Mohd Herwan Sulaiman

@ump.edu.my

Faculty of Electrical & Electronics Engineering Technology
Universiti Malaysia Pahang



              

https://researchid.co/mherwan

Mohd Herwan Sulaiman obtained his B. Eng. (Hons) in Electrical-Electronics, M. Eng (Electrical-Power) and PhD (Electrical Engineering) from Universiti Teknologi Malaysia (UTM) in 2002, 2007 and 2012 respectively. He is currently serves as an Associate Professor at Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang (UMP). His research interests are power system optimization and swarm intelligence applications to power system studies. He has authored and co-authored more than 100 technical papers in the international journals and conferences and also has been invited as a Journal reviewer for several international impact journals in the field of power system, soft computing application and many more. He is one of the inventors of the new nature inspired algorithm namely Barnacle Mating Optimizer. He is also a Senior Member of IEEE. His website can be accessed through .

RESEARCH INTERESTS

Power system optimization and swarm intelligence applications to power system studies

169

Scopus Publications

Scopus Publications

  • Gooseneck barnacle optimization algorithm: A novel nature inspired optimization theory and application
    Marzia Ahmed, Mohd Herwan Sulaiman, Ahmad Johari Mohamad, and Mostafijur Rahman
    Elsevier BV


  • An application of deep learning for lightning prediction in East Coast Malaysia
    Mohd Herwan Sulaiman, Amir Izzani Mohamed, and Zuriani Mustaffa
    Elsevier BV

  • The important contribution of renewable energy technologies in overcoming Pakistan's energy crisis: Present challenges and potential opportunities
    Rafiq Asghar, Mohd H Sulaiman, Zuriani Mustaffa, Nasim Ullah, and Waqas Hassan
    SAGE Publications
    In recent years, the environmental and economic consequences of coal, gasoline and other conventional energy sources have been widely discussed. Currently, Pakistan's energy output is highly dependent on these resources; as a consequence, the country is facing a severe energy crisis. The government spends more than $3.7 billion annually on fossil fuel imports, which has a significant impact on an already vulnerable economy. Moreover, the country is ranked as the seventh-most impacted region by climate change, making it critical for the government to take proactive measures. Renewable energy that is both economical and sustainable will become a realistic and viable choice for satisfying Pakistan's current and future energy demands. This article provides a detailed overview of Pakistan's long-standing energy scarcity concerns and discusses the country's current renewable energy challenges and opportunities. Additionally, Pakistan's efforts in the realm of renewable energy are compared to those of other Asian nations in order to understand how these countries are advancing renewable energy sources. Numerous statistics show that despite government initiatives, the amount of power generated from renewable sources falls short of the Indicative Generation Capacity Expansion Plan’s (IGCEP’s) 2025 and 2030 targets. Achieving these aspirational targets will require competent policies, incentives, technological expertise and substantial political and financial commitments. Hence, this article also advised policymakers and city municipalities on how to enhance energy infrastructure, knowledge and the capacity for overcoming future challenges. In contrast, the development of renewable energy in Pakistan has been an extremely successful endeavour, but an effective and efficient approach is necessary to leverage the benefits of rapid progress.


  • Using the evolutionary mating algorithm for optimizing deep learning parameters for battery state of charge estimation of electric vehicle
    Mohd Herwan Sulaiman, Zuriani Mustaffa, Nor Farizan Zakaria, and Mohd Mawardi Saari
    Elsevier BV


  • A Circular Eddy Current Probe Using Miniature Fluxgates for Multi-Orientation Slit Evaluation in Steel Components
    Mohd Mawardi Saari, Nurul A’in Nadzri, Mohd Aufa Hadi Putera Zaini, Mohd Herwan Sulaiman, and Toshihiko Kiwa
    Pleiades Publishing Ltd



