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

Observational Learning01:12

Observational Learning

841
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
841
Behavior Modification01:21

Behavior Modification

556
Behavioral approaches have often been criticized for ignoring mental processes and focusing solely on observable behavior. However, these approaches provide an optimistic perspective for individuals seeking to change their behaviors. Rather than concentrating on intrinsic personality traits, behavioral approaches suggest that even longstanding habits can be modified by changing the reward contingencies that maintain them.
A real-world application of operant conditioning principles is applied...
556
Classification of Signals01:30

Classification of Signals

1.3K
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...
1.3K
Real-World Application of Classical Conditioning01:15

Real-World Application of Classical Conditioning

1.3K
Classical conditioning not only includes the initial pairing of stimuli but also extends to more complex forms, such as higher-order conditioning. Higher-order conditioning involves creating associations beyond the primary conditioned stimulus, resulting in a chain of conditioned responses.
Higher-order, or second-order, conditioning occurs when a neutral stimulus becomes associated with an already established conditioned stimulus through repeated pairings. For instance, if a dog has been...
1.3K
Methods of Classification and Identification01:28

Methods of Classification and Identification

1.0K
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...
1.0K
Force Classification01:22

Force Classification

2.3K
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,...
2.3K

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

A new extended belief rule base method based on neighborhood covering reduction for diabetes diagnosis.

PloS one·2026
Same author

A New Interval Belief Rule Base Model Based on Hybrid Optimization and Adaptive Reference Intervals for Diesel Engine Health State Assessment.

Sensors (Basel, Switzerland)·2026
Same author

A Dual-Source Evidence-Driven Semi-Supervised Belief Rule Base for Fault Diagnosis.

Sensors (Basel, Switzerland)·2026
Same author

A Photovoltaic Power Prediction Method Based on Data-Driven Interval Construction Belief Rule Base.

Sensors (Basel, Switzerland)·2026
Same author

A new multilayer tree structure belief rule base-based prediction method for key indicators of flotation process.

PloS one·2026
Same author

DE-HRNet: Detail enhanced high-resolution network for human pose estimation.

PloS one·2025
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jan 18, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.3K

基于PLA-YOLO11n的课堂行为检测方法

Hongshuo Zhang1, Guohui Zhou1, Wei He1

  • 1School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了PLA-YOLO11n模型,用于准确检测课堂行为,以提高教学效率. 这种改进的模型在SCB2数据集上的平均平均精度 (mAP@0.5) 提高了3.8%.

关键词:
其他AIFI的AIFILSKA LSKA 在线观看这就是PConvv.这就是YOLOv11的意义.在课堂上行为检测,行为检测.

更多相关视频

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.8K
Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

5.8K

相关实验视频

Last Updated: Jan 18, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.3K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.8K
Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

5.8K

科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 教育技术的教育技术

背景情况:

  • 准确的学生行为检测对于分析学习状态和改善教学至关重要.
  • 现有的模型可能难以检测小目标或有效集成功能.

研究的目的:

  • 提出一个改进的课堂行为检测模型,PLA-YOLO11n,基于YOLO11架构.
  • 通过更好的分析,提高学生行为的检测,提高整体教学效率.

主要方法:

  • 开发了一种新的C3K2_PConv模块,将部分卷积集成到YOLO11脊柱和部层中.
  • 整合了大型内核自我注意力 (LSKA) 机制,以增强小目标特征表示.
  • 用注意力功能集成模块 (AIFI) 取代了SPPF,并添加了一个高分辨率检测头.

主要成果:

  • 与原来的YOLO11.11n相比,PLA-YOLO11n模型表现出优越的性能.
  • 在SCB2数据集中,平均平均精度 (mAP@0.5) 提高了3.8%.
  • 这些改进有效地改善了对课堂行为的检测.

结论:

  • 拟议的PLA-YOLO11n模型在课堂行为检测方面取得了重大进展.
  • 新型模块和机制有助于提高教育环境中的准确性和有效性.
  • 这种方法有可能对教学策略和学生学习分析产生积极影响.