Journal of Advanced Research Design

Harmonic Path Planning using Quarter-Sweep Boosted TOR Iterative Method

Sumiati Suparmin — 2025-05-23

This paper presents the study to examine the effectiveness of the application of Quarter Sweep Boosted TOR with the 9-Point Laplacian operator using the families of relaxation methods in the computation of Laplace equation solutions to obtain the harmonic potentials. This work is a continuation from the past study that applied the standard application 5-Point Laplacian to solve path planning issue which a mobile robot faces because of working in indoor environment. The robot can navigate from a given initial position to a goal position by following the safest path, ensuring it avoids any obstacles and minimizes the risk of collisions. By utilizing the equation of Laplace and computing the potential values’ distribution in the environments which have been simulated, the robot can determine the safest path that avoids obstacles which exists in the environment. This method ensures that the robot moves along a path where the potential for collisions is minimized. The findings confirm that QSBTOR outperforms Half Sweep Boosted TOR (HSBTOR) and Full Sweep Boosted TOR (FSBTOR). QSBTOR and HSBTOR show 73% and 50% reduction respectively, compared to FSBTOR in terms of computational complexity.

Hydrodynamic Response Assessment of Floating Photovoltaic System Subjected to Environmental Loadings

Ossama Yehia Gouda Ahmed Ghonim — 2025-05-23

Floating Photovoltaic systems (FPVs) represent a novel integration of solar energy technology with aquatic environments, marking a pivotal advancement in renewable energy applications. This paper introduces a new design for the floating system, specifically engineered to optimize performance under hydrodynamic conditions. Despite the significant focus on the field, the complex hydrodynamics of FPVs, particularly their resilience against varying offshore conditions, remain inadequately explored. This study seeks to bridge this knowledge gap by using an Excel-based analytical model to investigate how changes in design parameters, such as water depth and wave height, affect the surge, heave and pitch responses of the proposed design. Our findings indicate that water depth variations predominantly affect surge movement, while wave height significantly influences all hydrodynamic responses of the floater.

Interior Design and Commercial Signage Analysis of Muslim-Friendly Restaurants in the Capital Town and the Only City of Sultan Kudarat, Philippines

Mildred F. Accad — 2025-05-23

Architectural structure and interior designs of commercial establishments are significant factors in considering a Muslim -friendly tourism destination. The practice of Halal food is centred on Mindanao Island to address the community's needs, like looking for safer food and temporary shelter. The strict observance of Halal practices is expected. However, there are misconceptions about Halal in the community. This study determined the common interior designs of establishments with the Halal logo on their signages. Another focus is the floor plan, furniture and fixtures and their arrangement. It also analyses the commercial signs on the establishments as to the common colours, font styles and other special features and the implication of the Halal logo printed on the signage to customers' eyes. Descriptive quantitative and qualitative content analysis elicited information from the owners, customers and crews. The small and medium-scale food service providers with Halal logos posted in the signages are covered in this study. There are limited locations that offer Muslim-friendly food. It is usually cooked indoors or at home and displayed on the stalls. The eating and handwashing areas are also in limited space. Hence, the public market food stalls with Halal logos used temporary commercial signages like tarpaulin posted on the façade. The Halal logo is printed on the right side of the monotone or the green and white colour with simple font styles. Muslim customers are confident about eating food offered at Muslim -friendly establishments; however, non-Muslim passers-by searching for food disregard logo and their relevance to food safety practices. Thus, there is a need to massively disseminate information about the importance of Halal practices in the area.

Minimizing Total Production Cost in Hybrid Flow Shop Scheduling using Taguchi Enhanced Particle Swarm Optimization Algorithm

Wasif Ullah — 2025-05-23

This study uses metaheuristic optimization algorithms to minimize the total production cost (TPC) in a hybrid flow shop scheduling (HFS) environment. Scheduling jobs in manufacturing systems is vital for fulfilling customer demands and improving efficiency. In this research, four well-established metaheuristic algorithms, namely Tuned Particle Swarm Optimization (TPSO), Standard particle swarm optimization (PSO), Sine cosine algorithm (SCA) and Arithmetic optimization algorithm (AOA), were explored for TPC optimization in HFS using MATLAB (2022b). Through experimental analysis, TPSO consistently provided the best solutions regarding mean fitness, outperforming other algorithms in a maximum of 12 benchmark test problems. Taguchi's Design of Experiment (DOE) was utilized to identify the most influential parameter configurations for PSO. The findings highlight the effectiveness of TPSO in minimizing production costs and improving productivity in HFS. This research contributes to production scheduling and offers insights for organizations striving to optimize manufacturing systems utilizing the HFS environment.

