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

Cluster Sampling Method01:20

Cluster Sampling Method

12.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.0K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

125
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...
125
Sampling Plans01:23

Sampling Plans

214
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
214
Time-Series Graph00:54

Time-Series Graph

4.4K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.4K
Velocity and Position by Graphical Method01:34

Velocity and Position by Graphical Method

7.5K
Velocity and position can be calculated from the known function of acceleration as a function of time. The total area under the acceleration-time graph and the velocity-time graph gives the change in velocity and position, respectively. In the case of an airplane, its acceleration is tracked using the inertial navigation system. The pilot provides the input of the airplane's initial position and velocity before takeoff. The inertial navigation system then uses the acceleration data to...
7.5K
Classification of Signals01:30

Classification of Signals

538
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
538

You might also read

Related Articles

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

Sort by
Same author

Spatiotemporal Imputation of Traffic Emissions With Self-Supervised Diffusion Model.

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

Gender Differences of NLRP1 Inflammasome in Mouse Model of Alzheimer's Disease.

Frontiers in aging neuroscience·2020
Same author

LncRNA GACAT3 predicts poor prognosis and promotes cell proliferation in breast cancer through regulation of miR-497/CCND2.

Cancer biomarkers : section A of Disease markers·2018
Same author

Evidence for Inbreeding and Genetic Differentiation among Geographic Populations of the Saprophytic Mushroom Trogia venenata from Southwestern China.

PloS one·2016
Same author

The establishment of species-specific primers for the molecular identification of ten stored-product psocids based on ITS2 rDNA.

Scientific reports·2016
Same author

Evolution and prognosis of breast osteosarcoma: A case report.

Oncology letters·2016

Related Experiment Video

Updated: Jul 23, 2025

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

574

Self-Supervised Spatiotemporal Clustering of Vehicle Emissions With Graph Convolutional Network.

Lihong Pei, Yang Cao, Yu Kang

    IEEE Transactions on Neural Networks and Learning Systems
    |July 19, 2023
    PubMed
    Summary

    This study introduces a novel two-way self-supervised learning method to accurately track air pollution evolution from vehicle emissions. The approach enhances spatiotemporal clustering by mutually reinforcing spatial and temporal features for better pattern detection.

    More Related Videos

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    7.0K
    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
    08:27

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

    Published on: January 5, 2024

    1.2K

    Related Experiment Videos

    Last Updated: Jul 23, 2025

    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

    574
    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    7.0K
    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
    08:27

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

    Published on: January 5, 2024

    1.2K

    Area of Science:

    • Environmental Science
    • Computer Science
    • Data Science

    Background:

    • Spatiotemporal clustering of vehicle emissions is crucial for understanding road traffic air pollution evolution.
    • Existing Graph Convolutional Network (GCN) methods inadequately model the interplay between spatial and temporal emission variations.
    • This limitation leads to incomplete descriptions and inaccurate detection of air pollution patterns.

    Purpose of the Study:

    • To propose a novel two-way self-supervised spatiotemporal representation learning scheme for vehicle emissions.
    • To address the challenge of unsupervised learning in accurately detecting air pollution evolution patterns.
    • To improve the accuracy of spatiotemporal clustering by capturing the interactions between spatial and temporal features.

    Main Methods:

    • A two-way self-supervised scheme progressively learns temporal and spatial features in a mutually reinforced manner.
    • Initial temporal representations are captured using a pretrained BiLSTM network.
    • A Graph Convolutional Network (GCN) is employed for feature clustering, constrained by a two-way self-supervised mechanism and refined through joint optimization.

    Main Results:

    • The proposed method demonstrates superior performance compared to state-of-the-art approaches in spatiotemporal clustering of vehicle emissions.
    • Experimental results on the Xian city traffic emission dataset (2020) validate the effectiveness of the approach.
    • The method successfully captures and refines the evolution patterns of air pollution from road traffic.

    Conclusions:

    • The developed two-way self-supervised learning scheme effectively models spatiotemporal correlations in vehicle emissions.
    • This approach overcomes the limitations of existing methods by integrating spatial and temporal feature interactions.
    • The findings offer a significant advancement in accurately detecting and analyzing air pollution evolution patterns.