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Subject: search.filters.subject.530Institute: search.filters.UBSInstitut.Fraunhofer Institut für Produktionstechnik und Automatisierung (IPA)

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    Effects of load forecast deviation on the specification of energy storage systems
    (2023) Emde, Alexander; Märkle, Lisa; Kratzer, Benedikt; Schnell, Felix; Baur, Lukas; Sauer, Alexander
    The liberalization of the German energy market has created opportunities for end-consumers, including industrial companies, to actively participate in the electricity market. By making their energy loads more flexible, consumers can generate additional income and thus save money. Energy storage systems can be utilized to achieve the required flexibility by temporarily storing excess electrical energy in the form of heat, cold, or electricity for later use. This publication focuses on how the dimensionality of energy storage is influenced by load forecasting. The results show that inaccuracies in load forecasting lead to a direct over-dimensioning and thus, a deterioration of the economics of energy storage technologies. Using two scenario cases, it shows on the one hand how important good forecasts are and on the other hand that buffers must be included in the conceptual design in order to be able to compensate for forecast errors.
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    The effect of rod orientation on electrical anisotropy in silver nanowire networks for ultra-transparent electrodes
    (2016) Ackermann, Thomas; Neuhaus, Raphael; Roth, Siegmar
    Two-dimensional networks made of metal nanowires are excellent paradigms for the experimental observation of electrical percolation caused by continuous jackstraw-like physical pathways. Such systems became very interesting as alternative material in transparent electrodes, which are fundamental components in display devices. This work presents the experimental characterization of low-haze and ultra-transparent electrodes based on silver nanowires. The films are created by dip-coating, a feasible and scalable liquid film coating technique. We have found dominant alignment of the silver nanowires in withdrawal direction. The impact of this structural anisotropy on electrical anisotropy becomes more pronounced for low area coverage. The rod alignment does not influence the technical usability of the films as significant electrical anisotropy occurs only at optical transmission higher than 99 %. For films with lower transmission, electrical anisotropy becomes negligible. In addition to the experimental work, we have carried out computational studies in order to explain our findings further and compare them to our experiments and previous literature. This paper presents the first experimental observation of electrical anisotropy in two-dimensional silver nanowire networks close at the percolation threshold.
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    Optimizing NV magnetometry for magnetoneurography and magnetomyography applications
    (2023) Zhang, Chen; Zhang, Jixing; Widmann, Matthias; Benke, Magnus; Kübler, Michael; Dasari, Durga; Klotz, Thomas; Gizzi, Leonardo; Röhrle, Oliver; Brenner, Philipp; Wrachtrup, Jörg
    Magnetometers based on color centers in diamond are setting new frontiers for sensing capabilities due to their combined extraordinary performances in sensitivity, bandwidth, dynamic range, and spatial resolution, with stable operability in a wide range of conditions ranging from room to low temperatures. This has allowed for its wide range of applications, from biology and chemical studies to industrial applications. Among the many, sensing of bio-magnetic fields from muscular and neurophysiology has been one of the most attractive applications for NV magnetometry due to its compact and proximal sensing capability. Although SQUID magnetometers and optically pumped magnetometers (OPM) have made huge progress in Magnetomyography (MMG) and Magnetoneurography (MNG), exploring the same with NV magnetometry is scant at best. Given the room temperature operability and gradiometric applications of the NV magnetometer, it could be highly sensitive in the pT/Hz-range even without magnetic shielding, bringing it close to industrial applications. The presented work here elaborates on the performance metrics of these magnetometers to the state-of-the-art techniques by analyzing the sensitivity, dynamic range, and bandwidth, and discusses the potential benefits of using NV magnetometers for MMG and MNG applications.
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    Radarbasierte Terrainerfassung zur vorausschauenden Steuerung aktiver Prothesen der unteren Extremitäten
    (Stuttgart : Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA, 2022) Kleiner, Bernhard; Verl, Alexander (Prof. Dr.-Ing.)
