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Adaptive Optics and Applications, AOA 2022

作者： Zepp, Andreas Gladysz, Szymon Osten, Wolfgang Stein, Karin Fraunhofer IOSB Ettlingen Fraunhofer Institute of Optronics System Technologies and Image Exploitation Gutleuthausstraße 1 Ettlingen76275 Germany University of Stuttgart Institute for Applied Optics ITO Pfaffenwaldring 9 Stuttgart70569 Germany

ISBN: (纸本)9781557528209

The modal holographic wavefront sensor enables fast measurements of individual aberration modes without the need for time-consuming calculations. To minimize the effect of inter-modal crosstalk on the measurement accuracy, we present sensor optimization and show its performance in closed-loop adaptive optics in simulation. © 2022 The Author(s)

关键词： Atmospheric turbulence

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Experimental separation estimation of incoherent optical sources reaching the (quantum) Cramér-Rao bound

Experimental separation estimation of incoherent optical sou...

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2023 Photonics North, PN 2023

作者： Rouviere, Clementine Barral, David Grateau, Antonin Sorelli, Giacomo Karuseichyk, Ilya Walschaers, Mattia Treps, Nicolas Sorbonne Université Collège de France Laboratoire Kastler Brossel ENS-Université Psl Cnrs 24 Rue Lhomond Paris75005 France Fraunhofer Iosb Ettlingen Fraunhofer Institute of Optronics System Technologies and Image Exploitation Gutleuthausstr. 1 Ettlingen76275 Germany

ISBN: (纸本)9798350326734

Resolving light sources below the diffraction limit is a fundamental task both for astronomy and microscopy. Several recent works analysed this problem through the lens of quantum parameter estimation theory and proved that the separation between two point sources can be estimated at the quantum limit using intensity measurements after spatial-mode demultiplexing. We implement this technique and provide an optimal estimator based on a linear combination of demultiplexed intensity measurements [1-3]. Our experimental setup allows for the generation of the images of two sources, with tunable mutual coherence, as well as for spatial mode demultiplexing to estimate their separation [4]. © 2023 IEEE.

关键词： Light sources

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Experimental separation estimation of incoherent optical sources reaching the Cramér-Rao bound

Experimental separation estimation of incoherent optical sou...

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Rouvière, Clémentine Barral, David Grateau, Antonin Sorelli, Giacomo Karuseichyk, Ilya Walschaers, Mattia Treps, Nicolas Laboratoire Kastler Brossel ENS-Université PSL CNRS Sorbonne Université Collège de France 24 Rue Lhomond Paris75005 France Fraunhofer IOSB Ettlingen Fraunhofer Institute of Optronics System Technologies and Image Exploitation Gutleuthausstr. 1 Ettlingen76275 Germany

ISBN: (纸本)9798350345995

Resolving light sources below the diffraction limit is a fundamental task both for astronomy and microscopy. Several recent works analysed this problem through the lens of quantum parameter estimation theory and proved that the separation between two point sources can be estimated at the quantum limit using intensity measurements after spatial-mode demultiplexing. We implement this technique and provide an optimal estimator based on a linear combination of demultiplexed intensity measurements [1-3]. Our experimental setup (see Fig. 1 (left)) allows for the generation of the images of two sources, with tunable mutual coherence, as well as for spatial mode demultiplexing to estimate their separation [4]. © 2023 IEEE.

关键词： Light sources

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Towards a Stateless Cloud Native and Cloud Agnostic Digital Twin Platform for the Digital Product Passport: Out-of-the-box, Reliable, and Scaling

Towards a Stateless Cloud Native and Cloud Agnostic Digital ...

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International Conference on Emerging technologies and Factory Automation (ETFA)

作者： Magnus Redeker Sven Simikin Frank Marek Fraunhofer IOSB IOSB-INA Lemgo Fraunhofer Institute of Optronics System Technologies and Image Exploitation New York University Shanghai China Novatio-Solutions GmbH Germany

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

Interoperable digital twins (DT) simplify and automate the implementation of Industry 4.0 (I4.0) use cases like Collaborative Condition Monitoring (CCM), Product and Corporate Carbon Footprint (PCF, CCF) and the Digital Product Passport (DPP) - aiming at adaptivity, resource-efficiency, user-friendliness, compliance with legal requirements and value-creation. Platforms accelerate the development and deployment of DTs and services and ensure their resilient operation in order to capture especially all relevant shop-floor information, for example to aggregate PCFs, CCFs, and DPPs or to report on sustainability - automatically. However, platform operators should in particular avoid cloud provider lock-ins to be able to react flexibly to use case requirements at any time. The technology must be manageable and costs must be controllable. The aim is to achieve a break-even point between security, costs, future viability and controllability, short implementation time and motivated use. Such requirements can currently only be met with cloud infrastructures. An agnostic design ensures that switching infrastructures is possible and can be done both securely and quickly. To this end, this paper develops an out-of-the-box cloud native and cloud agnostic DT platform providing specialized, stateless, loosely coupled DT services interacting within a service mesh. The developed platform meets the requirements, can easily be executed in any public, private and hybrid cloud, and can be scaled horizontally without any further ado.

