@misc{7976, author = {{Gassenmeier, Veronika and Deppe, Sahar and Hernández Rodriguez, Tanja and Kuhfuß, Fabian and Moser, André and Hass, Volker C. and Kuchemüller, Kim B. and Pörtner, Ralf and Möller, Johannes and Ifrim, George Adrian and Frahm, Björn}}, booktitle = {{Current Research in Biotechnology}}, issn = {{2590-2628 }}, pages = {{102--119}}, publisher = {{Elsevier}}, title = {{{Model-assisted DoE applied to microalgae processes, Current Research in Biotechnology}}}, doi = {{10.1016/j.crbiot.2022.01.005}}, volume = {{4}}, year = {{2022}}, } @misc{7977, abstract = {{Kinetic growth models are a useful tool for a better understanding of microalgal cultivation and for optimizing cultivation conditions. The evaluation of such models requires experimental data that is laborious to generate in bioreactor settings. The experimental shake flask setting used in this study allows to run 12 experiments at the same time, with 6 individual light intensities and light durations. This way, 54 biomass data sets were generated for the cultivation of the microalgae Chlorella vulgaris. To identify the model parameters, a stepwise parameter estimation procedure was applied. First, light-associated model parameters were estimated using additional measurements of local light intensities at differ heights within medium at different biomass concentrations. Next, substrate related model parameters were estimated, using experiments for which biomass and nitrate data were provided. Afterwards, growth-related model parameters were estimated by application of an extensive cross validation procedure.}}, author = {{Kuhfuß, Fabian and Gassenmeier, Veronika and Deppe, Sahar and Ifrim, George Adrian and Hernández Rodriguez, Tanja and Frahm, Björn}}, booktitle = {{Bioprocess and Biosystems Engineering}}, issn = {{1615-7605}}, pages = {{15--30}}, publisher = {{Springer}}, title = {{{View on a mechanistic model of Chlorella vulgaris in incubated shake flasks}}}, doi = {{10.1007/s00449-021-02627-2}}, volume = {{45}}, year = {{2022}}, } @inproceedings{1994, abstract = {{In the filling and packaging industry, the trend is towards self-diagnosis, optimization, and quality monitoring of processes. The aim is to increase production volumes and the quality. These concepts require continuous monitoring and anomaly detection of the filling process. In addition, a root cause analysis of the failure is required because not every failure can be simulated or measured previously. Standard anomaly detection methods have no integrated root cause analysis. In this paper a fusion system is utilises for the detection of different unknown anomalies and also the failure source of them. The performance of this method is benchmarked with a real-word filling process.}}, author = {{Bator, Martyna and Dicks, Alexander and Deppe, Sahar and Lohweg, Volker}}, booktitle = {{24nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA2019) }}, isbn = {{978-1-7281-0304-4}}, issn = {{1946-0759}}, location = {{ Zaragoza, Spain }}, publisher = {{IEEE}}, title = {{{Anomaly Detection with Root Cause Analysis for Bottling Process}}}, doi = {{10.1109/ETFA.2019.8869514}}, year = {{2019}}, } @article{2010, author = {{Wissel, Christian and Deppe, Sahar and Lohweg, Volker}}, issn = {{0935-0187 }}, journal = {{SPS-Magazin Ausgabe 1+2/2018, TeDo Verlag GmbH, Marburg}}, number = {{1/2 }}, publisher = {{TeDo-Verl}}, title = {{{3D-Inspektion bei 600m/s - Feinste 3D-Strukturen inline mit hohem Tempo prüfen}}}, year = {{2018}}, } @article{2012, abstract = {{The rapid growth of optical imaging technologies increased the access and collection of data, which boosts the demand of data and knowledge discovery. This is a fast growing topic in several industry and research areas. Nowadays, a large number of images and signals must be analysed in order to gain and learn proper knowledge. Detecting images with similar contents without specifying an image, recently attracts the researches in image processing domain. Motif discovery in image processing aims to tackle the problem of deriving structures or detecting regularities in image databases. Most of the motif discovery methods solve this problem by converting images into one dimensional time series in a pre-processing step and then applying a motif discovery on these one dimensional time series for image motifs detection. Nevertheless, this conversion might lead to information loss and also the problem of