Tsunami Simulations in the Western Makran Using Hypothetical Heterogeneous Source Models from World’s Great Earthquakes

2019 ◽  
pp. 95-110
Author(s):  
Amin Rashidi ◽  
Zaher Hossein Shomali ◽  
Nasser Keshavarz Farajkhah
Keyword(s):  
Great Earthquakes ◽  
Source Models ◽  
Heterogeneous Source

Tsunami Simulations in the Western Makran Using Hypothetical Heterogeneous Source Models from World’s Great Earthquakes

2018 ◽  
Vol 175 (4) ◽  
pp. 1325-1340 ◽  
Author(s):  
Amin Rashidi ◽  
Zaher Hossein Shomali ◽  
Nasser Keshavarz Farajkhah
Keyword(s):  
Great Earthquakes ◽  
Source Models ◽  
Heterogeneous Source

Source models of great earthquakes from ultra low-frequency normal mode data

2014 ◽  
Vol 233 ◽  
pp. 41-67 ◽  
Author(s):  
K. Lentas ◽  
A.M.G. Ferreira ◽  
E. Clévédé ◽  
J. Roch
Keyword(s):  
Normal Mode ◽  
Low Frequency ◽  
Great Earthquakes ◽  
Source Models

scholarly journals A Unification of Heterogeneous Data Sources into a Graph Model in E-commerce

2021 ◽  
Author(s):  
Sonal Tuteja ◽  
Rajeev Kumar
Keyword(s):  
Large Scale ◽  
Graph Model ◽  
Heterogeneous Data ◽  
Data Models ◽  
Graph Models ◽  
Query Performance ◽  
Source Models ◽  
Database Size ◽  
Modelling Techniques ◽  
Heterogeneous Source

AbstractThe incorporation of heterogeneous data models into large-scale e-commerce applications incurs various complexities and overheads, such as redundancy of data, maintenance of different data models, and communication among different models for query processing. Graphs have emerged as data modelling techniques for large-scale applications with heterogeneous, schemaless, and relationship-centric data. Models exist for mapping different types of data to a graph; however, the unification of data from heterogeneous source models into a graph model has not received much attention. To address this, we propose a new framework in this study. The proposed framework first transforms data from various source models into graph models individually and then unifies them into a single graph. To justify the applicability of the proposed framework in e-commerce applications, we analyse and compare query performance, scalability, and database size of the unified graph with heterogeneous source data models for a predefined set of queries. We also access some qualitative measures, such as flexibility, completeness, consistency, and maturity for the proposed unified graph. Based on the experimental results, the unified graph outperforms heterogeneous source models for query performance and scalability; however, it falls behind for database size.

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.

AN INCOMPLETE HISTORY OF LATE HOLOCENE GREAT EARTHQUAKES AT THE NEHALEM RIVER ESTUARY, CENTRAL CASCADIA SUBDUCTION ZONE

2017 ◽  
Author(s):  
Alan R. Nelson ◽  
◽  
Yuki Sawai ◽  
Andrea D. Hawkes ◽  
Simon E. Engelhart ◽  
...  
Keyword(s):  
Subduction Zone ◽  
Late Holocene ◽  
River Estuary ◽  
Cascadia Subduction Zone ◽  
Great Earthquakes ◽  
History Of

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.

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