Designation: Lecturer
Email: [email protected]
1 The impact of COVID-19 lockdown on short-term load forecasting in Bangladesh has been investigated in this paper. Machine learning models have been proved to be the most efficient regarding such prediction. Models like Artificial Neural Network (ANN), Long Short-Term Memory (LSTM) and Random Forest (RF) have been used in this study to build robust models taking the COVID-19 lockdown situation into account. Data sets for the models were formulated by taking daily generation reports, weather indicators and holidays. This study aims to compare different machine learning models to find out the best model for load forecasting keeping into account the impact of COVID-19 lockdown. The results of these methods have been compared based on accuracy metrics. It has been observed that LSTM shows the least error among the compared models.
(Link)2 This study proposes a simulation-based design for a Silicon-On-Insulator (SOI) ring resonator with a Figure of Merit (FOM) of 56.15 and a high sensitivity of up to 730 nm/RIU. The Finite-Difference Time-Domain (FDTD) technique was used to assess and evaluate the design quantitatively. Our design demonstrates higher sensitivity compared to many recent studies conducted on SOI-based sensors. The device structure follows a conventional ring resonator arrangement with a single waveguide, incorporating a 2D graphene layer on top of the SiO2 wafer and a gold nano-disc positioned at the center of the ring. Our findings highlight the device's susceptibility to refractive index variations, making it a desirable choice for various sensing applications. We have investigated the sensor's capabilities for sensing different concentrations of milkmilk. Graphene and gold materials enhance the device's response to light and provide comparatively higher sensitivity. The suggested design can serve as a blueprint for device fabrication, considering the practicality of implementing an SOI-based device using standard techniques for silicon processing.
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