TY - JOUR A1 - Jahr, Andreas A1 - Corves, Burkhard A1 - Batos, Andrej A1 - Schumacher, Mathias T1 - Simulation landtechnischer Schnitt- und Strömungsprozesse JF - agricultural engineering.eu N2 - Der Einsatz moderner Simulationsverfahren beschränkt sich im Bereich der Landmaschinenentwicklung in der Regel auf Anwendungen, die aus der Automobiltechnik bekannt sind. Darüber hinaus könnten diese Verfahren auch im Rahmen der Auslegung von Erntemaschinen hinsichtlich des Ernteprozesses einen maßgeblichen Beitrag zur weiteren Ressourcenschonung und Energieverbrauchsminderung leisten. Im Folgenden wird ein Ansatz zur Simulation erntetechnischer Schnittprozesse mit Hilfe der Mehrkörpersimulation (MKS) vorgestellt. Ferner soll die Anwendung der Strömungssimulation (CFD) am Beispiel eines Schlegelhäckslers gezeigt werden. KW - Strömungssimulation (CFD) KW - Mehrkörpersimulation (MKS) KW - Mähdrescherhäcksler KW - Schnittprozess Y1 - 2010 U6 - https://doi.org/10.15150/lt.2010.518 SN - 2943-5641 N1 - Diese Veröffentlichung beruht auf den Ergebnissen eines ProInno II-Projektes [1], das vom BMWi gefördert wurde und in Zusammenarbeit mit der Ennepetaler Schneid- und Mähtechnik GmbH & Co. KG (www.esm-ept.de) entstand VL - 65 IS - 5 SP - 372 EP - 375 PB - Kuratorium für Technik und Bauwesen in der Landwirtschaft e. V. (KTBL) ER - TY - BOOK A1 - Bongartz, R. A1 - Trofimov, Igor A1 - Jahr, Andreas ED - Pohl, Michael T1 - Optimierung der Zeitfestigkeit geschweißter rotationssymmetrischer Bauteile durch Randschichtverfestigung der Schweißzone T3 - Konstruktion, Werkstoffentwicklung und Schadensanalyse : [Tagung Werkstoffprüfung 2010, 02. und 03. Dezember 2010 in Neu-Ulm] KW - Werkstoffprüfung Y1 - 2010 SN - 9783514007789 SP - 131 EP - 136 PB - Verlag Stahleisen CY - Düsseldorf ER - TY - CHAP A1 - Fendrich, Sandra A1 - Fendrich, Sandra A1 - Jordan, Erwin A1 - Maykus, Stephan A1 - Müller, Regine A1 - Pothmann, Jens T1 - Diskussionspapier „Hilfen zur Erziehung in Nordrhein-Westfalen“ BT - ISA-Jahrbuch zur Sozialen Arbeit 2010 Y1 - 2010 SN - 978-3-8309-2442-5 SP - 126 EP - 150 PB - Waxmann CY - Münster ER - TY - BOOK ED - Müller, Regine ED - Nüsken, Dirk T1 - Child Protection in Europe: Von den Nachbarn lernen – Kinderschutz qualifizieren KW - Kinderschutz KW - Europa Y1 - 2010 SN - 9783830924036 PB - Waxmann CY - Münster, New York, München, Berlin ER - TY - CHAP A1 - Müller, Regine ED - Müller, Regine ED - Nüsken, Dirk T1 - „Child Protective Service“ im Vergleich. Ein Modell der wohlfahrtsstaatlichen Verortung der Fachkräfte im Kinderschutz T2 - Child Protection in Europe: Von den Nachbarn lernen – Kinderschutz qualifizieren KW - Kinderschutz KW - Fachkraft KW - Wohlfahrtsstaat KW - Europa Y1 - 2010 SN - 9783830924036 SP - 31 EP - 54 PB - Waxmann CY - Münster, New York, München, Berlin ER - TY - CHAP A1 - Wolf, Christian A1 - Gaida, Daniel A1 - Stuhlsatz, André A1 - McLoone, S. A1 - Bongards, Michael T1 - A Comparison of Performance of Advanced Pattern Recognition Methods for Organic Acid Prediction in Biogas Plants Using UV/vis Spectroscopic Online-Measurements T2 - Proceedings of the 2010 International Conference on Life System Modeling and Simulation (LSMS), Wuxi, China, 2010 Y1 - 2010 ER - TY - THES A1 - Stuhlsatz, André T1 - Machine learning with Lipschitz classifiers N2 - 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. Y1 - 2010 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:101:1-201104192945 PB - Otto-von-Guericke-Universität Magdeburg CY - Magdeburg 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 - Hörner, Fernand T1 - »ICH WEIß NOCH GENAU, WIE DAS ALLES BEGANN.« SAMPLING ALS KULTURELLE TEILHABE JF - Samples: Open Access Journal für Popular Music Studies KW - Popmusik KW - Hip-Hop KW - Sampling (Musik) Y1 - 2010 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hebis:26-opus-84026 UR - https://gfpm-samples.de/index.php/samples/article/view/102/94 SN - 1612-8001 VL - 9 PB - Arbeitskreis Studium Populärer Musik e.V. CY - Dortmund ER - TY - GEN A1 - Ern, Christiane A1 - Heinzl, Simeon T1 - Kurt Salmon Ass. In Düsseldorf T2 - AIT KW - Ern+Heinzl Architekten Y1 - 2010 SN - 0173-8046 IS - 4 SP - 92 EP - 97 PB - Koch ER -