inability to discover shifted and multi-scale image motifs of different size. Contrary to other approaches, here, a method is proposed to find image motifs of different size in image data sets by employing images in original dimension (2D) without converting them to one dimensional time series. The proposed approach consists of three steps: Mapping or transformation, feature extraction and measuring similarities. First, images are inspected by the Complex Quad Tree Wavelet Packet transform, which provides broad frequency analysis of an image in various scales. Next, statistical features are extracted from the wavelet coefficients. Finally, image motifs are detected by measuring the similarity of the features applying various similarity measures. Here, the performance of six similarity measures are benchmarked in details. Moreover, the efficiency of the proposed method is demonstrated on a data set with images from diverse applications such as hand gesture, text recognition, leaf and plant identification, etc. Additionally, the robustness of this method is examined with the image data overlaying with distortions such as noise and blur.}}, author = {{Deppe, Sahar and Lohweg, Volker}}, issn = {{1942-2679}}, journal = {{International Journal On Advances in Intelligent Systems}}, keywords = {{processing, Wavelet transformation.}}, number = {{3/4}}, pages = {{434 -- 446}}, publisher = {{IARIA Journals}}, title = {{{Evaluation of Similarity Measures for Shift-Invariant Image Motif Discovery}}}, volume = {{10}}, year = {{2017}}, } @inproceedings{2022, abstract = {{Nowadays, the boost of optical imaging technologies results in more data with a faster rate are being collected. Consequently, data and knowledge discovery science has become an attractive and a fast growing topic in several industry and research area. Motif discovery in image processing aims to tackle the problem of deriving structures or detecting regularities in image databases. Most of the motif discovery methods first convert images into time series and then attempt to find motifs in such data. This might lead to information loss and also the problem of inability to detect shifted and multi-scale image motifs of different size. Here, a method is proposed to find image motifs of different size in image datasets by applying images in original dimension without converting them to time series. Images are inspected by the Complex Quad Tree Wavelet Packet transform which provides broad frequency analysis of an image in various scales. Next, features are extracted from the wavelet coefficients. Finally, image motifs are detected by measuring the similarity of the features. The performance of the proposed method is demonstrated on a dataset with images from diverse applications, such as hand gesture, text recognition, leaf and plant identification, etc. }}, author = {{Deppe, Sahar and Lohweg, Volker}}, booktitle = {{PESARO 2017 The Seventh International Conference on Performance, Safety and Robustness in Complex Systems and Applications}}, editor = {{Leister, Wolfgang}}, issn = {{2308-3700}}, keywords = {{Motif discovery, Image processing, Wavelet transformation}}, location = {{Venice, Italy }}, pages = {{27--32}}, publisher = {{The Seventh International Conference on Performance, Safety and Robustness in Complex Systems and Applications; Special track MAIS: Machine Learning Algorithms in Image and Signal Processing}}, title = {{{Shift-Invariant Motif Discovery in Image Processing 'Best Paper Award'}}}, year = {{2017}}, } @article{2023, abstract = {{Last decades witness a huge growth in medical applications, genetic analysis,and in performance of manufacturing technologies and automatised productionsystems. A challenging task is to identify and diagnose the behavior of suchsystems, which aim to produce a product with desired quality. In order to con-trol the state of the systems, various information is gathered from differenttypes of sensors (optical, acoustic, chemical, electric, and thermal). Time seriesdata are a set of real-valued variables obtained chronologically. Data miningand machine learning help derive meaningful knowledge from time series.Such tasks include clustering, classification, anomaly detection andmotif discov-ery. Motif discovery attempts tofind meaningful, new, and unknown knowledgefrom data. Detection of motifs in a time series is beneficial for, e.g., discovery ofrules or specific events in a signal. Motifs provide useful information for theuser in order to model or analyze the data. Motif discovery is applied to variousareas as telecommunication, medicine, web, motion-capture, and sensornetworks. This contribution provides a review of the existing publications intime series motif discovery along with advantages and disadvantages of existingapproaches. Moreover, the research issues and missing points in thisfield arehighlighted. The main objective of this focus article is to serve as a glossary forresearchers in thisfield.