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

Design of Columns under a Centric Load01:17

Design of Columns under a Centric Load

551
The design of columns under centric load is a fundamental aspect of structural engineering and is critical for ensuring the stability and integrity of structures. Euler's and Secant's formulas are central to understanding and calculating the critical load and deformation behaviors of columns, providing a basis for safe and effective structural design.
Euler's formula is applicable under the assumption that the column is a perfect, straight, homogenous prism, and it is operating...
551
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

553
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
553
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

272
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
272
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

246
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
246
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

529
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
529
Convolution Properties II01:17

Convolution Properties II

583
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
583

You might also read

Related Articles

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

Sort by
Same author

Safe Fall: Use of Predictive Modeling and Machine Vision Techniques for Fall Analysis and Fall Quality.

Sensors (Basel, Switzerland)·2026
Same author

Expanding Domain-Specific Datasets with Stable Diffusion Generative Models for Simulating Myocardial Infarction.

International journal of neural systems·2025
Same author

Distinguishing Patient Profiles of Suicidal Ideation and Behavior: The Influence of Repetitive Negative Thinking, Internal and External Entrapment, and Defeat within the Integrated Motivational-Volitional Model in a Suicide Prevention Program.

The Psychiatric quarterly·2025
Same author

Understanding Robot Gesture Perception in Children with Autism Spectrum Disorder during Human-Robot Interaction.

International journal of neural systems·2025
Same author

Editorial for the Special Issue on "Feature Papers in Section AI in Imaging".

Journal of imaging·2024
Same author

Optical Flow-Based Obstacle Detection for Mid-Air Collision Avoidance.

Sensors (Basel, Switzerland)·2024

Related Experiment Video

Updated: Jan 29, 2026

Generating De Novo Antigen-specific Human T Cell Receptors by Retroviral Transduction of Centric Hemichain
08:48

Generating De Novo Antigen-specific Human T Cell Receptors by Retroviral Transduction of Centric Hemichain

Published on: October 25, 2016

9.0K

Model-Centric or Data-Centric Approach? A Case Study on the Classification of Surface Defects in Steel Hot Rolling

Francisco López de la Rosa1,2, José L Gómez-Sirvent1,3, Roberto Sánchez-Reolid1,3

  • 1Insituto de Investigación en Informática de Albacete (I3A), Calle de la Investigación, 2, 02071 Albacete, Spain.

Sensors (Basel, Switzerland)
|January 28, 2026
PubMed
Summary
This summary is machine-generated.

Data quality and quantity significantly impact convolutional neural network (CNN) performance in industrial applications, outweighing model complexity. Careful data analysis is crucial before selecting CNN models for efficiency and profit.

Keywords:
automated inspection systemsconvolutional neural networksdata augmentationimage preprocessingreliability

More Related Videos

Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations
11:36

Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

Published on: April 21, 2023

3.0K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K

Related Experiment Videos

Last Updated: Jan 29, 2026

Generating De Novo Antigen-specific Human T Cell Receptors by Retroviral Transduction of Centric Hemichain
08:48

Generating De Novo Antigen-specific Human T Cell Receptors by Retroviral Transduction of Centric Hemichain

Published on: October 25, 2016

9.0K
Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations
11:36

Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

Published on: April 21, 2023

3.0K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K

Area of Science:

  • Industrial applications
  • Machine learning
  • Computer vision

Background:

  • Convolutional neural networks (CNNs) are vital for industrial applications.
  • Model selection for CNNs depends on data and computational resources.
  • Optimizing CNN performance requires understanding data's role.

Purpose of the Study:

  • To analyze the influence of data quantity and quality on CNN model performance.
  • To compare the impact of data characteristics versus model complexity.
  • To guide CNN model selection in industrial settings.

Main Methods:

  • Utilized image preprocessing and data augmentation techniques.
  • Generated synthetic data to train CNN models of varying complexity.
  • Conducted experiments using the NEU Steel Surface Defects Database.

Main Results:

  • Data quality and quantity demonstrated a greater influence on CNN performance than model choice.
  • The study quantified the impact of data variations on model outcomes.
  • Model depth was found to be less critical than data attributes.

Conclusions:

  • Data quality and quantity are paramount for successful industrial CNN applications.
  • Prioritizing data analysis and resource assessment is essential before model selection.
  • Researchers should tailor CNN choices to specific industrial data and resource constraints.