Support Vector Machines for Control of Multimodal Processes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F22%3A00354007" target="_blank" >RIV/68407700:21730/22:00354007 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-030-96302-6_35" target="_blank" >http://dx.doi.org/10.1007/978-3-030-96302-6_35</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-96302-6_35" target="_blank" >10.1007/978-3-030-96302-6_35</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Support Vector Machines for Control of Multimodal Processes

Popis výsledku v původním jazyce

In recent manufacturing processes, the number of common causes of variation increases with the complexity of processes, leading to different shifts of the in-control process between multiple modes. Such a multimodal process violates the normality assumption, which decreases the efficiency of the commonly used methods and often disables the usage of SPC. This paper investigates the performance of one-class support vector machine (OSVM) in a multimodal setting. We have generated 5-modal synthetic data set with two correlated variables that violate the normality assumption. These methods were compared on the horizontally, vertically, and diagonally shifted out-of-control data. We have found that OSVM outperforms the other two commonly used SPC methods, which demonstrates that its more flexible decision boundary can naturally wrap the data from multimodal processes and can bring benefits to the control of modern complex processes.

Název v anglickém jazyce

Support Vector Machines for Control of Multimodal Processes

Popis výsledku anglicky

In recent manufacturing processes, the number of common causes of variation increases with the complexity of processes, leading to different shifts of the in-control process between multiple modes. Such a multimodal process violates the normality assumption, which decreases the efficiency of the commonly used methods and often disables the usage of SPC. This paper investigates the performance of one-class support vector machine (OSVM) in a multimodal setting. We have generated 5-modal synthetic data set with two correlated variables that violate the normality assumption. These methods were compared on the horizontally, vertically, and diagonally shifted out-of-control data. We have found that OSVM outperforms the other two commonly used SPC methods, which demonstrates that its more flexible decision boundary can naturally wrap the data from multimodal processes and can bring benefits to the control of modern complex processes.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021)

ISBN

978-3-030-96302-6

ISSN

2367-3370

e-ISSN

2367-3389

Počet stran výsledku

10

Strana od-do

384-393

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

online

Datum konání akce

15. 12. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000774224200035