TY - CHAP A1 - Hoffmann, Rüdiger A1 - Wolff, Matthias T1 - Towards hierarchical cognitive systems for intelligent signal processing T2 - ICT Innovations 2012, Secure and Intelligent Systems, Ohrid, Macedonia, Sep. 2012, WEB proceedings Y1 - 2012 SN - 1857-7288 SP - 613 EP - 618 PB - ICT ACT ER - TY - GEN A1 - Ju, Yong Chul A1 - Kraljevski, Ivan A1 - Neunübel, Heiko A1 - Tschöpe, Constanze A1 - Wolff, Matthias T1 - Acoustic Resonance Testing of Small Data on Sintered Cogwheels T2 - Sensors N2 - Based on the fact that cogwheels are indispensable parts in manufacturing, we present the acoustic resonance testing (ART) of small data on sintered cogwheels for quality control in the context of non-destructive testing (NDT). Considering the lack of extensive studies on cogwheel data by means of ART in combination with machine learning (ML), we utilize time-frequency domain feature analysis and apply ML algorithms to the obtained feature sets in order to detect damaged samples in two ways: one-class and binary classification. In each case, despite small data, our approach delivers robust performance: All damaged test samples reflecting real-world scenarios are recognized in two one-class classifiers (also called detectors), and one intact test sample is misclassified in binary ones. This shows the usefulness of ML and time-frequency domain feature analysis in ART on a sintered cogwheel dataset. KW - acoustic resonance testing (ART) non-destructive testing (NDT) KW - deep learning KW - machine learning KW - small-data KW - non-destructive testing (NDT) Y1 - 2022 U6 - https://doi.org/10.3390/s22155814 SN - 1424-8220 VL - 22 IS - 15 ER - TY - CHAP A1 - Fellbaum, Klaus A1 - Hoffmann, Rüdiger ED - Hoffmann, Rüdiger T1 - 20 Jahre Sprachsignalverarbeitung im Spiegel einer Konferenz T2 - Elektronische Sprachsignalverarbeitung 2009, Tagungsband der 20. Konferenz, Dresden, 21. bis 23. September 2009, Band 1 Y1 - 2009 SN - 978-3-941298-31-6 SP - 248 EP - 253 PB - TUDpress CY - Dresden ER - TY - CHAP A1 - Fellbaum, Klaus T1 - Übersichtsvortrag: Einsatz der Sprachtechnologie für behinderte und ältere Menschen Y1 - 2008 ER - TY - VIDEO A1 - Maier, Isidor Konrad T1 - A Decomposition Algorithm for Numerals based on Arithmetics N2 - Presentation of a Numeral Decomposition Algorithm. KW - Numeral Decomposition, Machine Learning, Reinforcement Learning Y1 - 2022 UR - https://zenodo.org/record/7501280 U6 - https://doi.org/10.5281/zenodo.7501280 ER - TY - GEN A1 - Maier, Isidor Konrad A1 - Kuhn, Johannes A1 - Duckhorn, Frank A1 - Kraljevski, Ivan A1 - Sobe, Daniel A1 - Wolff, Matthias A1 - Tschöpe, Constanze T1 - Word Class Based Language Modeling: A Case of Upper Sorbian, LREC 2022, Marseille T2 - Language Resources and Evaluation Conference 2022 (LREC 2022), Marseille, 2022-06-13 Y1 - 2022 U6 - https://doi.org/10.5281/zenodo.7501145 ER - TY - GEN A1 - Kraljevski, Ivan A1 - Duckhorn, Frank A1 - Tschöpe, Constanze A1 - Wolff, Matthias T1 - Machine Learning for Anomaly Assessment in Sensor Networks for NDT in Aerospace T2 - IEEE Sensors Journal N2 - 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. KW - Machine learning KW - Non-destructive testing KW - Ultrasonic transducers Y1 - 2021 UR - https://ieeexplore.ieee.org/document/9366491 U6 - https://doi.org/10.1109/JSEN.2021.3062941 SN - 1558-1748 VL - 21 IS - 9 SP - 11000 EP - 11008 ER - TY - CHAP A1 - Hampicke, Maik T1 - Zum Einsatz und Nutzen elektronischer Sprachkomponenten im Wohnbereich für die Zielgruppe der älteren Menschen Y1 - 2004 ER - TY - CHAP A1 - Kraljevski, Ivan A1 - Strecha, Guntram A1 - Wolff, Matthias A1 - Jokisch, Oliver A1 - Hoffmann, Rüdiger T1 - Using unified automatic speech recognition and synthesis system for cross-language acoustic modeling T2 - Proceedings Digitalna Obrada Govora I Slike (DOGS 2012) Y1 - 2012 SN - 978-86-7892-439-2 SP - 4 EP - 11 CY - Kovacica, Serbia ER - TY - CHAP A1 - Tschöpe, Constanze A1 - Wolff, Matthias T1 - Processing and evaluation of gear data using statistical classifiers T2 - Proceedings of the 6th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2012), Vienna, Sep. 2012 Y1 - 2012 SN - 978-395-02481-9-7 CY - Vienna ER - TY - CHAP A1 - Duckhorn, Frank A1 - Strecha, Guntram A1 - Wolff, Matthias A1 - Hoffmann, Rüdiger T1 - Entwicklung und Performance eines Sprachdialogsystems mit