Relevant bibliographies by topics / Rain tracking

Academic literature on the topic 'Rain tracking'

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Published: 14 March 2023

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Journal articles on the topic "Rain tracking"

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Niemczynowicz, Janusz. "Storm tracking using rain gauge data." Journal of Hydrology 93, no. 1-2 (August 1987): 135–52. http://dx.doi.org/10.1016/0022-1694(87)90199-5.

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Corcoran, Padraig. "Topology Based Object Tracking." Mathematical and Computational Applications 24, no. 3 (September 18, 2019): 84. http://dx.doi.org/10.3390/mca24030084.

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A model for tracking objects whose topological properties change over time is proposed. Such changes include the splitting of an object into multiple objects or the merging of multiple objects into a single object. The proposed model employs a novel formulation of the tracking problem in terms of homology theory whereby 0-dimensional homology classes, which correspond to connected components, are tracked. A generalisation of this model for tracking spatially close objects lying in an ambient metric space is also proposed. This generalisation is particularly suitable for tracking spatial-temporal phenomena such as rain clouds. The utility of the proposed model is demonstrated with respect to tracking communities in a social network and tracking rain clouds in radar imagery.
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He, Ting, Thomas Einfalt, Jianxin Zhang, Jiyao Hua, and Yang Cai. "New Algorithm for Rain Cell Identification and Tracking in Rainfall Event Analysis." Atmosphere 10, no. 9 (September 10, 2019): 532. http://dx.doi.org/10.3390/atmos10090532.

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This study proposes a new algorithm termed rain cell identification and tracking (RCIT) to identify and track rain cells from high resolution weather radar data. Previous algorithms have limitations when tracking non-consequent rain cells owing to their use of maximum correlation coefficient methods and their lack of an alternative way to handle the variation stages of rain cells during their life cycles. To address these deficiencies, various methods are implemented in the new algorithm. These include the particle image velocimetry (PIV) method for motion estimation and the rain cell matching rule to obtain the stage changes of rain cells. High resolution (5 min and 1 km) radar data from three rainy days over the German federal state North Rhine Westphalia (NRW) are used in this study. The performance of the identification module for the new algorithm is accessed by two object-oriented verification methods: structure–amplitude–location (SAL) and geometric index, while the performance of the tracking module is compared with TREC and SCOUT tracking algorithms and evaluated by the contingency table verification approach. Results suggest that the performance of the new algorithm is better than reference tracking method. Application of the RCIT algorithm to the selected cases shows that the inner structure of rainfall events in the experimental region present extreme value distributions, with most rainfall events having a short duration with less intensity. The new algorithm can effectively capture the stage changes of rain cells during their life cycles. The proposed algorithm can serve as the basis for further hydro-meteorological applications such as spatial and temporal analysis of rainfall events and short-term flood forecasting.
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Hambali, Roby, Djoko Legono, and Rachmad Jayadi. "THE APPLICATION OF PYRAMID LUCAS-KANADE OPTICAL FLOW METHOD FOR TRACKING RAIN MOTION USING HIGH-RESOLUTION RADAR IMAGES." Jurnal Teknologi 83, no. 1 (December 7, 2020): 105–15. http://dx.doi.org/10.11113/jurnalteknologi.v83.14494.

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Short-duration rainfall characteristics in the form of certain intensity, time, and spatial distribution become valuable contribution for lahar flow disaster mitigation in a mountainous region. Due to mitigation purpose, such information can be provided through the rainfall nowcasting process. One of the promising rainfall nowcasting applications is the extrapolation-based method. Rain motion tracking is a crucial part of the rainfall nowcasting based on this method. This paper discusses the application of Pyramid Lucas-Kanade Optical Flow (PLKOF) method on the rain motion tracking analysis using 150x150m resolution radar image. The study of rain motion tracking is carried out using 112 successive rainfall images with 10-minutes time interval originating from Mt. Merapi X-band multiparameter radar. The rainfall movement patterns in short duration are presented in the displacement vector (u,v) images and scatter diagrams of rain motions at x- and y-directions. From the simulations, it was found that the average displacement of rain motions in the Mt. Merapi region is 9 pixels (8.3 km/h) with the dominant direction is northeast. The results show that PLKOF is relatively good at detecting small displacements, yet unable to identify the occurrence of rain growth and decay properly. The ability of PLKOF method in predicting the position of rain cell displacement is satisfied as indicated by the POD, CSI, and FAR indexes.
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Palharini, Rayana, Daniel Vila, Daniele Rodrigues, Rodrigo Palharini, Enrique Mattos, and Eduardo Undurraga. "Analysis of Extreme Rainfall and Natural Disasters Events Using Satellite Precipitation Products in Different Regions of Brazil." Atmosphere 13, no. 10 (October 14, 2022): 1680. http://dx.doi.org/10.3390/atmos13101680.

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The number of natural disasters triggered by extreme events is increasing worldwide and significantly impacts modern society. Extreme rainfall is one of the most important factors contributing to these events. A better understanding of the physical process that causes extreme rainfall can allow rapid responses from decision-makers to lessen the impact of natural disasters on the local population. Satellite monitoring is widely used for this purpose and is essential for regions where terrestrial observations are limited or non-existent. The primary purpose of this study is to describe the performance of satellite products for extreme rainfall events that caused natural disasters in various climate regimes in Brazil and discuss the contribution of mesoscale convective systems (MCS) to these events. We defined regions based on the climatological rainfall distribution. Cases with rain values above the 99th percentile during 2012–2016 were considered statistically extreme. Our analysis is based on three datasets, with precipitation from (i) rain gauge stations, (ii) different satellite-based estimates, and (iii) mesoscale convective tracking data. The methodology was based on identifying extreme rainfall events, analyzing the performance of satellite precipitation estimates and, finally, quantifying the influence of convective systems on extreme rain. Although all regions of Brazil may be affected by natural disasters caused by extreme rains, the results suggest that the impacts caused in each region are different in magnitude. Convective systems explained over 90% of extreme rains in the case analyzed in Brazil’s south and about 60% to 90% of extreme rains in the case analyzed in the Northeast. In general, satellite products have identified rain events; however, in the southern region of Brazil, products have tended to overestimate rainfall, while other regions have tended to underestimate extreme rain values. The methods used in satellite precipitation estimation products have limitations to accurately identifying specific extreme rain events.
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Juráš, Peter. "Measurement and CFD Simulation of Wind-Driven Rain Using Eulerian Multiphase Model." Advanced Materials Research 1041 (October 2014): 265–68. http://dx.doi.org/10.4028/www.scientific.net/amr.1041.265.

