Browsing by Keyword "SDG 15 - Life on Land"
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Item On the Applicability of Ant Colony Optimization to Non-Intrusive Load Monitoring in Smart Grids(Springer Berlin Heidelberg, 2015-11) Gonzalez-Pardo, Antonio; Del Ser, Javier; Camacho, David; Puerta, José M.; Gámez, José A.; Dorronsoro, Bernabé; Baruque, Bruno; Troncoso, Alicia; Barrenechea, Edurne; Galar, Mikel; IAAlong with the proliferation of the Smart Grid, power load disaggregation is a research area that is lately gaining a lot of popularity due to the interest of energy distribution companies and customers in identifying consumption patterns towards improving the way the energy is produced and consumed (via e.g. demand side management strategies). Such data can be extracted by using smartmeters, but the expensive cost of incorporating a monitoring device for each appliance jeopardizes significantly the massive implementation of any straightforward approach. When resorting to a single meter to monitor the global consumption of the house at hand, the identification of the different appliances giving rise to the recorded consumption profile renders a particular instance of the so-called source separation problem, for which a number of algorithmic proposals have been reported in the literature. This paper gravitates on the applicability of the Ant Colony Optimization (ACO) algorithm to perform this power disaggregation treating the problem as a Constraint Satisfaction Problem (CSP). The discussed experimental results utilize data contained in the REDD dataset, which corresponds to real power consumption traces of different households. Although the experiments carried out in this work reveal that the ACO solver can be successfully applied to the Non-Intrusive Load Monitoring problems, further work is needed towards assessing its performance when tackling more diversea ppliance models and noisy power load traces.Item Private/Public Funding Strategies for Interactive Robotics Companies(Springer Science and Business Media Deutschland GmbH, 2022-07-02) Rentería-Bilbao, Arantxa; Robótica Médica; Tecnalia Research & InnovationThe aim of is paper is to provide a structured view of funding possibilities for companies in the field of interactive robotics. The paper presents a set of funding sources which are of interests for start-ups, spin-off and young companies working in the design and development of interactive robots, including wearable devices. This paper is divided into three chapters, covering private funding (types of existing funding sources, their specific target companies and requirements, and the reasons on how and why investors invest), public funding (mainly coming from European grants under several programs) and the conclusions.Item Quantile regression forests-based modeling and environmental indicators for decision support in broiler farming(2019-06) Diez-Olivan, Alberto; Averós, Xavier; Sanz, Ricardo; Sierra, Basilio; Estevez, Inma; Tecnalia Research & InnovationAn efficient and sustainable animal production requires fine-tuning and control of all the parameters involved. But this is not a simple task. Animal farming is a complex biological system in which environmental parameters and management practices interact in a dynamic way. In addition, the typical non-linear response of biological processes implies that relationships across parameters that are critical to assure animal welfare and performance are difficult to determine. In this paper a novel decision support system based on environmental indicators and on weights, leg problems and mortality rates is proposed to address this issue. The data-driven modeling process is performed by a quantile regression forests approach that allows estimating growth, welfare and mortality parameters on the basis of environmental deviations from optimal farm conditions. Resulting models also provide confidence intervals able to deal with uncertainty. They are deployed in farm, offering an accessible tool for farmers, veterinarians and technical personnel. Experimental results involving 20 flocks of broiler meat chickens from different farms show the validity of the system, obtaining robust prediction intervals and high accuracy, namely over 81% for every model. The in-field use of the proposed approach will facilitate an efficient and animal welfare-friendly production management.