@article{15001, author = {{Couso, Ines and Borgelt, Christian and Hüllermeier, Eyke and Kruse, Rudolf}}, issn = {{1556-603X}}, journal = {{IEEE Computational Intelligence Magazine}}, pages = {{31--44}}, title = {{{Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning}}}, doi = {{10.1109/mci.2018.2881642}}, year = {{2019}}, } @article{15002, abstract = {{Many problem settings in machine learning are concerned with the simultaneous prediction of multiple target variables of diverse type. Amongst others, such problem settings arise in multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. These subfields of machine learning are typically studied in isolation, without highlighting or exploring important relationships. In this paper, we present a unifying view on what we call multi-target prediction (MTP) problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research.}}, author = {{Waegeman, Willem and Dembczynski, Krzysztof and Hüllermeier, Eyke}}, issn = {{1573-756X}}, journal = {{Data Mining and Knowledge Discovery}}, number = {{2}}, pages = {{293--324}}, title = {{{Multi-target prediction: a unifying view on problems and methods}}}, doi = {{10.1007/s10618-018-0595-5}}, volume = {{33}}, year = {{2019}}, } @inproceedings{15003, author = {{Mortier, Thomas and Wydmuch, Marek and Dembczynski, Krzysztof and Hüllermeier, Eyke and Waegeman, Willem}}, booktitle = {{Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019}}, title = {{{Set-Valued Prediction in Multi-Class Classification}}}, year = {{2019}}, } @inbook{15004, author = {{Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke}}, booktitle = {{Discovery Science}}, isbn = {{9783030337773}}, issn = {{0302-9743}}, title = {{{Feature Selection for Analogy-Based Learning to Rank}}}, doi = {{10.1007/978-3-030-33778-0_22}}, year = {{2019}}, } @inbook{15005, author = {{Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke}}, booktitle = {{KI 2019: Advances in Artificial Intelligence}}, isbn = {{9783030301781}}, issn = {{0302-9743}}, title = {{{Analogy-Based Preference Learning with Kernels}}}, doi = {{10.1007/978-3-030-30179-8_3}}, year = {{2019}}, } @inbook{15006, author = {{Nguyen, Vu-Linh and Destercke, Sébastien and Hüllermeier, Eyke}}, booktitle = {{Discovery Science}}, isbn = {{9783030337773}}, issn = {{0302-9743}}, title = {{{Epistemic Uncertainty Sampling}}}, doi = {{10.1007/978-3-030-33778-0_7}}, year = {{2019}}, } @inproceedings{15007, author = {{Melnikov, Vitaly and Hüllermeier, Eyke}}, booktitle = {{Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101)}}, title = {{{Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA}}}, doi = {{10.1016/j.jmva.2019.02.017}}, year = {{2019}}, } @inproceedings{15009, author = {{Epple, Nico and Dari, Simone and Drees, Ludwig and Protschky, Valentin and Riener, Andreas}}, booktitle = {{2019 IEEE Intelligent Vehicles Symposium (IV)}}, isbn = {{9781728105604}}, title = {{{Influence of Cruise Control on Driver Guidance - a Comparison between System Generations and Countries}}}, doi = {{10.1109/ivs.2019.8814100}}, year = {{2019}}, } @inproceedings{15011, author = {{Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}}, booktitle = {{Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019}}, editor = {{Hoffmann, Frank and Hüllermeier, Eyke and Mikut, Ralf}}, isbn = {{978-3-7315-0979-0}}, location = {{Dortmund}}, pages = {{135--146}}, publisher = {{KIT Scientific Publishing, Karlsruhe}}, title = {{{Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking}}}, year = {{2019}}, } @inproceedings{15013, author = {{Brinker, Klaus and Hüllermeier, Eyke}}, booktitle = {{Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases}}, title = {{{A Reduction of Label Ranking to Multiclass Classification}}}, year = {{2019}}, } @inproceedings{15014, author = {{Hüllermeier, Eyke and Couso, Ines and Diestercke, Sebastian}}, booktitle = {{Proceedings SUM 2019, International Conference on Scalable Uncertainty Management}}, title = {{{Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants}}}, year = {{2019}}, } @article{15015, author = {{Henzgen, Sascha and Hüllermeier, Eyke}}, issn = {{1556-4681}}, journal = {{ACM Transactions on Knowledge Discovery from Data}}, pages = {{1--36}}, title = {{{Mining Rank Data}}}, doi = {{10.1145/3363572}}, year = {{2019}}, } @inproceedings{15024, abstract = {{Abstract. Within the scope of this study, an intrinsically lubricated deep drawing die fabricated via laser beam melting (LBM) is investigated. In contrast to the common objective of generating highly dense LBM components, this work endeavors to achieve intended micro-scale porosity. By utilizing permeable structures, in-process closed-loop control of lubrication during the forming operations is feasible. Based on a modified AM scan strategy, the required filigree, porous structures can be generated. Thus, in the present work three permeable specimens are additively generated from the maraging steel 1.2709. The cylindrical specimens are then analyzed via light microscopy (LM), microcomputer tomography (microCT), and with regard to the oil throughput rate. Subsequently, an intrinsically lubricated, AM deep drawing tool die is manufactured and experimentally tested. The findings reveal interesting results for deep drawn specimens with AM deep drawing dies.