Accessible flood modeling for low-resource, data-scarce communities currently does not exist. This paper proposes using drone imagery to compensate for the lack of other flood modeling data (i.e. streamflow measurements). Three flood models were run for Dzaleka Refugee Camp, located in Dowa, Malawi. Two of the models (the Soil and Water Assessment Tool (SWAT) and the Hydrologic Engineering Center River Analysis System (HEC-RAS)) are commonly used hydrological-hydraulic based models. The third model, the Water Caused Erosion Patterns (WCEP) model, was proposed by the author to capitalize on the high-resolution drone imagery using geological-geomorphological information. The drone imagery used in this study has a resolution of 3.5cm and shows erosion patterns throughout the refugee camp. By comparing the erosion patterns to flow direction of the surface, the erosion patterns were determined to be water caused or not water caused, the erosion patterns considered water caused were defined as high-risk flood areas, creating the WCEP model. The three models were compared using locations of collapsed houses throughout the camp. It was found that the WCEP model represents the location of collapsed houses significantly better (misclassification rate below 17%) than the SWAT or HEC-RAS models (misclassification rate below 54%, and 67% respectively). The WCEP model was combined with the best hydrological-hydraulic model (SWAT) to create a hydrogeomorphological model which capitalizes on both the drone imagery and the hydrological process. / Master of Science / The negative impact flooding has on communities can be reduced through flood modeling. But commonly used flood models are not accessible to data-scarce communities because of the historical data the models require. This paper explores using aerial imagery taken by a drone to make-up for the lack of historical data at Dzaleka Refugee Camp located in Dowa, Malawi. Drone imagery has a very high spatial resolution (3.5cm), so it is able to provide a lot of details, including marks that show an increase of flooding in certain areas and elevation information. The flood model presented in this paper is created using the found flood marks in drone imagery. The presented model is then compared to two commonly used flood models, and all three flood models are compared to locations of houses that collapsed from flooding throughout the refugee camp. The model created using drone imagery did the best job predicting high-risk locations with misclassification rates below 17%. The drone imagery model was then combined with a commonly used model to create a more comprehensive flood model, capitalizing on all available data.

This paper considers a study of the thermo-elastic behaviour of a disc brake during heavy braking. The work is concerned with working towards developing design advice that provides uniform heating of the disc, and equally important, even dissipation of heat from the disc blade. The material presented emanates from a combination of modeling, on-vehicle testing but mainly laboratory observations and subsequent investigations. The experimental work makes use of a purpose built high speed brake dynamometer which incorporates the full vehicle suspension for controlled simulation of the brake and vehicle operating conditions. Advanced instrumentation allows dynamic measurement of brake pressure fluctuations, disc surface temperature and discrete vibration measurements. Disc run-out measurements using non-contacting displacement transducers show the disc taking up varying orders of deformation ranging from first to third order during high speed testing. This surface interrogation during braking identifies disc deformation including disc warping, 'ripple' and the effects of 'hot spotting'. The mechanical measurements are complemented by thermal imaging of the brake, these images showing the vane and vent patterns on the surface of the disc. The results also include static surface scanning, or geometry analysis, of the disc which is carried out at appropriate stages during testing. The work includes stress relieving of finished discs and subsequent dynamometer testing. This identifies that in-service stress relieving, due to high heat input during braking, is a strong possibility for the cause of disc 'warping'. It is also seen that an elastic wave is established during a braking event, the wave disappearing on release of the brake.