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This paper deals with measurements of wind-driven rain intensity in Meteorological garden of Slovak hydrometeorological institute and subsequent modelling of free standing wind-driven rain gauge in OpenFoam CFD simulation tool using Eulerian multiphase model instead of common used Langrangian particle tracking model.
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Leal, Helvecio B., Alan J. P. Calheiros, Henrique M. J. Barbosa, Adriano P. Almeida, Arturo Sanchez, Daniel A. Vila, Sâmia R. Garcia, and Elbert E. N. Macau. "Impact of Multi-Thresholds and Vector Correction for Tracking Precipitating Systems over the Amazon Basin." Remote Sensing 14, no. 21 (October 28, 2022): 5408. http://dx.doi.org/10.3390/rs14215408.

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Different algorithms for forecasting and tracking meteorological systems have been developed over the years. Many of them are used to study cloud propagation, precipitation and lightning for nowcasting. Therefore, it is necessary to define carefully the parameters (e.g., intensity thresholds and minimum size) that impact tracking of these variables. In order to represent the physical aspects of rain propagation over the Amazon region, several methods of correction and displacement detection were studied. Different parameters were used to validate the methods based on the extrapolated rain cell. A probability detection of 78.4% and 68.6% was achieved for 20 dBZ thresholds during the wet and dry season, respectively. However, the POD decreases for higher reflectivity thresholds. The results for corrections by Inner Nuclei showed that embedded convection can dictate the propagation of rain cells. Split and merge corrections performed well; however, they applied only to a few cases. Corrections performed better for precipitating systems with larger areas and longer duration. The correction methods showed similar skills for both seasons. Which shows that they are able to monitor rain cells throughout the year. The automated combination of different methods for the 20 dBZ threshold proved to be the best choice for tracking rainfall in the Amazon region.
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Yan, Rong Ge, Yu Long Jia, Li Hua Zhu, and Qing Xin Yang. "Giant Magnetostrictive Freezing Rain Sensor." Advanced Materials Research 902 (February 2014): 163–66. http://dx.doi.org/10.4028/www.scientific.net/amr.902.163.

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As a disastrous weather, hazards of freezing rain can not be ignored. The important thing to be solved at present is using advanced technology and material to correctly detect and improve the forecasting ability of freezing rain. Based on the damage of freezing rain and excellent properties of the giant magnetostrictive materials, this paper gives a giant magnetostrictive freezing rain sensor. When there is different thickness of freezing rain, natural frequency of the sensor will change. Resonance is regained by adjusting the frequency of the power. From natural frequency change, the thickness of the freezing rain can be known. Using COMSOL software, modal analysis of different thickness freezing rain for the giant magnetostrictive freezing rain sensor is studied. The results show that there is big difference in natural frequency with difference thickness of freezing rain, which is easy to achieve automatic frequency tracking and monitor.
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Dell’Acqua, Fabio. "Rain pattern tracking by means of COTREC and modal matching." Optical Engineering 41, no. 2 (February 1, 2002): 278. http://dx.doi.org/10.1117/1.1432668.

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Moseley, Christopher, Peter Berg, and Jan O. Haerter. "Probing the precipitation life cycle by iterative rain cell tracking." Journal of Geophysical Research: Atmospheres 118, no. 24 (December 16, 2013): 13,361–13,370. http://dx.doi.org/10.1002/2013jd020868.

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Dissertations / Theses on the topic "Rain tracking"

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Morgan, Jake Roberts. "Tracking the little black 'rain' clouds: an enviro-economic analysis of ambient air pollution effects on pediatric asthma." Thesis, Montana State University, 2012. http://etd.lib.montana.edu/etd/2012/morgan/MorganJ0512.pdf.

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Epidemiological studies routinely demonstrate a positive correlation between ambient air pollution and respiratory aggravation. Potential benefits from federal regulations to economic and physical health of individuals, however, are not well understood by either law makers or voters. As the EPA implements new regulations targeting SO ₂ emissions, robust empirical analysis can frame the policy issue as one of statistical and economic analysis. The Asthma Call Back Survey extension of the Behavioral Risk Factor Surveillance System collects detailed data on symptoms, intervention methods, and demographics of individuals with asthma as well as associated ZIP-level spatial identification. The EPA's Acid Rain emissions database records detailed emission data for every power plant in the country. Together, these data can be used to link detailed asthma information with nearby plant emission levels, providing the foundation for an in-depth enviro-economic analysis of SO &#8322 effects on pediatric asthma aggravation and potential long term human capital investment. Using a zero-inflated negative binomial regression technique, the study estimates the effect that pollution has on days of missed school the provides insight into educational effects of emissions and presents the issue as one important to child development. The results and policy-oriented sensitivity analysis demonstrate the clear benefits of coal power plant pollution reduction, and suggest policy and education solutions which can mitigate negative asthma outcomes and promote educational attainments.
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Paduru, Anirudh. "Fast Algorithm for Modeling of Rain Events in Weather Radar Imagery." ScholarWorks@UNO, 2009. http://scholarworks.uno.edu/td/1097.

