Temperature coefficients of FLATCON® modules

A method for the derivation of the temperature coefficient of Fresnel lens based CPV modules is presented. The method is applied on FLATCON® modules. Their temperature coefficients are presented and discussed

Energy harvesting efficiency of III-V multi-junction concentrator solar cells under realistic spectral conditions

A theoretical analysis on the spectral sensitivity of the bandgap design of four practical III-V triple-junction solar cell structures is presented. The underlying solar cell model uses the detailed balance method. Six locations on Earth are investigated for which the monthly average of the measured aerosol optical depth and the precipitable water are used to calculate direct solar spectra with a discretisation of one spectrum per hour. The model is used to analyze the solar cell designs for the highest yearly energy production. Furthermore, the ideal bandgap combination for a maximal energy harvest is calculated for each location. It is shown that the metamorphic solar cell structure of Ga0.35In0.65P/Ga0.83In 0.17As/Ge with transparencies optimized for the standard AM1.5d reference spectrum leads to the highest energy harvesting efficiencies and shows the lowest spectral sensitivity. The standard lattice-matched structure of Ga0.50In0.50P/Ga0.99In0.10As/Ge shows the hi ghest spectral sensitivity with up to 10.8%rel difference in the yearly energy harvesting

A theoretical analysis on the energy production of III-V multi-junction solar cells under realistic spectral conditions

In this paper we present a methodology which uses the detailed balance method to determine the optimum bandgap combination of III-V triple-junction solar cells for the highest yearly energy production. As an example for the methodology we analyse two geographical locations on Earth with distinct spectral conditions. For these places the monthly average of the measured aerosol optical depth and the precipitable water are used to calculate direct solar spectra with a discretisation of one spectrum per hour. The model is used to analyse the spectral sensitivity of the bandgap design of four practical III-V triple-junction solar cell structures. In addition, the impact of the designated operating temperature is investigated. Furthermore, the ideal bandgap combination for a maximal energy harvest is calculated for each location. It is shown that structures optimized for the standard AM1.5d reference spectrum yield nearly optimal energy harvesting efficiencies at geographical locations with “red-rich” spectral conditions. However, the choice of the right bandgap combination is essential. By contrast, structures should be re-optimized for locations with a high share of blue light

Einsum networks: Fast and scalable learning of tractable probabilistic circuits

Probabilistic circuits (PCs) are a promising avenue for probabilistic modeling, as they permit a wide range of exact and efficient inference routines. Recent “deep-learning-style” implementations of PCs strive for a better scalability, but are still difficult to train on real-world data, due to their sparsely connected computational graphs. In this paper, we propose Einsum Networks (EiNets), a novel implementation design for PCs, improving prior art in several regards. At their core, EiNets combine a large number of arithmetic operations in a single monolithic einsum-operation, leading to speedups and memory savings of up to two orders of magnitude, in comparison to previous implementations. As an algorithmic contribution, we show that the implementation of Expectation-Maximization (EM) can be simplified for PCs, by leveraging automatic differentiation. Furthermore, we demonstrate that EiNets scale well to datasets which were previously out of reach, such as SVHN and CelebA, and that they can be used as faithful generative image models

Efficient and Robust Machine Learning for Real-World Systems

While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation and the vision of the Internet-of-Things fuel the interest in resource efficient approaches. These approaches require a carefully chosen trade-off between performance and resource consumption in terms of computation and energy. On top of this, it is crucial to treat uncertainty in a consistent manner in all but the simplest applications of machine learning systems. In particular, a desideratum for any real-world system is to be robust in the presence of outliers and corrupted data, as well as being `aware' of its limits, i.e.\ the system should maintain and provide an uncertainty estimate over its own predictions. These complex demands are among the major challenges in current machine learning research and key to ensure a smooth transition of machine learning technology into every day's applications. In this article, we provide an overview of the current state of the art of machine learning techniques facilitating these real-world requirements. First we provide a comprehensive review of resource-efficiency in deep neural networks with focus on techniques for model size reduction, compression and reduced precision. These techniques can be applied during training or as post-processing and are widely used to reduce both computational complexity and memory footprint. As most (practical) neural networks are limited in their ways to treat uncertainty, we contrast them with probabilistic graphical models, which readily serve these desiderata by means of probabilistic inference. In that way, we provide an extensive overview of the current state-of-the-art of robust and efficient machine learning for real-world systems

A Novel Way to Measure and Predict Development: A Heuristic Approach to Facilitate the Early Detection of Neurodevelopmental Disorders

Purpose of Review: Substantial research exists focusing on the various aspects and domains of early human development. However, there is a clear blind spot in early postnatal development when dealing with neurodevelopmental disorders, especially those that manifest themselves clinically only in late infancy or even in childhood. Recent Findings: This early developmental period may represent an important timeframe to study these disorders but has historically received far less research attention. We believe that only a comprehensive interdisciplinary approach will enable us to detect and delineate specific parameters for specific neurodevelopmental disorders at a very early age to improve early detection/diagnosis, enable prospective studies and eventually facilitate randomised trials of early intervention. Summary: In this article, we propose a dynamic framework for characterising neurofunctional biomarkers associated with specific disorders in the development of infants and children. We have named this automated detection ‘Fingerprint Model’, suggesting one possible approach to accurately and early identify neurodevelopmental disorders