Experiencias compartidas sobre detección de micotoxinas de Fusarium a las harinas de soja, trigo y otros cultivos

Experiencias compartidas sobre detección de micotoxinas de Fusarium a las harinas de soja, trigo y otros cultivosFil: Peruzzo, Alejandra. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias; Argentin

Delivery status of the ELI-NP gamma beam system

International audienceThe ELI-NP GBS is a high intensity and monochromatic gamma source under construction in Magurele (Romania). The design and construction of the Gamma Beam System complex as well as the integration of the technical plants and the commissioning of the overall facility, was awarded to the Eurogammas Consortium in March 2014. The delivery of the facility has been planned in for 4 stages and the first one was fulfilled in October 31st 2015. The engineering aspects related to the delivery stage 1 are presented

Erratum to: EuPRAXIA Conceptual Design Report – Eur. Phys. J. Special Topics 229, 3675-4284 (2020), https://doi.org/10.1140/epjst/e2020-000127-8

International audienceThe online version of the original article can be found at http://https://doi.org/10.1140/epjst/e2020-000127-8</A

Experiencias compartidas sobre detección de micotoxinas de Fusarium a las harinas de soja, trigo y otros cultivos

Experiencias compartidas sobre detección de micotoxinas de Fusarium a las harinas de soja, trigo y otros cultivosFil: Peruzzo, Alejandra. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias; Argentin

Switched adaptation strategies for integral sliding mode control: Theory and application

Integral sliding mode (SM) control is an interesting approach, as it can maintain the good chattering alleviation property of higher-order SMs while making the reaching phase less critical and keeping the controlled system trajectory on a suitably selected sliding manifold since the initial time instant. In order to make such a method more robust and to improve its flexibility by the adaptation of its parameters to the current system condition, in this paper, a switched strategy is proposed. Specifically, the suboptimal Second-order SM algorithm is considered as a basis in its integral formulation, and the switching strategy is designed by partitioning the so-called auxiliary system state space in a finite number of regions. The proposed method allows one to improve the transient performance by adapting the gains through these regions, thus implying an energy saving capability. The proposal is theoretically analyzed and, in order to test its performance, the control of the lateral dynamics of ground vehicles is used as a case study. Specifically, yaw-rate tracking is considered, as it is made difficult by parametric uncertainties and nonlinear effects that arise especially with large steering angles. Extensive simulation tests are carried out using standard validation maneuvers, which favorably witness the performance of the new control algorithm

Aggregation of nonlinearly enhanced experts with application to electricity load forecasting

Combining the predictions of different base experts is a well known approach used to improve the accuracy of time series forecasts. Forecast aggregation is becoming crucial in many fields, including electricity forecasting, as Internet of Things and Cloud technology give access to larger numbers of sensor data, time series and predictions from external providers. In this context, it is not uncommon that the failure of some experts causes relevant drops in the performances of the aggregated forecast when classical techniques based on linear averaging are applied. This might be a symptom of suboptimality of the individual experts, that do not fully exploit important predictors, e.g. calendar features that play a major role in the electrical demand profiles. In this work, we therefore present two non-linear strategies to obtain aggregated forecasts, starting from the availability of a set of base experts and the knowledge of some relevant predictor variables. The first approach, called aggregation of enhanced experts (AEE), enhances each individual expert and then feeds the enhanced forecasts into classical linear aggregation techniques. In the second approach, called enhanced aggregation of experts (EAE), the expert forecasts are nonlinearly combined with the predictor variables through an Artificial Neural Network (ANN). The case of missing expert forecasts is also considered via a statistically-based imputation method. The short-term prediction of German electrical load is used as a case study. Twelve base experts are enhanced with respect to calendar features, i.e. daytime and weekday. Compared to state-of-the-art aggregation methods applied to the not-enhanced set of experts, the proposed approaches not only improve the accuracy of aggregated forecast (up to 25% reduction of MAPE and RMSE), but are also robust with respect to missing experts