}}, author = {{Bader, Fabian and Hengsbach, Florian and Hoyer, Kay-Peter and Homberg, Werner and Schaper, Mirko}}, booktitle = {{PROCEEDINGS OF THE 22ND INTERNATIONAL ESAFORM CONFERENCE ON MATERIAL FORMING: ESAFORM 2019}}, title = {{{Intrinsically lubricated tool inserts for deep drawing applications generated by selective laser melting}}}, doi = {{10.1063/1.5112720}}, year = {{2019}}, } @article{15028, abstract = {{Friction-spinning is an incremental forming process, which is accompanied by complex thermal and mechanical loads in the tool and the formed part. To influence the process temperature, two main process parameters, i.e. the rotation speed and the feed rate, can be adapted. With the objective to improve the tool performance and the quality of the workpiece, this study focuses on a coating concept for friction-spinning tools made of high speed steel (HS6 5 2C, 1.3343). On the one hand, atmospheric plasma sprayed (APS) Al2O3 and ZrO2-8Y2O3 coatings serve as a thermal insulator, and, on the other hand, physically vapor deposited (PVD) TiAlSi7.9N and CrAlSi7.5N films are applied to increase the hardness and wear resistance of the tools. In addition, duplex coatings, combining the APS and PVD technique, are synthesized to influence both the heat transfer and the tribological properties of friction-spinning tools. Subsequently, all coated tools are tested in a friction-spinning process to form flanges made of AW-6060 (AlMgSi 3.3206) tube materials. The tool temperatures are determined in-situ to investigate the impact of the tool coating on the process temperature. Compared to an uncoated tool, the alumina and zirconia coatings contribute to a reduction of the tool temperature by up to half, while the PVD films increase the hardness of the tool by 20 GPa. Furthermore, it is shown that the surface quality of thermally sprayed (TS) or PVD coated tools is directly related to the surface roughness of the resulting workpiece. }}, author = {{Tillmann, Wolfgang and Fehr, Alexander and Stangier, Dominic and Dildrop, Markus and Homberg, Werner and Lossen, Benjamin and Hijazi, Dina}}, issn = {{0944-6524}}, journal = {{Production Engineering}}, pages = {{449--457}}, title = {{{Al2O3/ZrO2-8Y2O3 and (Cr,Ti)AlSiN tool coatings to influence the temperature and surface quality in friction-spinning processes}}}, doi = {{10.1007/s11740-019-00899-y}}, year = {{2019}}, } @phdthesis{15030, abstract = {{Working-media-based forming processes (WMBF) represent a great potential regarding the production of complex sheet-metal lightweight components with excellent surface quality, shape accuracy and dimensional stability. The working-media-based forming processes characterize the sheet-metal forming process, where the sheet metal blank is formed during the forming process by means of a (quasi-)static or dynamic working media pressure into a contouring forming tool. Although the WMBF offers improved utilization of the formability of the used materials compared to conventional sheet metal forming processes, there are limits in the production of complex deeper or sharp edged components with (quasi-)static and dynamic WMBF processes, which can not be overcome by using these methods alone. In order to overcome this, multi-level WMBF process sequences for components with spherical and stepped geometries are developed in this work. Here the developed strategies combine the advantages of (quasi-)static and dynamic WMBF processes. Furthermore, based on analytical, experimental and numerical investigations, innovative process management strategies were derived, which completely compensate the local wall thickness changes, make better use of existing material resources and thus enable the safe production of mentioned geometries.}}, author = {{Djakow, Eugen}}, keywords = {{High Speed Forming}}, pages = {{188}}, publisher = {{Shaker}}, title = {{{Ein Beitrag zur kombinierten (quasi-)statischen und dynamischen Umformung von blechförmigen Halbzeugen}}}, doi = {{ISBN 978-3-8440-6723-1}}, year = {{2019}}, } @article{15031, author = {{Linnemann, M. and Psyk, V. and Djakow, Eugen and Springer, R. and Homberg, W. and Landgrebe, D.}}, issn = {{2351-9789}}, journal = {{Procedia Manufacturing}}, pages = {{21--26}}, title = {{{High-Speed Incremental Forming – New Technologies For Flexible Production Of Sheet Metal Parts}}}, doi = {{10.1016/j.promfg.2018.12.038}}, year = {{2019}}, } @article{15036, author = {{Piper, M. and Zibart, A. and Djakow, Eugen and Springer, R. and Homberg, W. and Kenig, E.Y.}}, issn = {{1359-4311}}, journal = {{Applied Thermal Engineering}}, pages = {{142--146}}, title = {{{Heat transfer enhancement in pillow-plate heat exchangers with dimpled surfaces: A numerical study}}}, doi = {{10.1016/j.applthermaleng.2019.02.082}}, year = {{2019}}, } @inproceedings{15080, author = {{Hartel, Rita and Dunst, Alexander}}, booktitle = {{International Conference on Multimedia Modeling, MMM}}, isbn = {{9783030057152}}, issn = {{0302-9743}}, location = {{Thessaloniki, Greece}}, pages = {{662--671}}, publisher = {{Springer}}, title = {{{How Good Is Good Enough? Establishing Quality Thresholds for the Automatic Text Analysis of Retro-Digitized Comics}}}, doi = {{10.1007/978-3-030-05716-9_59}}, year = {{2019}}, } @article{15121, author = {{Bolenz, Lukas and Fischer, Florian and Toye, Dominique and Kenig, Eugeny}}, issn = {{0009-286X}}, journal = {{Chemie Ingenieur Technik}}, pages = {{1892--1896}}, title = {{{Tomographische Untersuchung der Fluiddynamik viskoser Systeme in Packungskolonnen}}}, doi = {{10.1002/cite.201900077}}, year = {{2019}}, } @article{15123, author = {{Bolenz, Lukas and Toye, Dominique and Kenig, Eugeny}}, journal = {{Chemical Engineering Transactions}}, pages = {{913--918}}, title = {{{Liquid Flow Morphology of Viscous Systems in Structured Packings: Investigations by X-ray Tomography}}}, doi = {{10.3303/CET1974153}}, volume = {{74}}, year = {{2019}}, }