Scott Idubor

This work presents the modeling, simulation and analysis of a Home Energy Management System (HEMS) specifically designed to manage domestic load. The aim of this project is to model and analyze the HEMS for efficient energy harvesting, storage and consumption. To implement this, the HEMS system was modeled and simulated using MATLAB/Simulink. Each subsystem of the HEMS; the PV system, DC bus, DC-DC converter, DC/AC inverter, battery subsystem, home subsystem, AC/grid interface are modelled using the Simulink blocks and all design considerations are taken account for. The system is rated at 5kw and it was designed to power two test loads of 3KW each which was connected to the home energy management system (HEMS) i.e a total 6KW load. In this project, we used Simulink to simulate a photovoltaic system, grid power and a battery connected to a home energy management system (HEMS) as complementary power sources to address issues of power shortages and to also minimize and control the rate of energy consumption in homes thus reducing the cost of power consumption as much as possible.
Having designed, simulated and analyzed the HEMS, the results were studied and the system was effective in managing the loads under different grid and power scenarios. The system’s response during a 6-second simulation period showed how the system managed the two 3kW loads under different scenarios. The PV system initially powers both loads, drawing the 1KW deficit from the Grid. A grid outage is then simulated, and the loads previously powered by the sun and grid are then powered by the battery system, reducing grid usage and reliance. The grid is later restored and it resynchronizes with the system. This indicates the system success in managing the load under different power and grid scenarios.

This paper presents the development of an Automated Audio Translation System (AATS) specifically designed for translating spoken Ewe, a Western Nigerian language predominantly spoken within the Yoruba people into English. The project addresses the growing need for effective communication tools in multilingual contexts, particularly in regions where Ewe is widely spoken. The proposed system leverages advanced machine learning techniques, including automatic speech recognition (ASR), natural language processing (NLP), and neural machine translation (NMT) to facilitate real-time audio translation.
The methodology involves the collection of a diverse corpus of Ewe audio recordings paired with their English translations, which serves as the training dataset for the ASR and NMT components. We employ deep learning architectures, such as recurrent neural networks (RNNs) and transformer models, to enhance the accuracy and fluency of the translation output. Evaluation metrics, including Word Error Rate (WER) and BLEU scores, are utilized to assess the performance of the
system against baseline models.
Preliminary results indicate that the AATS achieves a significant improvement in translation
accuracy compared to traditional translation methods. This research not only contributes to the field of computational linguistics but also aims to promote cultural exchange and accessibility by
bridging language barriers