Journal articles: 'Velocity Uncertainty'

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Avila Farenzena, Bruno, and Jorge Hugo Silvestrini. "Density currents front velocity uncertainty." Computers & Fluids 232 (January 2022): 105209. http://dx.doi.org/10.1016/j.compfluid.2021.105209.

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Chen, Yue, Harold E. Bedell, Laura J. Frishman, and Dennis M. Levi. "Stimulus uncertainty affects velocity discrimination." Vision Research 38, no. 9 (May 1998): 1265—IN2. http://dx.doi.org/10.1016/s0042-6989(97)00282-4.

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Poliannikov, Oleg V., and Alison E. Malcolm. "The effect of velocity uncertainty on migrated reflectors: Improvements from relative-depth imaging." GEOPHYSICS 81, no. 1 (January 1, 2016): S21—S29. http://dx.doi.org/10.1190/geo2014-0604.1.

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We have studied the problem of uncertainty quantification for migrated images. A traditional migrated image contains deterministic reconstructions of subsurface structures. However, input parameters used in migration, such as reflection data and a velocity model, are inherently uncertain. This uncertainty is carried through to the migrated images. We have used Bayesian analysis to quantify the uncertainty of the migrated structures by constructing a joint statistical distribution of the location of these structures. From this distribution, we could deduce the uncertainty in any quantity derived from these structures. We have developed the proposed framework using a simple model with velocity uncertainty in the overburden, and we estimated the absolute positions of the horizons and the relative depth of one horizon with respect to another. By quantifying the difference in the corresponding uncertainties, we found that, in this case, the relative depths of the structures could be estimated much better than their absolute depths. This analysis justifies redatuming below an uncertain overburden for the purposes of the uncertainty reduction.

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Steinbock, J., A. Weissenbrunner, M. Juling, T. Lederer, and P. U. Thamsen. "Uncertainty evaluation for velocity–area methods." Flow Measurement and Instrumentation 48 (April 2016): 51–56. http://dx.doi.org/10.1016/j.flowmeasinst.2015.09.007.

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Zhang, Jiacheng, Sayantan Bhattacharya, and Pavlos P. Vlachos. "Uncertainty of PIV/PTV based Eulerian pressure estimation using velocity uncertainty." Measurement Science and Technology 33, no. 6 (March 10, 2022): 065303. http://dx.doi.org/10.1088/1361-6501/ac56bf.

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Abstract This work introduces a method to estimate the uncertainty of the pressure fields reconstructed from particle image velocimetry / particle tracking velocimetry (PIV/PTV) measurements by propagating the instantaneous velocity vector uncertainty through the pressure reconstruction. The uncertainty propagations through the calculation and integration of pressure gradients are modelled as linear transformations. The autocorrelation coefficient was modelled and incorporated in the uncertainty estimation to reproduce the effect of the autocorrelation of velocity errors on the reconstructed pressure’s accuracy. The method was first tested on synthetic velocity fields contaminated with varying levels of artificial noise correlated in space, time, or between components. The error analysis shows that the proposed method could predict the spatiotemporal variations of the pressure errors. The estimated pressure uncertainty also captures the effects of the velocity noise level, the autocorrelation, and the different pressure-gradient integration methods, with more than 80% accuracy in most test cases. The method was applied to an experimental vortex ring flow with planar PIV and a laminar pipe flow with volumetric PTV. The error analysis shows that the obtained pressure uncertainty possessed similar spatial and statistical distributions as the pressure errors. The results also indicate that the performance of the proposed uncertainty estimation method depends on the accuracy of the velocity uncertainty. The proposed uncertainty estimation method exhibits reliability in obtaining the local and instantaneous pressure uncertainty from the PIV/PTV measurements.

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Grubb, H., A. Tura, and C. Hanitzsch. "Estimating and interpreting velocity uncertainty in migrated images and AVO attributes." GEOPHYSICS 66, no. 4 (July 2001): 1208–16. http://dx.doi.org/10.1190/1.1487067.

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Estimating a suitable velocity field for use in prestack depth migration is inherently uncertain because of limitations on the available data and estimation techniques. This uncertainty affects both the migrated depth of structures and their amplitudes in the inverted images. These effects can be estimated by performing multiple migrations with a set of velocity fields and colocating features in the migrated images. This lets us examine the imaging procedure’s sensitivity to changes in the velocity field so we can assess both structural and amplitude uncertainties in migrated images. These two types of uncertainties affect interpretation in different ways. For instance, with structural uncertainty interpretation we consider the change in migrated location of structures when deciding on drilling locations, optimizing well trajectories, or computing uncertainty in volumetric calculations. With amplitude uncertainty or amplitude versus offset (AVO) uncertainty interpretation, we consider (1) uncertainty in crossplots of pairs of AVO attributes at a point of interest or (2) uncertainty of the attribute values along identified structures. For any interpretation informing a decision, the uncertainty can help estimate risk. Our data processing approach is based on amplitude‐preserving prestack depth migration followed by AVO inversion, or AVO migration/inversion. It is valid for estimating AVO attributes in simple to moderately complex structural settings. Our methods of assessing the effect of velocity uncertainty can also be applied when obtaining structural uncertainties for a complex overburden geology or amplitude uncertainties in conventional NMO‐based AVO analysis. They may also be applied straightforwardly to any poststack attribute analysis. Key to the approach is the availability of multiple velocity fields to generate multiple migrated images. In our application, an automatic algorithm samples possible fields, but the set of fields to consider could be generated from another source, such as interpretation.

