scholarly journals Visual analysis of density and velocity profiles in dense 3D granular gases

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dmitry Puzyrev ◽  
David Fischer ◽  
Kirsten Harth ◽  
Torsten Trittel ◽  
Raúl Cruz Hidalgo ◽  
...  
Keyword(s):  
Visual Analysis ◽  
Three Dimensional ◽  
Ground Truth ◽  
Optical Observations ◽  
Granular Gases ◽  
Optical Analysis ◽  
Analysis Methods ◽  
Leidenfrost Effect ◽  
Side Walls ◽  
Ground Truth Information

AbstractGranular multiparticle ensembles are of interest from fundamental statistical viewpoints as well as for the understanding of collective processes in industry and in nature. Extraction of physical data from optical observations of three-dimensional (3D) granular ensembles poses considerable problems. Particle-based tracking is possible only at low volume fractions, not in clusters. We apply shadow-based and feature-tracking methods to analyze the dynamics of granular gases in a container with vibrating side walls under microgravity. In order to validate the reliability of these optical analysis methods, we perform numerical simulations of ensembles similar to the experiment. The simulation output is graphically rendered to mimic the experimentally obtained images. We validate the output of the optical analysis methods on the basis of this ground truth information. This approach provides insight in two interconnected problems: the confirmation of the accuracy of the simulations and the test of the applicability of the visual analysis. The proposed approach can be used for further investigations of dynamical properties of such media, including the granular Leidenfrost effect, granular cooling, and gas-clustering transitions.

scholarly journals TOWARDS GLOBALLY CONSISTENT SCAN MATCHINGWITH GROUND TRUTH INTEGRATION

2015 ◽  
Vol XL-3/W2 ◽  
pp. 59-64
Author(s):  
J. Gailis ◽  
A. Nüchter
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Simultaneous Localization And Mapping ◽  
Ground Truth ◽  
Mapping Method ◽  
Coordinate Frame ◽  
Scan Matching ◽  
Localization And Mapping ◽  
Real World Datasets ◽  
Ground Truth Information

The scan matching based simultaneous localization and mapping method with six dimensional poses is capable of creating a three dimensional point cloud map of the environment, as well as estimating the six dimensional path that the vehicle has travelled. The essence of it is the registering and matching of sequentially acquired 3D laser scans, while moving along a path, in a common coordinate frame in order to provide 6D pose estimations at the respective positions, as well as create a three dimensional map of the environment. An approach that could drastically improve the reliability of acquired data is to integrate available ground truth information. This paper is about implementing such functionality as a contribution to 6D SLAM (simultaneous localization and mapping with 6 DoF) in the 3DTK – The 3D Toolkit software (Nüchter and Lingemann, 2011), as well as test the functionality of the implementation using real world datasets.

The Influence of Blade Wakes on the Performance of Interturbine Diffusers

1996 ◽  
Vol 118 (2) ◽  
pp. 347-352 ◽  
Author(s):  
R. G. Dominy ◽  
D. A. Kirkham
Keyword(s):  
Performance Modeling ◽  
Three Dimensional ◽  
Secondary Flows ◽  
Two Dimensional ◽  
Correct Performance ◽  
Analysis Methods ◽  
Flow Factor ◽  
The Mean ◽  
Lp Turbine

Interturbine diffusers provide continuity between HP and LP turbines while diffusing the flow upstream of the LP turbine. Increasing the mean turbine diameter offers the potential advantage of reducing the flow factor in the following stages, leading to increased efficiency. The flows associated with these interturbine diffusers differ from those in simple annular diffusers both as a consequence of their high-curvature S-shaped geometry and of the presence of wakes created by the upstream turbine. It is shown that even the simplest two-dimensional wakes result in significantly modified flows through such ducts. These introduce strong secondary flows demonstrating that fully three-dimensional, viscous analysis methods are essential for correct performance modeling.

scholarly journals Assessing biases, relaxing moralism: On ground-truthing practices in machine learning design and application

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172110135
Author(s):  
Florian Jaton
Keyword(s):  
Machine Learning ◽  
William James ◽  
A Priori ◽  
Learning Algorithms ◽  
Three Dimensional ◽  
Ground Truth ◽  
Machine Learning Algorithms ◽  
Ground Truthing ◽  
Set Up ◽  
The Moment

