The current global health emergency triggered by the pandemic COVID-19 is one of the greatest challenges we face in this generation. Computational simulations have played an important role to predict the development of the current pandemic. Such simulations enable early indications on the future projections of the pandemic and is useful to estimate the efficiency of control action in the battle against the SARS-CoV-2 virus. The SEIR model is a well-known method used in computational simulations of infectious viral diseases and it has been widely used to model other epidemics such as Ebola, SARS, MERS, and influenza A. This paper presents a modified SEIRS model with additional exit conditions in the form of death rates and resusceptibility, where we can tune the exit conditions in the model to extend prediction on the current projections of the pandemic into three possible outcomes; death, recovery, and recovery with a possibility of resusceptibility. The model also considers specific information such as ageing factor of the population, time delay on the development of the pandemic due to control action measures, as well as resusceptibility with temporal immune response. Owing to huge variations in clinical symptoms exhibited by COVID-19, the proposed model aims to reflect better on the current scenario and case data reported, such that the spread of the disease and the efficiency of the control action taken can be better understood. The model is verified using two case studies based on the real-world data in South Korea and Northern Ireland.
As smartphones become increasingly powerful and ubiquitous, integrating them into intelligent lighting systems can boost both convenience and energy efficiency. This paper presents an intelligent lighting system prototype with enhanced security features for smart homes. The custom-built Android mobile application made use of the onboard ambient light sensor to run a novel closed-loop feedback algorithm to implement daylight harvesting. A cost analysis shows that the whole system setup is slightly cheaper than commercial products and due to its daylight harvesting capabilities, has potential for monetary savings in the long run, outperforming current commercial products.
Machine learning can be used to automatically process sensor data and create data-driven models for prediction and classification. However, in applications such as fault diagnosis, faults are rare events and learning models for fault classification is complicated because of lack of relevant training data. A hybrid diagnosis system design is proposed which combines model-based residuals with incremental anomaly classifiers. The proposed method is able to identify unknown faults and also classify multiple-faults using only single-fault training data. A physical model and data collected from an internal combustion engine are used to verify the proposed method.
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