Konferensartikel

Perspectives on Industrial Optimization based on Big Data Technology and Soft Computing through Image Coding

Yukinori Suzuki
Information and Electronic Engineering, Muroran Institute of Technology, Japan

Ladda ner artikelhttp://dx.doi.org/10.3384/ecp171421019

Ingår i: Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016

Linköping Electronic Conference Proceedings 142:150, s. 1019-1025

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Publicerad: 2018-12-19

ISBN: 978-91-7685-399-3

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

Abstract

Industrial systems are being rapidly innovated due to recent information technology of IoT and fruitful results of arti?cial intelligence. We discuss roles of big data technologies and soft computing to optimize industrial systems and to design robust systems through image coding. We show a code book (CB) design for vector quantization (VQ) to discuss roles of soft computing and big data technology. The CBs were designed by conventional clustering algorithms. However, these conventional algorithms cannot provide CBs that encode and/or decode images with high image quality and low bits rate. We show a perspectives to overcome this problem to integrate big data technology and soft computing.

Nyckelord

industrial optimization, big data, soft computing, image coding

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