Browsing by Subject "Data fusion"
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Item Detecting, identifying, and reconstructing fluid flows(2020-11) Wang, MengyingThe estimation of fluid flow from limited sensor measurements is central for many applications in physics and engineering. Sensing instruments allow us to detect the spatiotemporal signatures generated by the surrounding flows, such that the information about what generated those flows can be learned. Moreover, we can model the flow dynamics if the flow type is classified by analyzing those signatures. The flow reconstruction allows us to estimate the flow field by fusing information from model predictions and measurements. Subsequently, control law can be designed based on the estimation for various applications, such as drag reduction and transient energy suppression. In this thesis, we focus on the sensing and estimation of two typical fluid flows: (1) vortex wakes, and (2) wall-bounded turbulence. The first part of this thesis investigates the detection and identification of vortex wakes. Vortex wakes commonly arise in marine locomotion and can exhibit markedly different dynamics. We propose a vortex wake detection protocol, such that different wakes can be classified using hydrodynamic signals measured at a single location on the nearby surface of a fish-like body. The model parameter of the wakes can be further identified given a short period of the vortex trajectories. In the second part of this thesis, we propose a multi-sensor fusion algorithm to reconstruct the turbulent flow by fusing a linear model, non-time-resolved field measurements, and judiciously placed point sensor measurements. Estimation of turbulent flows is more challenging, due to the breadth of spatial and temporal scales underlying dynamics. Field measurements are constrained to sub-Nyquist sampling frequencies, and point measurements provide spatially limited information. The multi-sensor fusion approach is demonstrated using direct numerical simulation data from the Johns Hopkins Turbulence Database. The results show that model-based multi-sensor fusion is a promising approach to reconstruct turbulence with improved spatiotemporal resolution.Item Enhanced Capabilities of BullReporter and BullConverter(Minnesota Department of Transportation, 2017-09) Kwon, Taek M.Bull-Converter/Reporter is a software stack for Weigh-In-Motion (WIM) data analysis and reporting tools developed by the University of Minnesota Duluth for the Minnesota Department of Transportation (MnDOT) to resolve problems associated with deployment of multi-vendor WIM systems in a statewide network. These data tools have been used by the MnDOT Office of Transportation System Management (OTSM) since their initial delivery in 2009. The objective of this project was to expand the current conversion capabilities of BullConverter to include more raw data formats from different companies and the current BullReporter functions to include new analysis and reporting capabilities. Data analysis needs change over time, and the members of the OTSM WIM section identified several new functions that would increase efficiency and improve quality of WIM data. This report describes the new reporting and conversion functions implemented in this project.Item Spatial parameters for transportation: A multi-modal approach for modelling the urban spatial structure using deep learning and remote sensing(Journal of Transport and Land Use, 2021) Stiller, Dorothee; Wurm, Michael; Stark, Thomas; d'Angelo, Pablo; Stebner, Karsten; Dech, Stefan; Taubenböck, HannesA significant increase in global urban population affects the efficiency of urban transportation systems. Remarkable urban growth rates are observed in developing or newly industrialized countries where researchers, planners, and authorities face scarcity of relevant official data or geo-data. In this study, we explore remote sensing and open geo-data as alternative sources to generate missing data for transportation models in urban planning and research. We propose a multi-modal approach capable of assessing three essential parameters of the urban spatial structure: buildings, land use, and intra-urban population distribution. Therefore, we first create a very high-resolution (VHR) 3D city model for estimating the building floors. Second, we add detailed land-use information retrieved from OpenStreetMap (OSM). Third, we test and evaluate five experiments to estimate population at a single building level. In our experimental set-up for the mega-city of Santiago de Chile, we find that the multi-modal approach allows generating missing data for transportation independently from official data for any area across the globe. Beyond that, we find the high-level 3D city model is the most accurate for determining population on small scales, and thus evaluate that the integration of land use is an inevitable step to obtain fine-scale intra-urban population distribution.