Relevant bibliographies by topics / Directional data

Academic literature on the topic 'Directional data'

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Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Directional data"

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Boulerice, Bernard, and Gilles R. Ducharme. "Decentered directional data." Annals of the Institute of Statistical Mathematics 46, no. 3 (September 1994): 573–86. http://dx.doi.org/10.1007/bf00773518.

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Ahmed, Mohammed Imran Basheer, Rim Ali Zaghdoud, Mohammed Salih Ahmed, Mousa Alrabeea, Abdullatif Alsuwaiti, Nawaf Alzaid, Ahmed Alyousef, et al. "Intelligent Directional Survey Data Analysis to Improve Directional Data Acquisition." Mathematical Modelling of Engineering Problems 10, no. 2 (April 28, 2023): 482–90. http://dx.doi.org/10.18280/mmep.100214.

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Novrianti, Novrianti, Rycha Melisa, and Rafhie Adrian. "Kick-Off Point (KOP) and End of Buildup (EOB) Data Analysis in Trajectory Design." Journal of Geoscience, Engineering, Environment, and Technology 2, no. 2 (June 1, 2017): 133. http://dx.doi.org/10.24273/jgeet.2017.2.2.302.

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Well X is a development well which is directionally drilled. Directional drilling is choosen because the coordinate target of Well X is above the buffer zone. The directional track plan needs accurate survey calculation in order to make the righ track for directional drilling. There are many survey calculation in directional drilling such as tangential, underbalance, average angle, radius of curvature, and mercury method. Minimum curvature method is used in this directional track plan calculation. This method is used because it gives less error than other method. Kick-Off Point (KOP) and End of Buildup (EOB) analysis is done at 200 ft, 400 ft, and 600 ft depth to determine the trajectory design and optimal inclination. The hole problem is also determined in this trajectory track design. Optimal trajectory design determined at 200 ft depth because the inclination below 35º and also already reach the target quite well at 1632.28 ft TVD and 408.16 AHD. The optimal inclination at 200 ft KOP depth because the maximum inclination is 18.87º which is below 35º. Hole problem will occur if the trajectory designed at 600 ft. The problems are stuck pipe and the casing or tubing will not able to bend.
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Mardia, K. V. "Directional data analysis:an overview." Journal of Applied Statistics 15, no. 2 (January 1988): 115–22. http://dx.doi.org/10.1080/02664768800000018.

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Jupp, P. E. "Residuals for directional data." Journal of Applied Statistics 15, no. 2 (January 1988): 137–47. http://dx.doi.org/10.1080/02664768800000021.

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Hoffmann, Dorothea, Bill Palmer, and Alice Gaby. "Geocentric directional systems in Australia: a typology." Linguistics Vanguard 8, s1 (January 1, 2022): 67–89. http://dx.doi.org/10.1515/lingvan-2021-0063.

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Abstract This paper presents the results of a survey of geocentric directional systems across Australia using published and unpublished material as well as fieldwork data, providing the first systematic overview of such systems in Australia. The 116 sampled varieties, spoken across diverse landscapes, exhibit variation within and across languages. Many make use of more than one directional system. This paper sets out to create a systematic typological overview of geocentric directionals in Australia taking into account cultural and topographic salience, revisiting existing classifications of directional systems.
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Andersson, Fredrik, Adriana Citlali Ramírez, Torgeir Wiik, and Viktor V. Nikitin. "Directional interpolation of multicomponent data." Geophysical Prospecting 65, no. 5 (February 1, 2017): 1246–63. http://dx.doi.org/10.1111/1365-2478.12478.

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Lund, Ulric. "Cluster analysis for directional data." Communications in Statistics - Simulation and Computation 28, no. 4 (January 1999): 1001–9. http://dx.doi.org/10.1080/03610919908813589.

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Grogan, Mairéad, and Rozenn Dahyot. "Shape registration with directional data." Pattern Recognition 79 (July 2018): 452–66. http://dx.doi.org/10.1016/j.patcog.2018.02.021.

