[{"language":[{"iso":"eng"}],"year":"2020","title":"Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-Supervised Sound Event Detection","author":[{"id":"34851","full_name":"Ebbers, Janek","last_name":"Ebbers","first_name":"Janek"},{"id":"242","last_name":"Haeb-Umbach","first_name":"Reinhold","full_name":"Haeb-Umbach, Reinhold"}],"date_updated":"2023-11-22T08:27:32Z","file":[{"content_type":"application/pdf","file_id":"20754","file_size":108326,"access_level":"open_access","file_name":"DCASE2020Workshop_Ebbers_Paper.pdf","date_updated":"2020-12-16T08:57:22Z","relation":"main_file","date_created":"2020-12-16T08:57:22Z","creator":"huesera"}],"date_created":"2020-12-16T08:55:27Z","type":"conference","department":[{"_id":"54"}],"publication":"Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020)","abstract":[{"lang":"eng","text":"In this paper we present our system for the detection and classification of acoustic scenes and events (DCASE) 2020 Challenge Task 4: Sound event detection and separation in domestic environments. We introduce two new models: the forward-backward convolutional recurrent neural network (FBCRNN) and the tag-conditioned convolutional neural network (CNN). The FBCRNN employs two recurrent neural network (RNN) classifiers sharing the same CNN for preprocessing. With one RNN processing a recording in forward direction and the other in backward direction, the two networks are trained to jointly predict audio tags, i.e., weak labels, at each time step within a recording, given that at each time step they have jointly processed the whole recording. The proposed training encourages the classifiers to tag events as soon as possible. Therefore, after training, the networks can be applied to shorter audio segments of, e.g., 200ms, allowing sound event detection (SED). Further, we propose a tag-conditioned CNN to complement SED. It is trained to predict strong labels while using (predicted) tags, i.e., weak labels, as additional input. For training pseudo strong labels from a FBCRNN ensemble are used. The presented system scored the fourth and third place in the systems and teams rankings, respectively. Subsequent improvements allow our system to even outperform the challenge baseline and winner systems in average by, respectively, 18.0% and 2.2% event-based F1-score on the validation set. Source code is publicly available at https://github.com/fgnt/pb_sed."}],"_id":"20753","ddc":["000"],"user_id":"34851","status":"public","has_accepted_license":"1","oa":"1","file_date_updated":"2020-12-16T08:57:22Z","citation":{"mla":"Ebbers, Janek, and Reinhold Haeb-Umbach. “Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-Supervised Sound Event Detection.” <i>Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020)</i>, 2020.","bibtex":"@inproceedings{Ebbers_Haeb-Umbach_2020, title={Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-Supervised Sound Event Detection}, booktitle={Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020)}, author={Ebbers, Janek and Haeb-Umbach, Reinhold}, year={2020} }","ama":"Ebbers J, Haeb-Umbach R. Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-Supervised Sound Event Detection. In: <i>Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020)</i>. ; 2020.","ieee":"J. Ebbers and R. Haeb-Umbach, “Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-Supervised Sound Event Detection,” 2020.","apa":"Ebbers, J., &#38; Haeb-Umbach, R. (2020). Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-Supervised Sound Event Detection. <i>Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020)</i>.","chicago":"Ebbers, Janek, and Reinhold Haeb-Umbach. “Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-Supervised Sound Event Detection.” In <i>Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020)</i>, 2020.","short":"J. Ebbers, R. Haeb-Umbach, in: Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020), 2020."