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

Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

591
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
591
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

214
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
214
Application of Nonlinear Inequalities01:29

Application of Nonlinear Inequalities

87
A nonlinear inequality describes a comparison involving an expression that curves or behaves more complexly than a straight line. These inequalities often appear in forms that include squares, products, or variables in the denominator.To solve such an inequality, one starts by rewriting it so that zero appears on one side. For example, the inequality:  can be factored as: This form makes it easier to identify the values that cause the expression to equal zero. In this case, the...
87
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

3.0K
In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
3.0K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

928
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...
928
Principle of Moments: Problem Solving01:30

Principle of Moments: Problem Solving

1.1K
The principle of moments is a fundamental concept in physics and engineering. It refers to the balancing of forces and moments around a point or axis, also known as the pivot. This principle is used in many real-life scenarios, including construction, sports, and daily activities like opening doors and pushing objects.
One such scenario involves a pole placed in a three-dimensional system with a cable attached. When a tension is applied to the cable, the moment about the z-axis passing through...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Transcriptomic Analysis Provides Insights to Reveal the <i>bmp6</i> Function Related to the Development of Intermuscular Bones in Zebrafish.

Frontiers in cell and developmental biology·2022
Same author

A prospective randomized controlled trial comparing the effect and safety of Piranha and VersaCut morcellation devices in transurethral holmium laser enucleation of the prostate.

International urology and nephrology·2022
Same author

The roles of inactivated vaccines in older patients with infection of Delta variant in Nanjing, China.

Aging·2022
Same author

Porosity Tunable Poly(Lactic Acid)-Based Composite Gel Polymer Electrolyte with High Electrolyte Uptake for Quasi-Solid-State Supercapacitors.

Polymers·2022
Same author

Effect of pyrolysis temperature on sulfur content, extractable fraction and release of sulfate in corn straw biochar.

RSC advances·2022
Same author

Preparation and properties of PTFE hollow fiber membranes for the removal of ultrafine particles in PM<sub>2.5</sub> with repetitive usage capability.

RSC advances·2022

Related Experiment Video

Updated: Nov 27, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.5K

Sequential Change-Point Detection via Online Convex Optimization.

Yang Cao1, Liyan Xie1, Yao Xie1

  • 1H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary

This study introduces a novel method for sequential change-point detection with unknown parameters using online convex optimization. The approach achieves near second-order asymptotic optimality for detecting distribution changes in data streams.

Keywords:
change-point detectiononline algorithmssequential methods

More Related Videos

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&#8212;Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.4K
Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

6.9K

Related Experiment Videos

Last Updated: Nov 27, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.5K
Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&#8212;Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.4K
Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

6.9K

Area of Science:

  • Statistics
  • Machine Learning
  • Data Science

Background:

  • Sequential change-point detection is crucial for monitoring data streams.
  • Detecting changes when distribution parameters are unknown presents significant challenges.
  • Existing methods like recursive maximum likelihood estimators have limitations.

Purpose of the Study:

  • To develop a versatile and asymptotically optimal procedure for sequential change-point detection with unknown parameters.
  • To address complex scenarios where traditional estimators fail.
  • To provide a robust framework for real-time data analysis.

Main Methods:

  • Utilized sequential likelihood ratios with non-anticipating estimators.
  • Employed online convex optimization algorithms, specifically online mirror descent.
  • Established a connection between change-point detection and online convex optimization theory.
  • Leveraged the logarithmic regret bound property of online mirror descent.

Main Results:

  • The proposed detection procedure demonstrates near second-order asymptotic optimality.
  • The false alarm rate (average run length) asymptotically meets the theoretical lower bound.
  • The method is validated through numerical simulations and real-world data analysis.
  • Online mirror descent offers a versatile alternative when recursive maximum likelihood estimators are intractable.

Conclusions:

  • The integration of online convex optimization provides a powerful and flexible approach to sequential change-point detection.
  • The developed method offers strong theoretical guarantees on performance.
  • This work advances the field of statistical monitoring and anomaly detection.