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 - 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 - TY - JOUR A1 - Gaida, Daniel A1 - Wolf, Christian A1 - Meyer, C. A1 - Stuhlsatz, André A1 - Lippel, Jens A1 - Bäck, T. A1 - Bongards, M. 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 - Wolf, Christian A1 - Gaida, Daniel A1 - Stuhlsatz, André A1 - Ludwig, Thomas A1 - McLoone, Seán A1 - Bongards, Michael T1 - Predicting organic acid concentration from UV/vis spectrometry measurements – a comparison of machine learning techniques JF - Transactions of the Institute of Measurement and Control Y1 - 2013 U6 - https://doi.org/10.1177/0142331211403797 VL - 35 IS - 1 SP - 5 EP - 15 PB - sage journals ER - TY - CHAP A1 - Becker, Martin A1 - Lippel, Jens A1 - Stuhlsatz, André T1 - Regularized Nonlinear Discriminant Analysis - An Approach to Robust Dimensionality Reduction for Data Visualization T2 - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 27.02.2017 - 01.03.2017, Porto, Portugal Y1 - 2017 SN - 978-989-758-228-8 U6 - https://doi.org/10.5220/0006167501160127 SP - 116 EP - 127 PB - SCITEPRESS - Science and Technology Publications ER -