},"quality_controlled":"1","project":[{"_id":"52","name":"PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing"}]},{"publication_status":"published","date_updated":"2023-12-13T10:43:53Z","publication_identifier":{"isbn":["978-3-030-58111-4"]},"author":[{"full_name":"Bossek, Jakob","first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","id":"102979"},{"full_name":"Doerr, Carola","first_name":"Carola","last_name":"Doerr"},{"full_name":"Kerschke, Pascal","first_name":"Pascal","last_name":"Kerschke"},{"first_name":"Aneta","last_name":"Neumann","full_name":"Neumann, Aneta"},{"first_name":"Frank","last_name":"Neumann","full_name":"Neumann, Frank"}],"title":"Evolving Sampling Strategies for One-Shot Optimization Tasks","year":"2020","doi":"10.1007/978-3-030-58112-1_8","language":[{"iso":"eng"}],"extern":"1","abstract":[{"text":"One-shot optimization tasks require to determine the set of solution candidates prior to their evaluation, i.e., without possibility for adaptive sampling. We consider two variants, classic one-shot optimization (where our aim is to find at least one solution of high quality) and one-shot regression (where the goal is to fit a model that resembles the true problem as well as possible). For both tasks it seems intuitive that well-distributed samples should perform better than uniform or grid-based samples, since they show a better coverage of the decision space. In practice, quasi-random designs such as Latin Hypercube Samples and low-discrepancy point sets are indeed very commonly used designs for one-shot optimization tasks. We study in this work how well low star discrepancy correlates with performance in one-shot optimization. Our results confirm an advantage of low-discrepancy designs, but also indicate the correlation between discrepancy values and overall performance is rather weak. We then demonstrate that commonly used designs may be far from optimal. More precisely, we evolve 24 very specific designs that each achieve good performance on one of our benchmark problems. Interestingly, we find that these specifically designed samples yield surprisingly good performance across the whole benchmark set. Our results therefore give strong indication that significant performance gains over state-of-the-art one-shot sampling techniques are possible, and that evolutionary algorithms can be an efficient means to evolve these.","lang":"eng"}],"publication":"Parallel Problem Solving from Nature (PPSN XVI)","department":[{"_id":"819"}],"keyword":["Continuous optimization","Fully parallel search","One-shot optimization","Regression","Surrogate-assisted optimization"],"type":"conference","date_created":"2023-11-14T15:58:53Z","status":"public","user_id":"102979","publisher":"Springer-Verlag","_id":"48849","page":"111–124","citation":{"bibtex":"@inproceedings{Bossek_Doerr_Kerschke_Neumann_Neumann_2020, place={Berlin, Heidelberg}, title={Evolving Sampling Strategies for One-Shot Optimization Tasks}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-58112-1_8\">10.1007/978-3-030-58112-1_8</a>}, booktitle={Parallel Problem Solving from Nature (PPSN XVI)}, publisher={Springer-Verlag}, author={Bossek, Jakob and Doerr, Carola and Kerschke, Pascal and Neumann, Aneta and Neumann, Frank}, year={2020}, pages={111–124} }","ama":"Bossek J, Doerr C, Kerschke P, Neumann A, Neumann F. Evolving Sampling Strategies for One-Shot Optimization Tasks. In: <i>Parallel Problem Solving from Nature (PPSN XVI)</i>. Springer-Verlag; 2020:111–124. doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_8\">10.1007/978-3-030-58112-1_8</a>","mla":"Bossek, Jakob, et al. “Evolving Sampling Strategies for One-Shot Optimization Tasks.” <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, Springer-Verlag, 2020, pp. 111–124, doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_8\">10.1007/978-3-030-58112-1_8</a>.","short":"J. Bossek, C. Doerr, P. Kerschke, A. Neumann, F. Neumann, in: Parallel Problem Solving from Nature (PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp. 111–124.","chicago":"Bossek, Jakob, Carola Doerr, Pascal Kerschke, Aneta Neumann, and Frank Neumann. “Evolving Sampling Strategies for One-Shot Optimization Tasks.” In <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, 111–124. Berlin, Heidelberg: Springer-Verlag, 2020. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_8\">https://doi.org/10.1007/978-3-030-58112-1_8</a>.","ieee":"J. Bossek, C. Doerr, P. Kerschke, A. Neumann, and F. Neumann, “Evolving Sampling Strategies for One-Shot Optimization Tasks,” in <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, 2020, pp. 111–124, doi: <a href=\"https://doi.org/10.1007/978-3-030-58112-1_8\">10.1007/978-3-030-58112-1_8</a>.","apa":"Bossek, J., Doerr, C., Kerschke, P., Neumann, A., &#38; Neumann, F. (2020). Evolving Sampling Strategies for One-Shot Optimization Tasks. <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, 111–124. