TY - CHAP A1 - Becker, Martin A1 - Lippel, Jens A1 - Zielke, Thomas T1 - Gradient Descent Analysis: On Visualizing the Training of Deep Neural Networks T2 - VISIGRAPP 2019: Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications : Prague, Czech Republic, February 25-27, 2019 Y1 - 2019 SN - 978-989-758-354-4 U6 - https://doi.org/10.5220/0007583403380345 VL - Volume: 3 SP - 338 EP - 345 PB - INSTICC ER - TY - CHAP A1 - Becker, Martin A1 - Lippel, Jens A1 - Zielke, Thomas ED - Vezzani, Roberto T1 - Dimensionality Reduction for Data Visualization and Linear Classification, and the Trade-off between Robustness and Classification Accuracy T2 - Proceedings of ICPR 2020: 25th International Conference on Pattern Recognition, 10-15 January 2021, Milan Y1 - 2021 SN - 978-1-72818-808-9 U6 - https://doi.org/10.1109/ICPR48806.2021.9412865 SP - 6478 EP - 6485 PB - IEEE CY - Piscataway, NJ ER - TY - CHAP A1 - Lippel, Jens A1 - Becker, Martin A1 - Zielke, Thomas ED - Obaidat, Mohammad S. ED - Ören, Tuncer ED - Szczerbicka, Helena T1 - Modeling Dynamic Processes with Deep Neural Networks: A Case Study with a Gas-fired Absorption Heat Pump T2 - SIMULTECH 2019: Proceedings of the 9th International Conference on Simulation and Modeling Methodologies, Technologies and Applications : Prague, Czech RepublicJuly 29-31, 2019 Y1 - 2019 SN - 978-989-758-381-0 U6 - https://doi.org/10.5220/0007932903170326 SP - 317 EP - 326 PB - INSTICC ER - TY - CHAP A1 - Zielke, Thomas A1 - Bernst, Irina A1 - Crombach, Tobias A1 - Becker, Martin A1 - Lippel, Jens ED - Gómez Chova, Luis ED - López Martínez, A. ED - Candel Torres, I. T1 - WHAT CAN BE LEARNED BY GRADING ANALYTICS? T2 - INTED 2018: 12th International Technology, Education and Development Conference : 5-7 March, 2018, Valencia (Spain) : conference proceedings Y1 - 2018 SN - 978-84-697-9480-7 U6 - https://doi.org/10.21125/inted.2018.0593 VL - 12th International Technology, Education and Development Conference SP - 3097 EP - 3106 PB - IATED Academy CY - Valencia, Spain ER - TY - CHAP A1 - Goebel, Johannes A1 - Frank, Lena A1 - Zielke, Thomas A1 - Becker, Martin A1 - Lippel, Jens ED - Deutscher Kälte- und Klimatechnischer Verein, T1 - Modellierung dynamischer Prozesse mit Deep Neural Networks am Beispiel einer Gas-Absorptionswärmepumpe T2 - Deutsche Kaelte-und Klimatagung 2017 Bremen KW - Abwasser-Gas-WP/KM KW - ZIES Y1 - 2017 SP - 1147 EP - 1160 PB - Curran Associates, Inc. CY - Red Hook ER - TY - CHAP A1 - Lippel, Jens A1 - Bihlmeier, Richard A1 - Stuhlsatz, André T1 - A Computer Vision Approach to Fertilizer Detection and Classification T2 - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications KW - Maschinelles Lernen KW - Maschinelles Sehen KW - Landwirtschaft KW - Düngemittel Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-54735 SP - 563 EP - 569 PB - SCITEPRESS - Science and Technology Publications ER - TY - JOUR A1 - Becker, Martin A1 - Lippel, Jens A1 - Stuhlsatz, André A1 - Zielke, Thomas T1 - Robust dimensionality reduction for data visualization with deep neural networks JF - Graphical Models Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-26904 SN - 1524-0703 VL - 108 PB - Elsevier ER - TY - CHAP A1 - Stuhlsatz, André A1 - Lippel, Jens A1 - Zielke, Thomas T1 - Feature Extraction for Simple Classification T2 - 2010 20th International Conference on Pattern Recognition, August 23 - 26, 2010 , Istanbul, Turkey Y1 - 2010 SN - 978-0-7695-4109-9 U6 - https://doi.org/10.1109/ICPR.2010.377 SN - 1051-4651 SP - 1525 EP - 1528 PB - IEEE ER - TY - JOUR A1 - Gaida, Daniel A1 - Wolf, Christian A1 - Meyer, C. A1 - Stuhlsatz, André A1 - Lippel, Jens A1 - Bäck, T. A1 - Bongards, Michael A1 - McLoone, S. T1 - State estimation for anaerobic digesters using the ADM1 JF - Water science and technology N2 - The optimization of full-scale biogas plant operation is of great importance to make biomass a competitive source of renewable energy. The implementation of innovative control and optimization algorithms, such as Nonlinear Model Predictive Control, requires an online estimation of operating states of biogas plants. This state estimation allows for optimal control and operating decisions according to the actual state of a plant. In this paper such a state estimator is developed using a calibrated simulation model of a full-scale biogas plant, which is based on the Anaerobic Digestion Model No.1. The use of advanced pattern recognition methods shows that model states can be predicted from basic online measurements such as biogas production, CH4 and CO2 content in the biogas, pH value and substrate feed volume of known substrates. The machine learning methods used are trained and evaluated using synthetic data created with the biogas plant model simulating over a wide range of possible plant operating regions. Results show that the operating state vector of the modelled anaerobic digestion process can be predicted with an overall accuracy of about 90%. This facilitates the application of state-based optimization and control algorithms on full-scale biogas plants and therefore fosters the production of eco-friendly energy from biomass. Y1 - 2012 U6 - https://doi.org/10.2166/wst.2012.286 VL - 66 IS - 5 SP - 1088 EP - 1095 PB - IWA Publishing ER - TY - JOUR A1 - Stuhlsatz, André A1 - Lippel, Jens A1 - Zielke, Thomas T1 - Feature extraction with deep neural networks by a generalized discriminant analysis JF - IEEE transactions on neural networks and learning systems N2 - We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space is bounded by the number of classes and that the optimal discriminant function is linear. Unfortunately, linear transformations are insufficient to extract optimal discriminative features from arbitrarily distributed raw measurements. The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion. We show that the feature extraction based on our approach displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection. In a series of experiments, we evaluate GerDA features with respect to dimensionality reduction, visualization, classification, and detection. Moreover, we show that GerDA DNNs can preprocess truly high-dimensional input data to low-dimensional representations that facilitate accurate predictions even if simple linear predictors or measures of similarity are used. Y1 - 2012 U6 - https://doi.org/10.1109/tnnls.2012.2183645 VL - 23 IS - 4 SP - 596 EP - 608 PB - IEEE ER -