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Yilmaz, Öz. "Circumventing velocity uncertainty in imaging complex structures." Leading Edge 37, no. 1 (January 2018): 14–18. http://dx.doi.org/10.1190/tle37010014.1.

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Renbiao Wu, Kunlong Gu, Jian Li, J. Habersat, and G. Maksymonko. "Propagation velocity uncertainty on GPR SAR processing." IEEE Transactions on Aerospace and Electronic Systems 39, no. 3 (July 2003): 849–61. http://dx.doi.org/10.1109/taes.2003.1238741.

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Coveney, Sam, Cesare Corrado, Caroline H. Roney, Daniel O’Hare, Steven E. Williams, Mark D. O’Neill, Steven A. Niederer, Richard H. Clayton, Jeremy E. Oakley, and Richard D. Wilkinson. "Gaussian process manifold interpolation for probabilistic atrial activation maps and uncertain conduction velocity." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, no. 2173 (May 25, 2020): 20190345. http://dx.doi.org/10.1098/rsta.2019.0345.

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In patients with atrial fibrillation, local activation time (LAT) maps are routinely used for characterizing patient pathophysiology. The gradient of LAT maps can be used to calculate conduction velocity (CV), which directly relates to material conductivity and may provide an important measure of atrial substrate properties. Including uncertainty in CV calculations would help with interpreting the reliability of these measurements. Here, we build upon a recent insight into reduced-rank Gaussian processes (GPs) to perform probabilistic interpolation of uncertain LAT directly on human atrial manifolds. Our Gaussian process manifold interpolation (GPMI) method accounts for the topology of the atrium, and allows for calculation of statistics for predicted CV. We demonstrate our method on two clinical cases, and perform validation against a simulated ground truth. CV uncertainty depends on data density, wave propagation direction and CV magnitude. GPMI is suitable for probabilistic interpolation of other uncertain quantities on non-Euclidean manifolds. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.

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Poliannikov, Oleg V., Michael Prange, Alison E. Malcolm, and Hugues Djikpesse. "Joint location of microseismic events in the presence of velocity uncertainty." GEOPHYSICS 79, no. 6 (November 1, 2014): KS51—KS60. http://dx.doi.org/10.1190/geo2013-0390.1.

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The locations of seismic events are used to infer reservoir properties and to guide future production activity, as well as to determine and understand the stress field. Thus, locating seismic events with uncertainty quantification remains an important problem. Using Bayesian analysis, a joint probability density function of all event locations was constructed from prior information about picking errors in kinematic data and explicitly quantified velocity model uncertainty. Simultaneous location of all seismic events captured the absolute event locations and the relative locations of some events with respect to others, along with their associated uncertainties. We found that the influence of an uncertain velocity model on location uncertainty under many realistic scenarios can be significantly reduced by jointly locating events. Many quantities of interest that are estimated from multiple event locations, such as fault sizes and fracture spacing or orientation, can be better estimated in practice using the proposed approach.

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Li, Shihao, Ting Wang, Rongjun Cheng, and Hongxia Ge. "An Extended Car-Following Model considering the Driver’s Desire for Smooth Driving and Self-Stabilizing Control with Velocity Uncertainty." Mathematical Problems in Engineering 2020 (August 29, 2020): 1–17. http://dx.doi.org/10.1155/2020/9546012.

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In this paper, an extended car-following model with consideration of the driver’s desire for smooth driving and the self-stabilizing control in historical velocity data is constructed. Moreover, for better reflecting the reality, we also integrate the velocity uncertainty into the new model to analyze the internal characteristics of traffic flow in situation where the historical velocity data are uncertain. Then, the model’s linear stability condition is inferred by utilizing linear stability analysis, and the modified Korteweg-de Vries (mKdV) equation is also obtained to depict the evolution properties of traffic congestion. According to the theoretical analysis, we observe that the degree of traffic congestion is alleviated when the control signal is considered, and the historical time gap and the velocity uncertainty also play a role in affecting the stability of traffic flow. Finally, some numerical simulation experiments are implemented and the experiments’ results demonstrate that the control signals including the self-stabilizing control, the driver’s desire for smooth driving, the historical time gap, and the velocity uncertainty are of avail to improve the traffic jam, which are consistent with the theoretical analytical results.

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Clasing, Robert, and Enrique Muñoz. "Estimating the Optimal Velocity Measurement Time in Rivers’ Flow Measurements: An Uncertainty Approach." Water 10, no. 8 (July 31, 2018): 1010. http://dx.doi.org/10.3390/w10081010.

