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

Pilot and Numeric Relaying01:21

Pilot and Numeric Relaying

157
Pilot relaying is a type of differential protection used in power systems. It compares electrical quantities at the terminals of equipment via a communication channel instead of direct relay interconnection. This method is essential for transmission lines where the terminals are far apart, typically up to 80 km for lines with 69 to 115 kV ratings. Four types of communication channels are used for pilot relaying:
157
Network Function of a Circuit01:25

Network Function of a Circuit

422
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.
422
IP3/DAG Signaling Pathway01:11

IP3/DAG Signaling Pathway

12.8K
Membrane lipids such as phosphatidylinositol (PI) are precursors for several membrane-bound and soluble second messengers. Specific kinases phosphorylate PI and produce phosphorylated inositol phospholipids. One such inositol phospholipids are the  phosphatidylinositol-4,5 bisphosphate [PI(4,5)P2], present in the inner half of the lipid bilayer. Upon ligand binding, GPCR stimulates Gq proteins to turn on phospholipase Cꞵ. Activated phospholipase Cꞵ cleaves PI(4,5)P2 and...
12.8K
Neuronal Communication01:28

Neuronal Communication

1.8K
Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
1.8K
Control Systems: Applications01:25

Control Systems: Applications

841
Electrical engineering plays a pivotal role in our daily lives, with control systems at the heart of many applications, from home appliances to sophisticated space shuttles. Control systems manage and regulate the behavior of devices and processes, ensuring they function safely, correctly, and efficiently.
In modern vehicles, control systems manage various functions to enhance performance and safety. The steering wheel and accelerator are primary inputs in a car's control system. The...
841
Semiconductors01:22

Semiconductors

1.0K
There is variation in the electrical conductivity of materials - metals, semiconductors, and insulators that are showcased with the help of the energy band diagrams.
Metals such as copper (Cu), zinc (Zn), or lead (Pb) have low resistivity and feature conduction bands that are either not fully occupied or overlap with the valence band, making a bandgap non-existent. This allows electrons in the highest energy levels of the valence band to easily transition to the conduction band upon gaining...
1.0K

You might also read

Related Articles

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

Sort by
Same author

Empty Versus Full Bladder Protocols in Radiotherapy Planning for Localised Prostate Cancer: A Dosimetric Study.

Journal of medical imaging and radiation oncology·2026
Same author

From Schreier graphs to secure s-boxes using a group-theoretic design framework.

Scientific reports·2026
Same author

Oil spills in inland freshwaters: A review of recent incidents, mitigation practices, and ecological impacts in the United States.

The Science of the total environment·2026
Same author

Systematic review of trends in deep learning for UAV cybersecurity.

Frontiers in artificial intelligence·2026
Same author

V-CHIMERA: An Immune-Inspired Verified Framework for Organizational Cyber Crisis Response Under Misinformation.

Biomimetics (Basel, Switzerland)·2026
Same author

Tri-stream multi-model architecture for real-time detection of BeiDou signal manipulation in UAV swarms.

Scientific reports·2026

Related Experiment Video

Updated: Oct 12, 2025

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
10:15

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Published on: February 3, 2021

3.9K

Securing industrial communication with software-defined networking.

Abhishek Savaliya1, Rutvij H Jhaveri1, Qin Xin2

  • 1Department of Computer Science and Engineering, Pandit Deendayal Energy University, India.

Mathematical Biosciences and Engineering : MBE
|November 24, 2021
PubMed
Summary
This summary is machine-generated.

This study enhances industrial network resilience using machine learning within Software-Defined Networking (SDN). An intelligent system predicts link failures and congestion, recommending secure paths for improved quality of service.

Keywords:
industrial cyber-physical systemsmachine learningnetwork securitysoftware-defined networking

More Related Videos

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.2K
Data Communication Based on MQTT in a Polymer Extrusion Process
08:15

Data Communication Based on MQTT in a Polymer Extrusion Process

Published on: July 15, 2022

3.6K

Related Experiment Videos

Last Updated: Oct 12, 2025

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
10:15

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Published on: February 3, 2021

3.9K
Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.2K
Data Communication Based on MQTT in a Polymer Extrusion Process
08:15

Data Communication Based on MQTT in a Polymer Extrusion Process

Published on: July 15, 2022

3.6K

Area of Science:

  • Computer Science
  • Network Engineering
  • Cybersecurity

Background:

  • Industrial Cyber-Physical Systems (CPSs) face network vulnerabilities like link failures and congestion.
  • Software-Defined Networking (SDN) offers programmability and visibility for enhanced network control.
  • OpenFlow protocol facilitates communication between SDN controllers and network devices.

Purpose of the Study:

  • To improve network resilience and security in industrial CPSs.
  • To develop an intelligent recommender system for real-time path selection.
  • To predict and classify network traffic for proactive fault management.

Main Methods:

  • Utilized SDN architecture with OpenFlow protocol.
  • Employed machine learning algorithms (logistic regression, KNN, SVM, decision tree) for traffic prediction.
  • Trained models on network data including link delay and traffic statistics.
  • Evaluated models on the Mininet emulation testbed.

Main Results:

  • Machine learning models achieved high accuracies (91-99%) in predicting network states.
  • The highest accuracy model was integrated into an intelligent recommender system.
  • The system effectively recommends resilient paths by avoiding predicted failures and congestion.

Conclusions:

  • Integrating machine learning with SDN enhances network resilience and security.
  • The intelligent recommender system provides real-time path selection for improved Quality of Service (QoS).
  • Proactive measures based on traffic prediction mitigate network attacks and improve system reliability.