关键词： Connectors Condition monitoring Costs Microservice architectures Switches Digital twins Servers Security Carbon footprint Portals

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Concept of an autonomous in-situ sensor system for monitoring object movements caused by sea swells

Concept of an autonomous in-situ sensor system for monitorin...

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OCEANS

作者： Michael Wenrich Helge Renkewitz Sascha Kosleck Chair of Ocean Engineering University of Rostock Rostock Germany System Technologies and Image Exploitation IOSB-AST Fraunhofer Institute of Optronics Ilmenau Germany

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

The concept of an autonomous in-situ sensor system, Motion Probe, for monitoring wave induced motions of underwater objects is presented. This cost-effective system integrates a pressure sensor alongside inertial and optionally global positioning sensors to collect data on the seastate, as well as the movement, acceleration, and orientation of the designated test object. Energy-efficient features, wireless charging, and Wi-Fi data transmission enhance user-friendly operation. An innovative housing design is introduced to protect the sensor from external influences, ensuring its reliability in marine environments. The paper outlines the electronic implementation, energy management, and pressure housing design, emphasizing the Motion Probe’s potential for various research applications. Testing phases include functional tests under controlled laboratory environments and long term sea trials, providing insights into wave inducted object dynamics.

关键词： Pressure sensors Oceans Laboratories Life estimation Reliability engineering Sensor systems Probes Monitoring Wireless fidelity Testing

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Attention-based Point Cloud Edge Sampling

arXiv

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arXiv 2023年

作者： Wu, Chengzhi Zheng, Junwei Pfrommer, Julius Beyerer, Jürgen Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Germany Fraunhofer Institute of Optronics System Technologies and Image Exploitation Iosb Germany

Point cloud sampling is a less explored research topic for this data representation. The most common sampling methods nowadays are still classical random sampling and farthest point sampling. With the development of neural networks, various methods have been proposed to sample point clouds in a task-based learning manner. However, these methods are mostly generative-based, other than selecting points directly with mathematical statistics. Inspired by the Canny edge detection algorithm for images and with the help of the attention mechanism, this paper proposes a non-generative Attention-based Point cloud Edge Sampling method (APES), which can capture the outline of input point clouds. Experimental results show that better performances are achieved with our sampling method due to the important outline information it learned. Copyright © 2023, The Authors. All rights reserved.

关键词： Sampling

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A Cross Branch Fusion-Based Contrastive Learning Framework for Point Cloud Self-supervised Learning

A Cross Branch Fusion-Based Contrastive Learning Framework f...

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International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： Chengzhi Wu Qianliang Huang Kun Jin Julius Pfrommer Jürgen Beyerer Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Germany Department of Automation Tsinghua University China System Technologies and Image Exploitation IOSB Fraunhofer Institute of Optronics Germany

ISBN: (数字)9798350362459

ISBN: (纸本)9798350362466

Contrastive learning is an essential method in self-supervised learning. It primarily employs a multi-branch strategy to compare latent representations obtained from different branches and train the encoder. In the case of multi-modal input, diverse modalities of the same object are fed into distinct branches. When using single-modal data, the same input undergoes various augmentations before being fed into different branches. However, all existing contrastive learning frameworks have so far only performed contrastive operations on the learned features at the final loss end, with no information exchange between different branches prior to this stage. In this paper, for point cloud unsupervised learning without the use of extra training data, we propose a Contrastive Cross-branch Attention-based framework for Point cloud data (termed PoCCA), to learn rich $3 D$ point cloud representations. By introducing sub-branches, PoCCA allows information exchange between different branches before the loss end. Experimental results demonstrate that in the case of using no extra training data, the representations learned with our self-supervised model achieve state-of-the-art performances when used for downstream tasks on point clouds.

关键词： Point cloud compression Representation learning Three-dimensional displays Training data Benchmark testing Data models Task analysis

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Multi-Target Regression and Cross-Validation for Non-isothermal Glass Molding Experiments with Small Sample Sizes

Multi-Target Regression and Cross-Validation for Non-isother...

