Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems

2023 , Rueden, Laura von , Mayer, Sebastian , Beckh, Katharina , Georgiev, Bogdan , Giesselbach, Sven , Heese, Raoul , Kirsch, Birgit , Walczak, Michal , Pfrommer, Julius , Pick, Annika , Ramamurthy, Rajkumar , Garcke, Jochen , Bauckhage, Christian , Schuecker, Jannis

Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of informed machine learning. In this paper, we present a structured overview of various approaches in this field. We provide a definition and propose a concept for informed machine learning which illustrates its building blocks and distinguishes it from conventional machine learning. We introduce a taxonomy that serves as a classification framework for informed machine learning approaches. It considers the source of knowledge, its representation, and its integration into the machine learning pipeline. Based on this taxonomy, we survey related research and describe how different knowledge representations such as algebraic equations, logic rules, or simulation results can be used in learning systems. This evaluation of numerous papers on the basis of our taxonomy uncovers key methods in the field of informed machine learning.

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Intelligent Assistants: Conceptual Dimensions, Contextual Model, and Design Trends

2022 , Dhiman, Hitesh , Wächter, Christoph , Fellmann, Michael , Röcker, Carsten

Intelligent assistants are an increasingly commonplace class of information systems spanning a broad range of form and complexity. But what characterizes an intelligent assistant, and how do we design better assistants? In the paper, the authors contribute to scientific research in the domain of intelligent assistants in three steps, each building on the previous. First, they investigate the historical context of assistance as human work. By examining qualitative studies regarding the work of human assistants, the authors inductively derive concepts crucial to modeling the context of assistance. This analysis informs the second step, in which they develop a conceptual typology of intelligent assistants using 111 published articles. This typology explicates the characteristics (what or how) of intelligent assistants and their use context (who or which). In the third and final step, the authors utilize this typology to shed light on historical trends and patterns in design and evaluation of intelligent assistants, reflect on missed opportunities, and discuss avenues for further exploration.

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Learning Petri net models from sensor data of conveying systems based on the merging of prefix and postfix trees

2022 , Windmann, Stefan

Petri nets are a common modeling approach for parallel processes such as transport operations in conveying systems. In industrial applications, the Petri net models are usually created manually, which involves a lot of effort, especially if the modeled systems change frequently. This paper introduces a new learning method to automatically generate Petri nets from sensor data acquired in conveying systems. The underlying approach is to create prefix and postfix trees of possible event sequences and to merge them into a compact graph, which can be transformed into a deterministic Petri net model of the conveying system. Experimental results show that the proposed method produces realistic Petri net models even for conveying systems with ambiguous events.

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Security in Simulation - New Authorization Opportunities in HLA 4

2021 , Möller, Björn , Karlsson, Mikael , Herzog, Reinhard , Wood, Doug

A new version of the High Level Architecture (HLA) [1], with the working name HLA 4 [2], is under development. One security aspect that this new version addresses is authorization. The current version of HLA provides no standardized way to control which federates are allowed in a federation. HLA 4 adds new functionality for authorization, enabling control of which federates are allowed to connect to a Runtime Infrastructure (RTI) and to join a particular federation. The new functionality is provided using a flexible plug-in design. Many types of authentication can be supported, for example passwords, X.509 certificates, and connection to Active/Open Directory servers. This paper describes a number of use cases for authentication in HLA federations. It also describes the technical design of the new security plug-in. Finally, it discusses the potential and limitations of the selected approach.

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MotorFactory: A Blender Add-on for Large Dataset Generation of Small Electric Motors

2022 , Wu, Chengzhi , Zhou, Kanran , Kaiser, Jan-Philipp , Mitschke, Norbert , Klein, Jan-Felix , Pfrommer, Julius , Beyerer, Jürgen , Lanza, Gisela , Heizmann, Michael , Furmans, Kai

To enable automatic disassembly of different product types with uncertain condition and degree of wear in remanufacturing, agile production systems that can adapt dynamically to changing requirements are needed. Machine learning algorithms can be employed due to their generalization capabilities of learning from various types and variants of products. However, in reality, datasets with a diversity of samples that can be used to train models are difficult to obtain in the initial period. This may cause bad performances when the system tries to adapt to new unseen input data in the future. In order to generate large datasets for different learning purposes, in our project, we present a Blender add-on named MotorFactory to generate customized mesh models of various motor instances. MotorFactory allows to create mesh models which, complemented with additional add-ons, can be further used to create synthetic RGB images, depth images, normal images, segmentation ground truth masks and 3D point cloud datasets with point-wise semantic labels. The created synthetic datasets may be used for various tasks including motor type classification, object detection for decentralized material transfer tasks, part segmentation for disassembly and handling tasks, or even reinforcement learning-based robotics control or view-planning.

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Sovereign Digital Consent through Privacy Impact Quantification and Dynamic Consent

2022 , Appenzeller, Arno , Hornung, Marina , Kadow, Thomas , Krempel, Erik , Beyerer, Jürgen

Digitization is becoming more and more important in the medical sector. Through electronic health records and the growing amount of digital data of patients available, big data research finds an increasing amount of use cases. The rising amount of data and the imposing privacy risks can be overwhelming for patients, so they can have the feeling of being out of control of their data. Several previous studies on digital consent have tried to solve this problem and empower the patient. However, there are no complete solution for the arising questions yet. This paper presents the concept of Sovereign Digital Consent by the combination of a consent privacy impact quantification and a technology for proactive sovereign consent. The privacy impact quantification supports the patient to comprehend the potential risk when sharing the data and considers the personal preferences regarding acceptance for a research project. The proactive dynamic consent implementation provides an implementation for fine granular digital consent, using medical data categorization terminology. This gives patients the ability to control their consent decisions dynamically and is research friendly through the automatic enforcement of the patients' consent decision. Both technologies are evaluated and implemented in a prototypical application. With the combination of those technologies, a promising step towards patient empowerment through Sovereign Digital Consent can be made.

