---
_id: '12800'
abstract:
- lang: eng
  text: his paper presents the cognitive module of the Cognitive Architecture for
    Artificial Intelligence (CAAI) in cyber-physical production systems (CPPS). The
    goal of this architecture is to reduce the implementation effort of artificial
    intelligence (AI) algorithms in CPPS. Declarative user goals and the provided
    algorithm-knowledge base allow the dynamic pipeline orchestration and configuration.
    A big data platform (BDP) instantiates the pipelines and monitors the CPPS performance
    for further evaluation through the cognitive module. Thus, the cognitive module
    is able to select feasible and robust configurations for process pipelines in
    varying use cases. Furthermore, it automatically adapts the models and algorithms
    based on model quality and resource consumption. The cognitive module also instantiates
    additional pipelines to evaluate algorithms from different classes on test functions.
    CAAI relies on well-defined interfaces to enable the integration of additional
    modules and reduce implementation effort. Finally, an implementation based on
    Docker, Kubernetes, and Kafka for the virtualization and orchestration of the
    individual modules and as messaging technology for module communication is used
    to evaluate a real-world use case.
author:
- first_name: Jan
  full_name: Strohschein, Jan
  last_name: Strohschein
- first_name: Andreas
  full_name: Fischbach, Andreas
  last_name: Fischbach
- first_name: Andreas
  full_name: Bunte, Andreas
  id: '58885'
  last_name: Bunte
- first_name: Heide
  full_name: Faeskorn-Woyke, Heide
  last_name: Faeskorn-Woyke
- first_name: Natalia
  full_name: Moriz, Natalia
  id: '44238'
  last_name: Moriz
- first_name: Thomas
  full_name: Bartz-Beielstein, Thomas
  last_name: Bartz-Beielstein
citation:
  ama: Strohschein J, Fischbach A, Bunte A, Faeskorn-Woyke H, Moriz N, Bartz-Beielstein
    T. Cognitive capabilities for the CAAI in cyber-physical production systems. <i>The
    International Journal of Advanced Manufacturing Technology</i>. 2021;115(11-12):3513-3532.
    doi:<a href="https://doi.org/10.1007/s00170-021-07248-3">10.1007/s00170-021-07248-3</a>
  apa: Strohschein, J., Fischbach, A., Bunte, A., Faeskorn-Woyke, H., Moriz, N., &#38;
    Bartz-Beielstein, T. (2021). Cognitive capabilities for the CAAI in cyber-physical
    production systems. <i>The International Journal of Advanced Manufacturing Technology</i>,
    <i>115</i>(11–12), 3513–3532. <a href="https://doi.org/10.1007/s00170-021-07248-3">https://doi.org/10.1007/s00170-021-07248-3</a>
  bjps: <b>Strohschein J <i>et al.</i></b> (2021) Cognitive Capabilities for the CAAI
    in Cyber-Physical Production Systems. <i>The International Journal of Advanced
    Manufacturing Technology</i> <b>115</b>, 3513–3532.
  chicago: 'Strohschein, Jan, Andreas Fischbach, Andreas Bunte, Heide Faeskorn-Woyke,
    Natalia Moriz, and Thomas Bartz-Beielstein. “Cognitive Capabilities for the CAAI
    in Cyber-Physical Production Systems.” <i>The International Journal of Advanced
    Manufacturing Technology</i> 115, no. 11–12 (2021): 3513–32. <a href="https://doi.org/10.1007/s00170-021-07248-3">https://doi.org/10.1007/s00170-021-07248-3</a>.'
  chicago-de: 'Strohschein, Jan, Andreas Fischbach, Andreas Bunte, Heide Faeskorn-Woyke,
    Natalia Moriz und Thomas Bartz-Beielstein. 2021. Cognitive capabilities for the
    CAAI in cyber-physical production systems. <i>The International Journal of Advanced
    Manufacturing Technology</i> 115, Nr. 11–12: 3513–3532. doi:<a href="https://doi.org/10.1007/s00170-021-07248-3">10.1007/s00170-021-07248-3</a>,
    .'
  din1505-2-1: '<span style="font-variant:small-caps;">Strohschein, Jan</span> ; <span
    style="font-variant:small-caps;">Fischbach, Andreas</span> ; <span style="font-variant:small-caps;">Bunte,
    Andreas</span> ; <span style="font-variant:small-caps;">Faeskorn-Woyke, Heide</span>
    ; <span style="font-variant:small-caps;">Moriz, Natalia</span> ; <span style="font-variant:small-caps;">Bartz-Beielstein,
    Thomas</span>: Cognitive capabilities for the CAAI in cyber-physical production
    systems. In: <i>The International Journal of Advanced Manufacturing Technology</i>
    Bd. 115. London [u.a.], Springer  (2021), Nr. 11–12, S. 3513–3532'
  havard: J. Strohschein, A. Fischbach, A. Bunte, H. Faeskorn-Woyke, N. Moriz, T.
