@inproceedings{1991, author = {{Funk, Mark and Scharf, Matthias and Dörksen, Helene and Danneel, Hans-Jürgen and Lohweg, Volker and Hübner, Michael and Schaede, Johannes and Stierman, Rob and Knobloch, Alexander and Le, Dinh Khoi and Gillich, Eugen and Mönks, Uwe}}, booktitle = {{ODS 2020 Review}}, location = {{San Francisco}}, title = {{{Creating a Self-authentication System for Smart Banknotes}}}, year = {{2020}}, } @inproceedings{2011, author = {{Lohweg, Volker and Funk, Mark and Scharf, Matthias and Dörksen, Helene and Danneel, Hans-Jürgen and Hübner, Michael and Schaede, Johannes and Thony, Emmanuel and Knobloch, Alexander and Lee, Dinh Khoi and Mönks, Uwe and Gillich, Eugen}}, booktitle = {{Optical Document Security - The Conference on Optical Security and Counterfeit Detection XII San Francisco}}, location = {{San Francisco, USA}}, title = {{{smartBN—Intelligent Protection and Authentication in Payment Transactions by Smart Banknotes}}}, 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}}, } @article{2030, author = {{Lohweg, Volker and Mönks, Uwe}}, issn = {{0022-6416}}, journal = {{Unternehmermagazin}}, number = {{3/4}}, title = {{{Schwellwerte und Sensoren - Predictive Maintenance in der Praxis}}}, volume = {{64}}, year = {{2016}}, } @inproceedings{2031, abstract = {{Currently, new research questions arise because of the paradigms of Industry 4.0, which aims to bring together mechatronic systems and information technologies. Its general idea is to create an Internet of Things consisting of communicating machines, which implement concepts for self-configuration, -diagnosis, and -optimisation. The realisation of these functionalities is in focus of current research and gains in importance not only in the industrial sector. The overall goal is to equip technical systems with intelligence to enable for autonomous behaviour. Therefore, tasks like information processing, extensive networking, or system monitoring using sensor and information fusion systems have to be reconsidered. This contribution focuses on the design and maintenance of sensor and information fusion systems and presents a preliminary evaluation of a design concept for such applications. The concept is developed to automatically configure sensor and information fusion systems, which is a time-consuming and complex task when carried out manually. It reduces the perceived complexity of the application and supports the designer during design and maintenance of the sensor and information fusion system.}}, author = {{Fritze, Alexander and Mönks, Uwe and Lohweg, Volker}}, booktitle = {{21th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2016)}}, title = {{{A Concept for Self-Configuration of Adaptive Sensor and Information Fusion Systems}}}, year = {{2016}}, } @inproceedings{2036, abstract = {{In industrial processes a vast variety of different sensors is increasingly used to measure and control processes, machines, and logistics. One way to handle the resulting large amount of data created by hundreds or even thousands of different sensors in an application is to employ information fusion systems. Information fusion systems, e.g. for condition monitoring, combine different sources of information, like sensors, to generate the state of a complex system. The result of such an information fusion process is regarded as a health indicator of a complex system. Therefore, information fusion approaches are applied to, e.g., automatically inform one about a reduction in production quality, or detect possibly dangerous situations. Considering the importance of sensors in the previously described information fusion systems and in industrial processes in general, a defective sensor has several negative consequences. It may lead to machine failure, e.g. when wear and tear of a machine is not detected sufficiently in advance. In this contribution we present a method to detect faulty sensors by computing the consistency between sensor values. The proposed sensor defect detection algorithm exemplarily utilises the structure of a multilayered group-based sensor fusion algorithm. Defect detection results of the proposed method for different test cases and the method's capability to detect a number of typical sensor defects are shown.