835 resultados para Idealized model for theory development


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The purpose of this research was to develop a working physical model of the focused plenoptic camera and develop software that can process the measured image intensity, reconstruct this into a full resolution image, and to develop a depth map from its corresponding rendered image. The plenoptic camera is a specialized imaging system designed to acquire spatial, angular, and depth information in a single intensity measurement. This camera can also computationally refocus an image by adjusting the patch size used to reconstruct the image. The published methods have been vague and conflicting, so the motivation behind this research is to decipher the work that has been done in order to develop a working proof-of-concept model. This thesis outlines the theory behind the plenoptic camera operation and shows how the measured intensity from the image sensor can be turned into a full resolution rendered image with its corresponding depth map. The depth map can be created by a cross-correlation of adjacent sub-images created by the microlenslet array (MLA.) The full resolution image reconstruction can be done by taking a patch from each MLA sub-image and piecing them together like a puzzle. The patch size determines what object plane will be in-focus. This thesis also goes through a very rigorous explanation of the design constraints involved with building a plenoptic camera. Plenoptic camera data from Adobe © was used to help with the development of the algorithms written to create a rendered image and its depth map. Finally, using the algorithms developed from these tests and the knowledge for developing the plenoptic camera, a working experimental system was built, which successfully generated a rendered image and its corresponding depth map.

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A range of societal issues have been caused by fossil fuel consumption in the transportation sector in the United States (U.S.), including health related air pollution, climate change, the dependence on imported oil, and other oil related national security concerns. Biofuels production from various lignocellulosic biomass types such as wood, forest residues, and agriculture residues have the potential to replace a substantial portion of the total fossil fuel consumption. This research focuses on locating biofuel facilities and designing the biofuel supply chain to minimize the overall cost. For this purpose an integrated methodology was proposed by combining the GIS technology with simulation and optimization modeling methods. The GIS based methodology was used as a precursor for selecting biofuel facility locations by employing a series of decision factors. The resulted candidate sites for biofuel production served as inputs for simulation and optimization modeling. As a precursor to simulation or optimization modeling, the GIS-based methodology was used to preselect potential biofuel facility locations for biofuel production from forest biomass. Candidate locations were selected based on a set of evaluation criteria, including: county boundaries, a railroad transportation network, a state/federal road transportation network, water body (rivers, lakes, etc.) dispersion, city and village dispersion, a population census, biomass production, and no co-location with co-fired power plants. The simulation and optimization models were built around key supply activities including biomass harvesting/forwarding, transportation and storage. The built onsite storage served for spring breakup period where road restrictions were in place and truck transportation on certain roads was limited. Both models were evaluated using multiple performance indicators, including cost (consisting of the delivered feedstock cost, and inventory holding cost), energy consumption, and GHG emissions. The impact of energy consumption and GHG emissions were expressed in monetary terms to keep consistent with cost. Compared with the optimization model, the simulation model represents a more dynamic look at a 20-year operation by considering the impacts associated with building inventory at the biorefinery to address the limited availability of biomass feedstock during the spring breakup period. The number of trucks required per day was estimated and the inventory level all year around was tracked. Through the exchange of information across different procedures (harvesting, transportation, and biomass feedstock processing procedures), a smooth flow of biomass from harvesting areas to a biofuel facility was implemented. The optimization model was developed to address issues related to locating multiple biofuel facilities simultaneously. The size of the potential biofuel facility is set up with an upper bound of 50 MGY and a lower bound of 30 MGY. The optimization model is a static, Mathematical Programming Language (MPL)-based application which allows for sensitivity analysis by changing inputs to evaluate different scenarios. It was found that annual biofuel demand and biomass availability impacts the optimal results of biofuel facility locations and sizes.

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Wind energy has been one of the most growing sectors of the nation’s renewable energy portfolio for the past decade, and the same tendency is being projected for the upcoming years given the aggressive governmental policies for the reduction of fossil fuel dependency. Great technological expectation and outstanding commercial penetration has shown the so called Horizontal Axis Wind Turbines (HAWT) technologies. Given its great acceptance, size evolution of wind turbines over time has increased exponentially. However, safety and economical concerns have emerged as a result of the newly design tendencies for massive scale wind turbine structures presenting high slenderness ratios and complex shapes, typically located in remote areas (e.g. offshore wind farms). In this regard, safety operation requires not only having first-hand information regarding actual structural dynamic conditions under aerodynamic action, but also a deep understanding of the environmental factors in which these multibody rotating structures operate. Given the cyclo-stochastic patterns of the wind loading exerting pressure on a HAWT, a probabilistic framework is appropriate to characterize the risk of failure in terms of resistance and serviceability conditions, at any given time. Furthermore, sources of uncertainty such as material imperfections, buffeting and flutter, aeroelastic damping, gyroscopic effects, turbulence, among others, have pleaded for the use of a more sophisticated mathematical framework that could properly handle all these sources of indetermination. The attainable modeling complexity that arises as a result of these characterizations demands a data-driven experimental validation methodology to calibrate and corroborate the model. For this aim, System Identification (SI) techniques offer a spectrum of well-established numerical methods appropriated for stationary, deterministic, and data-driven numerical schemes, capable of predicting actual dynamic states (eigenrealizations) of traditional time-invariant dynamic systems. As a consequence, it is proposed a modified data-driven SI metric based on the so called Subspace Realization Theory, now adapted for stochastic non-stationary and timevarying systems, as is the case of HAWT’s complex aerodynamics. Simultaneously, this investigation explores the characterization of the turbine loading and response envelopes for critical failure modes of the structural components the wind turbine is made of. In the long run, both aerodynamic framework (theoretical model) and system identification (experimental model) will be merged in a numerical engine formulated as a search algorithm for model updating, also known as Adaptive Simulated Annealing (ASA) process. This iterative engine is based on a set of function minimizations computed by a metric called Modal Assurance Criterion (MAC). In summary, the Thesis is composed of four major parts: (1) development of an analytical aerodynamic framework that predicts interacted wind-structure stochastic loads on wind turbine components; (2) development of a novel tapered-swept-corved Spinning Finite Element (SFE) that includes dampedgyroscopic effects and axial-flexural-torsional coupling; (3) a novel data-driven structural health monitoring (SHM) algorithm via stochastic subspace identification methods; and (4) a numerical search (optimization) engine based on ASA and MAC capable of updating the SFE aerodynamic model.

