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

1.2K
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,...
1.2K
Classification of Systems-I01:26

Classification of Systems-I

188
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:
188
Aggregates Classification01:29

Aggregates Classification

326
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...
326
Classification of Systems-II01:31

Classification of Systems-II

146
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,
146
Classification of Leukocytes01:30

Classification of Leukocytes

1.9K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
1.9K
Classification of Signals01:30

Classification of Signals

466
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...
466

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

Accuracy of Deep Learning Models in Detecting Mandibular Furcation Defects on Panoramic Radiographs.

Diagnostics (Basel, Switzerland)·2026
Same author

Diagnostic performance of systemic inflammatory and nutritional indices and their association with clinicopathological features in endometrial cancer: a retrospective study.

BMC women's health·2026
Same author

Integrating scanning electron microscopy, explainable deep learning, and ITS sequencing for accurate identification in some species Geastrum.

Scientific reports·2026
Same author

Compact Involutional Transformer for Automated Detection of Pediatric Tooth Number Anomalies on Panoramic Radiographs.

Journal of imaging informatics in medicine·2026
Same author

Enhancing crayfish sex identification with Kolmogorov-Arnold networks and stacked autoencoders.

Scientific reports·2025
Same author

Deep Learning Performance in Analyzing Nailfold Videocapillaroscopy Images in Systemic Sclerosis.

Diagnostics (Basel, Switzerland)·2025
Same journal

DARUMA: a gateway to fast and easy prediction of intrinsically disordered regions.

PeerJ. Computer science·2026
Same journal

Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.

PeerJ. Computer science·2026
Same journal

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network.

PeerJ. Computer science·2026
Same journal

An anomaly detection model for multivariate time series with anomaly perception.

PeerJ. Computer science·2026
Same journal

Retraction: A wormhole attack detection method for tactical wireless sensor networks.

PeerJ. Computer science·2026
Same journal

Evaluation of mental disorder with prioritization of its type by utilizing the bipolar complex fuzzy decision-making approach based on Schweizer-Sklar prioritized aggregation operators.

PeerJ. Computer science·2026
查看所有相关文章

相关实验视频

Updated: Jul 6, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K

使用深度学习与云图像进行云类型分类.

Mehmet Guzel1, Muruvvet Kalkan1, Erkan Bostanci1

  • 1Department of Computer Engineering, Ankara University, Ankara, Turkey.

PeerJ. Computer science
|January 10, 2024
PubMed
概括
此摘要是机器生成的。

这项研究使用深度学习和图像处理来从图像中分类云类型,通过Xception模型实现97.66%的准确性. 这种进步提高了天气预报和对危险条件的准备.

关键词:
在美国,CNN是CNN.云类型 云类型 云类型深度学习是一种深度学习.图像的分类图像的分类.转移学习转移学习

更多相关视频

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.8K

相关实验视频

Last Updated: Jul 6, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.8K

科学领域:

  • 气象学和大气科学.
  • 计算机科学,特别是人工智能和机器学习.

背景情况:

  • 云是天气模式的关键指标,影响日常生活,并为恶劣天气提供警告.
  • 准确的云层分类对于改善天气预报和能够采取积极措施应对危险天气事件至关重要.

研究的目的:

  • 开发一种自动化系统,根据形状和颜色等视觉特征对云层形成进行分类.
  • 通过先进的云分析,提高天气预报的准确性和可靠性.

主要方法:

  • 利用图像处理和深度学习技术进行云图像分类.
  • 评估了多个深度学习模型,包括MobileNet V2,Inception V3,EfficientNetV2L,VGG-16,Xception,ConvNeXtSmall和ResNet-152 V2.2等,这些模型都得到了广泛的应用.
  • 确定Xception模型是这个任务中最有效的.

主要成果:

  • 该Xception模型实现了97.66%的高分类准确度.
  • 展示了人工智能在准确检测和分类各种云类型方面的潜力.

结论:

  • 将人工智能驱动的云分类集成到天气预报系统中可以显著提高预测准确度.
  • 这项研究为云研究提供了一种新的方法,提高了天气准备和预报可靠性.