Suchergebnisse

Success/failure of adaptive control algorithms – especially those designed using the Linear Quadratic Gaussian criterion – depends on the quality of the process data used for model identification. One of the most harmful types of process data corruptions are outliers, i.e. 'wrong data' lying far away from the range of real data. The presence of outliers in the data negatively affects an estimation of the dynamics of the system. This effect is magnified when the outliers are grouped into blocks. In this paper, we propose an algorithm for outlier detection and removal. It is based on modelling the corrupted data by a two-component probabilistic mixture. The first component of the mixture models uncorrupted process data, while the second models outliers. When the outlier component is detected to be active, a prediction from the uncorrupted data component is computed and used as a reconstruction of the observed data. The resulting reconstruction filter is compared to standard methods on simulated and real data. The filter exhibits excellent properties, especially in the case of blocks of outliers.

[EN] The objective of this study was to estimate the response to selection for total body fat content of rabbits measured by computer tomography (CT). A divergent selection experiment was performed using Pannon Ka rabbits, which were previously selected for number of kits born alive. The so-called zero generation consisted of 351 Pannon Ka rabbits, from which the index, total body fat volume (cm3) divided by the body weight (kg), was measured. Rabbits with low and high fat index values were selected to form the parent groups of the lean and fat lines, respectively. The lines consisted of 55-72 females and 35-47 males, depending on the line and generation. After three generations, the rabbits were evaluated by means of a single trait animal model. The fat index showed a moderate heritability estimate (0.28±0.03). The magnitude of the common litter effect was small (0.10±0.02). The breeding values averaged per generation provided slightly asymmetrical responses. Based on the results, the divergent selection was successful in confirming that CT is a very suitable method for performing selection for body composition traits. ; EFOP-3.6.3-VEKOP-16-2017-00008 project. The project is co-financed by the European Union and the European Social Fund and the János Bolyai Research Scholarship of the Hungarian Academy of Sciences (BO/00871/19) ; Kasza, R.; Matics, Z.; Gerencsér, Z.; Donkó, T.; Radnai, I.; Szendrő, Z.; Nagy, I. (2020). Divergent selection for fat index in Pannon Ka rabbits: genetic parameters, selection response. World Rabbit Science. 28(3):129-133. https://doi.org/10.4995/wrs.2020.12733 ; OJS ; 129 ; 133 ; 28 ; 3 ; Al-Saef A.M., Khalil M.H., Al-Dobaib S.N., Al-Homidan A.H., García M.L., Baselga M. 2008. Comparing Saudi synthetic lines of rabbits with the founder breeds for carcass, lean composition and meat quality traits. Livest. Res. Rural Dev., 20: 1-12. ; Donkó T., Czakó B., Kovács Gy., Petneházy Ö., Kasza R., Szendrő Zs., Garamvölgyi R., Matics Zs. 2016. Total body fat content determination by means of ...

Angelika Varga has been supported by a European Union Marie Curie Intra-European Fellowship (254661), a Hungarian Social Renewal Operation Program (TÁMOP 4.1.2.E-13/1/KONV-2013-0010) and the Hungarian Brain Research program (KTIA_NAP_13-2-2014-0005) of the Hungarian Government. Agnes Jenes has been supported by a BJA/RCoA Project Grant. This work has also been supported, in part, by the BIOSS-2 Grant, Project A6. T

Jose Vicente Torres-Pérez was supported by the Chelsea and Westminster Health Charity, London, UK. Péter Sántha and Gábor Jancsó have been supported by the research grant K-101873 of the Hungarian Scientific Research Fund (OTKA). Peter Szucs, Angelika Varga and Miklós Sivadó have been supported by the KTIA_NAP_13–2–2014–0005 grant of the Hungarian Government. Peter Szucs has also been supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences. Joao Sousa-Valente was supported by the Fundaçao para a Ciencia e a Tecnologia, Portugal. Anna Andreou has been supported by the Migraine Trust, UK.