@misc{GottschaldReichAdam2021, author = {Gottschald, Jonas and Reich, Marius and Adam, Mario}, title = {Lernende Algorithmen - K{\"u}nstliche Intelligenz f{\"u}r W{\"a}rmenetze}, series = {hn21 Journal}, journal = {hn21 Journal}, number = {2021}, publisher = {Zentrum f{\"u}r Forschungskommunikation}, address = {K{\"o}ln}, pages = {8 -- 8}, year = {2021}, language = {de} } @misc{BackesAdam2012, author = {Backes, Klaus and Adam, Mario}, title = {KWKK-Anlagen im Feldtest (Teil 1)}, series = {SHT Sanit{\"a}r + Heizungstechnik}, volume = {77}, journal = {SHT Sanit{\"a}r + Heizungstechnik}, number = {3}, publisher = {Krammer}, issn = {0036-4401}, pages = {40 -- 44}, year = {2012}, language = {de} } @misc{BackesAdam2012, author = {Backes, Klaus and Adam, Mario}, title = {KWKK-Anlagen im Feldtest (Teil 2)}, series = {SHT Sanit{\"a}r + Heizungstechnik}, volume = {77}, journal = {SHT Sanit{\"a}r + Heizungstechnik}, number = {4}, publisher = {Kramer}, issn = {0036-4401}, pages = {50 -- 53}, year = {2012}, language = {de} } @misc{BackesAdamGottschald2012, author = {Backes, Klaus and Adam, Mario and Gottschald, Jonas}, title = {KWKK-Anlagen im Feldtest. Optimaler Betrieb durch Verbesserungen bei Regelung und Hydraulik}, series = {KK Die K{\"a}lte + Klimatechnik}, volume = {65}, journal = {KK Die K{\"a}lte + Klimatechnik}, number = {3}, publisher = {Alfons W. Gentner Verlag}, issn = {0343-2246}, pages = {20 -- 27}, year = {2012}, language = {de} } @misc{deCarvalhoHeinzeIlleetal.2012, author = {de Carvalho, Raphael and Heinze, Robert and Ille, Fabian and Adam, Mario and Kameier, Frank and Schauenberg, Udo}, title = {Verbesserte energetische Prognose von air conomy Modulen. Vergleich zwischen Design of Experiments (DoE) und Dimensionsanalyse (Teil 2)}, series = {HLH L{\"u}ftung/Klima, Heizung/Sanit{\"a}r, Geb{\"a}udetechnik}, volume = {63}, journal = {HLH L{\"u}ftung/Klima, Heizung/Sanit{\"a}r, Geb{\"a}udetechnik}, number = {9}, publisher = {Springer VDI-Verlag}, pages = {32 -- 36}, year = {2012}, language = {de} } @misc{AdamLohmann2011, author = {Adam, Mario and Lohmann, Sandra}, title = {Solare K{\"u}hlung - Dynamische Rechnersimulationen und Parametervariationen}, series = {KI K{\"a}lte - Luft - Klimatechnik}, volume = {47}, journal = {KI K{\"a}lte - Luft - Klimatechnik}, number = {5}, publisher = {H{\"u}thig}, organization = {H}, issn = {1865-5432}, pages = {22 -- 26}, year = {2011}, language = {de} } @article{DeckertSchomaker2022, author = {Deckert, Carsten and Schomaker, Rahel M.}, title = {Cultural tightness-looseness and national innovativeness: impacts of tolerance and diversity of opinion}, series = {Journal of Innovation and Entrepreneurship}, volume = {11}, journal = {Journal of Innovation and Entrepreneurship}, publisher = {Springer Nature}, issn = {2192-5372}, doi = {10.1186/s13731-022-00219-2}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-36515}, pages = {1 -- 19}, year = {2022}, abstract = {The links between innovativeness as a driver of economic performance, and the determinants of innovativeness have been investigated by management scholars and economists for decades, focusing mostly on "hard factors" as investment in research and development, or education. Focusing on a relatively neglected, but in times of globalization even more important aspect, the infuence of cultural characteristics on innovativeness, we apply diferent econometric models to test for links between cultural tightness and looseness on the one hand, and national innovativeness on the other hand. We fnd that cultural tightness—in the sense of homogenous and intolerant societies—has a negative link to national innovativeness, while cultural looseness—in the sense of tolerant and diverse societies—displays a positive link to national innovativeness.}, subject = {Innovation}, language = {en} } @article{KrebsMuellerBraun2021, author = {Krebs, Christian and M{\"u}ller, Patrick and Braun, Alexander}, title = {Impact of Windshield Optical Aberrations on Visual Range Camera Based Classification Tasks Performed by CNNs}, series = {London Imaging Meeting}, volume = {2021}, journal = {London Imaging Meeting}, number = {1}, publisher = {Ingenta connect}, isbn = {0-89208-346-6}, issn = {2694-118X}, doi = {10.2352/issn.2694-118X.2021.LIM-83}, pages = {83 -- 87}, year = {2021}, language = {de} } @inproceedings{MuellerLehmannBraun2020, author = {Mueller, Patrick and Lehmann, Matthias and Braun, Alexander}, title = {Simulating tests to test simulation}, series = {IS\&T International Symposium on Electronic Imaging 2020: Autonomous Vehicles and Machines, 26 January 2020 — 30 January 2020, Burlingame, CA, USA}, volume = {2020}, booktitle = {IS\&T International Symposium on Electronic Imaging 2020: Autonomous Vehicles and Machines, 26 January 2020 — 30 January 2020, Burlingame, CA, USA}, number = {16}, publisher = {Society for Imaging Science and Technology}, doi = {10.2352/ISSN.2470-1173.2020.16.AVM-149}, year = {2020}, language = {en} } @article{MuellerBrummelBraun2021, author = {M{\"u}ller, Patrick and Brummel, Mattis and Braun, Alexander}, title = {Spatial recall index for machine learning algorithms}, series = {London Imaging Meeting}, volume = {2021}, journal = {London Imaging