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Design of transformation initiatives implementing organisational agility: an empirical study

SN Business and Economics 2021年第6期1卷 1-28页

作者： Kovynyov, Ivan Buerck, Axel Mikut, Ralf Imperial College Business School London United Kingdom Kobaltblau Management Consultants GmbH Munich Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany

This study uses 125 responses from companies of all sizes predominantly headquartered in Germany, Switzerland, France and UK to reveal perceptions of the drivers of organisational agility. It further investigates current understanding of managing principles of multiple organisational dimensions such as culture, values, leadership, organisational structure, processes and others to achieve greater organisational agility. The data set is disaggregated into four major profiles of agile organisations: laggards, execution specialists, experimenters, and leaders. The approach to agile transformation is analysed by each of those profiles. While the positive effect from a more holistic approach is confirmed, leaders tend to focus more on processes and products rather than project work. Respondents perceive that IT, product development and research are most agile functions within their organisations, while human resources, finance and administration are considered being not agile. Furthermore, organisations with higher levels of organisational agility tend to use more than one agile scaling framework. Implications on theories of agile transformations and organisational design are discussed. © The Author(s) 2021.

关键词： Agile Agile organisations Agile transformation Organisational agility Organisational design

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Forecasting energy time series with profile neural networks 20

Forecasting energy time series with profile neural networks

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11th ACM International Conference on Future Energy Systems, e-Energy 2020

作者： Heidrich, Benedikt Turowski, Marian Ludwig, Nicole Mikut, Ralf Hagenmeyer, Veit Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Germany

ISBN: (纸本)9781450380096

Forecasting the energy demand is essential for network operators to balance the grid, in particular with the increasing share of renewable energy sources. Neural networks, especially deep neural networks, have shown promising results in recent forecasting tasks. However, they often struggle learning periodicities in time series efficiently. In line with the finding that deep learning can be improved with statistical information, we introduce profile neural networks based on the fast and promising convolutional neural networks. The underlying idea of profile neural networks is that decomposing periodic energy time series into a standard load profile, a trend, and a colorful noise module improves the forecasting accuracy. The proposed deep neural network architecture is applied to real-world electricity data from buildings on a university campus, more specifically of one building with strong seasonal variation and one building with weak seasonal variation. The new architecture outperforms current state-of-the-art deep learning benchmark models regarding the forecasting accuracy on forecast horizons of one day and one week-ahead, improving the mean absolute scaled error by up to 25%, as well as regarding the trade-off between training time and accuracy. © 2020 Owner/Author.

关键词： Time series

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Automated Style-Aware Selection of Annotated Pre-Training Databases in Biomedical Imaging

Automated Style-Aware Selection of Annotated Pre-Training Da...

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IEEE International Symposium on Biomedical Imaging

作者： Miguel Molina-Moreno Marcel P. Schilling Markus Reischl Ralf Mikut Department of Signal Theory and Communications Universidad Carlos III de Madrid Madrid Spain Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany

Due to the broad use of deep learning and its need for big data, annotated and available databases for different tasks are constantly appearing. Nevertheless, they often remain unexploited due to the difficulty of effectively performing transfer learning between different databases. In medical imaging, the task of transfer learning is challenging due to: the variety of image modalities, organ/cell shapes, etc., and the lack of available and annotated data. In this paper, we propose an automated pipeline for predicting the similarity values of new database compared to known annotated databases. The system consists of an autoencoder trained on a comprehensive loss function that considers image reconstruction, style features, and dataset membership. A similarity measure is defined based on the resulting 2D latent space, which is demonstrated to have a correlation with the pre-training results on not annotated databases. Hence, our similarity measure could be used to select the most suitable known database for transfer learning or domain adaptation.

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Spatial analysis of tails of air pollution PDFs in Europe

arXiv

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arXiv 2024年

作者： He, Hankun Schäfer, Benjamin Beck, Christian School of Mathematical Sciences Queen Mary University of London LondonE1 4NS United Kingdom Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany

Outdoor air pollution is estimated to cause a huge number of premature deaths worldwide, it catalyses many diseases on a variety of time scales, and it has a detrimental effect on the environment. In light of these impacts it is necessary to obtain a better understanding of the dynamics and statistics of measured air pollution concentrations, including temporal fluctuations of observed concentrations and spatial heterogeneities. Here we present an extensive analysis for measured data from Europe. The observed probability density functions (PDFs) of air pollution concentrations depend very much on the spatial location and on the pollutant substance. We analyse a large number of time series data from 3544 different European monitoring sites and show that the PDFs of nitric oxide (NO), nitrogen dioxide (NO2) and particulate matter (PM10 and PM2.5) concentrations generically exhibit heavy tails. These are asymptotically well approximated by q-exponential distributions with a given entropic index q and width parameter λ. We observe that the power-law parameter q and the width parameter λ vary widely for the different spatial locations. We present the results of our data analysis in the form of a map that shows which parameters q and λ are most relevant in a given region. A variety of interesting spatial patterns is observed that correlate to properties of the geographical region. We also present results on typical time scales associated with the dynamical behaviour. © 2024, CC BY.

