Control of a Hybrid OscillatingWater Column-Offshore Wind Turbine

Acceso restringido al texto completo pendiente de concesión de patenteThe wind energy sector is spreading its wings all over the world, and wind power is gradually displacingfossil fuels. Due to the impacts of climate change and global warming, renewable energy resources suchas wind and wave power are gaining popularity. It is essential to construct wind and wave supplyinfrastructure in order to tackle these challenges. Floating Offshore Wind Turbines (FOWT) have playeda game-changing role in gathering more clean, renewable wind and wave resources and producing morepower.Offshore energy machines offer greater potential than onshore machines due to larger capacity factors,more accessible area, and less visible effects. Oscillating water columns may be incorporated into theFOWTs' platform for harnessing both wind and wave power supply. The integrated system of FOWTOWCshas the potential to significantly reduce system costs by leveraging shared operation andmaintenance and common grid infrastructure. It can also improve the system's smoothed power outputand efficiency. However, one of the difficulties that lies ahead is the stability of FOWTs in order toreduce unwanted platform vibrations and capture as much energy as feasible. These undesirablemovements diminish aerodynamic efficiency, limit tower fatigue life, and raise loads on blades, rotorshaft, yaw bearing, and tower base. As a result, it is vital to keep the FOWT's platform movements withina reasonable range.In this thesis, four oscillating water columns (OWC) have been integrated into the FOWT's bargeplatform to decrease the system's oscillations. To analyse the behavior of the hybrid system, responseamplitude operators (RAO) have been evaluated. Using RAOs, a switching control method have beenintroduced to manage the transition between closing and opening OWCs' valves. The controller reducesthe oscillations in a barge-based FOWT supporting 5 MW wind turbine. The considered environmentalconditions consist of various sea states with low-rated, rated and above-rated wind speeds.The results show that the considered switching control strategy has been able to reduce the oscillations inthe barge-based FOWT efficiently. Consequently, this oscillation reduction lead to decease thefluctuations in the generated power output. Also, the results illustrate that the average power outputincreases in low-rated wind speeds.These results have been obtained using the MultiSurf, WAMIT, FAST, and MATLAB-Simulink tools.Finally, to assess the performance of the suggested technique, a comparison has been made between thecontrolled OWCs-based barge and the traditional barge-based platform.This thesis work is structured as follows: the first chapter provides an overview of FOWT types and waveenergy converters. It also discusses the advantages and disadvantages of the hybrid FOWT-oscillatingwater columns. The second chapter summarizes the current state of the art in FOWT stabilizing methods.The problem statement and thesis goals are then explained. Chapter 3 describes the performance ofOWCs in barge-based FOWTs for various sea conditions. In chapter 4, a switching control method ispresented to reduce oscillations in the hybrid FOWT-OWCs system when wind power is absent. Chapter5 develops a switching control approach to decrease system oscillations in diverse sea conditions andwind speed scenarios. In addition, the performance of the controlled OWCs-based barge platformplatform in terms of generated power has been examined in this chapter. Finally, in chapter 6, thefindings of the thesis and future works are summarized

Validation of Digital Surface Models (DSMs) Retrieved From Unmanned Aerial Vehicle (UAV) Point Clouds Using Geometrical Information From Shadows

Theoretically, the appearance of shadows in aerial imagery is not desirable for researchers because it leads to errors in object classification and bias in the calculation of indices. In contrast, shadows contain useful geometrical information about the objects blocking the light. Several studies have focused on estimation of building heights in urban areas using the length of shadows. This type of information can be used to predict the population of a region, water demand, etc., in urban areas. With the emergence of unmanned aerial vehicles (UAVs) and the availability of high- to super-high-resolution imagery, the important questions relating to shadows have received more attention. Three-dimensional imagery generated using UAV-based photogrammetric techniques can be very useful, particularly in agricultural applications such as in the development of an empirical equation between biomass or yield and the geometrical information of canopies or crops. However, evaluating the accuracy of the canopy or crop height requires labor-intensive efforts. In contrast, the geometrical relationship between the length of the shadows and the crop or canopy height can be inversely solved using the shadow length measured. In this study, object heights retrieved from UAV point clouds are validated using the geometrical shadow information retrieved from three sets of high-resolution imagery captured by Utah State University’s AggieAir UAV system. These flights were conducted in 2014 and 2015 over a commercial vineyard located in California for the USDA Agricultural Research Service Grape Remote sensing Atmospheric Profile and Evapotranspiration Experiment (GRAPEX) Program. The results showed that, although this approach could be computationally expensive, it is faster than fieldwork and does not require an expensive and accurate instrument such as a real-time kinematic (RTK) GPS