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_8\">https://doi.org/10.1007/978-3-030-58112-1_8</a>"},"place":"Berlin, Heidelberg"},{"abstract":[{"text":"The Traveling Salesperson Problem (TSP) is one of the best-known combinatorial optimisation problems. However, many real-world problems are composed of several interacting components. The Traveling Thief Problem (TTP) addresses such interactions by combining two combinatorial optimisation problems, namely the TSP and the Knapsack Problem (KP). Recently, a new problem called the node weight dependent Traveling Salesperson Problem (W-TSP) has been introduced where nodes have weights that influence the cost of the tour. In this paper, we compare W-TSP and TTP. We investigate the structure of the optimised tours for W-TSP and TTP and the impact of using each others fitness function. Our experimental results suggest (1) that the W-TSP often can be solved better using the TTP fitness function and (2) final W-TSP and TTP solutions show different distributions when compared with optimal TSP or weighted greedy solutions.","lang":"eng"}],"extern":"1","publication":"Parallel Problem Solving from Nature (PPSN XVI)","department":[{"_id":"819"}],"type":"conference","keyword":["Evolutionary algorithms","Node weight dependent TSP","Traveling Thief Problem"],"date_created":"2023-11-14T15:58:54Z","date_updated":"2023-12-13T10:44:54Z","publication_status":"published","author":[{"id":"102979","orcid":"0000-0002-4121-4668","first_name":"Jakob","last_name":"Bossek","full_name":"Bossek, Jakob"},{"full_name":"Neumann, Aneta","first_name":"Aneta","last_name":"Neumann"},{"first_name":"Frank","last_name":"Neumann","full_name":"Neumann, Frank"}],"publication_identifier":{"isbn":["978-3-030-58111-4"]},"year":"2020","title":"Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions","doi":"10.1007/978-3-030-58112-1_24","language":[{"iso":"eng"}],"citation":{"mla":"Bossek, Jakob, et al. “Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions.” <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, Springer-Verlag, 2020, pp. 346–359, doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_24\">10.1007/978-3-030-58112-1_24</a>.","bibtex":"@inproceedings{Bossek_Neumann_Neumann_2020, place={Berlin, Heidelberg}, title={Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-58112-1_24\">10.1007/978-3-030-58112-1_24</a>}, booktitle={Parallel Problem Solving from Nature (PPSN XVI)}, publisher={Springer-Verlag}, author={Bossek, Jakob and Neumann, Aneta and Neumann, Frank}, year={2020}, pages={346–359} }","ama":"Bossek J, Neumann A, Neumann F. Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions. In: <i>Parallel Problem Solving from Nature (PPSN XVI)</i>. Springer-Verlag; 2020:346–359. doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_24\">10.1007/978-3-030-58112-1_24</a>","ieee":"J. Bossek, A. Neumann, and F. Neumann, “Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions,” in <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, 2020, pp. 346–359, doi: <a href=\"https://doi.org/10.1007/978-3-030-58112-1_24\">10.1007/978-3-030-58112-1_24</a>.","apa":"Bossek, J., Neumann, A., &#38; Neumann, F. (2020). Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions. <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, 346–359. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_24\">https://doi.org/10.1007/978-3-030-58112-1_24</a>","chicago":"Bossek, Jakob, Aneta Neumann, and Frank Neumann. “Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions.” In <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, 346–359. Berlin, Heidelberg: Springer-Verlag, 2020. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_24\">https://doi.org/10.1007/978-3-030-58112-1_24</a>.","short":"J. Bossek, A. Neumann, F. Neumann, in: Parallel Problem Solving from Nature (PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp. 346–359."