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arXiv 2024年

作者： Demirel, Gökhan Grafenhorst, Simon Förderer, Kevin Hagenmeyer, Veit The Institute of Automation and Applied Informatics IAI Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany

This work analyzes the impact of varying concentrations mini-photovoltaic (MPV) systems, often referred to as balcony power plants, on the stability and control of the low-voltage (LV) grid. By local energy use and potentially reversing meter operation, we focus on how these MPV systems transform grid dynamics and elucidate consumer participation in the energy transition. We scrutinize the effects of these systems on power quality, power loss, transformer loading, and the functioning of other inverter-based voltage-regulating distributed energy resources (DER). Owing to the rise in renewable output from MPVs, the emerging bidirectional energy flow poses challenges for distribution grids abundant with DERs. Our case studies, featuring sensitivity analysis and comparison of distributed and decentralized DER control strategies, highlight that autonomous inverters are essential for providing ancillary services. With the growing use of battery energy storage (BES) systems in LV grids for these services, the need for adaptable DER control strategies becomes increasingly evident. Copyright © 2024, The Authors. All rights reserved.

关键词： Power quality

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Automated Characterisation of Printed Electronics Under Adjustable Ambient Conditions

Automated Characterisation of Printed Electronics Under Adju...

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International Conference on Semiconductor

作者： Martin Ungerer Zehua Chen Tim Pang Mach Klaus-Martin Reichert Ulrich Gengenbach Moritz Lindmüller Joachim R. Binder Markus Reischl Liane Koker Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany Institute for Applied Materials Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9798350352078

ISBN: (纸本)9798350352085

By means of functional printing, a variety of electronic devices (e.g. passives, semiconductors, sensors) and entire circuits can be printed. One field of application are flexible electronics. But the protection from environmental influences like humidity or oxygen is still a challenge. Often, encapsulation methods known from conventional semiconductor manufacturing can not be applied. Thus, new encapsulation materials and processes have to be developed and validated. To facilitate this manually very time-consuming task, an automated measurement setup for electrical characterisation of printed electronics under adjustable ambient conditions is introduced.

关键词： Encapsulation Printing Semiconductor device measurement Sensitivity Electric variables measurement Humidity Semiconductor device manufacture Sensors Protection Flexible electronics

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Investigation of solution processable moisture barrier films based on a polymer and filler materials

Investigation of solution processable moisture barrier films...

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IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS)

作者： Zehua Chen Ulrich Gengenbach Shant Gananian Daniel Moser Klaus-Martin Reichert Liane Koker Institute for Automation and Applied Informatics (IAI) Karlsruhe Institute of Technology (KIT) Eggenstein-Leopoldshafen Germany

ISBN: (数字)9798350383263

ISBN: (纸本)9798350383270

Flexible printed electronics (FPE) attract significant interest due to their potential applications, ranging from printed circuits to wearable devices. Encapsulation of FPE devices is crucial to protect them from environmental factors such as moisture and oxygen. Solution-processed encapsulation by incorporating fillers into the polymer matrix is a promising method to produce barrier films on FPE devices. In this work, glass flakes, graphene oxide, montmorillonite, and silica are examined as potential fillers in poly(vinyl alcohol) (PVA). An adapted water vapor transmission rate (WVTR) dry cup method is developed to select the most effective filler. The optimal concentration of the selected fillers in PVA is determined. Moreover, the barrier effect of selected PVA/filler films is validated by resistance measurements of inkjet printed silver tracks subjected to damp heat and room storage conditions.

关键词： Encapsulation Silicon compounds Silver Transmission line matrix methods Films Moisture Sensor systems

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Using weather data in energy time series forecasting: the benefit of input data transformations

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Energy informatics 2023年第1期6卷 44页

作者： Neumann, Oliver Turowski, Marian Mikut, Ralf Hagenmeyer, Veit Ludwig, Nicole Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Baden-Wuerttemberg Karlsruhe76344 Germany Cluster of Excellence Machine Learning University of Tübingen Maria-von-Linden Str. 6 Baden-Wuerttemberg Karlsruhe76344 Germany

Renewable energy systems depend on the weather, and weather information, thus, plays a crucial role in forecasting time series within such renewable energy systems. However, while weather data are commonly used to improve forecast accuracy, it still has to be determined in which input shape this weather data benefits the forecasting models the most. In the present paper, we investigate how transformations for weather data inputs, i. e., station-based and grid-based weather data, influence the accuracy of energy time series forecasts. The selected weather data transformations are based on statistical features, dimensionality reduction, clustering, autoencoders, and interpolation. We evaluate the performance of these weather data transformations when forecasting three energy time series: electrical demand, solar power, and wind power. Additionally, we compare the best-performing weather data transformations for station-based and grid-based weather data. We show that transforming station-based or grid-based weather data improves the forecast accuracy compared to using the raw weather data between 3.7 and 5.2%, depending on the target energy time series, where statistical and dimensionality reduction data transformations are among the best. © 2023, The Author(s).

