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IEEE International Conference on Industrial Engineering and Engineering Management

作者： A. Schmetz C. Vahl Z. Zhen D. Reibert S. Mayer D. Zontar J. Garcke C. Brecher Fraunhofer-Institute for Production Technology IPT Aachen Germany Fraunhofer-Center for Machine Learning and Fraunhofer-Institute for Algorithms and Scientific Computing SCAI Sankt Augustin Germany Laboratory for Machine Tools and Production Engineering (WZL) RWTH Aachen University Germany

ISBN: (纸本)9781665437721

Predictive maintenance is a prominent and active field for applications of machine learning in industry in recent years. The health and wear of equipment directly influences the productivity and quality of the production process. Especially in ultra-precision manufacturing, tool wear has a major impact on the achievable quality while the wear itself cannot be measured directly in-process. In this paper we present a machine learning-based classification of the tool wear in-process using acoustic emission sensors. To increase the interpretability of the process – to open the black box model – we apply a feature importance analysis and use the obtained feature importances to provide augmented data representations to the users. These representations increase the transparency of the model's decision process and assist the users in validating the model's decisions and gain new insight into the phenomenon of tool wear itself.

关键词： Productivity Industries Engineering management Machine learning Acoustic emission Industrial engineering Sensors

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Modern information technology: Information visualization, virtual environment, neo-geography, tangible images

Scientific Visualization

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scientific Visualization 2013年第4期5卷 1-17页

作者： Aleshin, A. Afanasiev, V. Brusentsev, P. Eremchenko, E. Klimenko, A. Klimenko, S. Nikitin, I. Nikitina, L. Pestrikov, V. Sourin, A. Sourina, O. Moscow Russia Barco Russia and CIS Moscow Russia Neo-geography Group Protvino Russia Institute of Computing for Physics and Technology Protvino Russia Fraunhofer Institute for Algorithms and Scientific Computing SCAI Germany Nanyang Technological University Singapore Singapore Fraunhofer Interactive Digital Media at NTU Singapore Singapore

The ever increasing complexity of the physical phenomena studied in scientific and engineering disciplines , requires the development of new approaches and powerful technique for processing and analysis of complex data. scientific Visualization developing methods and tools for understanding the problems to be solved by bringing in a person's ability to see and perceive the image. The article deals with the preconditions of occurrence of this scientific field, the stages of its formation as a scientific discipline and major achievements. Shows the transition from technology to render virtual environment. Currently, scientific visualization is quite complete scientific discipline. Purpose of virtual environments is to provide users with a virtual workspace, in which they can observe and investigate create the virtual real-time data, models and the scenes. Presents selected results of the development of scientific visualization and virtual environment from the Moscow institute of Physics and Technology and the institute of computing for Physics and Technology in cooperation with research centers in Germany and Singapore. In conclusion ascertained advantages of visualization technologies and virtual environments to improve the efficiency of fundamental research, to meet the challenges of science, education and industry. Promising areas of research in this subject area are information visualization , visual analytics, a new man-machine interfaces based on haptic devices and tangible images.

关键词： Data visualization

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On Multilevel Energy-Based Fragmentation Methods

arXiv

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arXiv 2024年

作者： Barker, James Griebel, Michael Hamaekers, Jan Institute for Numerical Simulation University of Bonn Friedrich-Hirzebruch-Allee 7 Bonn53115 Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven 1 Sankt Augustin53757 Germany

Energy-based fragmentation methods approximate the potential energy of a molecular system as a sum of contribution terms built from the energies of particular subsystems. Some such methods reduce to truncations of the many-body expansion (MBE);others combine subsystem energies in a manner inspired by the principle of inclusion/exclusion (PIE). The combinatorial technique of Möbius inversion of sums over partially ordered sets, which generalizes the PIE, is known to provide a non-recursive expression for the MBE contribution terms, and has also been connected to related cluster expansion methods. We build from these ideas a very general framework for decomposing potential functions into energetic contribution terms associated with elements of particular partially ordered sets (posets) and direct products thereof. Specific choices immediately reproduce not only the MBE, but also a number of other existing decomposition forms, including, e.g., the multilevel ML-BOSSANOVA schema. Furthermore, a different choice of poset product leads to a setup familiar from the combination technique for high-dimensional approximation, which has a known connection to quantum-chemical composite methods. We present the ML-SUPANOVA decomposition form, which allows the further refinement of the terms of an MBE-like expansion of the Born-Oppenheimer potential according to systematic hierarchies of ab initio methods and of basis sets. We outline an adaptive algorithm for the a posteori construction of quasi-optimal truncations of this decomposition. Some initial experiments are reported and *** Codes 35J10, 65D40, 65Z05, 92E10, 06A07 © 2024, CC BY.