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Weather radar imagery is important for several remote sensing applications including tracking of storm fronts and radar echo classification. In particular, tracking of precipitation events is useful for both forecasting and classification of rain/non-rain events since non-rain events usually appear to be static compared to rain events. Recent weather radar imaging-based forecasting approaches [3] consider that precipitation events can be modeled as a combination of localized functions using Radial Basis Function Neural Networks (RBFNNs). Tracking of rain events can be performed by tracking the parameters of these localized functions. The RBFNN-based techniques used in forecasting are not only computationally expensive, but also moderately effective in modeling small size precipitation events. In this thesis, an existing RBFNN technique [3] was implemented to verify its computational efficiency and forecasting effectiveness. The feasibility of modeling precipitation events using RBFNN effectively was evaluated, and several modifications to the existing technique have been proposed.
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Warlimont, Petra. "Application of the Tracking and Analysis Framework (TAF) to Assess the Effects of Acidic Deposition on Recreational Fishing in Maine Lakes." Fogler Library, University of Maine, 2002. http://www.library.umaine.edu/theses/pdf/WarlimontP2002.pdf.

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CAUTERUCCIO, ARIANNA. "The role of turbulence in particle-fluid interaction as induced by the outer geometry of catching-type precipitation gauges." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/999883.

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This thesis work investigates the particle-fluid interaction of hydrometeors along the terminal part of their fall trajectories, while approaching the collector of catching-type precipitation gauges in windy conditions. Both the turbulence generated by the bluff body aerodynamics of precipitation gauges when impacted by the wind and the free-stream turbulence inherent to the natural wind are addressed to assess their role in precipitation measurements. The bluff body aerodynamics of precipitation gauges induces deviations in the trajectories of the approaching hydrometeors due to the acceleration, updraft and turbulence development upstream and above the collector of the gauge. The resulting wind-induced errors were studied in the literature using different approaches – field measurement campaigns, numerical simulations and wind tunnel experiments. In this work, the numerical approach based on Computational Fluid Dynamic (CFD) simulation, which reduces, when compared with field observations, the time and resources needed to investigate different configurations by varying the wind speed, type of precipitation and gauge geometry, is employed. A Lagrangian Particle Tracking (LPT) model provides the catch ratios as a function of the particle size and wind speed. The LPT model, already available from the literature, was adapted to simulate the trajectories of water droplets when falling through the atmosphere and approaching the gauge collector by parameterizing liquid particles with spherical shape and using suitable drag coefficient equations. The first part of the work aims to validate the numerical approach against a dedicated, innovative and robust experimental campaign obtained by means of Wind Tunnel (WT) experiments (flow velocity measurements, Particle Image Velocimetry and video tracking of water drops) conducted in the wind tunnel facilities available at DICCA and at Politecnico di Milano (within the PRIN 20154WX5NA project). The video tracking experimental setup allowed to compare observed and simulated trajectories under various wind velocity and drop size conditions, and to validate the Lagrangian Particle Tracking model, here adapted to simulate particles falling at a different vertical velocity than the terminal one. Comparison and validation of numerical simulation results against field-measured data introduce the problem of confronting this simplified approach with the natural atmospheric conditions actually affecting operational instruments in the field. Natural wind fields are indeed characterized by turbulent fluctuations, especially near to the ground where precipitation gauges are located. Dedicated CFD simulations with various turbulence generating solutions, based on imposing specific boundary conditions or inserting suitable obstacles designed to achieve the desired level of free-stream turbulence upstream of the gauge, were performed. Wind tunnel measurements were performed in the DICCA facility using, as a turbulence-generating device, a fixed solid fence with a regular square mesh inserted upstream of a calyx shaped gauge. CFD simulations were performed reproducing the same conditions and results were validated by comparison with WT measurements. The comparison between the uniform and turbulent free-stream conditions showed that the normalized updraft in the upwind part, upstream of the centre of the collector, and the downdraft in the downwind part are less accentuated in the turbulent free-stream configuration than in uniform free-stream conditions. This is ascribable to the energy dissipation induced by turbulent fluctuations. The dissipative effect of the free-stream turbulence also has a damping role on the acceleration of the flow above the collector as demonstrated by CFD results. The overall free-stream turbulence effect on the collection performance of the gauges was quantified by computing and comparing the Collection Efficiency (CE) values in uniform and turbulent free-stream conditions. Results demonstrated that the CE values are higher in turbulent free-stream conditions. The effect of the free-stream turbulence on the collection efficiency of the Hotplate© snow gauge was investigated, and the literature turbulence intensity level (from Øistad, 2015) impacting on the gauge by was obtained in the simulation by imposing a constant turbulent kinetic energy value as a boundary condition upstream of the gauge. The calculated catch ratios are larger for the free-stream turbulence condition with respect to the uniform one for all characteristic sizes of snowflakes. Consequently, the same effect was observed in the calculated CE values. In addition, in order to introduce a realistic level of turbulence at the gauge collector elevation in the simulation, wind speed measurements obtained from a 3D ultrasonic anemometer in the Nafferton Farm site (UK), recorded at high frequency (20 Hz) and at the gauge elevation, were analysed to calculate the free-stream turbulence intensity values for various wind speeds. This was used to perform a CFD simulation on a chimney shaped gauge and to calculate its effect on the collection performance. To better reproduce the decay of the turbulence intensity in space and its effect on the gauge, Large Eddy Simulations (LES) were also performed in both uniform and turbulent free-stream conditions while simulating the trajectories of solid precipitation particles, which are more sensitive than raindrops to the turbulent fluctuations. Results, in terms of the catch ratio for each characteristic size of snowflakes, show a different behaviour when compared to the uniform conditions. A larger free-stream turbulence intensity induces a more pronounced undercatch for small size particles (less than 2 mm) with respect to the uniform case, while the undercatch is reduced for larger particles. This is due to the greater aptitude of the small size particles to follow the turbulent velocity fluctuations, while larger particles are more inertial, and to the reduced velocity components that particles cross in turbulent free-stream conditions near the gauge body. The obtained CE values are higher in turbulent free-stream conditions, confirming the observations already obtained for the airflow features, where a potential overestimation of the undercatch obtained in uniform free-stream conditions was hypothesized. Based on the CFD results and on the validation provided by wind tunnel observations it is possible to conclude that accounting for the free-stream airflow turbulence in the simulation is required to avoid underestimation of the collection efficiency of precipitation gauges. A turbulent free-stream is indeed the natural atmospheric condition of the wind impacting on operational precipitation gauges in the field. This work demonstrates that numerical derivation of correction curves for use in precipitation measurements as proposed hitherto in the literature is affected by a systematic overestimation of the wind-induced error due to the simplifying assumption of uniform free-stream conditions. Finally, in order to achieve results that can be used in an operational context, suitable Collection Efficiency (CE) curves and the associated adjustment curves, which directly provide the expected undercatch as a function of the wind speed and the measured precipitation intensity, were derived for two sample measurement instruments. The first one is best suited for rainfall measurements and is characterised by the common cylindrical shape of traditional catching type gauges, therefore a numerical formulation of the CE curves as a function of rainfall intensity is proposed. The second one, the Hotplate© gauge, is best suited for snowfall measurements and is characterised by an innovative measuring principle implying a dedicated geometry of the sensor. In this case, the numerically derived CE curves are expressed as a function of snowfall intensity. For the typical cylindrical gauge, the residual dependency of the CE curves on the rainfall intensity was investigated in order to obtain a single CE expression as a function of both the rainfall intensity and wind speed. The parameters of the Particle Size Distribution (PSD) for various classes of the RI were derived by literature data from the Italian territory. Then the variation of the PSD parameters as a function of the RI was obtained, and subsequently also the parameters of the sigmoidal curves, used to fit the numerical CE values, were parametrized with the RI. As a result, easy to use adjustment curves as a function of both the measured rainfall intensity and wind speed were derived. In the case of the Hotplate© snow gauge, the shape of the CE curves differs from the typical sigmoidal one due to its complex geometry. At low wind speed, the aerodynamic response of the gauge is predominant and CE values decrease with increasing the wind speed up to a wind threshold value beyond which the geometrical effect on the collection performance starts to be relevant and the CE increases. At very high wind speeds the geometrical contribution prevails and the CE becomes even larger than one.
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Caballero, Angel A. 1981. "A class-D-tracking-rail class-A audio power amplifier." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28384.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (p. 63-64).
A tracking-rail power audio amplifier was designed and partially implemented to deliver up to 100W into an 8Q load with very low signal distortion and high power efficiency. The design uses a class-A amplifier, known for its low signal distortion but low power efficiency (less than 50%), to amplify the audio signal. Class-D amplifiers, known for their high power efficiency (greater than 85%) but high signal distortion, provide a signal output that will serve as the supply voltages of the output stage of the class-A amplifier. Thus, the rails will track the audio signal, highly increasing the power efficiency of the Class-A amplifier. This amplifier can achieve a theoretical efficiency of 80%, but, in practice, it is closer to 70%.
by Angel A. Caballero.
M.Eng.
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Patel, Harshal. "Beam refinement and beam tracking using Machine Learning Techniques in 5G NR RAN." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21176.