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Wang, Guochang, Fode Zhang, and Heng Lian. "Directional regression for functional data." Journal of Statistical Planning and Inference 204 (January 2020): 1–17. http://dx.doi.org/10.1016/j.jspi.2019.03.011.

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Dissertations / Theses on the topic "Directional data"

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DEMNI, Houyem. "Depth-based classification approaches for directional data." Doctoral thesis, Università degli studi di Cassino, 2021. http://hdl.handle.net/11580/83781.

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Supervised learning tasks aim to define a data-based rule by which new objects are assigned to one of the given classes. To this end, a training set containing objects with known memberships is exploited. Directional data are points lying on the surface of circles, spheres or hyper-spheres. Given that they lie on a non-linear manifold, directional observations require specific methods to be analyzed. In this thesis, the main interest is to present novel methodologies and to perform reliable inferences for directional data, within the framework of supervised classification. First, a supervised classification procedure for directional data is introduced. The procedure is based on the cumulative distribution of the cosine depth, that is a directional distance-based depth function. The proposed method is compared with the max-depth classifier, a well-known depth-based classifier within the literature, through simulations and a real data example. Second, we study the optimality of the depth distribution and the max-depth classifiers from a theoretical perspective. More specifically, we investigate the necessary conditions under which the classifiers are optimal in the sense of the optimal Bayes rule. Then, we study the robustness of some directional depth-based classifiers in the presence of contaminated data. The performance of the depth distribution classifier, the max-depth classifier and the DD-classifier is evaluated by means of simulations in the presence of both class and attribute noise. Finally, the last part of the thesis is devoted to evaluate the performance of depth-based classifiers on a real directional data set.
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Correia, Arthur Endlein. "Methods and applications for geological directional data analysis." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/44/44141/tde-24082017-080342/.

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OpenStereo foi desenvolvido originalmente para preencher uma lacuna entre aplicativos de anáalise para geologia estrutural, como um software livre, gratuito e multi-plataforma. Ao longo dos anos ele adquiriu um grande núumero de usuários, com citações regulares. Este trabalho objetivou a reestruturação do OpenStereo como um todo, mudando-o para uma nova estrutura de interface gráfica e construíndo-o do zero visando desempenho, estabilidade e facilidade de manutenção e extensão. Diversas novas funcionalidades foram incluídas tais como projetos, conversão de notação de atitudes, ajuste de pequenos círculos, extração de atitudes de modelos tridimensionais e conversão de shapefiles de linhas para dados circulares. A pesquisa gerou dois subprodutos principais: um novo método gráfico para ajuste de pequenos círculos e a biblioteca de análise de dados estruturais Auttitude.
OpenStereo was originally developed to fill a gap among software packages for structural geology analysis, as a free open source cross-platform software. Over the years it has acquired a great number of users, with regular citations. This work aimed to restructure OpenStereo as a whole, changing to a new graphical interface framework and building it from the ground up for speed, stability, ease of maintenance and extension. Many new functionalities were also included, such as project management, structural attitudes notation handling, small circle fitting, extractions of attitudes from three-dimensional models and conversion of lines shapefiles to circular data. The research involved had two main byproducts, a new graphical method for small circle data fitting and a directional data analysis library, Auttitude.
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Kittivoravitkul, Sasivimol. "A bi-directional transformation approach for semistructured data integration." Thesis, Imperial College London, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.444093.

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黎文傑 and Man-kit Lai. "Some results on the statistical analysis of directional data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1994. http://hub.hku.hk/bib/B31211550.

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Lai, Man-kit. "Some results on the statistical analysis of directional data /." [Hong Kong : University of Hong Kong], 1994. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13787950.

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Zhang, Shaosong. "Analysis of WACSIS data using a directional hybrid wave model." Texas A&M University, 2005. http://hdl.handle.net/1969.1/4744.