    In dieser Arbeit wird das Thema der Anpassung aktiver Prothesen der unteren Extremitäten bei verschiedenen Untergründen behandelt. Bisherige Systeme werden auf Basis von Bewegung und Bodenreaktionskraft gesteuert und sind deswegen nicht für eine terrainabhängige Steuerung von Gelenkwinkeln und -momenten vor dem Betreten des Untergrunds geeignet. Dies ist für einen Komfort- und Sicherheitsgewinn jedoch notwendig. Daher wird in der vorliegenden Arbeit ein mobiles, radarbasiertes Messsystem vorgestellt, dass das vorliegende Terrain während der Bewegung erfasst und das Potenzial für eine vorausschauende Steuerung unter realen Einsatzbedingungen bietet. Damit wird die bisher fehlende visuelle Rückkopplung an den Bewegungsapparat durch den Menschen in Teilen kompensiert. Teil der Entwicklung ist ein Verfahren zur zweidimensionalen Kartierung der Umgebungsstruktur. Mittels Inertialsensorik wird die Bewegung rekonstruiert und aus den synchron erfassten Radar-Entfernungsmessdaten wird ein 2D-Scan in der Sagittalebene errechnet. Es wurden Messergebnisse relevanter Terrain-Übergänge exemplarisch untersucht und Hypothesen für die Rekonstruktion der Umgebungsstruktur aufgestellt. Auf dieser Basis wurde ein Verfahren zur Vereinzelung von Umgebungsstrukturen im Radarscan entwickelt. Die Erfassung markanter Ortsmerkmale ermöglichen eine Dimensionierung von Stufen, Treppen und Rampen. Ergebnisse aus einer Probandenstudie sowie aus Labormessungen zeigen Potential und Ausblick des Ansatzes und führen zu grundlegenden Erkenntnissen zum Einsatz des entwickelten radarbasierten Messsystems.
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    Quantum kernel methods and applications to differential equations
    (2024) Flórez Ablan, Roberto
    Quantum computers have the potential to surpass classical computers in specific tasks, promising advantages in many fields. Machine Learning (ML), a domain with significant societal impact, is a key area of interest for exploring the applications of quantum computing. Here, we investigate two research directions aimed at understanding how current quantum computers can be used to solve ML problems. First, we study Quantum Kernels (QKs). By calculating inner products between quantum states, QKs can be used to define similarity measures between points. QKs are a promising approach to Quantum Machine Learning (QML) but, in general, they have not been shown to outperform classical ML methods. A key reason for this is that QKs suffer from the exponential concentration problem. As the number of qubits increases, the kernel matrices become similar to the identity matrix, preventing generalization. One strategy to alleviate the exponential concentration problem is to rescale the data points that enter the quantum model. This technique is known as bandwidth tuning and has been shown to allow generalization in QKs. However, it has been numerically demonstrated that using this method results in QKs that cannot provide a quantum advantage over classical methods. In this thesis, we propose an explanation for this phenomenon. We show that due to the size of the rescaling factors, the QKs become similar to polynomial and RBF kernels, which are classically tractable. Second, we implemented a Differential Equation (DE) solver based on variational quantum methods. A Quantum Neural Network (QNN) or QK, is used to represent an ansatz for the solution of a DE. The DE information is included into a loss function, which is minimized using a classical optimizer. In the case of a QK, the optimized parameters are the coefficients of a linear combination of QKs evaluated at the data points. In the case of a QNN, the optimized parameters are the phases of the quantum gates. The QNN implementation was included into the open-source QML python library sQUlearn. A preliminary hyperparameter study was conducted for QKs. Based on our limited investigation, we conclude that QKs leveraging the fidelity between quantum states, known as Fidelity Quantum Kernels (FQKs), demonstrate superior performance compared to those employing a semi-classical approach, referred to as Projected Quantum Kernels (PQKs).