}}, author = {{Ehlenbröker, Jan-Friedrich and Mönks, Uwe and Lohweg, Volker}}, booktitle = {{Journal of Sensors and Sensor Systems, ISSN 2194-8771}}, publisher = {{Copernicus Publications}}, title = {{{Sensor Defect Detection in Multisensor Information Fusion}}}, year = {{2016}}, } @inproceedings{2037, abstract = {{The complexity of industrial applications has constantly increased over the last decades. New paradigms arise in the context of the fourth industrial revolution by bringing together mechatronic systems and information technologies. Tasks like information processing, extensive networking, or system monitoring using sensor and information fusion systems are incorporated with the aim to design applications that are capable for self-configuration, -diagnosis, and -optimisation. This contribution focuses on the design of sensor and information fusion systems. A methodology for the design process of such systems is proposed that serves as tool for auto-configuration to facilitate self-diagnosis and -optimisation.}}, author = {{Fritze, Alexander and Mönks, Uwe and Lohweg, Volker}}, booktitle = {{3rd International Conference on System-Integrated Intelligence - New Challenges for Product and Production Engineering }}, publisher = {{Paderborn, Germany}}, title = {{{A Support System for Sensor and Information Fusion System Design}}}, year = {{2016}}, } @article{2044, abstract = {{Sensors, and also actuators or external sources such as databases, serve as data sources in order to realise condition monitoring of industrial applications or the acquisition of characteristic parameters like production speed or reject rate. Modern facilities create such a large amount of complex data that a machine operator is unable to comprehend and process the information contained in the data. Thus, information fusion mechanisms gain increasing importance. Besides the management of large amounts of data, further challenges towards the fusion algorithms arise from epistemic uncertainties (incomplete knowledge) in the input signals as well as conflicts between them. These aspects must be considered during information processing to obtain reliable results, which are in accordance with the real world. The analysis of the scientific state of the art shows that current solutions fulfil said requirements at most only partly. This article proposes the multilayered information fusion system MACRO (multilayer attribute-based conflict-reducing observation) employing the μBalTLCS (fuzzified balanced two-layer conflict solving) fusion algorithm to reduce the impact of conflicts on the fusion result. The performance of the contribution is shown by its evaluation in the scope of a machine condition monitoring application under laboratory conditions. Here, the MACRO system yields the best results compared to state-of-the-art fusion mechanisms. The utilised data is published and freely accessible.}}, author = {{Mönks, Uwe and Dörksen, Helene and Lohweg, Volker and Hübner, Michael}}, issn = {{1424-8220}}, journal = {{Sensors}}, title = {{{Information Fusion of Conflicting Input Data}}}, doi = {{10.3390/s16111798}}, year = {{2016}}, } @article{2140, abstract = {{Recent industrial applications are implemented in a modular way, resulting in flexibility during the whole life cycle, i.e., setup, operation, and maintenance. This applies especially to larger applications like logistic, production, and printing processes. Their modular character is resulting from the constantly increasing complexity of such installations, which makes their supervision for securing reliable operation a difficult task: the data of hundreds (if not thousands) of signal sources must be acquired, communicated, and evaluated for system diagnosis. In this contribution we summarize the challenges arising in such applications and show that distributed sensor and information fusion for modular self-diagnosis tackles these challenges. Here, we propose an innovative distributed architecture encompassing intelligent sensor nodes, self-configuring real-time communication networks, and a suitable sensor and information fusion system for condition monitoring. New challenges arise in the context of distributed information fusion systems, which are identified and to which an outlook on future solutions is provided. A number of these solutions have already been discovered, implemented, and are evaluated in the context of a demonstrator, which resembles a real-world printing application.}}, author = {{Mönks, Uwe and Trsek, Henning and Dürkop, Lars and Geneiß, Volker and Lohweg, Volker}}, issn = {{0957-4158}}, journal = {{Mechatronics}}, keywords = {{Cyber-physical systems, Information fusion, Fusion system design, Intelligent sensors, Self-configuration, Intelligent networking}}, number = {{34}}, pages = {{63--71}}, publisher = {{Elsevier}}, title = {{{Towards distributed intelligent sensor and information fusion}}}, doi = {{10.1016/j.mechatronics.2015.05.005}}, year = {{2015}}, } @inproceedings{2145, abstract = {{One general problem is the detection of sensor defects. Defective sensors can have several negative consequences, e. g., they will lead to machine failure when wear and tear of a machine is not detected sufficiently in advance. In this contribution we present a method to detect faulty sensors by calculating the consistency between sensor values. Background for this consistency-driven approach is a sensor fusion algorithm which combines sensors to attributes. These attributes are generally created based on local or thematical proximity. Therefore a consistency based approach is promising.