@inproceedings{4780,
  author       = {{Bunte, Andreas and Wunderlich, Paul and Moriz, Natalia and Li, Peng and Mankowski, Andre and Rogalla, Antje and Niggemann, Oliver}},
  booktitle    = {{24nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  title        = {{{Why Symbolic AI is a Key Technology for Self-Adaption in the Context of CPPS}}},
  year         = {{2019}},
}

@misc{12808,
  abstract     = {{Along with the constantly increasing complexity of industrial automation systems, machine learning methods have been widely applied to detecting abnormal states in such systems. Anomaly detection tasks can be treated as one-class classification problems in machine learning. Geometric methods can give an intuitive solution to such problems. In this paper, we propose a new geometric structure, oriented non-convex hulls, to represent decision boundaries used for one-class classification. Based on this geometric structure, a novel boundary based one-class classification algorithm is developed to solve the anomaly detection problem. Compared with traditional boundary-based approaches such as convex hulls based methods and one-class support vector machines, the proposed approach can better reflect the true geometry of target data and needs little effort for parameter tuning. The effectiveness of this approach is evaluated with artificial and real world data sets to solve the anomaly detection problem in Cyber-Physical-Production-Systems (CPPS). The evaluation results also show that the proposed approach has higher generality than the used baseline algorithms.}},
  author       = {{Li, Peng and Niggemann, Oliver}},
  booktitle    = {{Engineering Applications of Artificial Intelligence}},
  issn         = {{1873-6769}},
  keywords     = {{One-class classification, n-dimensional oriented non-convex hull, Anomaly detection, CPPS}},
  publisher    = {{Elsevier BV}},
  title        = {{{Non-convex hull based anomaly detection in CPPS}}},
  doi          = {{10.1016/j.engappai.2019.103301}},
  volume       = {{87}},
  year         = {{2019}},
}

@misc{12834,
  abstract     = {{In the context of Industry 4.0, extensive deployment and application of advanced manufacturing equipment and various sensors is leading to a growing demand for data exchange between different devices. In smart factories, network transmission has multiprotocol features of wired/wireless communication, and different data flows have different real-time requirements. In this article, a heterogeneous network architecture based on software-defined network is proposed for realizing cross-network flexible forwarding of multisource manufacturing data and optimized utilization of network resources. Subsequently, the mechanism of cross-network fusion and scheduling (CNFS) is analyzed from the perspective of high dynamic characteristics and different delay requirements of data flows. Based on this analysis, a route-aware data flow dynamic reconstruction algorithm is proposed. The proposed algorithm improves the efficiency of manufacturing data cross-network fusion, especially for multivariety and small-batch intelligent manufacturing systems. Furthermore, for meeting the bandwidth requirements of different delay flows, a delay-sensitive network bandwidth scheduling algorithm is proposed. Finally, the effectiveness of the proposed CNFS mechanism is verified using a candy packaging intelligent production line prototype platform.}},
  author       = {{Wan, Jiafu and Yang, Jun and Wang, Shiyong and Li, Di and Li, Peng and Xia, Min}},
  booktitle    = {{IEEE Transactions on Industrial Informatics}},
  issn         = {{1941-0050}},
  keywords     = {{Heterogeneous networks, Real-time systems, Bandwidth, Job shop scheduling, Smart manufacturing, Computer architecture, Cross-network fusion, heterogeneous networks, network resource}},
  number       = {{9}},
  pages        = {{6059--6068}},
  publisher    = {{Institute of Electrical and Electronics Engineers (IEEE)}},
  title        = {{{Cross-Network Fusion and Scheduling for Heterogeneous Networks in Smart Factory}}},
  doi          = {{10.1109/tii.2019.2952669}},
  volume       = {{16}},
  year         = {{2019}},
}

@inproceedings{4788,
  author       = {{Bunte, Andreas and Li, Peng and Niggemann, Oliver}},
  booktitle    = {{International Conference on Agents and Artificial Intelligence (ICAART)}},
  publisher    = {{SCITEPRESS}},
  title        = {{{Mapping Data Sets to Concepts Using Machine Learning and a Knowledge Based Approach}}},
  year         = {{2018}},
}

@inproceedings{4789,
  author       = {{Bunte, Andreas and Li, Peng and Niggemann, Oliver}},
  booktitle    = {{3rd Conference on Machine Learning for Cyber Physical Systems and Industry 4.0 (ML4CPS)}},
  title        = {{{Learned Abstraction: Knowledge Based Concept Learning for Cyber Physical Systems}}},
  year         = {{2017}},
}