5,690 research outputs found

    Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions

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    In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request

    The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

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    The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art, and culture. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution

    Examples of works to practice staccato technique in clarinet instrument

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    Klarnetin staccato tekniğini güçlendirme aşamaları eser çalışmalarıyla uygulanmıştır. Staccato geçişlerini hızlandıracak ritim ve nüans çalışmalarına yer verilmiştir. Çalışmanın en önemli amacı sadece staccato çalışması değil parmak-dilin eş zamanlı uyumunun hassasiyeti üzerinde de durulmasıdır. Staccato çalışmalarını daha verimli hale getirmek için eser çalışmasının içinde etüt çalışmasına da yer verilmiştir. Çalışmaların üzerinde titizlikle durulması staccato çalışmasının ilham verici etkisi ile müzikal kimliğe yeni bir boyut kazandırmıştır. Sekiz özgün eser çalışmasının her aşaması anlatılmıştır. Her aşamanın bir sonraki performans ve tekniği güçlendirmesi esas alınmıştır. Bu çalışmada staccato tekniğinin hangi alanlarda kullanıldığı, nasıl sonuçlar elde edildiği bilgisine yer verilmiştir. Notaların parmak ve dil uyumu ile nasıl şekilleneceği ve nasıl bir çalışma disiplini içinde gerçekleşeceği planlanmıştır. Kamış-nota-diyafram-parmak-dil-nüans ve disiplin kavramlarının staccato tekniğinde ayrılmaz bir bütün olduğu saptanmıştır. Araştırmada literatür taraması yapılarak staccato ile ilgili çalışmalar taranmıştır. Tarama sonucunda klarnet tekniğin de kullanılan staccato eser çalışmasının az olduğu tespit edilmiştir. Metot taramasında da etüt çalışmasının daha çok olduğu saptanmıştır. Böylelikle klarnetin staccato tekniğini hızlandırma ve güçlendirme çalışmaları sunulmuştur. Staccato etüt çalışmaları yapılırken, araya eser çalışmasının girmesi beyni rahatlattığı ve istekliliği daha arttırdığı gözlemlenmiştir. Staccato çalışmasını yaparken doğru bir kamış seçimi üzerinde de durulmuştur. Staccato tekniğini doğru çalışmak için doğru bir kamışın dil hızını arttırdığı saptanmıştır. Doğru bir kamış seçimi kamıştan rahat ses çıkmasına bağlıdır. Kamış, dil atma gücünü vermiyorsa daha doğru bir kamış seçiminin yapılması gerekliliği vurgulanmıştır. Staccato çalışmalarında baştan sona bir eseri yorumlamak zor olabilir. Bu açıdan çalışma, verilen müzikal nüanslara uymanın, dil atış performansını rahatlattığını ortaya koymuştur. Gelecek nesillere edinilen bilgi ve birikimlerin aktarılması ve geliştirici olması teşvik edilmiştir. Çıkacak eserlerin nasıl çözüleceği, staccato tekniğinin nasıl üstesinden gelinebileceği anlatılmıştır. Staccato tekniğinin daha kısa sürede çözüme kavuşturulması amaç edinilmiştir. Parmakların yerlerini öğrettiğimiz kadar belleğimize de çalışmaların kaydedilmesi önemlidir. Gösterilen azmin ve sabrın sonucu olarak ortaya çıkan yapıt başarıyı daha da yukarı seviyelere çıkaracaktır

    Socio-endocrinology revisited: New tools to tackle old questions

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    Animals’ social environments impact their health and survival, but the proximate links between sociality and fitness are still not fully understood. In this thesis, I develop and apply new approaches to address an outstanding question within this sociality-fitness link: does grooming (a widely studied, positive social interaction) directly affect glucocorticoid concentrations (GCs; a group of steroid hormones indicating physiological stress) in a wild primate? To date, negative, long-term correlations between grooming and GCs have been found, but the logistical difficulties of studying proximate mechanisms in the wild leave knowledge gaps regarding the short-term, causal mechanisms that underpin this relationship. New technologies, such as collar-mounted tri-axial accelerometers, can provide the continuous behavioural data required to match grooming to non-invasive GC measures (Chapter 1). Using Chacma baboons (Papio ursinus) living on the Cape Peninsula, South Africa as a model system, I identify giving and receiving grooming using tri-axial accelerometers and supervised machine learning methods, with high overall accuracy (~80%) (Chapter 2). I then test what socio-ecological variables predict variation in faecal and urinary GCs (fGCs and uGCs) (Chapter 3). Shorter and rainy days are associated with higher fGCs and uGCs, respectively, suggesting that environmental conditions may impose stressors in the form of temporal bottlenecks. Indeed, I find that short days and days with more rain-hours are associated with reduced giving grooming (Chapter 4), and that this reduction is characterised by fewer and shorter grooming bouts. Finally, I test whether grooming predicts GCs, and find that while there is a long-term negative correlation between grooming and GCs, grooming in the short-term, in particular giving grooming, is associated with higher fGCs and uGCs (Chapter 5). I end with a discussion on how the new tools I applied have enabled me to advance our understanding of sociality and stress in primate social systems (Chapter 6)

