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MFO Tunned SVR Models for Analyzing Dimensional Characteristics of Cracks Developed on Steam Generator Tubes

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F22%3A10250907" target="_blank" >RIV/61989100:27230/22:10250907 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.webofscience.com/wos/woscc/full-record/WOS:000896013100001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:000896013100001</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3390/app122312375" target="_blank" >10.3390/app122312375</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    MFO Tunned SVR Models for Analyzing Dimensional Characteristics of Cracks Developed on Steam Generator Tubes

  • Original language description

    Accurate prediction of material defects from the given images will avoid the major cause in industrial applications. In this work, a Support Vector Regression (SVR) model has been developed from the given Gray Level Co-occurrence Matrix (GLCM) features extracted from Magnetic Flux Leakage (MFL) images wherein the length, depth, and width of the images are considered response values from the given features data set, and a percentage of data has been considered for testing the SVR model. Four parameters like Kernel function, solver type, and validation scheme, and its value and % of testing data that affect the SVR model&apos;s performance are considered to select the best SVR model. Six different kernel functions, and three different kinds of solvers are considered as two validation schemes, and 10% to 30% of the testing data set of different levels of the above parameters. The prediction accuracy of the SVR model is considered by simultaneously minimizing prediction measures of both Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) and maximizing R-2 values. The Moth Flame Optimization (MFO) algorithm has been implemented to select the best SVR model and its four parameters based on the above conflict three prediction measures by converting multi-objectives into a single object using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The performance of the MFO algorithm is compared statistically with the Dragon Fly Optimization Algorithm (DFO) and Particle Swarm Optimization Algorithm (PSO).

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20300 - Mechanical engineering

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

  • Name of the periodical

    Applied Sciences

  • ISSN

    2076-3417

  • e-ISSN

    2076-3417

  • Volume of the periodical

    12

  • Issue of the periodical within the volume

    23

  • Country of publishing house

    CH - SWITZERLAND

  • Number of pages

    25

  • Pages from-to

    nestrankovano

  • UT code for WoS article

    000896013100001

  • EID of the result in the Scopus database