Drivgods utgör ett hot mot dammsäkerheten vid vattenkraftsverk eftersom det ökar risken för blockering av kraftverkens utskov vilket i sin tur kan resultera i överströmning av dammen och dammbrott. Fram till idag har drivgods inte ansetts vara ett problem i Sverige. Flera allvarliga drivgodsincidenter utomlands i kombination med en prognostiserad ökning av flödesintensiteter och översvämningstillfällen i Svenska vattendrag har dock lett till ett ökat intresset hos Svenska dammägare. Det saknas idag tillförlitliga metoder för prognostisering av potentiella drivgodsmängder vid extremflöden, vilket försvårar planering av preventiva åtgärder. Denna studie har som mål att utveckla specifika metoder för automatiserad kvantifiering och geografisk analys av drivgods. En modell har utvecklats i ArcGIS Modelbuilder för (i) avgränsning och klassificering av riskområden längs ett vattendrag baserat på identifierade riskfaktorer för drivgods-bildning, (ii) kvantitativ estimering av trädvolym utifrån höjdrasterdataset framtagna från LIDAR punktmoln, och (iii) identifiering av trädindivider baserat på lokalisering av lokala maxpunkter i en digital ytmodell skapad från laser-data. I syfte att utvärdera metodens tillämpbarhet samt undersöka förekomsten av eventuella geografiska mönster i utbredningen av potentiella drivgodskvantiteter appliceras modellen på utvalda dammanläggningar längs med älven Ljungan i Sverige. Modellens noggrannhet i uppskattningen av trädindivider valideras i en fältkontroll. Resultaten indikerar en mycket god överrensstämmelse mellan modellerade och fältkontrollerade värden på trädantal, dock med något underestimerade modellerade värden. Ett större antal träd som riskerar att falla i älven identifierades vid högre vattenflöden som ett resultat av en generellt större geografisk omfattning på översvämmade ytor. Stora vegetationsvolymer identifierades främst i riskområden med en stor andel höga träd men även i riskområden med ett stort antal träd. I vissa riskområden erhölls större relativa vegetationsvolymer trots ett betydligt mindre relativt antal träd vilket antyder att både trädhöjd och trädantal styr vegetationsvolymers storlek. Resultaten av utförda statistiska och geografiska analyser förbättrar överblicken över drivgodsets volym och geografiska fördelning längs vattendrag vilket i sin tur även leder till en bättre förståelse för drivgodsproblemets magnitud för specifika dammanläggningar. Den automatiserade metoden genererar reproducerbara resultat som är jämförbara mellan olika dammanläggningar och vattendrag över tid. Metoden har stor potential för framtida drivgodsanalyser då den erbjuder möjligheten att identifiera riskområden och estimera drivgodskvantiteter inom dessa. / Floating debris poses a threat to dam safety at hydropower dams as it increases the risk of spillways becoming blocked, which can in turn result in dam overflow and failure. Until now it has not been considered a problem in Sweden. However, the occurrence of several major international incidents, together with a projected future increase in streamflow intensities and flood events in Swedish rivers has raised the interest of Swedish dam owners. Presently there is a lack of robust methods for forecasting potential magnitudes of floating debris in extreme streamflow scenarios, which limits planning of preventive measures. This study aims to develop explicit methods for automated quantification and geographical analysis of floating debris. A model is developed in ArcGIS Modelbuilder for (i) identification and classification of risk areas along a river based on identified risk factors for floating debris formation, (ii) quantitative estimation of vegetation volume from elevation raster datasets derived from LIDAR point clouds, and (iii) identification of trees within risk areas based on locating local maxima in a digital surface raster derived from laser-data. In order to evaluate the applicability of the method as well as to investigate the existence of geographical patterns in potential floating debris quantities, the model is applied on selected dam facilities along the Ljungan River in Sweden. The accuracy of modeled tree amounts is validated in a field study. The results indicate high correlation between modeled number of trees and ground truth data, though modeled values are slightly underestimated. A greater number of trees at risk of falling into the river were identified during higher streamflow events as a result of larger areas being flooded. Large volumes of vegetation were identified in risk areas with a high proportion of tall trees as well as in risk areas with a large number of trees. In some risk areas, greater relative vegetation volumes were obtained despite a significantly smaller relative number of trees. This suggests that vegetation volume as a factor depends both upon the number of trees as well as tree height. The results of performed statistical and geographical analyses provide a better overview of floating debris distribution along rivers, leading to a better understanding of potential magnitudes of the problem for specific dam facilities. The automated method generates reproducible results that are comparable between different dam facilities and rivers over time. The method has significant potential for future floating debris analyses as it offers the possibility to identify risk areas and estimate floating debris quantities within them.

