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

Survival Tree01:19

Survival Tree

131
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
131
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

221
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
221
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

525
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
525
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

94
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
94
Cancer Survival Analysis01:21

Cancer Survival Analysis

411
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
411

You might also read

Related Articles

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

Sort by
Same author

Identification of highly immunogenic endogenous dsRNAs from cellular MDA5 filaments.

bioRxiv : the preprint server for biology·2026
Same author

Multimerizing transcription factors FOXP3 and AIRE as chromatin architectural regulators.

Nature immunology·2026
Same author

FoxP3 forms a head-to-head dimer in vivo and stabilizes its multimerization on adjacent microsatellites.

Cell reports·2025
Same author

FoxP3 forms a head-to-head dimer in vivo and stabilizes its multimerization on adjacent microsatellites.

bioRxiv : the preprint server for biology·2025
Same author

PACT prevents aberrant activation of PKR by endogenous dsRNA without sequestration.

Nature communications·2025
Same author

Ultrastable and versatile multimeric ensembles of FoxP3 on microsatellites.

Molecular cell·2025

Related Experiment Video

Updated: Aug 15, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K

Shapelet selection based on a genetic algorithm for remaining useful life prediction with supervised learning.

Gilseung Ahn1, Min-Ki Jin2, Seok-Beom Hwang2

  • 1Big Data Group, Hyundai Motors Company, Seoul, 06796, Republic of Korea.

Heliyon
|December 29, 2022
PubMed
Summary

Optimizing remaining useful life (RUL) shapelets is crucial for accurate predictions. This study introduces a genetic algorithm to create optimal RUL shapelet sets, improving prediction performance and interpretability.

Keywords:
Feature selectionGenetic algorithmRUL shapelet selectionRemaining useful life prediction

More Related Videos

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.1K
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.7K

Related Experiment Videos

Last Updated: Aug 15, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K
Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.1K
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.7K

Area of Science:

  • Engineering
  • Data Science
  • Machine Learning

Background:

  • Remaining Useful Life (RUL) prediction is vital for asset management.
  • Traditional similarity-based RUL methods suffer from parameter sensitivity.
  • RUL shapelets offer improved prediction but require optimized sets.

Purpose of the Study:

  • To mathematically formalize the RUL shapelet composition problem.
  • To develop an optimized RUL shapelet set composition methodology.
  • To enhance RUL prediction performance and interpretability.

Main Methods:

  • Mathematical formalization of the RUL shapelet composition problem.
  • Analysis of characteristics for effective RUL shapelet sets.
  • Development of a genetic algorithm-based solution methodology.

Main Results:

  • The proposed genetic algorithm method significantly outperforms previous approaches.
  • Validated effectiveness through comprehensive experimental analysis.
  • Demonstrated applicability to diverse RUL prediction scenarios.

Conclusions:

  • The developed genetic algorithm provides an effective solution for RUL shapelet optimization.
  • Findings offer insights for future RUL shapelet-based research.
  • The methodology is adaptable for various RUL prediction challenges.