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Adaptive Filters Detection of State Change in Pseudonomas Putida Cultivation

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F22%3A43925439" target="_blank" >RIV/60461373:22340/22:43925439 - isvavai.cz</a>

  • Result on the web

    <a href="https://link.springer.com/chapter/10.1007/978-3-031-09073-8_45" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-09073-8_45</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-031-09073-8_45" target="_blank" >10.1007/978-3-031-09073-8_45</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Adaptive Filters Detection of State Change in Pseudonomas Putida Cultivation

  • Original language description

    This article presents a case study of adaptive novelty detection methods used for detection of fermentation process state change. The evaluated methods utilizing Normalized Least Mean Squares and Generalized Normalized Gradient Descent adaptive filters are Learning Entropy and Error and Learning Based Novelty Detection. Usability of these methods is presented on two case studies with cultivation of Pseudonomas Putida KT2442 yeast. Results for all combinations of adaptive filters and novelty detection methods are evaluated and presented.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Software Engineering Perspectives in Systems, Proceedings of 11th Computer Science On-line Conference 2022, Vol. 1

  • ISBN

    978-3-031-09069-1

  • ISSN

    2367-3370

  • e-ISSN

    2367-3389

  • Number of pages

    10

  • Pages from-to

    519-527

  • Publisher name

    Springer

  • Place of publication

    New York

  • Event location

    online

  • Event date

    Apr 26, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000892638700045