pela ajuda com as medidas de espessura de filmes finos de polímeros (pmma). Ao Prof. Dr. Sérgio Carlos Zilio pela ajuda com as medidas de índice de refração. Ao Prof. Dr. Ben-Hur V. Borges e ao Dr. John Weiner pela ajuda e sugestões nas simulações de FDTD. Aos técnicos do grupo de semicondutores Haroldo Arakaki e Carlos A. de Souza pelo suporte instrumental, tecnológico e suas sugestões/auxilio fundamentais durante este projeto.

No abstract

As artificial neural networks (ANN), they are computational models inspired by the way the nervous system of living beings work, these models can be used for processing and classification of data and applications, such as series and function prediction. Thus, this work used a time-delayed neural network (TDNN) to predict the demand for active energy on the P4 bus in the city of Presidente Prudente

Hospitals use germicidal lamps at ultraviolet wavelengths (UV-C -254 nm) to sterilize equipment, water and the environment in operating rooms. The food and medicine industries use them to disinfect various types of products, containers and packaging. Thistechnology is currently being used to disinfect environments, medical and hospital equipment and protective equipment in common use in the face of the current COVID-19 pandemic. In view of the UV-C applications to perform sterilization of environments andobjects, this project involves the development of a prototype using a hardware electronic prototyping platform of a chamber with portable UV lamps for disinfecting objectives. This prototype consists of a hardware module and a software module. In the hardware module, the necessary components for the assembly of the physical system were evaluated, as well as its basic functions, such as the activation of lamps and safety devices, while in the software module, the timing system, the activation and shutdown control were developed. of the lamps, the user interface and security, the exposure to radiation through a magnetic reed switch sensor to check the state of the door.

The algorithm of artificial neural networks (RNA), are computational models that can perform generalization, inferences, identification, and classification of information and patterns. Thus, in this work, a study was developed through the creation of a neural network classifying patterns to identify and classify the types of short circuits that occur in the electrical distribution system. Thus, a multilayer perceptron neural network was developed in the Matlab software with 3 hidden layers, 25 neurons in each hidden layer, and a hyperbolic tangent activation function. The PMC was trained using simulated short-circuit data in the ATPDraw software and presented an efficiency of 94.7% in the identification of short circuits in the validation stage. The trained network was also able to evaluate short circuits on an IEEE 9-bar test bus demonstrating the potential to be applied as an additional measure of network information in integrated operation centers (IOC).