Global ETD Search

1	Diffusion von Gold in Germanium Strohm, Andreas. January 1999 (has links) Stuttgart, Univ., Diplomarb., 1999.
2	FCS investigations on the diffusional behaviour of TNF-Receptors upon stimulation in living cells Gerken, Margarita. January 2008 (has links) Stuttgart, Univ., Diss., 2008.
3	Selbstdiffusion in Silizium-Germanium-Legierungen Strohm, Andreas. January 2002 (has links) Stuttgart, Univ., Diss., 2002.
4	Nonlinear anisotropic diffusion filters for the numerical approximation of conservation laws Grahs, Thorsten. Unknown Date (has links) (PDF) Techn. University, Diss., 2002--Braunschweig.
5	Geomorphological dating of scarps in temperate climate using a modified diffusion model Oemisch, Mamke. Unknown Date (has links) (PDF) University, Diss., 2004--Bonn.
6	Highway Traffic Forecasting with the Diffusion Model : An Image-Generation Based Approach / Vägtrafikprognos med Diffusionsmodellen : En bildgenereringsbaserad metod Chi, Pengnan January 2023 (has links) Forecasting of highway traffic is a common practice for real traffic information system, and is of vital importance to traffic management and control on highways. As a typical time-series forecasting task, we want to propose a deep learning model to map the historical sensory traffic values (e.g., speed, flow) to future traffic forecasts. Prevailing traffic forecasting methods focus on the graph representation of the urban road. However, compared to the dense connectivity of urban road networks, highway traffic flows normally run on road segments of serial topology. This indicates that the highway traffic flows do not have the same type of spatial interaction, therefore motivating us to resort to a new forecasting paradigm. While traffic patterns can be intuitively represented by spatial-temporal (ST) images, this study transforms the traffic forecasting task into the conditional image generation task. Our approach explores the inherent properties of ST-images from the perspectives of physical meaning and traffic dynamics. An innovative deep learning based architecture is designed to process the ST-image, and a diffusion model is trained to obtain traffic forecasts by generating future ST-image based on the historical STimages. We demonstrate the effectiveness of the architecture in processing ST-image through ablation studies and the effectiveness of the model through comparison with popular baseline models, i.e., LSTM and T-GCN. / Prognos av vägtrafik är en vanlig praxis för riktiga trafikinformationssystem och är av vital betydelse för trafikhantering och kontroll på motorvägar. Som en typisk tidsserieförutsägelseuppgift vill vi föreslå en djupinlärningsmodell för att kartlägga historiska sensoriska trafikvärden (t.ex. hastighet, flöde) till framtida trafikprognoser. Rådande trafikprognosmetoder fokuserar på grafrepresentationen av stadsvägar. Jämfört med den täta anslutningen av stadsvägnät, löper motorvägstrafik normalt på vägsegment med seriell topologi. Detta indikerar att motorvägstrafikflöden inte har samma typ av rumslig interaktion, vilket motiverar oss att använda en ny prognosparadigm. Medan trafikmönster intuitivt kan representeras av spatial-temporala (ST) bilder, omvandlar denna studie trafikprognosuppgiften till en uppgift för betingad bildgenerering. Vår metod utforskar de inneboende egenskaperna hos ST-bilder från perspektiven fysisk betydelse och trafikdynamik. En innovativ djupinlärningsbaserad arkitektur är utformad för att behandla STbilden, och en diffusionsmodell tränas för att erhålla trafikprognoser genom att generera framtida ST-bilder baserat på historiska ST-bilder. Vi demonstrerar effektiviteten hos arkitekturen genom avbränningsstudier och modellens effektivitet genom jämförelse med populära baslinjemodeller, dvs. LSTM och T-GCN. Diffusion model Traffic forecasting Generative model Image processing Spatial temporal modelling Diffusionsmodell Trafikprognos Generativ modell Bildbehandling Rumsligtemporal modellering Computer Sciences Datavetenskap (datalogi)