  • Improved Barnacle Mating Optimizer-Based Least Square Support Vector Machine to Predict COVID-19 Confirmed Cases with Total Vaccination
    Marzia Ahmed, Mohd Herwan Sulaiman, and Ahmad Johari Mohamad
    Walter de Gruyter GmbH
    Abstract Every country must have an accurate and efficient forecasting model to avoid and manage the epidemic. This paper suggests an upgrade to one of the evolutionary algorithms inspired by nature, the Barnacle Mating Optimizer (BMO). First, the exploration phase of the original BMO is enhanced by enforcing and replacing the sperm cast equation through Levy flight. Then, the Least Square Support Vector Machine (LSSVM) is partnered with the improved BMO (IBMO). This hybrid approach, IBMO-LSSVM, has been deployed effectively for time-series forecasting to enhance the RBF kernel-based LSSVM model since vaccination started against COVID-19 in Malaysia. In comparison to other well-known algorithms, our outcomes are superior. In addition, the IBMO is assessed on 19 conventional benchmarks and the IEEE Congress of Evolutionary Computation Benchmark Test Functions (CECC06, 2019 Competition). In most cases, IBMO outputs are better than comparison algorithms. However, in other circumstances, the outcomes are comparable.





  • Product Recommendation using Deep Learning in Computer Vision
    Sharvinteraan C. Mogan, Zuriani Mustaffa, Mohd Herwan Sulaiman, and Ferda Ernawan
    IEEE
    Recently, recommendation models have gained popularity due to their effectiveness in improving customer satisfaction and deriving sales. However, current product recommendation models have a drawback: they lack personalized and targeted advertisements for individual users. Consequently, the recommendations provided are random and not tailored to users' preferences. This limitation negatively impacts the system's ability to deliver relevant and personalized advertisements, leading to reduced user engagement and potentially lower conversion rates. Moreover, the absence of personalized advertisements can result in user dissatisfaction as they may receive recommendations that are irrelevant or not aligned with their interests and needs. To address these challenges, this study proposed a targeted product recommendation model using Deep Learning (DL) techniques in computer vision. The study utilizes the dataset of human images obtained from the Kaggle website, which includes details such as gender, class, and age. Findings of the study demonstrated a high level of accuracy in product recommendations, indicating the potential for significant improvements in addressing the issues. In conclusion, the proposed method achieves good accuracy in predicting the gender and age, and provides appropriate product recommendations based on these features.

  • Optimal Pneumatic Actuator Positioning and Dynamic Stability using Prescribed Performance Control with Particle Swarm Optimization: A Simulation Study
    Addie Irawan, Mohd Syakirin Ramli, Mohd Herwan Sulaiman, Mohd Iskandar Putra Azahar, and Abdul Hamid Adom
    ASCEE Publications
    This paper introduces an optimal control strategy for pneumatic servo systems (PSS) positioning using Finite-time Prescribed Performance Control (FT-PPC) with Particle Swarm Optimization (PSO). Pneumatic servo systems are widely used in industrial automation, as well as medical and cybernetics systems that involve robotics applications. Precision in pneumatic control is crucial not only for the sake of efficiency but also safety. The primary goal of the proposed control strategy is to optimize the convergence rate and finite time of the prescribed performance function in error transformation of the FT-PPC, as well as the Proportional, Integral and Derivative (PID) controller as the inner-loop controller for this system. The study utilizes a dynamic model of a pneumatic proportional valve with a double-acting cylinder (PPVDC) as the targeted plant and performs simulations with a multi-step input trajectory. This offline tuning method is essential for such nonlinear systems to be safely optimized, avoiding major damage to the real-time fine-tuned works on the controller. The results demonstrate that the proposed control strategy surpasses the performance of FT-PPC with a PID controller alone, significantly improving the system's performance, including suppressing overshoot and oscillation in the responses. Further validation through the actual system of PPVDC using the fine-tuned values of FT-PPC and PID with PSO is a future task and more challenging to come, as hardware constraints may vary with different environments such as temperatures.

