@incollection{EmergMeth_Rehmer_2022,
 abstract = {The plastic injection molding process has been established as the most widespread manufacturing process in the plastic processing industry. It is employed by almost 70 % of all plastic processing companies. Among the decisive factors contributing to its prevalence are the ability to manufacture parts with intricate geometries and a high degree of automation. There are approaches of varying complexity to control the part quality to reduce waste and increase the efficiency of the process: The industry standard is the control of the so-called machine-variables i.e. the process variables that are measured on the machine side of the process. This does not take into account any variables that reflect the true state of the emerging part. For this reason the scientific community aims to control process variables that are measured cavity-side, more precisely the pressure in the mold cavity. However the implementation of pressure control requires significant control knowledge and is not suitable for large-scale industrial application. The objective of this contribution is therefore to transform an ordinary machine-variable controlled injection molding machine to a Cyber Physical Production System (CPPS) via augmentation by a digital twin (DT). The DT will predict the part quality from process variables. To this end a state-of-the-art industrial injection molding machine was equipped with additional sensors that measure in-cavity process variables. Moreover an in-line quality measuring cell was added. By doing so all machine, processes and quality data required for data-driven modeling and perspectively control are acquired. Subsequently an internal dynamics approach for predicting final batch quality from process value trajectories is proposed and compared to the current state-of-the-art modeling approaches in a case study.},
 author = {Alexander Rehmer and Marco Klute and Andreas Kroll and Hans-Peter Heim},
 booktitle = {Modeling, Identification
and Control for Cyber-Physical Systems Towards Industry
4.0},
 chapter = {4},
 doi = {10.1016/B978-0-32-395207-1.00014-7},
 edition = {1},
 editor = {Paolo Mercorelli and Weicun Zhang and Hamidreza Nemati and YuMing Zhang},
 isbn = {978-0-323-95207-1},
 keywords = {Digital Twin, Plastic injection molding
Part quality prediction, OPC-UA
Machine
learning},
 language = {english},
 mrtnotes = {submitted, IdRNN},
 pages = {79--109},
 publisher = {Academic Press},
 series = {Emerging Methodologies and Applications in Modelling, Identification and Control},
 title = {A Digital Twin for Part QualityPrediction and Control in
Plastic Injection
Molding},
 url = {https://www.sciencedirect.com/science/article/abs/pii/B9780323952071000147},
 year = {2024}
}

@techreport{2022-Rehmer-TR-DIM,
 address = {Universität Kassel},
 author = {Rehmer, Alexander},
 language = {german},
 month = {December},
 mrtnr = {TR-032},
 owner = {Axel Dürrbaum},
 school = {FG Mess- und Regelungstechnik},
 timestamp = {2023.09.26},
 title = {Digital Twin of Injection Molding (DIM) -- Entwicklung einer datengetriebenen Repräsentation des Thermoplast-Spritzgießpro-zesses zur Optimierung der Bauteilgüte (durch direkte
Prozessregelung)},
 type = {Abschlussbericht},
 year = {2022}
}

@inproceedings{Rehmer-CCTA-2022,
 abstract = {Recurrent Neural Networks are applied in areas such as speech recognition, natural language and video processing, and the identification of nonlinear state space models. Conventional Recurrent Neural Networks, e.g. the Elman Network, are hard to train. A more recently developed class of recurrent neural networks, so-called Gated Units, outperform their counterparts on virtually every task. This paper aims to provide additional insights into the differences between RNNs and Gated Units in order to explain the superior perfomance of gated recurrent units. It is argued, that Gated Units are easier to optimize not because they solve the vanishing gradient problem, but because they circumvent the emergence of large local gradients.},
 address = {Trieste, Italy},
 author = {Alexander Rehmer and Marco Klute and Andreas Kroll
and Hans-Peter Heim},
 booktitle = {6th IEEE Conference on Control Technology and
Applications (CCTA)},
 journal = {IFAC-PapersOnLine},
 language = {english},
 month = {22.-25- August},
 mrtnote = {peer,IdRNN},
 organization = {IFAC},
 owner = {rehmer},
 pages = {1427-1432},
 publisher = {Elsevier},
 timestamp = {2019.11.25},
 title = {An internal dynamics approach to predicting batch-end product quality in plastic injection molding using Recurrent Neural
Networks},
 volume = {53},
 year = {2022}
}

