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

Multimachine Stability01:25

Multimachine Stability

Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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...

You might also read

Related Articles

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

Sort by
Same author

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

IEEE transactions on neural networks and learning systems·2026
Same author

Neural Spelling: A Spell-Based BCI System for Language Neural Decoding.

IEEE transactions on bio-medical engineering·2026
Same author

A Hybrid Covert Attention-Augmented Motor Imagery Paradigm for Brain-Computer Interfaces.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

A Deep Learning Prognostic Model for Diabetes Patients Using Bilateral Fundus Imaging.

Journal of diabetes science and technology·2026
Same author

Distinct effects of empathy on self-other processing revealed by different behavioral and EEG indices.

Cognitive, affective & behavioral neuroscience·2026
Same author

Biologically-constrained spiking neural network for neuromodulation in locomotor recovery after spinal cord injury.

PLoS computational biology·2026

Related Experiment Videos

Genetic algorithm-based neural fuzzy decision tree for mixed scheduling in ATM networks.

Chin-Teng Lin1, I-Fang Chung, Her-Chang Pu

  • 1Dept. of Electr. & Control Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 5, 2008
PubMed
Summary

This study introduces a novel Genetic Algorithm-based Neural Fuzzy Decision Tree (GANFDT) for efficient mixed scheduling in Asynchronous Transfer Mode (ATM) networks. The GANFDT ensures high system utilization while meeting Quality of Service (QoS) requirements in real-time environments.

Related Experiment Videos

Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Asynchronous Transfer Mode (ATM) networks are evolving to support diverse multimedia traffic with stringent Quality of Service (QoS) demands.
  • Efficient traffic control scheduling is crucial for ATM network performance, balancing system utilization and implementation complexity.
  • Existing algorithms like Rate Monotonic (RM) and Deadline Driven (DD) present trade-offs between simplicity and system efficiency.

Purpose of the Study:

  • To propose and evaluate a novel scheduling approach for ATM networks that combines the benefits of Rate Monotonic and Deadline Driven algorithms.
  • To address the challenge of schedulability testing for mixed scheduling algorithms in real-time environments.
  • To develop an efficient method for achieving high system utilization under hardware constraints in ATM networks.

Main Methods:

  • Implementation of a mixed scheduling algorithm integrating Rate Monotonic and Deadline Driven approaches.
  • Development of a Genetic Algorithm-based Neural Fuzzy Decision Tree (GANFDT) for real-time schedulability testing.
  • GANFDT combines Genetic Algorithms (GA) and neural fuzzy networks within a binary classification tree structure.

Main Results:

  • The proposed GANFDT effectively performs schedulability testing for the mixed scheduling algorithm.
  • Simulation results demonstrate that GANFDT enables efficient mixed scheduling in ATM networks.
  • The approach achieves high system utilization while respecting hardware limitations.

Conclusions:

  • The GANFDT provides an efficient and practical solution for mixed scheduling in ATM networks.
  • This method enhances the feasibility of supporting diverse multimedia traffic with guaranteed QoS.
  • The GANFDT approach offers a viable strategy for real-time traffic control in high-speed networks.