---
_id: '4780'
author:
- first_name: Andreas
full_name: Bunte, Andreas
id: '58885'
last_name: Bunte
- first_name: Paul
full_name: Wunderlich, Paul
id: '52317'
last_name: Wunderlich
- first_name: Natalia
full_name: Moriz, Natalia
id: '44238'
last_name: Moriz
- first_name: Peng
full_name: Li, Peng
id: '58937'
last_name: Li
- first_name: Andre
full_name: Mankowski, Andre
id: '51482'
last_name: Mankowski
- first_name: Antje
full_name: Rogalla, Antje
id: '66617'
last_name: Rogalla
- first_name: Oliver
full_name: Niggemann, Oliver
id: '10876'
last_name: Niggemann
citation:
ama: 'Bunte A, Wunderlich P, Moriz N, et al. Why Symbolic AI is a Key Technology
for Self-Adaption in the Context of CPPS. In: 24nd IEEE International Conference
on Emerging Technologies and Factory Automation (ETFA). Zaragoza, Spain; 2019.'
apa: Bunte, A., Wunderlich, P., Moriz, N., Li, P., Mankowski, A., Rogalla, A., &
Niggemann, O. (2019). Why Symbolic AI is a Key Technology for Self-Adaption in
the Context of CPPS. In 24nd IEEE International Conference on Emerging Technologies
and Factory Automation (ETFA). Zaragoza, Spain.
bjps: Bunte A et al. (2019) Why Symbolic AI Is a Key Technology for
Self-Adaption in the Context of CPPS. 24nd IEEE International Conference on
Emerging Technologies and Factory Automation (ETFA). Zaragoza, Spain.
chicago: Bunte, Andreas, Paul Wunderlich, Natalia Moriz, Peng Li, Andre Mankowski,
Antje Rogalla, and Oliver Niggemann. “Why Symbolic AI Is a Key Technology for
Self-Adaption in the Context of CPPS.” In 24nd IEEE International Conference
on Emerging Technologies and Factory Automation (ETFA). Zaragoza, Spain, 2019.
chicago-de: 'Bunte, Andreas, Paul Wunderlich, Natalia Moriz, Peng Li, Andre Mankowski,
Antje Rogalla und Oliver Niggemann. 2019. Why Symbolic AI is a Key Technology
for Self-Adaption in the Context of CPPS. In: 24nd IEEE International Conference
on Emerging Technologies and Factory Automation (ETFA). Zaragoza, Spain.'
din1505-2-1: 'Bunte, Andreas ; Wunderlich, Paul ; Moriz,
Natalia ; Li, Peng ; Mankowski, Andre ; Rogalla,
Antje ; Niggemann, Oliver:
Why Symbolic AI is a Key Technology for Self-Adaption in the Context of CPPS.
In: 24nd IEEE International Conference on Emerging Technologies and Factory
Automation (ETFA). Zaragoza, Spain, 2019'
havard: 'A. Bunte, P. Wunderlich, N. Moriz, P. Li, A. Mankowski, A. Rogalla, O.
Niggemann, Why Symbolic AI is a Key Technology for Self-Adaption in the Context
of CPPS, in: 24nd IEEE International Conference on Emerging Technologies and Factory
Automation (ETFA), Zaragoza, Spain, 2019.'
ieee: A. Bunte et al., “Why Symbolic AI is a Key Technology for Self-Adaption
in the Context of CPPS,” in 24nd IEEE International Conference on Emerging
Technologies and Factory Automation (ETFA), 2019.
mla: Bunte, Andreas, et al. “Why Symbolic AI Is a Key Technology for Self-Adaption
in the Context of CPPS.” 24nd IEEE International Conference on Emerging Technologies
and Factory Automation (ETFA), 2019.
short: 'A. Bunte, P. Wunderlich, N. Moriz, P. Li, A. Mankowski, A. Rogalla, O. Niggemann,
in: 24nd IEEE International Conference on Emerging Technologies and Factory Automation
(ETFA), Zaragoza, Spain, 2019.'
ufg: 'Bunte, Andreas et. al. (2019): Why Symbolic AI is a Key Technology
for Self-Adaption in the Context of CPPS, in: 24nd IEEE International Conference
on Emerging Technologies and Factory Automation (ETFA), Zaragoza, Spain.'
van: 'Bunte A, Wunderlich P, Moriz N, Li P, Mankowski A, Rogalla A, et al. Why Symbolic
AI is a Key Technology for Self-Adaption in the Context of CPPS. In: 24nd IEEE
International Conference on Emerging Technologies and Factory Automation (ETFA).
