TY - GEN A1 - Braun, Alexander T1 - Laserspektroskopische Messverfahren zur Temperaturbestimmung in Sprays Y1 - 2001 PB - Georg-August-Universität Göttingen CY - Göttingen ER - TY - JOUR A1 - Balzer, Chr. A1 - Braun, Alexander A1 - Hannemann, T. A1 - Paape, Chr. A1 - Ettler, M. A1 - Neuhauser, W. A1 - Wunderlich, Chr. T1 - Electrodynamically trapped Yb+ ions for quantum information processing JF - Physical Review A Y1 - 2006 U6 - https://doi.org/10.1103/PhysRevA.73.041407 SN - 2469-9934 VL - 73 IS - 4 PB - American Physical Society ER - TY - JOUR A1 - Heizmann, Michael A1 - Braun, Alexander A1 - Glitzner, Markus A1 - Günther, Matthias A1 - Hasna, Günther A1 - Klüver, Christina A1 - Krooß, Jakob A1 - Marquardt, Erik A1 - Overdick, Michael A1 - Ulrich, Markus T1 - Implementing machine learning: chances and challenges JF - at - Automatisierungstechnik Y1 - 2022 U6 - https://doi.org/10.1515/auto-2021-0149 SN - 2196-677X VL - 70 IS - 1 SP - 90 EP - 101 PB - De Gruyter CY - Berlin ER - TY - CHAP A1 - Christmann, Simon A1 - Busboom, Imke A1 - Feige, Volker K.S. A1 - Haehnel, Hartmut T1 - Towards Automated Quality Inspection Using a Semi-Mobile Robotized Terahertz System T2 - 2020 Third International Workshop on Mobile Terahertz Systems (IWMTS) KW - angle compensation KW - automatic alignment KW - Projekt Smart Production KW - robot KW - terahertz time-domain spectroscopy KW - thickness measurements Y1 - 2020 SN - 978-1-7281-5598-2 U6 - https://doi.org/10.1109/IWMTS49292.2020.9166259 SP - 1 EP - 5 PB - IEEE CY - Essen ER - TY - JOUR A1 - Schuller, Bjorn A1 - Vlasenko, Bogdan A1 - Eyben, Florian A1 - Wollmer, Martin A1 - Stuhlsatz, André A1 - Wendemuth, Andreas A1 - Rigoll, Gerhard T1 - Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies JF - IEEE Transactions on Affective Computing Y1 - 2010 U6 - https://doi.org/10.1109/t-affc.2010.8 VL - 1 IS - 2 SP - 119 EP - 131 PB - IEEE ER - TY - JOUR A1 - Lyalina, Yuliya A1 - Langmann, Reinhard A1 - Krisilov, V. T1 - The Interaction Model in iLearning Environments and its Use in the Smart Lab Concept JF - International Journal of Online Engineering Y1 - 2011 U6 - https://doi.org/10.3991/ijoe.v7i4.1793 VL - 7 IS - 4 PB - iJOE ER - TY - JOUR A1 - Queval, Loic A1 - Gottkehaskamp, Raimund T1 - Analytical Field Calculation of Modulated Double Helical Coils JF - IEEE Transactions on Applied Superconductivity Y1 - 2015 U6 - https://doi.org/10.1109/tasc.2015.2477377 VL - 25 IS - 6 SP - 1 EP - 7 PB - IEEE ER - TY - JOUR A1 - Franz, Jürgen T1 - Benefits of Communication Methods in Environmental Protection JF - IETE Technical Review Y1 - 1996 U6 - https://doi.org/10.1080/02564602.1996.11416600 VL - 13 IS - 3 SP - 153 EP - 162 PB - Taylor & Francis ER - TY - JOUR A1 - Jain, V. K. A1 - Franz, J. H. T1 - Some Design Issues in Infrared Wireless Networks JF - IETE Technical Review Y1 - 2004 U6 - https://doi.org/10.1080/02564602.2004.11417161 VL - 21 IS - 5 SP - 335 EP - 343 PB - Taylor & Francis ER - TY - JOUR A1 - Heinicke, Christian A1 - Baekler, Peter A1 - Hehl, Friedrich W. T1 - Einstein-aether theory, violation of Lorentz invariance, and metric-affine gravity JF - Physical Review D Y1 - 2005 U6 - https://doi.org/10.1103/physrevd.72.025012 VL - 72 IS - 2 PB - APS ER - TY - JOUR A1 - Baekler, Peter A1 - Hehl, Friedrich W. T1 - ROTATING BLACK HOLES IN METRIC-AFFINE GRAVITY JF - International Journal of Modern Physics D Y1 - 2006 U6 - https://doi.org/10.1142/s0218271806008589 VL - 15 IS - 5 SP - 635 EP - 668 PB - World Scientific ER - TY - JOUR A1 - Langmann, Reinhard T1 - Distributed Web-Based Control System JF - International Journal of Online Engineering (iJOE) Y1 - 2010 U6 - https://doi.org/10.3991/ijoe.v6i3.1345 VL - 6 IS - 3 