Ostwestfalen-Lippe University of Applied Sciences and Arts
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Das Rahmenwerk der Taskforce on Nature-related Financial Disclosures (TNFD) – Eine kritische Würdigung der Empfehlungen vom September 2023 zur Berichterstattung über naturbezogene Leistungsindikatoren und Kennzahlen
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Nachhaltigkeitsberichterstattung im Mittelstand – Hintergründe und Umsetzung europäischer Vorgaben, von: Hartke, V./Wilhelm, B., IDW Verlag, Düsseldorf 2022
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Immersive Exploration of Machine Learning Data Combining Visual Analytics with Explainable AI
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Adsorbed Gas Storage Digital Twin
No full textOne possibility for energy storage are fuels. With gaseous fuels like hydrogen or methane, significant efforts are necessary for a feasible storage in terms of compression or liquefaction. This is of particular importance in the mobility sector. An alternative to high-pressure or cryogenic gas storage is the storage by adsorption in porous media using nano-carbons, metal–organic frameworks, or metal hydrides as adsorbents. In order to assess the performance of the charging and discharging of adsorption tanks, the mass and energy balance as well as the phase equilibrium (adsorption isotherm) and, if present, the spatial distribution of properties has to be considered. In order to simplify the analysis and prediction of these models, an attempt is made to develop digital twins based on machine learning. Neural networks and Gaussian process regression are applied to replace the system of coupled nonlinear and differential equations. The data basis used is generated by simulations. Thus, it is possible to easily predict the performance of a storage tank for different gases or to determine an optimum storage device (material selection and tank design)
Assistance System for graph-based 3D Visualization of Design Structure Matrices
No full textThe increasing number of product artifacts (e.g., mechanical or electronic components, software functions, documents) confronts small and medium-sized companies with the challenge of assessing change effects. The lack of knowledge of artifact relationships causes problems, such as outdated documentation, lack of coordination with affected disciplines, or delayed changes. The Design Structure Matrix (DSM) can clearly represent the elements and relationships of complex systems. This paper presents an assistance system for intuitive visualization of engineering change effects using existing DSM-based methods for complexity management. The implemented algorithms compute graph layouts, cluster analyses, and change predictions in the form of change risk, time, and cost. An application example of a 3D-printed intelligent lamp demonstrates the approach's viability. The paper concludes with a discussion of the benefits and future activities
Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients
No full textPartial differential equations (PDEs) are important tools to model physical systems and including them into machine learning models is an important way of incorporating physical knowledge. Given any system of linear PDEs with constant coefficients, we propose a family of Gaussian process (GP) priors, which we call EPGP, such that all realizations are exact solutions of this system. We apply the Ehrenpreis-Palamodov fundamental principle, which works as a non-linear Fourier transform, to construct GP kernels mirroring standard spectral methods for GPs. Our approach can infer probable solutions of linear PDE systems from any data such as noisy measurements, or pointwise defined initial and boundary conditions. Constructing EPGP-priors is algorithmic, generally applicable, and comes with a sparse version (S-EPGP) that learns the relevant spectral frequencies and works better for big data sets. We demonstrate our approach on three families of systems of PDEs, the heat equation, wave equation, and Maxwell's equations, where we improve upon the state of the art in computation time and precision, in some experiments by several orders of magnitude
HUMAN’23: 6th Workshop on Human Factors in Hypertext
No full textHUMAN 2023 is the 6th workshop of a series for the ACM Hypertext conferences. The HUMAN workshop has a strong focus on the user and thus is complementary to the strong machine analytics research direction that could be experienced in previous conferences.The user-centric view on hypertext not only includes user interfaces and interaction, but also discussions about hypertext application domains as well as human-centered AI. Furthermore, the workshop raises the question of how original hypertext ideas (e. g., Doug Engelbart’s "augmenting human intellect" [7] or Jeff Conklin’s "hypertext as a computer-based medium for thinking and communication" [6]) can improve today’s hypertext systems