@inproceedings{LippelBihlmeierStuhlsatz2025, author = {Lippel, Jens and Bihlmeier, Richard and Stuhlsatz, Andr{\´e}}, title = {A Computer Vision Approach to Fertilizer Detection and Classification}, series = {Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications}, booktitle = {Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications}, publisher = {SCITEPRESS - Science and Technology Publications}, doi = {10.5220/0013189300003912}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-54735}, pages = {563 -- 569}, year = {2025}, subject = {Maschinelles Lernen}, language = {en} } @inproceedings{SchullerVlasenkoEybenetal.2015, author = {Schuller, Bj{\"o}rn and Vlasenko, Bogdan and Eyben, Florian and W{\"o}llmer, Martin and Stuhlsatz, Andr{\´e} and Wendemuth, Andreas and Rigoll, Gerhard}, title = {Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies (Extended Abstract)}, series = {In Proc. 6th biannual Conference on Affective Computing and Intelligent Interaction (ACII 2015), AAAC,}, booktitle = {In Proc. 6th biannual Conference on Affective Computing and Intelligent Interaction (ACII 2015), AAAC,}, year = {2015}, abstract = {As the recognition of emotion from speech has matured to a degree where it becomes applicable in real-life settings, it is time for a realistic view on obtainable performances. Most studies tend to overestimation in this respect: acted data is often used rather than spontaneous data, results are reported on pre-selected prototypical data, and true speaker disjunctive partitioning is still less common than simple cross-validation. A considerably more realistic impression can be gathered by inter-set evaluation: we therefore show results employing six standard databases in a cross-corpora evaluation experiment. To better cope with the observed high variances, different types of normalization are investigated. 1.8k individual evaluations in total indicate the crucial performance inferiority of inter- to intra-corpus testing.}, language = {en} } @article{BeckerLippelStuhlsatzetal.2020, author = {Becker, Martin and Lippel, Jens and Stuhlsatz, Andr{\´e} and Zielke, Thomas}, title = {Robust dimensionality reduction for data visualization with deep neural networks}, series = {Graphical Models}, volume = {108}, journal = {Graphical Models}, publisher = {Elsevier}, issn = {1524-0703}, doi = {10.1016/j.gmod.2020.101060}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-26904}, pages = {101060}, year = {2020}, language = {en} } @inproceedings{StuhlsatzLippelZielke2010, author = {Stuhlsatz, Andr{\´e} and Lippel, Jens and Zielke, Thomas}, title = {Feature Extraction for Simple Classification}, series = {2010 20th International Conference on Pattern Recognition, August 23 - 26, 2010 , Istanbul, Turkey}, booktitle = {2010 20th International Conference on Pattern Recognition, August 23 - 26, 2010 , Istanbul, Turkey}, publisher = {IEEE}, isbn = {978-0-7695-4109-9}, issn = {1051-4651}, doi = {10.1109/ICPR.2010.377}, pages = {1525 -- 1528}, year = {2010}, language = {en} } @inproceedings{StuhlsatzWedellMoorsetal.2019, author = {Stuhlsatz, Andr{\´e} and Wedell, Tobias and Moors, Mark and Schulze, Stephan}, title = {A smart measuring system for intelligent data acquisition in steel plants}, series = {METEC \& 4th ESTAD 2019, European Steel Technology and Application Day, CCD Congress Center D{\"u}sseldorf, 24-28 June 2019}, booktitle = {METEC \& 4th ESTAD 2019, European Steel Technology and Application Day, CCD Congress Center D{\"u}sseldorf, 24-28 June 2019}, publisher = {Steel Institute VDEh}, address = {D{\"u}sseldorf}, year = {2019}, abstract = {Sensing and acquiring reliable physical values are the fundamentals, not only for a predictive maintenance or quality assessment, but especially for big data analysis and sophisticated Industry 4.0 applications. In steel plants, physical values are distributed over the complete process chain of steel making while the environmental conditions are harsh with respect to high temperature, aggressive fluids, water, shock and dust. These conditions render the use of electronic devices focusing a consumer market impossible.The developed self-contained smart measuring system presented in this paper survives in harsh environments and is composed of small-sized modules providing miscellaneous functionalities. The high degree of modularity in hard-and software facilitates a cost-effective adaptation to many applications, like vibration monitoring, temperature logging or torque measurement. Different onboard measurement components are available yet, namely