Jove
Visualize
Contact Us

Related Concept Videos

Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

11.8K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
11.8K
Reducing Line Loss01:18

Reducing Line Loss

143
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
143
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

91
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...
91
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

621
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...
621
Signal Flow Graphs01:18

Signal Flow Graphs

169
Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
169
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

3.6K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
3.6K

You might also read

Related Articles

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

Sort by
Same author

A Contrastive Dual-Task Framework for Few-Shot Traffic Classification in IoT Networks.

Sensors (Basel, Switzerland)·2026
Same author

On the interface between biomaterials and two-dimensional materials for biomedical applications.

Advanced drug delivery reviews·2022
Same author

HARNAS: Human Activity Recognition Based on Automatic Neural Architecture Search Using Evolutionary Algorithms.

Sensors (Basel, Switzerland)·2021
Same author

Malicious Network Behavior Detection Using Fusion of Packet Captures Files and Business Feature Data.

Sensors (Basel, Switzerland)·2021
Same author

DRNet: A Depth-Based Regression Network for 6D Object Pose Estimation.

Sensors (Basel, Switzerland)·2021
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles
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 Experiment Video

Updated: May 31, 2025

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

478

Zero-Shot Traffic Identification with Attribute and Graph-Based Representations for Edge Computing.

Zikui Lu1, Zixi Chang2, Mingshu He3

  • 1School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Sensors (Basel, Switzerland)
|January 25, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces AG-ZSL, a novel zero-shot learning framework for encrypted traffic classification. It effectively identifies unknown traffic types by learning from traffic behavior and attributes, improving network security.

Keywords:
deep learningedge computinggraph neural networkstraffic classificationtraffic representationzero-shot learning

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

455
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

8.9K

Related Experiment Videos

Last Updated: May 31, 2025

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

478
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

455
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

8.9K

Area of Science:

  • Computer Science
  • Network Security
  • Machine Learning

Background:

  • Fine-grained traffic identification is crucial but challenging due to mobile device proliferation and network growth.
  • Existing machine learning and deep learning methods struggle with unseen traffic data due to reliance on training data distribution.

Purpose of the Study:

  • To propose AG-ZSL, a zero-shot learning framework for general encrypted traffic classification.
  • To address the limitations of current methods in handling unseen traffic samples.

Main Methods:

  • AG-ZSL learns two mapping functions: traffic behavior embeddings from interaction graphs and attribute embeddings from descriptions.
  • It minimizes the distance between these embeddings in a shared feature space.
  • A two-stage classification method using gradient rejection and K-Nearest Neighbors is employed.

Main Results:

  • AG-ZSL demonstrates exceptional performance in classifying both known and unknown traffic types.
  • The framework shows high effectiveness on Internet of Things (IoT) datasets.

Conclusions:

  • AG-ZSL offers a robust solution for general encrypted traffic classification, particularly for unseen samples.
  • The framework has significant potential for enhancing secure and efficient network traffic management at the network edge.