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

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

936
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
936
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.4K
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...
6.4K
Neural Control of Respiration01:18

Neural Control of Respiration

2.6K
The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
Two primary areas comprise the respiratory center: the medullary respiratory center in the medulla oblongata and the pontine respiratory group in the pons. The...
2.6K
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

482
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
482
Classification of Signals01:30

Classification of Signals

519
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...
519
Gas Chromatography: Types of Detectors-II01:19

Gas Chromatography: Types of Detectors-II

414
In gas chromatography, different detectors are employed to meet specific analytical needs. These detectors are often categorized based on their detection mechanisms and the types of compounds they are best suited to analyze. Thermal Conductivity Detectors (TCD), Flame Ionization Detectors (FID), and Electron Capture Detectors (ECD) represent common categories, each with unique operating principles and applications. However, beyond these, several other detectors are designed for more specialized...
414

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

Within-project and cross-project defect prediction based on model averaging.

Scientific reports·2025
Same author

YOLOv7-CSAW for maritime target detection.

Frontiers in neurorobotics·2023
Same author

Face Mask-Wearing Detection Model Based on Loss Function and Attention Mechanism.

Computational intelligence and neuroscience·2022
Same author

Time-varying feature selection for longitudinal analysis.

Statistics in medicine·2019
Same author

Prodrug AST-003 Improves the Therapeutic Index of the Multi-Targeted Tyrosine Kinase Inhibitor Sunitinib.

PloS one·2015
Same author

Great influence of geographic isolation on the genetic differentiation of Myriophyllum spicatum under a steep environmental gradient.

Scientific reports·2015
Same journal

AI-driven neuroanalytic modeling for mental health: multichannel CNN-based autism spectrum disorder detection via facial pattern analysis.

Frontiers in computational neuroscience·2026
Same journal

Modeling multiscale neural dynamics for EEG-based emotion recognition using an attentive wavelet-transformer framework.

Frontiers in computational neuroscience·2026
Same journal

New directions for complex systems in contemporary neuroscience: a morphodynamic and emergent function approach.

Frontiers in computational neuroscience·2026
Same journal

NMDA receptor kinetics drive distinct routes to chaotic firing in pyramidal neurons.

Frontiers in computational neuroscience·2026
Same journal

Schumann-anchored golden ratio organization of human neural oscillations.

Frontiers in computational neuroscience·2026
Same journal

Toward model-guided electrophysiology-Encoding of chirps in the electrosensory periphery of <i>Apteronotus leptorhynchus</i>.

Frontiers in computational neuroscience·2026
查看所有相关文章

相关实验视频

Updated: Jul 16, 2025

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

448

基于YOLOv8-MNC的吸烟行为检测算法

Zhong Wang1,2, Lanfang Lei1, Peibei Shi2

  • 1School of Artificial Intelligence and Big Data, Hefei University, Hefei, China.

Frontiers in computational neuroscience
|September 11, 2023
PubMed
概括
此摘要是机器生成的。

一个新的YOLOv8-MNC算法通过解决小物体挑战来改善吸烟检测. 这种新的方法提高了识别烟的准确性和稳定性,推进了用于行为分析的计算机视觉.

关键词:
卡拉菲 (Carafe) 是一种葡萄酒.在MHSASA中,MHSA是MHSA.NWD NWD NWD 的意思是北北方向.这就是YOLOv8的意义.吸烟行为检测检测 吸烟行为检测

更多相关视频

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking
14:21

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking

Published on: August 6, 2013

18.4K
Author Spotlight: Understanding Processing of Olfactory and Spatial Information by Brain with Real-Time Behavioral Analysis
06:21

Author Spotlight: Understanding Processing of Olfactory and Spatial Information by Brain with Real-Time Behavioral Analysis

Published on: September 20, 2024

823

相关实验视频

Last Updated: Jul 16, 2025

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

448
Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking
14:21

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking

Published on: August 6, 2013

18.4K
Author Spotlight: Understanding Processing of Olfactory and Spatial Information by Brain with Real-Time Behavioral Analysis
06:21

Author Spotlight: Understanding Processing of Olfactory and Spatial Information by Brain with Real-Time Behavioral Analysis

Published on: September 20, 2024

823

科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 检测吸烟行为是具有挑战性的,因为小,隐藏的物体,如烟.
  • 现有的深度学习模型在此类任务的准确性和稳定性方面扎.

研究的目的:

  • 引入一种新的吸烟检测算法,YOLOv8-MNC,以克服当前深度学习方法的局限性.
  • 为了提高在吸烟行为分析中检测小,隐藏物体的准确性和稳定性.

主要方法:

  • 开发了YOLOv8-MNC,在YOLOv8的基础上构建了一个专门的小目标检测层.
  • 集成的NWD损失通过减少对微小位置偏差的敏感性来提高训练准确性.
  • 集成的多头自我注意机制 (MHSA) 增强了全球特征学习和CARAFE的高效上采样,以尽量减少特征损失.

主要成果:

  • 在定制的吸烟行为数据集上,YOLOv8-MNC模型实现了85.887%的检测准确度.
  • 与以前的算法相比,平均平均精度 (mAP@0.5) 显著增加了5.7%.
  • 展示了对小,封闭物体的改进的检测精度和模型稳定性.

结论:

  • YOLOv8-MNC在吸烟行为检测方面取得了重大进展,解决了准确性和稳健性的关键挑战.
  • 该算法的增强性能表明其在相关的对象检测领域的潜在应用.
  • 未来的工作重点是改进YOLOv8-MNC技术并探索其更广泛的应用.