485 research outputs found
TANGO: Transparent heterogeneous hardware Architecture deployment for eNergy Gain in Operation
The paper is concerned with the issue of how software systems actually use
Heterogeneous Parallel Architectures (HPAs), with the goal of optimizing power
consumption on these resources. It argues the need for novel methods and tools
to support software developers aiming to optimise power consumption resulting
from designing, developing, deploying and running software on HPAs, while
maintaining other quality aspects of software to adequate and agreed levels. To
do so, a reference architecture to support energy efficiency at application
construction, deployment, and operation is discussed, as well as its
implementation and evaluation plans.Comment: Part of the Program Transformation for Programmability in
Heterogeneous Architectures (PROHA) workshop, Barcelona, Spain, 12th March
2016, 7 pages, LaTeX, 3 PNG figure
Research Naval Postgraduate School, v. 2, no. 10, September 2010
NPS Research is published by the Research and Sponsored Programs, Office of the Vice President and Dean of Research, in accordance with NAVSOP-35. Views and opinions expressed are not necessarily those of the Department of the Navy.Approved for public release; distribution is unlimited
Implementation of second-life batteries as energy storage systems enhancing the interoperability and flexibility of the energy infrastructure in tertiary buildings
The main focus of this project is to evaluate the implementation of second-life batteries for a building stock enabling the energy flexibility schemes like Demand Response (DR). This project will focus particularly on how the building stock and its energy infrastructure (energy storage systems, legacy-assets, communication devices and grid architecture, among others) can participate as innovative energy solutions of the next generation of smart-grids, acting as virtual power plants (VPP) in order to deploy the distributed generation (DG) concept in the actual energy field and paving the way to unlock the demand response (DR) market in the distribution energy network. In addition, the implementation of these technologies will led to plan different business models and the scalability of them in the tertiary building sector. Battery energy storage systems (BESSs) are already being deployed for several stationary applications in a techno-economical feasible way. This project focuses in the study to obtain potential revenues from BESSs built from EVs lithium-ion batteries with varying states of health (SoH). For this analysis, a stationary BESS sizing model is done, using the parameters of a 14 kWh new battery, but also doing a comparison with parameters if the same battery would be 11.2 kWh second-life battery. The comprehensive sizing model consists of several detailed sub-models, considering battery specifications, aging and an operational strategy plan, which allow a technical assessment through a determined time frame. Therefore, battery depreciation and energy losses are considered in this techno-economic analysis. Potential economical feasible applications of new and second-life batteries, such the integration of a Building Integrated Photovoltaics (BIPV), self-consumption schemes, feed-in-tariff schemes and frequency regulation as well as their combined operation are compared. The research includes different electricity price scenarios mostly from the current Spanish energy market. The operation and integration of ICT-IoT technology upgrades is found to have the highest economic viability for this specific case study. A detailed study for this project will enhance the relevant importance of these topics in the energy field and how it will be a disruptive solution for the initial problem statement. A general context is given in order to introduce the main and specific objectives thus to trace an adequate way to follow and achieve them. The development of this master thesis will be coupled with the Demand Response Integration technologies (DRIvE) [10] H2020 EU funded project, currently on-going, considering some of the energy consumption data and initial parameters from the selected case study at COMSA Corporación office building in Barcelona, Spain
Improving Activity Recognition Accuracy in Ambient-Assisted Living Systems by Automated Feature Engineering
Ambient-assisted living (AAL) is promising to become a supplement of the current care
models, providing enhanced living experience to people within context-aware homes and smart environments.
Activity recognition based on sensory data in AAL systems is an important task because 1) it can be
used for estimation of levels of physical activity, 2) it can lead to detecting changes of daily patterns that may
indicate an emerging medical condition, or 3) it can be used for detection of accidents and emergencies. To be
accepted, AAL systems must be affordable while providing reliable performance. These two factors hugely
depend on optimizing the number of utilized sensors and extracting robust features from them. This paper
proposes a generic feature engineering method for selecting robust features from a variety of sensors, which
can be used for generating reliable classi cation models. From the originally recorded time series and some
newly generated time series [i.e., magnitudes, rst derivatives, delta series, and fast Fourier transformation
(FFT)-based series], a variety of time and frequency domain features are extracted. Then, using two-phase
feature selection, the number of generated features is greatly reduced. Finally, different classi cation models
are trained and evaluated on an independent test set. The proposed method was evaluated on ve publicly
available data sets, and on all of them, it yielded better accuracy than when using hand-tailored features.
The bene ts of the proposed systematic feature engineering method are quickly discovering good feature
sets for any given task than manually nding ones suitable for a particular task, selecting a small feature set
that outperforms manually determined features in both execution time and accuracy, and identi cation of
relevant sensor types and body locations automatically. Ultimately, the proposed method could reduce the
cost of AAL systems by facilitating execution of algorithms on devices with limited resources and by using
as few sensors as possible.info:eu-repo/semantics/publishedVersio
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