Relevant bibliographies by topics / Temporal window estimation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Temporal window estimation'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Temporal window estimation"

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Heagerty, Patrick, Michael D. Ward, and Kristian Skrede Gleditsch. "Windows of Opportunity: Window Subseries Empirical Variance Estimators in International Relations." Political Analysis 10, no. 3 (2002): 304–17. http://dx.doi.org/10.1093/pan/10.3.304.

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We show that temporal, spatial, and dyadic dependencies among observations complicate the estimation of covariance structures in panel databases. Ignoring these dependencies results in covariance estimates that are often too small and inferences that may be more confident about empirical patterns than is justified by the data. In this article, we detail the development of a nonparametric approach, window subseries empirical variance estimators (WSEV), that can more fully capture the impact of these dependencies on the covariance structure. We illustrate this approach in a simulation as well as with a statistical model of international conflict similar to many applications in the international relations literature.
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Taroni, Matteo, Giorgio Vocalelli, and Andrea De Polis. "Gutenberg–Richter B-Value Time Series Forecasting: A Weighted Likelihood Approach." Forecasting 3, no. 3 (August 6, 2021): 561–69. http://dx.doi.org/10.3390/forecast3030035.

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We introduce a novel approach to estimate the temporal variation of the b-value parameter of the Gutenberg–Richter law, based on the weighted likelihood approach. This methodology allows estimating the b-value based on the full history of the available data, within a data-driven setting. We test this methodology against the classical “rolling window” approach using a high-definition Italian seismic catalogue as well as a global catalogue of high magnitudes. The weighted likelihood approach outperforms competing methods, and measures the optimal amount of past information relevant to the estimation.
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Agron, Danielle Jaye S., Jae-Min Lee, and Dong-Seong Kim. "Nozzle Thermal Estimation for Fused Filament Fabricating 3D Printer Using Temporal Convolutional Neural Networks." Applied Sciences 11, no. 14 (July 12, 2021): 6424. http://dx.doi.org/10.3390/app11146424.

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A preventive maintenance embedded for the fused deposition modeling (FDM) printing technique is proposed. A monitoring and control integrated system is developed to reduce the risk of having thermal degradation on the fabricated products and prevent printing failure; nozzle clogging. As for the monitoring program, the proposed temporal neural network with a two-stage sliding window strategy (TCN-TS-SW) is utilized to accurately provide the predicted thermal values of the nozzle tip. These estimated thermal values are utilized to be the stimulus of the control system that performs countermeasures to prevent the anomaly that is bound to happen. The performance of the proposed TCN-TS-SW is presented in three case studies. The first scenario is when the proposed system outperforms the other existing machine learning algorithms namely multi-look back LSTM, GRU, LSTM, and the generic TCN architecture in terms of obtaining the highest training accuracy and lowest training loss. TCN-TS-SW also outperformed the mentioned algorithms in terms of prediction accuracy measured by the performance metrics like RMSE, MAE, and R2 scores. In the second case, the effect of varying the window length and the changing length of the forecasting horizon. This experiment reveals the optimized parameters for the network to produce an accurate nozzle thermal estimation.
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Tadić, Jovan M., Xuemei Qiu, Scot Miller, and Anna M. Michalak. "Spatio-temporal approach to moving window block kriging of satellite data v1.0." Geoscientific Model Development 10, no. 2 (February 15, 2017): 709–20. http://dx.doi.org/10.5194/gmd-10-709-2017.

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Abstract. Numerous existing satellites observe physical or environmental properties of the Earth system. Many of these satellites provide global-scale observations, but these observations are often sparse and noisy. By contrast, contiguous, global maps are often most useful to the scientific community (i.e., Level 3 products). We develop a spatio-temporal moving window block kriging method to create contiguous maps from sparse and/or noisy satellite observations. This approach exhibits several advantages over existing methods: (1) it allows for flexibility in setting the spatial resolution of the Level 3 map, (2) it is applicable to observations with variable density, (3) it produces a rigorous uncertainty estimate, (4) it exploits both spatial and temporal correlations in the data, and (5) it facilitates estimation in real time. Moreover, this approach only requires the assumption that the observable quantity exhibits spatial and temporal correlations that are inferable from the data. We test this method by creating Level 3 products from satellite observations of CO2 (XCO2) from the Greenhouse Gases Observing Satellite (GOSAT), CH4 (XCH4) from the Infrared Atmospheric Sounding Interferometer (IASI) and solar-induced chlorophyll fluorescence (SIF) from the Global Ozone Monitoring Experiment-2 (GOME-2). We evaluate and analyze the difference in performance of spatio-temporal vs. recently developed spatial kriging methods.
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Yang, Zhijia, Wujiao Dai, Rock Santerre, Cuilin Kuang, and Qiang Shi. "A Spatiotemporal Deformation Modelling Method Based on Geographically and Temporally Weighted Regression." Mathematical Problems in Engineering 2019 (December 27, 2019): 1–11. http://dx.doi.org/10.1155/2019/4352396.

