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5th IEEE International Conference on Flexible and Printable Sensors and Systems, FLEPS 2023

作者： Petanil, Lisa Schweizer, Anja Pylatiuk, Christian Stiller, Peter Reichert, Klaus-Martin Gengenbach, Ulrich Ungerer, Martin Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9781665457330

Inkjet-printing of electrochemical sensors is a rapidly developing fabrication method. Careful selection of pre-processing methods, inkjet-printing parameters, and post-processing methods is crucial for the features of the final sensor. In particular the interaction of inks and substrate is essential for setting the pre- and post-processing conditions as well as the inkjet-printing parameters. Here, commercial gold, silver, and passivation inks are printed onto a Polyimide substrate and investigated with different processing parameters. A literature search has shown that there is a lack of systematic investigation of the processing conditions for this material combination. Optimal processing parameters are presented here for this combination of materials. With the identified processing parameters, we reproducibly achieve low resistivities for gold and silver nanoparticle inks. © 2023 IEEE.

关键词： Silver nanoparticles

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Experimental Analysis of Immersion & Invariance Adaptive Control for an Interleaved DC/DC Boost Converter with Unknown Load Type

Experimental Analysis of Immersion & Invariance Adaptive Con...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Wachter, Jan Gröll, Lutz Hagenmeyer, Veit Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Karlsruhe Germany

ISBN: (纸本)9798350396782

The retrofit of the energy system leads to new challenges for the control of power electronic devices. To account for parametric uncertainties of passive elements, or unknown ambient conditions in real-world applications, the use of proportional-integral (PI) controllers in cascaded schemes is common. Observer based adaptive control approaches can be leveraged to accommodate such uncertainties and at the same time provide parameter estimates and enable model based control approaches. From an application perspective, the question arises about the consequences of wrong modelling assumptions, for example regarding the often unknown load type. In this work an interleaved DC/DC boost converter is considered, that supplies a DC-bus to which generally unknown loads can be connected, such as a grid-tied three phase inverter. This paper illustrates the design of an observer based adaptive controller using the Immersion & Invariance framework with an energy shaping plus damping injection passivity based controller. A highly simplified, large signal plant model as well as the assumption of a purely resistive load are used for the design. The provided experimental results show that the adaptive controller is able to cope with violations of the modeling assumptions concerning the load type in a real-world application scenario. © 2023 IEEE.

关键词： adpative control interleaved dc/dc boost converter modelling assumption violation power electronics

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Flatness of Some DC Microgrid Topologies 8

Flatness of Some DC Microgrid Topologies

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8th IEEE Workshop on the Electronic Grid, eGRID 2023

作者： Kastner, Adam Groll, Lutz Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9798350327007

It is well-known that the state-space averaged models of buck and boost converters possess the property of differential flatness, which can prove beneficial to control design. This paper investigates the differential flatness of systems that consist of buck and boost converters interconnected at their outputs in three structures, which model DC microgrids with different properties. We present the mathematical form of potential flat outputs which depends on the number of converters of each type. Flatness-based feedforward control is demonstrated in simulations for all three types of interconnection. A numerical solution of the implicit flat parametrization is used where the explicit flat parametrization is not available. © 2023 IEEE.

关键词： Boost converter

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On Higher-Order Averaging and Flatness 9

On Higher-Order Averaging and Flatness

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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Kastner, Adam Gröll, Lutz Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9798350311402

Averaging methods are widely used for the control-oriented modeling of systems where high-frequency actuation is used to steer the average of the control variables, such as switching power converters or mechanical systems with vibrational control. Higher-order averaging provides approximations for the trajectories with improved accuracy compared to the commonly used first-order averaging method. Furthermore, differential flatness is a property of dynamical systems which allows parametrizing state and input trajectories from a flat output without integration. In this contribution we investigate the relation between higher-order averaging and flatness via examples of electrical and mechanical systems. © 2023 IEEE.

关键词： Dynamical systems

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Towards a Real-World Dispatchable Feeder 8

Towards a Real-World Dispatchable Feeder

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8th IEEE Workshop on the Electronic Grid, eGRID 2023

作者： Beichter, Sebastian Beichter, Maximilian Werling, Dorina Galenzowski, Johannes Weise, Victor Hildenbrand, Christoph Wiegel, Friedrich Mikut, Ralf Waczowicz, Simon Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany

ISBN: (纸本)9798350327007

With the integration of renewable energy sources, electric vehicles, and batteries at the low-voltage distribution grid level, residential buildings are increasingly evolving from consumers to prosumers. As the prosumers burden the distribution grid with their uncertain interactions, the need for solutions to counteract this uncertainty becomes more important. One such solution is schedule-based control strategies, such as the dispatchable feeder. Thereby, the dispatchable feeder uses intelligent managed flexible components such as battery energy storage system (BESS) and controllable loads to follow a committed schedule. While the concept of the dispatchable feeder is widely discussed in the literature, the practical challenges associated with its real-world implementation remain under-explored. Therefore, this paper identifies challenges associated with the real-world implementation of the dispatchable feeder in residential buildings. Further, it discusses important future research questions regarding the concept and its valuable integration into the real world. It also proposes an experimental research infrastructure for systematically investigating the raised questions using the real-world laboratory of the Energy Lab 2.0. © 2023 IEEE.

