2,433 research outputs found
"Le present est plein de l’avenir, et chargé du passé" : Vorträge des XI. Internationalen Leibniz-Kongresses, 31. Juli – 4. August 2023, Leibniz Universität Hannover, Deutschland. Band 2
[No abstract available]Deutschen Forschungsgemeinschaft (DFG)/Projektnr. 517991912VGH VersicherungNiedersächsisches Ministerium für Wissenschaft und Kultur (MWK
Leveraging elasticity theory to calculate cell forces: From analytical insights to machine learning
Living cells possess capabilities to detect and respond to mechanical features of their surroundings. In traction force microscopy, the traction of cells on an elastic substrate is made visible by observing substrate deformation as measured by the movement of embedded marker beads. Describing the substrates by means of elasticity theory, we can calculate the adhesive forces, improving our understanding of cellular function and behavior. In this dissertation, I combine analytical solutions with numerical methods and machine learning techniques to improve traction prediction in a range of experimental applications. I describe how to include the normal traction component in regularization-based Fourier approaches, which I apply to experimental data. I compare the dominant strategies for traction reconstruction, the direct method and inverse, regularization-based approaches and find, that the latter are more precise while the former is more stress resilient to noise. I find that a point-force based reconstruction can be used to study the force balance evolution in response to microneedle pulling showing a transition from a dipolar into a monopolar force arrangement. Finally, I show how a conditional invertible neural network not only reconstructs adhesive areas more localized, but also reveals spatial correlations and variations in reliability of traction reconstructions
Cloud fragmentation and chemical evolution of the high-mass star-forming region G327.3-0.6
In the struggle to understand how stars form in a cluster, it is important to study the morphology, kinematics and chemistry of the star-forming clouds. This thesis focuses on the high-mass star-forming region G327.3-0.6, which is a 3 pc filament at a distance of 3.3 kpc, hosting one hot molecular core and a set of cold dense cores. It was observed with the Atacama Large Millimetre/Sub-millimetre Array (ALMA) at 1.3 mm with high resolution 2". The data were self-calibrated to improve the signal to noise ratio by a factor of 2. The dendrogram algorithm together with the background subtraction were adopted to determine 66 compact cores. Minimum spanning tree determined a median core separation at 0.15pc and possible hierarchical fragmentation, which was supported by the two-point correlation function. Core mass function (CMF) was fitted with an index of -0.83, which is a hint of high-mass star-forming regions. The fragmentation in the filament was dominated by thermal support in small scale (~0.15pc) and by turbulence in large scale (~0.4pc). With toolbox XCLASS, 26 molecules and 39 isotopes were identified in the hot core spectrum, and a temperature of 270K was derived. The temperature error is around 60%. The moment maps were derived for 42 molecular transitions and analyzed by the Histogram of Oriented Gradient (HOG), indicating correlations between DCN and continuum, SiO and H2CO/CH3OH. Principal component analysis (PCA) and clustering algorithm were applied to the average spectra of each core to classify the evolutionary stages. Four groups are found with chemical and physical distinctions, suggesting the excitation temperature of CH3OH to be a good evolutionary indicator. The infrared environment is complex and may associated with photon-dissociation regions (PDRs)
"Le present est plein de l’avenir, et chargé du passé" : Vorträge des XI. Internationalen Leibniz-Kongresses, 31. Juli – 4. August 2023, Leibniz Universität Hannover, Deutschland. Band 3
[No abstract available]Deutschen Forschungsgemeinschaft (DFG)/Projektnr. 517991912VGH VersicherungNiedersächsisches Ministerium für Wissenschaft und Kultur (MWK
Aerial Drone-based System for Wildfire Monitoring and Suppression
