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LLM-based code generation could save significant manual efforts in industrial automation, where control engineers manually produce control logic for sophisticated production processes.
Diagnosis of plant-wide oscillation through data-driven analysis and process understanding
Nina F Thornhill, John W Cox, and Michael A Paulonis. 2003 · 2003
Earlier work this paper cites.
Computer-aided design and implementation of interlock control code. In 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control . IEEE, 2653–2658
Rainer Drath, Alexander Fay, and Till Schmidberger. 2006 · 2006
Earlier work this paper cites.
Collaborative process automation systems
Martin Hollender. 2010 · 2010
Earlier work this paper cites.
Model-driven development of industrial process control applications
David Hästbacka, Timo Vepsäläinen, and Seppo Kuikka. 2011 · 2011
Earlier work this paper cites.
An MDD process for IEC 61131-based industrial automation systems. In ETFA2011 . IEEE, 1–8
Kleanthis Thramboulidis and Georg Frey. 2011 · 2011
Earlier work this paper cites.
Extracting UML models from images. In 2013 5th International Conference on Computer Science and Information Technology . IEEE, 169–178
Bilal Karasneh and Michel RV Chaudron. 2013 · 2013
Earlier work this paper cites.
OpenPLC: An open source alternative to automation. In IEEE Global Humanitarian Technology Conference (GHTC 2014) . IEEE, 585–589
Thiago Rodrigues Alves, Mario Buratto, Flavio Mauricio De Souza, and Thelma Virginia Rodrigues. 2014 · 2014
Earlier work this paper cites.
Automatic derivation of qualitative plant simulation models from legacy piping and instrumentation diagrams
Esteban Arroyo, Mario Hoernicke, Pablo Rodríguez, and Alexander Fay. 2016 · 2016
Earlier work this paper cites.
Automatic HTML code generation from mock-up images using machine learning techniques. In 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT) . IEEE, 1–4
Batuhan Aşıroğlu, Büşta Rümeysa Mete, Eyyüp Yıldız, Yağız Nalçakan, Alper Sezen, Mustafa Dağtekin, and Tolga Ensari. 2019 · 2019
Cited alongside, same era.
A digitization and conversion tool for imaged drawings to intelligent piping and instrumentation diagrams (P&ID)
Sung-O Kang, Eul-Bum Lee, and Hum-Kyung Baek. 2019 · 2019
Cited alongside, same era.
Features recognition from piping and instrumentation diagrams in image format using a deep learning network
Eun-Seop Yu, Jae-Min Cha, Taekyong Lee, Jinil Kim, and Duhwan Mun. 2019 · 2019
Cited alongside, same era.
A classification framework for automated control code generation in industrial automation
Heiko Koziolek, Andreas Burger, Marie Platenius-Mohr, and Raoul Jetley. 2020a · 2020
Cited alongside, same era.
Deep neural network for automatic image recognition of engineering diagrams
Automatically recognizing the semantic elements from UML class diagram images
Fangwei Chen, Li Zhang, Xiaoli Lian, and Nan Niu. 2022 · 2022
Later among the works it cites.
End-to-end digitization of image format piping and instrumentation diagrams at an industrially applicable level
Byung Chul Kim, Hyungki Kim, Yoochan Moon, Gwang Lee, and Duhwan Mun. 2022 · 2022
Later among the works it cites.
HTML Code Generation from Website Images and Sketches using Deep Learning-Based Encoder-Decoder Model. In 2022 IEEE 4th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA) . IEEE, 133–138
D Yashaswini, Nikhil Kumar, et al · 2022
Later among the works it cites.
On the assessment of generative AI in modeling tasks: an experience report with ChatGPT and UML
Javier Cámara, Javier Troya, Lola Burgueño, and Antonio Vallecillo. 2023 · 2023
Closest in time.
Large Language Models: A Comprehensive Survey of its Applications, Challenges, Limitations, and Future Prospects
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Dong-Yeol Yun, Seung-Kwon Seo, Umer Zahid, and Chul-Jin Lee. 2020 · 2020
Cited alongside, same era.
Topnav: Efficiently navigating through industrial process plant topologies. In 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) . IEEE, 1–8
Andreas Berlet, Julius Rückert, Heiko Koziolek, Rainer Drath, and Mike Barth. 2021 · 2021
Cited alongside, same era.
Deep-learning-based recognition of symbols and texts at an industrially applicable level from images of high-density piping and instrumentation diagrams
Hyungki Kim, Wonyong Lee, Mijoo Kim, Yoochan Moon, Taekyong Lee, Mincheol Cho, and Duhwan Mun. 2021 · 2021
Cited alongside, same era.
Rule-based code generation in industrial automation: four large-scale case studies applying the cayenne method. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering in Practice . 152–161
Heiko Koziolek, Andreas Burger, Marie Platenius-Mohr, Julius Rückert, Hadil Abukwaik, Raoul Jetley, and Abdulla P P. 2020b
Cited in the paper.
Muhammad Usman Hadi, Rizwan Qureshi, Abbas Shah, Muhammad Irfan, Anas Zafar, Muhammad Bilal Shaikh, Naveed Akhtar, Jia Wu, Seyedali Mirjalili, et al · 2023
Closest in time.
Toward automatic generation of control structures for process flow diagrams with large language models
Edwin Hirtreiter, Lukas Schulze Balhorn, and Artur M Schweidtmann. 2023 · 2023
Closest in time.
ChatGPT for PLC/DCS Control Logic Generation. In Proc. IEEE Int. Conf. on Emerging Technologies and Factory Automation (ETFA2023) (2023-09-15)
Heiko Koziolek, Sten Gruener, and Virendra Ashiwal. [n. d.] · 2023
Closest in time.