@inproceedings{2098,
  author       = {{Glock, Stefan and Voth, Karl and Schaede, Johannes and Lohweg, Volker}},
  title        = {{{A Framework for Possibilistic Multi-source Data Fusion with Monitoring of Sensor Reliability, World Conference on Soft Computing}}},
  year         = {{2011}},
}

@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{2101,
  author       = {{Lohweg, Volker and Glock, Stefan and Voth, Karl}},
  publisher    = {{Intech Publishers}},
  title        = {{{A Possibilistic Framework for Sensor Fusion with Monitoring of Sensor Reliability, Sensor Fusion – Foundation and Applications}}},
  doi          = {{10.5772/17384}},
  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}},
}

@inbook{2076,
  abstract     = {{Segmentation and feature extraction algorithms based on Wavelet Transform or Wavelet Packet Transform are established in pattern recognition. Especially in the field of texture analysis they are known to be practical. One difficulty of texture analysis was in the past the characterization of different printing processes. In this paper we present a new algorithmic concept to feature extraction of textures, printed by different printing techniques, without the necessity of a previous teaching phase. The typical characters of distinct printed textures are extracted by first order statistical moments of wavelet coefficients. The algorithm uses the 2D incomplete shift invariant Wavelet Packet Transform, resulting in a fast execution time of O(<i>N</i>log<sub>2</sub>(<i>N</i>)). Since the incomplete shift invariant Wavelet Packet Transform was exclusively defined for 1D-signals, it has been modified in this research. The application describes the detection of different printed security textures. }},
  author       = {{Glock, Stefan and Gillich, Eugen and Schaede, Johannes and Lohweg, Volker}},
  booktitle    = {{Pattern Recognition}},
  editor       = {{Denzler, J. and Notni, G. and Süße, H.}},
  isbn         = {{978-3-642-03797-9}},
  keywords     = {{Discrete Wavelet Transform, Wavelet Transform, Wavelet Packet, Decomposition Level, Printing Technique}},
  pages        = {{422--431}},
  publisher    = {{Springer}},
  title        = {{{Feature Extraction Algorithm for Banknote Textures based on Incomplete Shift Invariant Wavelet Packet Transform}}},
  doi          = {{https://doi.org/10.1007/978-3-642-03798-6_43}},
  volume       = {{5748}},
  year         = {{2009}},
}

@inproceedings{2077,
  abstract     = {{In this paper we present an optical measurement system approach for quality analysis of brakes which are used in high-speed trains. The brakes consist of the so called brake discs and pads. In a deceleration process the discs will be heated up to 500°C. The quality measure is based on the fact that the heated brake discs should not generate hot spots inside the brake material. Instead, the brake disc should be heated homogeneously by the deceleration. Therefore, it makes sense to analyze the number of hot spots and their relative gradients to create a quality measure for train brakes. In this contribution we present a new approach for a quality measurement system which is based on an image analysis and classification of infra-red based heat images. Brake images which are represented in pseudo-color are first transformed in a linear grayscale space by a hue-saturation-intensity (HSI) space. This transform is necessary for the following gradient analysis which is based on gray scale gradient filters. Furthermore, different features based on Haralick's measures are generated from the gray scale and gradient images. A following Fuzzy-Pattern-Classifier is used for the classification of good and bad brakes. It has to be pointed out that the classifier returns a score value for each brake which is between 0 and 100% good quality. This fact guarantees that not only good and bad bakes can be distinguished, but also their quality can be labeled. The results show that all critical thermal patterns of train brakes can be sensed and verified.}},
  author       = {{Glock, Stefan and Hausmann, Stefan and Gerke, Sebastian and Warok, Alexander and Spiess, Peter and Witte, Stefan and Lohweg, Volker}},
  booktitle    = {{Optical measurement systems for industrial inspection VI : 15 - 18 June 2009, Munich, Germany / sponsored by SPIE Europe. Peter H. Lehmann, ed. ; Pt. 2 }},
  isbn         = {{ 9780819476722 }},
  location     = {{München}},
  publisher    = {{SPIE }},
  title        = {{{Optical classification for quality and defect analysis of train brakes}}},
  doi          = {{https://doi.org/10.1117/12.827457}},
  volume       = {{7389}},
  year         = {{2009}},
}