PB - iJOE ER - TY - JOUR A1 - Baekler, Peter A1 - Hehl, Friedrich W. A1 - Nester, James M. T1 - Poincaré gauge theory of gravity: Friedman cosmology with even and odd parity modes: Analytic part JF - Physical Review D Y1 - 2011 U6 - https://doi.org/10.1103/physrevd.83.024001 VL - 83 IS - 2 PB - APS ER - TY - JOUR A1 - Baekler, Peter A1 - Hehl, Friedrich W. T1 - Beyond Einstein–Cartan gravity: quadratic torsion and curvature invariants with even and odd parity including all boundary terms JF - Classical and Quantum Gravity Y1 - 2011 U6 - https://doi.org/10.1088/0264-9381/28/21/215017 VL - 28 IS - 21 PB - IOP ER - TY - JOUR A1 - Langmann, Reinhard A1 - Stiller, Michael T1 - Industrial Cloud – Status und Ausblick JF - HMD Praxis der Wirtschaftsinformatik Y1 - 2015 U6 - https://doi.org/10.1365/s40702-015-0162-0 VL - 52 IS - 5 SP - 647 EP - 664 PB - Springer Nature ER - TY - JOUR A1 - Richter, Jessica A1 - Steenmann, Anna A1 - Schellscheidt, Benjamin A1 - Licht, Thomas T1 - On-Chip Diffusion Bonding creates Stable Interconnections Usable at Temperatures over 300°C JF - International Symposium on Microelectronics Y1 - 2019 U6 - https://doi.org/10.4071/2380-4505-2019.1.000530 VL - 2019 IS - 1 SP - 000530 EP - 000534 PB - IMAPS ER - TY - JOUR A1 - Kagermann, Dominik A1 - Lang, Kati A1 - Roscher, Angélique T1 - Mehr Lernerfolg durch die Digitalisierung der Lehre JF - Sales Excellence Y1 - 2020 U6 - https://doi.org/10.1007/s35141-020-0392-9 VL - 29 IS - 11 SP - 20 EP - 23 PB - Springer Fachmedien ER - TY - JOUR A1 - Heizmann, Michael A1 - Braun, Alexander A1 - Hüttel, Markus A1 - Klüver, Christina A1 - Marquardt, Erik A1 - Overdick, Michael A1 - Ulrich, Markus T1 - Artificial intelligence with neural networks in optical measurement and inspection systems JF - at - Automatisierungstechnik Y1 - 2020 U6 - https://doi.org/10.1515/auto-2020-0006 VL - 68 IS - 6 SP - 477 EP - 487 PB - De Gruyter ER - TY - JOUR A1 - Di Gioia, Antonio A1 - Brown, Ian P. A1 - Nie, Yue A1 - Knippel, Ryan A1 - Ludois, Daniel C. A1 - Dai, Jiejian A1 - Hagen, Skyler A1 - Alteheld, Christian T1 - Design and Demonstration of a Wound Field Synchronous Machine for Electric Vehicle Traction With Brushless Capacitive Field Excitation JF - IEEE Transactions on Industry Applications N2 - This paper describes the modeling, optimization, mechanical design, and experimental characterization of a high power density wound field synchronous machine (WFSM) for electric vehicle traction applications. The WFSM is designed for brushless rotor field excitation using an axial flux hydrodynamic capacitive power coupler (CPC). The flexible design environment, which was used for large-scale multiobjective optimization of the WFSM, is described. A prototype WFSM, spray cooled with automatic transmission fluid, with an 80-kW output at a base speed of 4000 r/min has been tested. The prototyped WFSM achieves volumetric torque and power densities of 17.22 N·m/L and 7.19 kW/L referred to the envelope cylindrical volume of the active materials plus spray cooling rings. The specific torque and power densities are 4.69 N·m/kg and 1.95 kW/kg referred to the mass of the active materials and the shaft. The prototyped CPC provided up to 1-kW excitation power with a mass 600 g and volume of 0.271 L. Y1 - 2018 U6 - https://doi.org/10.1109/tia.2017.2784799 VL - 54 IS - 2 SP - 1390 EP - 1403 PB - IEEE ER - TY - CHAP A1 - Müller, Patrick A1 - Lehmann, Matthias A1 - Braun, Alexander ED - Kress, Bernard C. ED - Schelkens, Peter T1 - Optical quality metrics for image restoration T2 - Digital Optical Technologies 2019 N2 - Image restoration is a process used to remove blur (from different sources like object motion or aberrations) from