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

Two-Dimensional Force System: Problem Solving01:29

Two-Dimensional Force System: Problem Solving

1.2K
Solving problems related to two-dimensional force systems is an essential aspect of mechanics and engineering. By applying the principles of vector analysis and force equilibrium, one can determine the effect of multiple forces acting on an object in a two-dimensional space.
The first step to solving a two-dimensional force system problem is to draw a free-body diagram of the object under consideration. This diagram helps identify all the external forces acting on the object, including their...
1.2K
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

1.3K
A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
1.3K
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

704
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
704

You might also read

Related Articles

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

Sort by
Same author

Embedded Printing of Integrated Quantum Dot Waveguide Deformation Sensors.

Sensors (Basel, Switzerland)·2026
Same author

Correction: Meyer zu Westerhausen et al. Optimisation of Sensor and Sensor Node Positions for Shape Sensing with a Wireless Sensor Network-A Case Study Using the Modal Method and a Physics-Informed Neural Network. <i>Sensors</i> 2025, <i>25</i>, 5573.

Sensors (Basel, Switzerland)·2025
Same author

Full-Factorial Rheological Investigation of Carbopol ETD2020 for Embedded Printing: Effects of pH and Carbomer Concentration.

Materials (Basel, Switzerland)·2025
Same author

Reliability Assessment of Wireless Sensor Networks by Strain-Based Region Analysis for Redundancy Estimation in Measurements on the Example of an Aircraft Wing Box.

Sensors (Basel, Switzerland)·2024

Related Experiment Video

Updated: Jan 18, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.1K

Optimisation of Sensor and Sensor Node Positions for Shape Sensing with a Wireless Sensor Network-A Case Study Using

Sören Meyer Zu Westerhausen1, Imed Hichri1, Kevin Herrmann1

  • 1Institute of Product Development, Leibniz University Hannover, An der Universität 1, 30823 Garbsen, Germany.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
Summary

This study presents a Python tool for optimal sensor placement and wireless sensor network (WSN) node positioning for accurate structural health monitoring (SHM). The integrated approach enhances shape sensing capabilities for optimizing structural components.

Keywords:
optimal sensor placementsensor node position optimisationshape sensingstructural health monitoringwireless sensor network

More Related Videos

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

9.8K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.8K

Related Experiment Videos

Last Updated: Jan 18, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.1K
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

9.8K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.8K

Area of Science:

  • Structural Engineering
  • Sensor Networks
  • Machine Learning

Background:

  • Operational condition data from structural components is crucial for product optimization through load adaptation.
  • Optimal sensor placement is essential for high-quality shape and load sensing, especially in large-scale structures.
  • Existing research often addresses sensor placement or node positioning separately, not holistically.

Purpose of the Study:

  • To develop a unified methodology for optimal sensor placement and wireless sensor network (WSN) node positioning.
  • To implement this methodology in a Python tool for practical application.
  • To demonstrate the effectiveness of the optimized WSN for real-time shape sensing on a test component.

Main Methods:

  • Application of the modal method for shape sensing.
  • Utilization of a physics-informed neural network (PINN) for inverse problems in shape sensing (iPINN).
  • Implementation of a WSN using strain gauges, HX711 A/D converters, and Arduino Nano 33 IoT microprocessors, with data processing on a Python Flask server.

Main Results:

  • Successful realization of an optimized WSN on a demonstration part under test bench loading.
  • Demonstration of high-accuracy shape sensing capabilities enabled by the integrated methodology.
  • Validation of the Python tool's applicability for optimizing sensor placement and WSN configuration.

Conclusions:

  • The presented methodology and Python tool effectively integrate optimal sensor placement and WSN node positioning.
  • The developed system achieves high-accuracy shape sensing, crucial for structural health monitoring and product optimization.
  • This approach provides a valuable framework for real-time monitoring and data processing of structural components.