Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

Filters

6 5
Reset filters

Settings
Now showing 1 - 10 of 11
No Thumbnail Available
Publication

New active learning algorithms for near-infrared spectroscopy in agricultural applications

2021 , Krause, Julius , Günder, Maurice , Schulz, Daniel , Gruna, Robin

The selection of training data determines the quality of a chemometric calibration model. In order to cover the entire parameter space of known influencing parameters, an experimental design is usually created. Nevertheless, even with a carefully prepared Design of Experiment (DoE), redundant reference analyses are often performed during the analysis of agricultural products. Because the number of possible reference analyses is usually very limited, the presented active learning approaches are intended to provide a tool for better selection of training samples.

View
No Thumbnail Available
Publication

Generative Machine Learning for Resource-Aware 5G and IoT Systems

2021 , Piatkowski, Nico , Mueller-Roemer, Johannes Sebastian , Hasse, Peter , Bachorek, Adam , Werner, Tim , Birnstill, Pascal , Morgenstern, Andreas , Stobbe, Lutz

Extrapolations predict that the sheer number of Internet-of-Things (IoT) devices will exceed 40 billion in the next five years. Hand-crafting specialized energy models and monitoring sub-systems for each type of device is error prone, costly, and sometimes infeasible. In order to detect abnormal or faulty behavior as well as inefficient resource usage autonomously, it is of tremendous importance to endow upcoming IoT and 5G devices with sufficient intelligence to deduce an energy model from their own resource usage data. Such models can in-turn be applied to predict upcoming resource consumption and to detect system behavior that deviates from normal states. To this end, we investigate a special class of undirected probabilistic graphical model, the so-called integer Markov random fields (IntMRF). On the one hand, this model learns a full generative probability distribution over all possible states of the system-allowing us to predict system states and to measure the probability of observed states. On the other hand, IntMRFs are themselves designed to consume as less resources as possible-e.g., faithful modelling of systems with an exponentially large number of states, by using only 8-bit unsigned integer arithmetic and less than 16KB memory. We explain how IntMRFs can be applied to model the resource consumption and the system behavior of an IoT device and a 5G core network component, both under various workloads. Our results suggest, that the machine learning model can represent important characteristics of our two test systems and deliver reasonable predictions of the power consumption.

View
No Thumbnail Available
Publication

Reference Architecture Model. Version 3.0

2019 , Otto, Boris , Steinbuss, Sebastian , Teuscher, Andreas , Lohmann, Steffen , Bader, Sebastian , Birnstil, P. , Böhmer, M. , Brost, G. , Cirullies, J. , Eitel, A. , Ernst, T. , Geisler, S. , Gelhaar, J. , Gude, R. , Haas, C. , Huber, M. , Jung, C. , Jürjens, J. , Lange, C. , Lis, D. , Mader, C. , Menz, N. , Nagel, R. , Patzer, F. , Pettenpohl, H. , Pullmann, J. , Quix, C. , Schulz, D. , Schütte, J. , et al.

View
No Thumbnail Available
Publication

Situation responsive networking of mobile robots for disaster management

2014 , Kuntze, Helge-Björn , Frey, Christian W. , Emter, Thomas , Petereit, Janko , Tchouchenkov, Igor , Müller, Thomas , Tittel, Martin , Worst, Rainer , Pfeiffer, Kai , Walter, Moriz , Rademacher, Sven , Müller, Fabian

If a natural disaster like an earthquake or an accident in a chemical or nuclear plant hits a populated area, rescue teams have to get a quick overview of the situation in order to identify possible locations of victims, which need to be rescued, and dangerous locations, hich need to be secured. Rescue forces must operate quickly in order to save lives, and they often need to operate in dangerous enviroments. Hence, robot-supported systems are increasingly used to support and accelerate search operations. The objective of the SENEKA concept is the situation responsive networking of various robots and sensor systems used by first responders in order to make the search for victims and survivors more quick and efficient. SENEKA targets the integration of the robot-sensor network into the operation procedures of the rescue teams. The aim of this paper is to inform on the objectives and first research results of the ongoing joint research project SENEKA.

View
No Thumbnail Available
Publication

Grundlagen des Maschinellen Lernens

2021 , Bauckhage, Christian , Hübner, Wolfgang , Hug, Ronny , Paaß, Gerhard , Rüping, Stefan

Zu definieren, was die menschliche Intelligenz sowie intelligentes Handeln – und da­mit auch die Künstliche Intelligenz – ausmacht, ist außerordentlich schwer und be­schäftigt Philosophen und Psychologen seit Jahrtausenden. Allgemein anerkannt istaber, dass die Fähigkeit zu lernen ein zentrales Merkmal vonIntelligenzist. So ist auchdas Forschungsgebiet desMaschinellen Lernens(engl.machine learning, ML) ein zen­traler Teil der Künstlichen Intelligenz, das hinter vielen aktuellen Erfolgen von KI-Sys­temen steckt.