Yardangs, elongated landforms sculpted by wind erosion, are prominent features in the Campo de Piedra Pomez (CPP) region of the Puna-Altiplano Plateau, Argentina. This study explores their formation and evolution through the examination of a 6 km by 0.5 km area captured in 2019 and a 5 km by 0.5 km area in 2024. High-resolution drone imagery and satellite data were employed to classify and quantify yardangs, gravel, and underlying bedrock across a vast study area. The research reveals a variation in yardang distribution and morphology from northwest (windward) to southeast (leeward), noting a significant decrease in yardang and bedrock area, coupled with an increase in gravel coverage. This linear pattern suggests a progressive formation process, highlighting varying degrees of yardang maturity influenced by wind erosion and sediment transport. Digital Elevation Models (DEMs) indicated that elevated regions within the CPP have a higher concentration of yardangs, suggesting localized factors such as geological composition and wind exposure contribute to yardang development. Additionally, gravel analysis showed a distinct difference in size, shape, and composition along the windward-to-leeward transect: larger, more angular gravel with little quartz upwind, and smaller, well-rounded gravel with higher quartz content downwind. These findings highlight the role of prevailing northwest winds in shaping the yardangs and transporting sediment across the region. A key aspect of this research is the proposal of a staged progression model for yardang formation, where windward yardangs are less mature and downwind yardangs exhibit more advanced erosional features. This model provides a nuanced understanding of yardang evolution and highlights the dynamic nature of aeolian processes. Furthermore, the study draws parallels with similar landforms on Mars, Venus, and Titan, suggesting that the mechanisms of yardang formation on Earth can inform our understanding of aeolian processes on other planetary bodies. Overall, this study enhances the understanding of yardang formation and evolution, contributing valuable insights into the interaction between geological structures and atmospheric forces. The findings underscore the importance of high-resolution imagery and photogrammetry in geomorphological research and offer a foundation for future studies to explore the detailed mechanisms behind yardang formation on Earth and other planets.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第25432号 / 情博第870号 / 新制||情||146(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 大手 信人, 教授 土居 秀幸, 准教授 市川 光太郎 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Through a systematic exploration of varied but interconnected landscapes, this dissertation investigates how drone technology not only reshapes individual sectors but also interlinks them, fostering a cohesive advancement toward a more efficient and technologically integrated future. By focusing on drone applications in last-mile deliveries, medical supplies, and aerospace innovations, this work contributes robust insights into the strategic adoption of drone technology, offering guidelines for policymakers, industry stakeholders, and academic scholars aiming to navigate and leverage the potentials of this groundbreaking technological frontier. Each essay within this dissertation builds upon these themes, presenting in-depth analyses and discussions that bridge theoretical knowledge with practical applications. By addressing specific challenges and opportunities within each sector, this comprehensive study contributes to academic research. It provides actionable insights for practitioners and policymakers engaged in the cutting-edge realms of logistics, healthcare delivery, and aerospace development.

The use of autonomous aerial vehicles (UAVs) to explore unknown environments is a growing field of research; of particular interest is locating a target that emits a signal within an unknown environment. Several physical processes produce scalar signals that attenuate with distance from their source, such as chemical, biological, electromagnetic, thermal, and radar signals. The natural decay of the signal with increasing distance enables a gradient ascent method to be used to navigate toward the target. The UAVs navigate around obstacles whose positions are initially unknown; a hybrid controller comprised of overlapping control modes enables robust obstacle avoidance in the presence of exogenous inputs by precluding topological obstructions. Limitations of a distributed gradient augmentation approach to obstacle avoidance are discussed, and an alternative algorithm is presented which retains the robustness of the hybrid control while leveraging local obstacle position information to improve non-collision reliability. A heterogeneous swarm of multirotors demonstrates the target locating problem, sharing information over a multicast wireless private network in a fully distributed manner to form an estimate of the signal's gradient, informing the direction of travel toward the target. The UAVs navigate around obstacles, showcasing both algorithms developed for obstacle avoidance. Each UAV performs its own target seeking and obstacle avoidance calculations in a distributed architecture, receiving position data from an OptiTrack motion capture system, illustrating the applicability of the control law to real world challenges (e.g., unsynchronized clocks among different UAVs, limited computational power, and communication latency). Experimental and theoretical results are compared. / Master of Science / In this project, a new method for locating a target using a swarm of unmanned drones in an unknown environment is developed and demonstrated. The drones measure a signal such as a beacon that is being emitted by the target of interest, sharing their measurement information with the other drones in the swarm. The magnitude of the signal increases as the drones move toward the target, allowing the drones to estimate the direction to the target by comparing their measurements with the measurements collected by other drones. While seeking the target in this manner, the drones detect obstacles that they need to avoid. An issue that arises in obstacle avoidance is that drones can get stuck in front of an obstacle if they are unable to decide which direction to travel; in this work, the decision process is managed by combining two control modes that correspond to the two direction options available, using a robust switching algorithm to select which mode to use for each obstacle. This work extends the approach used in literature to include multiple obstacles and allow obstacles to be detected dynamically, enabling the drones to navigate through an unknown environment as they locate the target. The algorithms are demonstrated on unmanned drones in the VT SpaceDrones test facility, illustrating the capabilities and effectiveness of the methods presented in a series of scenarios.

