SLIIT International Conference on Engineering and Technology [SICET]
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SLIIT International Conference on Engineering and Technology is organized by the Faculty of Engineering. SICET welcomes submissions from various disciplines, focusing on emerging trends in Engineering, Technology, and Applied and Natural Sciences. The conference will encompass research in theory, practical applications, and education. This event offers a unique platform for academics, student researchers, and industry practitioners to present innovative ideas and engage with professionals from diverse engineering fields
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Publication Open Access Enhancing Patient Safety with the MedAlert SYstem (MASY): A Low- Cost Timer for Medication Administration Alerts(Faculty of Engineering, 2025-09-09) Rodrego, P; Fernando, N; Wijekoon, N; Wanigasekara, D; Herath, JWhen it comes to patient safety, the timely administration of medication tends to be a critical factor in almost all the healthcare related environments, especially in hospital wards of any scale and clinics where multiple patients simultaneously require any form of medication. In many small to medium scale hospital wards and rural healthcare environments, where commercially available systems which are developed for patient management tend to be extremely costly to deploy due to many factors such as the scale of the facility and lack of staff with the necessary knowledge to run and maintain such a sophisticated system, staff always seem to rely on logbooks for the purpose of tracking the issued doses and the times at which these specific medications are issued. This process is prone to human error, inefficiency, data tampering, and lack of accountability. This paper therefore presents an economical solution addressing the issues mentioned above: the MedAlert System (MASY), which is a low-cost, standalone timer unit specifically designed and developed for healthcare settings. This system, based on the popular Arduino open-source platform, enables medical staff members to manage multiple medication timers simultaneously with no reliance on any logbooks or other external references. This system also offers both visual and auditory alerts to ensure that medication is always administered at the correct time. Unlike commercial patient monitoring systems implemented in large-scale hospitals, MASY can operate independently, with no reliance on servers or internet connections. Due to its simplicity, this system can be easily modified and implemented according to local workflows. This paper discusses the system’s software, design, human machine interaction, and the future scope of the system’s development. It is believed that implementing such a simple device has the potential to reduce missed or delayed medication doses and, in turn, improve patient safety in almost any setting.Publication Open Access Development Of An Ai-Based Model With Low Computational Complexity For Accurate Solar Energy Forecasting(Faculty of Engineering, 2025-09-09) Chandrasinghe, S; Fernando, NThis paper introduces a short-term solar energy forecasting model that is designed with a focus on low computational complexity and addresses the challenges posed by fluctuations in solar energy generation, which are significantly influenced by environmental factors. These fluctuations can lead to instability when solar power generation systems are integrated into national energy grids, creating difficulties in maintaining a balanced supply and demand. If solar energy generation can be accurately forecasted before fluctuations occur, potential issues can be identified in advance, allowing for better management of the energy system, including optimizing storage facilities when energy generation is high. Current solar energy forecasting systems face significant challenges due to their high computational complexity, which results in increased power consumption and lower accuracy. To address these issues, this study focuses on the development of an artificial intelligence (AI)-based forecasting model using an Artificial Neural Network (ANN). The goal is to reduce the computational complexity of the model while maintaining high accuracy. To achieve this, various data analysis and complexity reduction techniques, such as variable reduction, pruning, and quantization, were applied. The performance of the optimized AI model was evaluated by comparing the forecasted values to actual solar energy generation data. The results demonstrate that the proposed model successfully reduces computational complexity while maintaining a satisfactory level of accuracy. This optimization makes the model more suitable for real-time forecasting, particularly in resource-constrained environments, and provides a more efficient approach to solar energy management. The findings of this study suggest that AI-based forecasting models can play a critical role in enhancing the integration of solar energy into national grids, ensuring a more reliable and sustainable energy supply. Further research could explore additional optimization techniques and the introduction of generalization techniques to improve transferability of the model and applicability across diverse geographical regions. Additionally, focus on utilizing AI techniques that minimize computational complexity without compromising the accuracy of the model, aiming to maintain high forecasting precision while optimizing the efficiency of the system.Publication Open Access Development of an AI-Based Model with Low Computational Complexity for Accurate Wind Energy Forecasting(Faculty of Engineering, 2025-09-09) Dilshan, S; Fernando, NMost countries primarily relay on fossil fuel for electricity generation, leading to fossil fuel depletion and environmental pollution. The countries are developed technologies for renewable energy generation. The wind energy being promoted as a superior renewable energy. However, wind energy has its challengers, particularly uncertainty that can affect overall system stability. The accurate short-term forecasting of wind energy was crucial for ensuring grid stability. Both physical and AI-based models can effectively be utilized for wind energy prediction. AI-based methodologies have shown superior effectiveness, efficiency, and accuracy when compared to traditional physical models. The lightweight AI-based forecasting model was particularly significant for processing devices, enabling faster computations and substantially more cost-effective forecasting. The research utilized simulation software to develop an Artificial Neural Network (ANN) model, initially incorporating eight meteorological parameters. Four of these parameters showed weak correlations and were subsequently removed from the model. Further optimization was achieved through pruning and quantization techniques, significantly reducing computational complexity. The optimized model demonstrates a notable reduction in both training time by 92.69% and inference time by 63.83%, while maintaining accuracy with only a marginal decrease of 3.99% compared to the initial model. These improvements were achieved with minimal loss in predictive accuracy, significantly reducing computational complexity. The study concludes that the optimized ANN model is wellsuited for real-time wind power forecasting, offering a balance between accuracy and computational efficiency. This approach not only facilitates better grid management but also extends the applicability of AI-based forecasting to devices with limited processing capabilities. Future work could explore additional complexity reduction techniques and broader deployment scenarios.Publication Open Access Optimum Synchronization of Grid-connected Renewable Energy Source(Sri Lanka Institute of Information Technology, 2023-03-25) Fernando, N; Ganepola, D; Hettiwatte, SIn the last decades, wind power production has become one of the major concerns to investigate in enhancing the utilization of renewable energy resources in microgrids. Wind power can regulate environmental-friendly power generation which helps to satisfy the power demand in the grid whenever it is essential. This research has been carried out for analyzing behavior of Wind Energy Conversion System (WECS) and appropriate technique for grid synchronization in optimum way. Therefore, this includes the analysis of synchronization procedures and design an optimization technique for synchronization of WECS which is connected to the grid via an inverter. Also, it comprises existing renewable energy systems and applications on synchronization techniques. Mainly, this paper proposes an optimal synchronizing control scheme which verifies deterministic and reliable reconnection to the grid. The control scheme was designed using MATLAB Simulink software and the results were interpreted that the concept is efficient and reliable to optimize the microgrid operations.