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Predictive analytics in quality assurance for assembly processes: lessons learned from a case study at an industry 4.0 demonstration cell

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Procedia CIRP 2021年 104卷 641-646页

作者： Peter Burggräf Johannes Wagner Benjamin Heinbach Fabian Steinberg Alejandro R. Pérez M. Lennart Schmallenbach Jochen Garcke Daniela Steffes-lai Moritz Wolter Chair of International Production Engineering and Management (IPEM) Universität Siegen Paul-Bonatz-Straße 9-11 57076 Siegen Germany Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Schloss Birlinghoven 1 53757 Sankt Augustin Germany Fraunhofer Center for Machine Learning Schloss Birlinghoven 1 53757 Sankt Augustin Germany Institute for Numerical Simulation Universität Bonn Endenicher Allee 19b 53115 Bonn Germany Institute for Computer Science Universität Bonn Endenicher Allee 19a 53115 Bonn Germany

Quality assurance (QA) is an important task in manufacturing to assess whether products meet their specifications. However, QA might be expensive, time-consuming, or incomplete. This paper presents a solution for predictive analytics in QA based on machine sensor values during production while employing specialized machine-learning models for classification in a controlled environment. Furthermore, we present lessons learned while implementing this model, which helps to reduce complexity in further industrial applications. The paper’s outcome proves that the developed model was able to predict product quality, as well as to identify the correlation between machine-status and faulty product occurrence.

关键词： Machine-Learning Predictive Quality Production Quality Assurance Classification

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Predicting missing links using PyKEEN

Predicting missing links using PyKEEN

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2019 ISWC Satellite Tracks (Posters and Demonstrations, Industry, and Outrageous Ideas), ISWC 2019-Satellites

作者： Ali, Mehdi Hoyt, Charles Tapley Domingo-Fernández, Daniel Lehmann, Jens Smart Data Analytics Group University of Bonn Germany Department of Enterprise Information Systems Fraunhofer Institute for Intelligent Analysis and Information Systems Sankt Augustin and Dresden Germany Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin Germany

PyKEEN is a framework, which integrates several approaches to compute knowledge graph embeddings (KGEs). We demonstrate the usage of PyKEEN in an biomedical use case, i.e. we trained and evaluated several KGE models on a biological knowledge graph containing genes’ annotations to pathways and pathway hierarchies from well-known databases. We used the best performing model to predict new links and present an evaluation in collaboration with a domain expert*. Copyright 2019 for this paper by its authors.

关键词： Knowledge graph

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SCAIView - A semantic search engine for biomedical research utilizing a microservice architecture 14

SCAIView - A semantic search engine for biomedical research ...

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Posters and Demos Track of the 14th International Conference on Semantic Systems, SEMPDS 2018

作者： Dörpinghaus, Jens Klein, Jürgen Darms, Johannes Madan, Sumit Jacobs, Marc Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin Germany

Biological and medical researchers explore the mechanisms of living organisms and tend to gain a better understanding of underlying fundamental biological processes of life. To tackle such complex tasks they constantly need to gather and accumulate new knowledge by performing experiments and studying scientific literature. We will present the novel semantic search engine "SCAIView" for knowledge discovery and retrieval and, additionally, discuss the most recent paradigm shifts in communication technologies, which leads to a completely new architecture that improves scalability, achieves better interoperability, and also increases fault-tolerance. © 2018 CEUR-WS. All Rights Reserved.

关键词： Fault tolerance

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Improving document retrieval with a clustering based relevance feedback system 11

Improving document retrieval with a clustering based relevan...

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11th IADIS International Conference Information Systems 2018, IS 2018

作者： Darms, Johannes Dörpinghaus, Jens Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin Germany

ISBN: (纸本)9789898533746

Relevance feedback for document retrieval systems is a technique where user feedback is used to improve a query response. In this work we propose a system that uses multiple clusterings and a semi-supervised heuristic to improve a query response. The heuristic creates an optimal cluster w.r.t. the relevance feedback based on multiple clusterings. We justify the explicit separation of the optimization process and the clustering process by time and space constrains. The evaluation of the heuristic on a corpus containing 1.660 documents from MEDLINE showed promising results. We were able to obtain better results as a single clustering after a few iterations. © 2018 IADIS. All rights reserved.

