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16th International Conference on Knowledge Management and Information Systems, KMIS 2024 as part of 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2024

作者： Schmurr, Philipp Schmidt, Andreas Stucky, Karl-Uwe Suess, Wolfgang Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Kaiserstr. 12 Karlsruhe Germany

ISBN: (纸本)9789897587160

The goal of this work is to allow domain experts to properly perform data integration themselves and not to rely on external resources. This way the long-term data integration quality is not endangered and therefore cost for external resources can be saved. To achieve this, we propose a new approach that enables data integration based on entity-relationship (ER) models derived from arbitrary data sources. ER models are abstract and simply define all entities and relations needed for integration, which makes them easy to understand. Strategies to extract ER models from various standard data sources - relational databases, XML files and OWL data - are presented and a concept on how to extend it to arbitrary other data sources is introduced. Furthermore, the extracted models are a foundation to perform graphical data integration into an ontology based model and, thus, contribute to a harmonized knowledge management in heterogeneous data and information environments. It can be summarized as a strategy to improve the interoperability of existing data according to the FAIR principles. © 2024 by SCITEPRESS – Science and technology Publications, Lda.

关键词： Data collection

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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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Application of Model-Free Control to Reduce the Total Harmonic Distortion of Inverters 8

Application of Model-Free Control to Reduce the Total Harmon...

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8th IEEE Workshop on the Electronic Grid, eGRID 2023

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

ISBN: (纸本)9798350327007

With the fast rising number of power electronic devices in the electric grid, the harmonic emissions of those devices can lead to significant power quality issues. This is reflected in the recent addition of harmonic stability as one of the grid stability types. The reduction of harmonic emissions is an important task in the development of resilient carbon neutral grids. This paper describes the application of the model-free control concept for grid-Tied inverters as an easily implementable extension for dq0-frame based current control loops, which are often used in cascaded control schemes. The approach incorporates the information contained in the output current derivative. To balance phase-error and measurement noise suppression, the derivative is obtained using an algebraic numerical differentiator. The reduction of the total harmonic distortion (THD) is demonstrated for two use-cases in simulation, in order to show the transferability of the results. Further, measurement data from a hardware implementation of the concept is studied to verify the behavior under real-world conditions. For this, the influence of the tuning parameters is discussed along with limitations of the approach. © 2023 IEEE.

关键词： Power quality

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Distributed AC Optimal Power Flow: A Scalable Solution for Large-Scale Problems

arXiv

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arXiv 2025年

作者： Dai, Xinliang Jiang, Yuning Guo, Yi Jones, Colin N. Diehl, Moritz Hagenmeyer, Veit Institute for Automation and Applied Informatics The Karlsruhe Institute of Technology Germany Automatic Control Laboratory EPFL Switzerland Urban Energy Systems Laboratory Swiss Federal Laboratory for Materials Science and Technology Switzerland Department of Mathematics University of Freiburg Germany

This paper introduces a novel distributed optimization framework for large-scale AC Optimal Power Flow (OPF) problems, offering both theoretical convergence guarantees and rapid convergence in practice. By integrating smoothing techniques and the Schur complement, the proposed approach addresses the scalability challenges and reduces communication overhead in distributed AC OPF. Additionally, optimal network decomposition enables efficient parallel processing under the single program multiple data (SPMD) paradigm. Extensive simulations on large-scale benchmarks across various operating scenarios indicate that the proposed framework is 2 to 5 times faster than the state-of-the-art centralized solver IPOPT on modest hardware. This paves the way for more scalable and efficient distributed optimization in future power system applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Parallel processing systems

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Semantic CIM2Matpower: Ontology-Based Network Topology Processor and Converter for CIM Node-Breaker Models 7

Semantic CIM2Matpower: Ontology-Based Network Topology Proce...