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The gauging process can be very extensive and time-consuming due to the procedures involved. Since velocity measurement time (VMT) is one of the main variables that would allow gauging times to be reduced, this study seeks to determine the optimal point VMT and, thereby, reduce the overall gauging time. An uncertainty approach based on the USGS area-velocity method and the GLUE methodology applied to eight gauging samples taken in shallow rivers located in South-central Chile was used. The average point velocity was calculated as the average of 1 to 70 randomly selected instant velocity samples (taken every one second). The time at which the uncertainty bands reached a stability criterion (according to both width and slope stability) was considered to be the optimum VMT since the variations were negligible and it does not further contribute to a less uncertain solution. Based on the results, it is concluded that the optimum point VMT is 17 s. Therefore, a point velocity measurement of 20 s is recommended as the optimal time for gauging in shallow rivers.

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Amaru, Maisha, Cory Hoelting, Natalia Ivanova, and Konstantin Osypov. "Introduction to this special section: Velocity-model uncertainty." Leading Edge 36, no. 2 (February 2017): 126. http://dx.doi.org/10.1190/tle36020126.1.

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Caiado, Camila C. S., Richard W. Hobbs, and Michael Goldstein. "Bayesian Strategies to Assess Uncertainty in Velocity Models." Bayesian Analysis 7, no. 1 (March 2012): 211–34. http://dx.doi.org/10.1214/12-ba707.

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Pradhan, Anshuman, Nader C. Dutta, Huy Q. Le, Biondo Biondi, and Tapan Mukerji. "Approximate Bayesian inference of seismic velocity and pore-pressure uncertainty with basin modeling, rock physics, and imaging constraints." GEOPHYSICS 85, no. 5 (June 26, 2020): ID19—ID34. http://dx.doi.org/10.1190/geo2019-0767.1.

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We have introduced a methodology for quantifying seismic velocity and pore-pressure uncertainty that incorporates information regarding the geologic history of a basin, rock physics, well log, drilling, and seismic data. In particular, our approach relies on linking velocity models to the basin modeling outputs of porosity, mineral volume fractions, and pore pressure through rock-physics models. We account for geologic uncertainty by defining prior probability distributions on lithology-specific porosity compaction model parameters, permeability-porosity model parameters, and heat-flow boundary condition. Monte Carlo basin simulations are performed by sampling the prior uncertainty space. We perform probabilistic calibration of the basin model outputs by defining data likelihood distributions to represent well data uncertainty. Rock physics modeling transforms the basin modeling outputs to give us multiple velocity realizations used to perform multiple depth migrations. We have developed an approximate Bayesian inference framework that uses migration velocity analysis in conjunction with well data for updating velocity and basin modeling uncertainty. We apply our methodology in 2D to a real field case from the Gulf of Mexico; our methodology allows for building a geologic and physical model space for velocity and pore-pressure prediction with reduced uncertainty.

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Gavrilyuk, Sergey. "‘Uncertainty’ principle in two fluid–mechanics." ESAIM: Proceedings and Surveys 69 (2020): 47–55. http://dx.doi.org/10.1051/proc/202069047.

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Hamilton’s principle (or principle of stationary action) is one of the basic modelling tools in finite-degree-of-freedom mechanics. It states that the reversible motion of mechanical systems is completely determined by the corresponding Lagrangian which is the difference between kinetic and potential energy of our system. The governing equations are the Euler-Lagrange equations for Hamil- ton’s action. Hamilton’s principle can be naturally extended to both one-velocity and multi-velocity continuum mechanics (infinite-degree-of-freedom systems). In particular, the motion of multi–velocity continuum is described by a coupled system of ‘Newton’s laws’ (Euler-Lagrange equations) for each component. The introduction of dissipative terms compatible with the second law of thermodynamics and a natural restriction on the behaviour of potential energy (convexity) allows us to derive physically reasonable and mathematically well posed governing equations. I will consider a simplest example of two-velocity fluids where one of the phases is incompressible (for example, flow of dusty air, or flow of compressible bubbles in an incompressible fluid). A very surprising fact is that one can obtain different governing equations from the same Lagrangian. Different types of the governing equations are due to the choice of independent variables and the corresponding virtual motions. Even if the total momentum and total energy equations are the same, the equations for individual components differ from each other by the presence or absence of gyroscopic forces (also called ‘lift’ forces). These forces have no influence on the hyperbolicity of the governing equations, but can drastically change the distribution of density and velocity of components. To the best of my knowledge, such an uncertainty in obtaining the governing equations of multi- phase flows has never been the subject of discussion in a ‘multi-fluid’ community.

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Le, Hong Quan, Dong Tran, Van Tien Nguyen, and Dac The Nguyen. "A machine learning approach for calibrating seismic interval velocity in 3D velocity model." Petrovietnam Journal 10 (November 1, 2022): 12–18. http://dx.doi.org/10.47800/pvj.2022.10-02.