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Abstract: Growing needs of communication, demands a higher data transmission rate in 5G NR. In the 3rd generation partnership project (3GPP), frames are used to schedule the data to be transferred between the cellular base station (gNB) and user equipment (UE). These frames are further divided into slots and a fixed number of slots are used for uplink and downlink. In downlink, several slots are being utilized for CSI-RS report, containing the best narrow beams and their power (RSRP). In this thesis, cell downlink capacity is improved by using supervised learning algorithms. The narrow beam is selected using machine learning, no longer using the scheduled slots in downlink, these slots are further utilized in data transmission, resulting in improved cell capacity. Supervised learning algorithms namely, Support Vector Machines (SVM), K-nearest Neighbor (k-NN), and Logistic Regression (LR) are compared, collecting the data using the 5G simulator at Ericsson AB, Lund and training them to classic narrow beams. The SVM algorithm is found to outperform other algorithms with an accuracy of 78.5% plus 19.6% of neighbor beam selection. The accuracy of the algorithm varies depending on the scenario and the quantity of training data used. Plugging-in the SVM algorithm into the simulator, the average throughput of multiple users (2, 5, 10,20, 30, and 40) is collected varying different user speeds (1m/s, 5m/s, and 10m/s) and different SSB intervals (20ms and 40ms). For 40ms SSB interval, 40 users, and user speed 10m/s, the average gain in throughput is found to be 46.6%. Similarly, for 20msSSB interval, 30 users, and user speed 10m/s, the average throughput gain is 21.15%. Keywords: 5G NR, 3GPP, Beamforming, Supervised learning, Machine learning, SVM, and Multi-class classification (MCC).  Aim and Objective: The goal of this thesis study is to investigate the usage of machine learning algorithms for optimizing the beam tracking process. Machine learning algorithms can be used to make beam tracking more intelligent, robust, and less resource-demanding. Different machine learning methods are explored to find the best possible beam for a user, and the performance of the algorithm is then compared with the baseline(3GPP) algorithm to find the most suitable algorithm. The machine learning algorithms are trained by using as input, the beam selection result of the baseline algorithm. Then, a moving UE is utilized to extract the measurements. Finally, each algorithm is evaluated with respect to the performance of the baseline algorithm. The best performing algorithm is plugged into the Ericsson simulator to select the best narrow beam and is used to collect the KPIs. Finally, the gain in total throughput is evaluated for a different number of users, moving with different speeds. Methodology: SLR and Experiment is conducted. Results: The three algorithms which are SVM, k-NN, and Logistic Regression were trained to classify 12 different classes, using the same data, to be able to make fair comparisons of their performances. Here, the results are divided into several sections. Firstly, the shortlisted algorithms are compared using a 10-fold cross-validation technique. Secondly, performances of single UE and multiple UE scenarios are shown separately. The best performance of each algorithm is presented in the form of a confusion matrix, showing the number of classified narrow beams. The X-axis in the matrix represents the labels classified by machine learning algorithms and Y-axis represents the true label (selected as per 3GPP algorithm).  Conclusion: In this thesis, the usage of a machine learning algorithm for selecting a narrow beam inside a wide beam is examined. Three different algorithms were considered namely, Support Vector Machines (SVM), k-Nearest Neighbor (kNN), and Logistic Regression(LR) using SLR methodology. Out of which the SVM algorithm is found to perform best, with an accuracy of 78.5% when tested offline. This algorithm is plugged into the Ericsson simulator and tested to perform acceptably with an accuracy of 78.3%and 19.6% of times the UE selects a neighbor narrow beam. Side lobes that exist in a beam tend to mislead the machine learning model, resulting in misclassification. Thus, most of the inaccurate narrow beam classification is found to be inside lobes. Using SVM has improved the average downlink throughput significantly in multiple users scenario, by not using CSI-RS measurements in the shared channel. This leads to more availability of slots for data transmission. Therefore, an increase of29.40% in average downlink throughput for 40ms CSI-RS reporting interval and 40UEs is found compared to baseline algorithm also, an increase of 21.15% in average downlink throughput for 20ms CSI-RS reporting interval and 30 UEs scenario. Average downlink throughput increment was greater for 20ms interval, compared to40ms CSI-RS reporting interval. This proves that frequent use of machine learning updating the narrow beam leads to better throughput. Hence, the increased capacity of the cell. Finally, all the aims and objectives in the study are achieved and the research questions mentioned in the thesis are answered and justified.
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Condori, Marcos Ademir Tejada. "Extensão da transformada imagem-floresta diferencial para funções de conexidade com aumentos baseados na raiz e sua aplicação para geração de superpixels." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-31072018-161103/.

