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.

Energy Stored in a Capacitor: Problem Solving

Energy Stored in a Capacitor: Problem Solving

In 1749, Benjamin Franklin coined the word battery for a series of capacitors connected to store energy. Capacitors store electric potential energy that can be released over a short time. This property means capacitors have a wide range of applications.
Capacitor-discharge ignition is a type of ignition system commonly found in small engines where the energy released from a capacitor ignites an induction coil that, in turn, fires the spark plug.
To calculate the energy stored in a capacitor of...

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...

Energy Stored in Capacitors

Energy Stored in Capacitors

A parallel plate capacitor, when connected to a battery, develops a potential difference across its plates. This potential difference is key to the operation of the capacitor, as it determines how much electrical energy the capacitor can store.
By integrating the equation that relates voltage and current in a capacitor, one can derive an equation for the voltage across the capacitor at any given time. This equation is crucial in understanding and predicting the behavior of capacitors in...

Energy Stored in a Capacitor

Energy Stored in a Capacitor

When an archer pulls the string in a bow, he saves the work done in the form of elastic potential energy. When he releases the string, the potential energy is released as kinetic energy of the arrow. A capacitor works on the same principle in which the work done is saved as electric potential energy. The potential energy (UC) could be calculated by measuring the work done (W) to charge the capacitor.

Spherical and Cylindrical Capacitor

Spherical and Cylindrical Capacitor

A spherical capacitor consists of two concentric conducting spherical shells of radii R1 (inner shell) and R2 (outer shell). The shells have equal and opposite charges of +Q and −Q, respectively. For an isolated conducting spherical capacitor, the radius of the outer shell can be considered to be infinite.
Conventionally, considering the symmetry, the electric field between the concentric shells of a spherical capacitor is directed radially outward. The magnitude of the field,...

You might also read

Related Articles

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

Sort by

Same author

Microstructure and Mechanical Properties of In Situ Al<sub>3</sub>Zr/Al-5Cu-0.6Mn-0.15Ti Heat-Resistant Aluminum Matrix Composites Based on Nominal Al<sub>3</sub>Zr Contents.

Materials (Basel, Switzerland)·2026

Same author

Hybrid strategy improved dung beetle optimization algorithm based 2D kapur entropy image segmentation method.

Scientific reports·2026

Same author

LysePred: A Multiscale Convolutional Neural Network for Predicting Hemolytic Activity of Antimicrobial Peptides.

ACS synthetic biology·2026

Same author

Orbital Stabilization of Anionic Redox via Surface Restructuration for Li-Rich Mn-Based Layered Oxides.

Angewandte Chemie (International ed. in English)·2026

Same author

Cod protein modifications induced by lipid oxidation-derived aldehydes: Insights into multi-aldehydes environment.

Food chemistry·2026

Same author

Noninvasive PPROM risk stratification with explainable AI using routine antenatal CRP and albumin.

NPJ digital medicine·2026

Search research articles

Related Experiment Video

Updated: Nov 2, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

High precision implicit function learning for forecasting supercapacitor state of health based on Gaussian process

Jiahao Ren^1,2, Junfei Cai^1,2, Jinjin Li^3,4

¹National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai, 200240, China.

Scientific Reports

|June 9, 2021

Summary

State of health prediction for supercapacitors is crucial for reliable operation. A new Gaussian process regression-implicit function learning method accurately predicts supercapacitor health using minimal data, significantly reducing errors.

More Related Videos

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Related Experiment Videos

Last Updated: Nov 2, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Area of Science:

Energy Storage Systems
Materials Science
Machine Learning

Background:

State of Health (SOH) prediction is vital for supercapacitor lifetime management and failure prevention.
Gaussian Process Regression (GPR) is effective for SOH prediction due to its ability to model nonlinearities and track SOH degradation.
Traditional GPR methods are limited by data requirements and computational time for function screening.

Purpose of the Study:

To develop a novel GPR-implicit function learning approach for supercapacitor SOH prediction.
To overcome the limitations of traditional GPR methods regarding data scarcity and computational cost.
To enhance the precision and efficiency of SOH prediction for energy storage devices.

Main Methods:

Proposed a GPR-implicit function learning algorithm that utilizes prior knowledge to derive mean and covariance functions.
Applied the implicit function learning to a preliminary dataset, avoiding extensive screening of explicit functions.
Trained the model using a small fraction (5% and 1%) of available cycle data.

Main Results:

Achieved an average Root Mean Square Error (RMSE) of 0.0056 F and average Mean Absolute Percentage Error (MAPE) of 0.6% when training on 5% of data.
Demonstrated prediction accuracy for the remaining 95% of cycles, reducing errors by over three times compared to previous studies.
Obtained low prediction errors (Average RMSE: 0.0094 F, Average MAPE: 1.01%) even when training on only 1% of the data.

Conclusions:

The GPR-implicit function learning model offers a highly precise and data-efficient solution for supercapacitor SOH prediction.
This approach significantly reduces the amount of training data required, making it suitable for scenarios with limited property data.
The findings highlight the potential of GPR-implicit function learning for advanced state of health monitoring in energy storage devices.