    The Role of Transient Vibration of the Skull on Concussion

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    Concussion is a traumatic brain injury usually caused by a direct or indirect blow to the head that affects brain function. The maximum mechanical impedance of the brain tissue occurs at 450±50 Hz and may be affected by the skull resonant frequencies. After an impact to the head, vibration resonance of the skull damages the underlying cortex. The skull deforms and vibrates, like a bell for 3 to 5 milliseconds, bruising the cortex. Furthermore, the deceleration forces the frontal and temporal cortex against the skull, eliminating a layer of cerebrospinal fluid. When the skull vibrates, the force spreads directly to the cortex, with no layer of cerebrospinal fluid to reflect the wave or cushion its force. To date, there is few researches investigating the effect of transient vibration of the skull. Therefore, the overall goal of the proposed research is to gain better understanding of the role of transient vibration of the skull on concussion. This goal will be achieved by addressing three research objectives. First, a MRI skull and brain segmentation automatic technique is developed. Due to bones’ weak magnetic resonance signal, MRI scans struggle with differentiating bone tissue from other structures. One of the most important components for a successful segmentation is high-quality ground truth labels. Therefore, we introduce a deep learning framework for skull segmentation purpose where the ground truth labels are created from CT imaging using the standard tessellation language (STL). Furthermore, the brain region will be important for a future work, thus, we explore a new initialization concept of the convolutional neural network (CNN) by orthogonal moments to improve brain segmentation in MRI. Second, the creation of a novel 2D and 3D Automatic Method to Align the Facial Skeleton is introduced. An important aspect for further impact analysis is the ability to precisely simulate the same point of impact on multiple bone models. To perform this task, the skull must be precisely aligned in all anatomical planes. Therefore, we introduce a 2D/3D technique to align the facial skeleton that was initially developed for automatically calculating the craniofacial symmetry midline. In the 2D version, the entire concept of using cephalometric landmarks and manual image grid alignment to construct the training dataset was introduced. Then, this concept was extended to a 3D version where coronal and transverse planes are aligned using CNN approach. As the alignment in the sagittal plane is still undefined, a new alignment based on these techniques will be created to align the sagittal plane using Frankfort plane as a framework. Finally, the resonant frequencies of multiple skulls are assessed to determine how the skull resonant frequency vibrations propagate into the brain tissue. After applying material properties and mesh to the skull, modal analysis is performed to assess the skull natural frequencies. Finally, theories will be raised regarding the relation between the skull geometry, such as shape and thickness, and vibration with brain tissue injury, which may result in concussive injury

