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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

7.4K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
7.4K
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

1.2K
Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
1.2K
Detection of Black Holes01:10

Detection of Black Holes

2.3K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.3K
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

1.4K
The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
1.4K
Atomic Absorption Spectroscopy: Interference01:25

Atomic Absorption Spectroscopy: Interference

1.4K
Interference leads to systematic error in atomic absorption (AA) measurements by enhancing or diminishing the analytical signal or the background. These interferences can be grouped into three main categories: spectral interference, chemical interference, and physical interference.
Spectral interference occurs when signals from other elements or molecules overlap with the analyte signal, falsely elevating or masking the analyte's absorbance. This interference can be corrected using Zeeman,...
1.4K
Classification of Signals01:30

Classification of Signals

1.0K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.0K

You might also read

Related Articles

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

Sort by
Same author

Interaction between dynamic reinforcement learning and working memory of pigeon: A comparative modeling study.

The Journal of experimental biology·2026
Same author

Utilizing the Relationship Between Unconstrained and Constrained Pareto Fronts for Constrained Multiobjective Optimization.

IEEE transactions on cybernetics·2022
Same author

Prognostic Signatures and Therapeutic Value Based on the Notch Pathway in Renal Clear Cell Carcinoma.

Oxidative medicine and cellular longevity·2022
Same author

Performance assessment of sample-specific network control methods for bulk and single-cell biological data analysis.

PLoS computational biology·2021
Same author

CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE).

IEEE transactions on medical imaging·2019
Same author

Prevention effect of low-temperature atomization inhalation for radiation induced oral mucositis in patients with head and neck cancer undergoing radiotherapy.

Cancer management and research·2019

Related Experiment Video

Updated: Oct 22, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.5K

Research on the Fastest Detection Method for Weak Trends under Noise Interference.

Guang Li1, Jing Liang2, Caitong Yue2

  • 1School of Electrical Engineering and Automation, Henan Institute of Technology, Xinxiang 453003, China.

Entropy (Basel, Switzerland)
|August 27, 2021
PubMed
Summary

This study introduces a new trend anomaly detection algorithm, SNWFD-DS, designed to identify weak anomalies in noisy industrial data. The method significantly improves detection accuracy and reduces delays, outperforming traditional approaches.

Keywords:
data streammachine learningsliding windowwindow length

More Related Videos

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
Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
06:04

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Published on: January 17, 2025

855

Related Experiment Videos

Last Updated: Oct 22, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.5K
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
Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
06:04

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Published on: January 17, 2025

855

Area of Science:

  • Industrial data analysis
  • Machine learning algorithms
  • Time series analysis

Background:

  • Trend anomaly detection is crucial for industrial data streams.
  • Noisy data hinders the detection of weak anomalies.
  • Existing methods struggle with noise interference.

Purpose of the Study:

  • To develop a novel algorithm for detecting weak trend anomalies in noisy industrial data streams.
  • To improve the speed and accuracy of anomaly detection.
  • To address limitations of traditional trend anomaly detection methods.

Main Methods:

  • Adoption of the "sliding nesting" algorithm.
  • Calculation of data weights using variable weights within windows.
  • Modification of the traditional trend anomaly detection score calculation.
  • Implementation of the SNWFD-DS algorithm.

Main Results:

  • SNWFD-DS effectively detects weak trend abnormalities despite noise interference.
  • The new algorithm significantly reduces detection delays compared to other methods.
  • On-site oil drilling data tests confirm improved detection accuracy under noise.

Conclusions:

  • The SNWFD-DS algorithm offers significant advantages for trend anomaly detection in industrial settings.
  • This method enhances the ability to identify subtle anomalies in real-time data.
  • The approach is valuable for both academic research and industrial applications.