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Velocity model technique is routinely used to convert data from the time-to-depth domain to support prospect evaluation, reservoir modelling, well engineering, and further drilling operation. In Vietnam, the conventional velocity model building workflow oversimplifies the interval velocities as only well interval velocities are populated into 2D grids for depth conversion or oversimplified calibration interval velocities by applying a single scaling factor function. This study explores the 3D velocity model workflow to obtain accurate and high-resolution interval velocities using a machine learning approach for both fields A and B in Cuu Long basin, offshore Vietnam. To design an effective approach to depth conversion, the anisotropy factor analysis was performed to understand the differences between the seismic and well interval velocities in geological layer in the 3D structural model. The seismic interval velocity was multiplied by the anisotropy factor to achieve the scaling seismic interval velocity. The scaling seismic interval velocity, elastic attributes, geometric attributes, structural and stratigraphic attributes were used as training features (variables) for predicting interval velocity using the supervised learning algorithm in the machine learning model. Supervised learning offers an opportunity to develop an expert-knowledge-based automated system, which incorporates both domain knowledge and quantitative data mining [1]. The random forest regression algorithms were selected for predicting interval velocity after evaluating several machine learning algorithms. To provide insight into the uncertainty of final interval velocity, a depth uncertainty analysis was conducted using a blind well test for 24 wells and 7 horizons. The comprehensive 3D velocity model using machine learning approach was built for the first time in Cuu Long basin, offshore Vietnam. The result showed the machine learning algorithm can address the disadvantages of conventional velocity calibration to create highly accurate depth representations of the subsurface including a measure of the uncertainty.

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Saitoh, Yohei, Zhiwei Luo, and Keiji Watanabe. "Adaptive Modular Vector Field Control for Robot Contact Tasks in Uncertain Environments." Journal of Robotics and Mechatronics 16, no. 4 (August 20, 2004): 374–80. http://dx.doi.org/10.20965/jrm.2004.p0374.

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We propose adaptive modular vector field control (AMVFC) for a robot manipulator to interact with uncertain environmental geometric constraints. Starting from an uncertain geometric model of the environment, we first parameterize the desired velocity vector field of the robot using the weighted combination of a set of basis vector fields. Then, to overcome the influence of environmental model uncertainty, we add force feedback to adjust robot dynamics and the weight parameters of the desired velocity field for the robot to approach the real environment. Simulation of a robot interacting with uncertain circles and an ellipse demonstrates the effectiveness of our approach.

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Messud, Jérémie, Patrice Guillaume, and Gilles Lambaré. "On tomography velocity uncertainty in relation with structural imaging." GEOPHYSICS 86, no. 4 (July 1, 2021): U89—U107. http://dx.doi.org/10.1190/geo2020-0603.1.

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Evaluating structural uncertainties associated with seismic imaging and target horizons can be of critical importance for decision making related to oil and gas exploration and production. An important breakthrough for industrial applications has been made with the development of industrial approaches to velocity model building. We have developed an extension of these approaches, sampling an equiprobable contour of the tomography posterior probability density function (PDF) rather than the full PDF, and using nonlinear slope tomography. Our approach allows for assessing the quality of uncertainty-related assumptions (linearity and Gaussian hypothesis within the Bayesian theory) and estimating volumetric migration positioning uncertainties (a generalization of horizon uncertainties), in addition to the advantages in terms of computational efficiency. We derive the theoretical concepts underlying this approach and unify our derivations with those of previous publications. Because the method works in the full model space rather than in a preconditioned one, we split the analysis into resolved and unresolved tomography spaces. We argue that resolved space uncertainties are to be used in further steps leading to decision making and can be related to the output of methods that work in a preconditioned model space. Unresolved space uncertainties represent a qualitative by-product specific to our method, strongly highlighting the most uncertain gross areas, thus useful for quality control. These concepts are developed on a synthetic data set. In addition, the industrial viability of the method is determined on two different 3D field data sets. The first one consists of a merge of different seismic surveys in the North Sea and indicates the corresponding structural uncertainties. The second one consists of a marine data set and indicates the impact of structural uncertainties on gross-rock volume computation.

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Buchem, Moritz, Julian Arthur Pawel Golak, and Alexander Grigoriev. "Vessel velocity decisions in inland waterway transportation under uncertainty." European Journal of Operational Research 296, no. 2 (January 2022): 669–78. http://dx.doi.org/10.1016/j.ejor.2021.04.026.

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Moss, R. E. S. "Quantifying Measurement Uncertainty of Thirty-Meter Shear-Wave Velocity." Bulletin of the Seismological Society of America 98, no. 3 (June 1, 2008): 1399–411. http://dx.doi.org/10.1785/0120070101.

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Ryu, Chung-Ho, Gyu-Hwan Hwang, Yong-Sik Jang, Moon-Ki Kim, and Ik-Hwan Choi. "A Study on Radial Velocity Transformation and Uncertainty Propagation." Journal of the Korea Institute of Military Science and Technology 16, no. 2 (April 5, 2013): 199–206. http://dx.doi.org/10.9766/kimst.2013.16.2.199.

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Annoni, Massimiliano. "Water jet velocity uncertainty in laser Doppler velocimetry measurements." Measurement 45, no. 6 (July 2012): 1639–50. http://dx.doi.org/10.1016/j.measurement.2012.01.035.

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Ni, Chuen-Fa, and Shu-Guang Li. "Modeling groundwater velocity uncertainty in nonstationary composite porous media." Advances in Water Resources 29, no. 12 (December 2006): 1866–75. http://dx.doi.org/10.1016/j.advwatres.2006.01.003.