关键词： Time series

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Intrinsic Explainable Artificial Intelligence Using Trainable Spatial Weights on Numerical Weather Predictions 24

Intrinsic Explainable Artificial Intelligence Using Trainabl...

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Proceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems

作者： Oliver Neumann Maximilian Beichter Benedikt Heidrich Nils Friederich Veit Hagenmeyer Ralf Mikut Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Karlsruhe Institute of Technology (KIT) Germany

ISBN: (纸本)9798400704802

Addressing the volatility of renewable energies like solar and wind is crucial for the energy system’s stability and optimal utilization of renewable energies. Accurate energy forecasts are important to improve scheduling. Electrical demand and renewable energies are weather-dependent and Numerical Weather Predictions have proven to be beneficial for energy forecasts due to their fine-grained spatial resolution. State-of-the-art Deep Learning approaches for energy forecasting are black-box models. However, decisions in energy systems depend on energy forecasts, and, thus, it is important that models are explainable and trustworthy. Explainable Artificial Intelligence techniques exist that add explainability to energy forecasting models, but all existing methods are only post-hoc or do not use weather data on large spatial areas. This paper introduces a novel approach to forecast energy that scales and adds intrinsic explainability by design. Therefore, we use trainable spatial weights to make accurate forecasts on large spatial areas. The trained weights can be interpreted spatially to enhance explainability and increase trust. Furthermore, the spatial weights enable a wide range of future work, including postprocessing, subregion forecasting, hierarchical learning, and spatial-temporal weights.

关键词： deep learning electrical demand energy forecasting energy transition explainable artificial intelligence neural networks numerical weather predictions solar power wind power

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Digital Twin Architecture to Use for Optimizing an AoD-Printing Process

Digital Twin Architecture to Use for Optimizing an AoD-Print...

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Design, Test, Integration and Packaging of MEMS/MOEMS

作者： Hanna Pfannenstiel Martin Ungerer Ingo Sieber Institute for Automation and Applied Informatics (IAI) Karlsruher Institute for Technology (KIT) Leopoldshafen-Eggenstein Germany

ISBN: (数字)9798350378269

ISBN: (纸本)9798350378276

Digital Twins have been gaining relevancy over the past decade as computing power becomes increasingly more powerful and cheaper. However, most digital twins require an already profound knowledge of their physical counterpart in order to mimic it. This paper proposes a data architecture which enables a digital twin to learn from its physical counterpart and help to build models in applications where little established models are available. The digital twin in this paper focuses on accompanying an additive manufacturing process.

关键词： Computational modeling Memory Computer architecture Packaging Three-dimensional printing Data models Digital twins

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Efficient Time-Delay System Optimization with Auto-Configured Metaheuristics

Efficient Time-Delay System Optimization with Auto-Configure...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Senkerik, Roman Byrski, Aleksander Guzowski, Hubert Janku, Peter Kadavy, Tomas Oplatkova, Zuzana Kominkova Matusu, Radek Pluhacek, Michal Pekar, Libor Smolka, MacIej Viktorin, Adam Tomas Bata University A.I. Lab Faculty of Applied Informatics Department of Informatics and Artificial Intelligence Zlin Czech Republic Tomas Bata University Faculty of Applied Informatics Department of Automation and Control Engineering Zlin Czech Republic Institute of Computer Science Agh University of Science and Technology Krakow Poland