关键词： Search engines

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Sparse Grids and Applications - Stuttgart 2014

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2018年

作者： Jochen Garcke Dirk Pflüger

ISBN: (纸本)9783319803098

This volume of LNCSE is a collection of the papers from the proceedings of the third workshop on sparse grids and applications. Sparse grids are a popular approach for the numerical treatment of high-dimensional problems. Where classical numerical discretization schemes fail in more than three or four dimensions, sparse grids, in their different guises, are frequently the method of choice, be it spatially adaptive in the hierarchical basis or via the dimensionally adaptive combination technique. Demonstrating once again the importance of this numerical discretization scheme, the selected articles present recent advances on the numerical analysis of sparse grids as well as efficient data structures. The book also discusses a range of applications, including uncertainty quantification and plasma physics.

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Universal translation algorithm for formulation of transport network problems 8

Universal translation algorithm for formulation of transport...

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8th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2018

作者： Baldin, Anton Cassirer, Kläre Clees, Tanja Klaassen, Bernhard Nikitin, Igor Nikitina, Lialia Torgovitskaia, Inna Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin53754 Germany

ISBN: (纸本)9789897583230

The formulation of transport network problems is represented as a translation between two domain specific languages: From a network description language, used by network simulation community, to a problem description language, understood by generic non-linear solvers. A universal algorithm for this translation is developed, an estimation of its computational complexity given, and an efficient application of the algorithm demonstrated on a number of realistic examples. Typically, for a large gas transport network with about 10K elements the translation and solution of non-linear system together require less than 1 sec on the common hardware. The translation procedure incorporates several preprocessing filters, in particular, topological cleaning filters, which accelerate the solution procedure by factor 8. Copyright © 2018 by SCITEPRESS-Science and Technology Publications, Lda. All rights reserved.

关键词： Complex networks

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Assessing the landscape of Alzheimer’s disease cohort data

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Alzheimer's & Dementia 2020年第S4期16卷

作者： Colin Birkenbihl Yasamin Salimi Daniel Domingo-Fernández Holger Fröhlich Martin Hofmann-Apitius Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Sankt Augustin Germany Bonn-Aachen International Center for IT (b-it) Bonn Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Sankt Augustin Germany

Background Data collected in cohort studies lay the groundwork for a plethora of Alzheimer’s disease (AD) research endeavours. While there exist numerous cohort datasets in our field, few dedicated efforts have focused on comparing these datasets to each other. In recent years, large research consortia, such as EMIF and ROADMAP, were formed to investigate and evaluate the AD data landscape. To the best of our knowledge, such previous approaches have solely investigated cohorts at the metadata level without considering the cohort dataset itself. In this work, we explored eight major AD cohort datasets with the aim of 1) characterizing their underlying data, 2) assessing the quantity and availability of data, 3) evaluating the interoperability across distinct cohort datasets, and 4) presenting our findings in an interactive web application. Thus, we allow researchers to investigate the AD data landscape and find the most suited datasets for their research. Method We collected data from multiple major AD cohort studies. Through intensive manual curation of these datasets, we identified which data modalities can be found in the shared data. Using descriptive statistics and visualizations, we highlighted differences and similarities between cohorts focusing on key demographic characteristics and AD biomarkers. Finally, we assessed and compared the longitudinal follow-up data for each study. Result We show that demographics can vary substantially across cohorts (Table 1). The overlap between datasets is limited with regard to assessed measurements (Figure 1). Additionally, distributions of the same variables often differ across cohorts due to differences in processing and normalization; thus, hampering dataset interoperability. By investigating longitudinal follow-up data, we demonstrate that although there is reasonable longitudinal coverage on cognitive assessments, important biomarkers were only collected for a small fraction of study participants over time (Figure 2).