This thesis presents a complete and fully automatic method for estimating the volume of an airbag, through all stages of its inflation, with multiple synchronized high-speed cameras. Using recorded contours of the inflating airbag, its visual hull is reconstructed with a novel method: The intersections of all back-projected contours are first identified with an accelerated epipolar algorithm. These intersections, together with additional points sampled from concave surface regions of the visual hull, are then Delaunay triangulated to a connected set of tetrahedra. Finally, the visual hull is extracted by carving away the tetrahedra that are classified as inconsistent with the contours, according to a voting procedure. The volume of an airbag's visual hull is always larger than the airbag's real volume. By projecting a known synthetic model of the airbag into the cameras, this volume offset is computed, and an accurate estimate of the real airbag volume is extracted. Even though volume estimates can be computed for all camera setups, the cameras should be specially posed to achieve optimal results. Such poses are uniquely found for different airbag models with a separate, fully automatic, simulated annealing algorithm. Satisfying results are presented for both synthetic and real-world data.

The goal of the first project was to evaluate strategies for determining the in vitro intrinsic clearance (CLint) of dextrorphan (DR) as metabolized by the UGT2B7 enzyme to obtain dextrorphan glucuronide (DR-G). A direct injection liquid chromatography-mass spectrometry (LC-MS) method was used to monitor products using the pseudo-first-order (PFO) model. Standard enzymatic incubations were also quantified using LC-MS. These data were fit utilizing both PFO and Michaelis-Menten (MM) models to determine estimates of kinetic parameters. The CLint was determined to be 0.28 (± 0.08) µL/min/mg protein for a baculovirus insect cell-expressed UGT2B7 enzyme. This is the first confirmation that dextrorphan is specifically metabolized by UGT2B7 and the first report of these kinetic parameters. Simulated chromatographic data were used to determine the precision and accuracy in the estimation of peak volumes in comprehensive two-dimensional liquid chromatography (2D-LC). Volumes were determined both by summing the areas in the second dimension chromatograms via the moments method and by fitting the second dimension areas to a Gaussian peak. When only two second dimension signals are substantially above baseline, the accuracy and precision are poor because the solution to the Gaussian fitting algorithm is indeterminate. The fit of a Gaussian peak to the areas of the second dimension peaks is better at predicting the peak volume when there are at least three second dimension injections above the limit of detection. Based on simulations where the sampling interval and sampling phase were varied, we conclude for well-resolved peaks that the optimum precision in peak volumes in 2D separations will be obtained when the sampling ratio is approximately two. This provides an RSD of approximately 2 % for the signal-to-noise (S/N) used in this work. The precision of peak volume estimation for experimental data was also assessed, and RSD values were in the 4-5 % range. We conclude that the poorer precision found in the 2D-LC experimental data as compared to 1D-LC is due to a combination of factors, including variations in the first dimension peak shape related to undersampling and loss in S/N due to the injection of multiple smaller peaks onto the second dimension column.

The thesis work evaluates a method to estimate the volume of stone and gravelpiles using only a cellphone to collect video and sensor data from the gyroscopesand accelerometers. The project is commissioned by Escenda Engineering withthe motivation to replace more complex and resource demanding systems with acheaper and easy to use handheld device. The implementation features popularcomputer vision methods such as KLT-tracking, Structure-from-Motion, SpaceCarving together with some Sensor Fusion. The results imply that it is possible toestimate volumes up to a certain accuracy which is limited by the sensor qualityand with a bias. / I rapporten framgår hur volymen av storskaliga objekt, nämligen grus-och stenhögar,kan bestämmas i utomhusmiljö med hjälp av en mobiltelefons kamerasamt interna sensorer som gyroskop och accelerometer. Projektet är beställt avEscenda Engineering med motivering att ersätta mer komplexa och resurskrävandesystem med ett enkelt handhållet instrument. Implementationen använderbland annat de vanligt förekommande datorseendemetoderna Kanade-Lucas-Tommasi-punktspårning, Struktur-från-rörelse och 3D-karvning tillsammans medenklare sensorfusion. I rapporten framgår att volymestimering är möjligt mennoggrannheten begränsas av sensorkvalitet och en bias.

