NetCDF: Performance and Storage Optimization of Meteorological Data

2020 ◽  
Author(s):  
Valentín Kivachuk Burdá ◽  
Michaël Zamo
Keyword(s):  
Neural Network ◽  
Cloud Cover ◽  
Deep Neural Network ◽  
Meteorological Data ◽  
General Assembly ◽  
File Format ◽  
Meteorological Field ◽  
File Formats ◽  
Storage Optimization ◽  
And Storage

<p>Any software relies on data, and the meteorological field is not an exception. The importance of using correct and accurate data is as important as using it efficiently. GRIB and NetCDF are the most popular file formats used in Meteorology, being able to store exactly the same data in any of them. However, they differ in how they internally treat the data, and transforming from GRIB (a simpler file format) to NetCDF is not enough to ensure the best efficiency for final applications.</p><p>In this study, we improved the performance and storage of <em>ARPEGE cloud cover forecasts post-processing with convolutional neural network</em> and <em>Precipitation Nowcasting using Deep Neural Network</em> projects (proposed in other sessions for the EGU general assembly). The data treatments of both projects were studied and different NetCDF capabilities were applied in order to obtain significantly faster execution times (up to 60 times faster) and more efficient space usage.</p>

scholarly journals Towards Supporting IoT System Designers in Edge Computing Deployment Decisions

2021 ◽  
Author(s):  
◽  
Majid Ashouri
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Edge Computing ◽  
Use Case ◽  
Bandwidth Utilization ◽  
Quality Requirements ◽  
Network Layers ◽  
Supporting Tool ◽  
Computational Resources ◽  
And Storage

The rapidly evolving Internet of Things (IoT) systems demands addressing new requirements. This particularly needs efficient deployment of IoT systems to meet the quality requirements such as latency, energy consumption, privacy, and bandwidth utilization. The increasing availability of computational resources close to the edge has prompted the idea of using these for distributed computing and storage, known as edge computing. Edge computing may help and complement cloud computing to facilitate deployment of IoT systems and improve their quality. However, deciding where to deploy the various application components is not a straightforward task, and IoT system designer should be supported for the decision. To support the designers, in this thesis we focused on the system qualities, and aimed for three main contributions. First, by reviewing the literature, we identified the relevant and most used qualities and metrics. Moreover, to analyse how computer simulation can be used as a supporting tool, we investigated the edge computing simulators, and in particular the metrics they provide for modeling and analyzing IoT systems in edge computing. Finally, we introduced a method to represent how multiple qualities can be considered in the decision. In particular, we considered distributing Deep Neural Network layers as a use case and raked the deployment options by measuring the relevant metrics via simulation.

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

Underwater Fish Images Classification by Deep Neural Network

2020 ◽  
Author(s):  
Ala Supriya ◽  
Chiluka Venkat ◽  
Aliketti Deepak ◽  
GV Hari Prasad
Keyword(s):  
Neural Network ◽  
Deep Neural Network
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