Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

152
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
152
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

173
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
173
Neural Circuits01:25

Neural Circuits

1.7K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Research on design of soft gripper by dielectric elastomer actuator based on quadrangular star honeycomb support.

PloS one·2026
Same author

Development and validation of a dual-channel deep learning for continuous acute kidney injury prediction in critically ill patients.

Renal failure·2026
Same author

Chrysanthemum-like nanodrug assembled from hyaluronan-doxorubicin/cisplatin conjugate for targeted synergistic chemotherapy of hepatocellular carcinoma.

International journal of biological macromolecules·2026
Same author

Chemodivergent C-H Activation and C-C Coupling between Indoles and Alkyne-Tethered <i>N</i>-Cyanosulfonamides via Substrate-Controlled Group Migration.

Organic letters·2026
Same author

Efficacy predictions for omalizumab treatment based on basophil CD203c expression in patients with allergic rhinitis by basophil activation test -- a real-life, pilot study.

The World Allergy Organization journal·2026
Same author

Construction and validation of a machine learning-based prediction model for postoperative complications in patients with chronic otitis media.

Frontiers in medicine·2026

Related Experiment Video

Updated: Sep 24, 2025

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

656

Dynamic graph convolutional network for assembly behavior recognition based on attention mechanism and multi-scale

Chengjun Chen1, Xicong Zhao2, Jinlei Wang2

  • 1School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao, 266520, Shandong, China. Chencj@qut.edu.cn.

Scientific Reports
|May 5, 2022
PubMed
Summary

This study introduces a graph convolutional network model for intelligent assembly behavior recognition in workshops. The model enhances efficiency and safety by accurately identifying 15 types of production behaviors with 93.1% accuracy.

More Related Videos

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

4.2K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

532

Related Experiment Videos

Last Updated: Sep 24, 2025

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

656
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

4.2K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

532

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Industrial Engineering

Background:

  • Workshop production efficiency and safety are critical.
  • Automated recognition of assembly behaviors is needed.
  • Existing methods may lack accuracy in complex scenarios.

Purpose of the Study:

  • To develop an intelligent model for recognizing workshop production assembly behaviors.
  • To improve production assembly efficiency and ensure worker safety.
  • To leverage advanced deep learning techniques for behavior analysis.

Main Methods:

  • A graph convolutional network (GCN) model was proposed for assembly behavior recognition.
  • The model incorporates an attention mechanism to focus on salient image features.
  • Multi-scale feature fusion was employed to enhance feature extraction.
  • A dataset of 15 workshop production behaviors was created and utilized.

Main Results:

  • The proposed model achieved high accuracy in assembly behavior recognition.
  • An average recognition accuracy of 93.1% was obtained on the custom dataset.
  • The attention mechanism improved the focus on critical visual cues.
  • Multi-scale feature fusion enhanced the model's ability to capture diverse features.

Conclusions:

  • The developed GCN model with attention and multi-scale fusion is effective for assembly behavior recognition.
  • The approach significantly improves recognition accuracy, contributing to enhanced production efficiency and safety.
  • This intelligent system offers a promising solution for real-time monitoring in industrial settings.