TY - CHAP A1 - Kirsch, Maximilian A1 - Mataré, Victor A1 - Ferrein, Alexander A1 - Schiffer, Stefan T1 - Integrating golog++ and ROS for Practical and Portable High-level Control T2 - 12th International Conference on Agents and Artificial Intelligence Y1 - 2020 U6 - http://dx.doi.org/10.5220/0008984406920699 ER - TY - JOUR A1 - Khayyam, Hamid A1 - Jamali, Ali A1 - Bab-Hadiashar, Alireza A1 - Esch, Thomas A1 - Ramakrishna, Seeram A1 - Jalili, Mahdi A1 - Naebe, Minoo T1 - A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modeling with Application in Industry 4.0 JF - IEEE Access N2 - To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements. Y1 - 2020 U6 - http://dx.doi.org/10.1109/ACCESS.2020.2999898 SN - 2169-3536 VL - 8 IS - Art. 9108222 SP - 111381 EP - 111393 PB - IEEE CY - New York, NY ER - TY - JOUR A1 - Khayyam, Hamid A1 - Jamali, Ali A1 - Bab-Hadiashar, Alireza A1 - Esch, Thomas A1 - Ramakrishna, Seeram A1 - Jalil, Mahdi A1 - Naebe, Minoo T1 - A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modelling with Application in Industry 4.0 JF - IEEE Access N2 - To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements. Y1 - 2020 SN - 2169-3536 U6 - http://dx.doi.org/10.1109/ACCESS.2020.2999898 SP - 1 EP - 12 PB - IEEE CY - New York, NY ER - TY - JOUR A1 - Ketelhut, Maike A1 - Brügge, G. M. A1 - Göll, Fabian A1 - Braunstein, Bjoern A1 - Albracht, Kirsten A1 - Abel, Dirk T1 - Adaptive iterative learning control of an industrial robot during neuromuscular training JF - IFAC PapersOnLine N2 - To prevent the reduction of muscle mass and loss of strength coming along with the human aging process, regular training with e.g. a leg press is suitable. However, the risk of training-induced injuries requires the continuous monitoring and controlling of the forces applied to the musculoskeletal system as well as the velocity along the motion trajectory and the range of motion. In this paper, an adaptive norm-optimal iterative learning control algorithm to minimize the knee joint loadings during the leg extension training with an industrial robot is proposed. The response of the algorithm is tested in simulation for patients with varus, normal and valgus alignment of the knee and compared to the results of a higher-order iterative learning control algorithm, a robust iterative learning control and a recently proposed conventional norm-optimal iterative learning control algorithm. Although significant improvements in performance are made compared to the conventional norm-optimal iterative learning control algorithm with a small learning factor, for the developed approach as well as the robust iterative learning control algorithm small steady state errors occur. KW - Iterative learning control KW - Robotic rehabilitation KW - Adaptive control Y1 - 2020 U6 - http://dx.doi.org/10.1016/j.ifacol.2020.12.741 SN - 2405-8963 VL - 53 IS - 2 SP - 16468 EP - 16475 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Kerres, Karsten A1 - Gredigk-Hoffmann, Sylvia A1 - Jathe, Rüdiger A1 - Orlik, Stefan A1 - Sariyildiz, Mustafa A1 - Schmidt, Torsten A1 - Sympher, Klaus-Jochen A1 - Uhlenbroch, Adrian T1 - Future approaches for sewer system condition assessment JF - Water Practice & Technology N2 - Different analytical approaches exist to describe the structural substance or wear reserve of sewer systems. The aim is to convert engineering assessments of often complex defect patterns into computational algorithms and determine a substance class for a sewer section or manhole. This analytically determined information is essential for strategic rehabilitation planning processes up to network level, as it corresponds to the most appropriate rehabilitation type and can thus provide decision-making support. Current calculation methods differ clearly from each other in parts, so that substance classes determined by the different approaches are only partially comparable with each other. The objective of the German R&D cooperation project ‘SubKanS’ is to develop a methodology for classifying the specific defect patterns resulting from the interaction of all the individual defects, and their severities and locations. The methodology takes into account the structural substance of sewer sections and manholes, based on real data and theoretical considerations analogous to the condition classification of individual defects. The result is a catalogue of defect patterns and characteristics, as well as associated structural substance classifications of sewer systems (substance classes). The methodology for sewer system substance classification is developed so that the classification of individual defects can be transferred into a substance class of the sewer section or manhole, eventually taking into account further information (e.g. pipe material, nominal diameter, etc.). The result is a validated methodology for automated sewer system substance classification. Y1 - 2020 U6 - http://dx.doi.org/10.2166/wpt.2020.027 SN - 1751-231X IS - 15 (2) SP - 386 EP - 393 PB - IWA Publishing CY - London ER - TY - JOUR A1 - Keller, Johannes A1 - Rath, Volker A1 - Bruckmann, Johanna A1 - Mottaghy, Darius A1 - Clauser, Christoph A1 - Wolf, Andreas A1 - Seidler, Ralf A1 - Bücker, H. Martin A1 - Klitzsch, Norbert T1 - SHEMAT-Suite: An open-source code for simulating flow, heat and species transport in porous media JF - SoftwareX N2 - SHEMAT-Suite is a finite-difference open-source code for simulating coupled flow, heat and species transport in porous media. The code, written in Fortran-95, originates from geoscientific research in the fields of geothermics and hydrogeology. It comprises: (1) a versatile handling of input and output, (2) a modular framework for