Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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Quantum computer-aided job scheduling for storage and retrieval systems

2024 , Windmann, Stefan

In this paper, a quantum computer-aided approach to job scheduling for automated storage and retrieval systems is introduced. The approach covers application cases, where various objects need to be transported between storage positions and the order of transport operations can be freely chosen. The objective of job scheduling is to arrange the transport operations in a sequence, where the cumulative costs of the transport operations and empty runs between subsequent transport operations are minimized. The scheduling problem is formulated as an asymmetric quadratic unconstrained binary optimization (QUBO) problem, in which the transport operations are modeled as nodes and empty runs are modeled as edges, with costs assigned to each node and each edge. An Quantum Approximate Optimization Algorithm (QAOA) is used to solve the QUBO. Evaluations of the quantum computer-aided job scheduling approach have been conducted on the IBM Q System One quantum computer in Ehningen. In particular, the running time for the solution of the QUBO has been investigated, as well as the scalability of the approach with respect to the required number of qubits.

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The K-functional and reiteration theorems for left and right spaces, Part I

2024 , Doktorski, Leo , Fernández-Martínez, Pedro , Signes, Teresa

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Exploring decentralized data management: a case study of changing energy suppliers in Germany

2024 , Rülicke, Linda , Fehrle, Florian , Martin, Arne , Monti, Antonello , Berkhout, Volker , Warweg, Oliver , Möller, Sven

This paper presents an innovative approach to decentralized data management in the German energy market, focusing on the use of decentralized data management with the help of Data Spaces to facilitate the automated change of energy suppliers within 24 h. The central focus of this research is the MakoMaker Space, a demonstrator project that employs the Connector from the Eclipse Data Space Components. The MakoMaker project demonstrates the successful automation of energy supplier changes, emphasizing the preservation of customer data sovereignty. It shows an alternative approach to the process, putting the customer into the center. Customers retain control of their data, which is accessible to providers as needed. While the paper discusses the potential for further enhancements, such as the integration of an identity provider and the development of a sustainable business model for service coordination, the primary focus is on the demonstrator’s successful application in a pilot setting.

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Temperature investigation of low SWaP thulium-doped fiber lasers

2024 , Panitzek, Dieter , Romano, Clément , Eichhorn, Marc , Kieleck, Christelle

We investigate the temperature dependence of an in-band core-pumped thulium-doped fiber laser with a low SWaP (size, weight, and power) architecture. The temperature investigation is carried out both experimentally and numerically by a simulation model. We demonstrate experimentally that the investigated setup is resistant for temperatures till 353 K. In addition, we explain the observed behavior by considering the temperature depended spectroscopic parameters of thulium-doped silica fibers. Finally, a numerical investigation is carried out for higher temperatures up to 573 K and higher output powers up to 12 W as well as for different wavelengths and show that the considered fiber lasers works still efficient at these temperature ranges. We show the reliability of the considered thulium-doped fiber laser architecture for applications in harsh environment.

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Sensor-based characterization of construction and demolition waste at high occupancy densities using synthetic training data and deep learning

2024 , Kronenwett, Felix , Maier, Georg , Leiss, Norbert , Gruna, Robin , Thome, Volker , Längle, Thomas

Sensor-based monitoring of construction and demolition waste (CDW) streams plays an important role in recycling (RC). Extracted knowledge about the composition of a material stream helps identifying RC paths, optimizing processing plants and form the basis for sorting. To enable economical use, it is necessary to ensure robust detection of individual objects even with high material throughput. Conventional algorithms struggle with resulting high occupancy densities and object overlap, making deep learning object detection methods more promising. In this study, different deep learning architectures for object detection (Region-based CNN/Region-based Convolutional Neural Network (Faster R-CNN), You only look once (YOLOv3), Single Shot MultiBox Detector (SSD)) are investigated with respect to their suitability for CDW characterization. A mixture of brick and sand-lime brick is considered as an exemplary waste stream. Particular attention is paid to detection performance with increasing occupancy density and particle overlap. A method for the generation of synthetic training images is presented, which avoids time-consuming manual labelling. By testing the models trained on synthetic data on real images, the success of the method is demonstrated. Requirements for synthetic training data composition, potential improvements and simplifications of different architecture approaches are discussed based on the characteristic of the detection task. In addition, the required inference time of the presented models is investigated to ensure their suitability for use under real-time conditions.