Zaragoza, Spain; 2019.'
date_created: 2021-02-02T08:08:34Z
date_updated: 2023-03-15T13:49:55Z
department:
- _id: DEP5023
language:
- iso: eng
place: Zaragoza, Spain
publication: 24nd IEEE International Conference on Emerging Technologies and Factory
Automation (ETFA)
status: public
title: Why Symbolic AI is a Key Technology for Self-Adaption in the Context of CPPS
type: conference
user_id: '68554'
year: 2019
...
---
_id: '12808'
abstract:
- lang: eng
text: 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.
article_number: '103301'
author:
- first_name: Peng
full_name: Li, Peng
id: '58937'
last_name: Li
- first_name: Oliver
full_name: Niggemann, Oliver
id: '10876'
last_name: Niggemann
citation:
ama: Li P, Niggemann O. Non-convex hull based anomaly detection in CPPS. Engineering
Applications of Artificial Intelligence. 2019;87. doi:10.1016/j.engappai.2019.103301
apa: Li, P., & Niggemann, O. (2019). Non-convex hull based anomaly detection
in CPPS. Engineering Applications of Artificial Intelligence, 87,
Article 103301. https://doi.org/10.1016/j.engappai.2019.103301
bjps: Li P and Niggemann O (2019) Non-Convex Hull Based Anomaly Detection
in CPPS. Engineering Applications of Artificial Intelligence 87.
chicago: Li, Peng, and Oliver Niggemann. “Non-Convex Hull Based Anomaly Detection
in CPPS.” Engineering Applications of Artificial Intelligence 87 (2019).
https://doi.org/10.1016/j.engappai.2019.103301.
chicago-de: Li, Peng und Oliver Niggemann. 2019. Non-convex hull based anomaly detection
in CPPS. Engineering Applications of Artificial Intelligence 87. doi:10.1016/j.engappai.2019.103301,
.
din1505-2-1: 'Li, Peng ; Niggemann,
Oliver: Non-convex hull based anomaly detection in CPPS. In: Engineering
Applications of Artificial Intelligence Bd. 87. Amsterdam [u.a.], Elsevier
BV (2019)'
havard: P. Li, O. Niggemann, Non-convex hull based anomaly detection in CPPS, Engineering
Applications of Artificial Intelligence. 87 (2019).
ieee: 'P. Li and O. Niggemann, “Non-convex hull based anomaly detection in CPPS,”
Engineering Applications of Artificial Intelligence, vol. 87, Art. no.
103301, 2019, doi: 10.1016/j.engappai.2019.103301.'
mla: Li, Peng, and Oliver Niggemann. “Non-Convex Hull Based Anomaly Detection in
CPPS.” Engineering Applications of Artificial Intelligence, vol. 87, 103301,
2019, https://doi.org/10.1016/j.engappai.2019.103301.
short: P. Li, O. Niggemann, Engineering Applications of Artificial Intelligence
87 (2019).
ufg: 'Li, Peng/Niggemann, Oliver: Non-convex hull based anomaly detection
in CPPS, in: Engineering Applications of Artificial Intelligence 87 (2019).'
van: Li P, Niggemann O. Non-convex hull based anomaly detection in CPPS. Engineering
Applications of Artificial Intelligence. 2019;87.
date_created: 2025-04-16T09:51:12Z
date_updated: 2025-06-26T13:34:10Z
department:
- _id: DEP5023
doi: 10.1016/j.engappai.2019.103301
external_id:
isi:
- '000506715100040'
intvolume: ' 87'
isi: '1'
keyword:
- One-class classification
- n-dimensional oriented non-convex hull
- Anomaly detection
- CPPS
language:
- iso: eng
place: Amsterdam [u.a.]
publication: Engineering Applications of Artificial Intelligence
publication_identifier:
eissn:
- 1873-6769
issn:
- 0952-1976
publication_status: published
publisher: Elsevier BV
status: public
title: Non-convex hull based anomaly detection in CPPS
type: scientific_journal_article
user_id: '83781'
volume: 87
year: '2019'
...
