Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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Application of LSTM Networks for Water Demand Prediction in Optimal Pump Control

2021 , Kühnert, Christian , Gonuguntla, Naga , Krieg, Helene , Nowak, Dimitri , Thomas, Jorge A.

Every morning, water suppliers need to define their pump schedules for the next 24 h for drinking water production. Plans must be designed in such a way that drinking water is always available and the amount of unused drinking water pumped into the network is reduced. Therefore, operators must accurately estimate the next day's water consumption profile. In real-life applications with standard consumption profiles, some expert system or vector autoregressive models are used. Still, in recent years, significant improvements for time series prediction have been achieved through special deep learning algorithms called long short-term memory (LSTM) networks. This paper investigates the applicability of LSTM models for water demand prediction and optimal pump control and compares LSTMs against other methods currently used by water suppliers. It is shown that LSTMs outperform other methods since they can easily integrate additional information like the day of the week or national holidays. Furthermore, the online- and transfer-learning capabilities of the LSTMs are investigated. It is shown that LSTMs only need a couple of days of training data to achieve reasonable results. As the focus of the paper is on the real-world application of LSTMs, data from two different water distribution plants are used for benchmarking. Finally, it is shown that the LSTMs significantly outperform the system currently in operation.

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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.

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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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Research Data in the Fraunhofer Digital Project. Creating a FAIR Research Data Infrastructure and Culture

2018 , Beyan, O. , Wuchner, Andrea , Eisengräber-Pabst, Dirk , Quix, C. , Zaschke, Christian , Schumacher, Oliver

The Fraunhofer Society, as the leading organization for applied research in Europe, conducts its research activities by 72 institutes and research units at locations throughout Germany. The Fraunhofer Digital project, a part of the Fraunhofer 2022 Agenda, introduces the vision, that research and administrative data will be linked, aggregated and analyzed in order to optimize the research and development processes and support management decisions. As a part of this vision, the Research Data in the Fraunhofer Data Space project develops methods to reuse the research data in a broader sense, and integrate research data silos in various institutes into Fraunhofer Data Space. In this work we present the concept, early outcomes, and a FAIRness assessment of the research data repository approach. In our assessment we used the FAIR metrics, and as a result developed a set of recommendations. These recommendations will be utilized to establish a digital research data infrastructure as well as a research data reuse culture in Fraunhofer. They can be also useful for other large scale institutions with heterogeneous research communities.

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Towards Distributed Healthcare Systems - Virtual Data Pooling Between Cancer Registries as Backbone of Care and Research

2021 , Appenzeller, Arno , Bartholomäus, Sebastian , Breitschwerdt, Rüdiger , Claussen, Carsten , Geisler, Sandra , Hartz, Tobias , Kachel, Philipp , Krempel, Erik , Robert, Sebastian , Zeissig, Sylke Ruth

German cancer registries offer a systematic approach for the collection, storage, and management of data on patients with cancer and related diseases. Much hope in research and healthcare in general is depending on such register-based analyses in order to comprehensively consider the features of a highly diverse population. Next to the data collection the cancer registries are responsible for data protection. To fulfill legal regulations, access to data has to be controlled in a strict way leading to sometimes bureaucratic and slow processes. The situation is especially complicated in Germany, since cancer data is distributed over numerous federal cancer registries. If a nationwide data evaluation is conducted a research team has to negotiate a separate contract with each cancer registry. In a joint work in progress effort of cancer registries, technical, medical, and economical experts we propose a different solution for cooperative data processing. Our approach aims for combining data in a virtual pool based on the selection criteria of individual requests from researchers. To achieve our goal, we adapt the Fraunhofer Medical Data Space as enabling technology. The architecture we propose will allow us to pool data of multiple partners regulated by data access policies. In doing so, each of the data sources can introduce its own rules and specifications on how data is used. Additionally, we add a digital consent management that will allow individual patients to decide how their data is used. Finally, we show the high potential of the cooperative analysis of distributed cancer data supported by the proposed solution in our approach.

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Multimedia analysis platform for crime prevention and investigation. Results of MAGNETO project

2021 , Perez, Francisco J. , Garrido, Victor J. , Garcia, Alberto , Zambrano, Marcelo , Kozik, Rafal , Choras, Michal , Mühlenberg, Dirk , Pallmer, Dirk , Müller, Wilmuth

Nowadays, the use of digital technologies is promoting three main characteristics of information, i.e. the volume, the modality and the frequency. Due to the amount of information generated by tools and individuals, it has been identified a critical need for the Law Enforcement Agencies to exploit this information and carry out criminal investigations in an effective way. To respond to the increasing challenges of managing huge amounts of heterogeneous data generated at high frequency, the paper outlines a modular approach adopted for the processing of information gathered from different information sources, and the extraction of knowledge to assist criminal investigation. The proposed platform provides novel technologies and efficient components for processing multimedia information in a scalable and distributed way, allowing Law Enforcement Agencies to make the analysis and a multidimensional visualization of criminal information in a single and secure point.

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The Virtual Caliper: Rapid Creation of Metrically Accurate Avatars from 3D Measurements

2019 , Pujades, Sergi , Mohler, Betty , Thaler, Anne , Tesch, Joachim , Mahmood, Naureen , Hesse, Nikolas , Bülthoff, Heinrich H. , Black, Michael J.

Creating metrically accurate avatars is important for many applications such as virtual clothing try-on, ergonomics, medicine, immersive social media, telepresence, and gaming. Creating avatars that precisely represent a particular individual is challenging however, due to the need for expensive 3D scanners, privacy issues with photographs or videos, and difficulty in making accurate tailoring measurements. We overcome these challenges by creating "The Virtual Caliper", which uses VR game controllers to make simple measurements. First, we establish what body measurements users can reliably make on their own body. We find several distance measurements to be good candidates and then verify that these are linearly related to 3D body shape as represented by the SMPL body model. The Virtual Caliper enables novice users to accurately measure themselves and create an avatar with their own body shape. We evaluate the metric accuracy relative to ground truth 3D body scan data, compare the method quantitatively to other avatar creation tools, and perform extensive perceptual studies. We also provide a software application to the community that enables novices to rapidly create avatars in fewer than five minutes. Not only is our approach more rapid than existing methods, it exports a metrically accurate 3D avatar model that is rigged and skinned.

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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.

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Auswirkungen der Coronapandemie auf die Entwicklung von Kommunen und Landkreisen in Deutschland

2021 , Ottendörfer, Eva , Bieker, Susanne , Kaiser, Urban , Frieling, Hendrik , Gölz, Sebastian , Neumann, Marcel , Guckenbiehl, Pascal , Henze-Sakowsky, Annika , Schmitt, Anna , Richter, Christine , Pollmer, Uta

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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.

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