@inproceedings{TschoepeWolffDuckhorn, author = {Tsch{\"o}pe, Constanze and Wolff, Matthias and Duckhorn, Frank}, title = {Zustands{\"u}berwachung von Magnetventilen anhand der Schaltger{\"a}usche}, series = {ZfP in Forschung, Entwicklung und Anwendung, Potsdam, 26. - 28. Mai 2014, DGZfP-Jahrestagung 2014}, booktitle = {ZfP in Forschung, Entwicklung und Anwendung, Potsdam, 26. - 28. Mai 2014, DGZfP-Jahrestagung 2014}, publisher = {DGZfP}, address = {Berlin}, isbn = {978-3-940283-61-0}, language = {de} } @incollection{WolffTschoepeRoemer, author = {Wolff, Matthias and Tsch{\"o}pe, Constanze and R{\"o}mer, Ronald}, title = {Quo vadis, UASR?}, series = {Systemtheorie Signalverarbeitung Sprachtechnologie}, booktitle = {Systemtheorie Signalverarbeitung Sprachtechnologie}, editor = {Mehnert, Dieter and Kordon, Ulrich and Wolff, Matthias}, publisher = {TUDpress}, address = {Dresden}, isbn = {978-3-944331-19-5}, pages = {276 -- 285}, language = {de} } @inproceedings{TschoepeWolff, author = {Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Processing and evaluation of gear data using statistical classifiers}, series = {Proceedings of the 6th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2012), Vienna, Sep. 2012}, booktitle = {Proceedings of the 6th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2012), Vienna, Sep. 2012}, address = {Vienna}, isbn = {978-395-02481-9-7}, language = {en} } @misc{KraljevskiDuckhornJuetal., author = {Kraljevski, Ivan and Duckhorn, Frank and Ju, Yong Chul and Tsch{\"o}pe, Constanze and Richter, Christian and Wolff, Matthias}, title = {Acoustic Resonance Recognition of Coins}, series = {2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 25-28 May 2020, Dubrovnik, Croatia}, journal = {2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 25-28 May 2020, Dubrovnik, Croatia}, publisher = {IEEE Xplore}, isbn = {978-1-7281-4460-3}, doi = {10.1109/I2MTC43012.2020.9129256}, pages = {6}, abstract = {In this study, we compare different machine learning approaches applied to acoustic resonance recognition of coins. Euro-cents and Euro-coins were classified by the sound emerging when throwing the coins onto a hard surface.The used dataset is a representative example of a small data which was collected in carefully prepared experiments.Due to the small number of coin specimens and the count of the collected observations, it was interesting to see whether deep learning methods can achieve similarly or maybe even better classification performances compared with more traditional methods.The results of the multi-class prediction of coin denominations are presented and compared in terms of balanced accuracy and Matthews Correlation Coefficient metrics. The feature analysis methods combined with the employed classifiers achieved acceptable results, despite the relatively small dataset.}, language = {en} } @misc{KraljevskiDuckhornJuetal., author = {Kraljevski, Ivan and Duckhorn, Frank and Ju, Yong Chul and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Acoustic Resonance Testing of Glass IV Bottles}, series = {Artificial Intelligence Applications and Innovations : 16th IFIP WG 12.5 International Conference, AIAI 2020, Neos Marmaras, Greece, June 5-7, 2020, Proceedings, Part II}, volume = {Cham}, journal = {Artificial Intelligence Applications and Innovations : 16th IFIP WG 12.5 International Conference, AIAI 2020, Neos Marmaras, Greece, June 5-7, 2020, Proceedings, Part II}, editor = {Maglogiannis, Ilias and Iliadis, Lazaros S. and Pimenidis, Elias}, publisher = {Springer International Publishing}, isbn = {978-3-030-49186-4}, issn = {1868-4238}, doi = {10.1007/978-3-030-49186-4_17}, pages = {195 -- 206}, abstract = {In this paper, acoustic resonance testing on glass intravenous (IV) bottles is presented. Different machine learning methods were applied to distinguish acoustic observations of bottles with defects from the intact ones. Due to the very limited amount of available specimens, the question arises whether the deep learning methods can achieve similar or even better detection performance compared with traditional methods.