high sensitive bridge amplifiers for strain measurements, accelerometers, gyroscopes, orientation-, temperature- and humidity sensors as well as an unique system identification. A communication module enables wireless transmission of the acquired data via Bluetooth or NFC online. Moreover, different power supply features are supported by the power module: Ultra-low power modes for long-life battery use, recharging of lithium cells, and an inductive power supply for wireless power transfer for applications with moving or rotating components. The heart of the system is a powerful ARM based microcontroller which enables an intelligent analysis of the data in situ. This is especially important where data size and complexity is the relevant factor for example in area-wide sensor networks.}, language = {en} } @inproceedings{StuhlsatzMeyerEybenetal.2011, author = {Stuhlsatz, Andr{\´e} and Meyer, Christine and Eyben, Florian and Zielke, Thomas and Meier, Gunter and Schuller, Bjorn}, title = {Deep neural networks for acoustic emotion recognition: Raising the benchmarks}, series = {2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, booktitle = {2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, publisher = {IEEE}, doi = {10.1109/ICASSP.2011.5947651}, pages = {5688 -- 5691}, year = {2011}, abstract = {Deep Neural Networks (DNNs) denote multilayer artificial neural networks with more than one hidden layer and millions of free parameters. We propose a Generalized Discriminant Analysis (GerDA) based on DNNs to learn discriminative features of low dimension optimized with respect to a fast classification from a large set of acoustic features for emotion recognition. On nine frequently used emotional speech corpora, we compare the performance of GerDA features and their subsequent linear classification with previously reported benchmarks obtained using the same set of acoustic features classified by Support Vector Machines (SVMs). Our results impressively show that low-dimensional GerDA features capture hidden information from the acoustic features leading to a significantly raised unweighted average recall and considerably raised weighted average recall.}, language = {en} } @incollection{StuhlsatzMeierWendemuth2007, author = {Stuhlsatz, Andr{\´e} and Meier, Hans-G{\"u}nter and Wendemuth, Andreas}, title = {Maximum Margin Classification on Convex Euclidean Metric Spaces}, series = {Advances in Soft Computing}, volume = {AINSC,volume 45}, booktitle = {Advances in Soft Computing}, editor = {Kurzynski, Marek and Puchala, Edward and Wozniak, Michal and Zolnierek, Andrzej}, publisher = {Springer Nature}, address = {Berlin, Heidelberg}, isbn = {9783540751748}, issn = {1867-5662}, doi = {10.1007/978-3-540-75175-5_27}, pages = {216 -- 223}, year = {2007}, language = {en} } @inproceedings{StuhlsatzMeierWendemuth2008, author = {Stuhlsatz, Andr{\´e} and Meier, Hans-G{\"u}nter and Wendemuth, Andreas}, title = {Making the Lipschitz Classifier Practical via Semi-infinite Programming}, series = {2008 Seventh International Conference on Machine Learning and Applications, 11-13 December 2008, San Diego}, booktitle = {2008 Seventh International Conference on Machine Learning and Applications, 11-13 December 2008, San Diego}, publisher = {IEEE}, isbn = {978-0-7695-3495-4}, doi = {10.1109/ICMLA.2008.26}, pages = {40 -- 47}, year = {2008}, language = {en} } @article{SchullerVlasenkoEybenetal.2010, author = {Schuller, Bjorn and Vlasenko, Bogdan and Eyben, Florian and Wollmer, Martin and Stuhlsatz, Andr{\´e} and Wendemuth, Andreas and Rigoll, Gerhard}, title = {Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies}, series = {IEEE Transactions on Affective Computing}, volume = {1}, journal = {IEEE Transactions on Affective Computing}, number = {2}, publisher = {IEEE}, doi = {10.1109/t-affc.2010.8}, pages = {119 -- 131}, year = {2010}, language = {en} } @inproceedings{Stuhlsatz2007, author = {Stuhlsatz, Andr{\´e}}, title = {Recognition of ultrasonic multi-echo sequences for autonomous symbolic indoor tracking}, series = {Sixth International Conference on Machine Learning and Applications (ICMLA 2007), 13-15 December 2007, Cincinnati}, booktitle = {Sixth International Conference on Machine Learning and Applications (ICMLA 2007), 13-15 December 2007, Cincinnati}, publisher = {IEEE}, isbn = {978-0-7695-3069-7}, doi = {10.1109/ICMLA.2007.30}, pages = {178 -- 185}, year = {2007}, language = {en} } @article{GaidaWolfMeyeretal.2012, author = {Gaida, Daniel and Wolf, Christian and Meyer, C. and Stuhlsatz, Andr{\´e} and Lippel, Jens and B{\"a}ck, T. and Bongards, Michael and McLoone, S.