Using WebGL for Teaching Bone Identification

2018 ◽  
Author(s):  
Mikayle A. Holm ◽  
Erik Gaasedelen ◽  
Paul A. Iaizzo
Keyword(s):  
Human Anatomy ◽  
Data Sets ◽  
Imaging Data ◽  
3D Objects ◽  
Education Community ◽  
Interactive Application ◽  
Set Up ◽  
Teaching Programs ◽  
Visible Body ◽  
2D Images

Newly developed interactive tutorials and applications which teach human anatomy are often set up as pay-to-play websites. Examples of these include the Visible Body app1 and the 3D Organon Anatomy2. Though these applications can be very educational, they may be costly, thus many students and members of the education community will not access these programs because of the upfront charges. These teaching programs are also frequently anatomically limited because they utilize idealized models, like KineMan3, instead of renderings or imaging data sets obtained from humans (clinical or from cadavers). This characteristic may make them useful study tools, but will not best prepare future doctors, nurses, and other health professionals for true, variable patient anatomies they will encounter in their various practices. Further, such students would likely gain more by studying 3D objects of real human anatomies instead of 2D images. We have designed a strategy to bring 3D human anatomies from real cadavers to the scientific and education communities completely open source (free of charge). Our interactive application is geared toward students of all ages (grade school to medical school) or by anyone interested in learning more about human bone anatomy.

scholarly journals Generation of geometric interpolations of building types with deep variational autoencoders

2020 ◽  
Vol 6 ◽  
Author(s):  
Jaime de Miguel Rodríguez ◽  
Maria Eugenia Villafañe ◽  
Luka Piškorec ◽  
Fernando Sancho Caparrini
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Large Data ◽  
Learning Model ◽  
Large Data Sets ◽  
Data Sets ◽  
Connectivity Map ◽  
Data Set ◽  
3D Objects ◽  
Machine Learning Model

Abstract This work presents a methodology for the generation of novel 3D objects resembling wireframes of building types. These result from the reconstruction of interpolated locations within the learnt distribution of variational autoencoders (VAEs), a deep generative machine learning model based on neural networks. The data set used features a scheme for geometry representation based on a ‘connectivity map’ that is especially suited to express the wireframe objects that compose it. Additionally, the input samples are generated through ‘parametric augmentation’, a strategy proposed in this study that creates coherent variations among data by enabling a set of parameters to alter representative features on a given building type. In the experiments that are described in this paper, more than 150 k input samples belonging to two building types have been processed during the training of a VAE model. The main contribution of this paper has been to explore parametric augmentation for the generation of large data sets of 3D geometries, showcasing its problems and limitations in the context of neural networks and VAEs. Results show that the generation of interpolated hybrid geometries is a challenging task. Despite the difficulty of the endeavour, promising advances are presented.

Bayesian Trigonometric Support Vector Classifier

2003 ◽  
Vol 15 (9) ◽  
pp. 2227-2254 ◽  
Author(s):  
Wei Chu ◽  
S. Sathiya Keerthi ◽  
Chong Jin Ong
Keyword(s):  
Loss Function ◽  
Gaussian Processes ◽  
Likelihood Function ◽  
Support Vector ◽  
Data Sets ◽  
Model Adaptation ◽  
Bayesian Techniques ◽  
Benchmark Data ◽  
Support Vector Classifier ◽  
Set Up

This letter describes Bayesian techniques for support vector classification. In particular, we propose a novel differentiable loss function, called the trigonometric loss function, which has the desirable characteristic of natural normalization in the likelihood function, and then follow standard gaussian processes techniques to set up a Bayesian framework. In this framework, Bayesian inference is used to implement model adaptation, while keeping the merits of support vector classifier, such as sparseness and convex programming. This differs from standard gaussian processes for classification. Moreover, we put forward class probability in making predictions. Experimental results on benchmark data sets indicate the usefulness of this approach.

Human aorta: preliminary results with virtual endoscopy based on three-dimensional MR imaging data sets.

Radiology ◽  
1996 ◽  
Vol 199 (1) ◽  
pp. 37-40 ◽  
Author(s):  
C P Davis ◽  
M E Ladd ◽  
B J Romanowski ◽  
S Wildermuth ◽  
J F Knoplioch ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Three Dimensional ◽  
Virtual Endoscopy ◽  
Human Aorta ◽  
Data Sets ◽  
Imaging Data ◽  
Preliminary Results

Development of Reliable NARX Models of Gas Turbine Cold, Warm and Hot Start-Up

2017 ◽  
Author(s):  
Hilal Bahlawan ◽  
Mirko Morini ◽  
Michele Pinelli ◽  
Pier Ruggero Spina ◽  
Mauro Venturini
Keyword(s):  
Gas Turbine ◽  
Training Data ◽  
Series Data ◽  
Data Sets ◽  
Control Logic ◽  
Start Up ◽  
Hot Start ◽  
Narx Models ◽  
Set Up ◽  
Rapid Transients