View
No Thumbnail Available
Publication

Deutsche Normungsroadmap Künstliche Intelligenz

2020 , Adler, R. , Kolomiichuk, Sergii , Hecker, Dirk , Lämmel, Philipp , Ma, Jackie , Marko, Angelina , Mock, Michael , Nagel, Tobias , Poretschkin, Maximilian , Rennoch, Axel , Röhler, Marcus , Ruf, Miriam , Schönhof, Raoul , Schneider, Martin A. , Tcholtchev, Nikolay , Ziehn, Jens , Böttinger, Konstantin , Jedlitschka, Andreas , Oala, Luis , Sperl, Philip , Wenzel, Markus , et al.

Die deutsche Normungsroadmap Künstliche Intelligenz (KI) verfolgt das Ziel, für die Normung Handlungsempfehlungen rund um KI zu geben, denn sie gilt in Deutschland und Europa in fast allen Branchen als eine der Schlüsseltechnologien für künftige Wettbewerbsfähigkeit. Die EU geht davon aus, dass die Wirtschaft in den kommenden Jahren mit Hilfe von KI stark wachsen wird. Umso wichtiger sind die Empfehlungen der Normungsroadmap, die die deutsche Wirtschaft und Wissenschaft im internationalen KI-Wettbewerb stärken, innovationsfreundliche Bedingungen schaffen und Vertrauen in die Technologie aufbauen sollen.

View
No Thumbnail Available
Publication

IDS Reference Architecture Model. Industrial Data Space. Version 2.0

2018 , Otto, Boris , Lohmann, Steffen , Steinbuss, Sebastian , Teuscher, Andreas , Auer, Soeren , Boehmer, Martin , Bohn, Juergen , Brost, Gerd , Cirullies, Jan , Ciureanu, Constantin , Corsi, Eva , Danielsen, Soeren , Eitel, Andreas , Ernst, Thilo , Geisler, Sandra , Gelhaar, Joshua , Gude, Roland , Haas, Christian , Heiles, Juergen , Hierro, Juanjo , Hoernle, Joachim , Huber, Manuel , Jung, Christian , Juerjens, Jan , Kasprzik, Anna , Ketterl, Markus , Koetzsch, Judith , Koehler, Jacob , Lange, Christoph , Langer, Dorothea , Langkau, Joerg , Lis, Dominik , Loeffler, Sven , Loewen, Ulrich , Mader, Christian , Menz, Nadja , Mueller, Andreas , Mueller, Bernhard , Nagel, Lars , Nagel, Ralf , Nieminen, Harri , Reitelbach, Thomas , Resetko, Aleksei , Pakkala, Daniel , Patzer, Florian , Pettenpohl, Heinrich , Pietzsch, Rene , Pullmann, Jaroslav , Punter, Matthijs , Quix, Christoph , Rohrmus, Dominik , Romer, Lena , Sandloehken, Joerg , Schoewe, Patrick , Schulz, Daniel , Schuette, Julian , Schweichhart, Karsten , Sol, Egbert-Jan , Sorowka, Peter , Spiegelberg, Gernot , Spiekermann, Markus , Spohn, Christian , Stoehr, Gerrit , Thess, Michael , Tramp, Sebastian , Wappler, Mona , Weiergraeber, Ann-Christin , Wenzel, Sven , Wolff, Oliver , Woerner, Heike

View
No Thumbnail Available
Publication

Tiefe neuronale Netze

2021 , Bauckhage, Christian , Hübner, Wolfgang , Hug, Ronny , Paaß, Gerhard

View
No Thumbnail Available
Publication

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

2019-03-29 , Rüden, Laura von , Mayer, Sebastian , Beckh, Katharina , Georgiev, Bogdan , Giesselbach, Sven , Heese, Raoul , Kirsch, Birgit , Pfrommer, Julius , Pick, Annika , Ramamurthy, Rajkumar , Schuecker, Jannis , Garcke, Jochen , Bauckhage, Christian , Walczak, Michal

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. First, we provide a definition and propose a concept for informed machine learning, which illustrates its building blocks and distinguishes it from conventional machine learning. Second, 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. Third, 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.

View
No Thumbnail Available
Publication

Kognitive Systeme und Robotik

2018 , Bauckhage, Christian , Bauernhansl, Thomas , Beyerer, Jürgen , Garcke, Jochen

Kognitive Systeme können komplexe Prozesse überwachen, analysieren und gewinnen daraus auch die Fähigkeit, in ungeplanten oder unbekannten Situationen richtig zu entscheiden. Fraunhofer-Experten setzen Verfahren des maschinellen Lernens ein, um neue kognitive Funktionen für Roboter und Automatisierungslösungen zu nutzen. Dazu statten sie Systeme mit Technologien aus, die von menschlichen Fähigkeiten inspiriert sind bzw. diese imitieren und optimieren. Der Bericht beschreibt diese Technologien, erläutert aktuelle Anwendungsbeispiele und entwirft Szenarien für zukünftige Anwendungsfelder.

View