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In order to reduce energy consumption of homes, it is important to make transparent which devices consume how much energy. However, power consumption is often only monitored aggregated at the house energy meter. Disaggregating this power consumption into the contributions of individual devices can be achieved using Machine Learning. Our work aims at making state of the art disaggregation algorithms accessibe for users of the open source home automation platform Home Assistant.
Development of an optimized LSO/LuYAP phoswich detector head for the Lausanne ClearPET demonstrator
(2006)
This paper describes the LSO/LuYAP phoswich detector head developed for the ClearPET small animal PET scanner demonstrator that is under construction in Lausanne within the Crystal Clear Collaboration. The detector head consists of a dual layer of 8×8 LSO and LuYAP crystal arrays coupled to a multi-anode photomultiplier tube (Hamamatsu R7600-M64). Equalistion of the LSO/LuYAP light collection is obtained through partial attenuation of the LSO scintillation light using a thin aluminum deposit of 20-35 nm on LSO and appropriate temperature regulation of the phoswich head between 30°C to 60°C. At 511keV, typical FWHM energy resolutions of the pixels of a phoswich head amounts to (28±2)% for LSO and (25±2)% for LuYAP. The LSO versus LuYAP crystal identification efficiency is better than 98%. Six detector modules have been mounted on a rotating gantry. Axial and tangential spatial resolutions were measured up to 4 cm from the scanner axis and compared to Monte Carlo simulations using GATE. FWHM spatial resolution ranges from 1.3 mm on axis to 2.6 mm at 4 cm from the axis.
In this paper, a coupled multiphase model considering both non-linearities of water retention curves and solid state modeling is proposed. The solid displacements and the pressures of both water and air phases are unknowns of the proposed model. The finite element method is used to solve the governing differential equations. The proposed method is demonstrated through simulation of seepage test and partially consolidation problem. Then, implementation of the model is done by using hypoplasticity for the solid phase and analyzing the fully saturated triaxial experiments. In integration of the constitutive law error controlling is improved and comparisons done accordingly. In this work, the advantages and limitations of the numerical model are discussed.
In this work, three patent pending calibration methods for heliostat fields of central receiver systems (CRS) developed by the Solar-Institut Jülich (SIJ) of the FH Aachen University of Applied Sciences are presented. The calibration methods can either operate in a combined mode or in stand-alone mode. The first calibration method, method A, foresees that a camera matrix is placed into the receiver plane where it is subjected to concentrated solar irradiance during a measurement process. The second calibration method, method B, uses an unmanned aerial vehicle (UAV) such as a quadrocopter to automatically fly into the reflected solar irradiance cross-section of one or more heliostats (two variants of method B were tested). The third calibration method, method C, foresees a stereo central camera or multiple stereo cameras installed e.g. on the solar tower whereby the orientations of the heliostats are calculated from the location detection of spherical red markers attached to the heliostats. The most accurate method is method A which has a mean accuracy of 0.17 mrad. The mean accuracy of method B variant 1 is 1.36 mrad and of variant 2 is 1.73 mrad. Method C has a mean accuracy of 15.07 mrad. For method B there is great potential regarding improving the measurement accuracy. For method C the collected data was not sufficient for determining whether or not there is potential for improving the accuracy.