Wan Azani Wan Mustafa
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Preferred name
Wan Azani Wan Mustafa
Official Name
Wan Azani, Wan Mustafa
Alternative Name
Mustafa, W.
Azani Mustafa, Wan
Mustaffa, Wan Azani
Wan Mustafa, Wan Azani
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Scopus Author ID
57219421621
Researcher ID
J-4603-2014

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Finite Element Analysis (FEA) of Fiber-Reinforced Polymer (FRP) Repair Performance for Subsea Oil and Gas Pipelines: The Recent Brief Review (2018-2022)

2023-11-01 , Shahid M.D.A. , Hashim M.H.M. , Wan Azani Wan Mustafa , Fadzil N.M. , Muda M.F.

Fiber-reinforced polymer (FRP) materials are used to reinforce and repair subsea pipelines in the oil and gas (O&G) industry due to their great strength-to-weight ratio as well as corrosion resistance. The extreme environmental conditions that subsea pipelines must endure include high pressure, high temperature, as well as corrosion. Modelling the effects of these conditions on the repaired pipeline can be challenging, and inaccuracies in modelling the underwater environment can lead to incorrect predictions regarding the repaired pipeline's behaviour. The finite element method (FEM)’s capabilities, as well as applications, are examined in this work. FEM is being used in place of time-consuming processes and conventional codes since they have demonstrated their strength as prediction tools. This article provides information on the to better understand the accuracy and reliability of the FEA modelling techniques used to simulate the behaviour of the FRP repair system under different operating conditions. The descriptions focus on the many types of composite fibres and the qualities that come from their components. Related document gathering was conducted from 2018-2022 and research on 53 papers were downloaded and acquired from the Scopus and Science Direct database. The review also discusses the insight into the practical implementation of finite element analysis (FEA) modelling involves understanding how FEA is used to solve real-world problems. Moreover, the findings show that by diversifying the FRP materials used in the repair design, the overall performance of the repaired pipeline can be optimized, leading to increased safety and reliability in subsea O&G operations. Also, the scope for future studies.

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PAPR Reduction in Cyclic Prefix OFDM (5G) System using Group Codeword Shifting Technique

2023-07-01 , Yusof A. , Abdullah E. , Idris A. , Wan Azani Wan Mustafa

Cyclic Prefix Orthogonal Frequency Division Multiplexing (CPOFDM) is a 5G multicarrier waveform that enables high data rates and spectrum efficiency improvements. The primary drawback of CPOFDM is that it has a high peak to average power ratio (PAPR), which is a characteristic of all multicarrier modulation techniques. We study the application of a Group Codeword Shift (GCS) approach to reduce the peak to average power ratio (PAPR) in a CPOFDM system in this article. Additionally, we compared the results of peak to average power ratio (PAPR) reduction with low complexity in CPOFDM using a Group Codeword Shift (GCS) approach, Selective Codeword Shift (SCS), Median Codeword and Conventional CPOFDM.

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Accuracy Analysis of Deep Learning Methods in Breast Cancer Classification: A Structured Review

2023-02-01 , Yusoff M. , Haryanto T. , Suhartanto H. , Wan Azani Wan Mustafa , Zain J.M. , Kusmardi K.

Breast cancer is diagnosed using histopathological imaging. This task is extremely time-consuming due to high image complexity and volume. However, it is important to facilitate the early detection of breast cancer for medical intervention. Deep learning (DL) has become popular in medical imaging solutions and has demonstrated various levels of performance in diagnosing cancerous images. Nonetheless, achieving high precision while minimizing overfitting remains a significant challenge for classification solutions. The handling of imbalanced data and incorrect labeling is a further concern. Additional methods, such as pre-processing, ensemble, and normalization techniques, have been established to enhance image characteristics. These methods could influence classification solutions and be used to overcome overfitting and data balancing issues. Hence, developing a more sophisticated DL variant could improve classification accuracy while reducing overfitting. Technological advancements in DL have fueled automated breast cancer diagnosis growth in recent years. This paper reviewed studies on the capability of DL to classify histopathological breast cancer images, as the objective of this study was to systematically review and analyze current research on the classification of histopathological images. Additionally, literature from the Scopus and Web of Science (WOS) indexes was reviewed. This study assessed recent approaches for histopathological breast cancer image classification in DL applications for papers published up until November 2022. The findings of this study suggest that DL methods, especially convolution neural networks and their hybrids, are the most cutting-edge approaches currently in use. To find a new technique, it is necessary first to survey the landscape of existing DL approaches and their hybrid methods to conduct comparisons and case studies.

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