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

Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.4K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.4K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.1K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.1K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

2.2K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
2.2K
Sampling Plans01:23

Sampling Plans

257
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
257
Convenience Sampling Method00:55

Convenience Sampling Method

9.6K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
9.6K
Systematic Sampling Method01:17

Systematic Sampling Method

11.1K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
11.1K

You might also read

Related Articles

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

Sort by
Same author

Machine-learning perspectives on Volterra system identification.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2025
Same author

Calibrating the Discrete Boundary Conditions of a Dynamic Simulation: A Combinatorial Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) Approach.

Sensors (Basel, Switzerland)·2024
Same author

Minimal Information Data-Modelling (MID) and an Easily Implementable Low-Cost SHM System for Use on a Short-Span Bridge.

Sensors (Basel, Switzerland)·2023
Same author

A Bayesian Method for Material Identification of Composite Plates via Dispersion Curves.

Sensors (Basel, Switzerland)·2023
Same journal

Computational modelling distinguishes diverse contributors to aneurysmal progression in the Marfan aorta.

Proceedings. Mathematical, physical, and engineering sciences·2025
Same journal

Inferring the shape of data: a probabilistic framework for analysing experiments in the natural sciences.

Proceedings. Mathematical, physical, and engineering sciences·2023
Same journal

The Elbert range of magnetostrophic convection. I. Linear theory.

Proceedings. Mathematical, physical, and engineering sciences·2022
Same journal

Soft wetting with (a)symmetric Shuttleworth effect.

Proceedings. Mathematical, physical, and engineering sciences·2022
Same journal

The quantum theory of time: a calculus for q-numbers.

Proceedings. Mathematical, physical, and engineering sciences·2022
Same journal

Integrable nonlinear evolution equations in three spatial dimensions.

Proceedings. Mathematical, physical, and engineering sciences·2022
See all related articles

Related Experiment Video

Updated: Sep 8, 2025

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

3.2K

A sampling-based approach for information-theoretic inspection management.

Lawrence A Bull1,2, Nikolaos Dervilis1, Keith Worden1

  • 1Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK.

Proceedings. Mathematical, Physical, and Engineering Sciences
|June 15, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new online learning method for structural health monitoring, reducing expert inspection costs. It uses an automatic regime to query informative measurements, enhancing damage detection with incremental data.

Keywords:
Active LearningDirichlet ProcessGenerative Mixture-ModelsSemi-Supervised LearningStructural Health Monitoring

More Related Videos

An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

7.3K
Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
16:23

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

Published on: February 26, 2014

14.5K

Related Experiment Videos

Last Updated: Sep 8, 2025

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

3.2K
An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

7.3K
Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
16:23

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

Published on: February 26, 2014

14.5K

Area of Science:

  • Engineering
  • Computer Science

Background:

  • Structural Health Monitoring (SHM) traditionally requires extensive data and costly expert inspections.
  • Data-driven SHM methods often need complete datasets before operation, which are rarely available.
  • Existing methods struggle to adapt to evolving system conditions and limited data.

Purpose of the Study:

  • To develop a cost-effective and adaptive SHM approach.
  • To reduce reliance on complete datasets and frequent expert interventions.
  • To enhance the efficiency of monitoring regimes by intelligently selecting informative measurements.

Main Methods:

  • A partially supervised learning approach is proposed for online classifier training.
  • The system learns incrementally, making predictions without requiring a full dataset upfront.
  • An automatic inspection regime queries measurements that are most informative to the evolving damage model.
  • Dirichlet process clustering is employed for online expert inspection management.

Main Results:

  • The proposed method effectively manages expert inspections in an online, data-driven manner.
  • It significantly reduces the cost associated with traditional expert assessments.
  • The approach demonstrates successful application on both simulated data and in situ bridge monitoring data.
  • The online learning classifier adapts to evolving system models and damage-sensitive features.

Conclusions:

  • Partially supervised online learning offers a viable solution for cost-effective SHM.
  • The automatic inspection regime optimizes resource allocation by focusing on informative data.
  • This method enhances the value and practicality of SHM systems, especially with incremental data availability.