  • Deep Learning-Based Technique for Sign Language Detection
    Zuriani Mustaffa, Nik Ahmad Farihin Mohd Zulkifli, Mohd Herwan Sulaiman, Ferda Ernawan, and Yagoub Abbker Adam
    IEEE
    Sign languages are a form of communication used by the deaf and hard-of-hearing community. Malay Sign Language (MSL) is the official sign language practiced in Malaysia, enabling communication through hand signs and facial expressions. Each sign and its combination hold a distinct meaning, making it challenging for individuals to casually learn MLS. Therefore, this study presents an object detection model that utilizes the Single Shot Detector (SSD) and Mobilenet to detect MLS in real time. The model focuses solely on detecting static signs that do not involve complex combinations. The datasets used for training consist of 2000 sign images collected from Kaggle website, as well as images captured using a personal camera. The datasets were divided into training, validation, and testing phases in an 80:10:10 ratio, respectively. In conclusion, this study successfully developed a real-time and accurate system for recognizing MSL using the SSD-Mobilenet model. This contribution has significant implications for the field of sign language recognition and can greatly improve communication access for individuals who are deaf or hard-of-hearing.

  • Optimal Finite- Time Prescribed Performance of Servo Pneumatic Positioning with PID Control Tuning using an Evolutionary Mating Algorithm
    Addie Irawan, Mohd Herwan Sulaiman, and Mohd Iskandar Putra Azahar
    IEEE
    This paper presents an optimum tuning on finite-time prescribed performance with PID (FT-PPC-PID) controller using the Evolutionary Mating Algorithm (EMA) approach for a pneumatic servo system’s (PSS) rod-piston positioning. The design objective is to optimize the convergence rate and finite time of the prescribed performance function in error transformation in parallel with PID controller’s gains. The multi-step input trajectory on the PPVDC model plant was used for simulations with specific load and random noise as disturbances. The results demonstrate that the controller optimized with EMA outperforms the same controller optimized with other methods in achieving dynamic multi-step positioning of the rod-piston. This highlights the significant enhancement in overall performance of PPVDC positioning, including the stability of its internal system, through the EMA-optimized finite-time prescribed performance controller with PID.

  • Non-Regularized Reconstruction of Magnetic Moment Distribution of Magnetic Nanoparticles using Barnacles Mating Optimizer
    Mohd Mawardi Saari, Mohd Herwan Sulaiman, Nurul Akmal Che Lah, Mohd Razali Daud, and Toshihiko Kiwa
    IEEE
    Core size estimation of magnetic nanoparticles (MNPs) using magnetization curves has been reliably utilized to obtain a fast and simple size estimation technique compared to transmission electron microscopy. This estimation technique involves solving the inverse problem of the magnetization curve. However, conventional methods, such as the singular value decomposition (SVD) or non-negative least squares (NNLS) algorithms, require a regularization threshold to mitigate the overfitting issues of an ill-conditioned problem. This prior information on the regularization requirement may lead to inaccurate magnetic moment reconstruction if the regularization degree is high due to broad distributions of the reconstructed magnetic moment. This research proposes a non-regularized reconstruction technique of magnetic moment distribution using the recent machine learning technique of the Barnacles Mating Optimizer (BMO) algorithm. A simulated magnetization curve of unimodal moment distributions from 1 mT to 1 T is used to minimize a model-free magnetic moment distribution. A reconstruction comparison among the BMO, Particle Swarm (PSO), Genetic Algorithm (GA), Sine Cosine Algorithm (SCA) optimizers, and NNLS method is presented. The magnetic moment reconstruction using the BMO algorithm shows significantly less noise and smooth distribution compared to the PSO and GA algorithms with fewer computation times. Furthermore, the constructed peaks’ position matches the original distribution and shows comparable performance with the conventional NNLS algorithm.

