AIoT tool integration for enriching teaching resources and monitoring student engagement

Electric vehicles equipped with lithium ion batteries (LIB) face safety concerns, primarily related to thermal effects. One critical component of LIB is the separator, which serves the purpose of preventing direct contact between the positive and negative electrodes while enabling the movement of lithium-ions. Any damage to the separator can result in short circuits and battery explosions. The properties of cellulose are compared to those of polyolefin through differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), weight loss measurements, and heat flow properties. These parameters play a vital role in determining the suitability of the material for LIB separators. The results obtained from the analysis are highly encouraging, as banana peel cellulose (BPC) exhibits a higher DSC value of 323.18°C, a moderate heat flow of 231.22J/g, a TGA result of 235°C, a weight loss of 59.37 percent, which is considered favorable, and a differential thermal analysis (DTA) result of 330.23°C. These findings position BPC as an excellent alternative material for separators in comparison to the existing polyolefin materials. Furthermore, an artificial neural network is employed to predict the performance of BPC material, with a binary classification system of 0 and 1, where 0 represents failure and 1 represents success. The model is trained using the input data, and it has been observed that the training process is successful, achieving an accuracy rate of 97.58%.

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Acesso Compartilhado à Recursos Físicos com Google Colab

Sabino,

Nacif,

Ferreira

2024

Anais Estendidos Do IV Simpósio Brasileiro De Educação Em Computação (EDUCOMP Estendido 2024)

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Este trabalho apresenta uma proposta para proporcionar aos alunos acesso a recursos de hardware para execução de projetos, mesmo em contextos onde a disponibilidade de tais recursos é limitada. Apresentamos um ambiente virtual de programação utilizando o Google Colab. Os alunos podem submeter seus códigos para execução em um recurso de hardware compartilhado, permitindo-lhes observar a execução em tempo real em um ambiente físico real e tangível. Esta abordagem não apenas facilita o acesso a recursos físicos com disponibilidade limitada, como reduz custos e motiva os estudantes com a visualização física dos resultados.

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AIoT tool integration for enriching teaching resources and monitoring student engagement

Cited by 3 publications

References 16 publications

Special issue on artificial intelligence of things in education (AIoT in education)

Special issue on artificial intelligence of things in education (AIoT in education)

Experimental Exploration of Cellulose Material for Battery Separators and Artificial Neural Network-Driven Predictive Modeling for Enhanced Thermal Safety in Electric Vehicles

Acesso Compartilhado à Recursos Físicos com Google Colab

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