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A segmentação de imagens é um problema muito importante em visão computacional, no qual uma imagem é dividida em regiões relevantes, tal como para isolar objetos de interesse de uma dada aplicação. Métodos de segmentação baseados na transformada imagem-floresta (IFT, Image Foresting Transform), com funções de conexidade monotonicamente incrementais (MI) têm alcançado um grande sucesso em vários contextos. Na segmentação interativa de imagens, na qual o usuário pode especificar o objeto desejado, novas sementes podem ser adicionadas e/ou removidas para corrigir a rotulação até conseguir a segmentação esperada. Este processo gera uma sequência de IFTs que podem ser calculadas de modo mais eficiente pela DIFT (Differential Image Foresting Transform). Recentemente, funções de conexidade não monotonicamente incrementais (NMI) têm sido usadas com sucesso no arcabouço da IFT no contexto de segmentação de imagens, permitindo incorporar informações de alto nível, tais como, restrições de forma, polaridade de borda e restrição de conexidade, a fim de customizar a segmentação para um dado objeto desejado. Funções não monotonicamente incrementais foram também exploradas com sucesso na geração de superpixels, via sequências de execuções da IFT. Neste trabalho, apresentamos um estudo sobre a Transformada Imagem-Floresta Diferencial no caso de funções NMI. Nossos estudos indicam que o algoritmo da DIFT original apresenta uma série de inconsistências para funções não monotonicamente incrementais. Este trabalho estende a DIFT, visando incorporar um subconjunto das funções NMI em grafos dirigidos e mostrar sua aplicação no contexto da geração de superpixels. Outra aplicação que é apresentada para difundir a relevância das funções NMI é o algoritmo Bandeirantes para perseguição de bordas e rastreamento de curvas.
Image segmentation is a problem of great relevance in computer vision, in which an image is divided into relevant regions, such as to isolate an object of interest for a given application. Segmentation methods with monotonically incremental connectivity functions (MI) based on the Image Foresting Transform (IFT) have achieved great success in several contexts. In interactive segmentation of images, in which the user is allowed to specify the desired object, new seeds can be added and/or removed to correct the labeling until achieving the expected segmentation. This process generates a sequence of IFTs that can be calculated more efficiently by the Differential Image Foresting Trans- form (DIFT). Recently, non-monotonically incremental connectivity functions (NMI) have been used successfully in the IFT framework in the context of image segmentation, allowing the incorporation of shape, boundary polarity, and connectivity constraints, in order to customize the segmentation for a given target object. Non-monotonically incremental functions were also successfully exploited in the generation of superpixels, via sequences of IFT executions. In this work, we present a study of the Differential Image Foresting Transform in the case of NMI functions. Our research indicates that the original DIFT algorithm presents a series of inconsistencies for non-monotonically incremental functions. This work extends the DIFT algorithm to NMI functions in directed graphs, and shows its application in the context of the generation of superpixels. Another application that is presented to spread the relevance of NMI functions is the Bandeirantes algorithm for curve tracing and boundary tracking.
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Chancellor, Edward, and Kasper Oikarinen. "1D LIDAR Speed and Motion for the Internet-of-Things : For Railroad Classification Yards." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299855.

