@misc{11978,
  author       = {{Gossen, Arthur and Katsch, Linda and Meyer, Mandy Isabel and Zimmer, Manuel and Bator, Martyna and Darvishi, Masoumeh and Holst, Christoph-Alexander and Lohweg, Volker and Schneider, Jan}},
  location     = {{Lemgo}},
  title        = {{{FoodLifeTimeTracking: Datengetriebene dynamische Haltbarkeitsvorhersage von Erfrischungsgetränken}}},
  year         = {{2024}},
}

@misc{12021,
  author       = {{Segermann, Jan and Luttmann, Mario and Blome, André and Feldt, Sebastian and Sivanesan, Sujee and Holst, Christoph-Alexander and Lohweg, Volker and Frahm, Björn and Müller, Ulrich}},
  keywords     = {{sourdough, fermentation, near-infrared spectroscopy, support vector machine}},
  location     = {{Lemgo}},
  title        = {{{Die Rolle von ML-Modellen in der Lebensmitteltechnologie: Eine Fallstudie zur Sauerteigfermentation mit NIR-Spektroskopie}}},
  year         = {{2024}},
}

@inproceedings{1993,
  abstract     = {{Industrial applications put special demands on machine learning algorithms. Noisy data, outliers, and sensor faults present an immense challenge for learners. A considerable part of machine learning research focuses on the selection of relevant, non-redundant features. This contribution details an approach to group and fuse redundant features prior to learning and classification. Features are grouped relying on a correlation-based redundancy measure. The fusion of features is guided by determining the majority observation based on possibility distributions. Furthermore, this paper studies the effects of feature fusion on the robustness and performance of classification with a focus on industrial applications. The approach is statistically evaluated on public datasets in comparison to classification on selected features only.}},
  author       = {{Holst, Christoph-Alexander and Lohweg, Volker}},
  booktitle    = {{at - Automatisierungstechnik 67 (10) }},
  pages        = {{853--865}},
  publisher    = {{De Gruyter}},
  title        = {{{Feature Fusion to Increase the Robustness of Machine Learners in Industrial Environments}}},
  year         = {{2019}},
}

@inproceedings{1995,
  abstract     = {{The repair of carbon fibre reinforced polymer structures of aircraft is increasingly conducted on site. Monitoring the curing process of polymers has the potential to decrease repair costs by time optimisation and quality control. In this paper Lamb waves are utilised to determine the degree of cure. Waves are excited and recorded by two piezoelectric transducers, one serving as an actuator and the other as a sensor. The recorded signals are processed with a complex wavelet transform, which allows more accurate feature extraction than calculating features in time domain. Extracted features are the transmitted signal energy of the wave and the resonance frequency of the curing polymer. Waves are excited at different frequencies to identify the current resonance frequency. Excitation at or near the current resonant frequency ensures that curing is monitored with maximum sensitivity.}},
  author       = {{Holst, Christoph-Alexander and Röckemann, Kristian and Steinmetz, Andreas and Lohweg, Volker}},
  booktitle    = {{5th IEEE International Forum on Research and Technologies for Society and Industry}},
  title        = {{{Lamb wave-based Cure Monitoring of Carbon Fibre Reinforced Polymers for On-site Aircraft Repairs}}},
  year         = {{2019}},
}

@inproceedings{1998,
  author       = {{Holst, Christoph-Alexander and Lohweg, Volker}},
  booktitle    = {{22nd International Conference on Information Fusion (FUSION)}},
  title        = {{{Improving Majority-guided Fuzzy Information Fusion for Industry 4.0 Condition Monitoring}}},
  year         = {{2019}},
}

@inproceedings{2003,
  abstract     = {{On-site aircraft repairs are gaining in importance due to the susceptibility of carbon fibre reinforced polymers to damage. Repairs themselves are required to be inspected for quality, preferably cost- and time-efficiently. This paper presents an approach for the inspection of repaired composites based on guided Lamb waves. The focus is on cost-effective signal excitation and effective signal processing. Lamb waves are excited with piezoelectric transducers at the resonance frequency of the material under test. Measured signals are processed with a complex wavelet transform to improve damage detection. The proposed approach is evaluated on two test specimens, one of which has a defect in the adhesive bond.}},
  author       = {{Holst, Christoph-Alexander and Röckemann, Kristian and Steinmetz, Andreas and Lohweg, Volker}},
  booktitle    = {{24nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA2019)}},
  title        = {{{Lamb Wave-based Quality Inspection of Repaired Carbon Fibre Reinforced Polymers for On-Site Aircraft Maintenance}}},
  year         = {{2019}},
}

