Self-consistent kinetic theory with nonlinear wave-particle resonances

We have developed, based on the oscillating-center transformation, a general theoretical approach for self-consistent plasma dynamics including, explicitly, effects of nonlinear(higherorder) wave-particle resonances. A specific example is then given for low-frequency responses of trapped particles in axisymmetric tokamaks. Possible applications to transport as well as nonlinear wave growth/damping are also briefly discussed.     

Achievable working range for solid all-desiccant air-conditioning systems under specific space comfort requirements

Solid desiccant air-conditioning systems present a promising solution, in terms of performance level and environmental protection, pointing out their potential to be coupled with thermal solar or waste heat energy source. Nevertheless, these systems are characterized by constraints to the load they can satisfy, through the trade-off between the dehumidification cooling capacity and the latent load of the conditioned space. On that level, one has to note that, for steady environmental conditions, the conditioned space does not present unique value of load, but range of load, corresponding to an array of acceptable temperature and (usually relative) humidity values. In this work a methodology is proposed for the definition of the system's achievable working range under specific set of space (comfort) requirements. Through this approach, the systems present greater potential for covering the space requirements, thus presenting more possibilities on a design basis, and more flexible control strategies, as well. The proposed methodology is presented and discussed through the case study of a solar desiccant air-conditioning system coupled to a typical residential building.     

Integration of InGaP/GaAs/Ge triple‐junction solar cells on deeply patterned silicon substrates

We report preliminary results on InGaP/InGaAs/Ge photovoltaic cells for concentrated terrestrial applications, monolithically integrated on engineered Si(001) substrates. Cells deposited on planar Ge/Si(001) epilayers, grown by plasma‐enhanced chemical vapor deposition, provide good efficiency and spectral response, despite the small thickness of the Ge epilayers and a threading dislocation density as large as 10⁷/cm². The presence of microcracks generated by the thermal misfit is compensated by a dense collection grid that avoids insulated areas. In order to avoid the excessive shadowing introduced by the use of a dense grid, the crack density needs to be lowered. Here, we show that deep patterning of the Si substrate in blocks can be an option, provided that a continuous Ge layer is formed at the top, and it is suitably planarized before the metalorganic chemical vapor deposition. The crack density is effectively decreased, despite that the efficiency is also lowered with respect to unpatterned devices. The reasons of this efficiency reduction are discussed, and a strategy for improvement is proposed and explored. Full morphological analysis of the coalesced Ge blocks is reported, and the final devices are tested under concentrated AM1.5D spectrum. Copyright © 2016 John Wiley & Sons, Ltd.     

Detection,localisation and tracking of pallets using machine learning techniques and 2D range data

The problem of autonomous transportation in industrial scenarios is receiving a renewed interest due to the way it can revolutionise internal logistics, especially in unstructured environments. This paper presents a novel architecture allowing a robot to detect, localise, and track (possibly multiple) pallets using machine learning techniques based on an on-board 2D laser rangefinder only. The architecture is composed of two main components: the first stage is a pallet detector employing a Faster Region-Based Convolutional Neural Network (Faster R-CNN) detector cascaded with a CNN-based classifier; the second stage is a Kalman filter for localising and tracking detected pallets, which we also use to defer commitment to a pallet detected in the first stage until sufficient confidence has been acquired via a sequential data acquisition process. For fine-tuning the CNNs, the architecture has been systematically evaluated using a real-world dataset containing 340 labelled 2D scans, which have been made freely available in an online repository. Detection performance has been assessed on the basis of the average accuracy over k-fold cross-validation, and it scored 99.58% in our tests. Concerning pallet localisation and tracking, experiments have been performed in a scenario where the robot is approaching the pallet to fork. Although data have been originally acquired by considering only one pallet as per specification of the use case we consider, artificial data have been generated as well to mimic the presence of multiple pallets in the robot workspace. Our experimental results confirm that the system is capable of identifying, localising and tracking pallets with a high success rate while being robust to false positives.