1,354,976 research outputs found
A novel optimization approach to forest height reconstruction from multi-baseline data
No full textThe paper deals with the problem of reconstructing the
height of forests from polarimetric/multi-baseline SAR data. The
approach consists of optimizing an objective functional defined
as the distance between the measured data and the data predicted
by the model at the actual estimate of the unknowns.
We indicate the role of global optimization on the performance of
the forest height reconstruction algorithm. As global optimizer, a
multilevel single-linkage method, which incorporates a local
optimization into the global search, is exploited, thus offering
computational efficiency and reliability. The performance of the
method are illustrated against numerically simulated data
Legendre quadrature for the discretization of 1D radiating panels
No full textIn [A. Capozzoli, C. Curcio, A. Liseno, MMS, Pizzo Calabro, Italy, 2022], the problem of modeling a source/scatterer using an equivalent radiator has been addressed and an approach has been given and numerically assessed. Once dimensioned the radiating panel, a practical implementation can be provided by a non-uniform array. The element positions should be chosen so that the array is capable to approximate, with an adequate accuracy, the fields radiated by the equivalent radiator. Here, the array element positioning is performed by exploiting a quadrature rule which takes into account that the singular functions supported on the region of interest associated to the most significant singular values of the radiation operator are related to those supported on the equivalent panel by a radiation integral. The quadrature rule enables also to choose a set of weights which are essential in the definition of the element excitation coefficients from the knowledge of the source distribution on the equivalent panel. For simplicity, a one-dimensional problem with a Legendre quadrature rule is considered. The approach is numerically assessed by checking the capability of the array to radiate, with a satisfactory degree of accuracy, the singular functions associated to the region of interest
New technique for wave-front reconstruction in optical telescopes
No full textA new approach to wave-front sensing for adaptive optics systems is presented. The method, which explicitly accounts for measurement errors, is based on a phase-retrieval algorithm whose performance with respect to trapping and stagnation problems is improved by use of a suitable mathematical formulation. The algorithm allows the determination of the expansion coefficients of the wave front in terms of Zernike polynomials. A numerical analysis shows the effectiveness of the algorithm even when the maximum optical path disturbance value is greater than two wavelengths and the measurement error is quite significant
Fault detection analysis using data mining techniques for a cluster of smart office buildings
No full textThere is an increasing need for automated fault detection tools in buildings. The total energy request in buildings can be significantly reduced by detecting abnormal consumption effectively. Numerous models are used to tackle this problem but either they are very complex and mostly applicable to components level, or they cannot be adopted for different buildings and equipment. In this study a simplified approach to automatically detect anomalies in building energy consumption based on actual recorded data of active electrical power for lighting and total active electrical power of a cluster of eight buildings is presented. The proposed methodology uses statistical pattern recognition techniques and artificial neural ensembling networks coupled with outliers detection methods for fault detection. The results show the usefulness of this data analysis approach in automatic fault detection by reducing the number of false anomalies. The method allows to identify patterns of faults occurring in a cluster of bindings; in this way the energy consumption can be further optimized also through the building management staff by informing occupants of their energy usage and educating them to be proactive in their energy consumption. Finally, in the context of smart buildings, the common detected outliers in the cluster of buildings demonstrate that the management of a smart district can be operated with the whole buildings cluster approach
Rethinking Athens Before the Persian Wars. Proceedings of the International Workshop at the Ludwig-Maximilians-Universität München (Munich, 23rd–24th February 2017)
No full textIn recent years, scholarly interest in Ancient Athens has been enlivened by spectacular archaeological discoveries. The new finds from the pre-Classical city called for a synoptic reassessment of the material remains, their interpretation and the previous methodological approaches, since the dense records of later historical phases had shaped the perception of Athens before the Persian Wars. Under theses premises, the International Workshop held at the Ludwig Maximilians-Universität München in February 2017 invited its participants to rethink early Athens. The papers assembled in this volume aim to question traditional perspectives and offer a multidisciplinary framework for the discussion of archaeological, literary and epigraphical testimonia
A hybrid evolutionary algorithm in the diagnosis of reflector distorsions from far-field amplitude pattern
No full textA new regularizing strategy in the image restoration and wave-front sensing by phase diversity
No full textSpecial Issue on Signal Recovery and Synthesis,July 199
Building energy consumption modeling with Neural Ensembling Approaches for fault detection analysis
No full textIn the paper a fault detection analysis through neural ensembling approaches is presented. Experimentation was carried out over two months monitoring data sets for the lighting energy consumption of an actual office building located at ENEA ‘Casaccia' Research Centre. Using a fault free data set for the training, the Artificial Neural Networks Ensembling (ANNE) were used for the estimation of hourly lighting energy consumption in normal operational conditions. The fault detection was performed through the analysis of the magnitude of residuals using a peak detection method. Moreover the peak detection method was applied directly to the testing data set. Finally a majority voting method to ensemble the results of different ANN classifiers was performed. Experimental results show the effectiveness of ensembling approaches in automatic detection of abnormal building lighting energy consumptio
A new regularizing strategy in the image restoration and wave-front sensing by phase diversity
No full textSpecial Issue on Signal Recovery and Synthesis,July 199
Field sampling of incoherent sources
No full textThe problem of characterizing random sources from near-field measurements performed on a domain DI and of devising the random field sampling procedure is tackled by a stochastic approach. The presented technique is an extension of that introduced in [A. Capozzoli, et al., Field sampling and field reconstruction: a new perspective, Radio Sci., vol. 45, 2010] and successfully adopted to experimentally characterize deterministic (CW) radiators and fields. Under the assumption that the source is wide sense stationary, quasi-monochromatic and incoherent, its intensity is reconstructed by time-domain field measurements aimed at extracting information from the mutual coherence of the field on DI. The linear relation between the field coherence on DI and the source intensity is inverted by using the Singular Value Decomposition (SVD) approach, properly representing the source intensity distribution by exploiting the a priori information (e.g., its size and shape) on the radiator. The sampling of the radiated random field is devised by a singular value optimization procedure of the relevant linear operator. Numerical results sketch the performance of the approach
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