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Need for Change: Business Functions Affected by the Use of Decentralized Information Systems 42

Need for Change: Business Functions Affected by the Use of D...

42nd International Conference on Information Systems: Building Sustainability and Resilience with IS: A Call for Action, ICIS 2021 TREOs

作者： Heines, Roger Kannengießer, Niclas Sturm, Benjamin Jung, Reinhard Sunyaev, Ali Institute of Information Management University of St.Gallen St.Gallen9000 Switzerland Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Karlsruhe76133 Germany

ISBN: (纸本)9781713893608

Driven by economic advantages and the idea of disintermediation of business processes, the decentralization of technical and economic systems has become a highly discussed topic in recent years. Extant research primarily investigated the technical implementations of decentralized information systems (IS) and their use by firms in business networks. It became clear that interorganizational relationships and business functions must be transformed to enable the use of decentralized IS (e.g., those related to how firms can be involved in the design, instantiation, operation, and governance of decentralized IS). However, the impact of transforming business functions of individual firms remains largely unclear, obfuscating a comprehensive understanding of the implication of decentralized IS use on internal organizational structures of firms. In this work, we focus on the identification of challenges for firms in using distributed ledger technology (DLT as a representative for decentralized IS) and their effects on the business functions of firms. © 2021 42nd International Conference on Information Systems, ICIS 2021 TREOs: "Building Sustainability and Resilience with IS: A Call for Action". All Rights Reserved.

关键词： Distributed ledger

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MLOps for Scarce Image Data: A Use Case in Microscopic Image Analysis

arXiv

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arXiv 2023年

作者： Sitcheu, Angelo Yamachui Friederich, Nils Baeuerle, Simon Neumann, Oliver Reischl, Markus Mikut, Ralf Institute for Automation and Applied Informatics Germany Institute of Biological and Chemical Systems Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany Robert Bosch GmbH Markwiesenstraße 46 Reutlingen72770 Germany

Nowadays, Machine Learning (ML) is experiencing tremendous popularity that has never been seen before. The operationalization of ML models is governed by a set of concepts and methods referred to as Machine Learning Operations (MLOps). Nevertheless, researchers, as well as professionals, often focus more on the automation aspect and neglect the continuous deployment and monitoring aspects of MLOps. As a result, there is a lack of continuous learning through the flow of feedback from production to development, causing unexpected model deterioration over time due to concept drifts, particularly when dealing with scarce data. This work explores the complete application of MLOps in the context of scarce data analysis. The paper proposes a new holistic approach to enhance biomedical image analysis. Our method includes: a fingerprinting process that enables selecting the best models, datasets, and model development strategy relative to the image analysis task at hand;an automated model development stage;and a continuous deployment and monitoring process to ensure continuous learning. For preliminary results, we perform a proof of concept for fingerprinting in microscopic image datasets. © 2023, CC BY.

关键词： Machine learning

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Validation Towards Realistic Synthetic Datasets in Production Planning

Validation Towards Realistic Synthetic Datasets in Productio...

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Simulation Winter Conference

作者： Jan Michael Spoor Marvin Matthes Martin Krockert Jens Weber Institute for Information Management in Engineering Karlsruhe Institute of Technology Germany Dept. of Informatics & Mathematics University of Applied Sciences Dresden Germany Faculty of Technology Baden-Wuerttemberg Cooperative State University Lörrach Germany

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

For large-scale simulations, a sufficient data amount is required. Despite an increasing data availability, it is still challenging to gather large-scale datasets, which are comprehensive, correct, accessible, and realistic, to validate new algorithms and models. An alternative is the use of synthetic data. Thus, we propose a novel methodology to generate realistic datasets. Based upon the statistical properties of real-world data, synthetic datasets are generated by ML models and filtered for anomalous values. The generated datasets are then compared to find the most suitable one. For this validation procedure, a modified Hopfield neural network model is extended to enable an analysis of sequences and to derive a comparison metric. The method demonstrates its applicability by providing an in-depth comparison of all tested data generators using a real-world dataset of a mid-size manufacturing company, whereby transformer-based generators proved most suitable. More diverse use cases should be evaluated in future research.

关键词： Measurement Production planning Hopfield neural networks Data collection Transformers Data models Generators Manufacturing Machinery Synthetic data

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A Real-Time PHIL Implementation of a Novel Nonlinear Distributed Control Strategy for a Multi-Terminal DC Microgrid

A Real-Time PHIL Implementation of a Novel Nonlinear Distrib...

