A NOVEL METHOD FOR STATISTICAL PATTERN RECOGNITION USING THE NETWORK THEORY AND A NEW HYBRID SYSTEM OF MACHINE LEARNING

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F19%3A00357261" target="_blank" >RIV/68407700:21730/19:00357261 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.17222/mit.2018.116" target="_blank" >https://doi.org/10.17222/mit.2018.116</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.17222/mit.2018.116" target="_blank" >10.17222/mit.2018.116</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A NOVEL METHOD FOR STATISTICAL PATTERN RECOGNITION USING THE NETWORK THEORY AND A NEW HYBRID SYSTEM OF MACHINE LEARNING

Popis výsledku v původním jazyce

The increase in wear resistance of cast irons after laser treatment is due not only to the corresponding structural and phase composition, but also to the improvement in the friction conditions due to the graphite retained in the laser impact zone. Also, laser hardening increases the wear resistance of steels and some other alloys in terms of the friction in alkaline and acidic environments. In this article we present a new method for a hybrid system of machine learning using a new method for statistical pattern recognition through network theory in robot laser hardening (RLH). We combined the method of multiple regression, the method of a support vector machine and the method of a neural network. For statistical pattern recognition we use the topological properties of network theory. The even distribution of the topological property 16-300 triads throughout the various levels of the organization and network in the microstructure of RLH indicates that there is a strong linkage across the network and an active connection among the needles of martensite.

Název v anglickém jazyce

A NOVEL METHOD FOR STATISTICAL PATTERN RECOGNITION USING THE NETWORK THEORY AND A NEW HYBRID SYSTEM OF MACHINE LEARNING

Popis výsledku anglicky

The increase in wear resistance of cast irons after laser treatment is due not only to the corresponding structural and phase composition, but also to the improvement in the friction conditions due to the graphite retained in the laser impact zone. Also, laser hardening increases the wear resistance of steels and some other alloys in terms of the friction in alkaline and acidic environments. In this article we present a new method for a hybrid system of machine learning using a new method for statistical pattern recognition through network theory in robot laser hardening (RLH). We combined the method of multiple regression, the method of a support vector machine and the method of a neural network. For statistical pattern recognition we use the topological properties of network theory. The even distribution of the topological property 16-300 triads throughout the various levels of the organization and network in the microstructure of RLH indicates that there is a strong linkage across the network and an active connection among the needles of martensite.

Druh

J_imp - Článek v periodiku v databázi Web of Science

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

Materials and Technology

ISSN

1580-2949

e-ISSN

1580-3414

Svazek periodika

53

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

SI - Slovinská republika

Počet stran výsledku

6

Strana od-do

95-100

Kód UT WoS článku

000458523900014

EID výsledku v databázi Scopus

2-s2.0-85064190935