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Author: search.filters.author.Herath, S

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    A Virtual Reality Immersive Experience of Sri Lanka's Sacred Heritage: VR Meditation Experience at the Ruwanweli Maha Seya using Heart Rate and HRV Tracking
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Herath, S; Hapangama, C; Gamage, U; Soyza, N; Gamage, I; Nismi, N
    Meditation is a widely recognized practice that reduces stress, improves focus, and promotes mental well-being. For various reasons, it has become difficult for religious devotees to travel to more intimate places of worship, such as their sacred sites, and to worship and meditate in harmonious environments. This system involves creating a Unity-based VR application that simulates several sacred places in Anuradhapura city, including four of the eight sacred places among the “Atamasthana"” and a short five-minute meditation session. A user evaluation was conducted with 25 participants. The results showed a decrease of 5.4 BPM in average heart rate and a 10 increase in heart rate variability after the meditation session. In this research, the sacred city of Anuradhapura has been used as a sample, but any religious sacred place can be used for this purpose. Through this harmonious environment recreated through Virtual Reality (VR), users can experience mental relaxation regardless of physical location. This research is more important because it combines cultural and religious preservation with modern technological innovations. By incorporating physiological sensations into a VR meditation experience, it is possible to enhance user engagement and monitor the results of the meditation. It can be understood that the physiological effects of this VR-based meditation are a good basis to advance both the study of digital wellness and cultural scarcity.
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    Graph Neural Network Based Surrogate Model for Design Informed Structural Optimization
    (Springer Science and Business Media Deutschland GmbH, 2025) Ariyasinghe, N; Weeratunga, H; Mallikarachchi, C; Herath, S
    Structural optimization of skeletal forms is crucial in weight-sensitive applications. Optimizing such structures often involves iterative, computationally intensive methods, which are inefficient under varying design parameters and constraints. This paper introduces a novel surrogate model based on Graph Neural Network (GNN) for real-time structural optimization, aimed at significantly reducing computational costs. In our approach, trusses composed of pin joints and connecting members are represented as graphs, where joints correspond to vertices and members to edges. This correspondence forms the use of Graph Neural Networks (GNNs) to predict topology and size-optimized truss structures. The GNN models the truss as a graph, with edges denoting member cross-sectional areas and nodes representing truss joints, based on input parameters such as geometry, load combinations, and boundary conditions. The resulting predicted structure reflects the optimized topology and member sizes. The proposed model bypasses the need for iterative computations by learning from a dataset comprising various problem definitions and their corresponding optimized results. This GNN-based optimization holds substantial promise for design scenarios requiring rapid and reliable optimization, demonstrating the potential for significant computational time savings while maintaining high accuracy in predicting near-optimal truss layouts. This is particularly significant in the context of sustainability, where industrial users can produce optimally designed structures with minimal material usage within a fraction of the computational power and time required for different applications. Testing results indicate that the model effectively generalizes across various design scenarios, providing near-optimal solutions with minimal computational effort. Specifically, the predicted structures exhibited a normalized root mean square error (NRMSE) of less than 10−3 and R2 values approaching unity. Additionally, predictions were made in under 0.01 s, demonstrating both accuracy and efficiency.