关键词： Adaptive algorithms

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On the effects of biased quantum random numbers on the initialization of artificial neural networks

arXiv

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arXiv 2021年

作者： Heese, Raoul Wolter, Moritz Mücke, Sascha Franken, Lukas Piatkowski, Nico Fraunhofer Center for Machine Learning and Fraunhofer Institute for Industrial Mathematics ITWM Germany Fraunhofer Center for Machine Learning and Fraunhofer Institute for Algorithms and Scientific Computing SCAI Germany Artificial Intelligence Group TU Dortmund University Germany Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS Germany

Recent advances in practical quantum computing have led to a variety of cloud-based quantum computing platforms that allow researchers to evaluate their algorithms on noisy intermediate-scale quantum (NISQ) devices. A common property of quantum computers is that they can exhibit instances of true randomness as opposed to pseudo-randomness obtained from classical systems. Investigating the effects of such true quantum randomness in the context of machine learning is appealing, and recent results vaguely suggest that benefits can indeed be achieved from the use of quantum random numbers. To shed some more light on this topic, we empirically study the effects of hardware-biased quantum random numbers on the initialization of artificial neural network weights in numerical experiments. We find no statistically significant difference in comparison with unbiased quantum random numbers as well as biased and unbiased random numbers from a classical pseudo-random number generator. The quantum random numbers for our experiments are obtained from real quantum hardware. Copyright © 2021, The Authors. All rights reserved.

关键词： Random number generation

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Iterative methods for reluctance based PEEC models

Iterative methods for reluctance based PEEC models

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International Zurich Symposium on Electromagnetic Compatibility (EMC Zurich)

作者： M.L. Zitzmann T. Clees R. Weigel Institute for Electronics Engineering University of Erlangen Nuremberg Erlangen Germany Algorithms and Scientific Computing Fraunhofer Institute Germany

This paper presents the application of iterative solution techniques to large sparse equation systems derived from PEEC modeling. The PEEC method (partial element equivalent circuit) is an approach to transform conducting objects into linear networks with basic electrical elements. Such equivalent circuit models can be simulated by conventional circuit solvers such as SPICE (simulation program for integrated circuit emphasis) based on the MNA (modified nodal analysis) approach. By applying appropriate sparsification techniques sparse system matrices can be obtained, adequate for iterative solvers. By using multilevel approaches linear complexity in time and memory requirements can be achieved in the best case. The suitability of different iterative methods as applied to such systems is compared by means of examples

关键词： Iterative methods Sparse matrices Circuit simulation Integrated circuit modeling Analytical models Iterative algorithms Equivalent circuits Transforms Integral equations Inductance

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A Graph-Theoretic Approach to the Train Marshalling Problem

A Graph-Theoretic Approach to the Train Marshalling Problem

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Federated Conference on Computer Science and Information Systems

作者： Jens Dorpinghaus Rainer Schrader Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Institut fur Informatik Universitat zu Koln

ISBN: (纸本)9781538623718

Rearranging cars of an incoming train in a hump yard is a widely discussed topic. We focus on the train marshalling problem where the incoming cars of a train are distributed to a certain number of sorting tracks. When pulled out again to build the outgoing train, cars sharing the same destination should appear consecutively. The goal is to minimize the number of sorting tracks. We suggest a graph-theoretic approach for this NP-complete problem. The idea is to partition an associated directed graph into what we call pseudochains of minimum length. We describe a greedy-type heuristic to solve the partitioning problem which, on random instances, performs better than the known heuristics for the train marshalling problem.

关键词： TRAIN Cars on-the-job training particle tracks Track tracks Tracks Heuristics REARRANGING grade Nondeterministic polynomial-time hard PULL-OUT

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Benchmark gas distribution network for cross-sectoral applications

Benchmark gas distribution network for cross-sectoral applic...

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International Energy and Sustainability Conference (IESC)

作者： Christian Brosig Silvan Fassbender Eberhard Waffenschmidt Sebastian Janocha Bernhard Klaassen Cologne Institute of Renewable Energy (ClRE) Cologne University of Applied Sciences Cologne Germany Rheinische NETZGesellschaft mbH (RNG) Cologne Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Sankt Augustin Germany

Cross-sectoral coupling of energy systems could provide the necessary flexibility for future energy systems with a high share of renewable energies. The gas grid implies large storage capacities and thus promises an economically attractive balance for fluctuating energy sources. A benchmark gas distribution network, coupled to the CIGRE benchmark network for electricity is presented within this paper, to universally investigate the impacts on gas grid infrastructure. The network includes interconnected meshed and radial topologies for both low and medium pressure gas networks. They have been synthesized from existing real networks in cooperation with the Rheinische NETZGesellschaft mbH. Applications range from analyzing network impacts of widespread CHP or fuel cell implementation, over comparing the cost-effectiveness of different future pathways between expansion and decommissioning of the gas grid network, to analyzing the impacts of distributed injection of renewable gas. The overall aim is to build a complete network benchmark for an integral cross-sectoral energy system including also heat.

