Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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  • Publication
    Explainable AI for sensor-based sorting systems
    ( 2023)
    Anneken, Mathias
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    Veerappa, Manjunatha
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    Huber, Marco
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    Kühnert, Christian
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    Kronenwett, Felix
    ;
    Maier, Georg
    Explainable artificial intelligence (XAI) can make machine learning based systems more transparent. This additional transparency can enable the use of machine learning in many different domains. In our work, we show how XAI methods can be applied to an autoencoder for anomaly detection in a sensor-based sorting system. The setup of the sorting system consists of a vibrating feeder, a conveyor belt, a line-scan camera and an array of fast-switching pneumatic valves. It allows the separation of a material stream into two fractions, realizing a binary sorting task. The autoencoder tries to mimic the normal behavior of the nozzle array and thus can detect abnormal behavior. The XAI methods are used to explain the output of the autoencoder. As XAI methods global and local approaches are used, which means we receive explanations for both a single result and the whole autoencoder. Initial results for both approaches are shown, together with possible interpretations of these results
  • Publication
    Are you sure? Prediction revision in automated decision-making
    ( 2021)
    Burkart, Nadia
    ;
    Robert, Sebastian
    ;
    Huber, Marco
    With the rapid improvements in machine learning and deep learning, decisions made by automated decision support systems (DSS) will increase. Besides the accuracy of predictions, their explainability becomes more important. The algorithms can construct complex mathematical prediction models. This causes insecurity to the predictions. The insecurity rises the need for equipping the algorithms with explanations. To examine how users trust automated DSS, an experiment was conducted. Our research aim is to examine how participants supported by an DSS revise their initial prediction by four varying approaches (treatments) in a between-subject design study. The four treatments differ in the degree of explainability to understand the predictions of the system. First we used an interpretable regression model, second a Random Forest (considered to be a black box [BB]), third the BB with a local explanation and last the BB with a global explanation. We noticed that all participants improved their predictions after receiving an advice whether it was a complete BB or an BB with an explanation. The major finding was that interpretable models were not incorporated more in the decision process than BB models or BB models with explanations.
  • Publication
    Modular and scalable automation for field robots
    ( 2021)
    Osten, Julia
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    Weyers, Catrin
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    Bregler, Kevin
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    Emter, Thomas
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    Petereit, Janko
    This article describes a modular and scalable charging and navigation concept for electrified field robots and other agricultural machines. The concept consists of an underbody charging system on a trailer and a modular navigation box. The underlying conductive charging process is compared to other charging techniques. Charging time in relation to charging current and mean power consumption in field use is displayed. In the navigation box, data of various sensors are combined by means of multi-sensor fusion regarding the precise time of arrival. Time synchronization is achieved by a novel method for compensating the data latency jitter by employing Kalman based timestamp filtering. Furthermore, navigation functionalities, such as motion planning and mapping, are presented.