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Research Report
(2024)
AbstractSanctions encompass a wide set of policy instruments restricting cross‐border economic activities. In this paper, we study how different types of sanctions affect the export behavior of firms to the targeted countries. We combine Danish register data, including information on firm‐destination‐specific exports, with information on sanctions imposed by Denmark from the Global Sanctions Database. Our data allow us to study firms' export behavior in 62 sanctioned countries, amounting to a total of 453 country‐years with sanctions over the period 2000–2015. Methodologically, we apply a two‐stage estimation strategy to properly account for multilateral resistance terms. We find that, on average, sanctions lead to a significant reduction in firms' destination‐specific exports and a significant increase in firms' probability to exit the destination. Next, we study heterogeneity in the effects of sanctions across (i) sanction types and sanction packages, (ii) the objectives of sanctions, and (iii) countries subject to sanctions. Results confirm that the effects of sanctions on firms' export behavior vary considerably across these three dimensions.
Diese Masterarbeit erforscht das Potenzial großer Sprachmodelle in der Bauindustrie mit einem Fokus auf digitale Transformation, Effizienzsteigerung und Nachhaltigkeit. Durch eine umfassende Literaturanalyse und qualitative Experteninterviews werden spezifische Anwendungsfälle, Herausforderungen bei der Implementierung und ethische sowie datenschutzrechtliche Überlegungen untersucht.
Die Arbeit hebt hervor, wie große Sprachmodelle die Planungsprozesse optimieren, das Risikomanagement verbessern und maßgeschneiderte Lösungen entwickeln können, um ökonomische und ökologische Vorteile zu erzielen. Zudem werden praxisorientierte Empfehlungen für eine erfolgreiche Integration dieser Technik in das Bauwesen präsentiert, die sowohl die technologische Machbarkeit als auch soziale Akzeptanz berücksichtigen.
Abschließend werden zukünftige Forschungsrichtungen aufgezeigt, die darauf abzielen, die digitale Transformation im Bauwesen unter Einbeziehung ethischer Standards und Datenschutz zu beschleunigen.
Die Ergebnisse dieser Arbeit demonstrieren das Potenzial von großen Sprachmodellen, traditionelle Bauprozesse zu revolutionieren, und betonen die Notwendigkeit einer sorgfältigen Implementierung, um die Vorteile dieser Technologie vollständig auszuschöpfen.
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.
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.
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.
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.
With the advancement in sensor technology and the trend shift of health measurement from treatment after diagnosis to abnormalities detection long before the occurrence, the approach of turning private spaces into diagnostic spaces has gained much attention. In this work, we designed and implemented a low-cost and compact form factor module that can be deployed on the steering wheel of cars as well as most frequently touch objects at home in order to measure physiological signals from the fingertip of the subject as well as environmental parameters. We estimated the heart rate and SpO2 with the error of 2.83 bpm and 3.52%, respectively. The signal evaluation of skin temperature shows a promising output with respect to environmental recalibration. In addition, the electrodermal activity sensor followed the reference signal, appropriately which indicates the potential for further development and application in stress measurement.