Production Process Improvement Towards Lean Process Effectiveness using FlexSim Simulation Software

Ahmad Nur Aizat Ahmad — 2025-05-23

Lean involves optimizing entire value streams to minimize waste and enhance customer value in production practices. Process improvement encompasses the identification, analysis and enhancement of existing business processes to optimize performance and align with best practice standards for a company's products or services. Companies face the imperative of increasing production to meet demand per day. The challenges include enhancing the process effectiveness in the production line. Process effectiveness measures a process's ability to produce desired results, evaluated qualitatively. FlexSim integrates simulation, artificial intelligence, three-dimensional computer image processing and data processing technology. The research objectives include identifying the operational process flow in the study company, proposing a new production process flow using FlexSim simulation software and evaluating the process flow for reducing lead time as part of process improvement. This research adopts a qualitative method, collecting data through observation and interviews. This research helps companies to increase the efficiency and effectiveness of their productivity. The data is analysed using simulation software, focusing on production process improvement to enhance process effectiveness in the PMI's company. The study's outcome reveals a 19.92% reduction in lead time achieved by consolidating processes and eliminating waste, such as waiting time, transportation and product motion. This reduction translates to cost savings and increased profitability, highlighting the positive impact of the implemented changes on the overall performance of the company.

Pothole Boundary Detection Algorithm using Image Segmentation Technique in Urban Road

Abdallah M. M. Alshami — 2025-05-23

Urban road safety is critically undermined by the presence of potholes, which necessitate accurate detection and assessment for effective maintenance. This study introduces a specialized algorithm designed to precisely detect and delineate the boundaries of potholes using advanced image segmentation techniques. Employing Otsu's method and Canny edge detection, the algorithm seeks to accurately identify and contour the edges of potholes in urban road imagery. The research extends beyond mere detection, focusing on the accurate characterization of potholes, determining their parameters and defining their regions. Implemented in Python and utilizing libraries such as OpenCV, NumPy and Matplotlib, the algorithm is developed to be modular, thoroughly documented and performance optimized. Real-world testing is a cornerstone of this project, with field tests conducted to ensure the algorithm's effectiveness across various road conditions. Additionally, the algorithm’s performance is meticulously evaluated against a diverse set of image datasets sourced from the internet, aiming to validate its robustness and reliability. The study's outcomes demonstrate the algorithm's potential to significantly contribute to road safety and infrastructure maintenance, offering a promising tool for urban road management authorities.

Real-Time Object Detection using Convolution Neural Network on Raspberry Pi Embedded Systems

Ahmad Luqman Abd Wahab — 2025-05-23

Object detection through colour filtering of pixel hue and saturation values is effective for objects lacking consistent distinguishable features. Traditional object recognition frameworks, however, are often passive and neglect the relationship between detection configuration and recognition performance, as well as the importance of feedback for image quality improvement. This limitation, combined with the inherent challenges of human colour detection, particularly for those with colour vision deficiencies, can impact tasks in sorting areas. To address these challenges, this project focuses on developing a real-time object detection system using convolutional neural networks (CNNs). The system integrates a camera detector, Raspberry Pi, servo motor, 5 V DC motor, infrared obstacle avoidance sensor and Liquid-Crystal Display (LCD) display. The dataset comprises 660 augmented chili images in red, green and yellow, used for both training and testing. During the final training epoch, the system achieved 100 % accuracy for both the training and validation datasets, demonstrating the efficacy of CNNs in real-time object detection and classification. To enhance the dataset, four types of image augmentations were applied: rotation along the centre, brightness increments and decrements and image sharpening. These augmentations increased the dataset size and improved the robustness of the neural network. The validation accuracy was only 3 % lower than the training accuracy, indicating minimal overfitting. This project highlights the advantages of using CNNs for precise colour-based object identification, addressing the inefficiencies of traditional methods and the limitations of human visual perception. By integrating advanced image processing techniques and machine learning algorithms, the system provides a robust and reliable solution for real-time object detection, applicable in various industrial sectors. This approach not only improves the accuracy and efficiency of object detection but also offers a practical solution for individuals with colour vision deficiencies, making it a valuable tool for diverse applications.

Assessing Coastal Erosion Risks: A Comparative Study of Coastal Vulnerability Index and Coastal Erosion Risk Assessment Methods

Azreen Shafiqa Nabilla Azlan — 2025-05-23

Coastal erosion poses significant environmental and socio-economic challenges, necessitating robust assessment methodologies for effective management. This study provides a comparative analysis of two widely used coastal risk assessment approaches: the Coastal Vulnerability Index (CVI) and the Coastal Erosion Risk Assessment (CERA). CVI evaluates broad-scale vulnerability based on physical and environmental indicators such as sea-level rise, shoreline erosion rates and geomorphology, making it suitable for large-scale coastal planning. In contrast, CERA integrates additional socio-economic and infrastructural factors to offer a more localized, high-resolution risk assessment, making it particularly useful for site-specific management and mitigation strategies. The study highlights key differences in spatial scope, data requirements and applicability, demonstrating that CVI is optimal for regional-scale vulnerability mapping, while CERA provides detailed risk classification essential for immediate intervention. The findings suggest that integrating both methodologies could enhance coastal risk assessments by combining CVI’s large-scale vulnerability insights with CERA’s detailed, site-specific risk evaluations. This hybrid approach would support more informed decision-making and adaptive strategies to mitigate coastal erosion impacts effectively.

