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

Force Classification01:22

Force Classification

2.8K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.8K

You might also read

Related Articles

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

Sort by
Same author

Automatic Tuning and Matching for NMR Probes Based on Physics-Informed Conditional Neural Processes.

Sensors (Basel, Switzerland)·2026
Same author

Mitochondrial Dysfunction Unravels the Potential Molecular Link Between Night Shift Work-Related Circadian Disruption and Elevated Blood Pressure in Human and Mouse Models.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Macrophage-mediated brain-bone marrow crosstalk promotes chronic stress-induced glioma growth.

Cancer cell·2026
Same author

CAF-derived PTGDS drives pancreatic cancer neuroendocrine differentiation and chemoresistance via PAQR9-MAPK.

Oncogenesis·2026
Same author

Integrated Stress Response and Necroptosis Drive Epithelial Dysfunction in Crohn's Disease: Repurposing Cancer Drugs for Permeability Barrier Healing.

Gastro hep advances·2026
Same author

Analysis of spatial heterogeneity evolution in Dashentang oyster reefs: anthropogenic activities and environmental factors.

Journal of environmental management·2026
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: May 3, 2026

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

1.0K

Shape Classification Using a Single Seal-Whisker-Style Sensor Based on the Neural Network Method.

Yitian Mao1, Yingxue Lv2, Yaohong Wang3

  • 1Department of Mechanics, School of Mechanical Engineering, Tianjin University, Tianjin 300072, China.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
Summary
This summary is machine-generated.

Inspired by seal whiskers, researchers developed a biomimetic sensor. This sensor, combined with a convolutional neural network (CNN), can identify underwater objects by analyzing fluid forces, paving the way for new aquatic sensing technologies.

Keywords:
biomimeticsconvolutional neural networkharbor seal whiskersshape classificationunderwater detection

More Related Videos

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.7K
Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

407

Related Experiment Videos

Last Updated: May 3, 2026

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

1.0K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.7K
Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

407

Area of Science:

  • Biomimetics and Sensor Technology
  • Hydrodynamics and Fluid Mechanics
  • Artificial Intelligence and Machine Learning

Background:

  • Aquatic animals like seals use whiskers for target identification and tracking.
  • This biological mechanism offers inspiration for developing advanced, low-power, portable, and eco-friendly sensors.
  • Existing sensing technologies may lack the sensitivity and adaptability of biological systems.

Purpose of the Study:

  • To design and test a seal-whisker-like cylindrical sensor for detecting underwater targets.
  • To train and evaluate a convolutional neural network (CNN) using force signals from the sensor.
  • To determine the effectiveness of this biomimetic approach in object identification and analyze key signal features.

Main Methods:

  • Fabrication of a single seal-whisker-mimicking cylinder.
  • Experimental measurement of forces (lift and drag) on the cylinder with nine different upstream targets.
  • Development and testing of a convolutional neural network (CNN) model using collected force signal datasets.
  • Application of Fourier analysis to understand signal characteristics and model performance.

Main Results:

  • The seal-whisker sensor combined with a CNN successfully identified underwater objects in most test cases.
  • Certain targets caused confusion, indicating limitations in the current model.
  • Increasing signal sample length improved accuracy but did not fully resolve target confusion.
  • High frequencies (>5 Hz) were found to be irrelevant for the CNN model's performance.
  • Lift signals provided more distinguishing features than drag signals for target differentiation.
  • Model efficacy correlated strongly with spectral feature discrepancies in lift signals.

Conclusions:

  • A biomimetic sensor inspired by seal whiskers, coupled with a CNN, shows significant potential for underwater object identification.
  • Lift signal analysis, particularly its spectral features, is crucial for distinguishing between different targets.
  • Further research may be needed to address confusions with specific targets and optimize sensor performance.