The Impact of Shadows on Partitioning of Radiometric Temperature to Canopy and Soil Temperature Based on the Contextual Two-Source Energy Balance Model (TSEB-2T)

Tests of the most recent version of the two-source energy balance model have demonstrated that canopy and soil temperatures can be retrieved from high-resolution thermal imagery captured by an unmanned aerial vehicle (UAV). This work has assumed a linear relationship between vegetation indices (VIs) and radiometric temperature in a square grid (i.e., 3.6 m x 3.6 m) that is coarser than the resolution of the imagery acquired by the UAV. In this method, with visible, near infrared (VNIR), and thermal bands available at the same high-resolution, a linear fit can be obtained over the pixels located in a grid, where the x-axis is a vegetation index (VI) and the y-axis is radiometric temperature. Next, with an accurate VI threshold that separates soil and vegetation pixels from one another, the corresponding soil and vegetation temperatures can be extracted from the linear equation. Although this method is simpler than other approaches, such as TSEB with Priestly-Taylor (TSEB-PT), it could be sensitive to VIs and the parameters that affect VIs, such as shadows. Recent studies have revealed that, on average, the values of VIs, such as normalized difference vegetation index (NDVI) and leaf area index (LAI), that are located in sunlit areas are greater than those in shaded areas. This means that involving or compensating for shadows will affect the linear relationship parameters (slope and bias) between radiometric temperature and VI, as well as thresholds that separate soil and vegetation pixels. This study evaluates the impact of shadows on the retrieval of canopy and soil temperature data from four UAV images before and after applying shadow compensation techniques. The retrieved temperatures, using the TSEB-2T approach, both before and after shadow correction, are compared to the average temperature values for both soil and canopy in each grid. The imagery was acquired by the Utah State University AggieAir UAV system over a commercial vineyard located in California as part of the USDA Agricultural Research Service Grape Remote sensing Atmospheric Profile and Evapotranspiration Experiment (GRAPEX) Program during 2014 to 2016. The results of this study show when it is necessary to employ shadow compensation methods to retrieve vegetation and soil temperature directly

Quality of work life and its related factors: A survey of nurses

Background: Improving the quality of work life (QWL) is a comprehensive process essential to attracting and retaining employees, especially in health care. Objectives: The purpose of the present study was to determine quality of nursing work life and its related factors at nurses Kashan city hospitals in 2014. Methods: This cross-sectional study was conducted on 157 ICU nurses from September to November, 2014 at 4 educational hospitals of Kashan, Iran. A three part questionnaire was used in this study: demographic and professional characteristics, quality of nursing work life (QNWL), and the national aeronautics and space administration task load index (NASA-TLX). Data were analyzed using the t-test, the Mann-Whitney U test, and the chi square and Fisher’s exact test with SPSS software, version 16. Results: The majority of the participants (N = 112) were female (83.3%), and the mean age of the subjects was 33 ± 6.98 years. Age, education, job position, job location, and a second nursing job in another hospital were found to predict QNWL. Among the six subscales of NASA-TLX, frustration and mental demand had the lowest and highest rating score, respectively. Temporal demand, frustration, and effort levels were significantly correlated with QNWL. Conclusions: It is necessary to pay more attention to the QNWL and its related factors, especially nursing workload, to improve quality of care

Multispectral Remote Sensing for Yield Estimation Using High-Resolution Imagery From an Unmanned Aerial Vehicle