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Power Tech Conference

作者： Ömer Ekin Filipe Perez Gilney Damm Veit Hagenmeyer Institute for Automation and Applied Informatics (IAI) Karlsruhe Institute of Technology (KIT) Karlsruhe Germany L2S Laboratory Paris-Saclay University Gif-sur-Y vette France IMSE Laboratory University Gustave Eiffel Marne-la-Vallee France

Multi-terminal DC Microgrids have great potential for integrating Renewable Energy Sources, Storage Technologies, and modern loads more efficiently because most of them operate on DC power. Besides the reduced conversion steps between the components, which avoids considerable losses, multi-terminal DC MicroGrids offer advantages due to the lack of skin effects and reactive power. Nevertheless, stabilizing such a DC network by controlling its power converters is a very challenging task in view of the fact that the classical solution, to use PI controllers based on linearized models, may lead to stable behavior only in a small region around the respective equilibrium point. In this paper, we present a real-time PHIL implementation of a novel nonlinear control scheme for a multi-terminal DC MicroGrid. The amount of needed capacitors is reduced significantly concerning previous approaches. The proposed nonlinear controller is compared with a cascaded PI controller based on a linearized model, the so-called vector control. Both approaches, nonlinear and linear, are implemented and validated on a real PHIL multi-terminal DC MicroGrid consisting of a battery, a PV, and two load ports. The real-time experimental results of PI-based and nonlinear control-based DC Microgrids are then compared. They show that the nonlinear control allows to better deal with disturbances and nonlinearities of the system, for several points of operation without re-tuning.

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Evaluation of semi-supervised learning using sparse labeling to segment cell nuclei

Evaluation of semi-supervised learning using sparse labeling...

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作者： Bruch, Roman Rudolf, Rüdiger Mikut, Ralf Reischl, Markus Institute of Molecular and Cell Biology Mannheim University of Applied Sciences Mannheim Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany

The analysis of microscopic images from cell cultures plays an important role in the development of drugs. The segmentation of such images is a basic step to extract the viable information on which further evaluation steps are build. Classical image processing pipelines often fail under heterogeneous conditions. In the recent years deep neuronal networks gained attention due to their great potentials in image segmentation. One main pitfall of deep learning is often seen in the amount of labeled data required for training such models. Especially for 3D images the process to generate such data is tedious and time consuming and thus seen as a possible reason for the lack of establishment of deep learning models for 3D data. Efforts have been made to minimize the time needed to create labeled training data or to reduce the amount of labels needed for training. In this paper we present a new semisupervised training method for image segmentation of microscopic cell recordings based on an iterative approach utilizing unlabeled data during training. This method helps to further reduce the amount of labels required to effectively train deep learning models for image segmentation. By labeling less than one percent of the training data, a performance of 90% compared to a full annotation with 342 nuclei can be achieved. © 2020 by Walter de Gruyter Berlin/Boston 2020.

关键词： Deep learning

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Non-linear, bivariate stochastic modelling of power-grid frequency applied to islands

Non-linear, bivariate stochastic modelling of power-grid fre...

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Power Tech Conference

作者： Ulrich Oberhofer Leonardo Rydin Gorjao G. Cigdem Yalcin Oliver Kamps Veit Hagenmeyer Benjamin Schafer Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany Faculty of Science and Technology Norwegian University of Life Sciences As Norway Department of Physics Istanbul University Istanbul Turkey Center for Nonlinear Sciences (CeNoS) University of Munster Germany

Mitigating climate change requires a transition away from fossil fuels towards renewable energy. As a result, power generation becomes more volatile and options for microgrids and islanded power-grid operation are being broadly discussed. Therefore, studying the power grids of physical islands, as a model for islanded microgrids, is of particular interest when it comes to enhancing our understanding of power-grid stability. In the present paper, we investigate the statistical properties of the power-grid frequency of three island systems: Iceland, Ireland, and the Balearic Islands. We utilise a Fokker-Planck approach to construct stochastic differential equations that describe market activities, control, and noise acting on power-grid dynamics. Using the obtained parameters we create synthetic time series of the frequency dynamics and compare them to empirical data. Our main contribution is to propose two extensions of stochastic power-grid frequency models and showcase the applicability of these new models to non-Gaussian statistics, as encountered in islands.

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Extrusion printing of radio frequency coplanar waveguides with commercial dispensing equipment

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Flexible and Printed Electronics 2025年第2期10卷

作者： Md Ashif, Nowab Reza Gengenbach, Ulrich Maier, Pascal Kotz, Alexander Koos, Christian Sieber, Ingo Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany Institute of Photonics and Quantum Electronics Karlsruhe Institute of Technology Karlsruhe Germany Institute of Microstructure Technology Karlsruhe Institute of Technology Karlsruhe Germany

Coplanar waveguides (CPWs) are fundamental components of radio frequency and microwave circuits used for a variety of applications in telecommunications, radar systems and wireless communications. There is a need for low-cost and easily accessible manufacturing techniques that enable the fabrication of CPWs using commercial materials and equipment, thus reducing production costs and facilitating easy access to these versatile transmission lines. In this paper, we present CPWs on alumina substrates fabricated with an extrusion printing process based on low-cost commercial dispensing equipment and a commercial silver ink. For a 4 mm long CPW, we observe an insertion loss below 1 dB and a return loss better than 10 dB for the frequency range from 40 MHz to 33.8 GHz. © 2025 The Author(s). Published by IOP Publishing Ltd.