subsurface parameter modeling, (3) a multi-level OpenMP parallelization, (4) parameter estimation and data assimilation by stochastic approaches (Monte Carlo, Ensemble Kalman filter) and by deterministic Bayesian approaches based on automatic differentiation for calculating exact (truncation error-free) derivatives of the forward code. Y1 - 2020 U6 - http://dx.doi.org/10.1016/j.softx.2020.100533 SN - 2352-7110 VL - 12 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Kasch, Susanne A1 - Schmidt, Thomas A1 - Eichler, Fabian A1 - Thurn, Laura A1 - Jahn, Simon A1 - Bremen, Sebastian T1 - Solution approaches and process concepts for powder bed-based melting of glass T2 - Industrializing Additive Manufacturing. Proceedings of AMPA2020 N2 - In the study, the process chain of additive manufacturing by means of powder bed fusion will be presented based on the material glass. In order to reliably process components additively, new concepts with different solutions were developed and investigated. Compared to established metallic materials, the properties of glass materials differ significantly. Therefore, the process control was adapted to the material glass in the investigations. With extensive parameter studies based on various glass powders such as borosilicate glass and quartz glass, scientifically proven results on powder bed fusion of glass are presented. Based on the determination of the particle properties with different methods, extensive investigations are made regarding the melting behavior of glass by means of laser beams. Furthermore, the experimental setup was steadily expanded. In addition to the integration of coaxial temperature measurement and regulation, preheating of the building platform is of major importance. This offers the possibility to perform 3D printing at the transformation temperatures of the glass materials. To improve the component’s properties, the influence of a subsequent heat treatment was also investigated. The experience gained was incorporated into a new experimental system, which allows a much better exploration of the 3D printing of glass. Currently, studies are being conducted to improve surface texture, building accuracy, and geometrical capabilities using three-dimensional specimen. The contribution shows the development of research in the field of 3D printing of glass, gives an insight into the machine and process engineering as well as an outlook on the possibilities and applications. KW - Glass powder KW - Laser processing KW - Additive manufacturing KW - Melting KW - L-PBF Y1 - 2020 SN - 978-3-030-54333-4 (Print) SN - 978-3-030-54334-1 (Online) U6 - http://dx.doi.org/10.1007/978-3-030-54334-1_7 N1 - International Conference on Additive Manufacturing in Products and Applications. 01.-03. September 2020. Zurich, Switzerland SP - 82 EP - 95 PB - Springer CY - Cham ER - TY - JOUR A1 - Jung, Alexander A1 - Staat, Manfred T1 - Erratum to "Modeling and simulation of human induced pluripotent stem cell-derived cardiac tissue" [GAMM-Mitteilungen, (2019), 42, 4, 10.1002/gamm.201900002] JF - GAMM-Mitteilungen Y1 - 2020 U6 - http://dx.doi.org/10.1002/gamm.202000011 SN - 1522-2608 N1 - Refers to: Modeling and simulation of human induced pluripotent stem cell-derived cardiac tissue. Alexander Jung, Manfred Staat. Volume 42, Issue 4. GAMM-Mitteilungen, 2019. https://doi.org/10.1002/gamm.201900002 VL - 43 IS - 4 PB - Wiley-VCH GmbH CY - Weinheim ER - TY - JOUR A1 - Jildeh, Zaid B. A1 - Kirchner, Patrick A1 - Oberländer, Jan A1 - Vahidpour, Farnoosh A1 - Wagner, Patrick H. A1 - Schöning, Michael Josef T1 - Development of a package-sterilization process for aseptic filling machines: A numerical approach and validation for surface treatment with hydrogen peroxide JF - Sensor and Actuators A: Physical N2 - Within the present work a sterilization process by a heated gas mixture that contains hydrogen peroxide (H₂O₂) is validated by experiments and numerical modeling techniques. The operational parameters that affect the sterilization efficacy are described alongside the two modes of sterilization: gaseous and condensed H₂O₂. Measurements with a previously developed H₂O₂ gas sensor are carried out to validate the applied H₂O₂ gas concentration during sterilization. We performed microbiological tests at different H₂O₂ gas concentrations by applying an end-point method to carrier strips, which contain different inoculation loads of Geobacillus stearothermophilus spores. The analysis of the sterilization process of a pharmaceutical glass vial is performed by numerical modeling. The numerical model combines heat- and advection-diffusion mass transfer with vapor–pressure equations to predict the location of condensate formation and the concentration of H₂O₂ at the packaging surfaces by changing the gas temperature. For a sterilization process of 0.7 s, a H₂O₂ gas concentration above 4% v/v is required to reach a log-count reduction above six. The numerical results showed the location of H₂O₂ condensate formation, which decreases with increasing sterilant-gas temperature. The model can be transferred to different gas nozzle- and packaging geometries to assure the absence of H₂O₂ residues. Y1 - 2020 U6 - http://dx.doi.org/10.1016/j.sna.2019.111691 SN - 0924-4247 VL - 303 IS - 111691 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Iomdina, Elena N. A1 - Kiseleva, Anna A. A1 - Kotliar, Konstantin A1 - Luzhnov, Petr V. T1 - Quantification of Choroidal Blood Flow Using the OCT-A System Based on Voxel Scan Processing T2 - 2020 International Conference on Biomedical Innovations and Applications (BIA) Y1 - 2020 SN - 978-1-7281-7073-2 U6 - http://dx.doi.org/10.1109/BIA50171.2020.9244511 SP - 41 EP - 44 ER -