Extended specifications and test data sets for data level comparisons of direct volume rendering algorithms

2001 ◽  
Vol 7 (4) ◽  
pp. 299-317 ◽  
Author(s):  
Kwansik Kim ◽  
C.M. Wittenbrink ◽  
A. Pang
Author(s):  
Kristian Krabbenhoft ◽  
J. Wang

A new stress-strain relation capable of reproducing the entire stress-strain range of typical soil tests is presented. The new relation involves a total of five parameters, four of which can be inferred directly from typical test data. The fifth parameter is a fitting parameter with a relatively narrow range. The capabilities of the new relation is demonstrated by the application to various clay and sand data sets.


2021 ◽  
Author(s):  
David Cotton ◽  

<p><strong>Introduction</strong></p><p>HYDROCOASTAL is a two year project funded by ESA, with the objective to maximise exploitation of SAR and SARin altimeter measurements in the coastal zone and inland waters, by evaluating and implementing new approaches to process SAR and SARin data from CryoSat-2, and SAR altimeter data from Sentinel-3A and Sentinel-3B. Optical data from Sentinel-2 MSI and Sentinel-3 OLCI instruments will also be used in generating River Discharge products.</p><p>New SAR and SARin processing algorithms for the coastal zone and inland waters will be developed and implemented and evaluated through an initial Test Data Set for selected regions. From the results of this evaluation a processing scheme will be implemented to generate global coastal zone and river discharge data sets.</p><p>A series of case studies will assess these products in terms of their scientific impacts.</p><p>All the produced data sets will be available on request to external researchers, and full descriptions of the processing algorithms will be provided</p><p> </p><p><strong>Objectives</strong></p><p>The scientific objectives of HYDROCOASTAL are to enhance our understanding  of interactions between the inland water and coastal zone, between the coastal zone and the open ocean, and the small scale processes that govern these interactions. Also the project aims to improve our capability to characterize the variation at different time scales of inland water storage, exchanges with the ocean and the impact on regional sea-level changes</p><p>The technical objectives are to develop and evaluate  new SAR  and SARin altimetry processing techniques in support of the scientific objectives, including stack processing, and filtering, and retracking. Also an improved Wet Troposphere Correction will be developed and evaluated.</p><p><strong>Project  Outline</strong></p><p>There are four tasks to the project</p><ul><li>Scientific Review and Requirements Consolidation: Review the current state of the art in SAR and SARin altimeter data processing as applied to the coastal zone and to inland waters</li> <li>Implementation and Validation: New processing algorithms with be implemented to generate a Test Data sets, which will be validated against models, in-situ data, and other satellite data sets. Selected algorithms will then be used to generate global coastal zone and river discharge data sets</li> <li>Impacts Assessment: The impact of these global products will be assess in a series of Case Studies</li> <li>Outreach and Roadmap: Outreach material will be prepared and distributed to engage with the wider scientific community and provide recommendations for development of future missions and future research.</li> </ul><p> </p><p><strong>Presentation</strong></p><p>The presentation will provide an overview to the project, present the different SAR altimeter processing algorithms that are being evaluated in the first phase of the project, and early results from the evaluation of the initial test data set.</p><p> </p>


2021 ◽  
Vol 10 (1) ◽  
pp. 105
Author(s):  
I Gusti Ayu Purnami Indryaswari ◽  
Ida Bagus Made Mahendra

Many Indonesian people, especially in Bali, make pigs as livestock. Pig livestock are susceptible to various types of diseases and there have been many cases of pig deaths due to diseases that cause losses to breeders. Therefore, the author wants to create an Android-based application that can predict the type of disease in pigs by applying the C4.5 Algorithm. The C4.5 algorithm is an algorithm for classifying data in order to obtain a rule that is used to predict something. In this study, 50 training data sets were used with 8 types of diseases in pigs and 31 symptoms of disease. which is then inputted into the system so that the data is processed so that the system in the form of an Android application can predict the type of disease in pigs. In the testing process, it was carried out by testing 15 test data sets and producing an accuracy value that is 86.7%. In testing the application features built using the Kotlin programming language and the SQLite database, it has been running as expected.


Sign in / Sign up

Export Citation Format

Share Document