    The influence of complex volcanic vent morphology on eruption dynamics

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    Vulkanausbrüche gelten als eine der spektakulärsten Naturgewalten unserer Erde. Gleichzeitig stellen sie jedoch auch eine Gefahr für die menschliche Gesundheit und Infrastruktur dar. Aufgrund ihrer Dynamik und ihres unberechenbaren Charakters geht von explosiven Vulkanausbrüchen eine besonders große Gefährdung des Menschen und seiner Umwelt aus. Im Zuge eines explosiven Ausbruchs werden heiße Gase und Pyroklasten in die Atmosphäre ausgeworfen. Obwohl das Monitoring aktiver Vulkane in den letzten Jahren immer weiter verbessert wurde, ist es immer noch schwierig eine konkrete Vorhersage zu den Ausbrüchen zu erstellen. Aufgrund ihrer Komplexität ist das Verhalten von Vulkanen nicht kalkulierbar. Bis heute ist weder eine Beobachtung, noch eine Messung der unterirdischen Rahmenbedingungen möglich, welche den Ausbruch steuern. Trotz dieser Unwägbarkeiten unterliegen Vulkanausbrüche dennoch physikalischen Gesetzmäßigkeiten, sodass die Möglichkeit besteht, die Prozesse im Untergrund eines Vulkans zu modellieren oder durch Experimente zu beschreiben. Aufgrund der Komplexität der Wechselwirkungen innerhalb des Systems Vulkan ist es erforderlich Experimente zunehmend realistischer zu gestalten. Sobald das ausgeworfene Material aus dem Krater austritt können wir den Ausbruch visuell Beobachten. In diesem Bereich ist das Verhalten des Ausbruchs vollständig von den Prozessen im Untergrund und von der Geometrie des Kraters abhängig. Im Vergleich zu den symmetrischen Kraterformen, welche in Experimenten und Modellen oft angenommen werden, sind die Krater in der Natur deutlich unregelmäßiger geformt. Ihre Geometrien sind oft eingekerbt und haben eine schräge Oberfläche. Zudem können sich die Kratergeometrien innerhalb kürzester Zeit verändern. Um den Einfluss der Prozesse im Untergrund zu verstehen müssen wir zuerst den Einfluss der beobachtbaren Parameter (z. B. Kratergeometrie) ergründen. Schlussendlich wird ein tiefergehendes Verständnis der Parameter, die Vulkanausbrüche steuern, zu einem Fortschritt und der Verbesserung der Gefährdungsanalysen führen. Um dies zu erreichen, habe ich Beobachtungen aus Feldkampagnen und Laborexperimenten kombiniert. Zunächst habe ich die Geometrien von Vulkankratern erfasst und deren zeitliche Entwicklung dokumentiert. Dazu haben ich die Geometrie der Krater in der Kraterterrasse des Strombolis in einer hohen Auflösung vermessen und die jeweils zugehörigen Explosionen beobachtet. Dabei konnte ich feststellen, dass sowohl die Intensität, als auch die Art und die Richtung der Ausbrüche durch Formveränderungen der Oberflächentopografie beeinflusst werden. Mittels Drohneneinsatz habe ich innerhalb eines Zeitraums von neun Monaten (Mai 2019–Januar 2020) fünf topografische Datensätze erstellt. In diesem Zeitraum war es möglich „normale“ Strombolianische Aktivität, starke Ausbrüche und sogar zwei Paroxysmen zu beobachten (3. Juli und 28. August 2019), sodass es möglich war, die verschiedenen Ausbruchstypen mit den vorherrschenden Ablagerungs- und Abtragungsprozessen zu verknüpfen. Zudem konnte ich die Anzahl der aktiven Krater, deren Positionen sowie deren Umgestaltung nachverfolgen. Da Veränderungen der Kratergeometrie und der Kraterposition auf eine Modifikation des Ausbruchsgeschehens hinweisen können, sind auch dies wichtige Faktoren für eine Gefährdungsanalyse. Die aus den Feldforschungen gewonnenen Daten zeigen deutlich die Komplexität, Vielseitigkeit und Variabilität der Formen vulkanischer Krater in einer nie da gewesenen zeitlichen und räumlichen Auflösung. Darüber hinaus haben die Beobachtungen der Vulkanausbrüche deutlich gemacht, wie stark die Beziehung zwischen dem Krater, der Kratergeometrie und dem Auswurf von pyroklastischem Material ist. Diese Erkenntnis hat eine große Bedeutung für die Gefährdungsanalyse, vor allem für Gebiete, die potentiell durch vulkanische Bomben und pyroklastischem Fallout bedroht sind. Im Anschluss habe ich eine Reihe von Dekompressionsexperimenten mit Kratergeometrien durchgeführt, welche auf den Beobachtungen am Stromboli aufbauen. Durch diese Experimente wurde der Zusammenhang zwischen Kratergeometrie und Ausbruchsdynamik bestätigt. Die verwendeten Geometrien haben eine geneigte Oberfläche mit einem Winkel von 5°, 15° und 30° und jeweils einer zylindrischen und einer trichterförmigen inneren Geometrie. Daraus ergeben sich sechs experimentelle Krater die mit folgenden experimentellen Bedingungen getestet wurden: Vier unterschiedliche Startdrücke (5, 8, 15 und 25 MPa) und zwei Gasvolumina (127.4cm3, 31.9cm3). Alle Experimente wurden bei Raumtemperatur und mit Argon durchgeführt. Trotz des vertikalen Aufbaus konnte man auf beiden Seiten des Kraters unterschiedlich große Winkel des austretenden Gases beobachten. Weiterhin war der Gasstrahl geneigt. Die Richtung der Neigung wurde durch die innere Geometrie be- stimmt. Bei einer zylindrischen Geometrie neigte sich der Gasstrahl in die Einfallsrichtung der geneigten Oberfläche. Im