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This study focuses on the analysis of measured directional seas using a nonlinear model, named Directional Hybrid Wave Model (DHWM). The model has the capability of decomposing the directional wave field into its free wave components with different frequency, amplitude, direction and initial phase based on three or more time series of measured wave properties. With the information of free waves, the DHWM can predict wave properties accurately up to the second order in wave steepness. In this study, the DHWM is applied to the analyses of the data of Wave Crest Sensor Inter-comparison Study (WACSIS). The consistency between the measurements collected by different sensors in the WACSIS project was examined to ensure the data quality. The wave characteristics at the locations of selected sensors were predicted in time domain and were compared with those recorded at the same location. The degree of agreement between the predictions and the related measurements is an indicator of the consistency among different sensors. To analyze the directional seas in the presence of strong current, the original DHWM was extended to consider the Doppler effects of steady and uniform currents on the directional wave field. The advantage of extended DHWM originates from the use of the intrinsic frequency instead of the apparent frequency to determine the corresponding wavenumber and transfer functions relating wave pressure and velocities to elevation. Furthermore, a new approach is proposed to render the accurate and consistent estimates of the energy spreading parameter and mean wave direction of directional seas based on a cosine-2s model. In this approach, a Maximum Likelihood Method (MLM) is employed. Because it is more tolerant of errors in the estimated cross spectrum than a Directional Fourier Transfer (DFT) used in the conventional approach, the proposed approach is able to estimate the directional spreading parameters more accurately and consistently, which is confirmed by applying the proposed and conventional approach, respectively, to the time series generated by numerical simulation and recorded during the WACSIS project.
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Tao, Ran. "Using directional change for information extraction in financial market data." Thesis, University of Essex, 2018. http://repository.essex.ac.uk/23341/.

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Directional change (DC) is a new concept for summarizing market dynamics. Instead of sampling the financial market at fixed intervals as in the traditional time series analysis, by contrast, DC is data-driven: the price change itself dictates when a price is recorded. DC provides us with a complementary way to extract information from data. The data sampled at irregular time intervals in DC allows us to observe features that may not be recognized under time series. In this thesis we propose our new method for the summarizing of financial markets through the use of the DC framework. Firstly, we define what is the vocabulary needed for a DC market summary. The vocabulary includes DC indicators and metrics. DC indicators are used to build a DC market summary for a single market. DC metrics help us quantitatively measure the differences between two markets under the directional change method. We demonstrate how such metrics could quantitatively measure the differences between different DC market summaries. Then, with real financial market data studied using DC, we aim to demonstrate the practicability of DC market analysis, as a complementary method to that of time series, in the analysis of the financial market.
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Ramler, Ivan Peter. "Improved statistical methods for k-means clustering of noisy and directional data." [Ames, Iowa : Iowa State University], 2008.

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Bhattacharya, Sumit. "A Real-Time Bi-Directional Global Positioning System Data Link Over Internet Protocol." Ohio University / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1121355433.

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Stylianidis, Matthaios. "Instability of a bi-directional TiFGAN in unsupervised speech representation learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302026.

