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 Experiment Video

Updated: Jul 1, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K

Optimizing Fuel Consumption Prediction Model Without an On-Board Diagnostic System in Deep Learning Frameworks.

Rıdvan Keskin1, Egemen Belge1, Senol Hakan Kutoglu2

  • 1Department of Electrical Electronics Engineering, Zonguldak Bulent Ecevit University, Zonguldak 67100, Türkiye.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
Summary

Related Concept Videos

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...

You might also read

Related Articles

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

Sort by
Same author

Analyzing Fault Reactivation Behavior Using InSAR, Stress Inversion, and Field Observations During the 2025 Sındırgı Earthquake Sequence, Simav Fault Zone, Western Türkiye.

Sensors (Basel, Switzerland)·2026
Same author

Delineating the Central Anatolia Transition Zone (CATZ): Constraints from Integrated Geodetic (GNSS/InSAR) and Seismic Data.

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

We developed a new model for predicting vehicle fuel consumption rate (FCR) using only throttle, speed, and acceleration data. This data-driven approach improves accuracy and efficiency, reducing emissions without needing complex vehicle specifics.

Area of Science:

  • Automotive Engineering
  • Machine Learning
  • Environmental Science

Background:

  • Accurate real-time fuel consumption rate (FCR) prediction is crucial for enhancing vehicle energy efficiency and reducing emissions.
  • Conventional methods often require specific vehicle parameters and environmental data, limiting their applicability.
  • On-board diagnostic (OBD) systems are typically used for FCR data acquisition but can be complex.

Purpose of the Study:

  • To propose a novel, data-driven FCR prediction model using readily available vehicle data.
  • To enhance prediction accuracy and reliability using deep learning and probabilistic methods.
  • To reduce reliance on complex vehicle-specific parameters and environmental conditions.

Main Methods:

  • Development of a Bayesian optimization and Monte Carlo (MC) Dropout-based long short-term memory (BMC-LSTM) network.
Keywords:
Bayesian optimizationdeep learningfuel consumption predictionlong short-term memorymachine learning

Related Experiment Videos

Last Updated: Jul 1, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K
  • Utilizing only vehicle throttle position, velocity, and acceleration data for prediction.
  • Integration of Bayesian optimization for hyperparameter tuning and MC-Dropout for uncertainty quantification.

    • Main Results:

    • The BMC-LSTM model achieved superior prediction accuracy compared to conventional LSTM, Bidirectional LSTM, XGBoost, support vector regression, and polynomial models.
    • The proposed method demonstrated a significantly higher R-squared score and reduced error metrics (MSE, RMSE, MAE).
    • The model provides calibrated predictions and robustness against distribution drift.

    Conclusions:

    • The BMC-LSTM model offers a cost-effective and accurate solution for real-time FCR prediction.
    • This approach simplifies FCR prediction by requiring only basic vehicle kinematic data at inference.
    • The method enhances energy efficiency and emission reduction potential in vehicles.