Kohlenmonoxid (CO) wird nach Einatmung weitgehend an Hämoglobin gebunden, eine minimale Bindung findet auch an Myoglobin statt. Die Kohlenmonoxid-Hämoglobinkonzentration (COHb) im Blut steigt nach Inhalation proportional zur inhalierten CO-Menge und zur Hämoglobinmasse an. Dieser Anstieg wird über die CO-Hämoxymetrie ermittelt und resultierend aus der CO-Verdünnung die Hämoglobinmenge berechnet. Über die Hämoglobinkonzentration und den Hämatokrit kann im Anschluss das Blutvolumen berechnet werden. Grundsätzlich ist dieses Verfahren seit über 100 Jahren bekannt und wird seit ca. 1995 als Routinemethode zur Blutvolumenbestimmung in der Sportmedizin verwendet. Es existieren darüber hinaus methodische Probleme durch die CO-Abatmung und die Evaluierung in großen Kollektiven. Die hier vorgestellte Methodik beinhaltet die Weiterentwicklung der CO-Methode zur Direktmessung im geschlossenen System. Die Probanden atmen dabei ein exakt definiertes Bolus-Volumen in einem geschlossenen Atmungssystem über 15 Minuten ein. Die maximale Arbeitsplatzkonzentration (MAK: COHb 5%), also die resultierende COHb-Konzentration im Blut bei einer CO-Langzeitexposition von 35 ppm, wird in der Regel nur leicht überschritten. Dazu wurden an 104 Probanden zwei Vergleichsmessungen in definiertem Abstand und an 20 Probanden Wiederholungsmessungen nach Blutspende zum Nachweis der Reliabilität und Validität durchgeführt. Zusätzlich ist die Abfallkinetik von COHb an 20 Probanden bestimmt worden. Im Ergebnis stellt sich methodenbedingt ein COHb-Steady-State nach 9 Minuten Rückatmung im geschlossenen System ein. Der Typical-Error der Messwiederholung der Methodik liegt bei 1,9% bzw. nach weiterer Modifizierung der Methodik bei 1,3%. Der Nachweis eines Blutverlustes von 490 ml im Rahmen einer Blutspende zeigt nur eine minimale Abweichung von 10 g Hämoglobinmasse zwischen gemessenem und kalkuliertem Verlust. Die Halbwertszeit von COHb wurde mit 135 min bestimmt. Die verwendete Methodik zeigt aufgrund der induzierten COHb-Steady-State-Kinetik Vorteile bei der Anwendung und Genauigkeit. Der Nachweis der Wiederholbarkeit und Messgenauigkeit konnte an einem hinreichend großen Kollektiv gezeigt werden. Bei Mehrfachanwendung bietet die Sensitivität der Methodik die Möglichkeit der Aufdeckung von Manipulationen des Blutes über Erythropoetin (EPO) oder Eigenbluttransfusion. Dabei bewegt sich die eingesetzte CO-Belastung während der Methode im Bereich des Konsums von wenigen Zigaretten.

In this thesis we investigate the problem of portfolio re-balancing for fixed periods using a cost/risk adaptation model and stochastic optimization. The cost/risk adaptation model takes theory of optimal liquidity costs and risk preference to build a universe in which we try to find better strategies than conventional ones. The results are focused on the comparison between the conventional execution strategies versus our developed model. We have found that our model outperforms the conventional methods for all assets that has been evaluated, and especially for investors whom value exposure to the markets higher.

<p dir="ltr">Several industrial and commercial bulk material management applications rely on accurate, current stockpile volume estimation. Proximal imaging and LiDAR sensing modalities can be used to derive stockpile volume estimates in outdoor and indoor storage facilities. Among available imaging and LiDAR sensing modalities, the latter is more advantageous for indoor storage facilities due to its ability to capture scans under poor lighting conditions. Evaluating volumes from such sensing modalities requires the pose (i.e., position and orientation) parameters of the used sensors relative to a common reference frame. For outdoor facilities, a Global Navigation Satellite System (GNSS) combined with an Inertial Navigation System (INS) can be used to derive the sensors’ pose relative to a global reference frame. For indoor facilities, GNSS signal outages will not allow for such capability. Prior research has developed strategies for establishing the sensor position and orientation for stockpile volume estimation while relying on multi-beam spinning LiDAR units. These approaches are feasible due to the large range and Field of View (FOV) of such systems that can capture the internal surfaces of barn and dome storage facilities.</p><p dir="ltr">The mechanical movement of multi-beam spinning LiDAR units together with the harsh conditions within indoor facilities (e.g., excessive humidity, dust, and corrosive environment in deicing salt storage facilities) limit the use of such systems. With the increasing availability of solid-state LiDAR units, there is an interest in exploring their potential for stockpile volume estimation. In spite of their higher robustness to harsh conditions, solid-state LiDAR units have shorter distance measurement range and limited FOV when compared with multi-beam spinning LiDAR. This research presents a strategy for the extrinsic calibration (i.e., estimating the relative pose parameters) of installed solid-state LiDAR units inside stockpile storage facilities. The extrinsic calibration is made possible using deployed spherical targets and a complete, reference scan of the facility from another LiDAR sensing modality. The proposed research introduces strategies for: 1) automated extraction of the spherical targets; 2) automated matching of these targets in the solid-state LiDAR and reference scans using invariant relationships among them; and 3) coarse-to-fine estimation of the calibration parameters. Experimental results in several facilities have shown the feasibility of using the proposed methodology to conduct the extrinsic calibration and volume evaluation with an error percentage less than 3.5% even with occlusion percentages reaching up to 50%.</p>