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[popis] => Data acquisition in smart homes is common activity to
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control systems into one smart solution with information
downloaded from internet as weather forecast, daytime, position
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saving money for energy.
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temperature/humidity monitoring connected to thermostat or AC
control. Modern smart homes try to interconnect all standalone
control systems into one smart solution with information
downloaded from internet as weather forecast, daytime, position
of the sun on the sky, actual season, etc. We present a method,
how to detect window ventilation from measured data so the
control system can automatically turn-off heating which leads to
saving money for energy.
[klicova_slova] => smart homes; learning thermostats; data analysis
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[citace_text] => VĚCHET, S.; HRBÁČEK, J.; KREJSA, J. Environmental Data Analysis for Learning Behavioral Patterns in Smart Homes. In Proceedings of the 2016 17th International Conference on Mechatronics – Mechatronika (ME) 2016. 1. Prague: Czech Technical University in Prague, 2016. p. 386-391. ISBN: 978-80-01-05882-4.
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author="Stanislav {Věchet} and Jan {Hrbáček} and Jiří {Krejsa}",
title="Environmental Data Analysis for Learning Behavioral Patterns in Smart Homes",
booktitle="Proceedings of the
2016 17th International Conference on Mechatronics –
Mechatronika (ME) 2016",
year="2016",
series="1",
pages="386--391",
publisher="Czech Technical University in Prague",
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ensure a stable, natural and energy saving environment inside
the building. However, typical usage of acquired data is limited to
temperature/humidity monitoring connected to thermostat or AC
control. Modern smart homes try to interconnect all standalone
control systems into one smart solution with information
downloaded from internet as weather forecast, daytime, position
of the sun on the sky, actual season, etc. We present a method,
how to detect window ventilation from measured data so the
control system can automatically turn-off heating which leads to
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transported in air within ducts and its size is detected by means of
Acoustic Emissions (AE) produced by the impact of powder on the
inner surface of the duct. The estimation of powder size
distribution from AE signals is the task addressed in this work.
Computer simulations show how the proposed strategies achieve a
relevant improvement of regression performance with respect to
the standard approach, using ANN directly on the dataset related
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publisher="Czech Technical University in Prague, Faculty of Electrical Engineering",
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expert systems for regression purposes. Therefore the usage of
already available data, related to environments showing similar
characteristics, could represent an effective approach to find a
good balance between regression performance and the amount of
data to gather for training. In this paper, the authors propose two
alternative strategies for improving the regression performance by
using heterogeneous data, i.e. data coming from diverse
environments with respect to the one taken as reference for testing.
These strategies are based on a standard machine learning
algorithm, i.e. the Artificial Neural Network (ANN). The employed
data came from measurements in industrial plants for energy
production through the combustion of coal powder. The powder is
transported in air within ducts and its size is detected by means of
Acoustic Emissions (AE) produced by the impact of powder on the
inner surface of the duct. The estimation of powder size
distribution from AE signals is the task addressed in this work.
Computer simulations show how the proposed strategies achieve a
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the standard approach, using ANN directly on the dataset related
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[citace_rtf] =>
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title="Heuristic approaches to stochastic quadratic assignment problem: VO and MM cases",
booktitle="Proceedings of 22nd International Conference on Soft Computing MENDEL 2016",
year="2016",
journal="Mendel Journal series",
volume="2016",
number="2016",
pages="117--122",
publisher="Brno University of Technology",
address="Brno, Czech Republic",
isbn="978-80-214-5365-4",
issn="1803-3814"
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[duvernost_udaju_id] => S
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[citace_text] => MARADA, T. The 3D lasser range finder for autonomous mobile robots: Design and Construction. In Engineering Mechanics 2007. 2007. Praha: Institute of Thermomechanics, Academy of Sciences of the Czech Republic, v.v.i., Prague, 2007. p. 175-176. ISBN: 978-80-87012-06-2.
[citace_html] => MARADA, T. The 3D lasser range finder for autonomous mobile robots: Design and Construction. In Engineering Mechanics 2007. 2007. Praha: Institute of Thermomechanics, Academy of Sciences of the Czech Republic, v.v.i., Prague, 2007. p. 175-176. ISBN: 978-80-87012-06-2.
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author="Tomáš {Marada}",
title="The 3D lasser range finder for autonomous mobile robots: Design and Construction",
booktitle="Engineering Mechanics 2007",
year="2007",
series="2007",
number="1",
pages="175--176",
publisher="Institute of Thermomechanics, Academy of Sciences of the Czech Republic, v.v.i., Prague",
address="Praha",
isbn="978-80-87012-06-2"
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[nazev_en] => The 3D lasser range finder for autonomous mobile robots: Design and Construction
[popis_en] => The paper is focus on design of the 3D laser range finder designed for autonomous mobile systems. Next can be sensor module use for indoor and outdoor 3D sensing applications for example exploration, survey, mapping and surveillance at a cost-saving price. The 3D laser has been built on the base of a 2D range finder by the extension with standard servomotor.
[klicova_slova_en] => Autonomous robot; 3D lasser range finder; Mapping
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