Emergencies associated with incorrect operation of relay protection and automation (RPA) devices due to saturation of current transformers (CTs) can lead to great economic damage. The article presents an analysis of r...
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System identification, as a rich and vital discipline, provides a practical and general methodology and tool for quantitatively modelling the input-output relationships of dynamical systems. Sparse nonlinear system id...
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Robots for automated assembly are being progressively implemented in the aerospace manufacturing sector. The dim and complex internal structure of the aircrafts significantly complicates the operation of robotic arms ...
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As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around...
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As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around sharing and discussing current events. Within these communities, users are enabled to share their opinions about each event. Using Sentiment Analysis to understand the polarity of each message belonging to an event, as well as the entire event, can help to better understand the general and individual feelings of significant trends and the dynamics on online social networks. In this context, we propose a new ensemble architecture, EDSAEnsemble (Event Detection Sentiment Analysis Ensemble), that uses Event Detection and Sentiment Analysis to improve the detection of the polarity for current events from Social Media. For Event Detection, we use techniques based on Information Diffusion taking into account both the time span and the topics. To detect the polarity of each event, we preprocess the text and employ several Machine and Deep Learning models to create an ensemble model. The preprocessing step includes several word representation models: raw frequency, TFIDF, Word2Vec, and Transformers. The proposed EDSA-Ensemble architecture improves the event sentiment classification over the individual Machine and Deep Learning models. Authors
This article presents an optimized control strategy tailored for DC islanded microgrids, featuring a voltage controller based on mixed H2/H∞ state feedback using linear matrix inequalities (LMIs) and a power controll...
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Common spatial patterns (CSP) is widely employed for spatial filtering and feature extraction in electroencephalogram (EEG)-based brain-computer interfaces (BCIs), However, the non-stationary nature of EEG signals can...
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In the pursuit of sustainable electricity generation from offshore wind and wave energy, the combination of Floating Offshore Wind Turbines (FOWTs) and Oscillating Water Columns (OWCs) has emerged as a promising solut...
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UAVs are becoming increasingly prevalent in a wide range of fields, including surveillance, photography, agriculture, transportation, and communications. Hence, research institutions have developed a range of linear a...
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In this paper, we explore how to use a Nvidia Jetson Nano and Python to create a system that detects weariness in a person's face using computer vision and machine learning techniques. The system captures the pers...
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In this proposal, a predictive control scheme is considered for PMSG based wind turbine system in order to maximize power extraction. The main goal is to maintain the turbine tip-speed ratio (and consequently the powe...
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