Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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  • Publication
    Multi-target regression and cross-validation for non-isothermal glass molding experiments with small sample sizes
    ( 2023)
    Mende, Hendrik
    ;
    Kiroriwal, Saksham
    ;
    Pfrommer, Julius
    ;
    Schmitt, Robert H.
    ;
    Beyerer, Jürgen
    Machine learning has become a core part of smart factories and Industry 4.0. In our work, we extend the use of machine learning for quality prediction of a thin glass product formed using a Non-isothermal Glass Moulding (NGM) process. As the form shape of a glass lens requires multiple variables to describe, Multi-Target Regression (MTR) is suitable for the same. Many MTR models are able to provide intuitive insights into the prediction target(s). We present a data pipeline that employs bootstrapping-inspired sampling for robust feature selection, modelling and validation for small dataset. The results demonstrate how MTR models can be used for prediction with dataset with high dimensional time series input and multiple targets.
  • Publication
    Knowledge Engineering and Ontology for Crime Investigation
    ( 2022)
    Müller, Wilmuth
    ;
    Mühlenberg, Dirk
    ;
    Pallmer, Dirk
    ;
    Zeltmann, Uwe
    ;
    Ellmauer, Christian
    ;
    Demestichas, Konstantinos
    Building upon the possibilities of technologies like ontology engineering, knowledge representational models, and semantic reasoning, our work presented in this paper, which has been performed within the collaborative research project PREVISION (Prediction and Visual Intelligence for Security Information), co-funded by the European Commission within Horizon 2020 programme, is going to support Law Enforcement Agencies (LEAs) in their critical need to exploit all available resources, and handling the large amount of diversified media modalities to effectively carry out criminal investigation. A series of tools have been developed within PREVISION which provide LEAs with the capabilities of analyzing and exploiting multiple massive data streams coming from social networks, the open web, the Darknet, traffic and financial data sources, etc. and to semantically integrate these into dynamic knowledge graphs that capture the structure, interrelations and trends of terrorist groups and individuals and Organized Crime Groups (OCG). The paper at hand focuses on the developed ontology, the tool for Semantic Reasoning and the knowledge base and knowledge visualization.
  • Publication
    Improving Driver Performance and Experience in Assisted and Automated Driving with Visual Cues in the Steering Wheel
    ( 2022)
    Diederichs, Frederik
    ;
    Muthumani, Arun
    ;
    Feierle, Alexander
    ;
    Galle, Melanie
    ;
    Mathis, Lesley-Ann
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    Bopp-Bertenbreiter, Anja Valeria
    ;
    Widlroither, Harald
    ;
    Bengler, Klaus
    In automated driving it is important to ensure drivers’ awareness of the currently active level of automation and to support transitions between those levels. This is possible with a suitable human-machine interface (HMI). In this driving simulator study, two visual HMI concepts (Concept A and B ) were compared with a baseline for informing drivers about three modes: manual driving, assisted driving, and automated driving. The HMIs, consisting of LED strips on the steering wheel that differed in luminance, color, and pattern, provided continuous information about the active mode and announced transitions. The assisted mode was conveyed in Concept A using a combination of amber and blue LEDs, while in Concept B only amber LEDs were used. During automated driving Concept A displayed blue LEDs and Concept B, turquoise. Both concepts were compared to a baseline HMI, with no LEDs. Thirty-eight drivers with driving licence were trained and participated. Objective measures (hands-on-wheel time, takeover time, and visual attention) are reported. Self-reported measures (mode awareness, trust, user experience, and user acceptance) from a previous publication are briefly repeated in this context (Muthumani et al.). Concept A showed 200 ms faster hands-on-wheel times than the baseline, while in Concept B several outliers were observed that prevented significance. The visual HMIs with LEDs did not influence the eyes-on-road time in any of the automation levels. Participants preferred Concept B, with more prominent differentiation between the automation levels, over Concept A.
  • Publication
    Knowledge Engineering for Crime Investigation
    ( 2022)
    Müller, Wilmuth
    ;
    Mühlenberg, Dirk
    ;
    Pallmer, Dirk
    ;
    Zeltmann, Uwe
    ;
    Ellmauer, Christian
    ;
    Pérez Carrasco, Francisco José
    ;
    Garcia, Alberto Garcia
    ;
    Demestichas, Konstantinos
    ;
    Peppes, Nikolaos
    ;
    Touska, Despoina
    ;
    Gkountakos, Konstantinos
    ;
    Muńoz Navarro, Eva
    ;
    Martinez, Santiago
    Building upon the possibilities of technologies like ontology engineering, knowledge representational models, text mining, and semantic reasoning, our work presented in this paper, which has been performed within the collaborative research project PREVISION (Prediction and Visual Intelligence for Security Information), co-funded by the European Commission within Horizon 2020 programme, is going to support Law Enforcement Agencies (LEAs) in their critical need to exploit all available resources, and handling the large amount of diversified media modalities to effectively carry out criminal investigation. A series of tools have been developed within PREVISION which provide LEAS with the capabilities of analyzing and exploiting multiple massive data streams coming from social networks, the open web, the Darknet, traffic and financial data sources, etc. and to semantically integrate these into dynamic knowledge graphs that capture the structure, interrelations and trends of terrorist groups and individuals and OGCs. The paper at hand focuses on the developed ontology and the tools for text mining, Extract Transform Load, Semantic Reasoning and the knowledge base and knowledge visualization.