Research Output
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PublicationDefinite time over-current protection on transmission line using MATLAB/Simulink( 2024-04-01)This paper has investigated the application of the definite time over-current (DTOC) which reacts to protect the breaker from damage during the occurrence of over-current in the transmission lines. After a distance relay, this kind of over-current relay is utilized as backup protection. The over-current relay will provide a signal after a predetermined amount of time delay, and the breaker will trip if the distance relay does not detect a line failure. As a result, this over-current relay functions with a time delay that is just slightly longer than the combined working times of the distance relay and the breaker. This DTOC is tested for various types of faults which are 3-phase fault occurring at load 1, 3-phase fault occurring at load 2, a 3-phase fault occurring before primary protection, and the behaviour of voltage and current with a failed primary protection. All the results will be obtained using the MATLAB/Simulink software package.
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PublicationAerial image semantic segmentation based on 3D fits a small dataset of 1D( 2023-12-01)Time restrictions and lack of precision demand that the initial technique be abandoned. Even though the remaining datasets had fewer identified classes than initially planned for the study, the labels were more accurate. Because of the need for additional data, a single network cannot categorize all the essential elements in a picture, including bodies of water, roads, trees, buildings, and crops. However, the final network gains some invariance in detecting these classes with environmental changes due to the different geographic positions of roads and buildings discovered in the final datasets, which could be valuable in future navigation research. At the moment, binary classifications of a single class are the only datasets that can be used for the semantic segmentation of aerial images. Even though some pictures have more than one classification, images of roads and buildings were only found in a significant number of samples. Then, the building datasets were pooled to produce a larger dataset and for the constructed models to gain some invariance on image location. Because of the massive disparity in sample size, road datasets needed to be integrated.
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PublicationExperimental Analysis of Flight Altitude for Enhanced Agricultural Drone Spraying Performance( 2023-01-01)Effective rice field management and the proper application of agricultural chemicals are crucial for ensuring agricultural product quality. These chemicals control weeds and protect against insect pests, which can harm crop yields and quality. This research explores the relationship between the altitude at which agricultural drones spray chemicals, spray uniformity, and chemical dispersion. The study assesses drone operations at heights of 1m, 1.5m, and 2m above hollow cone nozzles in 2.8m/s wind conditions. It aims to evaluate droplet uniformity and dispersion on water-sensitive paper placed on paddy plants, analyzed with ImageJ software. Results show that at 1.5m height, there's a significantly higher average droplet density, with 162.7 deposits/cm² in the upper region and 161.8 deposits/cm² in the lower region. Additionally, coverage is notably increased, at 55.21% for the upper region and 51.4% for the lower region. This research highlights the importance of optimal drone altitude for efficient chemical application in rice fields, improving crop protection and yield.
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PublicationStudy of eddy current density distribution in a contactless breast cancer detection mechanism using magnetic induction spectroscopy( 2017-01-01)Breast cancer is a throbbing disease that no longer needs an introduction. This is especially true among women due to their unique breast structure that naturally has more breast tissues compared to that of man’s. It is been forecasted that in 2015, a minimum of 60290 new cases of breast cancer will be reported. The goal of this study is to analytically evaluate the changes in the induced Eddy current densities as a function of di-electrical properties of the breast tissue with respect to tumor positioning as well as its size. This is achieved by running numerical simulations on the proposed mechanism of magnetic induction to detect tumors among healthy breast tissue via a 2D breast model configuration. The analytical results presented in this article, proved that the multi frequency magnetic induction principle is viable in detecting the breast lesions as small as 0.2 cm non-invasively through the distributions of the induced Eddy current density. While important pattern of the induced current were reflected when the tumors are located at the far ends of the breast diameter. The minimum results computational time with the proposed system is 10 s.
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PublicationApplication of coal bottom ash as raw material for concrete brick in housing construction( 2020-03-25)Coal and coal-fired power plants are crucial for supplying global energy demand. However, coal-fired power plants produce significant quantities of bottom ash as by-products. Through these power plants, it produces approximately 18 tons of bottom ash each day. Malaysia has the challenging task of dealing with an increasing amount of waste generated by power plants each year. The high cost of construction and operation of incineration plants coupled with a lack of landfills have given rise to the need for waste minimization and recycling. The use of coal ash in concrete is a new dimension in concrete mix design and if applied on large scale can revolutionize the construction industry, by economizing the construction cost and decreasing the ash content. The used of the coal bottom ash in the concrete brick can reduce the wastage of the coal bottom ash in Malaysia, beside that the cost of the housing can be reduced. In addition, the sustainability in the housing construction in Malaysia can be achieved using the wastage material in the construction.
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PublicationEnhancing UAV Safety: Accurate Distance Measurement with YOLOV8-based Measuring Application( 2023-01-01)This article introduces a lightweight and efficient model for measuring applications, aimed at enhancing the current UAV monitoring system. The primary objective of this project is to develop a measuring application capable of determining and displaying the distance between the camera on the UAV and the facial model. The YOLOV8 framework is employed as a detection model to identify and interpret objects within the region of interest. Additionally, the algorithm incorporates the concept of focal length in lenses to calculate the distance between the facial expressions of a human face and the camera. To assess the algorithm's accuracy, facial models were placed at various distances from the camera during testing. The predicted distance values obtained through the algorithm were then compared to the actual measured distances using a measuring tape. The results demonstrated a maximum tolerance of ±0.9 cm, indicating the algorithm's reliable performance in predicting distance measurements.
