Machine Learning Approach to Point Localization System
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F15%3AA1701E61" target="_blank" >RIV/61988987:17610/15:A1701E61 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Machine Learning Approach to Point Localization System
Original language description
The article introduces point localization systems in 3D Euclidean space based on neural networks. There are two models presented. The first one identified distances between a randomly generated point and a reference points in the defined domain. Then a neural network uses the obtained distances as its inputs to determine the actual position of the point in the domain space. Due to a relatively good accuracy that was obtained during the experimental study, the proposed model based on neural networks was used in the second model as an acoustic Motion Capturing system (MoCap). MoCap system is represented by a neural network that uses obtained distances between transmitters and a receiver as its inputs to determine an actual position of the receiver in space. We also propose a new way to minimize a training set by using ANFIS approach in this specific problem. All obtained results are summarized in the conclusion.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: IT4Innovations Centre of Excellence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
IEEE 13th International Symposium on Applied Machine Intelligence and Informatics
ISBN
978-1-4799-8221-9
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
313-317
Publisher name
IEEE
Place of publication
New York
Event location
Slovakia, Herľany
Event date
Jan 22, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000380524900051