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Sequence Networks of Rotating Machines

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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...

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Updated: Oct 31, 2025

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

An Interaction-Based Bayesian Network Framework for Surgical Workflow Segmentation.

Nana Luo^1,2,3, Atsushi Nara^2,3, Kiyoshi Izumi⁴

¹School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102612, China.

International Journal of Environmental Research and Public Health

|July 2, 2021

Summary

This study introduces a novel framework using real-time movement data to analyze surgical staff interactions and segment surgical workflows. The Bayesian network approach achieved 70% accuracy, enhancing surgical effectiveness assessment.

Keywords:

bayesian network individual interaction measurement surgical phases prediction zone position system

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Area of Science:

Medical Informatics
Surgical Workflow Analysis
Human-Computer Interaction

Background:

Surgical workflow recognition is vital for skill assessment and healthcare improvement.
Current methods rely heavily on signal, video, or image data, lacking staff interaction insights.
Gathering data on surgical staff cooperation is challenging.

Purpose of the Study:

To explore cooperation networks among surgical staff roles (surgeon, nurses, anesthetist).
To segment surgical workflows using real-time movement data.
To assess surgical effectiveness through interaction analysis.

Main Methods:

Collected real-time movement data of surgical staff during neurosurgery using a Zone Position System (ZPS).
Developed an interaction-based framework for surgical workflow recognition.
Integrated a Bayesian Network (BN) to address uncertainties in workflow classification.

Main Results:

The proposed Bayesian Network method achieved 70% accuracy in surgical workflow recognition.
The framework successfully captured and explained interactions and cooperation among surgical staff.
Real-time movement data provided high-frequency, high-resolution insights into OR dynamics.

Conclusions:

The interaction-based framework with a Bayesian Network effectively recognizes surgical workflows.
This approach enhances the assessment of surgical effectiveness by analyzing staff cooperation.
Movement data analysis offers a novel method for understanding complex operating room dynamics.