Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Signal Flow Graphs

Signal Flow Graphs

Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...

Energy Diagrams - I

Energy Diagrams - I

The dynamics of a mechanical system can be easily understood by interpreting a potential energy diagram. Since energy is a scalar quantity, the interpretation of the dynamics of the system becomes even simpler.
Take the example of a skater on a parabolic ramp. The potential energy at different points along the ramp will be proportional to the height of the ramp, which varies quadratically with the horizontal position on the ramp. As the skater moves down the ramp from the highest position,...

You might also read

Related Articles

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

Sort by

Same author

Correction: Safety and efficacy of nab-paclitaxel combined with nedaplatin and bevacizumab in patients with platinum-resistant epithelial ovarian cancer: a retrospective cohort study.

Journal of cancer research and clinical oncology·2026

Same author

Late-Stage <i>N</i>-Adamantylation of Pyridines.

Journal of the American Chemical Society·2026

Same author

Tisotumab vedotin versus chemotherapy in Chinese patients with recurrent or metastatic cervical cancer with disease progression on or after systemic therapy.

Chinese medical journal·2026

Same author

Hypermethylation of ZNF154 promotes malignant potential of ovarian cancer cells by diminishing ZNF154/KAP1-mediated ROMO1 repression.

Cell death & disease·2026

Same author

Safety and efficacy of nab-paclitaxel combined with nedaplatin and bevacizumab in patients with platinum-resistant epithelial ovarian cancer: a retrospective cohort study.

Journal of cancer research and clinical oncology·2026

Same author

HRD in endometrial cancer: LST loss drives distinct genomic profile and platinum response.

NPJ precision oncology·2026

Same journal

Using artificial intelligence to improve governance and public services in Africa.

Frontiers in big data·2026

Same journal

Case count metric for comparative analysis of entity resolution results.

Frontiers in big data·2026

Same journal

Data field theory: a geometric framework for learning on Riemannian manifolds with synthetic validation and limitation analysis.

Frontiers in big data·2026

Same journal

Correction: Explainable gradient convolutional vector fuzzy pattern analysis based on ensemble model for facial expression recognition.

Frontiers in big data·2026

Same journal

When uncertainty guides learning: a highly effective approach to kidney disease classification in CT imaging.

Frontiers in big data·2026

Same journal

Democratizing cloud data lake analytics: natural language access to Apache Iceberg via LLM agents.

Frontiers in big data·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 27, 2025

A Protocol for Real-time 3D Single Particle Tracking

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

Graph learning for particle accelerator operations.

Song Wang¹, Chris Tennant², Daniel Moser²

¹Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, United States.

Frontiers in Big Data

|April 26, 2024

Summary

This summary is machine-generated.

This study introduces a new graph learning method to classify particle accelerator beamline performance using unlabeled data. This approach visualizes operational data, offering valuable feedback for accelerator operations.

Keywords:

Graph Neural Network graph learning algorithm particle accelerator self-supervised learning (SSL)supervised training

More Related Videos

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

A Basic Positron Emission Tomography System Constructed to Locate a Radioactive Source in a Bi-dimensional Space

A Basic Positron Emission Tomography System Constructed to Locate a Radioactive Source in a Bi-dimensional Space

Published on: February 1, 2016

Related Experiment Videos

Last Updated: Jun 27, 2025

A Protocol for Real-time 3D Single Particle Tracking

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

A Basic Positron Emission Tomography System Constructed to Locate a Radioactive Source in a Bi-dimensional Space

A Basic Positron Emission Tomography System Constructed to Locate a Radioactive Source in a Bi-dimensional Space

Published on: February 1, 2016

Area of Science:

Physics
Materials Science
Medical Applications

Background:

Particle accelerators are vital tools in scientific research and medicine.
Analyzing complex beamline data is challenging for ensuring optimal operations.

Purpose of the Study:

To develop a novel graph learning approach for classifying beamline configurations.
To enable self-supervised learning from historical, unlabeled beamline data.
To provide visual feedback on accelerator operations.

Main Methods:

Transforming beamline component data into a heterogeneous graph.
Utilizing a self-supervised training strategy with fine-tuning on labeled data.
Extracting low-dimensional representations for visualization and analysis.

Main Results:

Successful classification of operational beamline configurations as good or bad.
Identification of distinct regions in the latent space corresponding to good and bad states.
Development of a visualization tool for accelerator operational data.

Conclusions:

The proposed graph learning method offers a paradigm shift in analyzing complex beamline data.
This approach can provide valuable, actionable feedback to accelerator operators.
The technique enhances the efficiency and understanding of particle accelerator operations.