关键词： Probability density function

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Understanding electricity prices beyond the merit order principle using explainable AI

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Energy and AI 2023年第3期13卷 149-159页

作者： Julius Trebbien Leonardo Rydin Gorjao Aaron Praktiknjo Benjamin Schafer Dirk Witthaut Forschungszentrum Julich Institute for Energy and Climate Research–Systems Analysis and Technology Evaluation(IEK-STE)52428 JülichGermany University of Cologne Institute for Theoretical PhysicsZülpicher Str.7750937 CologneGermany Faculty of Science and Technology Norwegian University of Life Sciences1432 AsNorway Chair for Energy System Economics Institute for Future Energy Consumer Needs and Behavior(FCN)E.ON Energy Research CenterRWTH Aachen UniversityMathieustr.1052074 AachenGermany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology76344 Eggenstein-LeopoldshafenGermany JARA-ENERGY 52074 AachenGermany

Electricity prices in liberalized markets are determined by the supply and demand for electric power,which are in turn driven by various external influences that vary strongly in *** perfect competition,the merit order principle describes that dispatchable power plants enter the market in the order of their marginal costs to meet the residual load,*** difference of load and renewable *** market models are based on this principle when attempting to predict electricity prices,yet the principle is fraught with assumptions and simplifications and thus is limited in accurately predicting *** this article,we present an explainable machine learning model for the electricity prices on the German day-ahead market which foregoes of the aforementioned assumptions of the merit order *** model is designed for an ex-post analysis of prices and builds on various external *** SHapley Additive exPlanation(SHAP)values we disentangle the role of the different features and quantify their importance from empiric data,and therein circumvent the limitations inherent to the merit order *** show that load,wind and solar generation are the central external features driving prices,as expected,wherein wind generation affects prices more than solar ***,fuel prices also highly affect prices,and do so in a nontrivial ***,large generation ramps are correlated with high prices due to the limited flexibility of nuclear and lignite ***,we offer a model that describes the influence of the main drivers of electricity prices in Germany,taking us a step beyond the limited merit order principle in explaining the drivers of electricity prices and their relation to each other.

关键词： Electricity prices Merit order principle Explainable artificial intelligence Machine learning Fuel prices Energy market

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Modes of Energy Packets in the Energy Packet Grid

Modes of Energy Packets in the Energy Packet Grid

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IEEE Electronic Power Grid (eGrid)

作者： Klemens Schneider Marcel Weßbecher Dominik Schulz Veit Hagenmeyer Marc Hiller Martina Zitterbart Institute of Telematics Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Institute of Electrical Engineering Karlsruhe Institute of Technology (KIT) Karlsruhe Germany

We advance the concept of the Energy Packet Grid (EP Grid), which is an alternative paradigm for the architecture and operation of the power grid. In particular, we introduce the concept of energy packet (EP) shapes, which are different ways to define energy packets. Additionally, we present so-called EP modes, which are different ways to conduct an EP transfer. We introduce the current-mode, the power-mode, and the mixed-mode. The EP modes are demonstrated with prototype EP devices in our real-world laboratory and in simulations.

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Real-Time Coordination of Integrated Transmission and Distribution Systems: Flexibility Modeling and Distributed NMPC Scheduling

arXiv

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arXiv 2024年

作者： Dai, Xinliang Guo, Yi Jiang, Yuning Jones, Colin N. Hug, Gabriela Hagenmeyer, Veit Institute for Automation and Applied Informatics KIT Germany Power Systems Laboratory ETH Zürich Switzerland Automatic Control Laboratory EPFL Switzerland Urban Energy Systems Laboratory EMPA Switzerland

This paper proposes a real-time distributed operational architecture to coordinate integrated transmission and distribution systems (ITD). At the distribution system level, the distribution system operator (DSO) calculates the aggregated flexibility of all controllable devices by power-energy envelopes and provides them to the transmission system operator (TSO). At the transmission system level, a distributed nonlinear model predictive control (NMPC) approach is proposed to coordinate the economic dispatch of multiple TSOs, considering the aggregated flexibility of all distribution systems. The subproblems of the proposed approach are associated with different TSOs and individual time periods. In addition, the aggregated flexibility of controllable devices in distribution networks is encapsulated, re-calculated, and communicated through the power-energy envelopes, facilitating a reduction in computational complexity and eliminating redundant information exchanges between TSOs and DSOs, thereby enhancing privacy and security. The framework's effectiveness and applicability in real-world scenarios are validated through simulated operational scenarios on a summer day in Germany, highlighting its robustness in the face of significant prediction mismatches due to severe weather conditions. Copyright © 2024, The Authors. All rights reserved.