---
_id: '12834'
abstract:
- lang: eng
text: 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:
- first_name: Jiafu
full_name: Wan, Jiafu
last_name: Wan
- first_name: Jun
full_name: Yang, Jun
last_name: Yang
- first_name: Shiyong
full_name: Wang, Shiyong
last_name: Wang
- first_name: Di
full_name: Li, Di
last_name: Li
- first_name: Peng
full_name: Li, Peng
id: '58937'
last_name: Li
- first_name: Min
full_name: Xia, Min
last_name: Xia
citation:
ama: Wan J, Yang J, Wang S, Li D, Li P, Xia M. Cross-Network Fusion and Scheduling
for Heterogeneous Networks in Smart Factory. IEEE Transactions on Industrial
Informatics. 2019;16(9):6059-6068. doi:10.1109/tii.2019.2952669
apa: Wan, J., Yang, J., Wang, S., Li, D., Li, P., & Xia, M. (2019). Cross-Network
Fusion and Scheduling for Heterogeneous Networks in Smart Factory. IEEE Transactions
on Industrial Informatics, 16(9), 6059–6068. https://doi.org/10.1109/tii.2019.2952669
bjps: Wan J et al. (2019) Cross-Network Fusion and Scheduling for
Heterogeneous Networks in Smart Factory. IEEE Transactions on Industrial Informatics
16, 6059–6068.
chicago: 'Wan, Jiafu, Jun Yang, Shiyong Wang, Di Li, Peng Li, and Min Xia. “Cross-Network
Fusion and Scheduling for Heterogeneous Networks in Smart Factory.” IEEE Transactions
on Industrial Informatics 16, no. 9 (2019): 6059–68. https://doi.org/10.1109/tii.2019.2952669.'
chicago-de: 'Wan, Jiafu, Jun Yang, Shiyong Wang, Di Li, Peng Li und Min Xia. 2019.
Cross-Network Fusion and Scheduling for Heterogeneous Networks in Smart Factory.
IEEE Transactions on Industrial Informatics 16, Nr. 9: 6059–6068. doi:10.1109/tii.2019.2952669,
.'
din1505-2-1: 'Wan, Jiafu ; Yang,
Jun ; Wang, Shiyong ; Li, Di ; Li,
Peng ; Xia, Min: Cross-Network
Fusion and Scheduling for Heterogeneous Networks in Smart Factory. In: IEEE
Transactions on Industrial Informatics Bd. 16. New York, NY, Institute of
Electrical and Electronics Engineers (IEEE) (2019), Nr. 9, S. 6059–6068'
havard: J. Wan, J. Yang, S. Wang, D. Li, P. Li, M. Xia, Cross-Network Fusion and
Scheduling for Heterogeneous Networks in Smart Factory, IEEE Transactions on Industrial
Informatics. 16 (2019) 6059–6068.
ieee: 'J. Wan, J. Yang, S. Wang, D. Li, P. Li, and M. Xia, “Cross-Network Fusion
and Scheduling for Heterogeneous Networks in Smart Factory,” IEEE Transactions
on Industrial Informatics, vol. 16, no. 9, pp. 6059–6068, 2019, doi: 10.1109/tii.2019.2952669.'
mla: Wan, Jiafu, et al. “Cross-Network Fusion and Scheduling for Heterogeneous Networks
in Smart Factory.” IEEE Transactions on Industrial Informatics, vol. 16,
no. 9, 2019, pp. 6059–68, https://doi.org/10.1109/tii.2019.2952669.
short: J. Wan, J. Yang, S. Wang, D. Li, P. Li, M. Xia, IEEE Transactions on Industrial
Informatics 16 (2019) 6059–6068.
ufg: 'Wan, Jiafu u. a.: Cross-Network Fusion and Scheduling for Heterogeneous
Networks in Smart Factory, in: IEEE Transactions on Industrial Informatics
16 (2019), H. 9, S. 6059–6068.'
van: Wan J, Yang J, Wang S, Li D, Li P, Xia M. Cross-Network Fusion and Scheduling
for Heterogeneous Networks in Smart Factory. IEEE Transactions on Industrial Informatics.