}}, author = {{Ehlenbröker, Jan-Friedrich and Mönks, Uwe and Lohweg, Volker}}, booktitle = {{AMA Conferences 2015, SENSOR 2015 - IRS2 2015}}, isbn = {{978-3-9813484-8-4 }}, pages = {{878 -- 883}}, publisher = {{AMA-Fachverband}}, title = {{{Consistency Based Sensor Defect Detection }}}, year = {{2015}}, } @inproceedings{2155, abstract = {{Today, mobile devices (smartphones, tablets, etc.) are widespread and of high importance for their users. Their performance as well as versatility increases over time. This leads to the opportunity to use such devices for more specific tasks like image processing in an industrial context. For the analysis of images requirements like image quality (blur, illumination, etc.) as well as a defined relative position of the object to be inspected are crucial. Since mobile devices are handheld and used in constantly changing environments the challenge is to fulfill these requirements. We present an approach to overcome the obstacles and stabilize the image capturing process such that image analysis becomes significantly improved on mobile devices. Therefore, image processing methods are combined with sensor fusion concepts. The approach consists of three main parts. First, pose estimation methods are used to guide a user moving the device to a defined position. Second, the sensors data and the pose information are combined for relative motion estimation. Finally, the image capturing process is automated. It is triggered depending on the alignment of the device and the object as well as the image quality that can be achieved under consideration of motion and environmental effects.}}, author = {{Henning, Kai-Fabian and Fritze, Alexander and Gillich, Eugen and Mönks, Uwe and Lohweg, Volker}}, booktitle = {{IST/SPIE Electronic Imaging 2015, Digital Photography and Mobile Imaging XI}}, keywords = {{Image processing, Image acquisition, Mobile devices Sensors, Image fusion, Motion estimation, Cameras}}, pages = {{1--12}}, publisher = {{SPIE}}, title = {{{Stable Image Acquisition for Mobile Image Processing Applications}}}, doi = {{10.1117/12.2076146}}, year = {{2015}}, } @inproceedings{2146, abstract = {{The increased deployment of information technology for information processing, extensive networking, and system/environment monitoring using sensor and information fusion systems are essential characteristics of cyber-physical systems. They allow an autonomous recognition and evaluation of the system's status leading to autonomous reactions improving or maintaining the status to operate adaptively, robustly, anticipatory, and user-friendly. Assisting the operator in handling such complex systems is rather important and requires self-configuration, self-diagnosis, and self-optimization capabilities. In this paper, a new assisted design methodology for sensor and information fusion systems is proposed. It is based on an innovative system architecture consisting of the information fusion system itself, intelligent adaptable sensors, and the communication architecture of the "Intelligent Technical Systems OstWestfalenLippe" (it's OWL) Leading-Edge Cluster project "Intelligent Networking" providing an intelligent network for self-configuration and the required real-time data exchange.}}, author = {{Mönks, Uwe and Trsek, Henning and Dürkop, Lars and Geneiß, Volker and Lohweg, Volker}}, booktitle = {{2nd International Conference on System-integrated Intelligence}}, issn = {{2212-0173}}, pages = {{35--45}}, title = {{{Assisting the Design of Sensor and Information Fusion Systems}}}, doi = {{https://doi.org/10.1016/j.protcy.2014.09.032}}, volume = {{15}}, year = {{2014}}, } @inproceedings{2157, abstract = {{Information fusion systems are crucial for the success of the upcoming fourth industrial revolution. In this emerging field, cyber-physicals systems play a major role. These are physical processing systems equipped with sensory devices which interconnect over communication networks for distributed cognitive information processing applications. Cyber-physical systems are generally limited in computational resources. Due to