Vertical Takeoff and Landing (VTOL) Unmanned Aerial Vehicles (UAVs) possess several beneficial attributes, including requiring minimal space to takeoff, hover, and land. The tailsitter is a type of VTOL airframe that combines the benefits of VTOL capability with the ability to achieve efficient horizontal flight. One type of tailsitter, the Quadrotor Biplane (QRBP), can transition the vehicle from hover as a quadrotor to horizontal flight as a biplane. The vehicle used in this thesis is a QRBP designed with special considerations for fully autonomous operation in an outdoor environment in the presence of model uncertainties. QRBPs undergo a rotation of 90° about its pitch axis during transition from vertical to horizontal flight that induces strong aerodynamic forces that are difficult to model, thus necessitating the use of a robust control method to overcome the resulting uncertainties in the model. A feedback-linearizing controller augmented with an H-Infinity robust control is developed to regulate the altitude and pitch angle of the vehicle for the whole flight regime, including the ascent, transition forward, and landing. The performance of the proposed control design is demonstrated through numerical simulations in MATLAB and outdoor flight tests. The H-Infinity controller successfully tracks the prescribed trajectory, demonstrating its value as a computationally inexpensive, robust control technique for QRBP tailsitter UAVs. / Master of Science / Vertical Takeoff and Landing (VTOL) Unmanned Aerial Vehicles (UAVs) are a special type of UAV that can takeoff, hover, and land vertically, which lends several benefits. VTOL aircraft have recently gained popularity due to their potential to serve as fast and efficient payload delivery vehicles for e-commerce. One type of VTOL aircraft, the Quadrotor Biplane (QRBP) combines the ability of a quadrotor aircraft to hover, with the efficient horizontal flight of a biplane. Such a vehicle is able to takeoff and land in confined spaces, and also travel large distances on a single battery. However, the takeoff maneuver of a QRBP involves pitching from vertical to horizontal flight, which causes the vehicle to experience strong aerodynamic effects that are difficult to accurately model. Thus, to autonomously perform this unique maneuver, a robust control technique is necessary. A robust UAV controller is one that functions even when there is a degree of uncertainty in the predicted behavior of the vehicle, such as differences between estimated and actual vehicle parameters, or the presence of external disturbances such as wind. Therefore, a robust controller known as H-Infinity is developed to regulate the altitude and pitch angle of the QRBP as it takes off, transitions to forward flight, flies as a biplane, transitions back to vertical flight, and lands. The performance of the proposed control design is validated using numerical simulations performed in MATLAB, and flight tests. The H-Infinity controller successfully tracks the prescribed trajectory, demonstrating its value as a reliable, computationally inexpensive, robust control technique for QRBP UAVs.

The twospotted spider mite (TSSM), Tetranychus urticae Koch, is a common polyphagous pest in peanut agroecosystems. The mite has caused serious economic losses to peanut farmers in the Virginia-Carolina area, where approximately 20% of the peanuts are produced annually in the United States. Peanut farmers depend on pesticides to control mite populations. Because TSSM has developed resistance to many acaricides and there are restrictions on the use of pesticides, an alternative approach, such as precision pest management, is needed that would reduce the amount of pesticides that must be applied. This study was initiated to determine whether precision pest management is a feasible management strategy for use against TSSM populations in peanut. Two requirements of the precision management approach are that maps of the spatial distribution of TSSM populations can be developed and the pattern of distribution changes little over time to allow management strategies to be implemented. To this end, a study of four commercial peanut fields located in two counties of southeastern Virginia was conducted to characterize the spatial distribution of TSSM populations. Intensive sampling of TSSM populations was conducted within each of the fields. The results showed that there was a general increase in TSSM populations during the early phases of sampling. Fields with low densities of TSSM populations had a spatial distribution that was either uniform or random; in fields with relatively higher densities, TSSM populations usually were aggregated. Little or no change in the spatial distribution of TSSM occurred from week to week in all fields that were sampled. Where changes in the distribution were observed, these were apparently caused by the application of a pesticide by the grower. The study also looked at remote sensing technology as an alternative to intensive sampling within peanut fields. Research was conducted under laboratory conditions to determine whether damage caused by feeding TSSM could be detected spectrally before symptoms become visible. The study showed that after eight days leaves of peanut plants subjected to low soil moisture levels had significantly lower reflectance ratios (mean = 9.4766; a = 0.05) than plants given medium (mean = 10.0186) or high (mean = 10.5413) soil moisture levels. After 10 days, there were significant differences (P < 0.05) in the mean reflectance ratios of peanut leaves exposed to four levels of spider mite densities (0, 5, 10, 20 mites/leaf) and the three levels of soil moisture. However, no significant interaction was observed between soil moisture and spider mite density (P = 0.8710). The mean reflectance ratio for 20 TSSM per leaf was found to be significantly lower than 0, 5, and 10 TSSM per leaf at all levels of moisture (low, medium, and high). The results suggested that remote sensing could be used to detect and map plant damage caused by feeding of spider mites before visual symptoms of damage are observed. The study also attempted to develop a platform for using remote sensing technology in the field. An Unmanned Air Vehicle (UAV) was evaluated that carried a remote sensing system. The UAV remote sensing system was flown over peanut fields where it captured images, which were analyzed to show the spatial distribution of plant stress. Further studies are needed to relate the distribution of plant stress or damage observed by the UAV with the distribution of TSSM densities within peanut fields. Once this has been accomplished, low-altitude remote sensing could be used as an alternative to sampling for building maps of the spatial distribution of TSSM populations for precision pest management. / Master of Science

