278 research outputs found
Supplemental Material - Stop the Bleed: A Prospective Evaluation and Comparison of Tourniquet Application in Security Personnel Versus Civilian Population
No full textSupplemental Material for Stop the Bleed: A Prospective Evaluation and Comparison of Tourniquet Application in Security Personnel Versus Civilian Population by Patrizio Petrone, Gerard Baltazar, Ricardo A. Jacquez, Meredith Akerman, Collin E. M. Brathwaite, and D’Andrea K. Joseph in The American Surgeon</p
Dual-Kalman-Filter-Based Identification and Real-Time Optimization of PV Systems
No full textIn this paper, the use of the Dual Kalman Filter for the identification of photovoltaic system parameters is presented. The system includes the photovoltaic source, the dc/dc converter and the battery/dc bus and both its states and parameters in the actual operating conditions are identified. In particular, the proposed approach gives the confidence interval for the system settling time, which is used for the real-time optimization of the perturbative maximum power point tracking algorithm. The proposed technique is implemented by using a Field-Programmable Gate Array and it is validated by means of both simulation and experimental results
Supplemental Material - The First Coronavirus Disease 2019 Pandemic Wave and the Effect on Healthcare Trainees: A National Survey Study
No full textSupplemental Material for The First Coronavirus Disease 2019 Pandemic Wave and the Effect on Healthcare Trainees: A National Survey Study by Helen H. Liu, Patrizio Petrone, Meredith Akerman, Raelina S. Howell, Andrew H. Morel, Amir H. Sohail, Cindy Alsamarraie, Barbara Brathwaite, Wendy Kinzler, James Maurer, and Collin E. M. Brathwaite in The American Surgeon</p
Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network
No full textIn this paper, an artificial neural network (ANN) is used for isolating faults and degradation phenomena occurring in photovoltaic (PV) panels. In the literature, it is well known that the values of the single diode model (SDM) associated to the PV source are strictly related to degradation phenomena and their variation is an indicator of panel degradation. On the other hand, the values of parameters that allow to identify the degraded conditions are not known a priori because they can be different from panel to panel and are strongly dependent on environmental conditions, PV technology and the manufacturing process. For these reasons, to correctly detect the presence of degradation, the effect of environmental conditions and fabrication processes must be properly filtered out. The approach proposed in this paper exploits the intrinsic capability of ANN to map in its architecture two effects: (1) the non-linear relations existing among the SDM parameters and the environmental conditions, and (2) the effect of the degradation phenomena on the I−V curves and, consequently, on the SDM parameters. The ANN architecture is composed of two stages that are trained separately: one for predicting the SDM parameters under the hypothesis of healthy operation and the other one for degraded condition. The variation of each parameter, calculated as the difference of the output of the two ANN stages, will give a direct identification of the type of degradation that is occurring on the PV panel. The method was initially tested by using the experimental I−V curves provided by the NREL database, where the degradation was introduced artificially, later tested by using some degraded experimental I−V curves.Photovoltaic Materials and Device
Method to decimate the samples necessary to identify one characteristic curve of at least one power supply modul and computer program for associated distributor
No full textMethod to evaluate the need to perform a reconfiguration step of two or more photovoltaic panels
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