关键词： Steel Benchmark testing Fuel cells Hydrogen Cogeneration Regulators Couplings

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Grid-enabled virtual screening against malaria

Grid-enabled virtual screening against malaria

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作者： Jacq, N. Salzemann, J. Jacq, F. Legré, Y. Medernach, E. Montagnat, J. Maaß, A. Breton, V. Schwichtenberg, H. Sridhar, M. Kasam, V. Zimmermann, M. Hofmann, M. Breton, V. Laboratoire de Physique Corpusculaire Université Blaise Pascal IN2P3-CNRS UMR 6533 24 Avenue des Landais 63177 Aubière France Communication and Systèmes Grenoble France Informatique Signaux et Systèmes Université de Nice Sophia Antipolis CNRS UMR 6070 Sophia Antipolis France Department of Simulation Engineering Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin Germany Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin Germany

WISDOM is an international initiative to enable a virtual screening pipeline on a Grid infrastructure. Its first attempt was to deploy large scale in silico docking on a public Grid infrastructure. Protein-ligand docking is about computing the binding energy of a protein target to a library of potential drugs using a scoring algorithm. Previous deployments were either limited to one cluster, to Grids of clusters in the tightly protected environment of a pharmaceutical laboratory or to desktop Grids. The first large scale docking experiment ran on the EGEE Grid production service from 11 July 2005 to 19 August 2005 against targets relevant to research on malaria and saw over 41 million compounds docked for the equivalent of 80 years of CPU time. Up to 1,700 computers were simultaneously used in 15 countries around the world. Issues related to the deployment and the monitoring of the in silico docking experiment as well as experience with Grid operation and services are reported in the paper. The main problem encountered for such a large scale deployment was the Grid infrastructure stability. Although the overall success rate was above 80%, a lot of monitoring and supervision was still required at the application level to resubmit the jobs that failed. But the experiment demonstrated how Grid infrastructures have a tremendous capacity to mobilize very large CPU resources for well targeted goals during a significant period of time. This success leads to a second computing challenge targeting avian flu neuraminidase N1. © Springer Science + Business Media B.V. 2007.

关键词： Grid computing

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Privacy Risk Assessment for Synthetic Longitudinal Health Data

Privacy Risk Assessment for Synthetic Longitudinal Health Da...

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69th Annual Meeting of the German Association of Medical Informatics, Biometry and Epidemiology, GMDS 2024

作者： Schneider, Julian Walter, Marvin Otte, Karen Meurers, Thierry Perrar, Ines Nöthlings, Ute Adams, Tim Fröhlich, Holger Prasser, Fabian Fluck, Juliane Kühnel, Lisa Knowledge Management ZB MED - Information Centre for Life Sciences Cologne Germany Medical Informatics Group Berlin Institute of Health at Charité - Universitätsmedizin Berlin Berlin Germany Institute of Nutritional and Food Sciences - Nutritional Epidemiology University of Bonn Bonn Germany Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing SCAI Sankt Augustin Germany The Agricultural Faculty University of Bonn Bonn Germany Faculty of Technology Bielefeld University Bielefeld Germany

ISBN: (纸本)9781643685366

Introduction A modern approach to ensuring privacy when sharing datasets is the use of synthetic data generation methods, which often claim to outperform classic anonymization techniques in the trade-off between data utility and privacy. Recently, it was demonstrated that various deep learning-based approaches are able to generate useful synthesized datasets, often based on domain-specific analyses. However, evaluating the privacy implications of releasing synthetic data remains a challenging problem, especially when the goal is to conform with data protection guidelines. Methods Therefore, the recent privacy risk quantification framework Anonymeter has been built for evaluating multiple possible vulnerabilities, which are specifically based on privacy risks that are considered by the European Data Protection Board, i.e. singling out, linkability, and attribute inference. This framework was applied to a synthetic data generation study from the epidemiological domain, where the synthesization replicates time and age trends previously found in data collected during the DONALD cohort study (1312 participants, 16 time points). The conducted privacy analyses are presented, which place a focus on the vulnerability of outliers. Results The resulting privacy scores are discussed, which vary greatly between the different types of attacks. Conclusion Challenges encountered during their implementation and during the interpretation of their results are highlighted, and it is concluded that privacy risk assessment for synthetic data remains an open problem. © 2024 The Authors.

关键词： Differential privacy

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A simple iterative approach to parameter optimization 00

A simple iterative approach to parameter optimization

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Proceedings of the fourth annual international conference on Computational molecular biology

作者： Alexander Zein Ralf Zimmer Thomas Lengauer GMD - German National Research Center for Information Technology Institute for Algorithms and Scientific Computing (SCAI) Schloβ Birlinghoven D-53754 Sankt Augustin Germany

ISBN: (纸本)9781581131864

Various bioinformatics problems require optimizing several different properties simultaneously. For example, in the protein threading problem, a linear scoring function combines the values for different properties of possible sequence-to-structure alignments into a single score to allow for unambigous optimization. In this context, an essential question is how each property should be weighted. As the native structures are known for some sequences, the implied partial ordering on optimal alignments may be used to adjust the weights. To resolve the arising interdependence of weights and computed solutions, we propose a novel approach: iterating the computation of solutions (here: threading alignments) given the weights and the estimation of optimal weights of the scoring function given these solutions via a systematic calibration method. We show that this procedure converges to structurally meaningful weights, that also lead to significantly improved performance on comprehensive test data sets as measured in different ways. The latter indicates that the performance of threading can be improved in general.

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