}, title = {State estimation for anaerobic digesters using the ADM1}, series = {Water science and technology}, volume = {66}, journal = {Water science and technology}, number = {5}, publisher = {IWA Publishing}, doi = {10.2166/wst.2012.286}, pages = {1088 -- 1095}, year = {2012}, abstract = {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.}, language = {en} } @article{StuhlsatzLippelZielke2012, author = {Stuhlsatz, Andr{\´e} and Lippel, Jens and Zielke, Thomas}, title = {Feature extraction with deep neural networks by a generalized discriminant analysis}, series = {IEEE transactions on neural networks and learning systems}, volume = {23}, journal = {IEEE transactions on neural networks and learning systems}, number = {4}, publisher = {IEEE}, doi = {10.1109/tnnls.2012.2183645}, pages = {596 -- 608}, year = {2012}, abstract = {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.}, language = {en} } @incollection{BeckerLippelStuhlsatz2017, author = {Becker, Martin and Lippel, Jens and Stuhlsatz, Andr{\´e}}, title = {Regularized Nonlinear Discriminant Analysis - An Approach to Robust Dimensionality Reduction for Data Visualization}, series = {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}, booktitle = {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}, publisher = {SCITEPRESS - Science and Technology Publications}, isbn = {978-989-758-228-8}, doi = {10.5220/0006167501160127}, pages = {116 -- 127}, year = {2017}, language = {en} } @article{WolfGaidaStuhlsatzetal.2013, author = {Wolf, Christian and Gaida, Daniel and Stuhlsatz, Andr{\´e} and Ludwig, Thomas and McLoone, Se{\´a}n and Bongards, Michael}, title = {Predicting organic acid concentration from UV/vis spectrometry measurements - a comparison of machine learning techniques}, series = {Transactions of the Institute of Measurement and Control}, volume = {35}, journal = {Transactions of the Institute of Measurement and Control}, number = {1}, publisher = {sage journals}, doi = {10.1177/0142331211403797}, pages = {5 -- 15}, year = {2013}, language = {en} } @inproceedings{StuhlsatzLippelZielke2010, author = {Stuhlsatz, Andr{\´e} and Lippel, Jens and Zielke, Thomas}, title = {Discriminative feature extraction with Deep Neural Networks}, series = {The 2010 International Joint Conference on Neural Networks (IJCNN), 18.07.2010 - 23.07.2010, Barcelona, Spain}, booktitle = {The 2010 International Joint Conference on Neural Networks (IJCNN), 18.07.2010 - 23.07.2010, Barcelona, Spain}, publisher = {IEEE}, isbn = {978-1-4244-6916-1}, doi = {10.1109/IJCNN.2010.5596329}, pages = {1 -- 8}, year = {2010}, language = {en} } @inproceedings{WolfGaidaStuhlsatzetal.2010, author = {Wolf, Christian and Gaida, Daniel and Stuhlsatz, Andr{\´e} and McLoone, S. and Bongards, Michael}, title = {A Comparison of Performance of Advanced Pattern Recognition Methods for Organic Acid Prediction in Biogas Plants Using UV/vis Spectroscopic Online-Measurements}, series = {Proceedings of the 2010 International Conference on Life System Modeling and Simulation (LSMS), Wuxi, China, 2010}, booktitle = {Proceedings of the 2010 International Conference on Life System Modeling and Simulation (LSMS), Wuxi, China, 2010}, year = {2010}, language = {en} } @inproceedings{GaidaWolfStuhlsatzetal.2011, author = {Gaida, Daniel and Wolf, Christian and Stuhlsatz, Andr{\´e} and Lippel, Jens and B{\"a}ck, T. and Bongards, Michael and McLoone, S.}, title = {State Estimation for Anaerobic Digesters using the ADM1}, series = {Proceedings of International IWA-Symposium on Anaerobic Digestion of Solid Waste and Energy Crops, Vienna, Austria, 2011}, booktitle = {Proceedings of International IWA-Symposium on Anaerobic Digestion of Solid Waste and Energy Crops, Vienna, Austria, 2011}, year = {2011}, language = {en} } @inproceedings{StuhlsatzMeierKatzetal.2006, author = {Stuhlsatz, Andr{\´e} and Meier, Hans-G{\"u}nter and Katz, Michael and Wendemuth, Andreas}, title = {Support Vector Machines for Postprocessing of Speech Recognition Hypotheses}, series = {Proceedings of the International Conference on Telecommunications and Multimedia (TEMU), Heraklion (Crete), Greece, 2006}, booktitle = {Proceedings of the International Conference on Telecommunications and Multimedia (TEMU), Heraklion (Crete), Greece, 2006}, year = {2006}, language = {en} } @misc{HeimannPlociennikMuelleretal.2009, author = {Heimann, Thomas and Plociennik, Uwe and M{\"u}ller, J{\"u}rgen and Stuhlsatz, Andr{\´e}}, title = {Verfahren und Anlage zum Stranggiessen eines