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A major challenge in the application of machine learning in the speech domain is the unavailability of annotated data. Supervised machine learning techniques are highly dependent on the amount of labelled data and the quality of the labels. On the other hand, unsupervised training methods do not require labels and hence allow for the use of much larger unlabelled datasets. In this thesis work we investigate the use of an unsupervised training method for learning representations of speech data. More specifically, we extend an existing Wasserstein Generative Adversarial Network (WGAN) architecture called the Time-Frequency GAN (TiFGAN), originally purposed for unconditional speech generation, into a bi-directional architecture capable of learning representations. We investigate the abilities of our proposed bi-directional architecture (BiTiFGAN) in learning speech representations by evaluating the learned representations in the supervised task of keyword detection using the Speech Commands dataset. We observe that the training of our model is characterized by instability and in an attempt to stabilize training we try several different configurations for our architecture and training parameters. Mode collapse in the encoder is a common problem across our experiments, decreasing the performance acquired with the learned representations and making training unstable. Nonetheless, by increasing the capacity of our BiTiFGAN discriminator we successfully learn representations that are competitive when compared to our baseline representations such as the Mel-frequency Cepstrum Coefficients (MFCC) or Filter Bank Energy (FBANK) features.
En återkommande utmaning i tillämpningen av maskininlärning i taldomänen är bristen på annoterad data. Övervakad maskininlärning beror i hög grad på mängden och kvalitén på annoterad data. I motsats till övervakad inlärning kan icke-övervakade tekniker för maskininlärning lära sig från icke-annoterad data och gör det därför praktiskt genomförbart att använda enorma mängder data för träning. I det här arbetet undersöker vi hur en icke-övervakad träningsmetod kan användas för att lära sig representationer av tal-data. Vi utökar en existerande arkitektur, Time-Frequency GAN (TiFGAN), en version av Was- serstein Generative Adversarial Networks (WGAN) ursprungligen utvecklad för obetingad tal-generering, till en tvåvägs-arkitektur som kan lära sig representationer. Vi undersöker hur väl vår föreslagna utökade tvåvägs-arkitektur (BiTiFGAN) kan lära sig representationer för tal genom att utvärdera de inlärda representationerna i en övervakad uppgift för nyckelords-igenkänning baserad på datamängden Speech Commands. Vi observerar att träningen av vår modell karaktäriseras av instabilitet och för att försöka stabilisera den utforskar vi flera olika variationer av arkitektur och tränings-parametrar. Vi finner att modal-kollaps (eng. mode collapse) i kodaren är ett återkommande problem i våra experiment och försämrar de inlärda representationernas prestanda samt är en bidragande faktor till instabilitet i träningen. Trots det kan vi genom att öka kapaciteten i vår BiTiFGAN-diskriminator framgångsrikt träna representationer som är jämförbara med baslinjer för tal-representation: Mel- Frequency Cepstrum Coefficients (MFCC) och Filter Bank Energy (FBANK).
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Books on the topic "Directional data"

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Bagchi, Parthasarathy. Bayesian analysis of directional data. Toronto: [s.n.], 1987.

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IESNA Computer Committee. Angular Positioning Sub-Committee., ed. IESNA guide for directional positioning of photometric data. New York, NY: Illuminating Engineering Society of North America, 1997.

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Spatial statistics and spatio-temporal data: Covariance functions and directional properties. Chichester, West Sussex, U.K: Wiley, 2011.

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Ralph, Grabowski, ed. MicroStation for AutoCAD users: A bi-directional handbook. Albany, NY: Delmar Publishers, 1999.

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Conforti, Frank. MicroStation for AutoCAD users: A bi-directional user's handbook. 2nd ed. Santa Fe, NM: OnWord Press, 1996.

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Sengupta, Ashis. A likelihood integrated method for exploratory graphical analysis of change point problem with directional data. Ahmedabad: Indian Institute of Management, 2006.

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Iliff, Kenneth W. X-29A lateral-directional stability and control derivatives extracted from high-angle-of-attack flight data. [Washington, D.C]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program Center, 1996.

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Iliff, Kenneth W. X-29A lateral-directional stability and control derivatives extracted from high-angle-of-attack flight data. [Washington, D.C.]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1996.

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Iliff, Kenneth W. X-29A lateral-directional stability and control derivatives extracted from high-angle-of-attack flight data. [Washington, D.C.]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1996.

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Iliff, Kenneth W. X-29A lateral-directional stability and control derivatives extracted from high-angle-of-attack flight data. [Washington, D.C.]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1996.

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Book chapters on the topic "Directional data"

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Trauth, Martin H. "Directional Data." In MATLAB® Recipes for Earth Sciences, 413–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46244-7_10.

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Marques de Sá, Joaquim P. "Directional Data." In Applied Statistics Using SPSS, STATISTICA and MATLAB, 327–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05804-6_10.

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Trauth, Martin H. "Directional Data." In MATLAB® Recipes for Earth Sciences, 491–517. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38441-8_10.

