arXiv subjects

Janaka Ekanayake

Publications and source records attributed to Janaka Ekanayake.

5 recordsLinked to original sources

arXiv2026

Development of Multivariate Attention LSTM Model For Dynamic Line Rating Forecasting

As global fossil fuel reserves diminish, there's a growing impetus for nations to transition towards renewable energy sources. Sri Lanka, for instance, aims to generate 70% of its electricity from renewable sources by 2030. Achieving this target requires optimal use of the existing power transmission infrastructure, as expanding the grid is both time-consuming and expensive. Traditionally, Static Line Ratings (SLRs) are used to define line capacity, often resulting in underutilization. Dynamic Line Rating (DLR), which estimates line capacity in real time based on weather conditions, offers a more efficient solution. However, DLR prediction is highly sensitive to environmental variability and forecasting complexity. This study proposes a novel multivariate Long Short-Term Memory (LSTM) model enhanced with an attention mechanism for improved DLR forecasting. Unlike traditional models that treat weather variables independently, the proposed approach captures nonlinear interdependencies among key environmental features such as ambient temperature, cable temperature, wind speed, humidity, and solar irradiance. The attention mechanism dynamically prioritizes the most relevant inputs during forecasting, leading to improved performance. Experimental evaluation on real-world DLR data demonstrates that the proposed model achieves a prediction accuracy of 95.84%, surpassing the conventional LSTM model's 94.62%. This improvement highlights the model's superior ability to deliver accurate and robust DLR forecasts. The findings confirm that incorporating multivariate features with attention enhances forecasting precision, supporting more efficient transmission line utilization and higher renewable energy integration.

eess.SP↗

arXiv2025

AVSim -- Realistic Simulation Framework for Airborne and Vector-Borne Disease Dynamics

Computational disease modeling plays a crucial role in understanding and controlling the transmission of infectious diseases. While agent-based models (ABMs) provide detailed insights into individual dynamics, accurately replicating human motion remains challenging due to its complex, multi-factorial nature. Most existing frameworks fail to model realistic human motion, leading to oversimplified and less realistic behavior modeling. Furthermore, many current models rely on synthetic assumptions and fail to account for realistic environmental structures, transportation systems, and behavioral heterogeneity across occupation groups. To address these limitations, we introduce AVSim, an agent-based simulation framework designed to model airborne and vector-borne disease dynamics under realistic conditions. A distinguishing feature of AVSim is its ability to accurately model the dual nature of human mobility (both the destinations individuals visit and the duration of their stay) by utilizing GPS traces from real-world participants, characterized by occupation. This enables a significantly more granular and realistic representation of human movement compared to existing approaches. Furthermore, spectral clustering combined with graph-theoretic analysis is used to uncover latent behavioral patterns within occupations, enabling fine-grained modeling of agent behavior. We validate the synthetic human mobility patterns against ground-truth GPS data and demonstrate AVSim's capabilities via simulations of COVID-19 and dengue. The results highlight AVSim's capacity to trace infection pathways, identify high-risk zones, and evaluate interventions such as vaccination, quarantine, and vector control with occupational and geographic specificity.

eess.SY↗

arXiv2025

The Role of Social Interactions in Mitigating Psychological Distress During the COVID-19 Pandemic: A Study in Sri Lanka

Massive changes in many aspects related to social groups of different socioeconomic backgrounds were caused by the COVID-19 pandemic and as a result, the overall state of mental health was severely affected globally. This study examined how the pandemic affected Sri Lankan citizens representing a range of socioeconomic backgrounds in terms of their mental health. The data used in this research was gathered from 3,020 households using a nationwide face-to-face survey, from which a processed dataset of 921 responses was considered for the final analysis. Four distinct factors were identified by factor analysis (FA) that was conducted and subsequently, the population was clustered using unsupervised clustering to determine which population subgroups were affected similarly. Two such subgroups were identified where the respective relationships to the retrieved principal factors and their demographics were thoroughly examined and interpreted. This resulted in the identification of contrasting perspectives between the two groups toward the maintenance and the state of social relationships during the pandemic, which revealed that one group was more 'socially connected' in nature resulting in their mental state being comparatively better in coping with the pandemic. The other group was seen to be more 'socially reserved' showing an opposite reaction toward social connections while their mental well-being declined showing symptoms such as loneliness, and emptiness in response to the pandemic. The study examined the role of social media, and it was observed that social media was perceived as a substitute for the lack of social connections or primarily used as a coping mechanism in response to the challenges of the pandemic

physics.soc-ph↗

arXiv2022

Holistic Interpretation of Public Scenes Using Computer Vision and Temporal Graphs to Identify Social Distancing Violations

The COVID-19 pandemic has caused an unprecedented global public health crisis. Given its inherent nature, social distancing measures are proposed as the primary strategies to curb the spread of this pandemic. Therefore, identifying situations where these protocols are violated, has implications for curtailing the spread of the disease and promoting a sustainable lifestyle. This paper proposes a novel computer vision-based system to analyze CCTV footage to provide a threat level assessment of COVID-19 spread. The system strives to holistically capture and interpret the information content of CCTV footage spanning multiple frames to recognize instances of various violations of social distancing protocols, across time and space, as well as identification of group behaviors. This functionality is achieved primarily by utilizing a temporal graph-based structure to represent the information of the CCTV footage and a strategy to holistically interpret the graph and quantify the threat level of the given scene. The individual components are tested and validated on a range of scenarios and the complete system is tested against human expert opinion. The results reflect the dependence of the threat level on people, their physical proximity, interactions, protective clothing, and group dynamics. The system performance has an accuracy of 76%, thus enabling a deployable threat monitoring system in cities, to permit normalcy and sustainability in the society.

cs.CV↗

arXiv2021

A generalized forecasting solution to enable future insights of COVID-19 at sub-national level resolutions

COVID-19 continues to cause a significant impact on public health. To minimize this impact, policy makers undertake containment measures that however, when carried out disproportionately to the actual threat, as a result if errorneous threat assessment, cause undesirable long-term socio-economic complications. In addition, macro-level or national level decision making fails to consider the localized sensitivities in small regions. Hence, the need arises for region-wise threat assessments that provide insights on the behaviour of COVID-19 through time, enabled through accurate forecasts. In this study, a forecasting solution is proposed, to predict daily new cases of COVID-19 in regions small enough where containment measures could be locally implemented, by targeting three main shortcomings that exist in literature; the unreliability of existing data caused by inconsistent testing patterns in smaller regions, weak deploy-ability of forecasting models towards predicting cases in previously unseen regions, and model training biases caused by the imbalanced nature of data in COVID-19 epi-curves. Hence, the contributions of this study are three-fold; an optimized smoothing technique to smoothen less deterministic epi-curves based on epidemiological dynamics of that region, a Long-Short-Term-Memory (LSTM) based forecasting model trained using data from select regions to create a representative and diverse training set that maximizes deploy-ability in regions with lack of historical data, and an adaptive loss function whilst training to mitigate the data imbalances seen in epi-curves. The proposed smoothing technique, the generalized training strategy and the adaptive loss function largely increased the overall accuracy of the forecast, which enables efficient containment measures at a more localized micro-level.

stat.AP↗