Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Reconstruction of Signal using Interpolation

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...

End Point Prediction: Gran Plot

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...

Convolution: Math, Graphics, and Discrete Signals

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...

You might also read

Related Articles

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

Sort by

Same author

Fully and spontaneously degradable carboxymethyl chitosan/oxidized hyaluronic acid-based membrane promoting three-dimensional tissue remodeling in vitro and in vivo.

International journal of biological macromolecules·2026

Same author

Tracking the Night: Measuring Age and Sex Patterns in Sleep Duration Using Wearable Technology.

Sleep·2026

Same author

First sequence-verified detection of Perkinsus chesapeaki in Pacific oysters (Magallana gigas) from South Korea.

Journal of invertebrate pathology·2026

Same author

Quantitative PCR surveillance of Perkinsus marinus in Crassostrea virginica and Magallana gigas across the USA, Mexico, Brazil, and Korea.

Parasites, hosts and diseases·2026

Same author

Strain-dependent <i>toxT</i> expression, rather than ToxT activity, governs virulence gene regulation in <i>Vibrio cholerae</i>.

Frontiers in microbiology·2026

Same author

Analysis of uroflowmetry results in community-dwelling adult males: Results of 24 years of Korean population-based screening data.

Investigative and clinical urology·2026

Search research articles

Related Experiment Videos

PM10 and PM2.5 real-time prediction models using an interpolated convolutional neural network.

Sangwon Chae¹, Joonhyeok Shin¹, Sungjun Kwon¹

¹Department of Business Administration, Korea Polytechnic University, 237 Sangidaehak-ro, Siheung-si, 15073, Gyeonggi-do, Republic of Korea.

Scientific Reports

|June 8, 2021

Summary

This summary is machine-generated.

We developed a new model to predict air quality by forecasting particulate matter (PM) levels. This interpolated Convolutional Neural Network (ICNN) model accurately forecasts PM10 and PM2.5 concentrations in real-time.

Related Experiment Videos

Area of Science:

Environmental Science
Data Science
Artificial Intelligence

Background:

Particulate matter (PM) in the air is a key indicator of poor air quality.
Accurate real-time prediction of PM concentrations is crucial for public health and environmental monitoring.
Existing models may face challenges with irregular spatial data and real-time forecasting.

Purpose of the Study:

To propose a novel real-time prediction model for air quality, specifically focusing on particulate matter concentrations.
To enhance the accuracy and reliability of PM2.5 and PM10 forecasting.
To leverage spatio-temporal data using advanced machine learning techniques.

Main Methods:

Developed an interpolated Convolutional Neural Network (ICNN) model.
Applied interpolation to transform irregular spatial air quality and weather data into an equally spaced grid.
Utilized the CNN architecture for predicting PM10 and PM2.5 concentrations.

Main Results:

The ICNN model demonstrated effective prediction performance for both PM10 and PM2.5.
Achieved an R-squared value greater than 0.97 and a Root Mean Square Error (RMSE) of approximately 16% of the standard deviation.
Showcased high reliability in forecasting high concentrations, with a probability of detection >0.90 and a Critical Success Index >0.85.

Conclusions:

The proposed ICNN model offers a significant advancement in real-time air quality prediction.
The model effectively utilizes spatio-temporal information for accurate PM concentration forecasting.
This research opens new avenues for developing advanced air quality monitoring and prediction systems.