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This thesis is an investigation into the feasibility of one-dimensional Light Detection and Ranging (LIDAR) sensors for tracking the position and motion of trains on railroad classification yards. Carefully monitoring railway traffic in these areas is important, in order to avoid accidents, optimise logistical operations and hence reduce delays. However, existing technologies for tracking trains on regular stretches of train-line, including Radio Frequency Identification (RFID) and Global Positioning System (GPS), have various drawbacks when applied to classification yards. As such, it is pertinent to investigate the extent to which simple LIDAR sensors could be used for this purpose, as part of a basic Internet of Things (IoT) system. To tackle this problem, we considered different ways of positioning the sensors around railway tracks. We then proposed a floating average algorithm for calculating a target object’s velocity using continuous LIDAR distance readings. To know when to apply the algorithm as a train is passing the sensor, we observed how the distance readings varied as a model train passed the sensor. The data was used to construct a Finite-state machine (FSM) that can fully describe the status of trains as they pass the sensor. In order to test our solution, we constructed a prototype sensor node implementing the FSM and evaluated its performance first with a model train and then on actual commuter trains on an outdoors train platform. We found that one-dimensional LIDAR sensors could feasibly be deployed to monitor the position and motion of trains with a high degree of consistency and accuracy. However, LIDAR may need to be corroborated with other types of technology such as RFID so that trains can be distinguished from other moving objects.
Detta projekt undersöker möjligheten att använda endimensionella Light Detection and Ranging (LIDAR) sensorer för att spåra läge och rörelse av tåg på rangerbangårdar. Att övervaka tågtrafik i dessa områden är viktigt för att undvika trafikolyckor, optimera logistiska operationer och därmed minska förseningar. Dagens teknik för att spåra tåg på vanliga tågspår, till exempel Radio Frequency Identification (RFID) och Global Positioning System (GPS), har flera begränsningar när de ska användas till rangerbangårdar. Följaktligen så är det relevant att undersöka till vilken grad enkla LIDAR sensorer kan tillämpas för detta ändamål som en del av ett Internet of Things (IoT) system. För att lösa detta problem, övervägde vi olika sätt att placera sensorerna kring tågspår. Därefter implementerade vi en glidande medelvärdealgoritm för att beräkna målobjektets hastighet genom att använda kontinuerliga LIDAR avståndsmätningar. För att kunna veta när algoritmen skulle tillämpas när riktiga tåg passerade sensorn, noterade vi först hur avståndsmätningarna varierade när ett modelltåg passerade sensorn. Mätningarna användes sedan för att konstruera en ändlig tillståndsmaskin (FSM) som kan fullständigt beskriva statusen av tåget när det åker förbi sensorn. För att testa vår lösning, tillverkade vi en sensornodprototyp med vår FSM implementerad och utvärderade först dess prestationsförmåga med ett modelltåg och sedan med riktiga pendeltåg.Vi observerade att endimensionella LIDAR sensorer kan användas för att övervaka läge och hastighet av tåg med hög precision och konsekventa resultat. Däremot visade sig att LIDAR ska med fördel kombineras med andra typer av teknologi, som till exempel RFID, för att urskilja tåg från andra objekt i rörelse.
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Wu, Bing-Ze, and 吳秉澤. "A Study to Evaluate Identifying Velocity Field, Tracking Rain Cells and their Convection, as well as Quantitative Precipitation Nowcast by Designed Observing System Experiments." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/c6m75n.

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碩士
國立臺灣大學
土木工程學研究所
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There are two basic elements of quantitative precipitation nowcasting with radar echo observation: one is the horizontal velocity field of the rainfall system; the other is to identify the time variation of the rain cell and its vertical convection, or the time variation of the echo intensity (Convection), and the use of a large number of identified rain cells and their intensity changes to count the climate characteristics of the rain cell life history. Two kinds of methods for estimating the velocity field of the rainfall system are estimated by using several echo images adjacent to each other. One is the image matching method for tracking, which is Lagrangian framework, such as the TREC (Tracking Radar Echo by Correlation) algorithm. One of the images is translated grids of a horizontal image in two main axes, and compared with adjacent time images to find the number of translation grids and the distance with the highest correlation coefficient, and divide by the time difference of the adjacent images to obtain two Shifting component. Because the image comparison requires a number of pixels, the spatial variability of the velocity field is low; when the method is applied in Taiwan, the spatial variability of the horizontal velocity field of the near-topographic rainfall system is large, and the variation of the velocity field estimation of the TREC method is insufficient.The other is to assume that two velocity components at any position in the velocity field are expressed as a function of the space coordinate (self-variable), and the coefficient of the velocity function is estimated by the "regression method". This method uses the Eulerian framework. If the flow is beyond a grid in a time difference and the Courant-Friedrichs-Lewy condition is not satisfied, the regression method will fail, resulting in incorrect coefficient estimation and incorrect estimation of the velocity field. The ABLER (Advection-equation Based Lagrangian-Eulerian Regression) algorithm combines TREC''s image translation and the regression of linear velocity field function, which combines the detailed changes of the regression method, and can avoid violation of the Courant condition through image translation. Cheng(2017) proposed the Principle Velocity Transform (PVT) and the Piecewise-Linear and Jointly Optimized (PLJO) strategy to improved the linear velocity field ABLER (Advection-equation Based Lagrangian--Eulerian Regression) algorithm. the result is: the velocity field is more flexible, the speed estimation is more accurate, and the calculation speed is faster, but the study does not include rain cell identification and intensity change identification. The design of this study: 1. The velocity component function of the velocity field is linear, 2. The different rain cells have different convection growth or decay of the Observing System Experiment (OSE). Using the improved ABLER algorithm improved by Cheng(2017) to estimate the moving velocity field, and make two improvements: (1) Optimizing the piecewise linear strategy, changing the Downhill Simplex Search (DSS) to the BFGS method, and adopting the incremental parameter estimation. The method greatly speeds up the calculation of the optimization coefficient, but the source of the rain cell will still cause the error of the estimation of the velocity field coefficient. (2) Add the identification of rain cells, intensity growth and attenuation rate. The principle of rain cell identification is to identify the strong echo region by the isoline technique and the non-zero winding number principle, and then use the erosion and dilation in mathematical morphology to identify the core regions of the respective rain cells; In addition, the contours of the rain cells corresponding to the adjacent time echo images are used to cover regional quality differences, estimate growth or attenuation, and perform extrapolation estimation. Application OSE evaluation: A. velocity field estimation error; B. rain cell increase and decrease rate estimation error; C. rain cell identification range error and other factors, sensitivity to epitaxial quantitative estimation. The results show that the estimation error of the velocity field has the most significant impact on the accuracy of the forecast. Increasing the rain cell identification and the intensity identification and intensity adjustment of the core region can improve the technical score of the extended quantitative rainfall forecast.
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Hong, Sujin active 2008. "Transit proximity and trip-making characteristics : a study of 2007 Chicago metropolitan region travel tracking survey." Thesis, 2008. http://hdl.handle.net/2152/22376.