begrenzten Hardwareressourcen Y1 - 2010 ER - TY - RPRT A1 - Tschöpe, Constanze A1 - Duckhorn, Frank A1 - Wolff, Matthias T1 - Akustische Mustererkennung: Qualitätskontrolle - Vorausschauende Instandhaltung - Zustandsüberwachung Y1 - 2017 UR - https://www.ikts.fraunhofer.de/content/dam/ikts/forschung/energiesysteme_bio_medizintechnik/bio_nanotechnologie/akustische_diagnostik/IKTS_White_Paper_Akustische_Mustererkennung.pdf PB - Fraunhofer-IKTS CY - Dresden ER - TY - GEN A1 - Huber-Liebl, Markus A1 - Römer, Ronald A1 - Wirsching, Günther A1 - Schmitt, Ingo A1 - beim Graben, Peter A1 - Wolff, Matthias T1 - Quantum-inspired Cognitive Agents T2 - Frontiers in Applied Mathematics and Statistics N2 - The concept of intelligent agents is—roughly speaking—based on an architecture and a set of behavioral programs that primarily serve to solve problems autonomously. Increasing the degree of autonomy and improving cognitive performance, which can be assessed using cognitive and behavioral tests, are two important research trends. The degree of autonomy can be increased using higher-level psychological modules with which needs and motives are taken into account. In our approach we integrate these modules in architecture for an embodied, enactive multi-agent system, such that distributed problem solutions can be achieved. Furthermore, after uncovering some weaknesses in the cognitive performance of traditionally designed agents, we focus on two major aspects. On the one hand, the knowledge processing of cognitive agents is based on logical formalisms, which have deficiencies in the representation and processing of incomplete or uncertain knowledge. On the other hand, in order to fully understand the performance of cognitive agents, explanations at the symbolic and subsymbolic levels are required. Both aspects can be addressed by quantum-inspired cognitive agents. To investigate this approach, we consider two tasks in the sphere of Shannon's famous mouse-maze problem: namely classifying target objects and ontology inference. First, the classification of an unknown target object in the mouse-maze, such as cheese, water, and bacon, is based on sensory data that measure characteristics such as odor, color, shape, or nature. For an intelligent agent, we need a classifier with good prediction accuracy and explanatory power on a symbolic level. Boolean logic classifiers do work on a symbolic level but are not adequate for dealing with continuous data. Therefore, we demonstrate and evaluate a quantum-logic-inspired classifier in comparison to Boolean-logic-based classifiers. Second, ontology inference is iteratively achieved by a quantum-inspired agent through maze exploration. This requires the agent to be able to manipulate its own state by performing actions and by collecting sensory data during perception. We suggest an algebraic approach where both kinds of behaviors are uniquely described by quantum operators. The agent's state space is then iteratively constructed by carrying out unitary action operators, while Hermitian perception operators act as observables on quantum eigenstates. As a result, an ontology emerges as the simultaneous solution of the respective eigenvalue equations. Tags: btuktqiai; btuktcogsys; btukttheseus; btuktqtheseus Y1 - 2022 UR - https://www.frontiersin.org/articles/10.3389/fams.2022.909873 U6 - https://doi.org/10.3389/fams.2022.909873 SN - 2297-4687 IS - 8 SP - 1 EP - 31 ER - TY - GEN A1 - Kraljevski, Ivan A1 - Ju, Yong Chul A1 - Ivanov, Dmitrij A1 - Tschöpe, Constanze A1 - Wolff, Matthias T1 - How to Do Machine Learning with Small Data? - A Review from an Industrial Perspective T2 - arXiv N2 - Artificial intelligence experienced a technological breakthrough in science, industry, and everyday life in the recent few decades. The advancements can be credited to the ever-increasing availability and miniaturization of computational resources that resulted in exponential data growth. However, because of the insufficient amount of data in some cases, employing machine learning in solving complex tasks is not straightforward or even possible. As a result, machine learning with small data experiences rising importance in data science and application in several fields. The authors focus on interpreting the general term of "small data" and their engineering