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Kim, Jongmin, Dongsu Kim, Geunsoo Son, and Duhan Lee. "Improvement of Uncertainty Assessment of Discharge Estimated by Velocity-Area Method." E3S Web of Conferences 40 (2018): 06042. http://dx.doi.org/10.1051/e3sconf/20184006042.

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The present study was conducted to re-estimate the factors needed for the velocity-area method previously provided by ISO through precise actual scale experiments in order to verify the appropriateness of the errors of the individual factors presented by ISO 748 and ISO 1088. For this, a steady-state flow of a flow velocity of approximately 1 m/s, 7 m wide, and 1 m deep, was maintained in the mild slope channel located at the River Experiment Center (Andong) of the Korea Institute of Construction Technology. Under this condition, the objective was to measure the flow velocity very precisely with respect to the space by using a micro-ADV having a high accuracy of flow velocity measurement. The water depth was precisely measured before the generation of the flow by using Total Station. The ISO regulations and the results of the present experiment were applied to three different conditions. The uncertainty assessed by applying the results of the present experiment exceeded twice that of the uncertainty estimated by applying the uncertainty factors provided by ISO. The uncertainty of the lateral gap between measurement lines and the number of measurement points in the depth direction was dependent on the scale of rivers. However, ISO may have presented the uncertainty factors analyzed from the data obtained from a wide range of river scales. Therefore, the discharge estimated by the velocity-area method may be dependent on the scale of rivers. The errors of the individual factors of the velocity-area method derived from the present study may be applied to the estimation of the uncertainty of the discharge calculated by the velocity-area method in small rivers.

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Dong, Haocun, Jiaqi Yuan, and Zhongguo Nie. "Vehicle velocity estimation based on uncertainty theory for vehicle impact accidents." ITM Web of Conferences 17 (2018): 03002. http://dx.doi.org/10.1051/itmconf/20181703002.

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Price, S. F., and I. M. Whillans. "Delineation of a catchment boundary using velocity and elevation measurements." Annals of Glaciology 27 (1998): 140–44. http://dx.doi.org/10.3189/1998aog27-1-140-144.

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The determination of catchment boundaries is a major source of uncertainty in net balance studies on large ice sheets. Here, a method for defining a catchment boundary is developed using new measurements of ice-surface velocity and elevation near the Ice Stream B/C boundary in West Antarctica. An objective method for estimating confidence in the catchment boundary is proposed. Using elevation data, the resulting mean standard deviation in boundary location is 13 km in position or 6000 km2 in area. Applying a similar uncertainty to both sides of the Ice Stream Β catchment results in a catchment-area uncertainty of 9%. Much larger uncertainties arise when the method is applied to velocity data. The uncertainty in both cases is primarily determined by the density of field measurements and is proportionally similar for larger catchment basins. Differences in the position of the velocity-determined boundary and the elevation-determined boundary probably result from data sampling. The boundary positions determined here do not support the hypothesis that Ice Stream Β captured parts of the Ice Stream C catchment.

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Shrestha, Rajesh R., and Slobodan P. Simonovic. "Fuzzy set theory based methodology for the analysis of measurement uncertainties in river discharge and stage." Canadian Journal of Civil Engineering 37, no. 3 (March 2010): 429–40. http://dx.doi.org/10.1139/l09-151.

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The discharge and stage measurements in a river system are characterized by a number of sources of uncertainty, which affects the accuracy of a rating curve established from measurements. This paper presents a fuzzy set theory based methodology for consideration of different sources of uncertainty in the stage and discharge measurements and their aggregation into a combined uncertainty. The uncertainty in individual measurements of stage and discharge is represented using triangular fuzzy numbers, and their spread is determined according to the International Organization for Standardization (ISO) standard 748 guidelines. The extension principle based fuzzy arithmetic is used for the aggregation of various uncertainties into overall stage–discharge measurement uncertainty. In addition, a fuzzified form of ISO 748 formulation is used for the calculation of combined uncertainty and comparison with the fuzzy aggregation method. The methodology developed in this paper is illustrated with a case study of the Thompson River near Spences Bridge in British Columbia, Canada. The results of the case study show that the selection of number of velocity measurement points on a vertical is the largest source of uncertainty in discharge measurement. An increase in the number of velocity measurement points provides the most effective reduction in the overall uncertainty. The next most important source of uncertainty for the case study location is the number of verticals used for velocity measurements. The study also shows that fuzzy set theory provides a suitable methodology for the uncertainty analysis of stage–discharge measurements.

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Shirai, Katsuaki, Shohei Ishimura, Tsuyoshi Kawanami, and Shigeki Hirasawa. "WeD-3-3 DEVELOPMENT OF A NEW VELOCITY CALIBRATION METHOD FOR LASER VELOCIMETRY TOWARDS ACHIEVING SMALL MEASUREMENT UNCERTAINTY." Proceedings of JSME-IIP/ASME-ISPS Joint Conference on Micromechatronics for Information and Precision Equipment : IIP/ISPS joint MIPE 2015 (2015): _WeD—3–3–1—_WeD—3–3–3. http://dx.doi.org/10.1299/jsmemipe.2015._wed-3-3-1.

