SLIIT International Conference On Engineering and Technology Vol. 02 [SICET] 2023

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/3551

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Author: search.filters.author.Liyanage, MHas files: Yes

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    PublicationOpen Access
    Enhancement of Images Under Low Light Conditions Using Artificial Intelligence
    (Sri Lanka Institute of Information Technology, 2023-03-25) Marzook, M; Herath, M; Liyanage, M; Thilakanayake, T
    Images taken in low light conditions do not contain all the information well-lit images contain. Various features including the colours of objects, details and the quality are lost. Extracting these features from images is very important for any kind of application of it. This study proposes a model to enhance the features of the image taken under low light conditions, by delivering a solution which improves the quality of the image through Artificial Intelligence. Through the proposed method, the clarity of the image is improved, making it closer to a well-lit image equivalent. Both Image Processing and Deep Learning based techniques are explored, including Convolutional Neural Network (CNN) based generative models. The Generative models considered are Autoencoders (AE) and Generative Adversarial Networks (GANs). The study has been carried out by using several datasets combined together, which include image pairs of well-lit and low light images. A comparison between the two CNN-based generative models is carried out. Through the study, it is quantitatively found, by the Structural Similarity Index and supported by the Peak Signal to Noise Ratio, that the proposed CNNbased Autoencoder model overrides the proposed CNN-based GAN model. This is further supported by qualitative observations of the image results. Both models, however, greatly enhance the low light images, bringing to light features that were not visible beforehand, and also provide results with good colour accuracy. Through this research study, the methods and solutions to enhance low light images have been addressed, as well as providing a comparison between two suitable models, Autoencoders and GANs. The proposed solution is able to address many of the limitations existing in the extent literature.
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    PublicationOpen Access
    Simulating the Effects of Active Aerodynamics on the Suspension System of a Formula Student Race Car
    (Sri Lanka Institute of Information Technology, 2023-03-25) Epakande, C; Dayawansa, B; Liyanage, M
    Active aerodynamics is a growing topic in the automotive industry. With technological advancements at play, it has begun to spread across multiple avenues such as road vehicle ride comfort and the development of active suspension systems. However, the application of active aerodynamics in Formula cars has not been a commonly discussed topic. Furthermore, the effects of active aerodynamics on the suspension system have not been assessed for Formula Student race cars. Therefore, this study looks to obtain an understanding about how actively changing the Angles of Attack of an aerodynamic front wing and a rear wing would affect the suspension system of a Formula Student race car. The study was done by first choosing a wing profile using the XFLR5 software, modelling the front and rear wings using SolidWorks, according to the parametric guidelines of the Formula Student Competition for different angles of attack, analysing coefficients of lift and drag of the wings for each angle of attack using Ansys Workbench, and by performing full-vehicle acceleration and cornering analyses on MSC Adams Car to find how changing these coefficients affects the suspension dampers along the direction normal to the ground the vehicle travels on. This research would help understand the many forces acting on the suspension and to explore further developments in this area such as active aerodynamics in Formula Student race cars in the future.