this fact, signal processing algorithms cannot be implemented one-to-one. Instead, efforts must be spent in algorithm optimisation towards resource efficiency and reduced computational complexity. In this contribution, we present our optimisation approach by matrix decomposition of an evidence-based conflict-reducing fusion approach which after optimisation is applicable in resource-limited devices for cognitive signal processing. We evaluate the results by comparison with the algorithm's original definition and show the improvements achieved. }}, author = {{Mönks, Uwe and Lohweg, Volker}}, booktitle = {{4th Internation Workshop on Cognitive Information}}, issn = {{2327-1698 }}, title = {{{Fast Evidence-based Information Fusion}}}, doi = {{ 10.1109/CIP.2014.6844508}}, year = {{2014}}, } @inproceedings{2159, abstract = {{In this contribution we show enhancements of the safety of hazardous material stores by the usage of a condition monitoring system. Hazardous material stores function as a store for dangerous chemicals. We use fire simulations to simulate a fire and use the results of this simulation in our condition monitoring system in order to show the attainable gains. The used condition monitoring system utilises multiple sensors which are distributed inside and outside of the hazardous material store. The values of the sensors are combined over multiple levels into one state for the complete system. This allows us to significantly enhance the detection time of dangerous operating states, compared to the use of dedicated single sensors.}}, author = {{Ehlenbröker, Jan-Friedrich and Mönks, Uwe and Wesemann, Derk and Lohweg, Volker}}, booktitle = {{Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA)}}, isbn = {{ 978-1-4799-4845-1}}, title = {{{Condition monitoring for hazardous material storage}}}, doi = {{10.1109/etfa.2014.7005264}}, year = {{2014}}, } @inproceedings{2161, author = {{Dörksen, Helene and Mönks, Uwe and Lohweg, Volker}}, booktitle = {{19th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Barcelona}}, title = {{{Fast Classification in Industrial Big Data Environments}}}, year = {{2014}}, } @inproceedings{2164, author = {{Neumann, Richard and Dicks, Alexander and Mönks, Uwe and Lohweg, Volker}}, booktitle = {{24. Workshop Computational Intelligence}}, isbn = {{978-3-7315-0275-3}}, pages = {{315--332}}, publisher = {{VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA)}}, title = {{{Fuzzy Pattern Klassifikation von Datensätzen mit nichtkonvexen Objektmorphologien}}}, year = {{2014}}, } @article{2135, abstract = {{Eine Zustandsüberwachung elektrischer Antriebe erfolgt derzeit in der Regel durch Einsatz spezieller Sensorik, bspw. durch Vibrationsmessungen. Außerdem werden die Antriebe lediglich isoliert betrachtet, eine Zusammenführung anfallender Informationen eines räumlich verteilten Antriebsverbunds findet meist nicht statt. Es wird ein neuartiges Motor-as-Sensor-Konzept vorgeschlagen und validiert, das eine antizipatorische Zustandsüberwachung ohne Einsatz zusätzlicher Sensorik allein durch Verarbeitung der phasenbezogenen Motorströme ermöglicht. Zusätzlich wird ein Informationsfusionskonzept vorgestellt, das die Informationen aller im Verbund beteiligten Antriebe zusammenführt, um darüber eine mit weniger Unsicherheiten behaftete Aussage über den Zustand einer Applikation herbeizuführen. Das Hauptaugenmerk liegt hierbei insbesondere auf der Beherrschung der anfallenden riesigen Datenmengen. die zur Verarbeitung in eingebetteten Systemen reduziert werden müssen.}}, author = {{Mönks, Uwe and Bator, Martyna and Dicks, Alexander and Lohweg, Volker}}, isbn = {{978-3-942647-29-8}}, journal = {{Wissenschaftsforum Intelligente Technische Systeme (Heinz Nixdorf Institut, Paderborn)}}, pages = {{305--315}}, publisher = {{Universität Paderborn}}, title = {{{Informationsfusion mit verteilter elektromotorischer Sensorik im Maschinen- und Anlagenbau}}}, volume = {{9. Paderborner Workshop Entwurf mechatronischer Systeme}}, year = {{2013}}, } @inproceedings{2137, abstract = {{Systems for process automation become increasingly complex and also tend to be composed of autonomous subsystems, which is strongly driven by the progress made in information technology. An active field of research is the implementation of monitoring and control at sub-system level using cognitive approaches. In this paper we present a method for autonomous and sensorless condition monitoring of an electric drive train. Based on experiment design we measured phase currents of a physical demonstrator device including mechanical defects and extracted signal features using proper orthogonal decomposition. In favor of classification of different defect states we performed a linear discriminant analysis, which yields appropriate data for a Fuzzy-Pattern-Classification algorithm. As a result we were able to identify different reference defect states as well as previously unknown states.