关键词： Extrusion

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Heterogeneous dual-frequency iot network for vital data acquisition 7th

Heterogeneous dual-frequency iot network for vital data acqu...

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7th KES International Conference on Sustainable Design and Manufacturing, KES-SDM 2020

作者： Salem, Mahmoud El-Maddah, Islam Youssef, Khaled Elkaseer, Ahmed Scholz, Steffen Mohamed, Hoda Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Navigation Science and Space Technology BSU Beni-Suef Egypt Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany Faculty of Engineering Port Said University Port Said42526 Egypt

ISBN: (纸本)9789811581304

The IoT solutions for health informatics are suffering from restriction of using one frequency which makes the network is unable to communicate via different frequencies. Also, this limitation of using a single frequency fixes a single communication range between nodes without the capability to vary the covering rang. This could consume high power if the system has to transmit data on long distance due to the need to use signal repeater node/s. This paper reports the development of a new heterogeneous design of IoT network via unsymmetrical frequencies in order to sense, process, exchange and visualize two vital signs. The sensing and processing phases are performed via a convenient design of noninvasive vital data acquisition nods. These data acquisition nodes of vital signs are integrated into an embedded system with the IoT nodes in order to provide effective, accurate and real-time health monitoring for vital signs. The design of IoT broker node enables real-time analysis and concatenation of two vital signs. In addition, the broker facilitates the communication between nodes to each other over multi-frequencies in order to cover variant distances and thus to optimize the usage of power. Moreover, the proposed software of the IoT broker is able to address run-time issues of network nodes in real time without the need to reset the whole network. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.

关键词： Data acquisition

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Analytical Uncertainty Propagation for Multi-Period Stochastic Optimal Power Flow

arXiv

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arXiv 2022年

作者： Bauer, Rebecca Mühlpfordt, Tillmann Ludwig, Nicole Hagenmeyer, Veit Institute for Automation und applied Informatics Karlsruhe Institute of Technology Germany Cluster of Excellence "Machine Learning: New Perspectives for Science" University of Tübingen Germany

The increase in renewable energy sources (RESs), like wind or solar power, results in growing uncertainty also in transmission grids. This affects grid stability through fluctuating energy supply and an increased probability of overloaded lines. One key strategy to cope with this uncertainty is the use of distributed energy storage systems (ESSs). In order to securely operate power systems containing renewables and use storage, optimization models are needed that both handle uncertainty and apply ESSs. This paper introduces a compact dynamic stochastic chance-constrained DC optimal power flow (CC-OPF) model, that minimizes generation costs and includes distributed ESSs. Assuming Gaussian uncertainty, we use affine policies to obtain a tractable, analytically exact reformulation as a second-order cone problem (SOCP). We test the new model on five different IEEE networks with varying sizes of 5, 39, 57, 118 and 300 nodes and include complexity analysis. The results show that the model is computationally efficient and robust with respect to constraint violation risk. The distributed energy storage system leads to more stable operation with flattened generation profiles. Storage absorbed RES uncertainty, and reduced generation cost. © 2022, CC BY-NC-ND.

关键词： Electric power transmission networks

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Analytical Uncertainty Propagation for Multi-Period Stochastic Optimal Power Flow

SSRN

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SSRN 2022年

The increase in renewable energy sources (RESs), like wind or solar power, results in growing uncertainty also in transmission grids. This affects grid stability through fluctuating energy supply and an increased probability of overloaded lines. One key strategy to cope with this uncertainty is the use of distributed energy storage systems (ESSs). In order to securely operate power systems containing renewables and use storage, optimization models are needed that both handle uncertainty and apply ESSs. This paper introduces a compact dynamic stochastic chance-constrained optimal power flow (CC-OPF) model, that minimizes generation costs and includes distributed ESSs. Assuming Gaussian uncertainty, we use affine policies to obtain a tractable, analytically exact reformulation as a second-order cone problem (SOCP). We test the new model on five different IEEE networks with varying sizes of 5, 39, 57, 118 and 300 nodes and include complexity analysis. The results show that the model is computationally efficient and robust with respect to constraint violation risk. The distributed energy storage system leads to more stable operation with flattened generation profiles. Storage absorbed RES uncertainty, and reduced generation cost. © 2022, The Authors. All rights reserved.

关键词： Acoustic generators

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