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Optifab 2023

作者： Mende, Hendrik Kiroriwal, Saksham Pfrommer, Julius Schmitt, Robert H. Beyerer, Jürgen Fraunhofer Institute for Production Technology IPT Steinbachstr. 17 Aachen52074 Germany Fraunhofer Institute of Optronics System Technologies and Image Exploitation IOSB Fraunhoferstr. 1 Karlsruhe76131 Germany Laboratory for Machine Tools and Production Engineering WZL of RWTH Aachen University Campus-Boulevard 30 Aachen52074 Germany

ISBN: (纸本)9781510668058

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. © 2023 SPIE.

关键词： Forecasting

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Towards a One-Stop-Shop Solution for the Application of Data-Driven Value-Adding Services in Production 29

Towards a One-Stop-Shop Solution for the Application of Data...

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29th IEEE International Conference on Emerging technologies and Factory Automation, ETFA 2024

作者： Redeker, Magnus Quirin, Dennis Schroeder, Rafael Klausmann, Tobias Löwen, Alexander Wollbrink, Alexander Stichweh, Heiko Althoff, Simon Bender, Amelie Sextro, Walter Hesse, Marc Fraunhofer Iosb IOSB-INA Lemgo Fraunhofer Institute of Optronics System Technologies and Image Exploitation Germany Bielefeld University Cognitronics & Sensor Systems Germany Friedrich Remmert GmbH Germany R & D Systems Engineering Lenze Se Germany Paderborn University Faculty of Mechanical Engineering Dynamics and Mechatronics Paderborn33098 Germany Weidmüller Group Germany

ISBN: (纸本)9798350361230

The holistic application of data-driven value-adding services (DDSs) to shop-floor assets (SFAs;e.g. equipment, machines, components) is a major obstacle, particularly for small and medium-sized enterprises (SMEs): Due to the high complexity and required expertise in various disciplines. It is necessary to i) select suitable DDSs for a relevant component in production, ii) provide data for their training, iii) deploy the DDSs in the given IT/OT infrastructure, and iv) setup data-streams between SFAs, DDSs, and dashboards. Currently, many companies are implementing flagship projects that go through the necessary steps for individual components, but these isolated solutions are not scalable due to the need for manual intervention. This paper describes how to completely automatize the four steps and their consolidation in a scalable Industry 4.0 one-stop-shop solution. To achieve this goal, each asset is equipped with an Asset Administration Shell (AAS) that specifies, in particular, the represented asset's capabilities, the data required to apply such a capability and the data generated by a capability. Based on the AASs of SFAs, DDSs, and computing resources (CRs), a matching service suggests automatically which DDSs would add value to which SFAs and, for each SFA-DDS match, in which CR to deploy the DDS. A trade fair demonstration provides strong support for the statements made: The one-stop-shop solution including the automatic matching greatly simplifies the application of data-driven services and, in some cases, even makes it possible at all. © 2024 IEEE.

关键词： AAS-based one-stop-shop Industry 4.0-solution Data-driven value-adding services Shop-floor optimization

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Evaluation of an Automated Mapping from ICD-10 to SNOMED CT

Evaluation of an Automated Mapping from ICD-10 to SNOMED CT

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2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022

作者： Philipp, Patrick Veloso, Julia Appenzeller, Arno Hartz, Tobias Beyerer, Juergen Fraunhofer Institute of Optronics System Technologies and Image Exploitation Iosb Fraunhofer Center for Machine Learning Karlsruhe Germany Klinisches Krebsregister Niedersachen Hannover Germany Karlsruhe Institute of Technology Kit Vision and Fusion Laboratory Ies Karlsruhe Germany

ISBN: (纸本)9798350320282

The amount of data in the medical field is constantly increasing. But it is not only the sheer amount of information that is important, but also its quality and type of representation. While nomenclatures such as SNOMED CT (systematized Nomenclature of Medicine and Clinical Term) are suited for finegrained documentation and modern analysis, much information is also bound in classifications. This fact is often historical, as billing systems are typically based on classifications such as ICD-10 and then also had been used for documentation. Leveraging this information automatically is subject of this paper - i.e. enabling an automatic mapping from ICD-10 to SNOMED CT. Because this mapping provides a large set of SNOMED codes for each ICD-10 concept, the approach is non-trivial. In order to pick the best possible code, we propose to take advantage of the hierarchical structure of the SNOMED system to find the concept which lies closer to all candidates in the target system. In other words, our algorithm searches the lowest common ancestor (LCA) of all candidates. For evaluation, we studied 1692 codes from a real-world dataset. The results are promising and show that the proposed approach achieves good results in the majority of cases. © 2022 IEEE.

关键词： Classification (of information)

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