    Bartz-Beielstein, Cognitive capabilities for the CAAI in cyber-physical production
    systems, The International Journal of Advanced Manufacturing Technology. 115 (2021)
    3513–3532.
  ieee: 'J. Strohschein, A. Fischbach, A. Bunte, H. Faeskorn-Woyke, N. Moriz, and
    T. Bartz-Beielstein, “Cognitive capabilities for the CAAI in cyber-physical production
    systems,” <i>The International Journal of Advanced Manufacturing Technology</i>,
    vol. 115, no. 11–12, pp. 3513–3532, 2021, doi: <a href="https://doi.org/10.1007/s00170-021-07248-3">10.1007/s00170-021-07248-3</a>.'
  mla: Strohschein, Jan, et al. “Cognitive Capabilities for the CAAI in Cyber-Physical
    Production Systems.” <i>The International Journal of Advanced Manufacturing Technology</i>,
    vol. 115, no. 11–12, 2021, pp. 3513–32, <a href="https://doi.org/10.1007/s00170-021-07248-3">https://doi.org/10.1007/s00170-021-07248-3</a>.
  short: J. Strohschein, A. Fischbach, A. Bunte, H. Faeskorn-Woyke, N. Moriz, T. Bartz-Beielstein,
    The International Journal of Advanced Manufacturing Technology 115 (2021) 3513–3532.
  ufg: '<b>Strohschein, Jan u. a.</b>: Cognitive capabilities for the CAAI in cyber-physical
    production systems, in: <i>The International Journal of Advanced Manufacturing
    Technology</i> 115 (2021), H. 11–12,  S. 3513–3532.'
  van: Strohschein J, Fischbach A, Bunte A, Faeskorn-Woyke H, Moriz N, Bartz-Beielstein
    T. Cognitive capabilities for the CAAI in cyber-physical production systems. The
    International Journal of Advanced Manufacturing Technology. 2021;115(11–12):3513–32.
date_created: 2025-04-15T13:05:17Z
date_updated: 2025-06-26T13:39:22Z
department:
- _id: DEP5023
doi: 10.1007/s00170-021-07248-3
external_id:
  isi:
  - '000659025000010'
intvolume: '       115'
isi: '1'
issue: 11-12
keyword:
- Cognition
- Industry 40
- Big data platform
- Machine learning
- CPPS
- Optimization
- Algorithm selection
- Simulation
language:
- iso: eng
page: 3513-3532
place: London [u.a.]
publication: The International Journal of Advanced Manufacturing Technology
publication_identifier:
  eissn:
  - 1433-3015
  issn:
  - 0268-3768
publication_status: published
publisher: 'Springer '
status: public
title: Cognitive capabilities for the CAAI in cyber-physical production systems
type: scientific_journal_article
user_id: '83781'
volume: 115
year: '2021'
...
---
_id: '4518'
abstract:
- lang: eng
  text: This paper introduces CAAI, a novel cognitive architecture for artificial
    intelligence in cyber-physical production systems. The goal of the architecture
    is to reduce the implementation effort for the usage of artificial intelligence
    algorithms. The core of the CAAI is a cognitive module that processes the user's
    declarative goals, selects suitable models and algorithms, and creates a configuration
    for the execution of a processing pipeline on a big data platform. Constant observation
    and evaluation against performance criteria assess the performance of pipelines
    for many and different use cases. Based on these evaluations, the pipelines are
    automatically adapted if necessary. The modular design with well-defined interfaces
    enables the reusability and extensibility of pipeline components. A big data platform
    implements this modular design supported by technologies such as Docker, Kubernetes,
    and Kafka for virtualization and orchestration of the individual components and
    their communication. The implementation of the architecture is evaluated using
    a real-world use case. The prototypic implementation is accessible on GitHub and
    contains a demonstration.
author:
- first_name: Andreas
  full_name: Fischbach, Andreas
  last_name: Fischbach
- first_name: Jan
  full_name: Strohschein, Jan
  last_name: Strohschein
- first_name: Andreas
  full_name: Bunte, Andreas
  id: '58885'
  last_name: Bunte
- first_name: Jörg
  full_name: Stork, Jörg
  last_name: Stork
- first_name: Heide
  full_name: Faeskorn-Woyke, Heide
  last_name: Faeskorn-Woyke
- first_name: Natalia
  full_name: Moriz, Natalia
  id: '44238'
  last_name: Moriz
- first_name: Thomas
  full_name: Bartz-Beielstein, Thomas
  last_name: Bartz-Beielstein
citation:
  ama: Fischbach A, Strohschein J, Bunte A, et al. CAAI -- A Cognitive Architecture
    to Introduce Artificial Intelligence in Cyber-Physical Production Systems. <i>The
    International Journal of Advanced Manufacturing Technology</i>. 2020;111(1/2):609-626.
