Deep-Learning-Based Adaptive Advertising with Augmented Reality

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.

Wind Booster Optimization for On-Site Energy Generation Using Vertical-Axis Wind Turbines

Large cities have a significant area of buildings with roofs that are not used most of the time. Vertical-axis wind turbines are suitable for this kind of on-site renewable energy generation. Since wind speeds are not high in these cities, a suitable solution to improve energy generation is to add a Wind Booster. This paper presents a methodology useful for selecting and optimizing the main components of a Wind Booster. As a case of study, we present this methodology in a Wind Booster for a Vertical Axis Wind Turbine (VAWT) that considers the wind flow’s specific behavior in a particular city. The final Wind Booster design is state of the art and makes use of Computational Fluid Dynamics (CFD) and Design of Experiments (DOE) techniques. We experimented with the conditions of Mexico City, obtaining a 35.23% increase in torque with the optimized Wind Booster configuration. The results obtained show the potential of this methodology to improve the performance of this kind of system. Moreover, since wind behavior is very different in each city, our proposal could be beneficial for researchers looking to implement the best possible wind turbine in their locality.

Career path level estimation and skill qualification feedback from textual descriptions

LinkedIn is a social medium oriented to professional career handling and networking. In it, users write a textual profile on their experience, and add skill labels in a free format. Users are able to apply for different jobs, but specific feedback on the appropriateness of their application according to their skills is not provided to them. In this work we particularly focus on applicants of the project management branch from information technologies—although the presented methodology could be extended to any area following the same mechanism. Using the information users provide in their profile, it is possible to establish the corresponding level in a predefined Project Manager career path (PM level). 1500+ experiences and skills from 300 profiles were manually tagged to train and test a model to automatically estimate the PM level. In this proposal we were able to perform such prediction with a precision of 98%. Additionally, the proposed model is able to provide feedback to users by offering a guideline of necessary skills to be learned to fulfill the current PM level, or those needed in order to upgrade to the following PM level. This is achieved through the clustering of skill qualification labels. Results of experiments with several clustering algorithms are provided as part of this work.

A Deep Neural Network Based Model for a Kind of Magnetorheological Dampers

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.