}}, author = {{Bayer, Christian and Bator, Martyna and Enge-Rosenblatt, Olaf and Mönks, Uwe and Dicks, Alexander and Lohweg, Volker}}, isbn = {{978-1-4799-0862-2}}, publisher = {{18th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}}, title = {{{Sensorless Drive Diagnosis Using Automated Feature Extraction, Significance Ranking and Reduction.}}}, doi = {{ 10.1109/ETFA.2013.6648126}}, year = {{2013}}, } @inproceedings{2141, abstract = {{Sensor and information fusion is recently a major topic which becomes important in machine diagnosis and conditioning for complex production machines and process engineering. It is a known fact that distributed automation systems have a major impact on signal processing and pattern recognition for machine diagnosis. Therefore, it is necessary to research and develop smart diagnosis methods which are applicable for distributed systems like resource-limited cyber-physical systems. In this paper we propose an new approach for sensor and information fusion based on Evidence Theory and socio-psychological decision-making. We show that context based condition monitoring is instantiated even in conflict situations, oc-curing in real life scenarios permanently. A simple but effective importance measure is proposed which controls the significance of conditioning propositions in a system.}}, author = {{Mönks, Uwe and Lohweg, Volker}}, booktitle = {{18th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}}, isbn = {{978-1-4799-0862-2}}, issn = {{1946-0759 }}, keywords = {{Decision making, Robot sensing systems, Reliability, Production, Context, Fuzzy set theory, Data integration}}, title = {{{Machine Conditioning by Importance Controlled Information Fusion}}}, doi = {{10.1109/ETFA.2013.6647984}}, year = {{2013}}, } @inproceedings{2142, abstract = {{Die aktive Zustandsüberwachung von Automatisierungssystemen rückt immer weiter in den Vordergrund und ist daher ein zentraler Forschungsgegenstand. In diesem Beitrag werden Ansätze der sensorlosen Überwachung eines Synchronmotors diskutiert. Basierend auf Messungen der Phasenströme des Motors werden mit der Hilbert-Transformation bzw. mit der Empirical Mode Decomposition charakteristische Merkmale aus den Signalen berechnet. Anschließend werden diese mittels Hauptkomponentenanalyse bzw. der linearen Diskriminanzanalyse reduziert. Die daraus berechneten Charakteristischen Merkmale dienen als Grundlage für die abschließende Fuzzy-Pattern-Klassifikation. Basierend auf dem erläuterten Ansatz ist die Identifikation typischer Betriebs- bzw. Fehlerzustände, aber auch das Erkennen nicht gelernter Zustände möglich. Das dabei vorgestellte Vorgehen ist vergleichsweise generisch und lässt sich gut auf andere Anwendungsgebiete übertragen.}}, author = {{Paschke, Fabian and Bayer, Christian and Bator, Martyna and Mönks, Uwe and Dicks, Alexander and Enge-Rosenblatt, Olaf and Lohweg, Volker}}, booktitle = {{23. Workshop Computational Intelligence 2013. Proceedings}}, editor = {{Hoffmann, F.}}, isbn = {{978-3-7315-0126-8}}, location = {{Dortmund}}, pages = {{211--225}}, publisher = {{KIT Scientific Publishing}}, title = {{{Sensorlose Zustandsüberwachung an Synchronmotoren.}}}, volume = {{46}}, year = {{2013}}, } @inproceedings{2144, author = {{Mönks, Uwe and Priesterjahn, Steffen and Lohweg, Volker}}, booktitle = {{23. Workshop Computational Intelligence, 05.-06.12.2013, Dortmund VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA), Düsseldorf }}, isbn = {{978-3-7315-0126-8}}, issn = {{1614-5267}}, pages = {{339--354}}, publisher = {{KIT Scientific Publishing}}, title = {{{Automated Fusion Attribute Generation for Conditioning Monitoring.}}}, doi = {{DOI: 10.5445/KSP/1000036887 }}, volume = {{46}}, year = {{2013}}, } @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{2113, abstract = {{In this paper, we sketch an idea for the integration of singleclass support vector machines (SVM) into fuzzy class learning. As result,we obtain robust and transparent rule-based fuzzy classification models suitable for online-classification tasks. In particular, the singleclass SVM is employed to extend the applicability of convex fuzzy classifica-tion models to nonconvex datainherent structures. The key point of thisextension is the preservation of the interpretability for both, the classlearning and the classification process. The feasibility of the approach isdemonstrated in the context of a banknote authentication application.