Implementasi Algoritma Convolutional Neural Network Pada Kendaraan Tanpa Awak Skala Kecil

Autonomous Vehicle is a vehicle capable of navigating the car independently without requiring input from the driver. This research aims to design and manufacture a prototype of an unmanned vehicle that can maneuver across a simple artificial road. This study also aims to analyze the performance of the NVIDIA Jetson Nano in processing deep learning models and driving actuators according to the predictions given by the model. The research stages include designing a prototype, creating an artificial path, taking image data, conducting training, and then implementing the training model on the car prototype. After testing the prototype, the training model made the correct steering angle prediction using epoch 50 with RMSE train and validation, 0.1792 and 0.1896, respectively. NVIDIA Jetson Nano also performs well in computing steering angle predictions with live input from the camera

Hybrid Neural Network Approach Based Tool for the Modelling of Photovoltaic Panels

A hybrid neural network approach based tool for identifying the photovoltaic one-diode model is presented. The generalization capabilities of neural networks are used together with the robustness of the reduced form of one-diode model. Indeed, from the studies performed by the authors and the works present in the literature, it was found that a direct computation of the five parameters via multiple inputs and multiple outputs neural network is a very difficult task. The reduced form consists in a series of explicit formulae for the support to the neural network that, in our case, is aimed at predicting just two parameters among the five ones identifying the model: the other three parameters are computed by reduced form. The present hybrid approach is efficient from the computational cost point of view and accurate in the estimation of the five parameters. It constitutes a complete and extremely easy tool suitable to be implemented in a microcontroller based architecture. Validations are made on about 10000 PV panels belonging to the California Energy Commission database

Clasificación de imágenes mediante algoritmos de Deep Learning: Mascarillas de COVID-19

La pandemia mundial causada por la Covid-19 ha provocado un antes y un después en nuestras vidas, tanto, que ahora llevar mascarilla con el fin de frenar su contagio es algo primordial e impensable en determinadas ocasiones. A raíz de la desesperación originada por este virus se ha incrementado el interés en métodos científicos que puedan ayudar a estabilizar y controlar la situación. Este proyecto gira en torno a este tema tan actual, ya que persigue alcanzar una eficiente clasificación de imágenes según se lleve mascarilla o no, así como diferenciando también si se lleva de forma incorrecta. Para desarrollarlo, se han empleado redes neuronales convolucionales basadas en Deep Learning, algunos populares paquetes básicos de aprendizaje automático como es Keras o TensorFlow y el lenguaje de programación Python 3.6. Los resultados obtenidos en este experimento, usando las herramientas presentadas y trabajando para lograr un ajuste de parámetros que optimice el resultado, terminan con una precisión del algoritmo máxima de un 95.31 % para el diseño final seleccionado.The global pandemic caused by Covid-19 has caused a before and after in our lives, so much that now wearing a mask in order to stop its contagion is essential and unthinkable in certain occasions. As a result of the desperation caused by this virus, there has been an increased interest in scientific methods that could help stabilize and control the situation. This project revolves around this very current topic, since it seeks to achieve an efficient images classification depending on whether a mask is worn or not, as well as differentiating whether it is worn incorrectly. To develop it, convolutional neural networks based on Deep Learning, some popular basic machine learning packages such as Keras or TensorFlow and the Python 3.6 programming language have been used. The results obtained in this experiment, using the tools presented and working to achieve a parameter adjustment that optimizes the result, ends with a maximum algorithm precision of 95.31%.Universidad de Sevilla. Grado en Ingeniería de las Tecnologías de Telecomunicació