S. Soutullo, E. Giancola, J.M. Franco, M. Botón, J.A. Ferrer, M.R. Heras
Volume 122, Pages 1-1152 (Septiembre 2017)
CISBAT 2017 International ConferenceFuture Buildings & Districts – Energy Efficiency from Nano to Urban Scale
A new simulation platform has been created to quantify the energy response of existing residential buildings with different refurbishment strategies. This tool has been developed as a multi-step form wizard to select the simulation options. The energy improvements have been calculated through the coupling between TRNSYS and GenOpt. Output information is: annual thermal loads, monthly loads reductions and cost estimations. Four refurbishment options have been evaluated considering different combined actions. The renovation of the building envelope is the most expensive action with the highest annual savings while the behavior of inhabitants is the cheapest option with the lowest annual savings.
J. M. Haut, M. E. Paoletti, A. Paz-Gallardo, J. Plaza, A. Plaza
Proceedings of the 17th International Conference on Computational and Mathematical Methods in Science and Engineering, CMMSE
Volume 3. Págs 1063-2321
17th International Conference on Computational and Mathematical Methods in Science and Engineering (CMMSE'17), July 4-8, 2017
Classification of remotely sensed hyperspectral images is a challenging task due the enormous amount of information comprised in these images, that contain hundreds of continuous spectral bands. This creates a need to develop new techniques for hyperspectral classification using high performance computing architectures. Despite the availability of multiple algorithms adapted to parallel environments (such as multicore computers or accelerators like field programmable gate arrays or graphics processing units, the application of cloud computing techniques has not been as widespread, although there are many potential advantages in exploiting cloud computing architectures for distributed hyperspectral image analysis. In this paper, we present a cloud implementation (developed using Apache Spark) of a successful technique for hyperspectral image classification: the multinomial logistic regression probabilistic classifier. Our experimental results suggest that cloud computing architectures allow for the efficient classification of large hyperspectral image data sets.