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The geographically and temporally weighted regression (GTWR) model is a dynamic model which considers the spatiotemporal correlation and the spatiotemporal nonstationarity. Taking into account these advantages, we proposed a spatiotemporal deformation modelling method based on GTWR. In order to further improve the modelling accuracy and efficiency and considering the application characteristics of deformation modelling, the inverse window transformation method is used to search the optimal fitting window width and furthermore the local linear estimation method is used in the fitting coefficient function. Moreover, a comprehensive model for the statistical tests method is proposed in GTWR. The results of a dam deformation modelling application show that the GTWR model can establish a unified spatiotemporal model which can represent the whole deformation trend of the dam and furthermore can predict the deformation of any point in time and space, with stronger flexibility and applicability. Finally, the GTWR model improves the overall temporal prediction accuracy by 43.6% compared to the single-point time-weighted regression (TWR) model.
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Cho, Woon Hyun, and Terry W. Spencer. "Estimation of polarization and slowness in mixed wavefields." GEOPHYSICS 57, no. 6 (June 1992): 805–14. http://dx.doi.org/10.1190/1.1443294.

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A new algorithm is developed for estimating the moveout velocities and polarization states in mixed wavefields recorded on multicomponent array data in the presence of random noise. The algorithm is applicable to a spatial and temporal data window in which more than two events are present. Three fundamental attributes of the waves are determined: polarization angle, apparent slowness, and the change in amplitude between adjacent detectors. In implementing the method, it is assumed that data is recorded at equispaced geophones located in a spatial window in which the three parameters are constant. Robustness is achieved by averaging the transfer matrix over all combinations of the subarrays that have the same transfer matrix. Application of a least‐squares criterion reduces the mathematics to an eigenvalue problem. The eigenvalues are complex, and their magnitude determines the amplitude change factor. The phase is a linear function of frequency with slope that determines the vertical slowness. The eigenvectors are the polarizations. The input data consists of the cross‐power spectra between subarrays that contain the same number of elements and are shifted by zero or one geophone separation. Examples illustrate the application of the algorithm to synthetic data. Numerical test results show that the performance of the method is not sensitive either to the time overlap between events or to the degree of similarity between waveforms.
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Alammari, Ammar, Ammar Ahmed Alkahtani, Mohd Riduan Ahmad, Ahmed Aljanad, Fuad Noman, and Zen Kawasaki. "Cross-Correlation Wavelet-Domain-Based Particle Swarm Optimization for Lightning Mapping." Applied Sciences 11, no. 18 (September 16, 2021): 8634. http://dx.doi.org/10.3390/app11188634.

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Several processing methods have been proposed for estimating the real pattern of the temporal location and spatial map of the lightning strikes. However, due to the complexity of lightning signals, providing accurate lightning maps estimation remains a challenging task. This paper presents a cross-correlation wavelet-domain-based particle swarm optimization (CCWD-PSO) technique for an accurate and robust representation of lightning mapping. The CCWD method provides an initial estimate of the lightning map, while the PSO attempts to optimize the trajectory of the lightning map by finding the optimal sliding window of the cross-correlation. The technique was further enhanced through the introduction of a novel lightning event extraction method that enables faster processing of the lightning mapping. The CCWD-PSO method was validated and verified using three narrow bipolar events (NBEs) flashes. The observed results demonstrate that this technique offers high accuracy in representing the real lightning mapping with low estimation errors.
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Peng, Hui, Juhong Tie, and Dequan Guo. "An Iterative Axial and Lateral Ultrasound Strain Estimator Using Subband Division." Journal of Medical Imaging and Health Informatics 10, no. 5 (May 1, 2020): 1057–68. http://dx.doi.org/10.1166/jmihi.2020.3024.

