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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Quantum ghost imaging using asynchronous detection

2021 , Pitsch, Carsten , Walter, Dominik , Grosse, Simon , Brockherde, Werner , Bürsing, Helge , Eichhorn, Marc

We present first results of a novel type of setup for quantum ghost imaging based on asynchronous single photon timing using single photon avalanche diode (SPAD) detectors. This scheme enables photon pairing with arbitrary path length difference and does, therefore, obviate the dependence on optical delay lines of current quantum ghost imaging setups [Nat. Commun. 6, 5913 (2015) [CrossRef]]. It is also, to our knowledge, the first quantum ghost imaging setup to allow three-dimensional imaging.

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Testing Automated Operation and Control Algorithms for Distribution Grids Using a Co-Simulation Environment

2021 , Schoen, Andrea , Ringelstein, Jan , Hammermeister, Irene , Braun, Martin , Wille-Haussmann, Bernhard , Marchand, Sophie , Ruhe, Stephan , Nicolai, Steffen , Braun, Martin

This publication presents a co-simulation framework that enables the coupling of various simulation components of energy systems that are not only modeled at three geographically distributed Fraunhofer Institutes, but are also vastly different in terms of their functionality, control algorithms, time resolution, speeds and used tools. This framework facilitates their joint usage despite these differences especially with regard to their time resolutions (real-time combined with non-real-time systems) and is applied in the DistributedGridLab of the Fraunhofer Cluster of Excellence Integrated Energy Systems (CINES) [1]. The DistributedGridLab thus allows users - e.g. grid operators, manufacturers and research institutes - to test their solutions before field deployment with special consideration of their interaction with other solutions at different remote testing facilities, and without the need to use the same software and hardware setups. A demonstrator of the DistributedGridLab is introduced where an electric energy system is modeled, which contains a medium voltage grid with three connected low voltage grids. The different control approaches interact to stabilize the entire system without a centrally controlled simulation of the systems.

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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.

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Optimal, blind-search modal wavefront correction in atmospheric turbulence. Part I: Simulations

2021 , Segel, Max , Gladysz, Szymon

Modal control is an established tool in adaptive optics. It allows not only for the reduction in the controllable degrees of freedom, but also for filtering out unseen modes and optimizing gain on a mode-by-mode basis. When Zernike polynomials are employed as the modal basis for correcting atmospheric turbulence, their cross-correlations translate to correction errors. We propose optimal modal decomposition for gradient-descent-based wavefront sensorless adaptive optics, which is free of this problem. We adopt statistically independent Karhunen-Loève functions for iterative blind correction and analyze performance of the algorithm in static as well as in dynamic simulated turbulence conditions.

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Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking

2021 , Maier, Georg , Pfaff, Florian , Pieper, Christoph , Gruna, Robin , Noack, Benjamin , Kruggel-Emden, Harald , Längle, Thomas , Hanebeck, Uwe D. , Wirtz, Siegmar , Scherer, Viktor , Beyerer, Jürgen

Sensor-based sorting is a machine vision application that has found industrial application in various fields. An accept-or-reject task is executed by separating a material stream into two fractions. Current systems use line-scanning sensors, which is convenient as the material is perceived during transportation. However, line-scanning sensors yield a single observation of each object and no information about their movement. Due to a delay between localization and separation, assumptions regarding the location and point in time for separation need to be made based on the prior localization. Hence, it is necessary to ensure that all objects are transported at uniform velocities. This is often a complex and costly solution. In this paper, we propose a new method for reliably separating particles at non-uniform velocities. The problem is transferred from a mechanical to an algorithmic level. Our novel advanced image processing approach includes equipping the sorter with an area-scan camera in combination with a real-time multiobject tracking system, which enables predictions of the location of individual objects for separation. For the experimental validation of our approach, we present a modular sorting system, which allows comparing sorting results using a line-scan and area-scan camera. Results show that our approach performs reliable separation and hence increases sorting efficiency.

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Cleaning strategies and cost modelling of experimental membrane-based desalination plants

2021 , Karimanzira, Divas , Went, Joachim , Neumann, Frank

In Project WASTEC, an experimental Reverse Osmosis (RO) desalination system was developed. It serves as a platform for testing new technologies. For this system, we solved two problems, which are described in this paper. Firstly, we developed and investigated strategies for scheduling chemical enhanced backwashing and chemical cleaning and secondly, due to the experimental nature of the project, several new technological developments with respect to materials and methods were integrated into the system and requires tools for evaluating the economic viability of the new technologies. In this task, the economics of membrane-based desalination will be investigated. Baseline systems of reverse osmosis and pretreatment systems (microfiltration and ultrafiltration) will be economically examined and compared for their investments and operational costs. Sensitivity of the different plant and membrane parameters to the cost will be studied. Results show that with respect to costs, for a 200m3/hr design capacity plant, a volume of water is produced by a MF process at a cost of $0.494 and at a cost of $0.486 by an ultrafiltration process microfiltration. The reverse osmosis process cannot be compared directly, but it required $ 0.49 / m3 for a plant with 56 m3/hour design capacity. The values are in line with the costs reported in literature for membrane-based filtration.

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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.

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SmartSpectrometer - Embedded Optical Spectroscopy for Applications in Agriculture and Industry

2021 , Krause, Julius , Grüger, Heinrich , Gebauer, Lucie , Zheng, Xiaorong , Knobbe, Jens , Pügner, Tino , Kicherer, Anna , Gruna, Robin , Längle, Thomas , Beyerer, Jürgen

The ongoing digitization of industry and agriculture can benefit significantly from optical spectroscopy. In many cases, optical spectroscopy enables the estimation of properties such as substance concentrations and compositions. Spectral data can be acquired and evaluated in real time, and the results can be integrated directly into process and automation units, saving resources and costs. Multivariate data analysis is needed to integrate optical spectrometers as sensors. Therefore, a spectrometer with integrated artificial intelligence (AI) called SmartSpectrometer and its interface is presented. The advantages of the SmartSpectrometer are exemplified by its integration into a harvesting vehicle, where quality is determined by predicting sugar and acid in grapes in the field.

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Adaptable Shack-Hartmann wavefront sensor with diffractive lenslet arrays to mitigate the effects of scintillation

2020 , Lechner, Daniel , Zepp, Andreas , Eichhorn, Marc , Gladysz, Szymon

Adaptive optics systems are used to compensate for distortions of the wavefront of light induced by turbulence in the atmosphere. Shack-Hartmann wavefront sensors are used to measure this wavefront distortion before correction. However, in turbulence conditions where strong scintillation (intensity fluctuation) is present, these sensors show considerably worse performance. This is partly because the lenslet arrays of the sensor are designed without regard to scintillation and are not adaptable to changes in turbulence strength. Therefore, we have developed an adaptable Shack-Hartmann wavefront sensor that can flexibly exchange its lenslet array by relying on diffractive lenses displayed on a spatial light modulator instead of utilizing a physical microlens array. This paper presents the principle of the sensor, the design of a deterministic turbulence simulation test-bed, and an analysis how different lenslet arrays perform in scintillation conditions. Our experiments with different turbulence conditions showed that it is advantageous to increase the lenslet size when scintillation is present. The residual phase variance for an array with 24 lenslets was up to 71% lower than for a 112 lenslet array. This shows that the measurement error of focal spots has a strong influence on the performance of a Shack-Hartmann wavefront sensor and that in many cases it makes sense to increase the lenslet size. With our adaptable wavefront sensor such changes in lenslet configurations can be done very quickly and flexibly.

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