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Ely, Gregory, Alison Malcolm, and Oleg V. Poliannikov. "Assessing uncertainties in velocity models and images with a fast nonlinear uncertainty quantification method." GEOPHYSICS 83, no. 2 (March 1, 2018): R63—R75. http://dx.doi.org/10.1190/geo2017-0321.1.

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Seismic imaging is conventionally performed using noisy data and a presumably inexact velocity model. Uncertainties in the input parameters propagate directly into the final image and therefore into any quantity of interest, or qualitative interpretation, obtained from the image. We considered the problem of uncertainty quantification in velocity building and seismic imaging using Bayesian inference. Using a reduced velocity model, a fast field expansion method for simulating recorded wavefields, and the adaptive Metropolis-Hastings algorithm, we efficiently quantify velocity model uncertainty by generating multiple models consistent with low-frequency full-waveform data. A second application of Bayesian inversion to any seismic reflections present in the recorded data reconstructs the corresponding structures’ position along with its associated uncertainty. Our analysis complements rather than replaces traditional imaging because it allows us to assess the reliability of visible image features and to take that into account in subsequent interpretations.

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Du, Yufeng, Neng Xiong, and Jun Lin. "Uncertainty analysis of velocity field calibration in wind tunnels based on GUM and MCM." Journal of Physics: Conference Series 2313, no. 1 (July 1, 2022): 012007. http://dx.doi.org/10.1088/1742-6596/2313/1/012007.

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Abstract The flow quality of wind tunnel is an important index to judge the performance of the wind tunnel, and it directly determines whether the aircraft can obtain high-quality test data, and directly relates to the ability of the wind tunnel to be applied to basic aerodynamic research. Velocity field calibration is necessary in flow field calibration and an important prerequisite for the accurate simulation of Mach number in wind tunnel tests. In this paper, the GUM method and the MCM method are used to compare and analyze the uncertainty of velocity field calibration of the high-speed wind tunnel, and the uncertainty of the Mach number in the range of 0.6 to 4 is solved. The uncertainty evaluation results of the two methods are basically the same, and are equivalent to the wind tunnel Mach number control accuracy value, which verifies the correctness of the evaluation results of the velocity field calibration uncertainty of high-speed wind tunnel, and the MCM method results verify the direct applicability of the GUM method for uncertainty evaluation in similar application scenarios.

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FURUICHI, Noriyuki. "GS01 Uncertainty of flowrate measurement using ultrasonic velocity profile method." Proceedings of the Fluids engineering conference 2014 (2014): _GS01–1_—_GS01–2_. http://dx.doi.org/10.1299/jsmefed.2014._gs01-1_.

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David Suits, L., TC Sheahan, KT Marosi, and DR Hiltunen. "Characterization of SASW Phase Angle and Phase Velocity Measurement Uncertainty." Geotechnical Testing Journal 27, no. 2 (2004): 11433. http://dx.doi.org/10.1520/gtj11433.

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Carlson, John B., Ben Craig, and Jeffrey C. Schwarz. "Structural uncertainty and breakpoint tests: an application to equilibrium velocity." Journal of Economics and Business 52, no. 1-2 (January 2000): 101–15. http://dx.doi.org/10.1016/s0148-6195(99)00027-2.

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Lee, Hyunho, and Jong-Jin Baik. "Effects of Uncertainty in Graupel Terminal Velocity on Cloud Simulation." Atmosphere 26, no. 3 (September 30, 2016): 435–44. http://dx.doi.org/10.14191/atmos.2016.26.3.435.

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Tiwari, Gaurav, and Gali Madhavi Latha. "Shear velocity-based uncertainty quantification for rock joint shear strength." Bulletin of Engineering Geology and the Environment 78, no. 8 (March 14, 2019): 5937–49. http://dx.doi.org/10.1007/s10064-019-01496-0.

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Popiolek, Z., F. E. Jørgensen, A. K. Melikov, M. C. G. Silva, and W. Kierat. "Assessment of Uncertainty in Measurements with Low Velocity Thermal Anemometers." International Journal of Ventilation 6, no. 2 (September 2007): 113–28. http://dx.doi.org/10.1080/14733315.2007.11683771.

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Yu, Tan, Yijun He, Jinbao Song, Hui Shen, Juanjuan Wang, and Guoping Gao. "Uncertainty in air–sea CO2 flux due to transfer velocity." International Journal of Remote Sensing 35, no. 11-12 (June 6, 2014): 4340–70. http://dx.doi.org/10.1080/01431161.2014.916046.

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Portune, Andrew R., and Corydon D. Hilton. "Determination of Uncertainty in Transition Velocity Estimates for Ceramic Materials." International Journal of Applied Ceramic Technology 10, no. 1 (July 9, 2012): 107–13. http://dx.doi.org/10.1111/j.1744-7402.2012.02807.x.

