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

SFG Algebra01:16

SFG Algebra

In Signal Flow Graph (SFG) algebra, the value a node represents is determined by the sum of all signals entering that node. This summed value is then transmitted through every branch leaving the node, making the SFG a powerful tool for visualizing and analyzing control systems.
Each node in an SFG corresponds to a variable, and the interactions between nodes are represented by branches with associated gains. When multiple branches lead into a node, the value at that node is the sum of the...

You might also read

Related Articles

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

Sort by
Same author

Bag of tricks: synergistic optimization of deep patch learning fuzzy systems with multi-strategy integration.

Scientific reports·2026
Same author

Expert experience-guided virtual datasets for adaptive automatic driving in metro trains.

Scientific reports·2026
Same author

Pyrazole-derived TRPC3 antagonist ameliorates synaptic dysfunctions and memory deficits in Alzheimer's disease models.

Molecular psychiatry·2026
Same author

Reduced Cortical Pyramidal Neuron Membrane Excitability and Synaptic Function in Parkinsonian Mice and Their Restoration by L-Dopa Treatment: Indirect Mediation by Striatal Dopaminergic Activity.

Brain sciences·2026
Same author

Decompression alone versus decompression with fusion in the treatment of lumbar degenerative spondylolisthesis: evaluating the overlapping meta-analyses.

Neurosurgical review·2026
Same author

Disentangling neuroimmune landscapes across peripheral activation paradigms resolves divergent glial state programs.

Research square·2026

Related Experiment Video

Updated: Jul 25, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.2K

Enhanced intelligent train operation algorithms for metro train based on expert system and deep reinforcement

Yunhu Huang1, Wenzhu Lai2, Dewang Chen3,4

  • 1College of Computer and Data Science, Minjiang University, Fuzhou, Fujian, China.

Plos One
|May 21, 2025
PubMed
Summary

This study introduces enhanced intelligent train operation (EITO) algorithms, combining expert knowledge and deep reinforcement learning. EITO optimizes energy consumption and passenger comfort, outperforming existing methods in metro systems.

More Related Videos

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.5K
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

2.5K

Related Experiment Videos

Last Updated: Jul 25, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.2K
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.5K
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

2.5K

Area of Science:

  • * Railway Engineering
  • * Artificial Intelligence
  • * Operations Research

Background:

  • * Automatic Train Operation (ATO) systems are increasingly adopted in metro networks for efficiency.
  • * Existing ATO systems face challenges in computational constraints, adaptability, and multi-objective optimization.
  • * There is a need for advanced algorithms to improve train operation performance beyond current capabilities.

Purpose of the Study:

  • * To develop novel enhanced intelligent train operation (EITO) algorithms addressing limitations of current ATO systems.
  • * To integrate expert knowledge with deep reinforcement learning for superior train control.
  • * To optimize multiple performance indicators, including energy consumption and passenger comfort, in real-time.

Main Methods:

  • * Proposed two enhanced intelligent train operation (EITO) algorithms: EITOE (expert system-based) and EITOP (deep reinforcement learning-based using Proximal Policy Optimization - PPO).
  • * Developed the double minimal-time distribution (DMTD) calculation method for extended coasting and energy optimization.
  • * Utilized real-world data from the Yizhuang Line of Beijing Metro (YLBS) for comparative testing.

Main Results:

  • * EITO algorithms demonstrated superior performance in energy consumption and passenger comfort compared to manual driving and existing intelligent driving algorithms (ITOR, STON).
  • * EITOP, utilizing PPO, achieved the best overall performance by optimizing multiple objectives online without reliance on offline speed profiles.
  • * Robustness of EITO was validated on complex metro lines with varying speed limits and gradients.

Conclusions:

  • * The proposed EITO algorithms, particularly EITOP, represent a significant advancement in automatic train operation.
  • * These algorithms effectively address computational constraints and multi-objective balancing, enhancing operational efficiency and passenger experience.
  • * EITO offers a practical and effective solution for optimizing metro train performance in diverse operational scenarios.