}}, author = {{Hempel, Arne-Jens and Hähnel, Holger and Mönks, Uwe and Lohweg, Volker}}, booktitle = {{BVAu 2012 - 3. Jahresolloquium "Bildverarbeitung in der Automation" Centrum Industrial IT, Lemgo,}}, keywords = {{fuzzy classification, pattern recognition, single-class support vector machine, data mining}}, publisher = {{inIT-Institut für industrielle Informationstechnik}}, title = {{{SVM-integrated Fuzzy Pattern Classification for Nonconvex Data-Inherent Structures Applied to Banknote Authentication}}}, year = {{2012}}, } @inproceedings{2117, abstract = {{The present work aims at a statistically motivated parameterisation for a fuzzy classification approach. Its key points are the determination of robust parameterisations for the data-driven fuzzy class learning based an statistical analyses as well as the preservation of the interpretability of the fuzzy class models and the classification process. In particular, order statistics and Monte Carlo methods are used to determine distributions and moments of class border parameters. These distributions and moments are further applied to evaluate the robustness of parameters of the current fuzzy classification model and to propose alternative robust parameterisations. The feasibility of the approach is demonstrated in the context of a machine diagnosis application.}}, author = {{Hähnel, Holger and Hempel, Arne-Jens and Mönks, Uwe and Lohweg, Volker}}, booktitle = {{22. Workshop Computational Intelligence, 06.-7.12.2012, Dortmund }}, editor = {{Hüllermeier, Eike and Hoffmann, Frank}}, isbn = {{978-3-86644-917-6}}, keywords = {{Fuzzy-Regelung}}, pages = {{115--131}}, publisher = {{VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA)}}, title = {{{Integration of Statistical Analyses for Parametrisation of the Fuzzy Pattern Classification}}}, year = {{2012}}, } @inproceedings{2119, abstract = {{In this paper, it is proposed a feature selection procedure based on Linear Discriminant Analysis. The aim behind this approach is to obtain a minimal set of features still enabling a separation between a number of different classes. Additionally, the reduced number of features implies faster computation and enables resource-limited hardware implementations for real-time signal processing applications. Also, incorporating only a small number of features retains the application's interpretability as a feature space of maximum three features can be visualised directly. Due to this, an expert can directly follow a decision system's answer. The proposed method has been evaluated in the context of an electric drive diagnosis application. In this scope, the LDA feature selection approach is at least as good as the benchmarked feature selection methods. When regarding only a minimal number of features, LDA outperforms the other approaches in terms of classification accuracy. As a secondary result. one can see how important a sensible choice of features is. While some arbitrary combinations produce completely inseparable feature spaces. Three are still combinations that can separate the classes even linearly such that no sophisticated classification concept (e.g. SVM) is needed. The authors are aware of the fact that the findings are shown only in the context of one specific application. Based on the work elaborated here, further research towards generalisation of the proposed approach is intended to be carried out. Additionally, the findings shall be examined using classifier concepts different from SVM, such as Fuzzy Pattern Classifiers.}}, author = {{Bator, Martyna and Dicks, Alexander and Mönks, Uwe and Lohweg, Volker}}, booktitle = {{22. Workshop Computational Intelligence, 06.-7.12.2012, Dortmund}}, editor = {{Frank Hoffmann, Eike Hüllermeier}}, isbn = {{978-3-86644-917-6}}, pages = {{163--177}}, publisher = {{VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA)}}, title = {{{Feature Extraction and Reduction Applied to Sensorless Drive Diagnosis}}}, year = {{2012}}, } @inproceedings{2099, abstract = {{In order to reduce time consuming and expensive flawed production in Security Printing Machines an inspection system for early recognition of consecutive errors is developed. It shall avoid printing errors by combining measuring data from several sensors with expert knowledge. The inspection quality is improved by acquiring several information sources, using different physical quantities, integrating expert knowledge and perception, extracting reasonable features, and generating intuitive results. The TLCS (Two Layer Conflict Solving) approach is based on the Evidence