As a component of the overall policy to defeat global terrorism and prevent attacks against the U.S., the Bush and Obama administrations have turned to unmanned aerial vehicles (UAVs), or drones. From 2004 to 2014, Pakistan has seen the largest volume of U.S. drone strikes targeting radical groups such as al Qaeda and the Taliban, a trend that is likely to continue for the foreseeable future. On the surface, using drones to eliminate terrorists while avoiding an official armed conflict aligns with the theory of neo- or structural realism developed by Kenneth Waltz. And yet although 9/11 served as the impetus for the U.S. to refocus attention on ameliorating the threat of terrorism and to initiate far-reaching measures to protect homeland security, there remains intense debate over whether or not the U.S. is actually more secure than it was prior to 9/11. While structural realism is still relevant to the current international system, the effects of drone strikes in Pakistan may set the U.S. on a path toward increasingly destabilizing situations that could lead to heightened insecurity and ultimately a change in power in the international system. The existing literature suggests that drone strikes in Pakistan are (1) leading to revenge-driven counter attacks, (2) intensifying radical anti-Americanism and creating more potential terrorists, (3) damaging the U.S. relationship with nuclear-armed Pakistan, (4) destabilizing the regions where drone attacks are launched, and (5) undermining American 'soft power.' The culmination of these five trends has the potential to disrupt the current balance of power in a way that is not in America's national interest. The unique security dilemma presented by the asymmetrical threat of terrorism and the asymmetrical response of drone strikes necessitates the continued evolution of neorealism as an IR theory. / Master of Arts

The thesis details the development of computer vision and path planning algorithms in order to map an area via UAV aerial imagery and aid a UGV in navigating a roadway when the road conditions are not previously known (i.e. disaster situations). Feature detection was used for transform calculation and image warping to create mosaics. A continuous extension using dynamic cropping based on newly gathered images was used to improve performance and computation time. Road detection using k-means segmentation and binary image morphing was applied to aerial imagery with image shifting tracked by the mosaicking to develop a large road map. Improvements to computation time were developed using k-means for calibration at intervals and nearest neighbor calculating for each image. This showed a greatly reduced computation time for a series of images with only 1-2% error compared to regular k-means segmentation. Path planning for the UAV utilized a traveling wave applied to the traveling salesman genetic algorithm solution to prioritize close targets and facilitate UGV deployment. Based on the large map of road locations and road detection method, the Rapidly-exploring Random Tree (RRT) algorithm was modified for real-time application and efficient data processing. Considerations of incomplete maps and goal adjustments was also incorporated. Finally, aerial imagery from an actual UAV flight was processed using these algorithms to validate and test flight parameters. Testing of different flight parameters showed the desired image overlay of 50% to give accurate mosaics. It also helped to develop a benchmark for the altitude, image resolution and frequency for flights. Vehicle requirements and algorithm limitations for future applications of this system are also discussed. / Master of Science