metallischen Produkts}, publisher = {Europ{\"a}isches Patentamt}, pages = {16}, year = {2009}, language = {de} } @phdthesis{Stuhlsatz2010, author = {Stuhlsatz, Andr{\´e}}, title = {Machine learning with Lipschitz classifiers}, publisher = {Otto-von-Guericke-Universit{\"a}t Magdeburg}, address = {Magdeburg}, doi = {10.25673/5050}, url = {http://nbn-resolving.de/urn:nbn:de:101:1-201104192945}, year = {2010}, abstract = {complex image, like for example that of a known person, and to distinguish it from other objects within half a second. While for a solution of this task the brain has access to a massive parallelism and a vast, hierarchically organized, and auto-associative memory, common computer architectures are just able to a sequential processing of information stored in a non auto-associative memory. Even modern, parallelly operating, multi-processor systems are far away from the performance of our brain. However, nowadays, it is possible to solve complex and memory extensive pattern recognition problems, like the recognition of handwritten digits or the transcription of speech, satisfactorily with a common computer by the use of modern statistical and algorithmic learning approaches. One of the most successful pattern recognition methods is the so-called Support Vector Machine (SVM). The SVM is based on the learning paradigm of structural risk minimization, which outperforms empirical approaches if only few data is available for solving the considered classification problem. Although the SVM has proven very good recognition performances in many cases, the SVM also comes up with limitations, for example if specific a priori knowledge shall be used. In particular, the increasing complexity of applications requires a high adaptivity of the classification method to the specific problem. Also concerning this point, the SVM is limited due to a restricted variety of implementable classification functions. The objective of the present thesis is the development of new learning algorithms for the classification of patterns, that on the one hand overcome the limitations of the SVM, but on the other hand are based on the same theoretical concepts facilitating the good performance of the SVM. Two new algorithms will be presented that are justified by a theoretical generalization of the SVM, and which will be utilized for the first time for a practical implementation. In contrast to the SVM, the new methods make accessible a much larger function class for constructing a classifier. This is an important prerequisite for flexible adaptation of the classifier to difficult classification tasks with particular requirements as well as for the integration of a priori knowledge about the problem at hand. In this work, the way to implementable algorithms leads across different mathematical reformulations of the original problem. Starting with the theoretical generalization of the SVM, it results a restricted optimization problem that is difficult to solve in general. In a first step, this problem is expressed in terms of a restricted minimax-problem by a modification of the suitable classification functions to a still very large function class consisting of (affine-)linear combinations of at least one-time continuously differentiable functions. In the next step, the minimax-problem is converted into a so-called Semi-Infinite Problem (SIP). It turns out, that this particular mathematical problem is appropriate in order to obtain a solution of the original problem for the considered function class using well-known optimization methods. To further exploit the problem structure, an equivalent dual problem is derived from the SIP. Therefore, we prove a duality theorem about the equality of the optimal values of the dual and the original problem. For solving the dual problem, a multilevel iterative approach is developed from which the proposed algorithms follow by pursuing different solution strategies. Moreover, all sub-optimization methods of any stage necessary for an implementation in software are developed. Namely, these are an adapted interior-point-method, a simulated annealing based search heuristics and a particular gradient decent approach. Furthermore, options are depicted for an improvement of efficiency for future implementations. Besides the emphasis on the theoretical development of new learning methods and their practical implementations, all algorithms were implemented in the MATLAB(R) programming environment for the experimental part of the present thesis. Hence, they are also available for further research