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Handheld spectral sensing devices should not mislead consumers as far as non-authentic food is concerned: A case study with adulteration of milk powder

2022 , Delatour, Thierry , Becker, Florian , Krause, Julius , Romero, Roman , Gruna, Robin , Längle, Thomas , Panchaud, Alexandre

With the rising trend of consumers being offered by start-up companies portable devices and applications for checking quality of purchased products, it appears of paramount importance to assess the reliability of miniaturized sensors embedded in such devices. Here, eight sensors were assessed for food fraud applications in skimmed milk powder. The performance was evaluated with dry- and wet-blended powders mimicking adulterated materials by addition of either ammonium sulfate, semicarbazide, or cornstarch in the range 0.5-10% of profit. The quality of the spectra was assessed for an adequate identification of the outliers prior to a deep assessment of performance for both non-targeted (soft independent modelling of class analogy, SIMCA) and targeted analyses (partial least square regression with orthogonal signal correction, OPLS). Here, we show that the sensors have generally difficulties in detecting adulterants at ca. 5% supplementation, and often fail in achieving adequate specificity and detection capability. This is a concern as they may mislead future users, particularly consumers, if they are intended to be developed for handheld devices available publicly in smartphone-based applications. Full article(This article belongs to the Special Issue Rapid Detection Methods for Food Fraud and Food Contaminants Series II).

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Model-assisted DoE applied to microalgae processes

2022 , Gassenmeier, Veronika , Deppe, Sahar , Hernández Rodríguez, Tanja , Kuhfuß, Fabian , Moser, Andre , Hass, Volker C. , Kuchemüller, Kim B. , Pörtner, Ralf , Möller, Johannes , Ifrim, George , Frahm, Björn

This study assesses the performance of the model-assisted Design of Experiment (mDoE) software toolbox for the design of two microalgae bioprocesses. The mDoE-toolbox was applied to maximize biomass growth for Desmodesmus pseudocommunis in a photobioreactor by varying the light intensity and pH and for Chlorella vulgaris in shake flasks, by varying the light intensity and duration. For both case studies, a mathematical mechanistic model was applied. In the first study only one experiment was necessary to adapt the mathematical model and identify a combination of light intensity and pH that improved biomass yield, as confirmed experimentally. In the second study, no well-established model was available for the specific experimental arrangement. On the basis of the literature, a mathematical model was constructed and a first cycle of mDoE was performed, thus identifying the desired factor combinations. Experiments confirmed the high biomass yield but revealed shortcomings of the model. The model was improved and a second cycle of mDoE was performed. The recommended factor combinations from both cycles were comparable. The mDoE was found to be a time-saving, cost-effective and useful method enabling the identification of factor combinations leading to high biomass production for the design of two different microalgae bioprocesses with low experimental effort.

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Anwendungsszenarien für AR in der Produktion: Use Cases und Technologielösungen

2022 , Deppe, Sahar , Brünninghaus, Marc , Voit, Michael , Röcker, Carsten

Mit der steigenden Leistungsfähigkeit von mobilen Computern und Anzeigegeräten hat sich die Nutzung von Augmented-Reality-Technologien in den letzten zehn Jahren verstärkt. Augmented Reality (AR) ist eine Technik, die es den Nutzern ermöglicht, mit ihrer physischen Umgebung durch die Überlagerung digitaler Informationen zu interagieren. Diese Technologie hebt bestimmte Merkmale der physischen Welt hervor, verbessert das Verständnis für diese Merkmale und bietet intelligente und zugängliche Einblicke. AR-Anwendungen haben das Potenzial, enorme Auswirkungen auf Branchen wie Produktion, Medizin, Forschung, Ausbildung und Unterhaltung zu bewirken. Der Fokus dieses Artikels liegt auf den AR-Anwendungen im Bereich der Produktion, die vor allem in den Bereichen Montage, Reparatur, Diagnose und Schulung eingesetzt werden. Außerdem werden die Effektivität und Effizienz von AR-Technologien in diesem Bereich anhand von vier dieser Anwendungen vorgestellt.

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Siamese recurrent neural networks for the robust classification of grid disturbances in transmission power systems considering unknown events

2022 , Kummerow, A. , Monsalve, C. , Bretschneider, P.

The automated identification and localisation of grid disturbances is a major research area and key technology for the monitoring and control of future power systems. Current recognition systems rely on sufficient training data and are very error-prone to disturbance events, which are unseen during training. This study introduces a robust Siamese recurrent neural network using attention-based embedding functions to simultaneously identify and locate disturbances from synchrophasor data. Additionally, a novel double-sigmoid classifier is introduced for reliable differentiation between known and unknown disturbance types and locations. Different models are evaluated within an open-set classification problem for a generic power transmission system considering different unknown disturbance events. A detailed analysis of the results is provided and classification results are compared with a state-of-the-art open-set classifier.

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