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Trauth, Martin H. "Directional Data." In Springer Textbooks in Earth Sciences, Geography and Environment, 429–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07719-7_10.

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Mahesh, K. C. "Directional Data Analysis." In Advances in Analytics and Applications, 21–30. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1208-3_3.

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El-Dawy, Eman, Hoda M. O. Mokhtar, and Ali El-Bastawissy. "Directional Skyline Queries." In Data and Knowledge Engineering, 15–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34679-8_2.

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Trauth, Martin H. "Statistics on Directional Data." In MATLAB® Recipes for Earth Sciences, 263–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72749-1_10.

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Trauth, Martin H. "Statistics on Directional Data." In MATLAB® Recipes for Earth Sciences, 311–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12762-5_10.

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Eargle, John M. "Directional Properties of Brass Instruments." In Electroacoustical Reference Data, 328–29. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2027-6_158.

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Eargle, John M. "Directional Properties of Woodwind Instruments." In Electroacoustical Reference Data, 330–31. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2027-6_159.

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Conference papers on the topic "Directional data"

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Goessling, Marc, and Shan Kang. "Directional decision lists." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7364077.

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Zheng, Yixian, Wenchao Wu, Huamin Qu, Chunyan Ma, and Lionel M. Ni. "Visual analysis of bi-directional movement behavior." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363802.

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Hauch, Konstantin, and Claudia Redenbach. "Mathematical morphology on directional data." In 2022 25th International Conference on Information Fusion (FUSION). IEEE, 2022. http://dx.doi.org/10.23919/fusion49751.2022.9841314.

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Weggenmann, Benjamin, and Florian Kerschbaum. "Differential Privacy for Directional Data." In CCS '21: 2021 ACM SIGSAC Conference on Computer and Communications Security. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3460120.3484734.

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Xu, Jizheng, Feng Wu, Jie Liang, and Wenjun Zhang. "Directional Lapped Transforms for Image Coding." In 2008 Data Compression Conference DCC. IEEE, 2008. http://dx.doi.org/10.1109/dcc.2008.69.

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Wang, Nan, and Hongning Wang. "Directional Multivariate Ranking." In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394486.3403051.

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Xu, Jizheng, and Feng Wu. "Subsampling-Adaptive Directional Wavelet Transform for Image Coding." In 2010 Data Compression Conference. IEEE, 2010. http://dx.doi.org/10.1109/dcc.2010.15.

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Mun, Sungkwang, and James E. Fowler. "Block Compressed Sensing of Images Using Directional Transforms." In 2010 Data Compression Conference. IEEE, 2010. http://dx.doi.org/10.1109/dcc.2010.90.

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Winther, A., D. J. Roper, and A. J. Sjaaholm. "Concepts of Directional Drilling Data Management." In Petroleum Computer Conference. Society of Petroleum Engineers, 1990. http://dx.doi.org/10.2118/20329-ms.

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Deo, M. C., and R. Burrows. "Extreme Wave Prediction Using Directional Data." In 20th International Conference on Coastal Engineering. New York, NY: American Society of Civil Engineers, 1987. http://dx.doi.org/10.1061/9780872626003.011.

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Reports on the topic "Directional data"

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Yeo, DongHun. Generation of Large Directional Wind Speed Datasets from Smaller Synoptic Storm or Thunderstorm Data Samples. National Institute of Standards and Technology, August 2012. http://dx.doi.org/10.6028/nist.tn.1756.

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Leblanc, S. G., P. Bicheron, J. M. Chen, M. Leroy, and J. Cihlar. Investigation of directional reflectance in boreal forests with an improved 4-Scale model and airborne POLDER data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1999. http://dx.doi.org/10.4095/218447.

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Grigoriu, Mircea. Algorithms for generating large sets of synthetic directional wind speed data for hurricane, thunderstorm, and synoptic winds. Gaithersburg, MD: National Bureau of Standards, 2009. http://dx.doi.org/10.6028/nist.tn.1626.