@inproceedings{2007,
  abstract     = {{Multisensor systems are susceptible to sensor ageing effects as well as to environmental changes. Due to these effects, the distribution of sensor measurements may change over time, which is referred to as sensor drift. A multisensor system which adapts to drift by self-monitoring is more durable, requires less manual maintenance, and provides information of higher quality. This contribution proposes an approach for detecting and adapting to sensor drift. The proposed detection algorithm determines the reliability of a sensor based on fuzzy pattern classifiers and a consistency measure. By this means, the inherent redundancy in multisensor systems is exploited to detect drift. Detected drift leads then to a retraining of the classifier on batched data guided by information fusion. The retraining incorporates the estimated magnitude of the drift. The proposed algorithms are evaluated in comparison with state-of-the-art methods in the scope of a publicly available dataset. It is shown that the drift detection algorithm yields results similar to the benchmark algorithm but is less computationally complex. Relearning with the drift-adapted approach results in more robust classifiers with regard to potential future drift.}},
  author       = {{Holst, Christoph-Alexander and Lohweg, Volker}},
  booktitle    = {{23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  keywords     = {{Multisensor systems, Temperature measurement, Current measurement, Redundancy, Pollution measurement, Detection algorithms}},
  location     = {{Torino, Italy}},
  title        = {{{A Conflict-Based Drift Detection And Adaptation Approach for Multisensor Information Fusion}}},
  doi          = {{10.1109/ETFA.2018.8502571}},
  year         = {{2018}},
}

@inproceedings{2009,
  abstract     = {{The aim of sensor orchestration is to design and organise multi-sensor systems both to reduce manual design efforts and to facilitate complex sensor systems. A sensor orchestration is required to adapt to non-stationary environments, even if it is applied in streaming data scenarios where labelled data are scarce or not available. Without labels in dynamic environments, it is challenging to determine not only the accuracy of a classifier but also its reliability. This contribution proposes monitoring algorithms intended to support sensor orchestration in classification tasks in non-stationary environments. Proposed measures regard the relevance of features, the separability of classes, and the classifier's reliability. The proposed monitoring algorithms are evaluated regarding their applicability in the scope of a publicly available and synthetically created collection of datasets. It is shown that the approach (i) is able to distinguish relevant from irrelevant features, (ii) measures class separability as class representations drift through feature space, and (iii) marks a classifier as unreliable if errors in the drift-adaptation occur.}},
  author       = {{Holst, Christoph-Alexander and Lohweg, Volker}},
  booktitle    = {{ACM International Conference on Computing Frontiers 2018}},
  location     = {{Ischia, Italy}},
  pages        = {{363 -- 370}},
  publisher    = {{ New York, NY }},
  title        = {{{Supporting Sensor Orchestration in Non-Stationary Environments}}},
  doi          = {{10.1145/3203217.3203228}},
  year         = {{2018}},
}

@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{2018,
  abstract     = {{Applying information fusion systems aims at gaining information of higher quality and simultaneously decreasing computational and communicational efforts. An increased availability of sensors in industrial machines, but also in everyday life, results in large amounts of potential features. Each feature entails computational and communicational costs. An information fusion system may not require all features, supported by the available sensors, to fulfil its purpose. Feature selection methods reduce the amount of features with the aim to maintain or even increase performance. This contribution proposes a feature selection approach exploiting the inherent conflict between features and utilising a state-ofthe-art information fusion operator. The performance of the proposed method is evaluated in the scope of a publicly available data set and benchmarked against an established feature selection method. It is shown that the proposed approach is faster and produces more accurate feature subsets containing very few features, although the established method produces slightly better performing subsets for large feature subsets.}},
  author       = {{Holst, Christoph-Alexander and Mönks, Uwe and Lohweg, Volker}},
  location     = {{Dortmund}},
  pages        = {{279--295}},
  publisher    = {{27. Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA)}},
  title        = {{{Conflict-based Feature Selection for Information Fusion Systems}}},
  doi          = {{10.5445/KSP/1000074341}},
  year         = {{2017}},
}