São Paulo Federal Institute of Education, Science and Technology

About: São Paulo Federal Institute of Education, Science and Technology is a education organization based out in São Paulo, Brazil. It is known for research contribution in the topics: Context (language use) & Computer science. The organization has 1707 authors who have published 2374 publications receiving 11333 citations.

Comparative analyzes of technological tools between industry 4.0 and smart cities approaches: the new society ecosystem

Abstract: Today the growth of modern cities is unprecedented in the history of urbanization and the urban environmental problems have also been increased. Unfortunately, there is no much time to modify past failures and improve the status quo, and ensure the protection of the environment. Consequently, it’s important to pay attention to the development of sustainable urban planning and its role in urban management issues is an objective that requires a new approach.On the other hand, Industry 4.0 (I.4.0), as called the 4th Industrial Revolution, carries impacts in the production on companies, the economy and society, with disruptive character, creating new markets and destabilizing the traditional way of doing business. Once I.4.0 is a strategic approach to the integration of advanced control systems with internet technology, enabling communication between people, products and complex systems, it’s expected to follow the same in the Smart Cities development.This article aims to relate technological tools of I.4.0 and the dimensions of “Smart Cities”, based on analytical framework for better understanding the emergence of new society ecosystem focused on the redefinition of the cities’ concept, urbanism and way of life, motivated by this new reconfiguration.

Statistical properties of intermittent bursts in the Texas Helimak

Abstract: We present investigations of intermittent turbulence in the Texas Helimak, a simple toroidal plasma device in which the turbulence properties are modified by applying a bias voltage. The analyzed turbulence presents high density bursts, detected by Langmuir probes measuring ion saturation current fluctuations. The turbulent time series is reproduced by a synthetic signal model that has bursts with the same temporal profile and random amplitudes, plus a low amplitude fluctuating stochastic background. Using this model, we identify two burst regimes, observed according to the radial position and external bias: in the first regime, the bursts occur in random instants, leading to a Poisson distribution of the time interval between bursts, while in the second regime, the time interval between large bursts is correlated and modeled by a Gamma distribution. Furthermore, we use the shape parameter k that measures the correlation between occurrence times of successive bursts to characterize the burst regime in most of the low field side. We find that in the region described by the second regime, the k values increase with positive applied bias.

Deep Reinforcement Learning for a Humanoid Robot Soccer Player

Abstract: This paper investigates the use of Deep Reinforcement Learning (DRL) applied to the humanoid robot soccer environment, where a robot must learn from basic to complex skills while it interacts with the environment through images received by its own camera. To do so, the Dueling Double DQN algorithm is used: it receives the images from the robot’s camera and decides on which discrete action should be performed, such as walk forward, turn to the left or kick the ball. The first experiments were performed in a robotic simulator in which the robot could learn, with DRL, three different tasks: to walk towards the ball, to act like a penalty taker and to act like a goalkeeper. In the second experiment, the learning obtained in the task to walk towards the ball was transferred to a real humanoid robot and a similar behavior could be observed, even though the environment was not exactly the same when the domain was changed. Results showed that it is possible to use DRL to learn tasks related to the role of a humanoid robot-soccer player, such as goalkeeper and penalty taker.

Using OGC Services to Interoperate Spatial Data Stored in SQL and NoSQL Databases

Abstract: Spatially-enabled social networks like Twitter and Foursquare have produced huge volumes of geo-referenced information which has been in general stored in NoSQL databases. The need to bring together the entire spectrum of geo-referenced information takes us to the traditional problem of interoperability between SQL and NoSQL spatial databases. This paper proposes a solution for the integration of geographic data stored in both SQL and NoSQL databases using OGC WMS and WFS interoperability services. Experiments conducted on PostgreSQL-PostGIS and CouchDB-GeoCouch spatial databases have demonstrated that it is possible to submit queries using the same syntax for SQL and NoSQL spatial databases in a simple and transparent manner for the user's application.

RBV in a context of supply chain management

Abstract: The aim of this study was to evaluate the supply chain management (SCM), strategic management (SM), and resource-based view (RBV) as background to business performance (BP). The study, exploratory in nature, was divided into two phases, qualitative and quantitative. In the first phase, data from ten companies were collected, later processed by content analysis. In the second phase, there was a pre-test, with subsequent data collection from 125 national companies associated with technology, processed by structural equation modeling. The results indicated that SCM favors SM and that both SCM and SM benefit RBV, therefore increasing BP. However, SM does not influence BP positively. Thus, we concluded that, due to the competition in the supply chain, focal companies have adopted strategies to integrate and coordinate the processes of their sectors, such as production, purchase, sales, and logistics to meet the goals of business performance.