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Baluga, Anthony, and Masato Nakane. Maldives Macroeconomic Forecasting:. Asian Development Bank, December 2020. http://dx.doi.org/10.22617/wps200431-2.

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This study aims to build an efficient small-scale macroeconomic forecasting tool for Maldives. Due to significant limitations in data availability, empirical economic modeling for the country can be problematic. To address data constraints and circumvent the “curse of dimensionality,” Bayesian vector autoregression estimations are utilized comprising of component-disaggregated domestic sectoral production, price, and tourism variables. Results demonstrate how this methodology is appropriate for economic modeling in Maldives. With the appropriate level of shrinkage, Bayesian vector autoregressions can exploit the information content of the macroeconomic and tourism variables. Augmenting for qualitative assessments, the directional inclination of the forecasts is improved.
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Fong, Elizabeth N., and Alan H. Goldfine. A data base directions information resource management. Gaithersburg, MD: National Bureau of Standards, 1986. http://dx.doi.org/10.6028/nbs.sp.500-139.

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Blaskiewicz M. and J. W. Glenn. Synopsis of BTA Steering Data and Future Directions. Office of Scientific and Technical Information (OSTI), October 1992. http://dx.doi.org/10.2172/1131604.

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Orekhvo, D. O., and R. V. Khrunichev. Remote training course "data Analysis", training direction 38.03.05 " Business Informatics". OFERNIO, June 2018. http://dx.doi.org/10.12731/ofernio.2018.23676.

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Williams, Michael, Marcial Lamera, Aleksander Bauranov, Carole Voulgaris, and Anurag Pande. Safety Considerations for All Road Users on Edge Lane Roads. Mineta Transportation Institute, March 2021. http://dx.doi.org/10.31979/mti.2021.1925.

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Edge lane roads (ELRs), also known as advisory bike lanes or advisory shoulders, are a type of shared street where two-way motor vehicle (MV) traffic shares a single center lane, and edge lanes on either side are preferentially reserved for vulnerable road users (VRUs). This work comprises a literature review, an investigation of ELRs’ operational characteristics and potential road user interactions via simulation, and a study of crash data from existing American and Australian ELRs. The simulation evaluated the impact of various factors (e.g., speed, volume, directional split, etc.) on ELR operation. Results lay the foundation for a siting criterion. Current American siting guidance relies only upon daily traffic volume and speed—an approach that inaccurately models an ELR’s safety. To evaluate the safety of existing ELRs, crash data were collected from ELR installations in the US and Australia. For US installations, Empirical Bayes (EB) analysis resulted in an aggregate CMF of .56 for 11 installations observed over 8 years while serving more than 60 million vehicle trips. The data from the Australian State of Queensland involved rural one-lane, low-volume, higher-speed roads, functionally equivalent to ELRs. As motor vehicle volume grows, these roads are widened to two-lane facilities. While the authors observed low mean crash rates on the one-lane roads, analysis of recently converted (from one-lane to two-lane) facilities showed that several experienced fewer crashes than expected after conversion to two-lane roads.
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9

Hoch, Brendon, and Samantha Cook. A 10-Year monthly climatology of wind direction : case-study assessment. Engineer Research and Development Center (U.S.), April 2023. http://dx.doi.org/10.21079/11681/46912.

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A 10-year monthly climatology of wind direction in compass degrees is developed utilizing datasets from the National Oceanic Atmospheric Administration, Climate Forecast System. Data retrieval methodologies, numerical techniques, and scientific analysis packages to develop the climatology are explored. The report describes the transformation of input data in Gridded Binary format to the Geographic Tagged Image File Format to support geospatial analyses. The specific data sources, software tools, and data-verification techniques are outlined.
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10

Mulligan, R. P. M., A. F. Bajc, F. R. B. Brunton, A. K. Burt, K M Dell, S. M. Hamilton, and E H Priebe. The Ontario Geological Survey's groundwater initiative: deliverables, data, derivatives and future direction. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2018. http://dx.doi.org/10.4095/306557.

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