}}, author = {{Deppe, Sahar and Lohweg, Volker}}, issn = {{2573-9468 }}, journal = {{ WIREs : Forensic science}}, number = {{2}}, publisher = {{Wiley-Blackwell }}, title = {{{Survey on Time Series Motif Discovery}}}, doi = {{ https://doi.org/10.1002/widm.1199}}, volume = {{7}}, year = {{2017}}, } @inproceedings{2033, abstract = {{Cash machines or automated teller machines (ATMs) are one of the typical ways to get cash around the world. Such machines are under a variety of criminal attacks. Most of the manipulations are performed through skimming. In 2014, such attacks led to a damage of approx. 280 million Euro within the EU. In this paper, we propose an approach to detect anomalies and attacks on ATMs via motif discovery. Motifs are frequently unknown occurring sequences or events in a time series signal. State of the ATM is captured by innovative piezoelectric sensor networks to analyse the occurring vibrations. The captured signals are inspected by the Complex Quad-Tree Wavelet Packet transform which provides broad frequency analysis of a signal in various scales. Next, features are extracted from the selected scale based on the information content, to detect motifs. Detected motifs provide the prototype patterns for anomaly detection or classification tasks.}}, author = {{Deppe, Sahar and Dicks, Alexander and Lohweg, Volker}}, booktitle = {{21th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2016), Berlin, }}, title = {{{Anomaly Detection on ATMs via Time Series Motif Discovery}}}, year = {{2016}}, } @inproceedings{2123, abstract = {{The means of data mining and machine learning tasks are important topics in signal processing fundamentals. An example of such tasks is motif discovery. This paper presents an efficient method for shift-invariant feature extraction in time-series motif discovery. The proposed method initiates from the machine learning procedure and tackles the drawbacks of existing methods. Moreover, the efficacy of the novel approach is benchmarked against various algorithms and data from diverse fields. }}, author = {{Deppe, Sahar and Lohweg, Volker}}, booktitle = {{25. Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA)}}, pages = {{23--45}}, title = {{{Shift-Invariant Feature Extraction for Time-Series Motif Discovery}}}, doi = {{10.5445/KSP/1000049620}}, year = {{2015}}, } @inproceedings{2124, abstract = {{Patente schützen das geistige Eigentum von Erfindern und verhindern, dass ihre neuen Ideen kopiert werden. Sie sind von großer Bedeutung für den wirtschaftlichen Erfolg eines Unternehmens. Vor einer geplanten Patentanmeldung ist es wichtig festzustellen, ob eine bestimmte Technik bereits patentiert ist und wie die Erfolgsaussichten beurteilt werden können. Aber auch die Identifizierung von Verstößen gegen eigene Patentanmeldungen ist für ein Unternehmen von äußerster Wichtigkeit. Verschiedene Techniken und Tools sind entwickelt worden, um Patentanalyse-Experten, Managern und Technologieämtern bei den unterschiedlichsten Anforderungen im Bezug auf eine Patentrecherche zu unterstützen.}}, author = {{Bator, Martyna and Deppe, Sahar and Lohweg, Volker}}, booktitle = {{25. Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA)}}, title = {{{Relevanzbewertung technischer Informationen mittels Data-Mining Verfahren am Anwendungsfall von Patentdokumenten}}}, year = {{2015}}, } @inproceedings{2125, author = {{Deppe, Sahar and Dörksen, Helene and Lohweg, Volker}}, booktitle = {{Workshop on Probabilistic Graphical Models}}, title = {{{Multi-Scale Motif Discovery in Image Processing}}}, year = {{2015}}, } @inproceedings{2165, author = {{Deppe, Sahar and Lohweg, Volker}}, booktitle = {{24. Workshop Computational Intelligence}}, isbn = {{978-3-7315-0275-3}}, pages = {{277--298}}, publisher = {{VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA)}}, title = {{{Identification of Multi-Scale Motifs}}}, year = {{2014}}, }