Peng Li

Orcid: 0000-0002-7181-7304

Affiliations:
  • Ostwestfalen-Lippe University of Applied Science, inIT, Lemgo, Germany


According to our database1, Peng Li authored at least 14 papers between 2015 and 2021.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2021
A Nonconvex Archetypal Analysis for One-Class Classification Based Anomaly Detection in Cyber-Physical Systems.
IEEE Trans. Ind. Informatics, 2021

2020
Cross-Network Fusion and Scheduling for Heterogeneous Networks in Smart Factory.
IEEE Trans. Ind. Informatics, 2020

Non-convex hull based anomaly detection in CPPS.
Eng. Appl. Artif. Intell., 2020

2019
On the Identification of Decision Boundaries for Anomaly Detection in CPPS.
Proceedings of the IEEE International Conference on Industrial Technology, 2019

Why Symbolic AI is a Key Technology for Self-Adaption in the Context of CPPS.
Proceedings of the 24th IEEE International Conference on Emerging Technologies and Factory Automation, 2019

2018
Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges.
IEEE Access, 2018

A Geometric Approach to Clustering Based Anomaly Detection for Industrial Applications.
Proceedings of the IECON 2018, 2018

Mapping Data Sets to Concepts using Machine Learning and a Knowledge based Approach.
Proceedings of the 10th International Conference on Agents and Artificial Intelligence, 2018

A Data Provenance based Architecture to Enhance the Reliability of Data Analysis for Industry 4.0.
Proceedings of the 23rd IEEE International Conference on Emerging Technologies and Factory Automation, 2018

2017
Learned Abstraction: Knowledge Based Concept Learning for Cyber Physical Systems.
Proceedings of the Machine Learning for Cyber Physical Systems, 2017

2016
Improving clustering based anomaly detection with concave hull: An application in fault diagnosis of wind turbines.
Proceedings of the 14th IEEE International Conference on Industrial Informatics, 2016

2015
Data Driven Modeling for System-Level Condition Monitoring on Wind Power Plants.
Proceedings of the 26th International Workshop on Principles of Diagnosis (DX-2015) co-located with 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (Safeprocess 2015), Paris, France, August 31, 2015

Bayesian predictive assistance system: An embedded application for resource optimization in industrial cleaning processes.
Proceedings of the 13th IEEE International Conference on Industrial Informatics, 2015

Data Driven Condition Monitoring of Wind Power Plants Using Cluster Analysis.
Proceedings of the 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, 2015


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