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Schlüßler, Raimund, Jürgen Czarske, and Andreas Fischer. "Uncertainty of flow velocity measurements due to refractive index fluctuations." Optics and Lasers in Engineering 54 (March 2014): 93–104. http://dx.doi.org/10.1016/j.optlaseng.2013.10.011.

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Sotoudeh, Nahale, Shahab Shiraghaee, Robin Andersson, Joel Sundstrom, Mehrdad Raisee, and Michel Cervantes. "PIV measurements in the draft tube of a down-scale propeller turbine: uncertainty analysis." IOP Conference Series: Earth and Environmental Science 1079, no. 1 (September 1, 2022): 012065. http://dx.doi.org/10.1088/1755-1315/1079/1/012065.

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Abstract In this study, the flow in the conical section of the draft tube of a propeller turbine has been investigated at the best efficiency point and part-load operating conditions using 2D and stereoscopic 3D particle image velocimetry. Since the flow in the turbine is periodic, it is necessary to study the mean flow field rather than the instantaneous one to identify the flow characteristics from a statistical standpoint. However, the statistical convergence of the obtained mean velocity is questionable. Thus, the current work proposes a methodology for investigating the convergence of mean velocity profiles based on the central limit theorem. The methodology is applied to the best efficiency point and part-load results. The results show that 3D PIV results have lower uncertainty than 2D PIV results because measuring the tangential velocity component affects uncertainty, only measured in 3D PIV. The uncertainty difference is more significant, especially in part-load operation, due to the presence of the rotating vortex rope, and therefore a more accurate measurement is necessary to produce a reliable mean flow field. Furthermore, the convergence of the mean velocity profile is faster, with lower uncertainty for best efficiency point results since, at the part-load condition, the tangential velocity component of the flow is higher. In addition, the converged mean velocity profiles show a backflow region with minor rotation in the center, surrounded by a high rotational axial flow during the part-load operation of the turbine.

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Ninos, Georgios, George Sofiadis, Aikaterini Skouroliakou, and Ioannis E. Sarris. "A Low-Cost Algorithm for Uncertainty Quantification Simulations of Steady-State Flows: Application to Ocular Hemodynamics." Symmetry 14, no. 11 (November 3, 2022): 2305. http://dx.doi.org/10.3390/sym14112305.

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An algorithm for the calculation of steady-state flowing under uncertain conditions is introduced in this work in order to obtain a probabilistic distribution of uncertain problem parameters. This is particularly important for problems with increased uncertainty, as typical deterministic methods are not able to fully describe all possible flow states of the problem. Standard methods, such as polynomial expansions and Monte Carlo simulations, are used for the formation of the generalized problem described by the incompressible Navier-Stokes equations. Since every realization of the uncertainty parameter space is coupled with non-linear terms, an incremental iterative procedure was adopted for the calculation. This algorithm adopts a Jacobi-like iteration methodology to decouple the equations and solve them one by one until there is overall convergence. The algorithm was tested in a typical artery geometry, including a bifurcation with an aneurysm, which consists of a well-documented biological flow test case. Additionally, its dependence on the uncertainty parameter space, i.e., the inlet velocity distribution, the Reynolds number variation, and parameters of the procedure, i.e., the number of polynomial expansions, was studied. Symmetry exists in probabilistic theories, similar to the one adopted by the present work. The results of the simulations conducted with the present algorithm are compared against the same but unsteady flow with a time-dependent inlet velocity profile, which represents a typical cardiac cycle. It was found that the present algorithm is able to correctly describe the flow field, as well as capture the upper and lower limits of the velocity field, which was made periodic. The comparison between the present algorithm and the typical unsteady one presented a maximum error of ≈2% in the common carotid area, while the error increased significantly inside the bifurcation area. Moreover, “sensitive” areas of the geometry with increased parameter uncertainty were identified, a result that is not possible to be obtained while using deterministic algorithms. Finally, the ability of the algorithm to tune the parameter limits was successfully tested.

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Latallerie, Franck, Christophe Zaroli, Sophie Lambotte, and Alessia Maggi. "Analysis of tomographic models using resolution and uncertainties: a surface wave example from the Pacific." Geophysical Journal International 230, no. 2 (March 8, 2022): 893–907. http://dx.doi.org/10.1093/gji/ggac095.

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SUMMARY Since most tomographic problems deal with imperfect data coverage and noisy data, an estimate of the seismic velocity in the Earth can only be a local average of the ‘true’ velocity with some attached uncertainty. We use the SOLA (subtractive optimally localized averages) method, a Backus–Gilbert-type method based on the resolution–uncertainty trade-off, to build a range of models of Rayleigh-wave velocities in the Pacific upper mantle. We choose one solution and show how to analyse the model using its resolution and uncertainties. We exploit the model statistics to evaluate the significance of deviations from a theoretical prediction: a half-space cooling model of the Pacific lithosphere. We investigate a slow-velocity anomaly located northeast of Hawaii, at about 200 km depth, and a pattern of alternatively slow- and fast-velocity bands, aligned approximately northwest to southeast, between 200 and 300 km depth. According to our resolution and uncertainty analyses, both features seem to be resolved.

