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

Determination of Expected Frequency01:08

Determination of Expected Frequency

2.2K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.2K
Response Surface Methodology01:16

Response Surface Methodology

147
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
147
Finding Critical Values for Chi-Square01:18

Finding Critical Values for Chi-Square

3.0K
Consider a curve representing sample data drawn randomly from a normally distributed population. One must construct confidence intervals to estimate or to test a claim regarding the population standard deviation. For example, a 95% confidence interval covers 95% of the area under the curve, and the remaining 5% is equally distributed on either side of the curve. To achieve such confidence intervals, one must determine the critical values. The critical values are simply the values separating the...
3.0K

You might also read

Related Articles

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

Sort by
Same author

rTM reprograms macrophages via the HIF-1α/METTL3/PFKM axis to protect mice against sepsis.

Cellular and molecular life sciences : CMLS·2024
Same author

ScRNA-seq and bulk RNA-seq identified NUPR1 as novel biomarkers related to CD4 + T cells infiltration for abdominal aortic aneurysm.

Molecular biology reports·2024
Same author

Targeting fibroblast activation protein with chimeric antigen receptor macrophages.

Biochemical pharmacology·2024
Same author

Reactive arthritis in connective tissue diseases; one should be vigilant for joint tuberculosis.

Tropical doctor·2024
Same author

A Nomogram for Predicting Cancer-Associated Venous Thromboembolism in Hospitalized Patients Receiving Chemoradiotherapy for Cancer.

Cancer control : journal of the Moffitt Cancer Center·2024
Same author

Automatic detection of epileptic seizure based on one dimensional cascaded convolutional autoencoder with adaptive window-thresholding.

Journal of neural engineering·2024

Related Experiment Video

Updated: Jul 13, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.5K

Random forest method for estimation of brake specific fuel consumption.

Qinsheng Yun1,2, Xiangjun Wang3, Chen Yao4

  • 1Naval University of Engineering, Wuhan, 430000, China. yunqinsheng@126.com.

Scientific Reports
|October 18, 2023
PubMed
Summary

Accurately estimating brake specific fuel consumption (BSFC) maps is vital for internal combustion engines. An improved random forest method enhances BSFC map estimation accuracy, outperforming existing techniques.

More Related Videos

Implementation of Portable Emissions Measurement Systems PEMS for the Real-driving Emissions RDE Regulation in Europe
09:34

Implementation of Portable Emissions Measurement Systems PEMS for the Real-driving Emissions RDE Regulation in Europe

Published on: December 4, 2016

28.2K
Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

3.4K

Related Experiment Videos

Last Updated: Jul 13, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.5K
Implementation of Portable Emissions Measurement Systems PEMS for the Real-driving Emissions RDE Regulation in Europe
09:34

Implementation of Portable Emissions Measurement Systems PEMS for the Real-driving Emissions RDE Regulation in Europe

Published on: December 4, 2016

28.2K
Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

3.4K

Area of Science:

  • Engineering
  • Thermodynamics
  • Data Science

Background:

  • Internal combustion engines are critical power sources, with energy efficiency measured by brake specific fuel consumption (BSFC).
  • BSFC maps are essential for engine analysis and optimization.
  • Current BSFC map estimation methods, like K-nearest neighbor and multi-layer perceptron, suffer from accuracy limitations, especially with sparse data.

Purpose of the Study:

  • To develop a more accurate method for estimating brake specific fuel consumption (BSFC) maps.
  • To improve upon existing techniques for handling distributed sampled data in BSFC map generation.

Main Methods:

  • An improved random forest method was proposed for BSFC estimation.
  • Polynomial features were utilized for nonlinear transformation to increase feature dimensions.
  • Particle swarm optimization algorithms were employed to optimize critical parameters of the random forest model.

Main Results:

  • The proposed improved random forest method demonstrated superior performance compared to common estimation techniques.
  • The method showed effectiveness in estimating 20%, 30%, and 40% of BSFC data across two datasets.
  • The enhanced method provides more accurate BSFC map estimations, particularly for distributed sampled data.

Conclusions:

  • The improved random forest method is a highly effective approach for BSFC map estimation.
  • This technique offers a significant advancement over traditional methods for internal combustion engine analysis.
  • The proposed method is suitable for accurate BSFC map generation, addressing limitations of current approaches.