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

Network Function of a Circuit01:25

Network Function of a Circuit

299
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
299
Generator Voltage Control01:21

Generator Voltage Control

161
Generator voltage control is crucial for maintaining the stable operation of synchronous generators and wind turbines. In older models, a DC generator driven by the rotor delivers DC power to the rotor's field winding, and the power is transferred through slip rings and brushes. In the latest models, static or brushless exciters are used. Static exciters rectify AC power from the generator terminals and then transfer the DC power directly to the rotor. Brushless exciters, on the other hand,...
161
Generating Electromagnetic Radiations01:10

Generating Electromagnetic Radiations

3.0K
The German physicist Heinrich Hertz (1857–1894) was the first to generate and detect certain types of electromagnetic waves in the laboratory. Starting in 1887, he performed a series of experiments that confirmed the existence of electromagnetic waves and verified that they travel at the speed of light. Hertz used an alternating-current RLC (resistor-inductor-capacitor) circuit that resonated at a known frequency and connected it to a loop of wire. High voltages induced across the gap in...
3.0K
DC Generator01:19

DC Generator

771
An alternator converts mechanical energy into electrical energy that varies sinusoidally, resulting in AC current. Meanwhile, a DC generator converts mechanical energy into electrical energy, which are DC pulses with the same polarity. The construction of a DC generator is similar to that of an alternator, except that the pair of slip rings is replaced by a single split ring, also called a commutator. The commutator functions like a periodic rotary switch; it changes the contacts with the...
771
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

105
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...
105
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

215
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
215

You might also read

Related Articles

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

Sort by
Same author

Ovarian leiomyoma: diagnostic challenges and imaging characteristics in a rare case.

BJR case reports·2026
Same author

Comment on 'A proposal for a new pathogenesis-guided classification for inherited epidermal differentiation disorders'.

The British journal of dermatology·2025
Same author

Testing the limits of large language models in debating humans.

Scientific reports·2025
Same author

Using LLMs to Infer Non-Binary COVID-19 Sentiments of Chinese Microbloggers.

Entropy (Basel, Switzerland)·2025
Same author

Diffusive persistence on disordered lattices and random networks.

Physical review. E·2024
Same author

Protect our environment from information overload.

Nature human behaviour·2024
Same journal

Machine Learning on Dynamic Functional Connectivity: Promise, Pitfalls, and Interpretations.

Information sciences·2026
Same journal

A multimodal machine learning approach to predict Fugl-Meyer scores and motor recovery potential in stroke rehabilitation: Toward precision-based therapies.

Information sciences·2025
Same journal

Two-Dimensional Semi-Nonnegative Matrix Factorization for Clustering.

Information sciences·2025
Same journal

Causality-aware Social Recommender System with Network Homophily Informed Multi-treatment Confounders.

Information sciences·2024
Same journal

An optimal Bayesian intervention policy in response to unknown dynamic cell stimuli.

Information sciences·2024
Same journal

HRL4EC: Hierarchical reinforcement learning for multi-mode epidemic control.

Information sciences·2023
See all related articles

Related Experiment Video

Updated: Jul 11, 2025

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
08:21

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

2.7K

A network generator for covert network structures.

Amr Elsisy1,2, Aamir Mandviwalla1,2, Boleslaw K Szymanski1,2,3

  • 1Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.

Information Sciences
|November 6, 2023
PubMed
Summary
This summary is machine-generated.

Researchers developed a new method to rewire covert networks, creating statistically similar synthetic networks. This helps identify stable structures within criminal or terrorist organizations for analysis and anonymization.

Keywords:
Covert networksHierarchical networksNetwork structure stabilityRandom weighted network generatorSocial networks

More Related Videos

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

590
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K

Related Experiment Videos

Last Updated: Jul 11, 2025

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
08:21

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

2.7K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

590
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K

Area of Science:

  • Network Science
  • Computational Social Science
  • Cybersecurity

Background:

  • Covert networks, such as criminal or terrorist organizations, operate with incomplete data due to members' efforts to conceal activities and associations.
  • Understanding the organizational structures of these networks is crucial for intelligence and security analysis.
  • Existing methods struggle to generate realistic synthetic networks that capture the complexities of covert structures.

Purpose of the Study:

  • To introduce a novel method for generating statistically similar synthetic covert networks.
  • To model both the edge structure and hierarchical organization of covert networks.
  • To provide tools for analyzing network stability and for anonymizing sensitive network data.

Main Methods:

  • A novel rewiring method for covert networks, parameterized by edge connectivity standard deviation.
  • Modeling higher-level organizational structures using multi-layer networks.
  • Utilizing the Stochastic Block Model for the lowest network level.
  • Generating numerous synthetic networks from original covert network data.

Main Results:

  • Generated synthetic networks are statistically similar to themselves and the original network.
  • Modeling edge structure and hierarchy together is essential for generating realistic networks.
  • A small percentage (18%) of synthetic network structures were consistently repeated, indicating stable organizational patterns.
  • Identified frequently repeating structures as strong candidates for ground truth network architecture.

Conclusions:

  • The developed method effectively generates synthetic covert networks that preserve statistical properties of the original.
  • The approach allows for the identification of stable, frequent structures within covert networks, aiding in analysis.
  • Synthetic networks can be utilized for anonymization and testing software in open research settings.