Satellites and autonomous unmanned aerial vehicles (UAVs) are two major platforms for acquiring remotely-sensed information of the earth’s surface. Due to the limitations of satellite-based imagery, such as coarse spatial resolution and fixed schedules, applications of UAVs as low-cost remote sensing systems are rapidly expanding in many research areas, particularly precision agriculture. UAVs can provide imagery with high spatial resolution (finer than 1 meter) and acquire information in visible, near infrared, and even thermal bands. In agriculture, vegetation characteristics such as health, water stress, and the amount of biomass, can be estimated using UAV imagery. In this study, three sets of high-resolution aerial imagery have been used for yield estimation based on vegetation indices. These images were captured by the Utah State University AggieAir™ UAV system flown in June 2017, August 2017, and October 2017 over a field experiment pasture site located in northern Utah. The pasture study area is primarily tall fescue. The field experiment includes 20 50 x 20-m plots, with 4 replications of 5 irrigation levels. Approximately 60 yield samples were harvested after each flight. Sample locations were recorded with high-accuracy real-time kinematic (RTK) GPS. In addition, the leaf area index (LAI) for each sample plot was measured using an optical sensor (LAI2200C) before harvesting. The relationship of yield for each sample versus vegetation indices (VIs) was explored. The VIs include the normalized difference vegetation index (NDVI), calculated using AggieAir imagery, and LAI measured using a ground-based sensor. The results of this study reveal the correlation between vegetation indices and the amount of biomass

DeepfakeArt Challenge: A Benchmark Dataset for Generative AI Art Forgery and Data Poisoning Detection

The tremendous recent advances in generative artificial intelligence techniques have led to significant successes and promise in a wide range of different applications ranging from conversational agents and textual content generation to voice and visual synthesis. Amid the rise in generative AI and its increasing widespread adoption, there has been significant growing concern over the use of generative AI for malicious purposes. In the realm of visual content synthesis using generative AI, key areas of significant concern has been image forgery (e.g., generation of images containing or derived from copyright content), and data poisoning (i.e., generation of adversarially contaminated images). Motivated to address these key concerns to encourage responsible generative AI, we introduce the DeepfakeArt Challenge, a large-scale challenge benchmark dataset designed specifically to aid in the building of machine learning algorithms for generative AI art forgery and data poisoning detection. Comprising of over 32,000 records across a variety of generative forgery and data poisoning techniques, each entry consists of a pair of images that are either forgeries / adversarially contaminated or not. Each of the generated images in the DeepfakeArt Challenge benchmark dataset has been quality checked in a comprehensive manner. The DeepfakeArt Challenge is a core part of GenAI4Good, a global open source initiative for accelerating machine learning for promoting responsible creation and deployment of generative AI for good

Spatial and Temporal Analysis of Precipitation and Effective Rainfall Using Gauge Observations, Satellite, and Gridded Climate Data for Agricultural Water Management in the Upper Colorado River Basin

Accurate spatial and temporal precipitation estimates are important for hydrological studies of irrigation depletion, net irrigation requirement, natural recharge, and hydrological water balances in defined areas. This analysis supports the verification of water savings (reduced depletion)from deficit irrigation of pastures in the Upper Colorado River Basin. The study area has diverse topography with scattered fields and few precipitation gauges that are not representative of the basin.Gridded precipitation products from TRMM-3B42, PRISM, Daymet, and gauge observations were evaluated on two case studies located in Colorado and Wyoming during the 2014–2016 irrigation seasons. First, the resolution at the farm level is discussed. Next, bias occurrence at different timescales (daily to monthly) is evaluated and addressed. Then, the coverage area of the gauge station, along with the impact of the dominant wind direction on the shape of the coverage area, is evaluated. Ultimately, available actual ET maps derived from the METRIC model are used to estimate spatial effective rainfall. The results show that the spatial resolutions of TRMM and PRISM are not adequate at the farm level, while Daymet is a better fit but lacks the adequate latency versus TRMM andPRISM. When compared against local weather station records, all three spatial datasets were found to have a bias that decreases at coarser temporal intervals. However, the performance of Daymet andPRISM at the monthly time step is acceptable, and they can be used for water resource management at the farm level. The adequacy of an existing gauge station for a given farm location depends on the willingness to accept the risk of the bias associated with a non-persistent, non-symmetric gauge coverage area that is highly correlated with the dominant wind direction. Among all goodness off it statistics considered in the study, the interpretation of the summation of error makes more sense for quantifying the rainfall bias and risk for the user. Finally, based on the USDA-SCS model and actual spatial ET, overall, seasonal effective rainfall tends to be less than 60% of total rainfall for agricultural lands