7	A Bayesian Reformulation of the Extended Drift-Diffusion Model in Perceptual Decision Making Fard, Pouyan R., Park, Hame, Warkentin, Andrej, Kiebel, Stefan J., Bitzer, Sebastian 10 November 2017 (has links) (PDF) Perceptual decision making can be described as a process of accumulating evidence to a bound which has been formalized within drift-diffusion models (DDMs). Recently, an equivalent Bayesian model has been proposed. In contrast to standard DDMs, this Bayesian model directly links information in the stimulus to the decision process. Here, we extend this Bayesian model further and allow inter-trial variability of two parameters following the extended version of the DDM. We derive parameter distributions for the Bayesian model and show that they lead to predictions that are qualitatively equivalent to those made by the extended drift-diffusion model (eDDM). Further, we demonstrate the usefulness of the extended Bayesian model (eBM) for the analysis of concrete behavioral data. Specifically, using Bayesian model selection, we find evidence that including additional inter-trial parameter variability provides for a better model, when the model is constrained by trial-wise stimulus features. This result is remarkable because it was derived using just 200 trials per condition, which is typically thought to be insufficient for identifying variability parameters in DDMs. In sum, we present a Bayesian analysis, which provides for a novel and promising analysis of perceptual decision making experiments. perzeptuelle Entscheidungsfindung Drift-Diffusionsmodell Bayes-Modelle Parameteranpassung exakte Eingangsmodellierung Modellvergleich Single-Trial-Modelle Technische Universität Dresden Publikationsfonds perceptual decision making drift-diffusion model Bayesian models parameter fitting exact input modeling model comparison single-trial models Technische Universität Dresden Publishing Fund ddc:610 rvk:XA 10000
8	A Bayesian Reformulation of the Extended Drift-Diffusion Model in Perceptual Decision Making Fard, Pouyan R., Park, Hame, Warkentin, Andrej, Kiebel, Stefan J., Bitzer, Sebastian 10 November 2017 (has links) Perceptual decision making can be described as a process of accumulating evidence to a bound which has been formalized within drift-diffusion models (DDMs). Recently, an equivalent Bayesian model has been proposed. In contrast to standard DDMs, this Bayesian model directly links information in the stimulus to the decision process. Here, we extend this Bayesian model further and allow inter-trial variability of two parameters following the extended version of the DDM. We derive parameter distributions for the Bayesian model and show that they lead to predictions that are qualitatively equivalent to those made by the extended drift-diffusion model (eDDM). Further, we demonstrate the usefulness of the extended Bayesian model (eBM) for the analysis of concrete behavioral data. Specifically, using Bayesian model selection, we find evidence that including additional inter-trial parameter variability provides for a better model, when the model is constrained by trial-wise stimulus features. This result is remarkable because it was derived using just 200 trials per condition, which is typically thought to be insufficient for identifying variability parameters in DDMs. In sum, we present a Bayesian analysis, which provides for a novel and promising analysis of perceptual decision making experiments. info:eu-repo/classification/ddc/610 ddc:610
9	Medical domain knowledge in domain-agnostic generative AI Kather, Jakob Nikolas, Ghaffari Laleh, Narmin, Foersch, Sebastian, Truhn, Daniel 31 May 2024 (has links) The text-guided diffusion model GLIDE (Guided Language to Image Diffusion for Generation and Editing) is the state of the art in text-to-image generative artificial intelligence (AI). GLIDE has rich representations, but medical applications of this model have not been systematically explored. If GLIDE had useful medical knowledge, it could be used for medical image analysis tasks, a domain in which AI systems are still highly engineered towards a single use-case. Here we show that the publicly available GLIDE model has reasonably strong representations of key topics in cancer research and oncology, in particular the general style of histopathology images and multiple facets of diseases, pathological processes and laboratory assays. However, GLIDE seems to lack useful representations of the style and content of radiology data. Our findings demonstrate that domain-agnostic generative AI models can learn relevant medical concepts without explicit training. Thus, GLIDE and similar models might be useful for medical image processing tasks in the future - particularly with additional domain-specific fine-tuning. info:eu-repo/classification/ddc/610 ddc:610