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Plattform W-Net 4.0: Dokumentation, Simulation und Betriebsoptimierung von rohrgebundenen Versorgungssystemen

2024 , Deuerlein, Jochen , Bernard, Thomas , Ziebarth, Mathias , Canzler, Armin , Keifenheim, Heiko , Wiese, Susanne , Parra, Salomé

In der Plattform W-Net 4.0 werden Geoinformationssystem, Simulationssoftware und Datenanalysetools in einer integrierten Plattform zusammengeführt. Damit werden kleine und mittlere Wasserversorger erstmals befähigt, einfach und ohne Risiko die Chancen der Digitalisierung umfänglich zu nutzen. Die Plattform kann auch für die Dokumentation, Simulation und Betriebsoptimierung von Fernwärme- und Gasnetzen sowie deren Koordination eingesetzt werden.

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High-power thulium-doped fiber MOPA emitting at 2036 nm

2024 , Romano, Clément , Panitzek, Dieter , Lorenz, Dominik , Forster, Patrick , Eichhorn, Marc , Kieleck, Christelle

An all-fiber laser system is presented with a simple MOPA configuration composed of a seed laser followed by a one-stage high-power amplifier. The seed delivers 10 W of output power at 2036 nm. The high-power amplifier operates with a high slope efficiency of 59 %. An output power of 937 W is demonstrated with a close to diffraction limited beam quality. No nonlinear effects were observed.

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The Drone-vs-Bird Detection Grand Challenge at ICASSP 2023: A Review of Methods and Results

2024 , Coluccia, Angelo , Fascista, Alessio , Sommer, Lars Wilko , Schumann, Arne , Dimou, Anastasios , Zarpalas, Dimitrios

This paper presents the 6th edition of the Drone-vs-Bird detection challenge, jointly organized with the WOSDETC workshop within the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2023. The main objective of the challenge is to advance the current state-of-the-art in detecting the presence of one or more Unmanned Aerial Vehicles (UAVs) in real video scenes, while facing challenging conditions such as moving cameras, disturbing environmental factors, and the presence of birds flying in the foreground. For this purpose, a video dataset was provided for training the proposed solutions, and a separate test dataset was released a few days before the challenge deadline to assess their performance. The dataset has continually expanded over consecutive installments of the Drone-vs-Bird challenge and remains openly available to the research community, for non-commercial purposes. The challenge attracted novel signal processing solutions, mainly based on deep learning algorithms. The paper illustrates the results achieved by the teams that successfully participated in the 2023 challenge, offering a concise overview of the state-of-the-art in the field of drone detection using video signal processing. Additionally, the paper provides valuable insights into potential directions for future research, building upon the main pros and limitations of the solutions presented by the participating teams.

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Towards intelligent energy management in energy communities: Introducing the district energy manager and an IT reference architecture for district energy management systems

2024 , Sauerbrey, Juliane , Bender, Tom , Flemming, Sebastian , Martin, Arne , Naumann, Steffi , Warweg, Oliver

District energy management offers possibilities for optimal energy usage on a local scale. However, it presents challenges due to multiple stakeholders, heterogeneous assets, and varying needs. In this article, an IT reference architecture for a cross-sectoral district energy management system is presented. This architecture addresses the aforementioned challenges and aims to optimize energy usage within the district. To define an optimal reference architecture, existing roles in the energy system are analyzed and mapped onto the structure of the district. 17 key roles for district energy management are identified, with the district energy manager being introduced as a new central role. A requirements analysis identifies the main tasks for an efficient district energy management, including forecasting, optimization and the management of flexibilities. During the design of the architecture, four primary software modules for data preprocessing, forecasting, optimization and balancing are defined. These are accompanied by five secondary, optional modules. The modularity of the system is prioritized, enabling customization to suit the specific needs of each district. The architecture comprehensively covers both technical and organizational aspects, taking into consideration the requirements of relevant key roles. By acting as a central unit within the district, this architecture facilitates holistic energy management.

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A survey of the state of the art in sensor-based sorting technology and research

2024 , Maier, Georg , Gruna, Robin , Längle, Thomas , Beyerer, Jürgen

Sensor-based sorting describes a family of systems that enable the removal of individual objects from a material stream. The technology is widely used in various industries such as agriculture, food, mining, and recycling. Examples of sorting tasks include the removal of fungus-infested grains, the enrichment of copper content in copper mining or the sorting of plastic waste according to the type of plastic. Sorting decisions are made based on information acquired by one or more sensors. A particular strength of the technology is the flexibility in sorting decisions, which is achieved by using various sensors and programming the data analysis. However, a comprehensive understanding of the process is necessary for the development of new sorting systems that can address previously unresolved tasks. This survey is aimed at innovative researchers and practitioners who are unfamiliar with sensor-based sorting or have only encountered certain aspects of the overall process. The references provided serve as starting points for further exploration of specific topics.

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