01:26

Classification of Systems-I

318
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
318
Aggregates Classification01:29

Aggregates Classification

387
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...
387
Classification of Systems-II01:31

Classification of Systems-II

242
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
242
Methods of Classification and Identification01:28

Methods of Classification and Identification

209
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
209
Classification of Signals01:30

Classification of Signals

908
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
908

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

Machine learning-based regression analysis of cyclopentane and cyclohexane molecular graphs.

Scientific reports·2026
Same author

Towards precision agriculture for assessing germination rates and density of rice seedling using hierarchical convolutional neural network on drone imagery.

Scientific reports·2026
Same author

Explainable artificial intelligence with pyramid vision transformer model for multi-class malignant cell classification on cytology slides.

Scientific reports·2026
Same author

Secure Elliptic Galois Cryptography Framework for robust real-time vehicle image classification using convolutional sparse autoencoder in intelligent transportation systems.

Scientific reports·2026
Same author

Forecasting of global water usage in agriculture and total global consumption by using the Bi-GRU model.

Scientific reports·2026
Same author

Improving spectral efficiency in distributed massive MIMO in multi-user downlink millimeter wave.

Scientific reports·2026
Same journal

MT-MRI for detection of renal interstitial fibrosis in renovascular disease.

Scientific reports·2026
Same journal

Detection of underground objects from GPR data using a lightweight YOLO-based approach.

Scientific reports·2026
Same journal

Early systemic inflammatory-metabolic trajectory phenotypes are associated with survival outcomes in metastatic renal cell carcinoma treated with nivolumab.

Scientific reports·2026
Same journal

Water balance components in a dry-seeded rice-wheat system: Untangling the effects of tillage and mulching practices.

Scientific reports·2026
Same journal

Topological approaches to quantum tensor train compression via ZX-calculus and SVD.

Scientific reports·2026
Same journal

determinants of flood impacts and adaptive capacity among market vendors in Walukuba-Masese, Jinja city, Uganda.

Scientific reports·2026
查看所有相关文章

相关实验视频

Updated: Sep 16, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.6K

在无人机网络中使用Snake优化算法与深度学习的多类空中图像分类.

Alanoud Al Mazroa1, Nuha Alruwais2, Muhammad Kashif Saeed3

  • 1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University (PNU), P.O. Box 84428, 11671, Riyadh, Saudi Arabia.

Scientific reports
|July 4, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新方法,用于使用深度学习和蛇优化算法对来自无人机网络的空中图像进行分类. 这种方法达到99.75%的准确性,大大改善了空中图像的分类.

关键词:
深度学习 (Deep Learning) 是一种深度学习.在DenseNet中,使用的是DenseNet.图像的分类图像的分类.蛇优化算法 蛇优化算法无人驾驶飞行器无人驾驶飞行器

更多相关视频

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

883
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

648

相关实验视频

Last Updated: Sep 16, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.6K
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

883
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

648

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 遥感 遥感 遥感 遥感

背景情况:

  • 多类空中图像分类 (AIC) 对于无人驾驶飞行器 (UAV) 网络在环境监测和基础设施检查等应用中至关重要.
  • 深度学习 (DL) 模型,特别是卷积神经网络 (CNN),擅长通过提取光谱和空间特征来分析复杂的空中图像.
  • 优化DL模型对于提高无人机系统中的AIC准确性至关重要.

研究的目的:

  • 为无人机网络提出一种新的方法,Snake优化算法与深度学习用于多类空中图像分类 (SOADL-MCAIC).
  • 使用先进的AI技术,提高多类空中图像分类的准确性和效率.
  • 开发一个强大的系统,以识别无人机拍摄的空中图像中的各种类别.

主要方法:

  • SOADL-MCAIC方法采用高斯过 (GF) 来进行图像预处理.
  • 一个高效的DenseNet模型被用于从空中图像中学习复杂的特征.
  • 蛇优化算法 (SOA) 用于高效密集网模型的超参数调整.
  • 核心极端学习机器 (KELM) 实现了对空中图像的最终分类.

主要成果:

  • 在UCM土地使用数据集上,SOADL-MCAIC方法实现了99.75%的优异分类准确度.
  • 拟议的方法在多类空中图像分类方面显示出与现有模型相比的显著改进.
  • 集成SOA用于超参数调有效提高了高效密集网络模型的性能.

结论:

  • SOADL-MCAIC方法为无人机网络中的多类空中图像分类提供了高度准确和有效的解决方案.
  • 这项研究通过改善监视,侦察和遥感能力,有助于自主空中系统的发展.
  • 提出的方法突出了将优化算法与深度学习结合起来,用于复杂的图像分析任务的潜力.