Falle einer trichterförmigen inneren Geometrie neigt sich der Gasstrahl entgegen der Einfallsrichtung. Der Winkel des Gasaustritts war bei einer zylindrischen inneren Geometrie immer größer als bei der trichterförmigen Geometrie. Sowohl die Winkel des Gasaustritts als auch die Neigung des Gasstrahls zeigten eine starke Reaktion auf eine Veränderung der Druckbedingung und Oberflächenneigung. Dabei zeigten sowohl der Austrittswinkel als auch die Neigung eine positive Korrelation mit dem Druck und der Oberflächenneigung. Hohe Druckbedingungen haben außerdem dafür gesorgt, dass für einen längeren Zeitraum Überdruckverhältnisse am Kraterausgang herrschten. Ein höheres Gasvolumen hat größere Gasaustrittswinkel ermöglicht. Zuletzt habe ich die Dekompressionsexperimente durch den Einsatz von Partikeln ergänzt, um so den Auswurf von Gas und Partikeln während eines explosiven Vulkanausbruchs nachzustellen. Dabei habe ich die beiden experimentellen Kratergeometrien aus den vorangegangenen Experimenten ausgewählt, welche den stärksten Einfluss auf die Gasdynamik aufgezeigt haben. Zusätzlich habe ich eine dritte Kratergeometrie verwendet, die dem aktiven Krater S1 auf Stromboli nachempfunden ist. Die Geometrie entspricht der Kratergeometrie aus der Vermessung im Mai 2019. Die S1 Geometrie zeichnet sich durch einen asymmetrischen Öffnungswinkel aus (~10° auf einer Seite, ~40° auf der anderen Seite). Zusätzlich zu den drei Kratergeometrien wurden unterschiedliche Partikel verwendet (Schlacke und Bims), mit jeweils drei unterschiedlichen Korngrößen (0.125–0.25, 0.5–1 und 1–2mm) und zwei Druckstufen (8 und 15MPa). Die Partikeldynamik, in der Nähe des experimentellen Kraters, wurde anhand der Winkel des Partikelauswurfs und der Geschwindigkeit der Partikel definiert und beschrieben. Dabei wurde festgestellt, dass die Geometrie des Kraters die Richtung und Neigung des Partikelauswurfswinkels und die Geschwindigkeit der Partikel bestimmt. Bei allen Kratergeometrien kam es zu einem asymmetrischen Partikelauswurf und im Falle von Bimspartikeln zudem zu einer ungleichmäßigen Geschwindigkeitsverteilung. Die Kombination aus Daten aus Feldkampagnen, Experimenten mit Gas und Experimenten mit zusätzlichen Partikeln zeigte deutlich den starken Einfluss der Kratergeometrie auf Eruptionen. In der Natur, führt eine modifizierte Kratergeometrie zu einem verändertem Auswurfsmuster der Pyroklasten. Im Labor haben komplexe Kratergeometrien zu geneigten Gasstrahlen, asymmetrischen Auswurfswinkeln von Gas- und Gaspartikeln und einer asymmetrischen Verteilung der Geschwindigkeit von Partikeln geführt. Auf Basis dieser Beobachtungen komme ich zu dem Schluss, dass asymmetrische Vulkankrater eine asymmetrische Verteilung von pyroklastischem Auswurf hervorrufen. Das führt zu einer bevorzugten Richtung für vulkanischen Fallout — und falls es zu einer kollabierenden Ausbruchsäule kommt — zu einer bevorzugten Richtung für pyroklastische Ströme. Der technische Fortschritt durch Drohnen, Photogrammmetrie und 3D Druck bietet einige Chancen für die Vulkanologie. Luftaufnahmen durch Drohnen ermöglichen eine schnelle, günstige und sichere Vermessung von Vulkankratern, auch in Zeiten erhöhter Aktivität. Zusammen mit Photogrammmetrie und 3D Druck lassen sich realitätsnahe Kratergeometrien erzeugen, für zunehmend realistische skalierte Laborexperimente.Volcanic eruptions are among the most violent displays of the Earth’s natural forces and threaten human health and infrastructure. Explosive eruptions are hazardous due to their impulsive and dynamic nature, ejecting gas and pyroclasts at high velocity and temperature into the atmosphere. In recent years, monitoring efforts have increased, but forecasting eruptions is still challenging as volcanoes are complex systems with the potential for inherently unpredictable behaviours. To date, the underlying boundary conditions are beyond observation and quantification. Still, they are constrained by physical laws and can be described through models and experiments. The complexity and interdependency of the parameters governing the dynamics of volcanic eruptions ask for increasingly realistic experiments to investigate the sub-surface conditions driving volcanic eruptions. Above the vent, in the near-vent region, the dynamics of explosive eruptions can first be visually observed. The characteristics at this stage are purely the result of the underlying boundary conditions and the exit (vent) geometry. Volcanic vents are rarely the symmetric features that are often assumed in models and experiments. They often exhibit highly irregular shapes with notched or slanted rims that can be transient. To eventually understand the unobservable boundary conditions, it is necessary to initially gain knowledge about the effect of the observable factors (i.e. vent geometry). This knowledge will ultimately improve the understanding of the parameters affecting an explosive event to develop accurate probabilistic hazard maps. To this end, a combination of field observations and laboratory experiments was used. First, I characterised vent