Institutional-Repository, University of Moratuwa
Welcome to the University of Moratuwa Digital Repository, which houses postgraduate theses and dissertations, research articles presented at conferences by faculties and departments, university-published journal articles and research publications authored by academic staff. This online repository stores, preserves and distributes the University's scholarly work. This service allows University members to share their research with a larger audience.
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Recent Submissions
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Graphene Oxide-based nanofluid for heat transfer applications
(IEEE, 2024) Arachchi, MDP; Sandaru, DMC; Rajapaksha, SM; Abeygunewardane, AAGA; Sitinamaluwa, HS
This research investigates the performance of Graphene Oxide-Deionized Water (GO-DI water) nanofluid, and partially reduced Graphene Oxide-Deionized water (prGODI water) nanofluid for enhanced heat transfer efficiency. GO and prGO were derived from Sri Lankan graphite via the modified hummers method followed by thermal reduction in a tube furnace. The effect of particle loading was analyzed on the viscosity, thermal conductivity (TC) and stability of nanofluid. The results show that the nanofluids beyond mass loading of 0.5 wt% of GO/prGO show poor stability. prGO was found to be more effective in enhancing the TC of the nanofluid, due to enhanced TC of the prGO particles. TC enhancement of nanofluids up to 30% was achieved, with the highest increment shown by the nanofluid with 0.75 wt% prGO. Furthermore, the thermal transport characteristics of the nanofluids were computationally modelled using finite element analysis. The average convection heat transfer coefficient (CHC) of 0.5 wt% prGO-based nanofluid showed a 52% increment, highlighting the effectiveness of prGO-based nanofluids. Importantly, the nanofluids with particle concentrations below 0.5 wt% show performance enhancement ratio (PER) values suitable for practical applications. The outcome of this research shows the potential of GO-based nanofluids as state-of-the-art heat transfer fluid to be used in the coolant industry.
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Calcium oxide nanoparticles from waste chicken eggshells as a fertilizer in agriculture
(IEEE, 2024) Weerakoon, WAKN; Udawala, RDNSS; Sewvandi, GA
In recent years, the use of nano fertilizers has gained significant interest due to their distinct physicochemical characteristics that enhance plant growth and productivity. There is a growing recognition in the importance of efficiently utilizing waste materials as society increasingly prioritizes waste reduction and sustainable resource management, which present opportunities to extract valuable resources and minimize environmental impact. The extraction of nano CaO from waste eggshells for agricultural applications represents an intersection of nanotechnology, agricultural innovation, and sustainable development. The synthesis of CaO nanoparticles was achieved through grinding and thermal decomposition processes. The synthesized nano CaO under optimized process parameters was analyzed using Thermogravimetric Analysis (TGA), Scanning Electron Microscopy (SEM), Fourier-Transform Infrared spectroscopy (FTIR), and X-ray Diffractometry (XRD). The highest CaO yield of 97.22% resulted from the optimized extraction process of wet mill grinding for 1 hour and calcination at 900 °C for 3 hours. The feasibility of the synthesized powder as a calcium-rich fertilizer was assessed through seed priming and soil pH tests. Our study reveals a remarkable potential of synthesized nano CaO as a calcium-rich fertilizer for robust seedling growth, plant vitality, and effective pH compensation in acidic soils, paving the way for more sustainable and productive agriculture.
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Use of federated learning for personal smart media cloud solutions
(IEEE, 2024) Kumarage, B; Harankahadeniya, H; Induranga, A; Perera, I; Gunasekera, K
The privacy of user data is a critical concern when it comes to media cloud platforms. Public media cloud services do not guarantee the privacy of their users’ data while offering smart features like facial recognition. Private cloud platforms, while ensuring privacy for stored content, cannot often deliver these smart features with continuously improving accuracy. This paper proposes PicsSmart, a novel approach for personal smart media cloud architecture that addresses these limitations. PicsSmart prioritizes user data privacy by keeping data on-premise within the personal cloud. It leverages federated learning to collaboratively train machine learning models across user devices, enabling continuous improvement of the accuracy of smart features it offers. Unlike traditional cloud platforms, PicsSmart allows for the attachment of various storage solutions, to overcome the storage limitations in cloud platforms. The results of the work demonstrate that PicsSmart effectively delivers smart features with increasing accuracy over time with the use of federated learning. It achieves high performance on diverse and heterogeneous user data while maintaining their privacy. PicsSmart offers a promising solution for users who are concerned about both data privacy and the benefits of smart media cloud functionalities. The implementation of PicsSmart is available at https://github.com/PicsSmart.
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Self-paced brain-computer interface on sensorimotor rhythms for controlling virtual objects
(IEEE, 2024) Athapattu, AD; Dassanayake, PSB; Nanayakkara, GSC; Liyanage, SN; Devindi, GAI; Ragel, R; Dissanayake, T; Nawinne, I
Non-invasive electroencephalogram (EEG) based Brain-Computer Interface (BCI) systems have become a fascinating study area in many research fields. A majority of the research in this area is conducted synchronously. Hence, at the time of the experiment, the user’s state of mind tends to be different from its natural behavior. As a solution to this problem, selfpaced BCI systems started gaining popularity in recent years. However, certain challenges remain to be addressed even with this method. Most of the research on self-paced BCI systems is focused on motor-imagery control, whereas research on nonmotor imagery mental tasks is limited. However, individuals with severe paralysis may face challenges in performing motor imagery tasks. In this research, we explore the possibility of using the techniques used in the motor-imagery method for nonmotor imagery mental tasks. The intention here is to use them in virtual object-controlling applications. The research was done with five different classification models using features from the Fast Fourier Transform (FFT) and Wavelet Transform (WT). The K-nearest neighbor model with features obtained with FFT continuously sustained its performance with a high 55% true positive rate.
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Performance evaluation of sustainable cement types in various concrete grades
(IEEE, 2024) Thejani, KWD; Baskaran, K
In the construction industry, the widespread use of OPC has raised concerns due to its significant CO2 emissions during production. As a response, there’s a growing trend among manufacturers to shift towards blended cement types, which offer potential sustainability benefits. Despite this shift, there remains a knowledge gap regarding the performance of concrete based on blended cement types, particularly in the context of the construction industry in SL. This study attempts to reduce this gap by exploring the sustainable cement types utilised in the construction industry and investigating their performance in concrete. The aim is to determine the critical factors that affect concrete quality when using blended cement and to investigate the attitudes & actions of the construction industry towards sustainable cement adoption. The study employs a methodology that involves performing experiments on three distinct grades of blended cement and conducting a field survey to gather feedback from industry professionals. Experimental results showed that concrete made with Blended Hydraulic Cement (BHC) takes a longer time to gain strength compared to OPC and Portland Composite Cement (PCC). However, results from the RCPT test indicated that concrete made with BHC is less permeable than that made with the other two types of cement.