Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...

Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...

Modeling and Similitude

Modeling and Similitude

Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...

The Uncertainty Principle

The Uncertainty Principle

Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...

You might also read

Related Articles

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

Sort by

Same author

A Maximum Entropy Model of the Distribution of Dengue Serotype in Mexico.

Transboundary and emerging diseases·2025

Same author

Measuring the Uncertainty of Predictions in Deep Neural Networks with Variational Inference.

Sensors (Basel, Switzerland)·2020

Same author

Design of a multiplex nested PCR for genotyping of the NSP4 from group A rotavirus.

Journal of virological methods·2008

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 10, 2025

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

From a Point Cloud to a Simulation Model-Bayesian Segmentation and Entropy Based Uncertainty Estimation for 3D

Christina Petschnigg¹, Markus Spitzner¹, Lucas Weitzendorf¹

¹BMW Group, Department of Factory Planning, Knorrstraße 147, 80788 Munich, Germany.

Entropy (Basel, Switzerland)

|April 3, 2021

Summary

This study presents a new method for 3D factory modeling using Bayesian neural networks for point cloud segmentation. This approach significantly improves the accuracy of environment models for process simulations in industrial planning.

Keywords:

Bayesian deep learning digital factory factory planning factory simulation photogrammetry point clouds uncertainty estimation

More Related Videos

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

Related Experiment Videos

Last Updated: Nov 10, 2025

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

Area of Science:

Industrial Engineering
Computer Vision
Robotics

Background:

3D modeling and process simulations are crucial for factory planning.
Existing data in brownfield sites are often outdated and incomplete, hindering accurate model generation.
Automated approaches for creating comprehensive factory models are lacking.

Purpose of the Study:

To develop a methodical approach for automated 3D factory model generation from digitized indoor environments.
To investigate the impact of uncertainty information from Bayesian segmentation on model accuracy.
To improve the accuracy of environment models for simulation purposes.

Main Methods:

Digitalization of large-scale indoor environments.
Application of a Bayesian neural network for point cloud segmentation and object identification.
Evaluation using a real-world dataset from an automotive production plant.

Main Results:

The Bayesian segmentation network outperformed the frequentist baseline.
Uncertainty information from the Bayesian framework enhanced environment model accuracy.
Considerable increase in the accuracy of model placement within simulation scenes was achieved.

Conclusions:

The proposed methodical approach enables automated generation of static environment or simulation models.
Bayesian neural networks offer a superior solution for point cloud segmentation in industrial settings.
The method significantly improves the accuracy and reliability of 3D factory models for planning and simulation.