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7th IEEE International Conference on Smart Energy Grid Engineering, SEGE 2019

作者： Schumilin, Artem Cakmak, Huseyin Kemal Stucky, Karl-Uwe Duepmeier, Clemens Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany

ISBN: (纸本)9781728124407

Smart electricity grids are key to a successful transition towards a future sustainable energy system. Network topology processing (NTP) is an important and frequently reoccurring task in most power grid analysis and simulation applications' including power flow calculations, short circuit and contingency analysis. NTP is applied to a detailed node-break-er representation of the power grid, as provided by the IEC Common Information Model (CIM). It is performed in order to obtain a reduced bus-branch representation, which serves as input to downstream analysis and simulation applications. The present paper introduces a novel approach to NTP that utilises a semantic knowledge base for network model management and exploits semantic reasoning over the formal CIM ontology and instance data. The proposed system is tested on publicly available ENTSO-E data, validated by converting the generated bus-branch model to Matpower and running power flow calculations in a power grid modelling and simulation framework. © 2019 IEEE.

关键词： Ontology

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Assignment of labour to a production line depending on lot size

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applied Mechanics and Materials 2014年 474卷 121-126页

作者： Kurnátová, Júlia Važan, Pavel Križanová, Gabriela Orihelová, Katarína Institute of Applied Informatics Automation and Mathematics Slovak University of Technology in Bratislava Slovakia

ISBN: (纸本)9783037859445

The paper describes the results of real simulation project in production line. The aim was to optimize the number of workers according to the different orders during one working week, to minimize time for orders realization with using the alternative technological processes, to minimize the length of order processing at maximum the number of workers on the shift. The simulation optimization was used for solution finding. © (2014) Trans Tech Publications, Switzerland.

关键词： Mechanics

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Prediction of selected production goals by classification methods

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applied Mechanics and Materials 2014年 474卷 115-120页

作者： Jurovata, Dominika Vazan, Pavel Kebisek, Michal Tanuska, Pavol Hrčka, Lukáš Institute of Applied Informatics Automation and Mathematics Slovak University of Technology in Bratislava Slovakia

ISBN: (纸本)9783037859445

The goal of this work was to use the process of knowledge discovery in planning and control of production processes. This work is focused on the prediction of the system behavior from the data of production process. The classification was used as a task of data mining. Some predictive models were created and the predictions of the production process behavior were realized by varying the input parameters using selected methods and techniques of data mining. It can be confirmed that the selected input parameters will lead to the fulfillment of the declared objectives of the process. The process of knowledge discovery has been implemented in the program STATISTICA Data Miner. © (2014) Trans Tech Publications, Switzerland.

关键词： Forecasting

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Dissolved ozone sensing in water and blood based on commercial screen-printed and BDD electrodes

Dissolved ozone sensing in water and blood based on commerci...

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2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023

作者： Petani, Lisa Llupa, Lea Peng, Zhen Ogata, Genki Koker, Liane Gengenbach, Ulrich Einaga, Yasuaki Pylatiuk, Christian Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Karlsruhe Germany Keio University Department of Chemistry Yokohama Japan

ISBN: (纸本)9798350300260

Dissolved ozone sensing in water is crucial for a variety of applications, including water treatment, food processing, and medical therapies, as ozone is widely used as an oxidising agent in these processes. Furthermore, dissolved ozone sensing in blood is important for medical therapies. We present a novel dissolved ozone sensor for measurement in water, based on commercially available screen-printed electrodes covered with different porous membranes made of polytetrafluoroethylene (PTFE) and non porous membranes made of polydimethylsiloxane (PDMS). With PTFE, a root mean squared error (RMSE), which is the difference between the novel sensor and the reference spectrometer, of 3.62 mg l-1 can be achieved, while with PDMS a RMSE of 6.34 mg l-1 is obtained. Also, we investigated the measurement of dissolved ozone in blood with membrane-free and electrolyte-free boron-doped diamond (BDD) electrodes. The proposed dissolved ozone sensors offer a promising solution for inexpensive monitoring of dissolved ozone concentrations in water and blood with a compact design. © 2023 IEEE.

关键词： amperometry disposable electrochemistry low-cost medical applications ozone therapy sensors water treatment

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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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