This paper documents the set-up and validation of nonlinear autoregressive exogenous (NARX) models of a heavy-duty single-shaft gas turbine. The considered gas turbine is a General Electric PG 9351FA located in Italy. The data used for model training are time series data sets of several different maneuvers taken experimentally during the start-up procedure and refer to cold, warm and hot start-up. The trained NARX models are used to predict other experimental data sets and comparisons are made among the outputs of the models and the corresponding measured data. Therefore, this paper addresses the challenge of setting up robust and reliable NARX models, by means of a sound selection of training data sets and a sensitivity analysis on the number of neurons. Moreover, a new performance function for the training process is defined to weigh more the most rapid transients. The final aim of this paper is the set-up of a powerful, easy-to-build and very accurate simulation tool which can be used for both control logic tuning and gas turbine diagnostics, characterized by good generalization capability.

scholarly journals FINE REGISTRATION OF KILO-STATION NETWORKS - A MODERN PROCEDURE FOR TERRESTRIAL LASER SCANNING DATA SETS

2016 ◽  
Vol XLI-B5 ◽  
pp. 485-492
Author(s):  
J.-F. Hullo
Keyword(s):  
Laser Scanning ◽  
Laser Scanner ◽  
Data Sets ◽  
Data Set ◽  
Information Models ◽  
3D Network ◽  
Laser Scanners ◽  
Building Information ◽  
Set Up ◽  
Fine Registration

We propose a complete methodology for the fine registration and referencing of kilo-station networks of terrestrial laser scanner data currently used for many valuable purposes such as 3D as-built reconstruction of Building Information Models (BIM) or industrial asbuilt mock-ups. This comprehensive target-based process aims to achieve the global tolerance below a few centimetres across a 3D network including more than 1,000 laser stations spread over 10 floors. This procedure is particularly valuable for 3D networks of indoor congested environments. In situ, the use of terrestrial laser scanners, the layout of the targets and the set-up of a topographic control network should comply with the expert methods specific to surveyors. Using parametric and reduced Gauss-Helmert models, the network is expressed as a set of functional constraints with a related stochastic model. During the post-processing phase inspired by geodesy methods, a robust cost function is minimised. At the scale of such a data set, the complexity of the 3D network is beyond comprehension. The surveyor, even an expert, must be supported, in his analysis, by digital and visual indicators. In addition to the standard indicators used for the adjustment methods, including Baarda’s reliability, we introduce spectral analysis tools of graph theory for identifying different types of errors or a lack of robustness of the system as well as <i>in fine</i> documenting the quality of the registration.

scholarly journals Measurements from mobile surface vehicles during LAPSE-RATE

2020 ◽  
Author(s):  
Gijs de Boer ◽  
Sean Waugh ◽  
Alexander Erwin ◽  
Steven Borenstein ◽  
Cory Dixon ◽  
...  
Keyword(s):  
Layer Structure ◽  
Unmanned Aircraft ◽  
Lapse Rate ◽  
Data Sets ◽  
San Luis Valley ◽  
San Luis ◽  
Remotely Piloted Aircraft ◽  
In Situ Sensors ◽  
Set Up ◽  
The One

Abstract. Between 14 and 20 July 2018, small unmanned aircraft systems (sUAS) were deployed to the San Luis Valley of Colorado (USA) alongside surface-based remote, in-situ sensors, and radiosonde systems as part of the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). The measurements collected as part of LAPSE-RATE targeted quantities related to enhancing our understanding of boundary layer structure, cloud and aerosol properties and surface-atmosphere exchange, and provide detailed information to support model evaluation and improvement work. Additionally, intensive intercomparison between the different unmanned aircraft platforms was completed. The current manuscript describes the observations obtained using three different types of surface-based mobile observing vehicles. These included the University of Colorado Mobile UAS Research Collaboratory (MURC), the National Oceanic and Atmospheric Administration National Severe Storms Laboratory Mobile Mesonet, and two University of Nebraska Combined Mesonet and Tracker (CoMeT) vehicles. Over the one-week campaign, a total of 143 hours of data were collected using this combination of vehicles. The data from these coordinated activities provide detailed perspectives on the spatial variability of atmospheric state parameters (air temperature, humidity, pressure, and wind) throughout the northern half of the San Luis Valley. These data sets have been checked for quality and published to the Zenodo data archive under a specific community set up for LAPSE-RATE (https://zenodo.org/communities/lapse-rate/) and are accessible at no cost by all registered users. The primary dataset DOIs are https://doi.org/10.5281/zenodo.3814765 (CU MURC measurements; de Boer et al., 2020d), https://doi.org/10.5281/zenodo.3738175 (NSSL MM measurements; Waugh, 2020) and https://doi.org/10.5281/zenodo.3838724 (UNL CoMeT measurements; Houston and Erwin., 2020).

Visible Body 3D Human Anatomy Atlas

2016 ◽  
Vol 17 (4) ◽  
pp. 47-49
Author(s):  
Dennis Brunning
Keyword(s):  
Human Anatomy ◽  
Visible Body
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