  • A Novel Hybrid Evolutionary Mating Algorithm for Covid19 Confirmed Cases Prediction based on Vaccination
    Marzia Ahmed, Ahmad Johari Mohamad, Mostafijur Rahman, Mohd Herwan Sulaiman, and Mohammod Abul Kashem
    IEEE
    Microorganisms may cause illness when they enter the body, multiply, and spread to other parts. The rapid spread of COVID-19 to neighboring countries is examined in this research. Anticipating a positive COVID-19 occurrence helps in determining risks and creating countermeasures. As a result, developing robust mathematical models with small error margins for predictions is crucial. Based on these findings, a combined method of evaluating confirmed cases of COVID-19 with universal immunization is recommended. First, the best hyperparameter values of the RBF kernel-based LSSVM (least square support vector machine) were determined using the most recent Evolutionary Mating Algorithm (EMA). After that, LSSVM will complete the task of prediction. This hybrid method has been utilized for time series forecasting in Malaysia since the country's immunization program against COVID-19 got underway. We evaluate our results next to those of well-known methodologies in nature-inspired metaheuristics.

  • Analysis of electric field behaviour for wind turbine blades under the influence of various gas
    Samer Wahdain
    Wydawnictwo SIGMA-NOT, sp. z.o.o.
    . Wind turbines are one of the most important natural sources of energy. The components of atmosphere gases in the surrounding wind turbines that are installed may significantly affect the increasing of electrical field resulting from lighting strikes. Here, we use the initiation and spread of electrical field in various gases O 2 , N 2 , Ar, Ne and SO 2 to examine the behaviour of electrical field on blade. This study uses the Finite Element Method to investigate the influence of gases on the lightning strike carbon fibre wind turbine blade. We use 3D modelling geometry i n this study to get accurate results for all sides of the blade. The generation of an impulse wave uses three stages with time varying from 0 to 60 µs. It was observed that N 2 and Air give the same reading because Nitrogen represents 72% of the air contents. Thus, our study elucidates that applying various gases can affect the electric field strength

  • Metaheuristic Approach for Optimizing Neural Networks Parameters in Battery State of Charge Estimation
    Zuriani Mustaffa, Mohd Herwan Sulaiman, and Azlan Abdul Aziz
    IEEE
    To accurately estimate the battery state of charge (SOC), it is vital to improve the performance of a battery-powered system. This paper employs the recent proposed Evolutionary Mating Algorithm (EMA) for optimizing the weights and biases of Feed-Forward Neural Network (FNN) in estimating the state of charge (SOC) of Lithium-ion batteries. SOC estimation is the critical aspect in battery management system (BMS) to ensure the reliable operation of electric vehicles (EV) since there are no direct way to measure it. In addition, it is very nonlinear due to variation of charge/discharge currents and temperature. EMA is the recent evolutionary algorithm based on mating theory and environmental factor will be used in this paper to optimize the weights and biases of FNN on a common Li-ion battery, multiple data measurements, drive cycles and training repetitions. The performance of EMA will be compared with other algorithms to show the effectiveness of EMA in solving the SOC estimation problem. Findings of the study demonstrate the superiority of EMA in estimating the SOC of the batteries in terms of Root Mean Square Error (RMSE), mean Absolute Error (MAE) and Standard Deviation.

  • Improved Barnacles Mating Optimizer for Loss Minimization Problem in Optimal Reactive Power Dispatch
    Mohd Herwan Sulaiman, Zuriani Mustaffa, Omar Aliman, and Mohd Mawardi Saari
    IEEE
    The solution of Optimal Reactive Power Dispatch (ORPD) can be treated as one of the sub-Optimal Power Flow (OPF) problems where the loss minimization is one of the objective functions to be solved. In this paper, an improvement of recent algorithm namely Improved Barnacles Mating optimizer (IBMO) is proposed to determine the best combination of control variables of power system’s components such as generator bus voltages, injected MVAR devices and transformer ratios so that the total transmission loss can be minimized. To assess the performance of IBMO in loss minimization of ORPD, IEEE 57-bus system will be used. The performance of IBMO will be compared with original BMO and Moth-Flame optimizer (MFO) to show the effectiveness of proposed improvement in solving the ORPD problem.