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Conventional ultrasound strain imaging usually only calculates the axial strain. Although axial strain is the main component of two dimensional strain field, lateral displacement and strain estimation can provide additional information of human mechanical properties. Shear strain and Poisson’s ratio can be estimated by using lateral strain estimation technique. Low lateral sampling rate and decorrelation noise of lateral radio frequency (RF) signal caused by axial displacement motion increase the difficulty of lateral strain estimation. Subband division technique is to divide a broadband signal into several narrowband signals. In this paper, the application of subband division technique in axial and lateral strain estimation is studied, and an iterative method for estimating axial and lateral strains is proposed based on subband technique. The subband division of this method is carried out along the axial direction, so that the bandwidth of the lateral subband signal is maintained and the quality of the lateral sub strain image is not reduced. In this paper, the number of subbands is three; the compounded lateral strain image is obtained by superimposing these sub strain images on the average. In each iteration, the temporal stretching technique is used to align the axial and lateral RF signals by using the axial and lateral displacement estimation information, which reduces the decorrelation noise of the RF signals. The length of temporal stretching window decreases with the number of iterations, so as to gradually improve the accuracy of temporal stretching. The phase zero algorithm is used to estimate the axial and lateral displacements. The effectiveness of this method is tested by simulations. The simulation results show that the elastographic signal-to-noise ratio (SNRe) of lateral strain image is increased by about 50%, the elastographic contrast noise ratio (CNRe) of lateral strain image is increased by about 120%, the SNRe of axial strain image is increased by about 4%, the CNRe of axial strain image is increased by 8%, and the signal-to-noise ratio of Poisson’s ratio image is increased by about 40%.
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Aldrin, Magne, Bjørnar Mortensen, Geir Storvik, Kjell Nedreaas, Asgeir Aglen, and Sondre Aanes. "Improving management decisions by predicting fish bycatch in the Barents Sea shrimp fishery." ICES Journal of Marine Science 69, no. 1 (November 20, 2011): 64–74. http://dx.doi.org/10.1093/icesjms/fsr172.

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Abstract Aldrin, M., Mortensen, B., Storvik, G., Nedreaas, K., Aglen, A., and Aanes, S. 2012. Improving management decisions by predicting fish bycatch in the Barents Sea shrimp fishery. – ICES Journal of Marine Science, 69: 64–74. When the bycatch of juvenile fish within the Barents Sea shrimp fishery is too large, the area is closed to fishing for a certain period. Bycatch is estimated from sampled trawl hauls, for which the shrimp yield is recorded, along with the total number of various bycatch fish species. At present, bycatch estimation is based on a simple estimator, the sum of the number of fish caught within the area of interest within a small time window, divided by the corresponding shrimp yield (in weight). No historical data are used. A model-based estimation is proposed in which spatio-temporal models are constructed for the variation in both the yield of shrimp and the amount of bycatch in space and time. The main effects are described through generalized additive models, and local dependence structures are specified through correlated random effects. Model estimation includes historical and recent data. Experiments with both simulated and real data show that the model-based estimator outperforms the present simple estimator when a low or moderate number of samples (e.g. <20) is available, whereas the two estimators are equally good when the number of samples is high.
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Atencia, A., M. C. Llasat, L. Garrote, and L. Mediero. "Effect of radar rainfall time resolution on the predictive capability of a distributed hydrologic model." Hydrology and Earth System Sciences Discussions 7, no. 5 (October 13, 2010): 7995–8043. http://dx.doi.org/10.5194/hessd-7-7995-2010.

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Abstract. The performance of distributed hydrological models depends on the resolution, both spatial and temporal, of the rainfall surface data introduced. The estimation of quantitative precipitation from meteorological radar or satellite can improve hydrological model results, thanks to an indirect estimation at higher spatial and temporal resolution. In this work, composed radar data from a network of three C-band radars, with 6-minutal temporal and 2 × 2 km2 spatial resolution, provided by the Catalan Meteorological Service, is used to feed the RIBS distributed hydrological model. A Window Probability Matching Method (gage-adjustment method) is applied to four cases of heavy rainfall to improve the observed rainfall sub-estimation in both convective and stratiform Z/R relations used over Catalonia. Once the rainfall field has been adequately obtained, an advection correction, based on cross-correlation between two consecutive images, was introduced to get several time resolutions from 1 min to 30 min. Each different resolution is treated as an independent event, resulting in a probable range of input rainfall data. This ensemble of rainfall data is used, together with other sources of uncertainty, such as the initial basin state or the accuracy of discharge measurements, to calibrate the RIBS model using probabilistic methodology. A sensitivity analysis of time resolutions was implemented by comparing the various results with real values from stream-flow measurement stations.
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Dissertations / Theses on the topic "Temporal window estimation"

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McGuire, Stephanie N. "Some aspects of the auditory processing of sinusoidally rippled spectra in humans." Thesis, University of Oxford, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.269304.

