In this paper, a deep neural network based model for a set of small-scale magnetorheological dampers (MRD) is developed where relevant parameters that have a physical meaning are inputs to the model. An experimental platform and a 3D-printing rapid prototyping facility provided a set of different conditions including MRD filled with two different MR fluids, which were used to train a Deep Neural Network (DNN), which is the core of the proposed model. Testing results indicate the model could forecast the hysteretic response of magnetorheological dampers for different load conditions and various physical configurations.

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Traditionally, approaches based on neural networks to solve the problem of disambiguation of the meaning of words (WSD) use a set of classifiers at the end, which results in a specialization in a single set of words-those for which they were trained. This makes impossible to apply the learned models to words not previously seen in the training corpus. This paper seeks to address a generalization of the problem of WSD in order to solve it through deep neural networks without limiting the method to a fixed set of words, with a performance close to the state-of-the-art, and an acceptable computational cost. We explore different architectures based on multilayer perceptrons, recurrent cells (Long Short-Term Memory-LSTM and Gated Recurrent Units-GRU), and a classifier model. Different sources and dimensions of embeddings were tested as well. The main evaluation was performed on the Senseval 3 English Lexical Sample. To evaluate the application to an unseen set of words, learned models are evaluated in the completely unseen words of a different corpus (Senseval 2 English Lexical Sample), overcoming the random baseline.INDEX TERMS Word sense disambiguation, recurrent neural networks, LSTM, multilayer perceptron, senseval english lexical sample test.

Nowadays, more than half of the world’s population lives in urban areas, and this number continues increasing. Consequently, there are more and more scientific publications that analyze health problems of people associated with living in these highly urbanized locations. In particular, some of the recent work has focused on relating people’s health to the quality and quantity of urban green areas. In this context, and considering the huge amount of land area in large cities that must be supervised, our work seeks to develop a deep learning-based solution capable of determining the level of health of the land and to assess whether it is contaminated. The main purpose is to provide health institutions with software capable of creating updated maps that indicate where these phenomena are presented, as this information could be very useful to guide public health goals in large cities. Our software is released as open source code, and the data used for the experiments presented in this paper are also freely available.

In this work we describe a system composed of deep neural networks that analyzes characteristics of customers based on their face (age, gender, and personality), as well as the ambient temperature, with the purpose of generating a personalized signal to potential buyers who pass in front of a beverage establishment; faces are automatically detected, displaying a recommendation using deep learning methods. In order to present suitable digital posters for each person, several technologies were used: Augmented reality, estimation of age, gender, and estimation of personality through the Big Five test applied to an image. The accuracy of each one of these deep neural networks is measured separately to ensure an appropriate precision over 80%. The system has been implemented into a portable solution, and is able to generate a recommendation to one or more people at the same time.