Md. Tasyrif bin Abdul Rahman
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Preferred name
Md. Tasyrif bin Abdul Rahman
Official Name
Md. Tasyrif, Abdul Rahman
Alternative Name
Abdul Rahman, Md Tasyrif
Rahman, M. D.Tasyrif Abdul
Main Affiliation
Scopus Author ID
36656452500
Researcher ID
FSF-6142-2022

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  • Publication
    Fatigue life investigation of UIC 54 rail profile for high speed rail
    ( 2017-10-29)
    Gurubaran Panerselvan
    ;
    Mohd Afendi Rojan
    ;
    Nur Fareisha M. A.
    ;
    Mohd Shukry Abdul Majid
    ;
    Haftirman I.
    ;
    Md. Tasyrif bin Abdul Rahman
    This study is to investigate the fatigue life of high speed rail in Malaysia. This paper describes about the experimental and simulation analysis investigation on fatigue life of rail profile UIC 54 using bulk specimen according to ASTM E 466-15 standard. The Fatigue life testing was performed in the fatigue testing machine (Instron 8800) 100 kN. Meanwhile, the fatigue life analysis was performed in ANSYS Workbench 14.5. Furthermore, the stress levels for experimental testing were applied as 16.7%, 25%, 35%, 50%, 58.3%, 66.77% and 75% with machine frequency of 20 Hz. Apart from that, the total fatigue life cycles for rail profile UIC 54 were acquired from both experimental and simulation. The fatigue life S-N curves were plotted and validated with the results of the simulation analysis with experimental results.
  • Publication
    Supervised segmentation on fusarium macroconidia spore in microscopic images via analytical approaches
    ( 2024-04-01)
    Azuddin K.A.
    ;
    Ahmad Kadri Junoh
    ;
    Ammar Zakaria
    ;
    Md. Tasyrif bin Abdul Rahman
    ;
    Nor N.M.I.M.
    ;
    Nishizaki H.
    ;
    Latiffah Z.
    ;
    Azuddin N.F.
    ;
    Abdullah M.Z.
    ;
    Terna T.P.
    Fungi are one of the major causes that contributed to plant diseases. There are lots of fungi species but it is estimated that only 10% have been described. There are two major approaches to identifying fungi species, morphological identification, and molecular test which need cautious clarification to make good interpretations and are time-consuming. In this paper, we propose a Machine Learning approach that involves the use of the K-Means clustering technique, and Decision Tree to highlight the observed fungi spore images taken under the microscopic view and discard background pixels to produce digital images database which later can be used for Deep Learning.
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