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Book chapters on the topic "Temporal window estimation"

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Böhm, Christian, Hans-Peter Kriegel, Peer Kröger, and Petra Linhart. "Selectivity Estimation of High Dimensional Window Queries via Clustering." In Advances in Spatial and Temporal Databases, 1–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11535331_1.

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Ezhilarasan, Murugesan, Kumar K. Nirmal, and P. Thambidurai. "An Efficient Algorithm for Fast Block Motion Estimation in High Efficiency Video Coding." In Emerging Technologies in Intelligent Applications for Image and Video Processing, 132–50. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9685-3.ch006.

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The Motion Estimation is an indispensable module in the design of video encoder. It employs Block Matching algorithm which involves searching a candidate block in the entire search window of the reference frame taking up to 80% of the total video encoding time. In order to increase the efficiency, several Block Matching Algorithms are employed to minimize the computational time involved in block matching. The chapter throws light on an efficient approach to be applied to the existing Block Matching Search techniques in HEVC which outperforms the various Block Matching algorithms. It involves two steps namely – Prediction and Refinement. The prediction step considers two parameters such as the temporal correlation and the direction to predict the MV of the candidate block. Several combinations of the search points are formulated in the refinement step of the algorithm to minimize the search time. The results depict that the Efficient Motion Estimation schemes provide a faster search minimizing the computational time upon comparison with the existing Motion Estimation algorithms.
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Grove, Matt. "The Archaeology of Group Size." In Social Brain, Distributed Mind. British Academy, 2010. http://dx.doi.org/10.5871/bacad/9780197264522.003.0019.

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This chapter aims to summarize the results of recent research producing estimates of hominin range areas, population sizes, and land use patterns based on archaeological data. Estimates of such variables are essential to any geographic or demographic discussion of human evolution, yet at present no generally applicable quantitative method is available to link them to the often abundant data of the archaeological record. Such data offer a unique window onto the patterns of adaptation characterizing prehistoric human populations, and developing a generic method to describe trajectories of change will allow researchers to compare range areas, population sizes and land use patterns between different regions and periods from throughout the vast spatio-temporal range of human evolution. The discussion gives particular emphasis to estimating a trajectory of group size through time from shortly after 2 million years ago until approximately 14,000 years ago.
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Al-Manji, Suad, Gordon Mitchell, and Amna Al Ruheili. "Arabian Sea Tropical Cyclones: A Spatio-Temporal Analysis in Support of Natural Hazard Risk Appraisal in Oman." In Agrometeorology. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.96961.

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Tropical cyclones [TCs] are a common natural hazard that have significantly impacted Oman. Over the period 1881–2019, 41 TC systems made landfall in Oman, each associated with extreme winds, storm surges and significant flash floods, often resulting in loss of life and substantial damage to infrastructure. TCs affect Omani coastal areas from Muscat in the north to Salalah in the south. However, developing a better understanding of the high-risk regions is needed, and is of particular interest in disaster risk reduction institutions in Oman. This study aims to find and map TC tracks and their spatio-temporal distribution to landfall in Oman to identify the high-risk areas. The analysis uses Kernel Density Estimation [KDE] and Linear Direction Mean [LDM] methods to better identify the spatio-temporal distribution of TC tracks and their landfall in Oman. The study reveals clear seasonal and monthly patterns. This knowledge will help to improve disaster planning for the high-risk areas.
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"Advances in Fish Tagging and Marking Technology." In Advances in Fish Tagging and Marking Technology, edited by Alistair J. Hobday and Doug Pincock. American Fisheries Society, 2012. http://dx.doi.org/10.47886/9781934874271.ch21.