关键词： Renewable energy

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Transformer training strategies for forecasting multiple load time series

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Energy informatics 2023年第1期6卷 20页

作者： Hertel, Matthias Beichter, Maximilian Heidrich, Benedikt Neumann, Oliver Schäfer, Benjamin Mikut, Ralf Hagenmeyer, Veit Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany

In the smart grid of the future, accurate load forecasts on the level of individual clients can help to balance supply and demand locally and to prevent grid outages. While the number of monitored clients will increase with the ongoing smart meter rollout, the amount of data per client will always be limited. We evaluate whether a Transformer load forecasting model benefits from a transfer learning strategy, where a global univariate model is trained on the load time series from multiple clients. In experiments with two datasets containing load time series from several hundred clients, we find that the global training strategy is superior to the multivariate and local training strategies used in related work. On average, the global training strategy results in 21.8% and 12.8% lower forecasting errors than the two other strategies, measured across forecasting horizons from one day to one month into the future. A comparison to linear models, multi-layer perceptrons and LSTMs shows that Transformers are effective for load forecasting when they are trained with the global training strategy. © 2023, The Author(s).

关键词： Time series

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Uncertainty-aware particle segmentation for electron microscopy at varied length scales

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npj Computational Materials 2024年第1期10卷 1961-1969页

作者： Luca Rettenberger Nathan J.Szymanski Yan Zeng Jan Schuetzke Shilong Wang Gerbrand Ceder Markus Reischl Institute for Automation and Applied Informatics(IAI) Karlsruhe Institute of Technology(KIT)KarlsruheGermany Department of Materials Science&Engineering UC Berkeley BerkeleyCA 94720USA Materials Sciences Division Lawrence Berkeley National LaboratoryBerkeleyCA 94720USA

Electron microscopy is indispensable for examining the morphology and composition of solid materials at the sub-micron *** study the powder samples that are widely used in materials development,scanning electron microscopes(SEMs)are increasingly used at the laboratory scale to generate large datasets with hundreds of *** these images to identify distinct particles and determine their morphology requires careful analysis,and automating this process remains *** this work,we enhance the Mask R-CNN architecture to develop a method for automated segmentation of particles in SEM *** address several challenges inherent to measurements,such as image blur and particle ***,our method accounts for prediction uncertainty when such issues prevent accurate segmentation of a *** that disparate length scales are often present in large datasets,we use this framework to create two models that are separately trained to handle images obtained at low or high *** testing these models on a variety of inorganic samples,our approach to particle segmentation surpasses an established automated segmentation method and yields comparable results to the predictions of three domain experts,revealing comparable accuracy while requiring a fraction of the *** findings highlight the potential of deep learning in advancing autonomous workflows for materials characterization.

关键词： particle scales hundreds

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Unsupervised GAN epoch selection for biomedical data synthesis

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Current Directions in Biomedical Engineering 2023年第1期9卷 467-470页

作者： Böhland, Moritz Bruch, Roman Löffler, Katharina Reischl, Markus Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen 76344 Germany

Supervised Neural Networks are used for segmentation in many biological and biomedical applications. To omit the time-consuming and tiring process of manual labeling, unsupervised Generative Adversarial Networks (GANs) can be used to synthesize labeled data. However, the training of GANs requires extensive computation and is often unstable. Due to the lack of established stopping criteria, GANs are usually trained multiple times for a heuristically fixed number of epochs. Early stopping and epoch selection can lead to better synthetic datasets resulting in higher downstream segmentation quality on biological or medical data. This article examines whether the Frechet Inception Distance (FID), the Kernel Inception Distance (KID), or the WeightWatcher tool can be used for early stopping or epoch selection of unsupervised GANs. The experiments show that the last trained GAN epoch is not necessarily the best one to synthesize downstream segmentation data. On complex datasets, FID and KID correlate with the downstream segmentation quality, and both can be used for epoch selection. © 2023 the author(s), published by Walter de Gruyter Berlin/Boston.

关键词： Generative adversarial networks

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A Novel Method for Topology Preserving Static Network Reduction 11

A Novel Method for Topology Preserving Static Network Reduct...

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11th IEEE International Conference on Smart Energy Grid Engineering, SEGE 2023

作者： Weber, Moritz Cakmak, Huseyin K. Kuhnapfel, Uwe Hagenmeyer, Veit Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Eggenstein-Leopoldshafen Germany

ISBN: (纸本)9798350340716

Modern power grids become increasingly interconnected, and renewable energy sources amplify regional differences in energy generation and consumption. As a consequence, power grid analyses need to consider larger grids to examine the interaction between different regions. A detailed simulation of complete systems is often not feasible due to computational limitations. Thus, network reduction techniques are employed to create computationally less expensive equivalents of less important parts of the network. In this paper, we present a novel static network reduction method that is focused on the preservation of the overall network topology. We implemented this method in Python with DIgSILENT PowerFactory for the network modeling. We evaluate our method on a model of the German transmission grid and compare it to traditional reduction methods. In contrast to these traditional methods, our method yields interpretable equivalents with a preserved network topology consisting of standard components, while achieving a comparable reduction rate and accuracy of power flow and short circuit results. © 2023 IEEE.

关键词： Network topology

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Mask R-CNN Outperforms U-Net in Instance Segmentation for Overlapping Cells

Mask R-CNN Outperforms U-Net in Instance Segmentation for Ov...

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作者： Rettenberger, Luca Rieken Münke, Friedrich Bruch, Roman Reischl, Markus Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany

U-Net is the go-to approach for biomedical segmentation applications. However, it is not designed to segment overlapping objects, a challenge Mask R-CNN has shown to have great potential in. Yet, Mask R-CNN receives little attention in biomedicine. Hence, we evaluate both approaches on a publicly available biomedical dataset. We find that Mask RCNN outperforms U-Net in segmenting overlapping cells and achieves comparable performance if they do not intersect. Our study provides valuable decision support to practitioners in selecting an appropriate method when solving instance segmentation tasks using deep learning, as well as important insights into enhancing the accuracy of such approaches in biomedical image analysis. © 2023 the author(s), published by Walter de Gruyter Berlin/Boston.

关键词： Computer vision

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