10	Stoffübertragung beim Spritzgießen Härtig, Thomas 22 March 2013 (has links) (PDF) Das Fügen mehrerer Komponenten während des Spritzgießprozesses wird bei vielen Spritzgießsonderverfahren angewandt. Diese Arbeit beschäftigt sich mit der Verbundbildung zwischen einem kalten Einlegeteil und der einströmenden Kunststoffschmelze beim Spritzgießen, im Folgenden Stoffübertragung genannt. Ein Großteil der Untersuchungen findet an Zweikomponenten-Zugstäben statt, wobei erste und zweite Komponente aus dem gleichen Thermoplast gefertigt werden. Mögliche Einflussfaktoren auf die Verbundfestigkeit werden zunächst im Theorieteil vorgestellt und diskutiert. Eine Auswahl relevanter Prozess- und Materialparameter wird dann in praktischen Versuchen detailliert analysiert. Es wird nach korrelierenden Tendenzen sowohl zwischen unterschiedlichen Verfahren als auch zwischen verschiedenen Kunststoffen gesucht. Mittels statistischer Versuchsplanung werden die Spritzgießparameterkombinationen nach Größe des Einflusses auf die Verbundfestigkeit sortiert. Dies trägt zum Verständnis der bei der Stoffübertragung ablaufenden Grundmechanismen bei. Weiterhin werden die Einflüsse der Prozessparameter auf das neue Verfahren der In-Mold Oberflächenmodifizierung, bei dem ein funktionaler Modifikator während des Spritzgießprozesses übertragen wird, mit den Ergebnissen der Zweikomponenten-Verbundfestigkeit verglichen. Abschließend wird auf die Besonderheiten bei der selektiven Stoffübertragung eingegangen und das neue Verfahren des In-Mold Printing vorgestellt. / The joining of two components by the process of injection molding is state of the art, although adhesion phenomena are not fully understood yet. The formation of bonds between a cold material, which was inserted or applied onto the surface of the cavity before injection molding, and an injected polymer melt is studied in this work. Providing sufficient bond strength, the material is transferred from the surface of the mold to the injection molded part. Possibly influencing factors on the bond strength are first identified, theoretically discussed, later in experiments varied and finally analyzed. Thereby correlating tendencies between different polymers and different in-mold technologies are observed. The relevant material and processing parameters are put in order by their influence on the bond strength using design of experiments. This helps to understand the mechanisms of the formation of bonds. The majority of the experiments is concerned with two component injection molding by measuring the bond strength of two component tensile bars, produced under varying processing conditions. In each case, first and second components are made of the same thermoplastic polymer. The thermal energy of the melt can be used also to initiate chemical reactions. This permits bonding of a thin layer of a functional polymer, which is applied onto the surface of the mold before injecting the melt, to the surface of the molded part. In this way, process-integrated surface modification during injection molding becomes possible. In a further attempt, patterns of paint are printed onto the surface of the mold by pad printing. During injection molding the paint is transferred completely to the surface of the polymeric part. Using this new technology of In-Mold Printing, fully finished surface decorated parts can be produced by injection molding. Spritzgießen Mehrkomponentenspritzgießen Stoffübertragung In-Mold Printing In-Mold Oberflächenmodifizierung Grenzfläche Grenzschicht Verbundfestigkeit Verbundhaftung Adhäsionsmechanismen Haftung Zweikomponentenspritzgießen Dekoration Kunststoffoberflächen Oberflächenmodifizierung Oberflächenfunktionalisierung two component injection molding multi component injection molding In-Mold Printing In-Mold Surface Modification material transfer interface boundary layer adhesion phenomena bond strength surface functionalization polymer surface decoration ddc:620 Spritzgießen Thermoplast Kunststoff-Metall-Verbund Kunststoff-Metall-Klebeverbindung Kunststoffverpackung Polymerschmelze Kunststoffschweißen Kunststofftechnik Kunststoffverarbeitung Kunststoffindustrie Kunststoffformteil Kunststoffherstellung Kunststoffkleben Kunststoff Kunststoffbauteil Formstoff <Kunststoff> Grenzfläche Grenzflächenphysik Grenzflächenreaktion Korngrenze Grenzschicht Spritzgussform Verbundverhalten Verbund Adhäsion Diffusionsmodell Diffusion Chemische Reaktion Reaktionsdynamik Modifizierung Modifikator Drucken Oberflächenstruktur Oberflächenveredelung Oberfläche <Motiv> Lotus-Effekt Technische Oberfläche Oberflächenprüfung Festkörperoberfläche Flüssigkeitsoberfläche Polymere Spritzgießwerkzeug Farbe <Motiv>

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