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<i>Abstract</i>.—Acoustic monitoring is a widely used low-cost technique for studying the movements of aquatic animals. One common deployment configuration is a linear array of receivers such that passage of a tagged animal across a line of receivers is detected. The typical goal is to estimate the fraction of a population that moves across the line. Receivers in an array can have nonoverlapping or overlapping detection probability envelopes. It has been assumed in the case of nonoverlapping arrays that the detection rate of tagged animals is proportional to the coverage of the line. Unfortunately, the estimation process is not as simple as previously believed. In fact the probability of detection is more Gaussian-shaped rather than uniform with distance from the receiver, and varies over time due to biotic and abiotic noise. Wind-generated noise in particular can have a major influence on the performance of receivers. Range testing, while important, will not solve the problem. In fact, the temporal complexity of this variable detection probability envelope renders a statistical solution improbable. Simulation modeling provides a potential alternative for estimating detection probability for nonoverlapping arrays, however, for most situations, the best solution is to design an overlapping array, with the detection range estimated for the worst environmental conditions.
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Conference papers on the topic "Temporal window estimation"

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Winstroth, Jan, and Joerg R. Seume. "Error Assessment of Blade Deformation Measurements on a Multi-Megawatt Wind Turbine Based on Digital Image Correlation." In ASME Turbo Expo 2015: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/gt2015-43622.

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Optical full-field measurement methods such as Digital Image Correlation (DIC) provide a new opportunity for measuring deformation and vibration in wind turbine rotor blades during operation, in high spatial and temporal resolution. Recent field tests on a multi-megawatt wind turbine have demonstrated the vast potential for full scale testing, however little is known about the overall accuracy of DIC measurements on wind turbines. The present work proposes using a virtual 3D wind turbine model for estimating the error associated with the optical measurements. The entire setup is simulated a priori and accurate error estimation becomes possible. The error estimation for a 3.2 MW wind turbine suggests that relative out-of-plane bending of the rotor blades can be measured with an accuracy of ±9.1 mm, relative in-plane bending of the rotor blades can be measured with an accuracy of ±10.2 mm, and relative blade torsion can be measured with an accuracy of ±0.07 deg. This corresponds to a relative error of 0.46% for out-of-plane bending, 1.11% for in-plane bending and 5.46% for blade torsion.
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Graf, Peter A., Ryan King, Katherine Dykes, Julian Quick, Levi Kilcher, and Jennifer Rinker. "Temporal Coherence Importance Sampling for Wind Turbine Extreme Loads Estimation." In AIAA Scitech 2019 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2019. http://dx.doi.org/10.2514/6.2019-1798.

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Wang, Yu, Hamn-Ching Chen, Guilherme Vaz, and Simon Mewes. "Verification Study of CFD Simulation of Semi-Submersible Floating Offshore Wind Turbine Under Regular Waves." In ASME 2021 3rd International Offshore Wind Technical Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/iowtc2021-3558.

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Abstract Utilization of Computational Fluid Dynamics (CFD) codes to perform hydrodynamic analysis of Floating Offshore Wind Turbines (FOWTs) is increasing recently. However, verification studies of the simulations that quantifying numerical uncertainties and permitting a detailed validation in a next phase is often disregarded. In this work, a verification study of CFD simulations of a semi-submersible FOWT design under regular waves is performed. To accomplish this goal, Response Amplitude Operators (RAOs) are derived from the computational results of the heave, surge and pitch motions. Four grids with different grid sizes with a constant refinement ratio are generated for verification of spatial convergence. Three different time increments are paired with each grid for verification of temporal convergence. The verification study is performed by estimation of the numerical errors and uncertainties using procedures proposed by Eca and Hoekstra [1].
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Yildirim, Nurseda, and Bahri Uzunoglu. "Spatial Clustering for Temporal Power Ramp Balance and Wind Power Estimation." In 2015 Seventh Annual IEEE Green Technologies Conference (GreenTech). IEEE, 2015. http://dx.doi.org/10.1109/greentech.2015.39.

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"Estimation of Spatially/Temporally Resolved Wind Inflow from Limited Measurements." In 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2013. http://dx.doi.org/10.2514/6.2013-748.

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Melville, W. Kendall, Lenonel Romero, and Jessica Kleiss. "Observations of Surface Waves and Wave Breaking: A Tribute to Nick Newman." In ASME 2008 27th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2008. http://dx.doi.org/10.1115/omae2008-57490.

