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Статті в журналах з теми "Rain tracking"
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.
Повний текст джерелаCorcoran, Padraig. "Topology Based Object Tracking." Mathematical and Computational Applications 24, no. 3 (September 18, 2019): 84. http://dx.doi.org/10.3390/mca24030084.
Повний текст джерелаHe, 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.
Повний текст джерелаHambali, 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.
Повний текст джерелаPalharini, 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.
Повний текст джерелаJuráš, 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.
Повний текст джерелаLeal, 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.
Повний текст джерелаYan, 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.
Повний текст джерелаDell’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.
Повний текст джерелаMoseley, 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.
Повний текст джерелаДисертації з теми "Rain tracking"
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.
Повний текст джерелаPaduru, Anirudh. "Fast Algorithm for Modeling of Rain Events in Weather Radar Imagery." ScholarWorks@UNO, 2009. http://scholarworks.uno.edu/td/1097.
Повний текст джерелаWarlimont, 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.
Повний текст джерелаCAUTERUCCIO, 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.
Повний текст джерелаCaballero, 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.
Повний текст джерелаIncludes 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.
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.
Повний текст джерелаCondori, 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/.
Повний текст джерелаImage 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.
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.
Повний текст джерелаDetta 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.
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.
Повний текст джерела國立臺灣大學
土木工程學研究所
107
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.
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.
Повний текст джерелаtext
Книги з теми "Rain tracking"
House of rain: Tracking a vanished civilization across the American Southwest. New York: Little, Brown and Co., 2006.
Знайти повний текст джерелаHouse of Rain: Tracking a Vanished Civilization Across the American Southwest. Little Brown & Company, 2007.
Знайти повний текст джерелаHouse of Rain: Tracking a Vanished Civilization Across the American Southwest. Little Brown & Company, 2007.
Знайти повний текст джерелаHouse of Rain: Tracking a Vanished Civilization Across the American Southwest. Back Bay Books, 2008.
Знайти повний текст джерелаHouse of Rain: Tracking a Vanished Civilization Across the American Southwest. Little Brown & Company, 2007.
Знайти повний текст джерелаHouse of Rain: Tracking a Vanished Civilization Across the American Southwest. Little Brown & Company, 2007.
Знайти повний текст джерелаChilds, Craig Leland. House of Rain: Tracking a Vanished Civilization Across the American Southwest. Little, Brown and Company, 2007.
Знайти повний текст джерелаHouse of Rain: Tracking a Vanished Civilization Across the American Southwest. Little Brown & Company, 2007.
Знайти повний текст джерелаPublishing, Mpire. Sweat Is Fat Crying, Make It Rain : 6x9 Gym Exercise Log: Gym Tracking Book. Independently Published, 2019.
Знайти повний текст джерелаQueens, 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.
Знайти повний текст джерелаЧастини книг з теми "Rain tracking"
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.
Повний текст джерелаSithamparanathan, 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.
Повний текст джерелаUlianov, 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.
Повний текст джерелаWang, 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.
Повний текст джерелаPatgar, 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.
Повний текст джерелаPatgar, 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.
Повний текст джерелаYadav, 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.
Повний текст джерелаLiu, 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.
Повний текст джерелаHu, 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.
Повний текст джерелаAydın, 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.
Повний текст джерелаТези доповідей конференцій з теми "Rain tracking"
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.
Повний текст джерелаLouchet, 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.
Повний текст джерелаGiannett, 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.
Повний текст джерелаPinage, 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.
Повний текст джерелаNagel, 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.
Повний текст джерела"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.
Повний текст джерелаMeissner, 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.
Повний текст джерелаPinage, 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.
Повний текст джерелаNguyen, 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.
Повний текст джерелаCorsini, 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.
Повний текст джерелаЗвіти організацій з теми "Rain tracking"
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.
Повний текст джерелаBalali, 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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