@article{2014,
  abstract     = {{Industrial applications are in transition towards modular and flexible architectures that are capable of self-configuration and -optimisation. This is due to the demand of mass customisation and the increasing complexity of industrial systems. The conversion to modular systems is related to challenges in all disciplines. Consequently, diverse tasks such as information processing, extensive networking, or system monitoring using sensor and information fusion systems need to be reconsidered. The focus of this contribution is on distributed sensor and information fusion systems for system monitoring, which must reflect the increasing flexibility of fusion systems. This contribution thus proposes an approach, which relies on a network of self-descriptive intelligent sensor nodes, for the automatic design and update of sensor and information fusion systems. This article encompasses the fusion system configuration and adaptation as well as communication aspects. Manual interaction with the flexibly changing system is reduced to a minimum.}},
  author       = {{Fritze, Alexander and Mönks, Uwe and Holst, Christoph-Alexander and Lohweg, Volker}},
  issn         = {{1424-8220}},
  journal      = {{Sensors}},
  keywords     = {{information fusion, intelligent sensor, knowledge-based system, self-configuration, sensor fusion}},
  number       = {{3}},
  title        = {{{An Approach to Automated Fusion System Design and Adaptation}}},
  doi          = {{ https://doi.org/10.3390/s17030601}},
  volume       = {{17}},
  year         = {{2017}},
}

@inproceedings{2107,
  abstract     = {{In this paper we propose a novel, extended perspective on evidential aggregation rules in machine condition monitoring. First, aspects regarding the interconnections between Dempster-Shafer, Fuzzy Set, and Possibility Theory are shown. Subsequently, a novel approach for direct determination of basic probability assignments using Fuzzy membership functions is proposed. Finally, it is applied to a pipe extrusion line's condition monitoring system, considering and reducing pairwise conflicts.}},
  author       = {{Mönks, Uwe and Voth, Karl and Lohweg, Volker}},
  booktitle    = {{IEEE CIP 2012, Third International Workshop on Cognitive Information Processing, May 28-30 2012, Parador de Baiona, Spain}},
  isbn         = {{978-1-4673-1877-8}},
  issn         = {{2327-1698 }},
  keywords     = {{Sensor phenomena and characterization, Production, Sensor fusion, Fuzzy set theory, Conferences, Possibility theory}},
  title        = {{{An Extended Perspective on Evidential Aggregation Rules in Machine Conditioning}}},
  doi          = {{10.1109/CIP.2012.6232905}},
  year         = {{2012}},
}

@inproceedings{2086,
  abstract     = {{Many of the existing fusion approaches based on Dempster-Shafer Theory (DST) tend to be unreliable in various scenarios. Therefore, this topic is still in discussion. In this work a Two-Layer Conflict Solving (TLCS) data fusion scheme is proposed which is based on Dempster-Shafer Theory and on Fuzzy-Pattern-Classification (FPC) concepts. The aim is to provide an approach to data fusion which provides a stable conflict scenario handling. Furthermore, the scheme can easily be extended to fuzzy classification and is applicable to sensor fusion applications. Therefore, the suggested approach will contribute as a novel fuzzy fusion method.}},
  author       = {{Lohweg, Volker and Mönks, Uwe}},
  booktitle    = {{The 2nd International Workshop on Cognitive Information Processing}},
  isbn         = {{978-1-4244-6457-9}},
  issn         = {{2327-1671 }},
  keywords     = {{Noise measurement, Fuzzy sets, Noise, Sensor fusion, Logic gates, Feature extraction, Fuses}},
  location     = {{Elba}},
  publisher    = {{14-16 June, 2010, Elba Island (Tuscany), Italy}},
  title        = {{{Sensor Fusion by Two-Layer Conflict Solving}}},
  doi          = {{10.1109/CIP.2010.5604094}},
  year         = {{2010}},
}

@article{2069,
  abstract     = {{During printed product manufacturing, measures are taken to ensure a certain level of printing quality and security via authentification  methods.  This  is  particularly  true  in  the  field  of  security  printing,  where  the  quality  standards,  which  must be reached by the end-products, i.e. banknotes, security documents and the like, are very high.  It  is  accepted,  that  print  defects  are  generated  because  printing  parameters  but  also  machine  parameters  will  change  unnoticed in production. Therefore, a new concept for a multi-sensory adaptive learning and classification model based on  Fuzzy-Pattern-Classifiers  for  data  inspection,  authentification  and  machine  conditioning  is  proposed.  This  kind  of  inspection concept, which combines optical, acoustical and other machine information, produces a large amount of data, which leads to multivariate methods for data analysis. Multivariate methods are useful for analysis of large and complex data  sets  that  consist  of  many  variables  measured  on  large  numbers  of  physical  data.  A  general  aim  is  to  improve  the  known  inspection  techniques  and  propose  an  inspection  methodology  that  can  ensure  a  comprehensive  quality  control  of  the  printed  substrates  processed  by  printing  presses,  especially  printing  presses  which  are  designed  to  process  substrates used in the course of the production of banknotes, security documents and others. }},
  author       = {{Dyck, Walter and Türke, Thomas and Schaede, Johannes and Lohweg, Volker}},
  journal      = {{Optical Document Security - The 2008 Conference on Optical Security and Counterfeit Deterrence; Reconnaissance International Publishers and Consultants, San Francisco, CA, USA}},
  keywords     = {{authentification, anti-counterfeit features, inspection, quality, sensor fusion, pattern recognition}},
  title        = {{{A New Concept on Quality Inspection and Machine Conditioning for Security Prints}}},
  year         = {{2008}},
}

@inproceedings{2068,
  abstract     = {{The production of printing goods is laborious. Furthermore, the print quality, especially in banknotes, must be assured. It is accepted, that print defects are generated because printing parameters, also machine parameters can change unnoticed. Therefore, a combined concept for a multi-sensory learning and classification model based on new adaptive fuzzy-pattern-classifiers for data inspection is proposed. This inspection concept, which combines optical, acoustical and other machine information, comes up with a large amount of data, which leads to multivariate methods for data analysis. Multivariate methods are useful for analysis of large and complex data sets that consist of many variables measured on large numbers of physical data.}},
  author       = {{Dyck, Walter and Türke, Thomas and Schaede, Johannes and Lohweg, Volker}},
  isbn         = {{978-1-4244-1565-6}},
  issn         = {{1551-2541 }},
  keywords     = {{Sensor fusion, Inspection, Optical sensors, Printing machinery, Data security, Data analysis, Production, Degradation, Principal component analysis, Karhunen-Loeve transforms}},
  pages        = {{accepted for publication}},
  publisher    = {{MLSP 2007 - International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING}},
  title        = {{{A Fuzzy-Pattern-Classifier-Based Adaptive Learning Model for Sensor Fusion}}},
  doi          = {{10.1109/MLSP.2007.4414320}},
  year         = {{2007}},
}