}, language = {en} } @inproceedings{TschoepeDuckhornHuberetal., author = {Tsch{\"o}pe, Constanze and Duckhorn, Frank and Huber, Markus and Meyer, Werner and Wolff, Matthias}, title = {A Cognitive User Interface for a Multi-Modal Human-Machine Interaction}, series = {Speech and computer : 20th International Conference, SPECOM 2018, Leipzig, Germany, September 18-22, 2018, proceedings}, booktitle = {Speech and computer : 20th International Conference, SPECOM 2018, Leipzig, Germany, September 18-22, 2018, proceedings}, editor = {Karpov, Alexey and Jokisch, Oliver and Potapova, Rodmonga}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-99578-6}, doi = {10.1007/978-3-319-99579-3}, pages = {707 -- 717}, language = {en} } @misc{DuckhornHuberMeyeretal., author = {Duckhorn, Frank and Huber, Markus and Meyer, Werner and Jokisch, Oliver and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Towards an Autarkic Embedded Cognitive User Interface}, series = {Proceedings Interspeech 2017, 20-24 August 2017, Stockholm}, journal = {Proceedings Interspeech 2017, 20-24 August 2017, Stockholm}, editor = {Lacerda, Francisco}, publisher = {ISCA}, doi = {10.21437/Interspeech.2017}, pages = {3435 -- 3436}, abstract = {ucuikt2015}, language = {en} } @inproceedings{TschoepeDuckhornRichteretal., author = {Tsch{\"o}pe, Constanze and Duckhorn, Frank and Richter, Christian and Bl{\"u}thgen, Peter and Wolff, Matthias}, title = {Intelligent Signal Processing on a Miniaturized Hardware Module}, series = {IEEE SENSORS Proceedings, Glasgow, Scotland, UK, Oct. 29 - Nov. 1, 2017}, booktitle = {IEEE SENSORS Proceedings, Glasgow, Scotland, UK, Oct. 29 - Nov. 1, 2017}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {978-1-5090-1012-7}, doi = {10.1109/ICSENS.2017.8234023}, language = {en} } @inproceedings{TschoepeWolff, author = {Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Mustererkennung in der technischen Diagnose}, series = {Tagungsband der 12. Tagung Technische Diagnostik 2016, 20.-21.10.2016, Hochschule Merseburg}, booktitle = {Tagungsband der 12. Tagung Technische Diagnostik 2016, 20.-21.10.2016, Hochschule Merseburg}, publisher = {Hochschule Merseburg}, address = {Merseburg}, isbn = {978-3-942703-64-2}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:3:2-68977}, pages = {124 -- 125}, language = {de} } @inproceedings{TschoepeDuckhornRichteretal., author = {Tsch{\"o}pe, Constanze and Duckhorn, Frank and Richter, Christian and Bl{\"u}thgen, Peter and Wolff, Matthias}, title = {An embedded system for acoustic pattern recognition}, series = {IEEE SENSORS Proceedings, Glasgow, Scotland, UK, Oct. 29 - Nov. 1, 2017}, booktitle = {IEEE SENSORS Proceedings, Glasgow, Scotland, UK, Oct. 29 - Nov. 1, 2017}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {978-1-5090-1012-7}, doi = {10.1109/ICSENS.2017.8234380}, pages = {3}, language = {en} } @inproceedings{TschoepeWolffSaeltzer, author = {Tsch{\"o}pe, Constanze and Wolff, Matthias and Saeltzer, G.}, title = {Estimating blood sugar from voice samples : a preliminary study}, series = {2015 International Conference on Computational Science and Computational Intelligence (CSCI 2015), Las Vegas, December 7-9, 2015}, booktitle = {2015 International Conference on Computational Science and Computational Intelligence (CSCI 2015), Las Vegas, December 7-9, 2015}, publisher = {IEEE}, doi = {10.1109/CSCI.2015.184}, pages = {804 -- 805}, language = {en} } @misc{TschoepeMuehleJuetal., author = {Tsch{\"o}pe, Constanze and M{\"u}hle, Maximilian and Ju, Yong Chul and Kraljevski, Ivan and Wolff, Matthias}, title = {K{\"u}nstliche Intelligenz in der ZfP - Welchen Beitrag kann KI in der ZfP leisten?}, series = {DGZfP-Jahrestagung 2021, 10.-11. Mai}, journal = {DGZfP-Jahrestagung 2021, 10.-11. Mai}, isbn = {978-3-947971-18-3}, pages = {17}, abstract = {K{\"u}nstliche Intelligenz zieht derzeit in alle Bereiche der Gesellschaft und des Lebens ein. Aber welchen Stellenwert hat sie momentan auf dem Gebiet der zerst{\"o}rungsfreien Pr{\"u}fung? Was kann KI leisten? Welche Herausforderungen m{\"u}ssen erfolgreich bew{\"a}ltigt werden? Gibt es das eine KI-Verfahren, welches prinzipiell f{\"u}r ZfP geeignet ist? Bei der Bauteil- und Materialpr{\"u}fung w{\"a}hrend und unmittelbar nach der Herstellung, der {\"U}berwachung von Verschleißteilen in Maschinen und Anlagen oder der Schadensdetektion an Bauteilen und Komponenten liefern ZfP-Verfahren Daten, die bewertet werden m{\"u}ssen. Obwohl inzwischen sehr leistungsf{\"a}hige Toolkits verf{\"u}gbar sind, erfordert der optimale Einsatz der KI f{\"u}r ein ZfP-Verfahren oftmals mehr. Die meisten Kunden m{\"o}chten nicht nur eine L{\"o}sung ihres Problems; sie wollen verstehen, warum die KI so und nicht anders entschieden hat, warum der Klassifikator das Bauteil einer bestimmten Klasse (z. B. gut/schlecht oder neuwertig/verschlissen/defekt) zugewiesen hat. Abh{\"a}ngig von der Klassifikationsaufgabe sowie der Art und der Anzahl der vorliegenden Daten kann ein geeignetes Verfahren bestimmt werden. Mit Methoden des maschinellen Lernens werden Modelle gebildet, welche die Basis f{\"u}r die KI-Verfahren zur Klassifikation bilden. Der Beitrag liefert einen {\"U}berblick {\"u}ber KI-Verfahren und deren Anwendungen in der zerst{\"o}rungsfreien Pr{\"u}fung. Zahlreiche Beispiele und Ergebnisse werden vorgestellt, um die Mannigfaltigkeit des Einsatzes in der ZfP und der bestehenden M{\"o}glichkeiten zu demonstrieren.