and crater shape changes at a frequently erupting volcano (Stromboli) to collect high-resolution geometric data of volcanic vents and observe the related explosion dynamics. As a result of topographic changes, variable eruption intensity, style and directionality could be detected. Five topographic data sets were acquired by unoccupied aerial vehicles (UAVs) over nine months (May 2019-January 2020). During this period, changes associated with "normal" Strombolian activity, "major explosions" and paroxysmal episodes (3 July and 28 August 2019) occurred. Hence, the topographic data made it possible to link the predominant constructive and destructive processes to these eruption styles. Furthermore, the number and position of active vents changed significantly, which is a critical parameter for hazard assessment as vent geometry and position can be linked to shifts in eruptive mechanisms. These field surveys highlight the geometric complexity and variability of volcanic vents at an unprecedented spatiotemporal resolution. Additionally, the observations of explosions suggested the paramount influence of crater and vent geometry on pyroclast ejection characteristics, a fact that has strong implications for areas potentially affected by bomb impact and pyroclastic fall out. Secondly, I designed a series of shock-tube experiments incorporating the geometry elements observed at Stromboli to quantify the influence of vent geometry and several boundary conditions. These experiments validated the link between vent geometry and explosion dynamics that was observed in the field. The novel geometry element is an inclined exit plane of 5°, 15° and 30° slant angle combined with a cylindrical and diverging inner geometry resulting in six vent geometries. All experiments were conducted with gas-only (Argon) at room temperature, four different starting pressures (5, 8, 15, 25 MPa) and two reservoir volumes (127.4 cm3, 31.9 cm3). Despite the vertical setup, the slanted geometry yielded both a laterally variable gas spreading angle and an inclination of the jets. The inner geometry controlled the jet inclination towards the dip direction of the slanted exit plane (cylindrical) and against the dip direction of the slanted exit plane (diverging). Cylindrical vents produced larger gas spreading angles than diverging vents. Both gas spreading angle and jet inclination were highly sensitive to the experimental pressure and the slant angle. They had a positive correlation with maximum gas spreading angle and jet inclination. Additionally, the pressure was positively correlated with the maximum duration of underexpanded characteristics of the jet. The gas volume only showed a positive correlation with the maximum gas spreading angle. Thirdly, I added particles to the experiments to mimic the ejection of gas-particle jets during explosive volcanic eruptions. For this set of experiments, the two geometries with the 30° slant angle from the previous experimental series were used as they exhibited the strongest effect on the gas ejection dynamics. They were supplemented by a third vent that resembled the "real" geometry of Stromboli’s active S1 vent as it was mapped in May 2019 and fabricated by 3D printing. The S1’s geometry is characterised by a ~ 10° divergence on one side and a ~ 40° divergence on the other side. Besides three vent geometries, two types of particles (scoria and pumice), each with three different grain size distributions (0.125– 0.25, 0.5–1, 1–2 mm) and two starting pressures (8, 15 MPa) were used. The near-vent vent dynamics were characterised as a function of particle spreading angle and particle ejection velocity. The vent geometry governed the direction and the magnitude of particle spreading, and the velocity of particles. All geometries yielded asymmetric particle spreading as well as a non-uniform velocity distribution in experiments with pumice particles. The combination of field observations, gas-only and gas-particle experiments demonstrated the prime control exerted by vent geometry. In nature, a modification of the vent led to modified pyroclast ejection patterns. In the laboratory the complex geometries facilitated inclined gas jets, an asymmetric gas and particle spreading angle, and an asymmetric particle ejection velocity distribution. These findings suggest that the asymmetry of volcanic vents and/or craters can promote the asymmetric distribution of volcanic ejecta.Which, in turn, will lead to a preferred direction of volcanic fallout and — in case a column collapse occurs — to a preferred direction of the ensuing pyroclastic density currents. The availability of new technology like unoccupied aerial vehicles, photogrammetry and 3D printing provides several opportunities for the volcanological community. Aerial observations allow a fast, inexpensive and safe way to collect geometrical data of volcanic vents and craters, even in times of elevated volcanic activity. In combination with photogrammetry and 3D printing, "real" vents can be produced for increasingly realistic scaled laboratory experiments

