Sazali Yaacob
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Preferred name
Sazali Yaacob
Official Name
Sazali, Yaacob
Alternative Name
Yaacob, S.
Bin Yaacob, Sazali
Yaacob, S. B.
Yaacob, Sazali B.
Main Affiliation
Scopus Author ID
6602262501

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  • Publication
    Failure envelope modelling of glass/epoxy composite pipes using system identification method
    ( 2017-11-07)
    Ang Jia Yi
    ;
    Mohd Shukry Abdul Majid
    ;
    Azuwir Mohd Nor
    ;
    Sazali Yaacob
    ;
    Mohd Ridzuan Mohd Jamir
    The paper aims to model the performance of the Glass Fibre Reinforced Epoxy (GRE) composite pipe under multiaxial loading via system identification approach. System identification modelling depends on the input and output data of the experimental result. In this study, the experimental data used are obtained from a pressurised test rig. The model is based on pure hydrostatic (2H: 1A) loading using GRE pipes with three different winding angles (±45°, ±55°, ±63°). Several models based on different model structures are derived for comparison to obtain the best modelling accuracy. The result shows that the transfer function method could model and has the highest efficiency compared with the experimental result. The ±45°pipe model have achieved 92.41% and 85.13% for both its hoop and axial model. The ±55°pipe model has achieved 96.64% and 86.1%. Follow by the ±63°which the best fit is 92.41% and 94.26%. At the last part of this research, the ±55°pipe model and experimental data has been use to identified when the damage occur and found that the axial strain of 78 bar can damage the experimental pipe in this research.
  • Publication
    Failure prediction of ±55° glass/epoxy composite pipes using system identification modelling
    ( 2017-10-29)
    Yi A.
    ;
    Mohd Shukry Abdul Majid
    ;
    Nor A.
    ;
    Mohd Ridzuan Mohd Jamir
    ;
    Sazali Yaacob
    Black-box modelling using system identification method to predict the performance of glass fibre reinforced epoxy (GRE) composite pipe under multiaxial loading stress ratio is presented. In this study, both linear and nonlinear models were derived namely; linear time-invariant parametric model and artificial neural network model. The models derived are to approximate the pure hydrostatic loading performance using GRE pipes with winding angles of ±55°. Three different linear model structures were derived, and the best fit model achieved at 96.64% of best fit. On the other hand, the Artificial Neural Network (ANN) modelling showed better accuracy with the best fit of 99.82%. Finally, the point of failure at which first damage takes place predicted by the models derived was validated using experimental data.
  • Publication
    Thought-actuated wheelchair navigation with communication assistance using statistical cross-correlation-based features and extreme learning machine
    (Wolters Kluwer ‑ Medknow, 2020)
    SatheesKumar Nataraj
    ;
    MP Paulraj
    ;
    Sazali Yaacob
    ;
    Abdul Hamid Adom
    Background: A simple data collection approach based on electroencephalogram (EEG) measurements has been proposed in this study to implement a brain–computer interface, i.e., thought‑controlled wheelchair navigation system with communication assistance. Method: The EEG signals are recorded for seven simple tasks using the designed data acquisition procedure. These seven tasks are conceivably used to control wheelchair movement and interact with others using any odd‑ball paradigm. The proposed system records EEG signals from 10 individuals at eight‑channel locations, during which the individual executes seven different mental tasks. The acquired brainwave patterns have been processed to eliminate noise, including artifacts and powerline noise, and are then partitioned into six different frequency bands. The proposed cross‑correlation procedure then employs the segmented frequency bands from each channel to extract features. The cross‑correlation procedure was used to obtain the coefficients in the frequency domain from consecutive frame samples. Then, the statistical measures (“minimum,” “mean,” “maximum,” and “standard deviation”) were derived from the cross‑correlated signals. Finally, the extracted feature sets were validated through online sequential‑extreme learning machine algorithm. Results and Conclusion: The results of the classification networks were compared with each set of features, and the results indicated that μ (r) feature set based on cross‑correlation signals had the best performance with a recognition rate of 91.93%.
  • Publication
    Analysis of accent-sensitive words in multi-resolution mel-frequency cepstral coefficients for classification of accents in Malaysian English
    ( 2013-06)
    M.A. Yusnita
    ;
    M.P. Paulraj
    ;
    Sazali Yaacob
    ;
    R. Yusuf
    ;
    Shahriman Abu Bakar
    This paper investigates the most accent-sensitive words for Malaysian English (MalE) speakers in multi-resolution 13 Mel-frequency cepstral coefficients. A text-independent accent system was implemented using different numbers of Mel-filters to determine the optimal settings for this database. Then, text-dependent accent systems were developed to rank the most accent-sensitive words for MalE speakers according to the classification rates. Prior work has also been conducted to test the significance of the wordlist for both gender and accent factors, and to investigate any interaction between these two factors. Experimental results show that male speakers have a higher intensity of accent effects compared with female speakers by 3.91% on text-independent and 3.47% on text-dependent tasks. Another finding has proven that by selecting appropriate words that carry severe accent markers could improve the task of speaker accent classification. An improvement of at most 8.45% and 8.91% was achieved on the male and female datasets, respectively, following vocabulary selection.
      1  14
  • Publication
    Home-based ankle rehabilitation system: Literature review and evaluation
    ( 2017-09-01)
    Lim Chee Chin
    ;
    Shafriza Nisha Basah
    ;
    Marwan Affandi
    ;
    Muhammad Nazrin Shah Shahrol Aman
    ;
    Sazali Yaacob
    ;
    Yeap Ewe Juan
    ;
    Mohamad Yazid Din
    Ankle sprain Injury is one of the most common ankle injuries due to domestic or sporting accidents. There is a need for greater demand for quick and effective ankle rehabilitation system (ARS). Nowadays, research on ARS has gained a great attention than manual clinical method in medical areas such as orthopedic injuries, pediatrics sport medicine and industrial services. It can improve the treatment conditions by reducing the dependency of doctors’ supervision, help patient with less movable to have home-based rehab exercise and help to speeds up recovery. There are currently available ARS that can provide effective ankle rehabilitation treatment such as Visual, Non-Visual and Robot-aided. In this paper, the critical review of ARS is conducted to evaluate the effectiveness of ARS in terms of provided setting criteria. The strengths, weaknesses, opportunities and threats of each ARS is discussed and compared to identify the most suitable home application of ARS for ankle sprain patient. From the comparison, the most suitable home application ARS is the visual marker-less based ARS system which give user-friendly, efficiency, validity in performance and cheaper cost.
      28  2