purposes in future. For the first time, classification results are explored and evaluated in comparison to the SVM on different data sets. As test data, an artificial 2d-dataset as well as two real-world datasets were used. In the concluding experiment, a scenario is prototypically considered to which the SVM is only inadequately applicable and which shall precisely prove the capability of the new methods in that case. It follows, regarding the considered datasets, the proposed learning methods reach comparably good classification accuracy like the SVM in standard applications. Moreover, the particular benefit of the new methods is reflected theoretically and experimentally in the ability to solve classification problems using decision functions that are not accessible to SVMs. Thereby, the underlying ideas, which make the SVM excel compared to other approaches with respect to generalization performances in case of few available learning information, are adequately transported into the proposed new environment. This opens the way for a design and a use of new classifiers that have not been implementable in a robust and generalizing basic concept so far.}, language = {en} } @article{Stuhlsatz2007, author = {Stuhlsatz, Andr{\´e}}, title = {HSVM - A SVM Toolkit for Segmented Speech Data}, series = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2007}, journal = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2007}, editor = {Fellbaum, Klaus}, isbn = {978-3-940046-40-6}, issn = {0940-6832}, pages = {107 -- 114}, year = {2007}, language = {en} } @inproceedings{StuhlsatzMeierKatzetal.2003, author = {Stuhlsatz, Andr{\´e} and Meier, Hans-G{\"u}nter and Katz, M. and Kr{\"u}ger, S. E. and Wendemuth, Andreas}, title = {Classification of Speech Recognition Hypotheses with Support Vector Machines}, series = {Proceedings of the Speech Processing Workshop: Magdeburg, Germany, September 09, 2003 ; in connection with the symposium of the German Association for Pattern Recognition (DAGM) 2003}, booktitle = {Proceedings of the Speech Processing Workshop: Magdeburg, Germany, September 09, 2003 ; in connection with the symposium of the German Association for Pattern Recognition (DAGM) 2003}, editor = {Wendemuth, Andreas}, publisher = {Otto-von-Guericke-Universit{\"a}t Magdeburg}, address = {Magdeburg}, isbn = {9783929757590}, year = {2003}, language = {en} } @inproceedings{GaidaStuhlsatzMeier2008, author = {Gaida, Daniel and Stuhlsatz, Andr{\´e} and Meier, Hans-G{\"u}nter}, title = {Fusion of Visual and Inertial Measurements for Pose Estimation}, series = {Proceedings of the Workshop on Emotion-, Speech- and Face Recognition with Advanced Classifiers: D{\"u}sseldorf, Germany, May 16 - 17, 2008 ; 3rd joined research workshop between Otto-von-Guericke University Magdeburg and the University of Applied Sciences FH D{\"u}sseldorf}, booktitle = {Proceedings of the Workshop on Emotion-, Speech- and Face Recognition with Advanced Classifiers: D{\"u}sseldorf, Germany, May 16 - 17, 2008 ; 3rd joined research workshop between Otto-von-Guericke University Magdeburg and the University of Applied Sciences FH D{\"u}sseldorf}, editor = {Wendemuth, Andreas and Meier, Hans-G{\"u}nter}, publisher = {Otto-von-Guericke University Magdeburg}, address = {Magdeburg}, isbn = {9783940961242}, pages = {39 -- 48}, year = {2008}, language = {en} } @inproceedings{StuhlsatzMeierWendemuth2008, author = {Stuhlsatz, Andr{\´e} and Meier, Hans-G{\"u}nter and Wendemuth, Andreas}, title = {A Dual Formulation to the Lipschitz Classifier}, series = {Proceedings of the Workshop on Emotion-, Speech- and Face Recognition with Advanced Classifiers: D{\"u}sseldorf, Germany, May 16 - 17, 2008 ; 3rd joined research workshop between Otto-von-Guericke University Magdeburg and the University of Applied Sciences FH D{\"u}sseldorf}, booktitle = {Proceedings of the Workshop on Emotion-, Speech- and Face Recognition with Advanced Classifiers: D{\"u}sseldorf, Germany, May 16 - 17, 2008 ; 3rd joined research workshop between Otto-von-Guericke University Magdeburg and the University of Applied Sciences FH D{\"u}sseldorf}, editor = {Wendemuth, Andreas and Meier, Hans-G{\"u}nter}, publisher = {Otto-von-Guericke University Magdeburg}, address = {Magdeburg}, isbn = {9783940961242}, pages = {1 -- 6}, year = {2008}, language = {en} } @book{Stuhlsatz2008, author = {Stuhlsatz, Andr{\´e}}, title = {Hybride Spracherkennung Eine HMM/SVM-Systemintegration}, publisher = {VDM Verlag}, address = {Saarbr{\"u}cken}, isbn = {9783639100624}, pages = {124}, year = {2008}, language = {en} }