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A Minimum Set-Cover Problem with several constraints

A Minimum Set-Cover Problem with several constraints

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Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Jens Dörpinghaus Carsten Düing Vera Weil Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin Germany Department for Computer Science University of Cologne Germany

A lot of problems in natural language processing can be interpreted using structures from discrete mathematics. In this paper we will discuss the search query and topic finding problem using a generic context-based approach. This problem can be described as a Minimum Set Cover Problem with several constraints. The goal is to find a minimum covering of documents with the given context for a fixed weight function. The aim of this problem reformulation is a deeper understanding of both the hierarchical problem using union and cut as well as the non-hierarchical problem using the union. We thus choose a modeling using bipartite graphs and suggest a novel reformulation using an integer linear program as well as novel graph-theoretic approaches.

关键词： Bipartite graph Search engines Computer science Natural language processing Search problems Dynamic programming Expert systems

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Predicting dementia in people with Parkinson’s disease

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npj Parkinson's Disease 2025年第1期11卷 1-10页

作者： Aborageh, Mohamed Hähnel, Tom Martins Conde, Patricia Klucken, Jochen Fröhlich, Holger Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Augustin Sankt 53757 Germany Department of Neurology University Hospital and Faculty of Medicine Carl Gustav Carus TUD Dresden University of Technology Dresden Germany Luxembourg Centre for Systems Biomedicine (LCSB) University of Luxembourg Esch-sur-Alzette Luxembourg Centre Hospitalier de Luxembourg (CHL) Luxembourg Luxembourg Bonn-Aachen International Center for Information Technology (B-IT) Rheinische Friedrich-Wilhelms-Universität Bonn Bonn 53115 Germany

Parkinson’s disease (PD) exhibits a variety of symptoms, with approximately 25% of patients experiencing mild cognitive impairment and 45% developing dementia within ten years of diagnosis. Predicting this progression and identifying its causes remains challenging. Our study utilizes machine learning and multimodal data from the UK Biobank to explore the predictability of Parkinson’s dementia (PDD) post-diagnosis, further validated by data from the Parkinson’s Progression Markers Initiative (PPMI) cohort. Using Shapley Additive Explanation (SHAP) and Bayesian Network structure learning, we analyzed interactions among genetic predisposition, comorbidities, lifestyle, and environmental factors. We concluded that genetic predisposition is the dominant factor, with significant influence from comorbidities. Additionally, we employed Mendelian randomization (MR) to establish potential causal links between hypertension, type 2 diabetes, and PDD, suggesting that managing blood pressure and glucose levels in Parkinson’s patients may serve as a preventive strategy. This study identifies risk factors for PDD and proposes avenues for prevention. © The Author(s) 2025.

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Parameter Identification in Cyclic Voltammetry of Alkaline Methanol Oxidation 8

Parameter Identification in Cyclic Voltammetry of Alkaline M...

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8th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2018

作者： Clees, Tanja Nikitin, Igor Nikitina, Lialia Pott, Sabine Krewer, Ulrike Haisch, Theresa Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin Germany Institute of Energy and Process Systems Engineering Technical University Braunschweig Germany

ISBN: (纸本)9789897583230

Alkaline methanol oxidation is an electrochemical process, perspective for the design of efficient high energy density fuel cells. The process involves a large number of elementary reactions, forming a complex reaction graph, and it is described by a system of non-linear differential equations of high order. The purpose of parameter identification is a reconstruction of reaction constants in this model on the basis of available experimental data. Cyclic voltammetry, a measurement of dynamical current-voltage characteristic of the cell, is especially suitable for analysis of electrochemical kinetics. In this paper we present several approaches for parameter identification in cyclic voltammetry of alkaline methanol oxidation. With the aid of global optimization methods and interactive parameter study we find four islands of solutions in parameter space, corresponding to different chemical mechanisms of the process. The main features of solutions are discussed and the reconstructed reaction constants are presented. Copyright © 2018 by SCITEPRESS-Science and Technology Publications, Lda. All rights reserved.

关键词： Differential equations

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