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The Gulf of Tehuantepec is well known for having strong offshore winds, which occur predominantly during the winter months when significant atmospheric pressure differences develop between the Gulf of Mexico and the Pacific Ocean, forcing winds through a mountain gap at the head of the gulf. During the Gulf of Tehuantepec Experiment (GOTEX), conducted in February 2004, we collected surface-wave and wave-breaking measurements using a conical scanning lidar (NASA Airborne Terrain Mapper, ATM) and a downward-looking video camera on the NSF/NCAR C-130 aircraft. We present ATM observations of surface waves as well as statistical and spectral descriptions of the wave field. We also present measurements of the occurrence and strength of breaking using digital video data to identify actively breaking waves. In order to infer the dynamics of breaking from simple physical models, the kinematics of the breaking must be separated from the kinematics of the underlying waves. This is done using linear and nonlinear estimates of the underlying orbital velocities from the spatio-temporal surface displacement data measured by the ATM. Nonlinear estimation of the orbital velocities is described in a companion paper in this symposium (Grue et al., OMAE 2008). Frequent breaking is observed under the strong wind forcing. Examples of the spatial structure of waves are compared to simultaneous video imagery, giving an indication of the scale at which wave breaking occurs. The GOTEX observations, to almost full wave development, are tested against the latest wind-wave numerical spectral models that include the effects of wave generation, nonlinear wave-wave interactions and wave dissipation due to breaking. This paper is dedicated to Nick Newman by the lead author in appreciation of Nicks encouragement and assistance when he (WKM) moved to MIT in the early 1980s.
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Wada, Ryota, Philip Jonathan, Takuji Waseda, and Shejun Fan. "Estimating Extreme Waves in the Gulf of Mexico Using a Simple Spatial Extremes Model." In ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/omae2019-95442.

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Abstract We seek to characterize the behavior of extreme waves in the Gulf of Mexico, using a 109 year-long wave hindcast (GOMOS). The largest waves in this region are driven by strong winds from hurricanes. Design of offshore production systems requires the estimation of extreme metocean conditions corresponding to return periods from 1 year to 10,000 years and beyond. For extrapolation to long return periods, estimation using data for around 100 years from a single location will incur large uncertainties. Approaches such as spatial pooling, cyclone track-shifting and explicit track modeling have been proposed to alleviate this problem. The underlying problem in spatial pooling is the aggregation of dependent data and hence underestimation of uncertainty using naïve analysis; techniques such as block-bootstrapping can be used to inflate uncertainties to more realistic levels. The usefulness of cyclone track-shifting or explicit track modeling is dependent on the appropriateness of the physical assumptions underpinning such a model. In this paper, we utilize a simple spatial statistical model for extreme value estimation of significant wave height under tropical cyclones, known as STM-E, proposed in Wada et al. (2018). The STM-E model was developed to characterize extreme waves offshore Japan, also dominated by tropical cyclones. The method relies on the estimation of two distributions from a sample of data, namely the distribution of spatio-temporal maximum (STM) and the exposure (E). In the current work, we apply STM-E to extreme wave analysis in Gulf of Mexico. The STM-E estimate provides a parsimonious spatially-smooth distribution of extreme waves, with smaller uncertainties per location compared to estimates using data from a single location. We also discuss the estimated characteristics of extreme wave environments in this region.
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Lv, Junjie, and Hao Quan. "Temporal Correlation Modeling of Wind Power Series Based on Gaussian Copula and Nonparametric Kernel Density Estimation." In 2020 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). IEEE, 2020. http://dx.doi.org/10.1109/appeec48164.2020.9220546.

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Alvarez-Legazpi, Paula, Marta Vargas-Mun˜oz, Jose´ Conrado Marti´nez-Acevedo, Joaqui´n Botella-Malago´n, and Manuel Rodri´guez-Ferna´ndez. "Cross Wind Protection Systems for High Speed Railway Lines." In 2010 Joint Rail Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/jrc2010-36112.

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Higher operating rail speeds and lighter rolling stock means that cross wind, a factor that had not been considered for railway operations until recent times, has acquired vital importance in keeping adequate safety levels for railway transport of passengers. The overturn risk for a train circulating on a high speed line is determined by three key issues: • TRAIN: its aerodynamic and dynamic characteristics. • LINE: radius, azimuth, type of infrastructure, etc. • WIND: speed and angle with the train: – Wind statistics at the cross wind detection stations. – Wind models with spatial extrapolation for estimating average and actual wind on each section of the line. – Temporal forecast models at the cross wind detection stations. The combination of a certain rail line and a specific vehicle allows the determination of the criticality of each site. Once the authoritative safety target has been defined, according to this overturn risk, the adequate operating procedures must be defined. There are three possible types of protection systems: • Passive protection: protection walls or wind screens. • Active protection: short term (minutes) wind alert systems that impose restrictions to train speed when strong cross wind conditions are predicted. • Special procedures to regulate railway traffic under critical wind conditions. This paper presented hereby describes the studies to determine the susceptible sections to be protected, focus afterwards, specifically on active protection systems themselves, and main actions for its implementation.
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Bolaños, Rodolfo, Anouk Lako, Patrick Dich Grode, Maziar Golestani, and Thor Ugelvig Petersen. "MetOcean-on-Demand for the US East Coast: Wave and Hydrodynamics Database." In ASME 2018 1st International Offshore Wind Technical Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/iowtc2018-1038.

