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

Discrete Fourier Transform01:15

Discrete Fourier Transform

263
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
263
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

312
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
312

You might also read

Related Articles

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

Sort by
Same author

Revealing Local Coordination-Modulated Oxygen Evolution Reactivity in High-Entropy Layered Double Hydroxides.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Engineering Strain-Stiffening Granular Hydrogels for 3D-Printed Tissue-Mimicry.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Emerging strategies for biofilm disruption in recurrent urinary tract infections.

Investigative and clinical urology·2026
Same author

The regenerative role of neural crest stem cells in physical stimuli-enhanced peripheral nerve repair.

Stem cell reports·2026
Same author

Mechanisms and clinical implications of bacterial persistence in recurrent urinary tract infections.

Investigative and clinical urology·2026
Same author

<sup>*</sup>OH Adsorption-Mediated Electrochemical Oxidation of 5-Hydroxymethylfurfural to Selective 2,5-Furandicarboxylic Acid at pH 12.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025

Related Experiment Video

Updated: Jun 26, 2025

Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach
09:32

Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach

Published on: September 26, 2019

7.2K

Deep Learning Model for Cosmetic Gel Classification Based on a Short-Time Fourier Transform and Spectrogram.

Jae Ho Sim1,2, Jengsu Yoo1, Myung Lae Lee1

  • 1Materials and Components Research Division, Superintelligence Creative research Laboratory, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Republic of Korea.

ACS Applied Materials & Interfaces
|May 13, 2024
PubMed
Summary

This study introduces a deep learning method to analyze cosmetic gel properties using friction signals. The optimized STFT-based 2D CNN model offers a reliable, objective alternative to traditional sensory evaluations.

Keywords:
convolution neural network (CNN)cosmetic geldeep learninglearning rate schedulerrheologyspectrogramtribology

More Related Videos

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

7.1K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.7K

Related Experiment Videos

Last Updated: Jun 26, 2025

Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach
09:32

Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach

Published on: September 26, 2019

7.2K
Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

7.1K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.7K

Area of Science:

  • Materials Science
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Cosmetics and topical medications are viscoelastic substances applied to skin and mucous membranes.
  • Human perception of these materials is complex, involving multiple sensory modalities.
  • Traditional sensory evaluations by expert panels have limitations due to individual variability.

Purpose of the Study:

  • To propose a deep-learning-based method for analyzing physical properties of cosmetic gels.
  • To systematically identify key physical characteristics influencing user experience.
  • To offer an objective alternative to subjective sensory evaluations.

Main Methods:

  • Time-series friction signals from cosmetic gels were measured.
  • Signals were preprocessed using short-time Fourier transform (STFT) and continuous wavelet transform (CWT).
  • A ResNet-based convolutional neural network (CNN) model was developed and optimized.

Main Results:

  • The STFT-based 2D CNN model demonstrated superior performance compared to CWT-based and 1D CNN models.
  • The optimized STFT-based 2D CNN model showed robustness and reliability via k-fold cross-validation.
  • Frequency factors changing over time were effectively distinguished and analyzed.

Conclusions:

  • The proposed deep learning approach offers a systematic and objective method for assessing cosmetic gel properties.
  • This technology has the potential to replace traditional expert panel evaluations.
  • Objective assessment of cosmetic user experience can be achieved, improving product development.