@inproceedings{Rehmer-ICSTCC-2022,
 abstract = {Recurrent Neural Networks are applied in areas such as speech recognition, natural language and video processing, and the identification of nonlinear state space models. Conventional Recurrent Neural Networks, e.g. the Elman Network, are hard to train. A more recently developed class of recurrent neural networks, so-called Gated Units, outperform their counterparts on virtually every task. This paper aims to provide additional insights into the differences between RNNs and Gated Units in order to explain the superior perfomance of gated recurrent units. It is argued, that Gated Units are easier to optimize not because they solve the vanishing gradient problem, but because they circumvent the emergence of large local gradients.},
 address = {Sinaia, Romania},
 author = {Alexander Rehmer and Andreas Kroll},
 booktitle = {Proceedings of the 26th International Conference on System Theory
Control and Computing
(ICSTCC)},
 journal = {IFAC-PapersOnLine},
 language = {english},
 month = {19.-21- October},
 mrtnote = {peer,IdRNN},
 owner = {rehmer},
 pages = {446-451},
 timestamp = {2019.11.25},
 title = {On affine quasi-LPV System Identification with unknown state-scheduling using (deep) Recurrent Neural
Networks},
 year = {2022}
}

@inproceedings{RehmerGMACI2022,
 address = {Berlin},
 author = {Alexander Rehmer and Andreas Kroll},
 booktitle = {32. Workshop Computational Intelligence},
 date = {2022},
 location = {Berlin},
 month = {1. - 2. Dezember 2022},
 mrtnote = {nopeer, IdRNN},
 organization = {GMA-FA 5.14},
 owner = {rehmer},
 pages = {133-150},
 publisher = {KIT Scientific Publishing},
 timestamp = {2021.08.24},
 title = {Eine Python-Toolbox zur datengetriebenen Modellierung des Spritzgieprozsses und Lösung von Optimalsteuerungsproblemen zur Steuerung der Bauteilqualität},
 url = {https://library.oapen.org/handle/20.500.12657/59840?show=full},
 year = {2022}
}

@inproceedings{RehmerECC2022_2,
 address = {London, UK},
 author = {Alexander Rehmer and Andreas Kroll},
 booktitle = {Preprints of the 20th European Control Conference
(ECC)},
 month = {12.-15. July},
 mrtnote = {peer,IdRNN},
 owner = {gringard},
 pages = {566-571},
 timestamp = {2017.12.13},
 title = {A Deep Recurrent Neural Network model for affine
quasi-LPV System identification},
 year = {2022}
}

@inproceedings{Rehmer_WCCI_2022,
 address = {Padua, Italy},
 author = {Alexander Rehmer and Andras Kroll},
 booktitle = {Preprints of the International Joint Conference on
Neural Networks (IJCNN 2022)},
 month = {18.-23. July},
 mrtnote = {nopeer,IdRNN},
 owner = {duerrbaum},
 pages = {1-8},
 timestamp = {2022.03.29},
 title = {The effect of the forget gate on bifurcation boundaries and dynamics in Recurrent Neural Networks and its implications for gradient-based optimization},
 year = {2022}
}

@inproceedings{Rehmer-IFAC-2020,
 abstract = {Recurrent Neural Networks are applied in areas such as speech recognition, natural language and video processing, and the identification of nonlinear state space models. Conventional Recurrent Neural Networks, e.g. the Elman Network, are hard to train. A more recently developed class of recurrent neural networks, so-called Gated Units, outperform their counterparts on virtually every task. This paper aims to provide additional insights into the differences between RNNs and Gated Units in order to explain the superior perfomance of gated recurrent units. It is argued, that Gated Units are easier to optimize not because they solve the vanishing gradient problem, but because they circumvent the emergence of large local gradients.},
 address = {Berlin, Germany},
 author = {Alexander Rehmer and Andreas Kroll},
 booktitle = {21th IFAC World Congress},
 journal = {IFAC-PapersOnLine},
 language = {english},
 month = {12.-17- July},
 mrtnote = {peer,IdRNN},
 number = {2},
 organization = {IFAC},
 owner = {rehmer},
 pages = {1243--1248},
 publisher = {Elsevier},
 timestamp = {2019.11.25},
 title = {On the vanishing and exploding gradient problem in
Gated Recurrent Units},
 volume = {53},
 year = {2020}
}

