| Smart factory of industry 4.0: Key technologies, application case, and challenges B Chen, J Wan, L Shu, P Li, M Mukherjee, B Yin Ieee Access 6, 6505-6519, 2017 | 1187 | 2017 |
| Cross-network fusion and scheduling for heterogeneous networks in smart factory J Wan, J Yang, S Wang, D Li, P Li, M Xia IEEE Transactions on Industrial Informatics 16 (9), 6059-6068, 2019 | 36 | 2019 |
| Data Driven Modeling for System-Level Condition Monitoring on Wind Power Plants. J Eickmeyer, P Li, O Givehchi, F Pethig, O Niggemann DX, 43-50, 2015 | 26 | 2015 |
| Non-convex hull based anomaly detection in CPPS P Li, O Niggemann Engineering Applications of Artificial Intelligence 87, 103301, 2020 | 17 | 2020 |
| Data driven condition monitoring of wind power plants using cluster analysis P Li, J Eickmeyer, O Niggemann 2015 International Conference on Cyber-Enabled Distributed Computing and …, 2015 | 15 | 2015 |
| Improving clustering based anomaly detection with concave hull: An application in fault diagnosis of wind turbines P Li, O Niggemann 2016 IEEE 14th International Conference on Industrial Informatics (INDIN …, 2016 | 13 | 2016 |
| A nonconvex archetypal analysis for one-class classification based anomaly detection in cyber-physical systems P Li, O Niggemann IEEE transactions on industrial informatics 17 (9), 6429-6437, 2020 | 10 | 2020 |
| A geometric approach to clustering based anomaly detection for industrial applications P Li, O Niggemann, B Hammer IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society …, 2018 | 10 | 2018 |
| Why symbolic ai is a key technology for self-adaption in the context of cpps A Bunte, P Wunderlich, N Moriz, P Li, A Mankowski, A Rogalla, ... 2019 24th IEEE International Conference on Emerging Technologies and Factory …, 2019 | 9 | 2019 |
| A data provenance based architecture to enhance the reliability of data analysis for Industry 4.0 P Li, O Niggemann 2018 IEEE 23rd International Conference on Emerging Technologies and Factory …, 2018 | 7 | 2018 |
| Mapping Data Sets to Concepts using Machine Learning and a Knowledge based Approach. A Bunte, P Li, O Niggemann ICAART (2), 430-437, 2018 | 6 | 2018 |
| Intelligente Zustandsüberwachung v. on Windenergie· anlangen als Cloud-Service J Eickmeyer, F Pethig, S Schriegel, O Niggemann, O Givechi, P Li, ... Automation, 2015 | 4 | 2015 |
| Learned Abstraction: Knowledge Based Concept Learning for Cyber Physical Systems. A Bunte, P Li, O Niggemann Machine Learning for Cyber Physical Systems: Selected papers from the …, 2020 | 3 | 2020 |
| On the identification of decision boundaries for anomaly detection in CPPS P Li, O Niggemann, B Hammer 2019 IEEE International Conference on Industrial Technology (ICIT), 1311-1316, 2019 | 3 | 2019 |
| Transformer in Reinforcement Learning for Decision-Making: A Survey W Yuan, J Chen, S Chen, L Lu, Z Hu, P Li, D Feng, F Liu, J Chen TechRxiv, 2023 | 1 | 2023 |
| Universal process optimization assistant for medium-sized manufacturing enterprises as self-learning expert system A Diedrich, J Eickmeyer, O Niggemann, P Li, T Hoppe, M Fuchs | 1 | 2017 |
| Bayesian predictive assistance system: An embedded application for resource optimization in industrial cleaning processes GM Shrestha, P Li, O Niggemann 2015 IEEE 13th International Conference on Industrial Informatics (INDIN …, 2015 | 1 | 2015 |
| A dynamic core evolutionary clustering algorithm based on saturated memory H Xie, P Li, Z Ding Autonomous Intelligent Systems 3 (1), 8, 2023 | | 2023 |
| A Rule Embbeding Method of Winrate Approximation for Texas Hold’em Z Hu, S Chen, W Yuan, P Li, M Zou, J Chen, J Chen 2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT), 1-9, 2022 | | 2022 |