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PublicationDevelopment of IoT-Enabled Smart Water Metering System( 2024-01-01)This paper introduces a smart water meter that utilizes the capabilities of the Internet of Things (IoT) to automate the collection of meter readings. The primary goal of this project is to create an IoT-based device for reading water meters, while simultaneously developing a compatible mobile application. Instead of relying on manual meter reading, which requires human effort, this project proposes the use of an IoT-enabled water meter to collect the data automatically. The device employs a camera and Convolutional Neural Network (CNN) for image processing, making it easy to detect the meter reading accurately. The IoT system architecture involves the use of an ESP32 CAM for data collection, a laptop as a gateway, and the Message Queuing Telemetry Transport (MQTT) protocol for data transfer. The collected data is stored in Firebase's real-time database, and the mobile application is designed to monitor and analyze the data. A functional prototype of the device is constructed and tested in a housing area. The collected data is then monitored through the developed mobile application. Lastly, the data is analyzed to assess the suitability of the proposed method, and recommendations for future improvements are provided.
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PublicationWireless Power Transfer for Smart Power Outlet( 2023-01-01)Wireless power transmission, based on electromagnetic principles, involves delivering electrical energy from a power source to an electrical load without physical conductors. This technology is precious when wires are impractical, unsafe, or unfeasible. In wireless power transmission, the paramount consideration is efficiency. Ensuring that a substantial portion of the energy generated reaches the intended receiver(s) is crucial for optimizing economic viability and minimizing power loss during transmission. Conventional power outlet sockets, often serving as extension points for multiple devices, are standard fixtures in small offices and homes. However, their wired nature limits distance introduces clutter, and raises safety concerns. Addressing these drawbacks, this paper presents a wireless power transfer solution - an intelligent power outlet. This innovative outlet is powered via wireless transmission, utilizing primary and secondary coils. Furthermore, the intelligent power outlet can be conveniently toggled ON/OFF using a remote control, enhancing its functionality and practicality.
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PublicationAdvancements in UAV image semantic segmentation: A comprehensive literature review( 2024-06-01)Unmanned Aerial Vehicles (UAVs) have revolutionized data acquisition across various domains, presenting immense potential for image processing and semantic segmentation. This literature review encompasses a thorough exploration of advancements, techniques, challenges, and datasets pertaining to UAV image semantic segmentation. It begins by introducing the fundamental concepts of UAVs, highlighting their pivotal role in capturing high-resolution imagery that serves diverse applications. The integration of deep learning algorithms with UAVs is emphasized, unlocking new horizons in autonomous flight, security, and environmental monitoring. Delving into the core principles of semantic segmentation, the review elucidates the critical task of classifying every pixel in an image. Convolutional Neural Networks (CNNs) are presented as the cornerstone technology, tracing their evolution from traditional CNNs to the highly adaptable Fully Convolutional Networks (FCNs). A substantial portion of the review is dedicated to FCNs, underscoring their ability to process images of varying dimensions while maintaining spatial coherence in the output. Their pivotal role in semantic segmentation, encompassing both classification and localization, is articulated. The subsequent sections delve into a comprehensive survey of state-of-the-art models, including SegNet, PSPNet, DeepLabNet, EfficientNet, DenseNet-C, and LinkNet. Each model's unique strengths and applications contribute to the evolving landscape of semantic segmentation tasks. The versatility of the U-Net architecture takes center stage in the latter parts of the review. Its fundamental structure is elucidated, followed by a comprehensive examination of its manifold adaptations—3D-U-Net, ResU-Net, U-Net++, Adversarial U-Net, Cascaded U-Net, and Improved U-Net 3+. These modifications address intrinsic challenges such as limited receptive fields and class imbalances, propelling U-Net to the forefront of image segmentation techniques. The subsequent sections pivot toward the application of U-Net in UAV image segmentation, illustrating its efficacy in diverse tasks, including land cover and crop classification. Nevertheless, persisting challenges, such as the scarcity of annotated datasets and the need for model generalization across varied environmental conditions, remain key areas of concern. The review culminates by underlining the significance of large, authentic datasets and data augmentation techniques. Furthermore, a brief exploration of publicly available UAV image datasets is presented, enhancing our understanding of the resources accessible for training and evaluating models. This comprehensive literature review encapsulates the dynamism of UAV image processing and semantic segmentation, illuminating recent developments and avenues for future research in this burgeoning field.
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PublicationClassification of semantic segmentation using fully convolutional networks based unmanned aerial vehicle application( 2023-06-01)The classification of semantic segmentation-based unmanned aerial vehicle (UAV) application based on the datasets used in this work and the necessary data preprocessing steps for the optimization and implementation of the models are also involved. The optimization of the various models was done using the evaluation metrics and loss functions because deep neural networks (DNNs) are just about writing a cost function and its subsequent optimization. convolutional neural network (CNN) is a common type of artificial neural network (ANN) that has found application in numerous tasks, such as image and video recognition, image classification, recommender systems, financial time series, medical image analysis, and natural language processing. CNN is developed to automatically and adaptively learn spatial feature hierarchies via backpropagation using numerous building blocks, such as pooling, convolution, and fully connected layers. The result of identification was excellent. The image segmentation was detected and comprehend the actual components of an image down to the pixel level. The result created an entire image segmentation masks with instances using the new label editor in the label box.
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