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Die Erfindung betrifft eine elektrische Insektenabwehrvorrichtung (1) zur Abwehr kriechender Insekten (2) umfassend eine Spannungserzeugungseinrichtung (4a, 4b) zur Erzeugung einer Abwehrspannung sowie umfassend zumindest zwei nebeneinander angeordnete und beabstandet zueinander verlaufende blanke elektrische Leiter (5a, 5b, 5c, 5d), die derart mit der Spannungserzeugungseinrichtung (4a, 4b) verbunden sind, dass die Abwehrspannung zwischen ihnen liegt. Die elektrischen Leiter sind als flache Leiterbahnen (5a, 5b, 5c, 5d) auf oder in der Oberfläche eines gemeinsamen Trägers (12, 13, 50, 60) ausgebildet, so dass ein Insekt (2) bei einem Berühren der Leiterbahnen (5a, 5b, 5c, 5d) einen elektrischen Schlag erhält.
A new method for design space exploration and optimization, Surrogate-Assisted Illumination (SAIL), is presented. Inspired by robotics techniques designed to produce diverse repertoires of behaviors for use in damage recovery, SAIL produces diverse designs that vary according to features specified by the designer. By producing high-performing designs with varied combinations of user-defined features a map of the design space is created. This map illuminates the relationship between the chosen features and performance, and can aid designers in identifying promising design concepts. SAIL is designed for use with compu-tationally expensive design problems, such as fluid or structural dynamics, and integrates approximative models and intelligent sampling of the objective function to minimize the number of function evaluations required. On a 2D airfoil optimization problem SAIL is shown to produce hundreds of diverse designs which perform competitively with those found by state-of-the-art black box optimization. Its capabilities are further illustrated in a more expensive 3D aerodynamic optimization task.
The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user. This technique to 'illuminate' the problem space through the lens of chosen features has the potential to be a powerful tool for exploring design spaces, but is limited by the need for numerous evaluations. The Surrogate-Assisted Illumination (SAIL) algorithm, introduced here, integrates approximative models and intelligent sampling of the objective function to minimize the number of evaluations required by MAP-Elites.
The ability of SAIL to efficiently produce both accurate models and diverse high-performing solutions is illustrated on a 2D airfoil design problem. The search space is divided into bins, each holding a design with a different combination of features. In each bin SAIL produces a better performing solution than MAP-Elites, and requires several orders of magnitude fewer evaluations. The CMA-ES algorithm was used to produce an optimal design in each bin: with the same number of evaluations required by CMA-ES to find a near-optimal solution in a single bin, SAIL finds solutions of similar quality in every bin.
Neuroevolution methods evolve the weights of a neural network, and in some cases the topology, but little work has been done to analyze the effect of evolving the activation functions of individual nodes on network size, an important factor when training networks with a small number of samples. In this work we extend the neuroevolution algorithm NEAT to evolve the activation function of neurons in addition to the topology and weights of the network. The size and performance of networks produced using NEAT with uniform activation in all nodes, or homogenous networks, is compared to networks which contain a mixture of activation functions, or heterogenous networks. For a number of regression and classification benchmarks it is shown that, (1) qualitatively different activation functions lead to different results in homogeneous networks, (2) the heterogeneous version of NEAT is able to select well performing activation functions, (3) the produced heterogeneous networks are significantly smaller than homogeneous networks.
The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user. This technique has the potential to be a powerful tool for design space exploration, but is limited by the need for numerous evaluations. The Surrogate-Assisted Illumination algorithm (SAIL), introduced here, integrates approximative models and intelligent sampling of the objective function to minimize the number of evaluations required by MAP-Elites.
The ability of SAIL to efficiently produce both accurate models and diverse high performing solutions is illustrated on a 2D airfoil design problem. The search space is divided into bins, each holding a design with a different combination of features. In each bin SAIL produces a better performing solution than MAP-Elites, and requires several orders of magnitude fewer evaluations. The CMA-ES algorithm was used to produce an optimal design in each bin: with the same number of evaluations required by CMA-ES to find a near-optimal solution in a single bin, SAIL finds solutions of similar quality in every bin.
Evolutionary illumination is a recent technique that allows producing many diverse, optimal solutions in a map of manually defined features. To support the large amount of objective function evaluations, surrogate model assistance was recently introduced. Illumination models need to represent many more, diverse optimal regions than classical surrogate models. In this PhD thesis, we propose to decompose the sample set, decreasing model complexity, by hierarchically segmenting the training set according to their coordinates in feature space. An ensemble of diverse models can then be trained to serve as a surrogate to illumination.
Reliable and regional differentiated power forecasts are required to guarantee an efficient and economic energy transition towards renewable energies. Amongst other renewable energy technologies, e.g. wind mills, photovoltaic (PV) systems are an essential component of this transition being cost-efficient and simply to install. Reliable power forecasts are however required for a grid integration of photovoltaic systems, which among other data requires high-resolution spatio-temporal global irradiance data.
Solar energy is one option to serve the rising global energy demand with low environmental impact. Building an energy system with a considerable share of solar power requires long-term investment and a careful investigation of potential sites. Therefore, understanding the impacts from varying regionally and locally determined meteorological conditions on solar energy production will influence energy yield projections.
Impact of atmospheric aerosols on photovoltaic energy production - Scenario for the Sahel zone
(2017)
Photovoltaic (PV) energy is one option to serve the rising global energy need with low environmental impact. PV is of particular interest for local energy solutions in developing countries prone to high solar insolation. In order to assess the PV potential of prospective sites, combining knowledge of the atmospheric state modulating solar radiation and the PV performance is necessary. The present study discusses the PV power as function of atmospheric aerosols in the Sahel zone for clear-sky-days. Daily yields for a polycrystalline silicon PV module are reduced by up to 48 % depending on the climatologically-relevant aerosol abundances.