scholarly journals Revised Magmatic Source Models for the 2015 Eruption at Axial Seamount Including Estimates of Fault‐Induced Deformation

2020 ◽  
Vol 125 (4) ◽  
Author(s):  
William L. Hefner ◽  
Scott L. Nooner ◽  
William W. Chadwick ◽  
DelWayne R. Bohnenstiehl
Keyword(s):  
Magmatic Source ◽  
Source Models ◽  
Axial Seamount

The Role of Computer Modeling in Electrocardiography

1990 ◽  
Vol 29 (04) ◽  
pp. 282-288 ◽  
Author(s):  
A. van Oosterom
Keyword(s):  
Electrical Activity ◽  
Computer Modeling ◽  
Body Surface ◽  
Electrical Current ◽  
The Body ◽  
Volume Conductor ◽  
Current Generator ◽  
Cardiac Electrical Activity ◽  
Source Models

AbstractThis paper introduces some levels at which the computer has been incorporated in the research into the basis of electrocardiography. The emphasis lies on the modeling of the heart as an electrical current generator and of the properties of the body as a volume conductor, both playing a major role in the shaping of the electrocardiographic waveforms recorded at the body surface. It is claimed that the Forward-Problem of electrocardiography is no longer a problem. Several source models of cardiac electrical activity are considered, one of which can be directly interpreted in terms of the underlying electrophysiology (the depolarization sequence of the ventricles). The importance of using tailored rather than textbook geometry in inverse procedures is stressed.

PETROLOGY AND PETROGENESIS OF ANDESITES FROM AXIAL SEAMOUNT, JUAN DE FUCA RIDGE

2017 ◽  
Author(s):  
Sara Renea Mills ◽  
◽  
Micheal Perfit ◽  
David A. Clague ◽  
Jennifer Brophy Paduan
Keyword(s):  
Juan De Fuca Ridge ◽  
Fuca Ridge ◽  
Juan De Fuca ◽  
Axial Seamount

scholarly journals Induced network-based transfer learning in injection molding for process modelling and optimization with artificial neural networks

2021 ◽  
Vol 112 (11-12) ◽  
pp. 3501-3513
Author(s):  
Yannik Lockner ◽  
Christian Hopmann
Keyword(s):  
Injection Molding ◽  
Transfer Learning ◽  
Injection Molding Process ◽  
Process Data ◽  
Molding Process ◽  
Melt Temperature ◽  
Processing Industry ◽  
Source Models ◽  
Plastics Processing ◽  
Artificial Neural

AbstractThe necessity of an abundance of training data commonly hinders the broad use of machine learning in the plastics processing industry. Induced network-based transfer learning is used to reduce the necessary amount of injection molding process data for the training of an artificial neural network in order to conduct a data-driven machine parameter optimization for injection molding processes. As base learners, source models for the injection molding process of 59 different parts are fitted to process data. A different process for another part is chosen as the target process on which transfer learning is applied. The models learn the relationship between 6 machine setting parameters and the part weight as quality parameter. The considered machine parameters are the injection flow rate, holding pressure time, holding pressure, cooling time, melt temperature, and cavity wall temperature. For the right source domain, only 4 sample points of the new process need to be generated to train a model of the injection molding process with a degree of determination R2 of 0.9 or and higher. Significant differences in the transferability of the source models can be seen between different part geometries: The source models of injection molding processes for similar parts to the part of the target process achieve the best results. The transfer learning technique has the potential to raise the relevance of AI methods for process optimization in the plastics processing industry significantly.

scholarly journals Development of Tools for Coastal Management in Google Earth Engine: Uncertainty Bathtub Model and Bruun Rule

2021 ◽  
Vol 13 (8) ◽  
pp. 1424
Author(s):  
Lucas Terres de Lima ◽  
Sandra Fernández-Fernández ◽  
João Francisco Gonçalves ◽  
Luiz Magalhães Filho ◽  
Cristina Bernardes
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Coastal Management ◽  
Google Earth ◽  
Environmental Data ◽  
Planetary Scale ◽  
Bruun Rule ◽  
Source Models ◽  
Google Earth Engine ◽  
Bathtub Model

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence of free and open-source models to estimate the sea-level impact can contribute to improve coastal management. This study aims to develop and validate two different models to predict the sea-level rise impact supported by Google Earth Engine (GEE)—a cloud-based platform for planetary-scale environmental data analysis. The first model is a Bathtub Model based on the uncertainty of projections of the sea-level rise impact module of TerrSet—Geospatial Monitoring and Modeling System software. The validation process performed in the Rio Grande do Sul coastal plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses the Bruun rule formula implemented in GEE and can determine the coastline retreat of a profile by creatting a simple vector line from topo-bathymetric data. The model shows a very high correlation (0.97) with a classical Bruun rule study performed in the Aveiro coast (NW Portugal). Therefore, the achieved results disclose that the GEE platform is suitable to perform these analysis. The models developed have been openly shared, enabling the continuous improvement of the code by the scientific community.

A note on source models for speech traffic

2007 ◽  
Vol 35 (3) ◽  
pp. 22-24 ◽  
Author(s):  
Michael Menth ◽  
Andreas Binzenhöfer ◽  
Stefan Mühleck
Keyword(s):  
Source Models

scholarly journals Determination of the Height of Hard X-Ray Sources in the Solar Atmosphere by Measurement of Photospheric Albedo Photons

1975 ◽  
Vol 68 ◽  
pp. 239-241
Author(s):  
John C. Brown ◽  
H. F. Van Beek
Keyword(s):  
Solar Atmosphere ◽  
The Other ◽  
X Ray ◽  
Theoretical Predictions ◽  
Source Models ◽  
The One

SummaryThe importance and difficulties of determining the height of hard X-ray sources in the solar atmosphere, in order to distinguish source models, have been discussed by Brown and McClymont (1974) and also in this Symposium (Brown, 1975; Datlowe, 1975). Theoretical predictions of this height, h, range between and 105 km above the photosphere for different models (Brown and McClymont, 1974; McClymont and Brown, 1974). Equally diverse values have been inferred from observations of synchronous chromospheric EUV bursts (Kane and Donnelly, 1971) on the one hand and from apparently behind-the-limb events (e.g. Datlowe, 1975) on the other.

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