This theoretical paper considers the morality of machine learning algorithms and systems in the light of the biases that ground their correctness. It begins by presenting biases not as a priori negative entities but as contingent external referents—often gathered in benchmarked repositories called ground-truth datasets—that define what needs to be learned and allow for performance measures. I then argue that ground-truth datasets and their concomitant practices—that fundamentally involve establishing biases to enable learning procedures—can be described by their respective morality, here defined as the more or less accounted experience of hesitation when faced with what pragmatist philosopher William James called “genuine options”—that is, choices to be made in the heat of the moment that engage different possible futures. I then stress three constitutive dimensions of this pragmatist morality, as far as ground-truthing practices are concerned: (I) the definition of the problem to be solved (problematization), (II) the identification of the data to be collected and set up (databasing), and (III) the qualification of the targets to be learned (labeling). I finally suggest that this three-dimensional conceptual space can be used to map machine learning algorithmic projects in terms of the morality of their respective and constitutive ground-truthing practices. Such techno-moral graphs may, in turn, serve as equipment for greater governance of machine learning algorithms and systems.

scholarly journals Three-dimensional visualization of ensemble weather forecasts – Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns

2015 ◽  
Vol 8 (7) ◽  
pp. 2355-2377 ◽  
Author(s):  
M. Rautenhaus ◽  
C. M. Grams ◽  
A. Schäfler ◽  
R. Westermann
Keyword(s):  
System Integration ◽  
Visual Analysis ◽  
Weather Forecasting ◽  
Three Dimensional ◽  
Conveyor Belt ◽  
Ensemble Prediction ◽  
Wind Fields ◽  
Uncertainty Visualization ◽  
Ensemble Prediction System ◽  
Weather Forecasts

Abstract. We present the application of interactive three-dimensional (3-D) visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 (THORPEX – North Atlantic Waveguide and Downstream Impact Experiment) campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts (WCBs) has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the European Centre for Medium Range Weather Forecasts (ECMWF) ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and grid spacing of the forecast wind field. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (3 to 7 days before take-off).

Investigation of Three-Dimensional Flow in Microchannels With Patterned Surfaces

2007 ◽  
Author(s):  
Auro Ashish Saha ◽  
Sushanta K. Mitra
Keyword(s):  
Surface Tension ◽  
Flow Instability ◽  
Numerical Study ◽  
Three Dimensional ◽  
Surface Characteristics ◽  
Bottom Wall ◽  
Surface Tension Effect ◽  
Hydrophobic Region ◽  
Side Walls ◽  
Dimensional Simulation

A three-dimensional numerical simulation of flow in patterned microchannel with alternate layers of hydrophilic and hydrophobic surfaces at the bottom wall is studied here. Surface characteristics of the microchannel are accounted by specifying the contact angle and the surface tension of the fluid. Meniscus profiles with varying amplitude and shapes are obtained under the different specified surface conditions. Flow instability increases as the fluid at the bottom wall traverses alternately from hydrophilic region to hydrophobic region. To understand the surface tension effect of the side walls, a two-dimensional numerical study has also been carried out for the microchannel and the results are compared with three-dimensional simulation. The surface tension effect of the side walls enhances the capillary effect for three-dimensional case.

scholarly journals TightCap: 3D Human Shape Capture with Clothing Tightness Field

2022 ◽  
Vol 41 (1) ◽  
pp. 1-17
Author(s):  
Xin Chen ◽  
Anqi Pang ◽  
Wei Yang ◽  
Peihao Wang ◽  
Lan Xu ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Ground Truth ◽  
Data Driven ◽  
Optimization Scheme ◽  
High Quality ◽  
3D Scan ◽  
Multi Stage ◽  
Geometry Image

In this article, we present TightCap, a data-driven scheme to capture both the human shape and dressed garments accurately with only a single three-dimensional (3D) human scan, which enables numerous applications such as virtual try-on, biometrics, and body evaluation. To break the severe variations of the human poses and garments, we propose to model the clothing tightness field—the displacements from the garments to the human shape implicitly in the global UV texturing domain. To this end, we utilize an enhanced statistical human template and an effective multi-stage alignment scheme to map the 3D scan into a hybrid 2D geometry image. Based on this 2D representation, we propose a novel framework to predict clothing tightness field via a novel tightness formulation, as well as an effective optimization scheme to further reconstruct multi-layer human shape and garments under various clothing categories and human postures. We further propose a new clothing tightness dataset of human scans with a large variety of clothing styles, poses, and corresponding ground-truth human shapes to stimulate further research. Extensive experiments demonstrate the effectiveness of our TightCap to achieve the high-quality human shape and dressed garments reconstruction, as well as the further applications for clothing segmentation, retargeting, and animation.

scholarly journals Monitoring of Sitting Postures With Sensor Networks in Controlled and Free-living Environments: Systematic Review

10.2196/21105 ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. e21105
Author(s):  
Arpita Mallikarjuna Kappattanavar ◽  
Nico Steckhan ◽  
Jan Philipp Sachs ◽  
Harry Freitas da Cruz ◽  
Erwin Böttinger ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Pressure Sensors ◽  
Ground Truth ◽  
Living Environment ◽  
Measurement Unit ◽  
Future Research ◽  
Free Living ◽  
Medium Quality ◽  
Underlying Causes ◽  
Future Work