    Synthese und Optimierung von Konstruktionsbäumen aus unstrukturierten räumlichen Daten

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    Sensorsysteme für die dreidimensionale Abtastung von Objektoberflächen sind in vielen Bereichen des täglichen Lebens omnipräsent. Moderne Smartphones und Spielekonsolen im Heimanwenderbereich sowie professionelle Systeme, z.B. eingesetzt zur Qualitätssicherung in Fertigungsprozessen, beinhalten Hard- und Softwarekomponenten zur Ermittlung räumlicher Daten. Aus entsprechenden Quellen stammende Datensätze bestehen meist aus einzelnen dreidimensionalen Punkten, die in unstrukturierter Form und potentiell messfehlerbehaftet vorliegen. Oft ist die Erzeugung dieser Punktwolke nur der erste Schritt innerhalb eines komplexen Verarbeitungsprozesses, an dessen Ende eine Repräsentation der räumlichen Daten steht, die für den entsprechenden Anwendungsfall als optimal angesehen wird. Ein solcher Anwendungsfall ist z.B. die automatische Erzeugung von Architekturplänen oder das Reverse Engineering (RE), also die Analyse des Aufbaus und der Funktionsweise eines Produkts. In beiden Fällen ist eine Repräsentation von Vorteil, die unnötige Details abstrahiert und dabei weiterführende Information über den Aufbau des Objekts und dessen elementare Bausteine beinhaltet. Eine solche Darstellung ist die sog. Constructive Solid Geometry (CSG)-Repräsentation, die Modelle als Baumstruktur bestehend aus Booleschen Mengenoperatoren in den inneren Knoten und geometrischen Primitiven in den äußeren Knoten beschreibt. Dabei ist die manuelle Erzeugung dieses sog. Konstruktionsbaums (KB) aus einer Punktwolke zeitaufwendig und für komplexe Datensätze kaum zu bewerkstelligen. Aus diesem Grund werden in dieser Arbeit Methoden vorgestellt, die das Problem der automatischen Synthese von KBs aus fehlerbehafteten Punktwolken robust und effizient lösen. Die vorgestellten Verfahren werden dabei in eine eigens entwickelte Prozess-Pipeline eingebettet und miteinander verknüpft. Den Anfang macht die Einführung eines Systems, das geometrische Primitive, wie Kugeln, Zylinder und allgemeine konvexe Polytope, mittels Maschinellem Lernen (ML) und Evolutionären Algorithmen (EA) in der Eingabepunktwolke detektiert und in diese einpasst. Dieser folgt die Vorstellung einer Methode, die das eigentliche KB-Syntheseproblem für bekannte Primitive löst und dazu auf graphbasierte Partitionierungs- und Vereinfachungsstrategien zur Steigerung von Laufzeiteffizienz und Robustheit zurückgreift. Da die Repräsentation als KB nicht eindeutig ist, lassen sich zusätzliche Metriken, wie z.B. die Baumgröße, bestimmen und existierende KBs entsprechend optimieren. Dieses Problem steht abschließend im Fokus dieser Arbeit, zu dessen vorgestellter Lösung ein Spektrum unterschiedlicher Lösungsstrategien evaluiert und diskutiert wird.Sensor systems for three-dimensional object surface scanning are omnipresent in many areas of daily life. Modern smartphones and game consoles for home users and professional systems, e.g., used for quality assurance in manufacturing processes, contain hard- and software components for measuring spatial data. Data sets originating from such sources usually consist of individual three-dimensional points which are unstructured and potentially subject to measurement errors. Often, the generation of this so-called point cloud is only the first step within a complex processing procedure which results in a spatial data representation that is considered optimal for a specific use case. Such a use case is, for example, the automatic generation of architectural plans or Reverse Engineering (RE), i.e., the analysis of a product's structure and functionality without any prior knowledge. In both cases, it is advantageous to obtain a representation that abstracts unnecessary details while providing more information about an object's structure and elementary building blocks. Such a representation is called Constructive Solid Geometry (CSG), which describes models as a tree structure consisting of Boolean set operators in the inner nodes and geometric primitives in the outer nodes. However, the manual generation of these so-called Construction Trees (CTs) based on a measured point cloud is time-consuming and hardly feasible for complex data sets. For this reason, this work presents methods that can robustly and efficiently solve the problem of automatically synthesizing CTs from error-prone point clouds. The presented methods are thereby embedded and interconnected in a newly developed process pipeline. At first, a system is introduced that detects and fits geometric primitives such as spheres, cylinders and general convex polytopes in the input point cloud using Machine Learning (ML) and Evolutionary Algorithms (EA). This is followed by an introduction of a method that solves the automatic CT synthesis problem for known primitives using graph-based partitioning and simplification strategies to increase runtime efficiency and robustness. Since the representation as CTs is not unique additional metrics such as tree size can be determined, and existing CTs can be optimized accordingly. This problem is the last this work addresses for which a spectrum of different solution strategies is evaluated and discussed