},"place":"Berlin, Heidelberg","status":"public","user_id":"102979","_id":"48852","publisher":"Springer-Verlag","page":"346–359"},{"date_updated":"2023-12-13T10:49:45Z","title":"Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem","year":"2020","author":[{"full_name":"Seiler, Moritz","last_name":"Seiler","first_name":"Moritz"},{"last_name":"Pohl","first_name":"Janina","full_name":"Pohl, Janina"},{"full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","first_name":"Jakob","last_name":"Bossek","id":"102979"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"last_name":"Trautmann","first_name":"Heike","full_name":"Trautmann, Heike"}],"publication_identifier":{"isbn":["978-3-030-58111-4"]},"doi":"10.1007/978-3-030-58112-1_4","language":[{"iso":"eng"}],"extern":"1","abstract":[{"lang":"eng","text":"In this work we focus on the well-known Euclidean Traveling Salesperson Problem (TSP) and two highly competitive inexact heuristic TSP solvers, EAX and LKH, in the context of per-instance algorithm selection (AS). We evolve instances with nodes where the solvers show strongly different performance profiles. These instances serve as a basis for an exploratory study on the identification of well-discriminating problem characteristics (features). Our results in a nutshell: we show that even though (1) promising features exist, (2) these are in line with previous results from the literature, and (3) models trained with these features are more accurate than models adopting sophisticated feature selection methods, the advantage is not close to the virtual best solver in terms of penalized average runtime and so is the performance gain over the single best solver. However, we show that a feature-free deep neural network based approach solely based on visual representation of the instances already matches classical AS model results and thus shows huge potential for future studies."}],"publication":"Parallel Problem Solving from {Nature} (PPSN XVI)","keyword":["Automated algorithm selection","Deep learning","Feature-based approaches","Traveling Salesperson Problem"],"type":"conference","department":[{"_id":"819"}],"date_created":"2023-11-14T15:59:00Z","status":"public","user_id":"102979","page":"48–64","publisher":"Springer-Verlag","_id":"48897","citation":{"mla":"Seiler, Moritz, et al. “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem.” <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>, Springer-Verlag, 2020, pp. 48–64, doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>.","apa":"Seiler, M., Pohl, J., Bossek, J., Kerschke, P., &#38; Trautmann, H. (2020). Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>, 48–64. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">https://doi.org/10.1007/978-3-030-58112-1_4</a>","ieee":"M. Seiler, J. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem,” in <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>, 2020, pp. 48–64, doi: <a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>.","short":"M. Seiler, J. Pohl, J. Bossek, P. Kerschke, H. Trautmann, in: Parallel Problem Solving from {Nature} (PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp. 48–64.","ama":"Seiler M, Pohl J, Bossek J, Kerschke P, Trautmann H. Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. In: <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>. Springer-Verlag; 2020:48–64. doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>","chicago":"Seiler, Moritz, Janina Pohl, Jakob Bossek, Pascal Kerschke, and Heike Trautmann. “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem.” In <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>, 48–64. Berlin, Heidelberg: Springer-Verlag, 2020. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">https://doi.org/10.1007/978-3-030-58112-1_4</a>.","bibtex":"@inproceedings{Seiler_Pohl_Bossek_Kerschke_Trautmann_2020, place={Berlin, Heidelberg}, title={Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>}, booktitle={Parallel Problem Solving from {Nature} (PPSN XVI)}, publisher={Springer-Verlag}, author={Seiler, Moritz and Pohl, Janina and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2020}, pages={48–64} }"},"place":"Berlin, Heidelberg"},{"date_updated":"2024-01-10T09:11:33Z","publication_status":"published","status":"public","year":"2020","title":"Erfolgsfaktoren digitaler Transformationsprozesse in KMU","author":[{"last_name":"Heitmann","first_name":"Ingmar","full_name":"Heitmann, Ingmar"}],"publication_identifier":{"isbn":["978-3-339-11532-4"]},"user_id":"21811","editor":[{"full_name":"Betz, Stefan","last_name":"Betz","first_name":"Stefan"}],"page":"249-276","publisher":"Dr. Kovac","_id":"50354","language":[{"iso":"ger"}],"publication":"Neuere Entwicklungen in Produktionn und Controlling","citation":{"ama":"Heitmann I. Erfolgsfaktoren digitaler Transformationsprozesse in KMU. In: Betz S, ed. <i>Neuere Entwicklungen in Produktionn und Controlling</i>. Dr. Kovac; 2020:249-276.","bibtex":"@inbook{Heitmann_2020, place={Hamburg}, title={Erfolgsfaktoren digitaler Transformationsprozesse in KMU}, booktitle={Neuere Entwicklungen in Produktionn und