Performance Evaluation of Ryu, OpenDayLight and Floodlight Controllers in Diverse Software-Defined Networking Topologies

Diong Hui — 2025-05-23

Software-Defined Networking (SDN) has been introduced as a new approach to networking for designing and managing computer networks. SDN architecture decouples control and data planes, enabling programmable network management. The significant component is the Controller, which is responsible for managing and distributing information to all network devices. While prior studies evaluate SDN controllers, comprehensive comparisons across multiple topologies and performance metrics remain limited. This paper addresses this gap by analysing and comparing the performance of the Ryu, OpenDayLight and Floodlight controllers under single, linear and tree network topologies using Mininet. The performance parameters considered include round-trip time (RTT), throughput, jitter and packet delivery ratio. The results indicate that in linear and tree topologies, Ryu exhibited the lowest RTT, highest bandwidth and lowest jitter compared to the ODL and Floodlight controllers. Conversely, in the single topology, Floodlight demonstrated the lowest jitter, while Ryu maintained the lowest RTT and highest bandwidth. In conclusion, Ryu is often preferred in scenarios requiring lower latency and higher throughput, particularly in more complex network topologies. However, Floodlight may still be advantageous in simpler topologies where minimizing jitter is critical. This comprehensive evaluation not only aids in controller selection but also informs future enhancements in SDN architecture and performance benchmarking methodologies, which will be implemented in modern data centres.

Validity of Upper Limb Risk Assessment Method for Assessing Work-Related Upper Limb Disorders at Workplace

Mohd Nasrull Abdol Rahman — 2025-05-23

This paper examines the construct validity of the newly developed observational tool called Upper Limb Risk Assessment (UPLIRA) tool for assessing work-related upper limb disorders (WULDs). Hence, this study aims to establish the validity of the UPLIRA tool. The construct validity for the UPLIRA method was verified using the known-groups technique by comparing two different groups of office workers (n = 70) and cashiers’ workers (n = 56). The Mann-Whitney test was used to compare the socio-demographic characteristics of these two groups and to evaluate the validity by comparing the questionnaire answers from the respondents. The construct validity shows that statistically significant for the 69% items of UPLIRA (p < 0.05). It shows that the UPLIRA assessment provided a good indication of work-related upper limb disorders (WULDs) which might be reported as pain, or discomfort in the upper body region. These results support that UPLIRA tool is valid instrument to measure exposure risk factors of the WULDs in industrial settings and epidemiological studies.

Reinforcement Learning Base Energy Efficient Route Optimization Model to Enhance Network Lifetime on the Internet of Vehicles

Quadeer Hussain — 2025-05-23

The Internet of Vehicles (IoV) has emerged as a promising paradigm to revolutionize transportation systems by enabling seamless communication and data exchange among vehicles and infrastructure. A crucial challenge in IoV is optimizing the routes of information, which is used to enhance network efficiency, reduce congestion, and enhance network lifetime. This research presents a centralized reinforcement base learning framework that leverages the power of reinforcement learning algorithms to optimize the routes by minimizing communication overhead. To evaluate the proposed approach, extensive simulations are conducted in a realistic IoV environment, incorporating various scenarios and traffic conditions. Comparative analyses are performed against LEACH, PEGASIS and EER_RL traditional widely used route optimization algorithms and heuristic-based methods to assess the effectiveness and efficiency of the reinforcement learning-based approach. Our findings demonstrate that the reinforcement learning-based route optimization approach exhibits superior performance in terms of reducing energy consumption, minimizing communication overhead, and enhancing network lifetime compared to conventional methods. This work opens new avenues for future research in leveraging RL algorithms for optimizing various aspects of IoV systems and addressing the challenges posed by dynamic and complex vehicular networks.

Harmonic Detection of Electric Current using Machine Learning: A Literature Review

Luqman Assaffat — 2025-05-23

The increased use of nonlinear devices makes distortions in the form of current and voltage waves unavoidable in power grids. As a result, harmonics, sub-harmonics and inter-harmonics often occur in the current and voltage spectrum. Harmonics cause many problems such as overpower loss, overvoltage, imbalance, flicker, relay miss operation, etc. Harmonic distortion of electric current can be measured directly in harmonically contaminated systems. Various standards have already been established in different measurement procedures and many other techniques have been proposed for analysing, characterizing and classifying power quality data. Extensive power quality monitoring is often recommended for industrial and distribution systems. Harmonic measurements are usually performed as part of more general power quality measurements to gain insight into the distribution system's state. Detection of electric current harmonics using machine learning has been widely done and provides excellent results. This study is based on a literature review of research journals on machine learning studies that detect electric current harmonics in electric power system networks.