El comportamiento geomecánico deformacional de los residuos urbanos : aplicación a un caso real

Las características de heterogeneidad de los residuos sólidos urbanos así como la degradación biológica de sus componentes orgánicos influyen en los aspectos geotécnicos de los vertederos. La magnitud y duración de los asientos son factores muy importantes en el estudio del comportamiento de vertederos. La velocidad de asiento disminuye con el tiempo pero se mantiene durante muchos años después de su clausura. Para reducir los asientos del relleno, uno de los métodos de tratamiento que se utiliza es la compactación dinámica de los residuos sólidos. En este trabajo se estudia la mejora, a través de la compactación dinámica por impacto tipo “Menard”, de un vertedero de residuos sólidos en Madrid y los asientos provocados por la aplicación de una sobrecarga. Se analiza el comportamiento de los residuos sólidos con tratamiento de mejora, así como la predicción de asientos a 10 años aplicando los modelos Sowers (1973), Yen & Scanlon (1975), Gandola et al. (1992) y Meruelo (1994). Heterogonous characteristics of solid urban residues as well as biological decomposition of its organic components affect the geotechnical aspects of the landfills. The magnitude and the duration of the landfill settlement are one of the significant factors in studying the behavior of landfills. Although the rate of the settlement decreases as the time passes, however it is still maintained during many years after its closure. One of the methods used to reduce the settlement waste is through the dynamic consolidation methods of the solid residues. In this work, by applying the “Menard” dynamic consolidation method, we are studying the improvement of solid residue landfill in Madrid and the settlements provoked by overloading. The behavior of the solid residues through the improvement treatments as well as 10 years ahead prediction are analyzed by applying the models by Sowers (1973), Yen & Scanlon (1975), Gandola et al. (1992) and Meruelo (1994).

Design and implementation of an adaptive dashboard and visualization interface for the dynamic optimization and control of Data Centers

Over the last few years, the Data Center market has increased exponentially and this tendency continues today. As a direct consequence of this trend, the industry is pushing the development and implementation of different new technologies that would improve the energy consumption efficiency of data centers. An adaptive dashboard would allow the user to monitor the most important parameters of a data center in real time. For that reason, monitoring companies work with IoT big data filtering tools and cloud computing systems to handle the amounts of data obtained from the sensors placed in a data center.Analyzing the market trends in this field we can affirm that the study of predictive algorithms has become an essential area for competitive IT companies. Complex algorithms are used to forecast risk situations based on historical data and warn the user in case of danger. Considering that several different users will interact with this dashboard from IT experts or maintenance staff to accounting managers, it is vital to personalize it automatically. Following that line of though, the dashboard should only show relevant metrics to the user in different formats like overlapped maps or representative graphs among others. These maps will show all the information needed in a visual and easy-to-evaluate way. To sum up, this dashboard will allow the user to visualize and control a wide range of variables. Monitoring essential factors such as average temperature, gradients or hotspots as well as energy and power consumption and savings by rack or building would allow the client to understand how his equipment is behaving, helping him to optimize the energy consumption and efficiency of the racks. It also would help him to prevent possible damages in the equipment with predictive high-tech algorithms.

Detecting, Localizing and Following Dynamic Objects with a Mini-UAV

This paper presents an approach for the detection, localization and following of dynamic terrestrial objects using a mini-UAV. The development is intended to be used for surveillance of large infrastructures. The detection algorithm is based on finding several pre-defined characteristics of the target, such as color, shape and size. The process used to localize the target, once it is detected, is based on an inversion of the Pinhole camera model. The task of following the Summit XL was designed to keep the target inside the field of view of the camera, and it was implemented in the form of a PID controller. The system has been tested both in simulation and with real robots, showing promising results.