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Institute
- Fakultät Architektur und Gestaltung (26)
- Fakultät Bauingenieurwesen (30)
- Fakultät Elektrotechnik und Informationstechnik (15)
- Fakultät Informatik (54)
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- Institut für Angewandte Forschung - IAF (45)
- Institut für Naturwissenschaften und Mathematik - INM (3)
- Institut für Optische Systeme - IOS (21)
- Institut für Strategische Innovation und Technologiemanagement - IST (48)
Die energetische Sanierung von Gebäuden ist von großer Relevanz, um die gesetzlichen Klimaziele zu erreichen. Die Methode des seriellen Sanierens spielt hierbei eine wichtige Rolle. Sie gilt als ganzheitliche Maßnahme zur energetischen Aufwertung von Bestandsgebäuden, durch die nicht nur die Gebäudehülle und die Anlagentechnik, wie etwa das Heizungssystem, effektiv verbessert werden, sondern auch eine Integration von Anlagen zur Strom- und Warmwasseraufbereitung erfolgt. Bei der seriellen Sanierung wird, in Anlehnung an die Industrie und an die modulare Bauweise, eine Vorfertigung der Fassaden-
und Dachelemente durchgeführt. Im Nachgang werden die einzelnen Bauelemente und Anlagen montiert bzw. installiert. Durch die Auslagerung der Produktion und durch die Vorfertigung der Elemente besteht das Potenzial, die Montagezeit und die damit verbundenen Einschränkungen vor Ort für die Bewohner deutlich zu reduzieren.
Apnea is a sleep disorder characterized by breathing interruptions during sleep, impacting cardiorespiratory function and overall health. Traditional diagnostic methods, like polysomnography (PSG), are unobtrusive, leading to noninvasive monitoring. This study aims to develop and validate a novel sleep monitoring system using noninvasive sensor technology to estimate cardiorespiratory parameters and detect sleep apnea. We designed a seamless monitoring system integrating noncontact force-sensitive resistor sensors to collect ballistocardiogram signals associated with cardiorespiratory activity. We enhanced the sensor’s sensitivity and reduced the noise by designing a new concept of edge-measuring sensor using a hemisphere dome and mechanical hanger to distribute the force and mechanically amplify the micromovement caused by cardiac and respiration activities. In total, we deployed three edge-measuring sensors, two deployed under the thoracic and one under the abdominal regions. The system is supported with onboard signal preprocessing in multiple physical layers deployed under the mattress. We collected the data in four sleeping positions from 16 subjects and analyzed them using ensemble empirical mode decomposition (EMD) to avoid frequency mixing. We also developed an adaptive thresholding method to identify sleep apnea. The error was reduced to 3.98 and 1.43 beats/min (BPM) in heart rate (HR) and respiration estimation, respectively. The apnea was detected with an accuracy of 87%. We optimized the system such that only one edge-measuring sensor can measure the cardiorespiratory parameters. Such a reduction in the complexity and simplification of the instruction of use shows excellent potential for in-home and continuous monitoring.
Die folgende Masterarbeit gibt eine Übersicht zu modernen AR-Technologien für den Einsatz in der Lehre mit dem Ziel eine geeignete Software zu identifizieren, die eine AR-Anwendungserstellung sowie die Integration dieser in das Vorlesungsgeschehen der HTWG ermöglicht. Diese Arbeit baut auf einer Literaturrecherche auf, welche den gegenwärtigen Einsatz von AR in der Lehrpraxis analysiert. Es wird der aktuelle Stand der Entwicklung in Bezug auf verschiedenste Hard- und Softwarelösungen dargestellt, einschließlich der Funktionsweise von AR-Anwendungen sowie relevanter Systemkomponenten. Anschließend werden sowohl der Einsatz von Augmented Reality im Bildungsbereich betrachtet als auch andere Sektoren wie Medizin und Industrie einbezogen, um eine umfassende Übersicht zu Fallstudien sowie Praxisbeispielen zu gewährleisten. Der Auswahlprozess der Software wird ebenfalls thematisiert und eine Anleitung zur Benutzung des gewählten Tools, Vuforia Studio, wird dargeboten.
Die Analyse ergab, dass AR-Anwendungen es den Schülern und Studierenden ermöglichen, aktiv am Unterricht bzw. den Vorlesungen teilzunehmen und Inhalte interaktiv zu erkunden, was das Interesse am Lehrinhalt steigert sowie eigenständiges Lernen fördert. Dem Einsatz von AR in der Lehre stehen jedoch Herausforderungen gegenüber. Insbesondere eine pädagogisch angemessene AR-Inhaltserstellung erweist sich als schwierig. Sowohl die Anfertigung
eines 3D-Modells als auch das Arbeiten mit Programmen wie Vuforia Studio selbst stellen sich als zeitintensiv und technologisch anspruchsvoll heraus. Von Seiten der Bildungseinrichtung müssen finanzielle Mittel bereitgestellt werden, denn ohne entsprechende Schulungen und Ressourcen wird auch die Bereitschaft der Lehrenden, sich mit neuen Technologien auseinanderzusetzen, nicht ausreichend sein, um hochwertige AR-Inhalte zu konzipieren. Obwohl die
technologische Infrastruktur zwar deutlich besser ausgebaut ist als noch vor einigen Jahren, vor allem, weil flächendeckendes Internet zur Verfügung steht und die Lernenden zum Großteil eigene Smartgeräte besitzen, ist eine kontinuierliche Investition in Hard- und Software sowie das Pflegen der gesammelten Daten und genutzten Server unerlässlich.
