Este artigo descreve um estudo comparativo entre soluções para a execução de atividades de laboratório de eletrônica durante o distanciamento social imposto pela crise do coronavírus. Alternativas de dados tabelados, o uso de simuladores, e a abordagem proposta de laboratório de ensino à distância são comparados sob aspectos de acessibilidade e aprendizagem do aluno, e custo para a escola. Um questionário (n=28) foi aplicado para obter diferentes perspectivas de técnicos, estudantes e professores. Resultados evidenciam maior interesse pela alternativa de laboratório à distância, e a preocupação de professores com a disponibilidade de internet estável aos alunos.
Using extensive databases and known algorithms to predict short-term energy consumption comprises most computational solutions based on artificial intelligence today. State-of-the-art approaches validate their prediction models in offline environments that disregard automation, quality monitoring, and retraining challenges present in online scenarios. The existing demand response initiatives lack personalization, thus not engaging consumers. Obtaining specific and valuable recommendations is difficult for most digital platforms due to their solution pattern: extensive database, specialized algorithms, and using profiles with similar aspects. The challenges and present personalization tactics have been researched by adopting a digital twin model. This study creates a different approach by adding structural topology to build a new category of recommendation platform using the digital twin model with real-time data collected by IoT sensors to improve machine learning methods. A residential study case with 31 IoT smart meter and smart plug devices with 19-month data (measurements performed each second) validated Digital Twin MLOps architecture for personalized demand response suggestions based on online short-term energy consumption prediction.
The Internet of Things enables the data collection of the physical world in real time, raising opportunities for cost reduction and optimization at large scale. However, creating an IoT solution that transforms these data into information by enabling new visualizations and interactions is not trivial. A remote digital electronics lab tool based on the MQTT protocol with energy monitoring, device control and integration with web and mobile dashboards is presented to support remote learning. The open source tool might be useful for teachers and technical staff of digital electronics lab courses by automating the remote lab devices management.
Este artigo descreve um dispositivo IoT que apresenta os dados coletados de um simulador de caminhada indoor em um dashboard web de forma autônoma e privativa (i.e., sem necessidade de conexão Internet e com autonomia de coleta de dados de 10 usuários durante 30 dias), e é integrado a funcionalidades multimídia: streaming de vídeo e rádio.
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