Wildfire, also known as forest fire or bushfire, being an uncontrolled fire crossing an area of combustible vegetation, has become an inherent natural feature of the landscape in many regions of the world. From local to global scales, wildfire has caused substantial social, economic and environmental consequences. Given the hazardous nature of wildfire, developing automated and safe means to monitor and fight the wildfire is of special interest. Unmanned aerial vehicles (UAVs), equipped with appropriate sensors and fire retardants, are available to remotely monitor and fight the area undergoing wildfires, thus helping fire brigades in mitigating the influence of wildfires. This thesis is dedicated to utilizing UAVs to provide automated surveillance, tracking and fire suppression services on an active wildfire event. Considering the requirement of collecting the latest information of a region prone to wildfires, we presented a strategy to deploy the estimated minimum number of UAVs over the target space with nonuniform importance, such that they can persistently monitor the target space to provide a complete area coverage whilst keeping a desired frequency of visits to areas of interest within a predefined time period. Considering the existence of occlusions on partial segments of the sensed wildfire boundary, we processed both contour and flame surface features of wildfires with a proposed numerical algorithm to quickly estimate the occluded wildfire boundary. To provide real-time situational awareness of the propagated wildfire boundary, according to the prior knowledge of the whole wildfire boundary is available or not, we used the principle of vector field to design a model-based guidance law and a model-free guidance law. The former is derived from the radial basis function approximated wildfire boundary while the later is based on the distance between the UAV and the sensed wildfire boundary. Both vector field based guidance laws can drive the UAV to converge to and patrol along the dynamic wildfire boundary. To effectively mitigate the impacts of wildfires, we analyzed the advancement based activeness of the wildfire boundary with a signal prominence based algorithm, and designed a preferential firefighting strategy to guide the UAV to suppress fires along the highly active segments of the wildfire boundary
Shaped-based IMU/Camera Tightly Coupled Object-level SLAM using Rao-Blackwellized Particle Filtering
Simultaneous Localization and Mapping (SLAM) is a decades-old problem. The classical solution to this problem utilizes entities such as feature points that cannot facilitate the interactions between a robot and its environment (e.g., grabbing objects). Recent advances in deep learning have paved the way to accurately detect objects in the image under various illumination conditions and occlusions. This led to the emergence of object-level solutions to the SLAM problem. Current object-level methods depend on an initial solution using classical approaches and assume that errors are Gaussian. This research develops a standalone solution to object-level SLAM that integrates the data from a monocular camera and an IMU (available in low-end devices) using Rao Blackwellized Particle Filter (RBPF). RBPF does not assume Gaussian distribution for the error; thus, it can handle a variety of scenarios (such as when a symmetrical object with pose ambiguities is encountered). The developed method utilizes shape instead of texture; therefore, texture-less objects can be incorporated into the solution. In the particle weighing process, a new method is developed that utilizes the Intersection over the Union (IoU) area of the observed and projected boundaries of the object that does not require point-to-point correspondence. Thus, it is not prone to false data correspondences. Landmark initialization is another important challenge for object-level SLAM. In the state-of-the-art delayed initialization, the trajectory estimation only relies on the motion model provided by IMU mechanization (during the initialization), leading to large errors. In this thesis, two novel undelayed initializations are developed. One relies only on a monocular camera and IMU, and the other utilizes an ultrasonic rangefinder as well. The developed object-level SLAM is tested using wheeled robots and handheld devices, and an error (in the position) of 4.1 to 13.1 cm (0.005 to 0.028 of the total path length) has been obtained through extensive experiments using only a single object. These experiments are conducted in different indoor environments under different conditions (e.g. illumination). Further, it is shown that undelayed initialization using an ultrasonic sensor can reduce the algorithm's runtime by half
New Music for a New World: Robert Ashley’s Television Operas
Robert Ashley defined the majority of his works as “television operas”—spoken narrative music for television broadcast. Analyzing Ashley’s works through their cross-disciplinarity, this thesis addresses the development of Ashley’s chosen medium; assesses his use of visual, linguistic, and musical structures; and interprets their basis in American cultural identity
Flow Dynamics and Aeroacoustics of Flow-induced Vibration of Bluff Bodies
Flow-induced vibration (FIV), a common phenomenon of fluid-structure interaction (FSI), is found everywhere and at all scales in the applications of marine, civil, aeronautical, and power engineering.