images by either non-blind or blind-deconvolution. The metrics commonly used to quantify the restoration process are peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Often only a small sample of test images are used (like Lena or the camera guy). In optical design research PSNR and SSIM are not normally used, here image quality metrics based on linear system theory (e.g. modulation transfer function, MTF) are used to quantify optical errors like spherical or chromatic aberration. In this article we investigate how different image restoration algorithms can be quantified by applying image quality metrics. We start with synthetic image data that is used in camera test stands (e.g. Siemens star etc.), apply two different spatially variant degradation algorithms, and restore the original image by a direct method (Wiener filtering within sub-images), and by an iterative method (alternating direction method of multipliers, ADMM). Afterwards we compare the quality metrics (like MTF curves) for the original, the degraded and the restored image. As a first result we show that restoration algorithms sometimes fail in dealing with non-natural scenes, e.g. slanted-edge targets. Further, these first results indicate a correlation between degradation and restoration, i.e. the restoration algorithms are not capable of removing the optically relevant errors introduced by the degradation, a fact neither visible nor available from the PSNR values. We discuss the relevance in the context of the automotive industry, where image restoration may yield distinct advantages for camera-based applications, but testing methods rely on the used image quality metrics. Y1 - 2019 U6 - https://doi.org/10.1117/12.2528100 VL - Proceedings, Vol. 11062 CY - Munich ER - TY - JOUR A1 - Lehmann, Matthias A1 - Wittpahl, Christian A1 - Zakour, Hatem Ben A1 - Braun, Alexander T1 - Resolution and accuracy of nonlinear regression of point spread function with artificial neural networks JF - Optical Engineering N2 - We had already demonstrated a numerical model for the point spread function (PSF) of an optical system that can efficiently model both the experimental measurements and the lens design simulations of the PSF. The novelty lies in the portability and the parameterization of this model, which allow for completely new ways to validate optical systems, which is especially interesting not only for mass production optics such as in the automotive industry but also for ophthalmology. The numerical basis for this model is a nonlinear regression of the PSF with an artificial neural network (ANN). After briefly describing both the principle and the applications of the model, we then discuss two optically important aspects: the spatial resolution and the accuracy of the model. Using mean squared error (MSE) as a metric, we vary the topology of the neural network, both in the number of neurons and in the number of hidden layers. Measurement and simulation of a PSF can have a much higher spatial resolution than the typical pixel size used in current camera sensors. We discuss the influence this has on the topology of the ANN. The relative accuracy of the averaged pixel MSE is below 10  −  4, thus giving confidence that the regression does indeed model the measurement data with good accuracy. This article is only the starting point, and we propose several research avenues for future work. Y1 - 2019 U6 - https://doi.org/10.1117/1.oe.58.4.045101 SN - 0091-3286 VL - 58 IS - 4 SP - 045101 PB - SPIE ER - TY - JOUR A1 - Lehmann, Matthias A1 - Wittpahl, Christian A1 - Zakour, Hatem Ben A1 - Braun, Alexander T1 - Modeling realistic optical aberrations to reuse existing drive scene recordings for autonomous