Background A majority of employees in the industrial world spend most of their working time in a seated position. Monitoring sitting postures can provide insights into the underlying causes of occupational discomforts such as low back pain. Objective This study focuses on the technologies and algorithms used to classify sitting postures on a chair with respect to spine and limb movements. Methods A total of three electronic literature databases were surveyed to identify studies classifying sitting postures in adults. Quality appraisal was performed to extract critical details and assess biases in the shortlisted papers. Results A total of 14 papers were shortlisted from 952 papers obtained after a systematic search. The majority of the studies used pressure sensors to measure sitting postures, whereas neural networks were the most frequently used approaches for classification tasks in this context. Only 2 studies were performed in a free-living environment. Most studies presented ethical and methodological shortcomings. Moreover, the findings indicate that the strategic placement of sensors can lead to better performance and lower costs. Conclusions The included studies differed in various aspects of design and analysis. The majority of studies were rated as medium quality according to our assessment. Our study suggests that future work for posture classification can benefit from using inertial measurement unit sensors, since they make it possible to differentiate among spine movements and similar postures, considering transitional movements between postures, and using three-dimensional cameras to annotate the data for ground truth. Finally, comparing such studies is challenging, as there are no standard definitions of sitting postures that could be used for classification. In addition, this study identifies five basic sitting postures along with different combinations of limb and spine movements to help guide future research efforts.

scholarly journals Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes

2020 ◽  
Author(s):  
Jiji Chen ◽  
Hideki Sasaki ◽  
Hoyin Lai ◽  
Yijun Su ◽  
Jiamin Liu ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Network Performance ◽  
Resolution Enhancement ◽  
Three Dimensional ◽  
Ground Truth ◽  
Time Lapse ◽  
Stimulated Emission ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Attention Networks

Abstract We demonstrate residual channel attention networks (RCAN) for restoring and enhancing volumetric time-lapse (4D) fluorescence microscopy data. First, we modify RCAN to handle image volumes, showing that our network enables denoising competitive with three other state-of-the-art neural networks. We use RCAN to restore noisy 4D super-resolution data, enabling image capture over tens of thousands of images (thousands of volumes) without apparent photobleaching. Second, using simulations we show that RCAN enables class-leading resolution enhancement, superior to other networks. Third, we exploit RCAN for denoising and resolution improvement in confocal microscopy, enabling ~2.5-fold lateral resolution enhancement using stimulated emission depletion (STED) microscopy ground truth. Fourth, we develop methods to improve spatial resolution in structured illumination microscopy using expansion microscopy ground truth, achieving improvements of ~1.4-fold laterally and ~3.4-fold axially. Finally, we characterize the limits of denoising and resolution enhancement, suggesting practical benchmarks for evaluating and further enhancing network performance.

Impact of image reconstruction parameters when using 3D DSA reconstructions to measure intracranial aneurysms

2017 ◽  
Vol 10 (3) ◽  
pp. 285-289 ◽  
Author(s):  
Katrina L Ruedinger ◽  
David R Rutkowski ◽  
Sebastian Schafer ◽  
Alejandro Roldán-Alzate ◽  
Erick L Oberstar ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Hounsfield Unit ◽  
Ground Truth ◽  
Individual Case ◽  
Dome Height ◽  
Patient Specific ◽  
Analysis Tool ◽  
Accuracy Of Measurements ◽  
Image Characteristic

Background and purposeSafe and effective use of newly developed devices for aneurysm treatment requires the ability to make accurate measurements in the angiographic suite. Our purpose was to determine the parameters that optimize the geometric accuracy of three-dimensional (3D) vascular reconstructions.MethodsAn in vitro flow model consisting of a peristaltic pump, plastic tubing, and 3D printed patient-specific aneurysm models was used to simulate blood flow in an intracranial aneurysm. Flow rates were adjusted to match values reported in the literature for the internal carotid artery. 3D digital subtraction angiography acquisitions were obtained using a commercially available biplane angiographic system. Reconstructions were done using Edge Enhancement (EE) or Hounsfield Unit (HU) kernels and a Normal or Smooth image characteristic. Reconstructed images were analyzed using the vendor's aneurysm analysis tool. Ground truth measurements were derived from metrological scans of the models with a microCT. Aneurysm volume, surface area, dome height, minimum and maximum ostium diameter were determined for the five models.ResultsIn all cases, measurements made with the EE kernel most closely matched ground truth values. Differences in values derived from reconstructions displayed with Smooth or Normal image characteristics were small and had only little impact on the geometric parameters considered.ConclusionsReconstruction parameters impact the accuracy of measurements made using the aneurysm analysis tool of a commercially available angiographic system. Absolute differences between measurements made using reconstruction parameters determined as optimal in this study were, overall, very small. The significance of these differences, if any, will depend on the details of each individual case.

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