Resultados totales (Incluyendo duplicados): 2
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RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/191866
Dataset. 2022

GROW-GREEN CORE KPIS

GROW-GREEN PILOTS MONITORING

  • Orozco Messana, Javier|||0000-0001-8611-8816
  • Calabuig Moreno, Raimon|||0000-0003-0810-881X
  • Vallés Planells, María Concepción|||0000-0002-5932-0485
  • Galiana Galán, Francisco|||0000-0001-7897-6538
  • Tudorie, Carla Ana-Maria|||0000-0002-3060-6199
  • Alfonso Solar, David|||0000-0003-0141-075X
  • Peñalvo López, Elisa|||0000-0002-3143-822X
  • Andrés Doménech, Ignacio|||0000-0003-4237-4863
[EN] The H2020 project “Green Cities for Climate and Water Resilience, Sustainable Economic Growth, Healthy Citizens and Environments" (GROW GREEN, Grant Agreement: 730283), developed green infrastructure pilots in: Manchester, Valencia and Wroclaw. The monitoring framework supported the pilot analysis and its impact assessment through the development of core Key Performance Indicators (KPIs) through all pilots. The historical evolution of these core KPIs are available on the Grow-Green Open Data platform sharing the software architecture for the smart city platform of Valencia City. It is an implementation of Telefónica’s Thinking Cities platform, which is based on the FIWARE standards and interfaces. All monitoring data are included on this dataset grouped on the core KPIs structure., This research was co-funded by the European Commission through the H2020 project “Green Cities for Climate and Water Resilience, Sustainable Economic Growth, Healthy Citizens and Environments (GROW GREEN)” Grant Agreement: 730283.

DOI: Dataset/10251/191866" target="_blank">http://hdl.handle.net/10251/191866, https://dx.doi.org/10.4995/Dataset/10251/191866
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/191866
HANDLE: Dataset/10251/191866" target="_blank">http://hdl.handle.net/10251/191866, https://dx.doi.org/10.4995/Dataset/10251/191866
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/191866
PMID: Dataset/10251/191866" target="_blank">http://hdl.handle.net/10251/191866, https://dx.doi.org/10.4995/Dataset/10251/191866
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/191866
Ver en: Dataset/10251/191866" target="_blank">http://hdl.handle.net/10251/191866, https://dx.doi.org/10.4995/Dataset/10251/191866
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/191866

RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/202500
Dataset. 2024

IMAGE DATABASE FOR THE SCIENTIFIC PAPER: DEEP LEARNING ALGORITHM, BASED ON CONVOLUTIONAL NEURAL NETWORKS, FOR EQUIVALENT ELECTRICAL CIRCUIT RECOMMENDATION FOR ELECTROCHEMICAL IMPEDANCE SPECTROSCOPY

  • Pérez Herranz, Valentín|||0000-0002-4010-0888
  • Giner Sanz, Juan José|||0000-0003-0441-6102
  • Sáez Pardo, Fermín
The present dataset is the database and image database used to train and test the Convolutional Neural Network models of the scientific paper: Deep Learning algorithm, based on convolutional neural networks, for electrical equivalent electrical circuit recommendation for Electrochemical Impedance Spectroscopy

DOI: Dataset/10251/202500" target="_blank">http://hdl.handle.net/10251/202500, https://dx.doi.org/10.4995/Dataset/10251/202500
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/202500
HANDLE: Dataset/10251/202500" target="_blank">http://hdl.handle.net/10251/202500, https://dx.doi.org/10.4995/Dataset/10251/202500
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/202500
PMID: Dataset/10251/202500" target="_blank">http://hdl.handle.net/10251/202500, https://dx.doi.org/10.4995/Dataset/10251/202500
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/202500
Ver en: Dataset/10251/202500" target="_blank">http://hdl.handle.net/10251/202500, https://dx.doi.org/10.4995/Dataset/10251/202500
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/202500

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