2019;16(9):6059–68.
date_created: 2025-04-23T08:35:16Z
date_updated: 2025-06-26T13:26:20Z
department:
- _id: DEP5023
doi: 10.1109/tii.2019.2952669
external_id:
isi:
- '000542966300043'
intvolume: ' 16'
isi: '1'
issue: '9'
keyword:
- Heterogeneous networks
- Real-time systems
- Bandwidth
- Job shop scheduling
- Smart manufacturing
- Computer architecture
- Cross-network fusion
- heterogeneous networks
- network resource
language:
- iso: eng
page: 6059-6068
place: New York, NY
publication: IEEE Transactions on Industrial Informatics
publication_identifier:
eissn:
- 1941-0050
issn:
- 1551-3203
publication_status: published
publisher: Institute of Electrical and Electronics Engineers (IEEE)
status: public
title: Cross-Network Fusion and Scheduling for Heterogeneous Networks in Smart Factory
type: scientific_journal_article
user_id: '83781'
volume: 16
year: '2019'
...
---
_id: '4788'
author:
- first_name: Andreas
full_name: Bunte, Andreas
id: '58885'
last_name: Bunte
- first_name: Peng
full_name: Li, Peng
id: '58937'
last_name: Li
- first_name: Oliver
full_name: Niggemann, Oliver
id: '10876'
last_name: Niggemann
citation:
ama: 'Bunte A, Li P, Niggemann O. Mapping Data Sets to Concepts Using Machine Learning
and a Knowledge Based Approach. In: International Conference on Agents and
Artificial Intelligence (ICAART). Madeira, Portugal: SCITEPRESS; 2018.'
apa: 'Bunte, A., Li, P., & Niggemann, O. (2018). Mapping Data Sets to Concepts
Using Machine Learning and a Knowledge Based Approach. In International Conference
on Agents and Artificial Intelligence (ICAART). Madeira, Portugal: SCITEPRESS.'
bjps: 'Bunte A, Li P and Niggemann O (2018) Mapping Data Sets to Concepts
Using Machine Learning and a Knowledge Based Approach. International Conference
on Agents and Artificial Intelligence (ICAART). Madeira, Portugal: SCITEPRESS.'
chicago: 'Bunte, Andreas, Peng Li, and Oliver Niggemann. “Mapping Data Sets to Concepts
Using Machine Learning and a Knowledge Based Approach.” In International Conference
on Agents and Artificial Intelligence (ICAART). Madeira, Portugal: SCITEPRESS,
2018.'
chicago-de: 'Bunte, Andreas, Peng Li und Oliver Niggemann. 2018. Mapping Data Sets
to Concepts Using Machine Learning and a Knowledge Based Approach. In: International
Conference on Agents and Artificial Intelligence (ICAART). Madeira, Portugal:
SCITEPRESS.'
din1505-2-1: 'Bunte, Andreas ; Li, Peng ; Niggemann,
Oliver: Mapping Data Sets to Concepts Using Machine Learning and a Knowledge
Based Approach. In: International Conference on Agents and Artificial Intelligence
(ICAART). Madeira, Portugal : SCITEPRESS, 2018'
havard: 'A. Bunte, P. Li, O. Niggemann, Mapping Data Sets to Concepts Using Machine
Learning and a Knowledge Based Approach, in: International Conference on Agents
and Artificial Intelligence (ICAART), SCITEPRESS, Madeira, Portugal, 2018.'
ieee: A. Bunte, P. Li, and O. Niggemann, “Mapping Data Sets to Concepts Using Machine
Learning and a Knowledge Based Approach,” in International Conference on Agents
and Artificial Intelligence (ICAART), 2018.
mla: Bunte, Andreas, et al. “Mapping Data Sets to Concepts Using Machine Learning
and a Knowledge Based Approach.” International Conference on Agents and Artificial
Intelligence (ICAART), SCITEPRESS, 2018.
short: 'A. Bunte, P. Li, O. Niggemann, in: International Conference on Agents and
Artificial Intelligence (ICAART), SCITEPRESS, Madeira, Portugal, 2018.'
ufg: 'Bunte, Andreas et. al. (2018): Mapping Data Sets to Concepts Using
Machine Learning and a Knowledge Based Approach, in: International Conference
on Agents and Artificial Intelligence (ICAART), Madeira, Portugal.'
van: 'Bunte A, Li P, Niggemann O. Mapping Data Sets to Concepts Using Machine Learning
and a Knowledge Based Approach. In: International Conference on Agents and Artificial
Intelligence (ICAART). Madeira, Portugal: SCITEPRESS; 2018.'
date_created: 2021-02-02T08:23:30Z
date_updated: 2023-03-15T13:49:55Z
department:
- _id: DEP5023
language:
- iso: eng
place: Madeira, Portugal
publication: International Conference on Agents and Artificial Intelligence (ICAART)
publisher: SCITEPRESS
status: public
title: Mapping Data Sets to Concepts Using Machine Learning and a Knowledge Based
Approach
type: conference
user_id: '68554'
year: 2018
...
