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

Introduction to Statistical Process Control01:15

Introduction to Statistical Process Control

67
Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...
67
The X̄ Chart00:58

The X̄ Chart

99
The  x̄ chart is a statistical tool for monitoring the means in a process.
The x̄ chart, often known as the individual control chart, is a crucial tool in statistical process control. It is designed to monitor process behavior and performance over time and is widely used in various industries to ensure that processes are operating at their optimum capacity and within specified limits.
A x̄ chart is constructed by plotting individual measurements of a quality...
99
The R Chart01:02

The R Chart

54
In statistical process control, control charts, particularly R charts, are instrumental in monitoring process variations and identifying non-random patterns that run charts might miss. R charts track the variability within process subgroups, which is crucial when standard deviation use is impractical or unknown process variations exist.
R charts are pivotal for pinpointing shifts in process variability. Stability is indicated when all data points remain within the defined upper and lower...
54
Interpreting R Charts01:22

Interpreting R Charts

49
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
49
Interpreting Run Charts01:25

Interpreting Run Charts

53
Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
53

You might also read

Related Articles

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

Sort by
Same author

[The effect of chelerythrine on the hypertrophy of cardiac myocytes of neonatal rats induced by different glucose levels and its mechanism].

Yao xue xue bao = Acta pharmaceutica Sinica·2009
Same author

Overexpression of Midkine promotes the viability of BA/F3 cells.

Biochemical and biophysical research communications·2009
Same author

Neuroprotection of ethanol against cerebral ischemia/reperfusion induced brain injury through GABA receptor activation.

Brain research·2009
Same author

[Open-path online monitoring of ambient atmospheric CO2 based on laser absorption spectrum].

Guang pu xue yu guang pu fen xi = Guang pu·2009
Same author

[Effects of Sarcandra glabra extract on immune activity in restraint stress mice].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica·2009
Same author

No association between the promoter polymorphisms of PAI-1 gene and sporadic Alzheimer's disease in Chinese Han population.

Neuroscience letters·2009

Related Experiment Video

Updated: May 27, 2025

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

2.9K

Research on variable-length control chart pattern recognition based on sliding window method and SECNN-BiLSTM.

Tao Zan1, Xiaolong Jia2,3, Xiaoyu Guo1

  • 1Beijing Key Laboratory of Advanced Manufacturing Technology, College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing, 100124, China.

Scientific Reports
|February 18, 2025
PubMed
Summary

This study introduces a novel method for recognizing variable-length control charts using Sliding Window and SECNN-BiLSTM deep learning. The developed system accurately identifies control chart patterns, improving statistical process control in manufacturing.

Keywords:
Cloud computingPattern recognitionSECNN-BiLSTMSliding window methodVariable-length control chart

More Related Videos

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

949
A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software
08:22

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

Published on: August 31, 2018

6.5K

Related Experiment Videos

Last Updated: May 27, 2025

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

2.9K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

949
A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software
08:22

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

Published on: August 31, 2018

6.5K

Area of Science:

  • Industrial Engineering
  • Computer Science
  • Data Science

Background:

  • Statistical Process Control (SPC) relies on control charts to monitor production processes.
  • Existing control chart recognition methods struggle with variable-length data, limiting their industrial application.
  • Accurate recognition of variable-length control charts is crucial for real-time process monitoring.

Purpose of the Study:

  • To propose a novel method for recognizing variable-length control charts.
  • To develop a cloud-edge integrated system for real-time control chart recognition.
  • To enhance the accuracy and efficiency of control chart pattern recognition in industrial settings.

Main Methods:

  • A variable-length control chart recognition method combining the Sliding Window Method with a SE-attention CNN and Bi-LSTM (SECNN-BiLSTM) network.
  • Transformation of one-dimensional, variable-length control chart data into two-dimensional matrices using a sliding window approach.
  • Development of a cloud-edge integrated system utilizing wireless digital calipers, embedded devices, and cloud computing.

Main Results:

  • The proposed SECNN-BiLSTM method demonstrated efficient and accurate recognition of variable-length control charts.
  • Simulations and engineering applications validated the effectiveness of the developed cloud-edge recognition system.
  • The method successfully addresses the limitations of fixed-length data recognition in existing SPC tools.

Conclusions:

  • The SECNN-BiLSTM method provides an effective solution for variable-length control chart recognition.
  • The cloud-edge integrated system enables practical, real-time application in industrial environments.
  • This work lays the groundwork for more advanced pattern recognition techniques in statistical process control.