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Influence of built environment on travel behavior has been recognized by several studies in last decade (Cervero 2003, 2004, Ewing at al 2003 and etc.). Easy access to the transit station and mixed land use has been largely emphasized by New Urbanist because of its influence on transit ridership and reduction of vehicle mile travel. However, empirical evidence that proximity of residential location to the transit station or mixed land use reduces auto dependency and encourages transit ridership has been lack for Chicago metropolitan region in spite of its long history of transit development. This study uses 2007 Chicago metropolitan region travel tracking study data and travel characteristics of residents living within walkable distance from the CAT or METRA rail station in Chicago Metropolitan region was analyzed in comparison with those of residents living beyond walkable distance from the rail station in order to find any difference in socio-demographic characteristics and travel characteristics. In general, households located within walkable distance (a quarter mile for this study) from the rail station are more likely to be low income households, to reside in a multifamily rental housing. Residents living within walkable distance show higher portion of African American or Asian proportion, of smaller-sized households (a single member household or childless household). They are likely to own fewer cars than residents living far from the rail station. With this observation of some difference in sociodemographic and travel characteristics between two groups, probability of transit use and rail use in a relationship with home location and job location were tested using binary logistic model. The result indicates that the number of household vehicles per person in the household influences negatively on residential location. The more available household cars per person, the less likely it is that a household is located within walkable distance from the rail station. Work location was also an important factor for transit or rail use. This provides evidence that providing mixed land use where jobs and housing are all provided within walkable distance from the transit station can increase transit use and reduces auto-dependency that current American society is facing severely.
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Books on the topic "Rain tracking"

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House of rain: Tracking a vanished civilization across the American Southwest. New York: Little, Brown and Co., 2006.

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House of Rain: Tracking a Vanished Civilization Across the American Southwest. Little Brown & Company, 2007.

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House of Rain: Tracking a Vanished Civilization Across the American Southwest. Little Brown & Company, 2007.

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House of Rain: Tracking a Vanished Civilization Across the American Southwest. Back Bay Books, 2008.

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House of Rain: Tracking a Vanished Civilization Across the American Southwest. Little Brown & Company, 2007.

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House of Rain: Tracking a Vanished Civilization Across the American Southwest. Little Brown & Company, 2007.

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Childs, Craig Leland. House of Rain: Tracking a Vanished Civilization Across the American Southwest. Little, Brown and Company, 2007.

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House of Rain: Tracking a Vanished Civilization Across the American Southwest. Little Brown & Company, 2007.

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Publishing, Mpire. Sweat Is Fat Crying, Make It Rain : 6x9 Gym Exercise Log: Gym Tracking Book. Independently Published, 2019.

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Queens, Cash Flow. Make It Rain: Blank Fill in Expense Tracker for Personal Household & Business Bill Tracking Funny Debt Organizer Log Funny Daily, Weekly & Monthly Income Track. Independently Published, 2020.

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Book chapters on the topic "Rain tracking"

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Ding, X., T. Denœux, and F. Helloco. "Tracking Rain Cells in Radar Images using Multilayer Neural Networks." In ICANN ’93, 962–67. London: Springer London, 1993. http://dx.doi.org/10.1007/978-1-4471-2063-6_284.

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Sithamparanathan, Kandeepan, and Radoslaw Piesiewicz. "Frequency Tracking Performance Using a Hyperbolic Digital-Phase Locked Loop for Ka-Band Communication in Rain Fading Channels." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 94–102. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04260-7_12.

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Ulianov, Cristian, Paul Hyde, and Ramy Shaltout. "Railway Applications for Monitoring and Tracking Systems." In Sustainable Rail Transport, 77–91. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58643-4_6.

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Wang, Zhechen, and Yingmin Jia. "Train Velocity Tracking Control with Considering Wheel-Rail Adhesion." In Proceedings of 2018 Chinese Intelligent Systems Conference, 421–33. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2288-4_41.

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Patgar, Tanuja, and Devi CS Kavitha. "Vision of Intelligent Control and Tracking Rail System: Global Evident Data." In On-Board Design Models and Algorithm for Communication Based Train Control and Tracking System, 1–14. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003294016-1.

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Patgar, Tanuja, and Devi CS Kavitha. "Predictive Analysis of Intelligent Rail Trip Detection Service Using Machine Learning." In On-Board Design Models and Algorithm for Communication Based Train Control and Tracking System, 101–14. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003294016-8.

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Yadav, Arun A., Chetan O. Yadav, and Paladugula V. Ramana. "Kinematical Synthesis and Numerical Analysis of Rail-Based Dual-Axis Solar Tracking System." In Lecture Notes in Electrical Engineering, 167–75. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4286-7_17.

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Liu, Keyan, Limin Jia, Yong Qin, Zhipeng Wang, Lei Tong, and Yixuan Geng. "Target Tracking for High-Speed Railway Catenary Based on Correlation Filtering Algorithm." In Proceedings of the 5th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2021, 243–50. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9909-2_27.

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Hu, Hailin, Fu Feng, Zhilin Lai, Jie Yang, and Tao Wang. "Optimization of Self-Learning Speed-Tracking Control for Permanent Magnet Synchronous Motor." In Proceedings of the 5th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2021, 232–40. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9905-4_28.

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Aydın, Ilhan, Erhan Akın, and Emre Güçlü. "An Autonomous UAV Based Rail Tracking and Sleeper Inspection with Light-Weight Line Segmentation Approach." In Lecture Notes in Networks and Systems, 317–24. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09176-6_37.

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Conference papers on the topic "Rain tracking"

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Costamagna, Eugenio, Fabio Dell'Acqua, and Paolo Gamba. "Global rain-pattern tracking in meteorological radar data." In Remote Sensing, edited by Jaqueline E. Russell. SPIE, 1999. http://dx.doi.org/10.1117/12.373043.

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Louchet, J., R. Mathurin, and B. Rottembourg. "Combinatorial optimization and linear prediction approaches to rain cell tracking." In 26th AIPR Workshop: Exploiting New Image Sources and Sensors, edited by J. Michael Selander. SPIE, 1998. http://dx.doi.org/10.1117/12.300045.