Meeting}, number = {1}, publisher = {Society for Imaging Science and Technology}, isbn = {0-89208-346-6}, doi = {10.2352/issn.2694-118X.2021.LIM-58}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-35657}, pages = {58 -- 62}, year = {2021}, abstract = {We present a novel metric Spatial Recall Index to assess the performance of machine-learning (ML) algorithms for automotive applications, focusing on where in the image which performance occurs. Typical metrics like intersection-over-union (IoU), precisionrecallcurves or average precision (AP) quantify the performance over a whole database of images, neglecting spatial performance variations. But as the optics of camera systems are spatially variable over the field of view, the performance of ML-based algorithms is also a function of space, which we show in simulation: A realistic objective lens based on a Cooke-triplet that exhibits typical optical aberrations like astigmatism and chromatic aberration, all variable over field, is modeled. The model is then applied to a subset of the BDD100k dataset with spatially-varying kernels. We then quantify local changes in the performance of the pre-trained Mask R-CNN algorithm. Our examples demonstrate the spatial dependence of the performance of ML-based algorithms from the optical quality over field, highlighting the need to take the spatial dimension into account when training ML-based algorithms, especially when looking forward to autonomous driving applications.}, language = {en} } @article{TsengMoslehMannanetal.2021, author = {Tseng, Ethan and Mosleh, Ali and Mannan, Fahim and St-Arnaud, Karl and Sharma, Avinash and Peng, Yifan and Braun, Alexander and Nowrouzezahrai, Derek and Lalonde, Jean-Fran{\c{c}}ois and Heide, Felix}, title = {Differentiable Compound Optics and Processing Pipeline Optimization for End-to-end Camera Design}, series = {ACM Transactions on Graphics}, volume = {40}, journal = {ACM Transactions on Graphics}, number = {2}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, issn = {1557-7368}, doi = {10.1145/3446791}, pages = {1 -- 19}, year = {2021}, abstract = {Most modern commodity imaging systems we use directly for photography--or indirectly rely on for downstream applications--employ optical systems of multiple lenses that must balance deviations from perfect optics, manufacturing constraints, tolerances, cost, and footprint. Although optical designs often have complex interactions with downstream image processing or analysis tasks, today's compound optics are designed in isolation from these interactions. Existing optical design tools aim to minimize optical aberrations, such as deviations from Gauss' linear model of optics, instead of application-specific losses, precluding joint optimization with hardware image signal processing (ISP) and highly parameterized neural network processing. In this article, we propose an optimization method for compound optics that lifts these limitations. We optimize entire lens systems jointly with hardware and software image processing pipelines, downstream neural network processing, and application-specific end-to-end losses. To this end, we propose a learned, differentiable forward model for compound optics and an alternating proximal optimization method that handles function compositions with highly varying parameter dimensions for optics, hardware ISP, and neural nets. Our method integrates seamlessly atop existing optical design tools, such as Zemax. We can thus assess our method across many camera system designs and end-to-end applications. We validate our approach in an automotive camera optics setting--together with hardware ISP post processing and detection--outperforming classical optics designs for automotive object detection and traffic light state detection. For human viewing tasks, we optimize optics and processing pipelines for dynamic outdoor scenarios and dynamic low-light imaging. We outperform existing compartmentalized design or fine-tuning methods qualitatively and quantitatively, across all domain-specific applications tested.}, language = {en} } @article{WittpahlDeeganBlacketal.2021, author = {Wittpahl, Christian and Deegan, Brian and Black, Bob and Braun, Alexander}, title = {An analytic-numerical image flicker study to test novel flicker metrics}, series = {Electronic Imaging: Society for Imaging Science and Technology}, journal = {Electronic Imaging: Society for Imaging Science and Technology}, number = {17}, publisher = {Society for Imaging Science and Technology}, issn = {2470-1173}, doi = {10.2352/ISSN.2470-1173.2021.17.AVM-183}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-35671}, pages = {1 -- 8}, year = {2021}, language = {en} } @article{WittpahlZakourLehmannetal.2018, author = {Wittpahl, Christian and Zakour, Hatem Ben and Lehmann, Matthias and Braun, Alexander}, title = {Realistic Image Degradation with Measured PSF}, series = {Electronic Imaging, Autonomous Vehicles and Machines 2018}, journal = {Electronic Imaging, Autonomous Vehicles and Machines 2018}, number = {17}, publisher = {Society for Imaging Science and Technology}, issn = {2470-1173}, doi = {10.2352/ISSN.2470-1173.2018.17.AVM-149}, pages = {149}, year = {2018}, language = {en} }