@techreport{2020-Kroll_ua-TB-PI2_NL_Dynamik,
 address = {Universität Kassel},
 author = {A. Kroll and A. Dürrbaum and A. Rehmer and
M. Gringard},
 institution = {FG Mess- und Regelungstechnik},
 language = {german},
 month = {September},
 mrtnote = {intern},
 mrtnr = {TR-028},
 owner = {duerrbaum},
 timestamp = {2017.06.29},
 title = {Task Force Nichtlineare Dynamik PI-II --
Abschlussbericht},
 type = {techreport},
 year = {2020}
}

@inproceedings{RehmerECC2019,
 abstract = {This paper is concerned with the test signal design for the identification of the partition parameters of locally affine Takagi-Sugeno-(TS-)Models. The basic idea is that data should be generated in local model transition areas in the scheduling space. A reference system output that represents the desired path in the scheduling space is forced upon the underlying system through a combination of a feed-forward and feedback control scheme. The system is then identified in an iterative closed-loop identification procedure. This method has been applied to an artificial system to demonstrate its potential.},
 address = {Naples, Italy},
 author = {Alexander Rehmer and Andreas Kroll},
 booktitle = {Preprints of the 18th European Control Conference
(ECC)},
 month = {25.-28. Juni},
 mrtnote = {peer,IdRNN},
 organization = {IFAC},
 owner = {rehmer},
 pages = {2504-2509},
 timestamp = {2018.11.14},
 title = {On Using Gated Recurrent Units for Nonlinear System
Identification},
 url = {https://www.ifac-control.org/events/european-control-conference-in-cooperation-with-ifac-ecc-2019},
 year = {2019}
}

@techreport{2018-Schlussbericht-FEE,
 address = {Universität Kassel},
 author = {Alexander Rehmer and Andreas Kroll and Benjamin Klöpper and Martin Hollender and Marcel Dix and Moncef Chioua and Jan Christoph Schlake and Hassan Enam Al Mawla and Martin Atzmüller and Andreas Schmidt and Gerd Stumme and Sebastian Heinze and Luise Schegner and Markus Graube and Leon Urbas and Ralf Klinkenberg and David Arnu and Edin Klapic and Fabian
Temme},
 doi = {10.2314/GBV:1029316821},
 institution = {FG Mess- und Regelungstechnik},
 language = {german},
 number = {01IS14006},
 owner = {duerrbaum},
 pagetotal = {83},
 timestamp = {2020.09.10},
 title = {FEE -- Frühzeitige Erkennung und Entscheidungsunterstützung für kritische Situationen im
Produktionsumfeld},
 type = {Schlussbericht},
 url = {https://www.tib.eu/de/suchen/id/TIBKAT:1029316821/},
 urldate = {10.9.2020},
 year = {2018}
}

@inproceedings{HeinzeAutomation2017,
 address = {Baden-Baden},
 author = {S. Heinze and M. Graube and L. Schegner and D. Arnu and R. Klinkenberg and A. Schmidt and M. Atzmüller and B. Klöpper and M. Dix and M. Hollender and M. Chioua and H. {Al Mawla} and A. Rehmer and A. Kroll and G. Stumme and L.
Urbas},
 booktitle = {Automation 2017},
 howpublished = {online},
 language = {deutsch},
 month = {27.-28. Juni},
 mrtnote = {FEE,nopeer},
 organization = {VDI/VDE-Gesellschaft Mess- und
Automatisierungstechnik (GMA)},
 owner = {almawla},
 publisher = {VDI},
 title = {Big Data in der Prozessindustrie: Frühzeitige Erkennung und
Entscheidungsunterstützung},
 url = {https://www.automatisierungskongress.de/},
 year = {2017}
}