---
_id: '4789'
author:
- first_name: Andreas
full_name: Bunte, Andreas
id: '58885'
last_name: Bunte
- first_name: Peng
full_name: Li, Peng
id: '58937'
last_name: Li
- first_name: Oliver
full_name: Niggemann, Oliver
id: '10876'
last_name: Niggemann
citation:
ama: 'Bunte A, Li P, Niggemann O. Learned Abstraction: Knowledge Based Concept Learning
for Cyber Physical Systems. In: 3rd Conference on Machine Learning for Cyber
Physical Systems and Industry 4.0 (ML4CPS). ; 2017.'
apa: 'Bunte, A., Li, P., & Niggemann, O. (2017). Learned Abstraction: Knowledge
Based Concept Learning for Cyber Physical Systems. In 3rd Conference on Machine
Learning for Cyber Physical Systems and Industry 4.0 (ML4CPS).'
bjps: 'Bunte A, Li P and Niggemann O (2017) Learned Abstraction: Knowledge
Based Concept Learning for Cyber Physical Systems. 3rd Conference on Machine
Learning for Cyber Physical Systems and Industry 4.0 (ML4CPS).'
chicago: 'Bunte, Andreas, Peng Li, and Oliver Niggemann. “Learned Abstraction: Knowledge
Based Concept Learning for Cyber Physical Systems.” In 3rd Conference on Machine
Learning for Cyber Physical Systems and Industry 4.0 (ML4CPS), 2017.'
chicago-de: 'Bunte, Andreas, Peng Li und Oliver Niggemann. 2017. Learned Abstraction:
Knowledge Based Concept Learning for Cyber Physical Systems. In: 3rd Conference
on Machine Learning for Cyber Physical Systems and Industry 4.0 (ML4CPS).'
din1505-2-1: 'Bunte, Andreas ; Li, Peng ; Niggemann,
Oliver: Learned Abstraction: Knowledge Based Concept Learning for Cyber
Physical Systems. In: 3rd Conference on Machine Learning for Cyber Physical
Systems and Industry 4.0 (ML4CPS), 2017'
havard: 'A. Bunte, P. Li, O. Niggemann, Learned Abstraction: Knowledge Based Concept
Learning for Cyber Physical Systems, in: 3rd Conference on Machine Learning for
Cyber Physical Systems and Industry 4.0 (ML4CPS), 2017.'
ieee: 'A. Bunte, P. Li, and O. Niggemann, “Learned Abstraction: Knowledge Based
Concept Learning for Cyber Physical Systems,” in 3rd Conference on Machine
Learning for Cyber Physical Systems and Industry 4.0 (ML4CPS), 2017.'
mla: 'Bunte, Andreas, et al. “Learned Abstraction: Knowledge Based Concept Learning
for Cyber Physical Systems.” 3rd Conference on Machine Learning for Cyber Physical
Systems and Industry 4.0 (ML4CPS), 2017.'
short: 'A. Bunte, P. Li, O. Niggemann, in: 3rd Conference on Machine Learning for
Cyber Physical Systems and Industry 4.0 (ML4CPS), 2017.'
ufg: 'Bunte, Andreas et. al. (2017): Learned Abstraction: Knowledge Based
Concept Learning for Cyber Physical Systems, in: 3rd Conference on Machine
Learning for Cyber Physical Systems and Industry 4.0 (ML4CPS).'
van: 'Bunte A, Li P, Niggemann O. Learned Abstraction: Knowledge Based Concept Learning
for Cyber Physical Systems. In: 3rd Conference on Machine Learning for Cyber Physical
Systems and Industry 40 (ML4CPS). 2017.'
date_created: 2021-02-02T08:24:27Z
date_updated: 2023-03-15T13:49:55Z
department:
- _id: DEP5023
language:
- iso: eng
publication: 3rd Conference on Machine Learning for Cyber Physical Systems and Industry
4.0 (ML4CPS)
status: public
title: 'Learned Abstraction: Knowledge Based Concept Learning for Cyber Physical Systems'
type: conference
user_id: '68554'
year: 2017
...