1,721,016 research outputs found
Cooperative neural system for particle classification in a cosmic ray space experiment
No full textA modular classification system based on neural networks, for the particle identification task in a physics experiment, is proposed. The system is oriented to prevent systematic classification errors that could occur analysing experimental data with different statistical feature distribution in respect of the expected ones. The system has been investigated in the context of the positron/proton classification problem in a cosmic ray space experiment, where the physics detectors are faced by the critical flight conditions. Finally, the experimental results shows as an adaptive training based on the real data can improve the classification model of the system
Parallel genetic algorithm for the design of neural networks: An application to the classification of remote sensed data
No full textInter-comparison of hydrological model simulations with time series of SAR-derived soil moisture maps
No full textA comparison between superficial soil moisture content, m(v), values predicted by the DREAM hydrologic model and those retrieved from time-series of ALOS/PALSAR and COSMO-SkyMed SAR data acquired in 2007 and 2010-2011 is presented. The area investigated is part of the Celone at Ponte Foggia-S. Severo river basin, which is a tributary of the Candelaro river, downstream of the S. Giusto Dam, in Puglia (Southern Italy). Results show a good agreement in terms of bias and rmse between the hydrologic modeled and SAR-retrieved m(v)-values, and open new opportunities for the use of SAR-derived m(v)-values to calibrate/validate hydrologic models in semi-arid areas
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A neural network for positron identification by a transition radiation detector
No full textA neural network algorithm has been applied in order to distinguish positrons from protons by a transition radiation detector (TRD). New variables are introduced, that simultaneously take into account spatial and energy TRD information. This method is found to be better than the one based on classical analysis: the results improve the detector performance in particle identification for efficiency higher than 90%. The high accuracy achieved with this method is used to identify positrons versus protons with 3 x 10(-3) contamination, as required by TRAMP-SI cosmic ray space experiment on the NASA Balloon-Borne Magnet Facility
On the use of temporal series of L- and X-band SAR data for soil moisture retrieval. Capitanata plain case study
No full textThis paper investigates the use of time series of ALOS/PALSAR-1 and COSMO-SkyMed data for the soil moisture retrieval (m(v)) by means of the SMOSAR algorithm. The application context is the exploitation of m(v) maps at a moderate spatial and temporal resolution for improving flood/drought monitoring at regional scale. The SAR data were acquired over the Capitanata plain in Southern Italy, over which ground campaigns were carried out in 2007, 2010 and 2011. The analysis shows that the m(v) retrieval accuracy is 5%-7% m(3)/m(3) at L- and X band, although the latter is restricted to a use over nearly bare soil only
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