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Giannett, Filippo, Ruggero Reggiannini, Marco Moretti, Simone Scarfone, Antonio Colicelli, Francesca Caparrini, Giacomo Bacci, et al. "Kalman Tracking of GEO Satellite Signal for Opportunistic Rain Rate Estimation." In 2018 15th International Symposium on Wireless Communication Systems (ISWCS). IEEE, 2018. http://dx.doi.org/10.1109/iswcs.2018.8491192.

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Pinage, Felipe, Jose Reginaldo Hughes Carvalho, and Jose Pinheiro de Queiroz Neto. "Natural Landmark Tracking Method to Support UAV Navigation over Rain Forest Areas." In 2012 Brazilian Symposium on Computing System Engineering (SBESC). IEEE, 2012. http://dx.doi.org/10.1109/sbesc.2012.28.

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Nagel, Dieter, and Christoph Neumann. "Tracking airborne targets through windmill areas and rain clutter with ground based radar." In 2016 17th International Radar Symposium (IRS). IEEE, 2016. http://dx.doi.org/10.1109/irs.2016.7497348.

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"Visual-based Natural Landmark Tracking Method to Support UAV Navigation over Rain Forest Areas." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004304304160419.

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Meissner, Thomas, and Frank J. Wentz. "Wind retrievals under rain for passive satellite microwave radiometers and its application to hurricane tracking." In 2008 Microwave Radiometry and Remote Sensing of the Environment (MICRORAD 2008). IEEE, 2008. http://dx.doi.org/10.1109/micrad.2008.4579492.

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Pinage, Felipe, Jose Reginaldo Hughes Carvalho, Emory Raphael Viana Freitas, and Jose Pinheiro de Queiroz Neto. "Feature Transform Technique for Combining Landmark Detection and Tracking of Visual Information of Large Rain Forest Areas." In 2013 Latin American Robotics Symposium and Competition (LARS/LARC). IEEE, 2013. http://dx.doi.org/10.1109/lars.2013.53.

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Nguyen, Son Hai, Mike Falco, Ming Liu, and David Chelidze. "Characterization of Fatigue Dynamics Under Deterministic and Stochastic Excitation." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71228.

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Estimating and tracking dynamics of crack growth is essential for fatigue failure prediction. A new experimental system coupling structural and crack growth dynamics was used to show fatigue damage accumulation is different under chaotic and stochastic loading, even when both excitations have similar spectral and statistical signatures. Furthermore, conventional rain-flow counting method considerably overestimates damage in case of chaotic forcing. Important nonlinear loading characteristics are identified to guide the new fatigue model development.
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Corsini, Alessandro, Alessio Castorrini, Enrico Morei, Franco Rispoli, Fabrizio Sciulli, and Paolo Venturini. "Modeling of Rain Drop Erosion in a Multi-MW Wind Turbine." In ASME Turbo Expo 2015: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/gt2015-42174.

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The actual strategy in offshore wind energy development is oriented to the progressive increase of the turbine diameter as well as the per unit power. Among many pioneering technological and aerodynamic issues linked to this design trend, the wind velocity at the blade tip region reaches very high values in normal operating conditions (typically between 90 to 110 m/s). In this range of velocity, the rain erosion phenomenon can have a relevant effect on the overall turbine performance in terms of power and energy production (up to 20% loss in case of deeply eroded leading edge). Therefore, as a customary approach erosion related issues are accounted for in the scheduling of the wind turbine maintenance. When offshore, on the other hand, the criticalities inherent to the cost of maintenance and operation monitoring suggest the rain erosion concerns to be tackled at the turbine design stage. In so doing, the use of computational tools to study the erosion phenomenon of wind turbines under severe meteorological conditions could define the base-line approach in the wind turbine blades design and verification. In this work, the authors present a report on numerical prediction of erosion on a 6 MW HAWT (horizontal axis wind turbine). Two different blade geometries of different aerodynamic loading, have been studied in a view to explore their sensitivity to rain erosion. The fully 3D simulations are carried out using an Euler-Lagrangian approach. Flow field simulations are carried out with the open-source code OpenFOAM, based on a finite volume approach, using Multiple Reference Frame methodology. Reynolds Averaged Navier-Stokes equations for incompressible steady flow were solved with a k-ε turbulence. An in-house code (P-Track) is used to compute the rain drops transport and dispersion, adopting the Particle Cloud Tracking approach (PCT), already validated on large industrial turbomachinery. At the impact on blade, erosion is modelled accounting for the main quantities affecting the phenomenon, which are impact velocity and material properties of the target surface. Results provide the regions of the two blades more sensitive to erosion, and the effect of the blade geometry on erosion attitude.
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Reports on the topic "Rain tracking"

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Barr, John K. An Application of Computerized Axial Tomography (CAT) Technology to Mass Raid Tracking. Fort Belvoir, VA: Defense Technical Information Center, August 1989. http://dx.doi.org/10.21236/ada214401.

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Balali, Vahid, Arash Tavakoli, and Arsalan Heydarian. A Multimodal Approach for Monitoring Driving Behavior and Emotions. Mineta Transportation Institute, July 2020. http://dx.doi.org/10.31979/mti.2020.1928.

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Studies have indicated that emotions can significantly be influenced by environmental factors; these factors can also significantly influence drivers’ emotional state and, accordingly, their driving behavior. Furthermore, as the demand for autonomous vehicles is expected to significantly increase within the next decade, a proper understanding of drivers’/passengers’ emotions, behavior, and preferences will be needed in order to create an acceptable level of trust with humans. This paper proposes a novel semi-automated approach for understanding the effect of environmental factors on drivers’ emotions and behavioral changes through a naturalistic driving study. This setup includes a frontal road and facial camera, a smart watch for tracking physiological measurements, and a Controller Area Network (CAN) serial data logger. The results suggest that the driver’s affect is highly influenced by the type of road and the weather conditions, which have the potential to change driving behaviors. For instance, when the research defines emotional metrics as valence and engagement, results reveal there exist significant differences between human emotion in different weather conditions and road types. Participants’ engagement was higher in rainy and clear weather compared to cloudy weather. More-over, engagement was higher on city streets and highways compared to one-lane roads and two-lane highways.
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