This paper describes the creation of the tool to approach to models of dispersion of pollutants, framed under a methodology of software development, which identified the sequence to follow in the life cycle extension development, through an incremental model in which the stages of the project were identified. At each stage a series of activities that helped define inputs and outputs in each was made. According the above in the first stage the functional requirements defined and nonfunctional, then in stage two architecture and graphical interface, followed by the coding stage extension and finally the stage of performance testing and user, in order to improve or correct the functionality of the extension.
Com a evolução constante da eletrônica, a necessidade de produzir protótipos em placa de circuito impresso é cada vez mais
cobrada e importante para se elaborar tecnologia de forma rápida, mas sem deixar a qualidade de produção baixa. Pensando
nisso, este artigo propõe o desenvolvimento de uma fresadora CNC com base no comando numérico computadorizado para a
confecção de trilhas na placa de circuito impresso de forma otimizada. Deste modo, são mostrados passo a passo os pilares
teóricos que compõem a base de conhecimento para que se possa entender e desenvolver a ferramenta que irá usinar e por
sua vez produzir o protótipo de forma eficaz. Os resultados obtidos em relação à montagem da ferramenta e o material
usinado foi classificado com satisfatório, já que a máquina CNC conseguiu atingir seus objetivos, perfurando, cortando a
placa e isolando as trilhas formando assim o circuito.
José Gleury Galvino Pereira e Adriana Maria Rebouças do Nascimento
In the last few years the resolution of NLP tasks with architectures composed of neural models has taken vogue. There are many advantages to using these approaches especially because there is no need to do features engineering. In this paper, we make a survey of a Deep Learning architecture that propose a resolutive approach to some classical tasks of the NLP. The Deep Learning architecture is based on a cutting-edge model that exploits both word-level and character-level representations through the combination of bidirectional LSTM, CNN and CRF. This architecture has provided cutting-edge performance in several sequential labeling activities for the English language. The architecture that will be treated uses the same approach for the Italian language. The same guideline is extended to perform a multi-task learning involving PoS labeling and sentiment analysis. The results show that the system performs well and achieves good results in all activities. In some cases it exceeds the best systems previously developed for Italian.
This paper implements Simultaneous Localization and Mapping (SLAM) technique to construct a map of a given environment. A Real Time Appearance Based Mapping (RTAB-Map) approach was taken for accomplishing this task. Initially, a 2d occupancy grid and 3d octomap was created from a provided simulated environment. Next, a personal simulated environment was created for mapping as well. In this appearance based method, a process called Loop Closure is used to determine whether a robot has seen a location before or not. In this paper, it is seen that RTAB-Map is optimized for large scale and long term SLAM by using multiple strategies to allow for loop closure to be done in real time and the results depict that it can be an excellent solution for SLAM to develop robots that can map an environment in both 2d and 3d.