ISBN: (纸本)9798350337020

This paper presents an experimental study that compares the performance of four selected metaheuristic algorithms for optimizing a time delay system model. Time delay system models are complex and challenging to optimize due to their inherent characteristics, such as non-linearity, multi-modality, and constraints. The study includes an explanation of the choice and core functionality of the selected algorithms, which are both baseline and state-of-the-art variants of self-organizing migrating algorithm (SOMA), state-of-the-art variant from the Success-History-based Adaptive Differential Evolution family of algorithms, with emphasis on diverse search (DISH algorithm), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. The hyperparameters of the metaheuristic algorithms were set using the iRace automatic algorithm configuration framework. The paper emphasizes the importance of metaheuristic algorithms in control engineering for time-delay systems to develop more effective and efficient control strategies and precise model identifications. The experimental results highlight the effectiveness of the state-of-the-art algorithms with specific adaptive mechanisms like population organization process, diverse search and adaptation mechanisms ensuring a gradual transition from exploration to exploitation. Overall, this study contributes to understanding the challenges and advantages of using metaheuristic algorithms in control engineering for time delay systems. The results provide valuable insights into the performance of modern metaheuristic algorithms and can help guide the selection of appropriate adaptive mechanisms of metaheuristics. © 2023 IEEE.

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Modification of rotor modal properties based on structural changes in the stiffness of its bearings and shaft 32

Modification of rotor modal properties based on structural c...

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32nd Joint Seminar Development of Materials Science in Research and Education, DMSRE 2023

作者： Bucha, P. Rolník, L. Lovíšková, J. Naď, M. Slovak University of Technology in Bratislava Faculty of Materials Science and Technology in Trnava Institute of Applied Informatics Automation and Mechatronics Ulica Jána Bottu 2781/25 Trnava917 24 Slovakia Slovak Academy of Sciences Institute of Informatics Dubravská cesta 9 Bratislava845 07 45 Slovakia

The rotor structures are fundamental mechanical systems that realize the transfer of rotary motion in technical devices and systems. In some operating regimes, the rotor systems are often subjected to undesirable and damaging phenomena resulting in resonant state. The ability to prevent or reduce the level of unwanted rotor vibrations in the given operating mode is an important task in the process of structural design of rotor structures. In general, the dynamic properties of rotor systems depend on their spatial properties - geometric parameters and material properties, i.e. distribution of mass and stiffness properties of rotor structures. For the structural design of the rotor or its structural modification, which will allow the redistribution of its spatial properties (mass and stiffness), two approaches based on changing the bearing stiffness and also on the shaft stiffness are presented in this paper. The main goal for modifying the spatial properties of the rotor is to get the critical rotor speed out of the operating speed range. © Published under licence by IOP Publishing Ltd.

关键词： Stiffness

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Impact of Annotation Noise on Histopathology Nucleus Segmentation

Impact of Annotation Noise on Histopathology Nucleus Segment...

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作者： Schilling, Marcel P. Ahuja, Niket Rettenberger, Luca Scherr, Tim Reischl, Markus Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany

Deep learning is often used for automated diagnosis support in biomedical image processing scenarios. Annotated datasets are essential for the supervised training of deep neural networks. The problem of consistent and noise-free annotation remains for experts such as pathologists. The variability within an annotator (intra) and the variability between annotators (inter) are current challenges. In clinical practice or biology, instance segmentation is a common task, but a comprehensive and quantitative study regarding the impact of noisy annotations lacks. In this paper, we present a concept to categorize and simulate various types of annotation noise as well as an evaluation of the impact on deep learning pipelines. Thereby, we use the multi-organ histology image dataset MoNuSeg to discuss the influence of annotator variability. We provide annotation recommendations for clinicians to achieve high-quality automated diagnostic algorithms. © 2022 The Author(s), published by De Gruyter.

关键词： Deep neural networks

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Annotation Efforts in Image Segmentation can be Reduced by Neural Network Bootstrapping

Annotation Efforts in Image Segmentation can be Reduced by N...

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作者： Rettenberger, Luca Schilling, Marcel Reischl, Markus Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany

Modern medical technology offers potential for the automatic generation of datasets that can be fed into deep learning systems. However, even though raw data for supporting diagnostics can be obtained with manageable effort, generating annotations is burdensome and time-consuming. Since annotating images for semantic segmentation is particularly exhausting, methods to reduce the human effort are especially valuable. We propose a combined framework that utilizes unsupervised machine learning to automatically generate segmentation masks. Experiments on two biomedical datasets show that our approach generates noticeably better annotations than Otsu thresholding and k-means clustering without needing any additional manual effort. Using our framework, unannotated datasets can be amended with pre-annotations fully unsupervised thus reducing the human effort to a minimum. © 2022 by Walter de Gruyter Berlin/Boston.

关键词： Image annotation

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