关键词： Predictive control systems

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Entomoscope: An Open-Source Photomicroscope for Biodiversity Discovery

TechRxiv

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TechRxiv 2023年

作者： Wührl, Lorenz Rettenberger, Luca Meier, Rudolf Hartop, Emily Graf, Julien Pylatiuk, Christian Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany Center for Integrative Biodiversity Discovery Leibniz Institute for Evolution and Biodiversity Science Museum für Naturkunde Berlin Germany

Understanding and combatting biodiversity loss are critical tasks facing our planet. They are made especially difficult because much of the earth’s biodiversity is concentrated in abundant and species-rich groups of invertebrates like insects. Traditionally, samples of insects have been analyzed manually by experts using morphology. Not only does this necessitate taxonomic expertise, but it is also error-prone, time-consuming, and often involves commercial microscopes that are too expensive for many countries in the Global South where most species are found. The alternative to expert sorting with morphology is the use of DNA barcoding. However, this requires a well-equipped laboratory and an entirely different skill set. We present an alternative solution: a low-cost, open-source photomicroscope for taking high-resolution, focus-stacked images that can be used for insect classification: the Entomoscope. We describe two different versions of the Entomoscope, a standalone version that can be operated without additional hardware and an even simpler Version, that requires a computer. We show that the optics are of sufficiently high quality to classify specimens with >95% accuracy into 15 different types of insects (mostly’families’ according to the Linnean classification). The classifier can be successively extended or individually trained for specific classification tasks. Here, we provide building instructions, 3D files, and a list of commercially available parts so that everyone can build their own Entomoscope. Open-source DIY hardware like the Entomoscope facilitates affordable, cutting-edge biodiversity research by entomologists around the globe. © 2023, CC BY.

关键词： Biodiversity

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Rapid Scalable Distributed Power Flow with Open-Source Implementation

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IFAC-PapersOnLine 2022年第13期55卷 145-150页

作者： Xinliang Dai Yichen Cai Yuning Jiang Veit Hagenmeyer Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany Laboratoire d'Automatique École Polytechnique Fédérale de Lausanne Lausanne Switzerland

This paper introduces a new method for solving the distributed ac power flow ( pf ) problem by further exploiting the problem formulation. We propose a new variant of the aladin algorithm devised specifically for this type of problem. This new variant is characterized by using a reduced modelling method of the distributed ac pf problem, which is reformulated as a zero-residual least-squares problem with consensus constraints. This pf is then solved by a Gauss-Newton based inexact aladin algorithm presented in the paper. An open-source implementation of this algorithm, called rapid pf +, is provided. Simulation results, for which the power system's dimension varies from 53 to 10224 buses, show great potential of this combination in the aspects of both the computing time and scalability.

关键词： Power Flow Large-scale ALADIN Distributed Optimization

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AI2Seg: A Method and Tool for AI-based Annotation Inspection of Biomedical Instance Segmentation Datasets

AI2Seg: A Method and Tool for AI-based Annotation Inspection...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Marcel P. Schilling Lukas Klinger Ulrike Schumacher Svenja Schmelzer Miguel Bordallo López Britta Nestler Markus Reischl Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany BioQuant Heidelberg University Heidelberg Germany Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland Institute for Applied Materials Karlsruhe Institute of Technology Karlsruhe Germany

In biomedical engineering, deep neural networks are commonly used for the diagnosis and assessment of diseases through the interpretation of medical images. The effectiveness of these networks relies heavily on the availability of annotated datasets for training. However, obtaining noise-free and consistent annotations from experts, such as pathologists, radiologists, and biologists, remains a significant challenge. One common task in clinical practice and biological imaging applications is instance segmentation. Though, there is currently a lack of methods and open-source tools for the automated inspection of biomedical instance segmentation datasets concerning noisy annotations. To address this issue, we propose a novel deep learning-based approach for inspecting noisy annotations and provide an accompanying software implementation, AI 2 Seg, to facilitate its use by domain experts. The performance of the proposed algorithm is demonstrated on the medical MoNuSeg dataset and the biological LIVECell dataset.

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