Controlling}, publisher={Dr. Kovac}, author={Heitmann, Ingmar}, editor={Betz, Stefan}, year={2020}, pages={249–276} }","mla":"Heitmann, Ingmar. “Erfolgsfaktoren digitaler Transformationsprozesse in KMU.” <i>Neuere Entwicklungen in Produktionn und Controlling</i>, edited by Stefan Betz, Dr. Kovac, 2020, pp. 249–76.","short":"I. Heitmann, in: S. Betz (Ed.), Neuere Entwicklungen in Produktionn und Controlling, Dr. Kovac, Hamburg, 2020, pp. 249–276.","chicago":"Heitmann, Ingmar. “Erfolgsfaktoren digitaler Transformationsprozesse in KMU.” In <i>Neuere Entwicklungen in Produktionn und Controlling</i>, edited by Stefan Betz, 249–76. Hamburg: Dr. Kovac, 2020.","apa":"Heitmann, I. (2020). Erfolgsfaktoren digitaler Transformationsprozesse in KMU. In S. Betz (Ed.), <i>Neuere Entwicklungen in Produktionn und Controlling</i> (pp. 249–276). Dr. Kovac.","ieee":"I. Heitmann, “Erfolgsfaktoren digitaler Transformationsprozesse in KMU,” in <i>Neuere Entwicklungen in Produktionn und Controlling</i>, S. Betz, Ed. Hamburg: Dr. Kovac, 2020, pp. 249–276."},"type":"book_chapter","department":[{"_id":"193"}],"place":"Hamburg","date_created":"2024-01-09T08:50:04Z"},{"department":[{"_id":"193"}],"type":"book_chapter","date_created":"2024-01-09T09:39:39Z","place":"Hamburg","citation":{"chicago":"Puls, Christoph. “Varianten- und Komplexitätsmanagement im produzierenden Gewerbe.” In <i>Neuere Entwicklungen in Produktionn und Controlling</i>, edited by Stefan Betz, 215–46. Hamburg: Dr. Kovac, 2020.","short":"C. Puls, in: S. Betz (Ed.), Neuere Entwicklungen in Produktionn und Controlling, Dr. Kovac, Hamburg, 2020, pp. 215–246.","apa":"Puls, C. (2020). Varianten- und Komplexitätsmanagement im produzierenden Gewerbe. In S. Betz (Ed.), <i>Neuere Entwicklungen in Produktionn und Controlling</i> (pp. 215–246). Dr. Kovac.","ieee":"C. Puls, “Varianten- und Komplexitätsmanagement im produzierenden Gewerbe,” in <i>Neuere Entwicklungen in Produktionn und Controlling</i>, S. Betz, Ed. Hamburg: Dr. Kovac, 2020, pp. 215–246.","ama":"Puls C. Varianten- und Komplexitätsmanagement im produzierenden Gewerbe. In: Betz S, ed. <i>Neuere Entwicklungen in Produktionn und Controlling</i>. Dr. Kovac; 2020:215-246.","bibtex":"@inbook{Puls_2020, place={Hamburg}, title={Varianten- und Komplexitätsmanagement im produzierenden Gewerbe}, booktitle={Neuere Entwicklungen in Produktionn und Controlling}, publisher={Dr. Kovac}, author={Puls, Christoph}, editor={Betz, Stefan}, year={2020}, pages={215–246} }","mla":"Puls, Christoph. “Varianten- und Komplexitätsmanagement im produzierenden Gewerbe.” <i>Neuere Entwicklungen in Produktionn und Controlling</i>, edited by Stefan Betz, Dr. Kovac, 2020, pp. 215–46."},"publication":"Neuere Entwicklungen in Produktionn und Controlling","editor":[{"full_name":"Betz, Stefan","last_name":"Betz","first_name":"Stefan"}],"user_id":"21811","_id":"50374","publisher":"Dr. Kovac","language":[{"iso":"ger"}],"page":"215-246","publication_status":"published","date_updated":"2024-01-10T09:17:52Z","author":[{"last_name":"Puls","first_name":"Christoph","full_name":"Puls, Christoph"}],"publication_identifier":{"isbn":["978-3-339-11532-4"]},"year":"2020","status":"public","title":"Varianten- und Komplexitätsmanagement im produzierenden Gewerbe"},{"date_updated":"2024-01-10T09:14:58Z","publication_status":"published","year":"2020","status":"public","title":"Digitalisierung des Produktionscontrollings in KMU","publication_identifier":{"isbn":["978-3-339-11532-4"]},"author":[{"first_name":"Christian","last_name":"Koch","full_name":"Koch, Christian"}],"user_id":"21811","editor":[{"last_name":"Betz","first_name":"Stefan","full_name":"Betz, Stefan"}],"page":"317-347","_id":"50365","language":[{"iso":"ger"}],"publisher":"Dr. Kovac","publication":"Neuere Entwicklungen in Produktionn und Controlling","citation":{"ieee":"C. Koch, “Digitalisierung des Produktionscontrollings in KMU,” in <i>Neuere Entwicklungen in Produktionn und Controlling</i>, S. Betz, Ed. Hamburg: Dr. Kovac, 2020, pp. 317–347.","apa":"Koch, C. (2020). Digitalisierung des Produktionscontrollings in KMU. In S. Betz (Ed.), <i>Neuere Entwicklungen in Produktionn und Controlling</i> (pp. 317–347). Dr. Kovac.","chicago":"Koch, Christian. “Digitalisierung des Produktionscontrollings in KMU.” In <i>Neuere Entwicklungen in Produktionn und Controlling</i>, edited by Stefan Betz, 317–47. Hamburg: Dr. Kovac, 2020.","short":"C. Koch, in: S. 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