}, language = {de} } @misc{KraljevskiRjelkaDuckhornetal., author = {Kraljevski, Ivan and Rjelka, Marek and Duckhorn, Frank and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Cross-Lingual Acoustic Modeling in Upper Sorbian - Preliminary Study}, series = {Elektronische Sprachsignalverarbeitung 2021 : Tagungsband der 32. Konferenz Berlin, 3.-5. M{\"a}rz 2021}, journal = {Elektronische Sprachsignalverarbeitung 2021 : Tagungsband der 32. Konferenz Berlin, 3.-5. M{\"a}rz 2021}, editor = {Hillmann, Stefan and Weiss, Benjamin and Michael, Thilo and M{\"o}ller, Sebastian}, publisher = {TUDpress}, address = {Dresden}, isbn = {978-3-959082-27-3}, issn = {0940-6832}, pages = {43 -- 50}, abstract = {In this paper, we present a preliminary study for acoustic modeling in Upper Sorbian, where a model of German was used in cross-lingual transfer learning. At first, we define the grapheme and phoneme inventories and map the target phonemes from the most similar German source equivalents. Phonetically balanced sentences for the recording prompts were selected from a combination of general and domain-specific textual data. The speech corpora with a total duration of around 11 hours was collected in controlled recording sessions involving an equal number of females, males, and children. The baseline acoustic model was employed to force-align the speech corpora given the knowledge-based phoneme mappings. How well the mappings were, was evaluated by the phoneme confusions in free-phoneme recognition. The new derived data-driven model with a reduced phoneme set was included in the adaptation and evaluation along with the baseline acoustic model. The model adaptation performance was cross-validated with the "Leave One Group Out" strategy. We observed major improvements in phoneme error rates after adaptation for the knowledge-based and data-driven phoneme mappings. The study confirmed the feasibility of transfer learning for acoustic model adaptation in the case of Upper Sorbian, at the same time demonstrating practical usability with a small vocabulary speech recognition application (Smart Lamp).}, language = {de} } @misc{KraljevskiDuckhornTschoepeetal., author = {Kraljevski, Ivan and Duckhorn, Frank and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Machine Learning for Anomaly Assessment in Sensor Networks for NDT in Aerospace}, series = {IEEE Sensors Journal}, volume = {21}, journal = {IEEE Sensors Journal}, number = {9}, issn = {1558-1748}, doi = {10.1109/JSEN.2021.3062941}, pages = {11000 -- 11008}, abstract = {We investigated and compared various algorithms in machine learning for anomaly assessment with different feature analyses on ultrasonic signals recorded by sensor networks. The following methods were used and compared in anomaly detection modeling: hidden Markov models (HMM), support vector machines (SVM), isolation forest (IF), and reconstruction autoencoders (AEC). They were trained exclusively on sensor signals of the intact state of structures commonly used in various industries, like aerospace and automotive. The signals obtained on artificially introduced damage states were used for performance evaluation. Anomaly assessment was evaluated and compared using various classifiers and feature analysis methods. We introduced novel methodologies for two processes. The first was the dataset preparation with anomalies. The second was the detection and damage severity assessment utilizing the intact object state exclusively. The experiments proved that robust anomaly detection is practically feasible. We were able to train accurate classifiers which had a considerable safety margin. Precise quantitative analysis of damage severity will also be possible when calibration data become available during exploitation or by using expert knowledge.}, language = {en} }