    doi:<a href="https://doi.org/10.1007/s00170-020-06094-z">10.1007/s00170-020-06094-z</a>
  apa: Fischbach, A., Strohschein, J., Bunte, A., Stork, J., Faeskorn-Woyke, H., Moriz,
    N., &#38; Bartz-Beielstein, T. (2020). CAAI -- A Cognitive Architecture to Introduce
    Artificial Intelligence in Cyber-Physical Production Systems. <i>The International
    Journal of Advanced Manufacturing Technology</i>, <i>111</i>(1/2), 609–626. <a
    href="https://doi.org/10.1007/s00170-020-06094-z">https://doi.org/10.1007/s00170-020-06094-z</a>
  bjps: <b>Fischbach A <i>et al.</i></b> (2020) CAAI -- A Cognitive Architecture to
    Introduce Artificial Intelligence in Cyber-Physical Production Systems. <i>The
    International Journal of Advanced Manufacturing Technology</i> <b>111</b>, 609–626.
  chicago: 'Fischbach, Andreas, Jan Strohschein, Andreas Bunte, Jörg Stork, Heide
    Faeskorn-Woyke, Natalia Moriz, and Thomas Bartz-Beielstein. “CAAI -- A Cognitive
    Architecture to Introduce Artificial Intelligence in Cyber-Physical Production
    Systems.” <i>The International Journal of Advanced Manufacturing Technology</i>
    111, no. 1/2 (2020): 609–26. <a href="https://doi.org/10.1007/s00170-020-06094-z">https://doi.org/10.1007/s00170-020-06094-z</a>.'
  chicago-de: 'Fischbach, Andreas, Jan Strohschein, Andreas Bunte, Jörg Stork, Heide
    Faeskorn-Woyke, Natalia Moriz und Thomas Bartz-Beielstein. 2020. CAAI -- A Cognitive
    Architecture to Introduce Artificial Intelligence in Cyber-Physical Production
    Systems. <i>The International Journal of Advanced Manufacturing Technology</i>
    111, Nr. 1/2: 609–626. doi:<a href="https://doi.org/10.1007/s00170-020-06094-z">10.1007/s00170-020-06094-z</a>,
    .'
  din1505-2-1: '<span style="font-variant:small-caps;">Fischbach, Andreas</span> ;
    <span style="font-variant:small-caps;">Strohschein, Jan</span> ; <span style="font-variant:small-caps;">Bunte,
    Andreas</span> ; <span style="font-variant:small-caps;">Stork, Jörg</span> ; <span
    style="font-variant:small-caps;">Faeskorn-Woyke, Heide</span> ; <span style="font-variant:small-caps;">Moriz,
    Natalia</span> ; <span style="font-variant:small-caps;">Bartz-Beielstein, Thomas</span>:
    CAAI -- A Cognitive Architecture to Introduce Artificial Intelligence in Cyber-Physical
    Production Systems. In: <i>The International Journal of Advanced Manufacturing
    Technology</i> Bd. 111, Springer (2020), Nr. 1/2, S. 609–626'
  havard: A. Fischbach, J. Strohschein, A. Bunte, J. Stork, H. Faeskorn-Woyke, N.
    Moriz, T. Bartz-Beielstein, CAAI -- A Cognitive Architecture to Introduce Artificial
    Intelligence in Cyber-Physical Production Systems, The International Journal of
    Advanced Manufacturing Technology. 111 (2020) 609–626.
  ieee: 'A. Fischbach <i>et al.</i>, “CAAI -- A Cognitive Architecture to Introduce
    Artificial Intelligence in Cyber-Physical Production Systems,” <i>The International
    Journal of Advanced Manufacturing Technology</i>, vol. 111, no. 1/2, pp. 609–626,
    2020, doi: <a href="https://doi.org/10.1007/s00170-020-06094-z">10.1007/s00170-020-06094-z</a>.'
  mla: Fischbach, Andreas, et al. “CAAI -- A Cognitive Architecture to Introduce Artificial
    Intelligence in Cyber-Physical Production Systems.” <i>The International Journal
    of Advanced Manufacturing Technology</i>, vol. 111, no. 1/2, 2020, pp. 609–26,
    <a href="https://doi.org/10.1007/s00170-020-06094-z">https://doi.org/10.1007/s00170-020-06094-z</a>.