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BACKGROUND: Decompressive laparotomy followed by temporary abdominal closure (TAC) is an established prophylaxis and treatment for abdominal compartment syndrome. The herein presented study aimed at the comparison of volume reserve capacity and development of intra-abdominal hypertension after forced primary abdominal closure and different TAC techniques in a porcine model. METHODS: Eight anesthesized and mechanically ventilated domestic pigs underwent a standardized midline laparotomy. A bag was placed into the abdominal cavity. Before abdominal closure, the bag was prefilled with 3,000 mL water to simulate increased intra-abdominal volume. The intra-abdominal pressure (IAP) was then increased in 2 mm Hg steps up to 30 mm Hg by adding volume (volume reserve capacity) to the intra-abdominal bag. Volume reserve capacity with the corresponding IAP were analyzed and compared for primary abdominal closure, bag silo closure, a zipper system, and vacuum-assisted closure (VAC) with different negative pressures (-50, -100, and -150 mm Hg). Hemodynamic and pulmonary parameters were monitored throughout the experiment. RESULTS: Volume reserve capacity was the highest for bag silo closure followed by the zipper system and VAC with primary abdominal closure providing the least volume reserve capacity in the whole IAP range. Of interest, VAC -50 mm Hg resulted in a lower volume reserve capacity when compared with VAC -100 and -150 mm Hg. Pulmonary and hemodynamic parameters demonstrated no significant differences between primary abdominal closure and the evaluated TAC techniques at all IAP levels. CONCLUSIONS: The present experimental in vivo study indicates that bag silo closure and zipper systems may be favorable TAC techniques after decompressive laparotomy. In contrast, the VAC techniques resulted in lower volume reserve capacity and therefore may bear an increased risk for recurrent intra-abdominal hypertension in the initial phase after decompressive laparotomy.

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The study conducted in a bacterial-based in vitro caries model aimed to determine whether typical inner secondary caries lesions can be detected at cavity walls of restorations with selected gap widths when the development of outer lesions is inhibited. Sixty bovine tooth specimens were randomly assigned to the following groups: test group 50 (TG50; gap, 50 microm), test group 100 (TG100; gap, 100 microm), test group 250 (TG250; gap, 250 microm) and a control group (CG; gap, 250 microm). The outer tooth surface of the test group specimens was covered with an acid-resistant varnish to inhibit the development of an outer caries lesion. After incubation in the caries model, the area of demineralization at the cavity wall was determined by confocal laser scanning microscopy. All test group specimens demonstrated only wall lesions. The CG specimens developed outer and wall lesions. The TG250 specimens showed significantly less wall lesion area compared to the CG (p < 0.05). In the test groups, a statistically significant increase (p < 0.05) in lesion area could be detected in enamel between TG50 and TG250 and in dentine between TG50 and TG100. In conclusion, the inner wall lesions of secondary caries can develop without the presence of outer lesions and therefore can be regarded as an entity on their own. The extent of independently developed wall lesions increased with gap width in the present setting.

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The IDA model of cognition is a fully integrated artificial cognitive system reaching across the full spectrum of cognition, from low-level perception/action to high-level reasoning. Extensively based on empirical data, it accurately reflects the full range of cognitive processes found in natural cognitive systems. As a source of plausible explanations for very many cognitive processes, the IDA model provides an ideal tool to think with about how minds work. This online tutorial offers a reasonably full account of the IDA conceptual model, including background material. It also provides a high-level account of the underlying computational “mechanisms of mind” that constitute the IDA computational model.

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Can one observe an increasing level of individual lack of orientation because of rapid social change in modern societies? This question is examined using data from a representative longitudinal survey in Germany conducted in 2002–04. The study examines the role of education, age, sex, region (east/west), and political orientation for the explanation of anomia and its development. First we present the different sources of anomie in modern societies, based on the theoretical foundations of Durkheim and Merton, and introduce the different definitions of anomia, including our own cognitive version. Then we deduce several hypotheses from the theory, which we test by means of longitudinal data for the period 2002–04 in Germany using the latent growth curve model as our statistical method. The empirical findings show that all the sociodemographic variables, including political orientation, are strong predictors of the initial level of anomia. Regarding the development of anomia over time (2002–04), only the region (west) has a significant impact. In particular, the results of a multi-group analysis show that western German people with a right-wing political orientation become more anomic over this period. The article concludes with some theoretical implications.