The study of FIV phenomenology, ranging from fatigue and concomitant damage of structures to its exploitation for energy extraction, has been an active area of fundamental research.
The research on the mechanism supporting the amplifying, stabilizing, and suppressing of FIV has practical implications for the structural design for optimal engineering fatigue control, energy utilization, etc. Moreover, the noise propagation generated from FIV is also accompanying environmental pollution that should not be ignored.
However, past research on the FIV supported by nonlinear spring and the corresponding detailed FSI characteristics are limited. The present study will conduct a numerical FIV study of bluff bodies mounted by linear and nonlinear springs, and analyze the impact of stiffness nonlinearity on the FIV responses, including the amplitude variation, phase change, frequency variation, and wake pattern. The technical method used in this part is direct Computational Fluid Dynamics/Computational Structural Dynamics (CFD/CSD) simulation with the full-order model (FOM), via the coupled Navier-Stokes and body-structure equations.
Additionally, the present study investigates the geometrical influences on FIV response and the mechanism underpinning the transfer from lock-in range to desynchronization or galloping range. Different body shapes, varied Reynolds numbers, and reduced velocity will involve many cases, as a result, expensive time will be consumed if the corresponding grids are generated and FOM calculations are carried out for each case. This part of the research will be mainly based on the data-driven stability analysis using the reduced-order model (ROM), and FOM based on CFD/CSD method will be used as supplementary for comparison. ROM could also provide the modal analysis and physical perspective that are not available for FOM.
Combining ROM and FOM methods, this thesis explores the mode transformation and interaction in the lock-in behavior of laminar flow past a circular cylinder. For the galloping analysis, it is observed very small changes in the windward interior angle of an isosceles-trapezoidal body can provoke or suppress galloping---indeed, a small decrease or increase (low to 1°) of the windward interior angle from a right angle (90°) can result in a significant enhancement and complete suppression, respectively, of the galloping oscillations.
This supports our hypothesis that the contraction and/or expansion of the cross-section in the streamline direction is significantly responsible for the galloping response.
Furthermore, one novel methodology of data-driven stability analysis via the superposition of 2-D reduced-order modes (SROM) for the purpose of performing modal analysis and stability predictions of 3-D flow-induced vibration with spanwise shear inflow is presented.
Lastly, noise propagation from energy harvesters based on the FIV mechanism also deserves attention. Owing that there is limited past research on noise propagation from oscillating cylinders, an investigation on aeroacoustics study of different oscillation patterns of single cylinder and tandem cylinders is carried out
Surgical Subtask Automation for Intraluminal Procedures using Deep Reinforcement Learning
Intraluminal procedures have opened up a new sub-field of minimally invasive surgery that use flexible instruments to navigate through complex luminal structures of the body, resulting in reduced invasiveness and improved patient benefits. One of the major challenges in this field is the accurate and precise control of the instrument inside the human body. Robotics has emerged as a promising solution to this problem. However, to achieve successful robotic intraluminal interventions, the control of the instrument needs to be automated to a large extent. The thesis first examines the state-of-the-art in intraluminal surgical robotics and identifies the key challenges in this field, which include the need for safe and effective tool manipulation, and the ability to adapt to unexpected changes in the luminal environment. To address these challenges, the thesis proposes several levels of autonomy that enable the robotic system to perform individual subtasks autonomously, while still allowing the surgeon to retain overall control of the procedure. The approach facilitates the development of specialized algorithms such as Deep Reinforcement Learning (DRL) for subtasks like navigation and tissue manipulation to produce robust surgical gestures. Additionally, the thesis proposes a safety framework that provides formal guarantees to prevent risky actions. The presented approaches are evaluated through a series of experiments using simulation and robotic platforms. The experiments demonstrate that subtask automation can improve the accuracy and efficiency of tool positioning and tissue manipulation, while also reducing the cognitive load on the surgeon. The results of this research have the potential to improve the reliability and safety of intraluminal surgical interventions, ultimately leading to better outcomes for patients and surgeons
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