Theory and uses conflict solving to fuse data. The first layer applies the Conflict Modified Dempster-Shafer-Theory (CMDST). Every two sensors‘ data are combined and conflicts are solved between individuals. In the second layer the data is fused using the results from the CMDST and the sensors’ original observations by the Group Conflict Redistribution (GCR). We introduce an improvement of the TLCS approach with reference to highly complex machine conditioning applications. In this context, the sensors are grouped to attributes applying expert knowledge. The fusion of the fuzzyfied sensor’s observations that are elements of one particular attribute is accomplished by the TLCS. Subsequently, the attributes’ conditions are merged using an Ordered Weighted Averaging Operator. In security printing machines the wiping unit is the most sensible part. It is responsible for removing surplus ink around the engravings. Even small parameter manipulations cause errors during the production. Experienced machine operators recognize errors before they occur and stabilize the production by changing wiping unit parameters mainly. The fusion approach is evaluated in a wiping simulator. Current, impact sound, temperature and force are acquired and processed. Wear, parameter changes, and mechanical disturbances are detected by the introduced algorithm.}}, author = {{Voth, Karl and Glock, Stefan and Mönks, Uwe and Türke, Thomas and Lohweg, Volker}}, booktitle = {{SENSOR+TEST Conference 2011,}}, isbn = {{978-3-9810993-9-3}}, pages = {{686--691}}, publisher = {{7 – 9 June 2011, Nürnberg, Germany }}, title = {{{Multi-sensory Machine Diagnosis on Security Printing Machines with Two Layer Conflict Solving}}}, doi = {{10.5162/sensor11/sp2.1}}, year = {{2011}}, } @inproceedings{2085, author = {{Lohweg, Volker and Gillich, Eugen and Glock, Stefan and Mönks, Uwe and Schaede, Johannes}}, booktitle = {{2. inIT KBA-Giori International Workshop on "Detection of Banknote Forgeries"}}, publisher = {{Orell Füssli, Zürich, 22-24 March 2010}}, title = {{{Intaglio Based Banknote Authentication}}}, year = {{2010}}, } @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}}, } @inproceedings{2087, abstract = {{It is likely in real-world applications that only little data isavailable for training a knowledge-based system. We present a method forautomatically training the knowledge-representing membership functionsof a Fuzzy-Pattern-Classification system that works also when only littledata is available and the universal set is described insufficiently. Actually,this paper presents how the Modified-Fuzzy-Pattern-Classifier’s member-ship functions are trained using probability distribution functions.}}, author = {{Mönks, Uwe and Lohweg, Volker and Petker, Denis}}, booktitle = {{IPMU 2010 - International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems}}, keywords = {{Fuzzy Logic, Probability Theory, Fuzzy-Pattern-Classification, Machine Learning, Artificial Intelligence, Pattern Recognition}}, publisher = {{28 Jun 2010 - 02 July 2010, Dortmund, Germany}}, title = {{{Fuzzy-Pattern-Classifier Training with Small Data Sets}}}, year = {{2010}}, } @inproceedings{2089, author = {{Lohweg, Volker and Mönks, Uwe}}, booktitle = {{Sensor Fusion ISBN 978-953-307-101-5 ed by Ciza Thomas}}, editor = {{Thomas (ed), Ciza}}, publisher = {{SCIYO}}, title = {{{Fuzzy-Pattern-Classifier based Sensor Fusion for Machine Conditioning}}}, year = {{2010}}, } @inproceedings{2092, author = {{Ehlenbröker, Jan-Friedrich and Mönks, Uwe and Lohweg, Volker}}, booktitle = {{1. Fachkolloquium "Bildverarbeitung in der Automation"}}, isbn = {{978-3-9814062-0-7}}, publisher = {{Centrum Industrial IT, Lemgo}}, title = {{{Surface Fingerprint Detection}}}, year = {{2010}}, } @inproceedings{2081, abstract = {{This paper presents a novel modular fuzzy patternclassifier design framework for intelligent automation systems, developed on the base of the established Modified Fuzzy PatternClassifier (MFPC) and that allows designing novel classifier mod-els which are hardware-efficiently implementable. The perfor-mances of novel classifiers using substitutes of MFPC’s geometricmean aggregator are benchmarked in the scope of an imageprocessing application against the MFPC to reveal classificationimprovement potentials for obtaining higher classification rates.}}, author = {{Mönks, Uwe and Lohweg, Volker and Larsen, Henrik Legind}}, booktitle = {{KI 2009 Workshop, Paderborn | September 15th, 2009, accepted for Publication}}, title = {{{Aggregation Operator Based Fuzzy Pattern Classifier Design, Machine Learning in Real-Time Applications (MLRTA 09)}}}, year = {{2009}}, }