  short: A. Fischbach, J. Strohschein, A. Bunte, J. Stork, H. Faeskorn-Woyke, N. Moriz,
    T. Bartz-Beielstein, The International Journal of Advanced Manufacturing Technology
    111 (2020) 609–626.
  ufg: '<b>Fischbach, Andreas u. a.</b>: CAAI -- A Cognitive Architecture to Introduce
    Artificial Intelligence in Cyber-Physical Production Systems, in: <i>The International
    Journal of Advanced Manufacturing Technology</i> 111 (2020), H. 1/2,  S. 609–626.'
  van: Fischbach A, Strohschein J, Bunte A, Stork J, Faeskorn-Woyke H, Moriz N, et
    al. CAAI -- A Cognitive Architecture to Introduce Artificial Intelligence in Cyber-Physical
    Production Systems. The International Journal of Advanced Manufacturing Technology.
    2020;111(1/2):609–26.
date_created: 2021-01-26T12:24:10Z
date_updated: 2025-06-26T13:38:24Z
department:
- _id: DEP5023
doi: 10.1007/s00170-020-06094-z
external_id:
  isi:
  - '000574389900002'
intvolume: '       111'
isi: '1'
issue: 1/2
keyword:
- CPPS
- Artificial intelligence
- Industry 40
- Reference architecture
- Optimization
- SMBO
- Cognition
- Big data platform
- Modularization
- AutoML
language:
- iso: eng
page: 609-626
publication: The International Journal of Advanced Manufacturing Technology
publication_identifier:
  eissn:
  - 1433-3015
  issn:
  - 0268-3768
publication_status: published
publisher: Springer
status: public
title: CAAI -- A Cognitive Architecture to Introduce Artificial Intelligence in Cyber-Physical
  Production Systems
type: journal_article
user_id: '83781'
volume: 111
year: '2020'
...
---
_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. <i>Engineering
    Applications of Artificial Intelligence</i>. 2019;87. doi:<a href="https://doi.org/10.1016/j.engappai.2019.103301">10.1016/j.engappai.2019.103301</a>
  apa: Li, P., &#38; Niggemann, O. (2019). Non-convex hull based anomaly detection
    in CPPS. <i>Engineering Applications of Artificial Intelligence</i>, <i>87</i>,
    Article 103301. <a href="https://doi.org/10.1016/j.engappai.2019.103301">https://doi.org/10.1016/j.engappai.2019.103301</a>
  bjps: <b>Li P and Niggemann O</b> (2019) Non-Convex Hull Based Anomaly Detection
    in CPPS. <i>Engineering Applications of Artificial Intelligence</i> <b>87</b>.
  chicago: Li, Peng, and Oliver Niggemann. “Non-Convex Hull Based Anomaly Detection
    in CPPS.” <i>Engineering Applications of Artificial Intelligence</i> 87 (2019).
    <a href="https://doi.org/10.1016/j.engappai.2019.103301">https://doi.org/10.1016/j.engappai.2019.103301</a>.
  chicago-de: Li, Peng und Oliver Niggemann. 2019. Non-convex hull based anomaly detection
    in CPPS. <i>Engineering Applications of Artificial Intelligence</i> 87. doi:<a
    href="https://doi.org/10.1016/j.engappai.2019.103301">10.1016/j.engappai.2019.103301</a>,
    .
  din1505-2-1: '<span style="font-variant:small-caps;">Li, Peng</span> ; <span style="font-variant:small-caps;">Niggemann,
    Oliver</span>: Non-convex hull based anomaly detection in CPPS. In: <i>Engineering
    Applications of Artificial Intelligence</i> 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,”
    <i>Engineering Applications of Artificial Intelligence</i>, vol. 87, Art. no.
    103301, 2019, doi: <a href="https://doi.org/10.1016/j.engappai.2019.103301">10.1016/j.engappai.2019.103301</a>.'
  mla: Li, Peng, and Oliver Niggemann. “Non-Convex Hull Based Anomaly Detection in
    CPPS.” <i>Engineering Applications of Artificial Intelligence</i>, vol. 87, 103301,
    2019, <a href="https://doi.org/10.1016/j.engappai.2019.103301">https://doi.org/10.1016/j.engappai.2019.103301</a>.
  short: P. Li, O. Niggemann, Engineering Applications of Artificial Intelligence
    87 (2019).
  ufg: '<b>Li, Peng/Niggemann, Oliver</b>: Non-convex hull based anomaly detection
    in CPPS, in: <i>Engineering Applications of Artificial Intelligence</i> 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'
...