and industrial application role. They give a brief overview of the most important industrial applications of machine learning and small data. Small data is defined in terms of various characteristics compared to big data, and a machine learning formalism was introduced. Five critical challenges of machine learning with small data in industrial applications are presented: unlabeled data, imbalanced data, missing data, insufficient data, and rare events. Based on those definitions, an overview of the considerations in domain representation and data acquisition is given along with a taxonomy of machine learning approaches in the context of small data. Y1 - 2023 UR - https://arxiv.org/abs/2311.07126 ER - TY - THES A1 - Lindemann, Jens T1 - Interpretation und Artikulation mit Äußerungs-Bedeutungs-Transduktoren N2 - In der vorliegenden Arbeit wird die Verwendung von endlichen Transduktoren zur bidirektionalen Übersetzung zwischen der syntaktischen und semantischen Ebene eines natürlichsprachlichen Dialogsystems untersucht. Zur Bedeutungsrepräsentation werden Merkmal-Werte-Relationen verwendet. Solche beschrifteten partiellen Ordnungen ermöglichen eine hierarchische Strukturierung von semantischen Begriffen und können durch gerichtete azyklische Graphen dargestellt werden. Auf Grundlage eines bedeutungsorientierten Sprachmodells wird eine mögliche Vorgehensweise zur Modellierung von Äußerungs-Bedeutungs-Transduktoren vorgestellt. Voraussetzung dafür ist eine Sprachdatensammlung, welche zum Beispiel mit Hilfe von Wizard-of-Oz Experimenten erstellt werden kann. Für die eigenen Untersuchungen wurden Transduktoren zu Sprachäußerungen der deutschen Lautsprache und der Deutschen Gebärdensprache konstruiert. Diese semantischen Grammatiken werden sowohl für die Interpretation als auch für die Artikulation eingesetzt. Auf der Perzeptionsseite ist somit die automatische Generierung von semantischen Zeichenketten zu einem gegebenen Worthypothesegraphen realisierbar. Durch die Klammerstruktur der semantischen Zeichenketten kann die dazugehörige Merkmal-Werte-Relation erzeugt werden. Diese Bedeutungsrepräsentation wird in der Beispielanwendung unter Verwendung des inversen Äußerungs-Bedeutungs-Transduktors direkt wieder artikuliert. Zuvor ist jedoch eine Linearisierung der Merkmal-Werte-Relation notwendig. Als Ergebnis dieser beiden Übersetzungsprozesse erhält man eine sinngemäße Wiedergabe zu der gegebenen Sprachzeichenfolge. Durch die Verwendung sprachabhängiger semantischer Grammatiken ist zudem auch eine bedeutungsorientierte Übersetzung des erkannten sprachlichen Ausdrucks realisierbar. Zusätzlich wurde auch ein aktuelles Verfahren zur semantischen Dekodierung mit endlichen Transduktoren umgesetzt, welches ein konzeptuelles stochastisches Sprachmodell nutzt. Damit ist eine vergleichende Bewertung dieser beiden Vorgehensweisen zur semantischen Interpretation möglich. Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus4-38910 UR - https://opus4.kobv.de/opus4-btu/frontdoor/index/index/docId/3891 N1 - Veröffentlichung erfolgte nur im Digitalen Repositorium ER - TY - GEN A1 - Maier, Isidor Konrad A1 - Wolff, Matthias T1 - A Decomposition Algorithm for Numerals based on Arithmetics N2 - Poster presentation for an idea to decompose numerals Y1 - 2022 U6 - https://doi.org/10.5281/zenodo.7501698 ER - TY - CHAP A1 - Ketzmerick, Bettina ED - Hess, Wolfgang ED - Stöber, Karlheinz T1 - Stimmklang und Sprechtempo als charakteristische Merkmale von Stimmen T2 - Elektronische Sprachsignalverarbeitung, Tagungsband der zwölften Konferenz, Bonn, 24. bis 26. September 2001 Y1 - 2001 SN - 3-935712-62-6 SP - 306 EP - 314 PB - w.e.b. CY - Dresden ER - TY - CHAP A1 - Pritsch, Marcus ED - Hess, Wolfgang ED - Stöber, Karlheinz T1 - Visuelle Sprechhilfen für Hörbehinderte T2 - Elektronische Sprachsignalverarbeitung, Tagungsband der zwölften Konferenz, Bonn, 24. bis 26. September 2001 Y1 - 2001 SN - 3-935712-62-6 SP - 131 EP - 135 PB - w.e.b. CY - Dresden ER - TY - CHAP A1 - Pritsch, Marcus T1 - Visuelle Kommunikationshilfen für Gehörlose Y1 - 2001 ER - TY - GEN A1 - Fellbaum, Klaus A1 - Hampicke, Maik A1 - Ketzmerick, Bettina A1 - Pritsch, Marcus T1 - Elektronische Sprachverarbeitung - Technik und Anwendungen T2 - Forum der Forschung Y1 - 2001 SN - 0947-6989 VL - 13 IS - 6 SP - 61 EP - 70 ER -