driving validation JF - Journal of Electronic Imaging N2 - Training autonomous vehicles requires lots of driving sequences in all situations. Collecting and labeling these drive scenes is a very time-consuming and expensive process. Currently, it is not possible to reuse these drive scenes with different optical properties, because there exists no numerically efficient model for the transfer function of the optical system. We present a numerical model for the point spread function (PSF) of an optical system that can efficiently model both experimental measurements and lens design simulations of the PSF. The numerical basis for this model is a nonlinear regression of the PSF with an artificial neural network. The novelty lies in the portability and the parameterization of this model. We present a lens measurement series, yielding a numerical function for the PSF that depends only on the parameters defocus, field, and azimuth. By convolving existing images and videos with this PSF, we generate images as if seen through the measured lens. The methodology applies to any optical scenario, but we focus on the context of autonomous driving, where the quality of the detection algorithms depends directly on the optical quality of the used camera system. With this model, it is possible to reuse existing recordings, with the potential to avoid millions of test drive miles. The parameterization of the optical model allows for a method to validate the functional and safety limits of camera-based advanced driver assistance systems based on the real, measured lens actually used in the product. Y1 - 2019 U6 - https://doi.org/10.1117/1.JEI.28.1.013005 SN - 1560-229X VL - 28 IS - 1 SP - 013005 PB - SPIE ER - TY - CHAP A1 - Lehmann, Matthias A1 - Wittpahl, Christian A1 - Zakour, Hatem Ben A1 - Braun, Alexander ED - Haverkamp, Nils ED - Youngworth, Richard N. T1 - Resolution and accuracy of non-linear regression of PSF with artificial neural networks T2 - SPIE Optical Systems Design: Optical Instrument Science, Technology, and Applications, 2018, Frankfurt, Germany N2 - In a previous work we have demonstrated a novel numerical model for the point spread function (PSF) of an optical system that can efficiently model both experimental measurements and lens design simulations of the PSF. The novelty lies in the portability and the parameterization of this model, which allows for completely new ways to validate optical systems, which is especially interesting for mass production optics like in the automotive industry, but also for ophtalmology. The numerical basis for this model is a non-linear regression of the PSF with an artificial neural network (ANN). In this work we examine two important aspects of this model: the spatial resolution and the accuracy of the model. Measurement and simulation of a PSF can have a much higher resolution then the typical pixel size used in current camera sensors, especially those for the automotive industry. We discuss the influence this has on on the topology of the ANN and the final application where the modeled PSF is actually used. Another important influence on the accuracy of the trained ANN is the error metric which is used during training. The PSF is a distinctly non-linear function, which varies strongly over field and defocus, but nonetheless exhibits strong symmetries and spatial relations. Therefore we examine different distance and similarity measures and discuss its influence on the modeling performance of the ANN. Y1 - 2018 U6 - https://doi.org/10.1117/12.2313144 N1 - Preprint bei ArXiv verfügbar. VL - Proc. SPIE, Vol. 10695 IS - 106950C SP - 52 EP - 63 PB - International Society for Optics and Photonics CY - Frankfurt ER -