Insgesamt bietet der Einsatz von Augmented Reality in der Lehre vielversprechende Möglichkeiten, um das Lernerlebnis zu verbessern und die Bildungsergebnisse zu optimieren, jedoch müssen die genannten Herausforderungen überwunden werden, um das gesamte Potenzial von Augmented Reality Technologien in der Lehre auszuschöpfen.
Incremental one-class learning using regularized null-space training for industrial defect detection
(2024)
One-class incremental learning is a special case of class-incremental learning, where only a single novel class is incrementally added to an existing classifier instead of multiple classes. This case is relevant in industrial defect detection scenarios, where novel defects usually appear during operation. Existing rolled-out classifiers must be updated incrementally in this scenario with only a few novel examples. In addition, it is often required that the base classifier must not be altered due to approval and warranty restrictions. While simple finetuning often gives the best performance across old and new classes, it comes with the drawback of potentially losing performance on the base classes (catastrophic forgetting [1]). Simple prototype approaches [2] work without changing existing weights and perform very well when the classes are well separated but fail dramatically when not. In theory, null-space training (NSCL) [3] should retain the basis classifier entirely, as parameter updates are restricted to the null space of the network with respect to existing classes. However, as we show, this technique promotes overfitting in the case of one-class incremental learning. In our experiments, we found that unconstrained weight growth in null space is the underlying issue, leading us to propose a regularization term (R-NSCL) that penalizes the magnitude of amplification. The regularization term is added to the standard classification loss and stabilizes null-space training in the one-class scenario by counteracting overfitting. We test the method’s capabilities on two industrial datasets, namely AITEX and MVTec, and compare the performance to state-of-the-art algorithms for class-incremental learning.
Particularly for manufactured products subject to aesthetic evaluation, the industrial manufacturing process must be monitored, and visual defects detected. For this purpose, more and more computer vision-integrated inspection systems are being used. In optical inspection based on cameras or range scanners, only a few examples are typically known before novel examples are inspected. Consequently, no large data set of non-defective and defective examples could be used to train a classifier, and methods that work with limited or weak supervision must be applied. For such scenarios, I propose new data-efficient machine learning approaches based on one-class learning that reduce the need for supervision in industrial computer vision tasks. The developed novelty detection model automatically extracts features from the input images and is trained only on available non-defective reference data. On top of the feature extractor, a one-class classifier based on recent developments in deep learning is placed. I evaluate the novelty detector in an industrial inspection scenario and state-of-the-art benchmarks from the machine learning community. In the second part of this work, the model gets improved by using a small number of novel defective examples, and hence, another source of supervision gets incorporated. The targeted real-world inspection unit is based on a camera array and a flashing light illumination, allowing inline capturing of multichannel images at a high rate. Optionally, the integration of range data, such as laser or Lidar signals, is possible by using the developed targetless data fusion method.
Das Freistellungssemester 2020 wurde für Recherchen zu unterschiedlichen Aufmaßsystemen in der historischen Bauforschung genutzt. Durch die Covid-19-Pandemie entwickelte sich das Arbeitsprogramm jedoch anders als geplant und verlagerte sich weitgehend in den virtuellen Raum. In der Disziplin der historischen Bauforschung entstand gerade durch die zeitweise Unmöglichkeit des Reisens und der Präsenzlehre ein intensiver Austausch zur Methodik in zahlreichen Onlinekonferenzen.
This paper broadens the resource-based approach to explaining survival of new technology-based firms (NTBFs) by focusing on the entrepreneur's ability to transform resources in response to triggers resulting from market interactions. Network theory is used to define a construct that allows determining the status of venture emergence (VE).The operationalization of the VE construct is built on the firm's value network maturity in the four market dimensions customer, investor, partner, and human resource. Business plans of NTBFs represent the artifact that contains this data in the form of transaction relation descriptions. Using content analysis, a multi-step combined human and computer coding process has been developed to empirically determine NTBFs' status of VE.Results of the business plan analysis suggests that the level of transaction relations allows to draw conclusions on the status of VE. Moreover, applying the developed process, a business plan coding test shows that the transaction relation based VE status significantly relates to NTBFs' survival capabilities.
Research Report
(2024)
We quantify the effects of GATT/WTO membership on trade and welfare. Using an extensive database covering manufacturing trade for 186 countries over the period 1980–2016, we find that the average partial equilibrium impact of GATT/WTO membership on trade among member countries is large, positive, and significant. We contribute to the literature by estimating country-specific estimates and find them to vary widely across the countries in our sample with poorer members benefitting more. Using these estimates, we simulate the general equilibrium effects of GATT/WTO on welfare, which are sizable and heterogeneous across members. We show that countries not experiencing positive trade effects from joining GATT/WTO can still gain in terms of welfare, due to lower import prices and higher export demand.