@article{2017-Kroll_et_al-atp_edition-Modellfabrik_muPlant,
 abstract = {Modellfabriken werden mittlerweile häufig in Forschung und Lehre eingesetzt, aber über ihren Aufbau und ihre Funktionen ist wenig zu lesen. Im Beitrag wird eine Übersicht über in Deuschland vorhandene Modellfabriken gegeben. Zudem werden Details der selbst konzipierten und realisierten Modellfabrik µPlant vorgestellt. Diese besteht aus mehreren mit Transportrobotern verbundenen Produktionsinseln/-zellen, die jeweils lokal über angepasste Automatisierungssysteme verfügen. Alle Module sind stofflich und informationstechnisch integriert und die Modellfabrik kann voll automatisiert betrieben werden. Der Beitrag richtet sich an Personen mit Interesse an Aufbau, Beschaffung oder Nutzung von Modellfabriken},
 author = {Andreas Kroll AND Axel Dürrbaum AND David Arengas AND Hassan {Al Mawla} AND Lars Kistner AND Alexander Rehmer},
 journal = {atp edition},
 keywords = {Modellfabrik, Modularisierung, dezentrale
Automation},
 language = {german},
 month = {September},
 mrtnote = {peer, muPlant, FEE},
 mrturla = {https://mrt-pc1.mrt.maschinenbau.uni-kassel.de/MRT/Bibliothek/Publikationen/2017-Kroll-atp-Modellfabrik_muPlant-PUB.pdf},
 number = {9},
 owner = {duerrbaum},
 pages = {40-53},
 timestamp = {2017.08.15},
 title = {µPlant: Eine automatisierungstechnisch-orientierte Modellfabrik für vernetzte heterogene
Systeme},
 url = {https://www.di-verlag.de/de/Zeitschriften/atp-edition/2017/09/Plant:-Modellfabrik-fuer-vernetzte-heterogene-Anlagen},
 volume = {59},
 year = {2017}
}

@inproceedings{RehmerIFAC2017,
 abstract = {Multivariate Statistical Process Control (MSPC) techniques such as Principal Component Analysis (PCA) and Partial Least Squares (PLS) have found wide application especially in the statistical modeling and monitoring of chemical processes. However, real industrial processes often violate the assumptions underlying MSPC since they exhibit timevarying and non-stationary behavior. Adaptive PCA-based monitoring procedures such as Moving Window PCA (MWPCA) and Recursive PCA (RPCA) have been proposed to tackle this issue. Although the parameter selection for those procedures is critical to their proper implementation, this topic is rarely covered in the literature. This paper examines two methods for MWPCA and RPCA parameter selection using the Tennessee Eastman process as an example. Based on the findings a novel procedure for RPCA parameter selection as well as a extension to RPCA will be proposed and demonstrated.},
 author = {Alexander Rehmer and Andreas Kroll},
 booktitle = {Proceedings of the 20th IFAC World Congress},
 mrtnote = {FEE,peer},
 organization = {IFAC},
 owner = {rehmer},
 pages = {15329-15334},
 timestamp = {2016.11.14},
 title = {An extension to RPCA parameter selection and process
monitoring},
 year = {2017}
}

@mastersthesis{MARehmer2016,
 address = {Universität Kassel},
 author = {Alexander Rehmer},
 month = {September},
 mrtnote = {education},
 mrtnr = {172},
 owner = {duerrbaum},
 school = {FG Mess- und Regelungstechnik},
 supervisor = {#bj#, #ak#, #hjs#},
 timestamp = {2015.09.18},
 title = {Erweiterung der Hauptkomponentenanalyse für die Massendatenanalyse zwecks
Prozessüberwachung},
 type = {Masterarbeit},
 year = {2016}
}

@mastersthesis{SemRehmer2014,
 address = {Universität Kassel},
 author = {Alexander Rehmer},
 month = {Oktober},
 mrtnote = {education},
 mrtnr = {153},
 owner = {jaeschke},
 school = {FG Mess- und Regelungstechnik},
 supervisor = {#bj#, #ak#},
 timestamp = {2014.11.05},
 title = {Principal Component Analysis (PCA) und Partial Least Squares (PLS) im
Big-Data-Kontext},
 type = {Oberseminararbeit},
 url = {https://mrt-pc1.mrt.maschinenbau.uni-kassel.de/MRT/Lehre/Aufgabenstellungen/2014-Rehmer-Sem-PCA_und_PLS_im_Big-Data-Kontext.pdf},
 year = {2014}
}

@mastersthesis{2013-Rehmer-BPS-mPlant,
 address = {Universität Kassel},
 author = {Alexander Rehmer},
 language = {german},
 month = {Oktober},
 mrtnote = {education, muPlant},
 mrtnr = {184},
 school = {FG Mess- und Regelungstechnik},
 supervisor = {#ak#, #ag#},
 title = {Wirtschaftlich-technische Konzipierung und Realisierung einer Befüll- und
Entleerstation},
 type = {Berufspraktische Studien},
 url = {https://mrt-pc1.mrt.maschinenbau.uni-kassel.de/MRT/Lehre/Aufgabenstellungen/2013-Rehmer-BPS-Abfuellstation.pdf},
 year = {2013}
}