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

1.7K
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,...
1.7K
Transformers in Distribution System01:27

Transformers in Distribution System

165
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
165
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

7.1K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
7.1K
Transformers01:26

Transformers

1.2K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.2K
Introduction to Learning01:18

Introduction to Learning

545
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
545
Masking and Demasking Agents01:19

Masking and Demasking Agents

2.7K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
2.7K

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

Evaluation of neo-innominate connection in bilateral bidirectional Glenn patients using computational flow modeling.

JTCVS structural and endovascular·2026
Same author

Numerical Analysis of Blood Flow Dynamics in Pediatric ECMO Circuits.

Annals of biomedical engineering·2026
Same author

FedLiverNet: a federated learning framework for privacy-preserving and efficient liver cancer detection.

Scientific reports·2026
Same author

Reply to Ozkok et al.

American journal of physiology. Heart and circulatory physiology·2026
Same author

SCAG-Net: Automated Brain Tumor Prediction from MRI Using Cuttlefish-Optimized Attention-Based Graph Networks.

Diagnostics (Basel, Switzerland)·2026
Same author

Predicting Autism Spectrum Disorder in Children Using Glowworm Optimization With Extreme Learning Machine Networks.

Brain and behavior·2026
Same journal

Correction: A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms.

Scientific reports·2026
Same journal

Poly(bromophenol blue)/CoSn(OH)<sub>6</sub> cubic particles modified pencil graphite electrode for electrochemical determination of diphenhydramine.

Scientific reports·2026
Same journal

Dietary Chlorella, Spirulina, and acidifier modulate jejunal cytokine-related gene expression in broiler chickens.

Scientific reports·2026
Same journal

Perceived physical activity barriers in university students: associations with fatigue and eating behaviours.

Scientific reports·2026
Same journal

Refuge limitation structures habitat use in agricultural landscapes: evidence from Sunda pangolins.

Scientific reports·2026
Same journal

Lightweight stateless transaction verification with outsourced witness updates for UTXO blockchains.

Scientific reports·2026
查看所有相关文章

相关实验视频

Updated: Sep 17, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.7K

先进的云入侵检测框架使用基于图形的特征转换器和对比学习.

Vijay Govindarajan1, Junaid Hussain Muzamal2

  • 1Colorado State University, Seattle, USA. vijay.govindarajan91@gmail.com.

Scientific reports
|July 2, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个用于云环境的新型入侵检测框架,通过集成图形神经网络和变压器自编码器,实现99.97%的准确性. 该系统有效地识别各种网络威胁,具有高精度和回忆.

相关实验视频

Last Updated: Sep 17, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.7K

科学领域:

  • 网络安全 网络安全
  • 网络安全 网络安全
  • 机器学习 机器学习

背景情况:

  • 云环境面临着复杂和不断变化的网络威胁.
  • 传统的入侵检测系统与云网络的规模和动态性作斗争.
  • 有效的检测需要先进的方法来分析复杂的网络流量模式.

研究的目的:

  • 为云环境开发一个模块化和可扩展的入侵检测框架.
  • 通过使用先进的机器学习技术来提高检测准确性和效率.
  • 为实时威胁识别提供可解释和实用的解决方案.

主要方法:

  • 模拟网络流作为图表以捕捉关系模式.
  • 使用图形神经网络 (GNN) 进行结构化嵌入提取.
  • 采用基于变压器的自动编码器和对比学习来进行精细的特征表示和分类.
  • 评估关于NSL-KDD和CIC-IDS2018数据集的框架.

主要成果:

  • 在二进制和多类场景中实现了99.97%的平均准确性.
  • 对于所有攻击类型,包括少数类型 (例如U2R,R2L) 证明了高精度和回忆.
  • 展示了低的错误阳性率和实时推断能力,使用适度的资源.

结论:

  • 拟议的框架为云网络的入侵检测提供了一个强大而准确的解决方案.
  • 集成基于图形的功能,自动编码器和对比学习显著提高了检测性能.
  • 该系统的可解释性 (通过SHAP) 和效率支持其在高通量环境中的实际部署.