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

Radial System Protection01:23

Radial System Protection

511
Radial systems employ time-delay overcurrent relays to reduce load interruptions. When a fault occurs, the nearest breaker opens first, while upstream breakers remain closed due to longer delay settings. This approach ensures minimal disruption to the rest of the system.
In a radial system with a fault downstream of the third breaker, ideally, only the third breaker will open, isolating the fault and interrupting the load connected beyond it. The second breaker has a longer delay setting,...
511
Fault Types01:18

Fault Types

601
When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
For line-to-line faults occurring between phases B and C, the...
601
Reclosers and Fuses01:26

Reclosers and Fuses

685
Automatic circuit reclosers enhance the protection of distribution circuits by interrupting and auto-reclosing an AC circuit according to a preset sequence. They effectively manage temporary faults on overhead distribution lines, often caused by tree limbs or wildlife, by briefly disrupting service to improve overall reliability. However, contact with reclosers or energized broken conductors on the ground can pose serious hazards.
A comprehensive protection scheme for radial distribution...
685
Bus Impedance Matrix01:24

Bus Impedance Matrix

622
Calculating subtransient fault currents for three-phase faults in an N-bus power system involves using the positive-sequence network. When a three-phase short circuit occurs at a specific bus, the analysis uses the superposition method to evaluate two separate circuits.
In the first circuit, all machine voltage sources are short-circuited, leaving only the prefault voltage source at the fault location. The positive-sequence bus impedance matrix can be determined by solving the nodal equations,...
622
Multimachine Stability01:25

Multimachine Stability

698
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:
698
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.3K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Machine Learning-Driven Prediction of Intensive Care Units Mortality and Length of Stay: A 11-Year Retrospective Study in Hong Kong Public Hospitals.

Journal of medical systems·2026
Same author

A High-Accuracy Probabilistic-Based Sigmoid Approximator Incorporating Memory-Saving and Time-Efficient Strategies.

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

D2Vformer: A Flexible Time-Series Prediction Model Based on Time-Position Embedding.

IEEE transactions on neural networks and learning systems·2025
Same author

A Fast Wang kWTA With Application in Sealed-Bid Uniform Price Auction.

IEEE transactions on neural networks and learning systems·2025
Same author

Robust Fault-Aware Extreme Learning Machine Based on Maximum Correntropy.

IEEE transactions on neural networks and learning systems·2025
Same author

Analysis and Design of a Distributed kWTA With Application in Sealed-Bid Auctions With Bidding Price Privacy Protection.

IEEE transactions on neural networks and learning systems·2025

Related Experiment Video

Updated: Apr 30, 2026

Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling
06:55

Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling

Published on: August 5, 2016

7.3K

RBF networks under the concurrent fault situation.

Chi-Sing Leung, John Pui-Fai Sum

    IEEE Transactions on Neural Networks and Learning Systems
    |May 9, 2014
    PubMed
    Summary

    This study addresses multiple fault sources in neural networks, specifically radial basis function (RBF) networks. A new formula estimates network performance with concurrent faults, aiding optimization without extensive testing.

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Neural Networks

    Background:

    • Existing research on neural network fault tolerance often overlooks multiple concurrent fault sources.
    • Real-world trained networks can be simultaneously affected by various fault types, impacting performance.

    Purpose of the Study:

    • To investigate the performance of radial basis function (RBF) networks under concurrent multiplicative weight noise and open weight faults.
    • To develop a method for estimating the generalization ability of faulty RBF networks without requiring test data or simulations.

    Main Methods:

    • Derivation of a Mean Prediction Error (MPE) formula to quantify generalization ability in faulty RBF networks.
    • Concurrent fault modeling including multiplicative weight noise and open weight faults.

    More Related Videos

    Development of a Novel Internal Fixation Model for Rat Radial Fractures: Fracture Healing Assessment and Dorsal Root Ganglion Isolation
    11:21

    Development of a Novel Internal Fixation Model for Rat Radial Fractures: Fracture Healing Assessment and Dorsal Root Ganglion Isolation

    Published on: March 13, 2026

    483

    Related Experiment Videos

    Last Updated: Apr 30, 2026

    Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling
    06:55

    Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling

    Published on: August 5, 2016

    7.3K
    Development of a Novel Internal Fixation Model for Rat Radial Fractures: Fracture Healing Assessment and Dorsal Root Ganglion Isolation
    11:21

    Development of a Novel Internal Fixation Model for Rat Radial Fractures: Fracture Healing Assessment and Dorsal Root Ganglion Isolation

    Published on: March 13, 2026

    483

    Main Results:

    • A novel MPE formula was derived to accurately estimate the generalization ability of RBF networks with multiple concurrent faults.
    • The MPE formula enables performance evaluation without a test set or generating numerous faulty network instances.
    • The study proposes optimization methods for regularization parameters and RBF width based on the MPE results.

    Conclusions:

    • The developed MPE formula offers a valuable tool for understanding and predicting the generalization performance of faulty RBF networks.
    • The findings facilitate the optimization of RBF network parameters to enhance fault tolerance against combined fault types.
    • This research contributes to more robust and reliable neural network designs in the presence of multiple faults.