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The U.S. East Coast has the potential for offshore wind deployment and contribute to the energy demands of the area. A safe and cost-effective development of offshore wind farms requires detailed and accurate meteorological and oceanographic (MetOcean) analysis and, thus, a wave and hydrodynamic database has been produced along the U.S. East Coast. The high-resolution wave and hydrodynamic models are calibrated and validated against various NOAA measurements along the U.S. East Coast. The wave model open lateral boundaries are forced by spectral data from a global wave model. Wind forcing for both models were taken from CFSR wind fields, and the hydrodynamic model was also forced by atmospheric pressure field. Spatially and temporally-varying water level and depth-averaged currents were used to force the wave model. The hydrodynamic model was forced by tidal elevations at the boundaries. The model’s resolution varies from 15 km along the offshore boundaries to approximately 3 km near the coastline. Wave spectra resolution was of 25 frequencies and 16 directions. New wave model features are explored, including the effect of atmospheric stability on wave growth, the air-sea density ratio and the current impact on the wave growth rate. These processes are now included during the calibration process of the wave model. Hydrodynamic model results show good agreement with surface elevation measurements, with no bias and small errors. Results of the wave model show overall good results, although some overestimation of the extreme events is noticeable for the studied cases. The high-quality wave and hydrodynamic models developed within this work will serve as a MetOcean database for developers or alternatively as forcing for establishing local high-resolution (100s m resolution) models, i.e. for complex bathymetry at specific wind farm sites. Further work includes longer-term validations, data assimilation and assessment of other processes such as bottom friction and their impact on the estimation of extreme waves.
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Reports on the topic "Temporal window estimation"

1

Walker, David T. Estimation of Temporally Evolving Typhoon Winds and Waves from Synthetic Aperture Radar. Fort Belvoir, VA: Defense Technical Information Center, January 2010. http://dx.doi.org/10.21236/ada542467.

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2

Walker, David. Estimation of Temporally Evolving Typhoon Winds and Waves from Synthetic Aperture Radar. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada598555.

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3

Walker, David. Estimation of Temporally Evolving Typhoon Winds and Waves from Synthetic Aperture Radar. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada557079.

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4

Downing, W. Logan, Howell Li, William T. Morgan, Cassandra McKee, and Darcy M. Bullock. Using Probe Data Analytics for Assessing Freeway Speed Reductions during Rain Events. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317350.

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Rain impacts roadways such as wet pavement, standing water, decreased visibility, and wind gusts and can lead to hazardous driving conditions. This study investigates the use of high fidelity Doppler data at 1 km spatial and 2-minute temporal resolution in combination with commercial probe speed data on freeways. Segment-based space-mean speeds were used and drops in speeds during rainfall events of 5.5 mm/hour or greater over a one-month period on a section of four to six-lane interstate were assessed. Speed reductions were evaluated as a time series over a 1-hour window with the rain data. Three interpolation methods for estimating rainfall rates were tested and seven metrics were developed for the analysis. The study found sharp drops in speed of more than 40 mph occurred at estimated rainfall rates of 30 mm/hour or greater, but the drops did not become more severe beyond this threshold. The average time of first detected rainfall to impacting speeds was 17 minutes. The bilinear method detected the greatest number of events during the 1-month period, with the most conservative rate of predicted rainfall. The range of rainfall intensities were estimated between 7.5 to 106 mm/hour for the 39 events. This range was much greater than the heavy rainfall categorization at 16 mm/hour in previous studies reported in the literature. The bilinear interpolation method for Doppler data is recommended because it detected the greatest number of events and had the longest rain duration and lowest estimated maximum rainfall out of three methods tested, suggesting the method balanced awareness of the weather conditions around the roadway with isolated, localized rain intensities.
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