    Internet of Things 2.0: Concepts, Applications, and Future Directions

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    Applications and technologies of the Internet of Things are in high demand with the increase of network devices. With the development of technologies such as 5G, machine learning, edge computing, and Industry 4.0, the Internet of Things has evolved. This survey article discusses the evolution of the Internet of Things and presents the vision for Internet of Things 2.0. The Internet of Things 2.0 development is discussed across seven major fields. These fields are machine learning intelligence, mission critical communication, scalability, energy harvesting-based energy sustainability, interoperability, user friendly IoT, and security. Other than these major fields, the architectural development of the Internet of Things and major types of applications are also reviewed. Finally, this article ends with the vision and current limitations of the Internet of Things in future network environments

    Autonomous Navigation in (the Animal and) the Machine

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    Understanding the principles underlying autonomous navigation might be the most enticing quest the computational neuroscientist can undertake. Autonomous operation, also known as voluntary behavior, is the result of higher cognitive mechanisms and what is known as executive function in psychology. A rudimentary knowledge of the brain can explain where and to a certain degree how parts of a computation are expressed. However, achieving a satisfactory understanding of the neural computation involved in voluntary behavior is beyond today’s neuroscience. In contrast with the study of the brain, with a comprehensive body of theory for trying to understand system with unmatched complexity, the field of AI is to a larger extent guided by examples of achievements. Although the two sciences differ in methods, theoretical foundation, scientific vigour, and direct applicability, the intersection between the two may be a viable approach toward understanding autonomy. This project is an example of how both fields may benefit from such a venture. The findings presented in this thesis may be interesting for behavioral neuroscience, exploring how operant functions can be combined to form voluntary behavior. The presented theory can also be considered as documentation of a successful implementation of autonomous navigation in Euclidean space. Findings are grouped into three parts, as expressed in this thesis. First, pertinent back- ground theory is presented in Part I – collecting key findings from psychology and from AI relating to autonomous navigation. Part II presents a theoretical contribution to RL theory developed during the design and implementation of the emulator for navigational autonomy, before experimental findings from a selection of published papers are attached as Part III. Note how this thesis emphasizes the understanding of volition and autonomous navigation rather than accomplishments by the agent, reflecting the aim of this project – to understand the basic principles of autonomous navigation to a sufficient degree to be able to recreate its effect by first principles
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