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Weremijewicz, Karolina, and Andrzej Gajewski. "Measurement Uncertainty Estimation for Laser Doppler Anemometer." Energies 14, no. 13 (June 25, 2021): 3847. http://dx.doi.org/10.3390/en14133847.

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Twenty percent of global electricity supplied to the buildings is used for preventing air temperature increase; its consumption for this prevention will triple by 2050 up to China’s present needs. Heat removed from the thermal power plants may drive cold generation in the absorption devices where mass and heat transfer are two-phase phenomena; hence liquid film break-up into the rivulets is extensively investigated, which needs knowledge of the velocity profiles. Laminar flow in a pipe is used in the preliminary study, velocity profile of developed flow is used as a benchmark. The study account writes the applied apparatus with their calibration procedure, and the uncertainty estimation algorithm. The calibration regression line with the slope close to one and a high Pearson’s coefficient value is the final outcome. Therefore, the apparatus may be applied in the principal research.

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Onagawa, Ryoji, Kae Mukai, and Kazutoshi Kudo. "Different planning policies for the initial movement velocity depending on whether the known uncertainty is in the cursor or in the target: Motor planning in situations where two potential movement distances exist." PLOS ONE 17, no. 3 (March 30, 2022): e0265943. http://dx.doi.org/10.1371/journal.pone.0265943.

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During goal-directed behaviors, individuals can be required to start a movement before deciding on the final goal. Previous studies have focused on the initial movement direction in situations involving multiple targets in different directions from the starting position and have shown that the movement is initiated in the average direction among the target directions. However, the previous studies only included situations with targets at equivalent distances, and the characteristics of motor planning in situations with multiple movement possibilities over different potential distances are unclear. In such situations, movement velocity is another important control variable. Furthermore, while previous studies examined situations with an uncertain motor target position, uncertainty can also exist in the effector position (e.g., body or tool locations). Therefore, we examined (1) whether the average output is confirmed in the initial movement velocity during execution in situations involving two potential movements with different distances. In addition, we examined (2) whether planning of the movement velocity can differ depending on the presence of uncertainty in the cursor or the target. In the main conditions, the participants were required to start a reaching movement with two potential movement distances; in the two-cursor condition, two cursors were presented before the start of the trial, and in the two-target condition, two targets were presented. As a control condition, a distance condition corresponding to each main condition was also performed. In the control condition, the initial movement velocity varied linearly with distance. Then, we tested whether the initial movement velocity in situations with two potential movement distances would follow the averaging output of the corresponding control condition. The results revealed that while the initial movement velocity in the two-target condition was slower than the averaging output, that in the two-cursor condition approached the averaging output. These results suggest that the velocity profile of the goal-directed movement is not simply averaged in a situation where two potential targets exist, and that there is a difference in the planning policy of the initial movement depending on whether the known uncertainty is for the movement goal or the effector.

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Hollister, Brad E., and Alex Pang. "Uncertainty Rank for Streamline Ensembles." Journal of Imaging Science and Technology 64, no. 2 (March 1, 2020): 20504–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2020.64.2.020504.

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Abstract Traditional spaghetti plots from ensemble data provide no explicit information as to the uncertainty of the realization flow paths. While intuitive assessment can be used when visualizing streamline density directly in such a plot, the display is often cluttered and difficult to interpret. The authors present a method to measure uncertainty and visualize member streamlines from an ensemble of vector fields. The method incorporates velocity probability density as a feature along each member streamline. The authors show visualizations of two different data sets using the proposed method.

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Bae, Jae-Hyeon, Kyoungsik Chang, Gong-Hee Lee, and Byeong-Cheon Kim. "Bayesian Inference of Cavitation Model Coefficients and Uncertainty Quantification of a Venturi Flow Simulation." Energies 15, no. 12 (June 7, 2022): 4204. http://dx.doi.org/10.3390/en15124204.

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In the present work, uncertainty quantification of a venturi tube simulation with the cavitating flow is conducted based on Bayesian inference and point-collocation nonintrusive polynomial chaos (PC-NIPC). A Zwart–Gerber–Belamri (ZGB) cavitation model and RNG k-ε turbulence model are adopted to simulate the cavitating flow in the venturi tube using ANSYS Fluent, and the simulation results, with void fractions and velocity profiles, are validated with experimental data. A grid convergence index (GCI) based on the SLS-GCI method is investigated for the cavitation area, and the uncertainty error (UG) is estimated as 1.12 × 10−5. First, for uncertainty quantification of the venturi flow simulation, the ZGB cavitation model coefficients are calibrated with an experimental void fraction as observation data, and posterior distributions of the four model coefficients are obtained using MCMC. Second, based on the calibrated model coefficients, the forward problem with two random inputs, an inlet velocity, and wall roughness, is conducted using PC-NIPC for the surrogate model. The quantities of interest are set to the cavitation area and the profile of the velocity and void fraction. It is confirmed that the wall roughness with a Sobol index of 0.72 has a more significant effect on the uncertainty of the cavitating flow simulation than the inlet velocity of 0.52.

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Liu, Wen-Cheng, Wei-Che Huang, and Chih-Chieh Young. "Uncertainty Analysis for Image-Based Streamflow Measurement: The Influence of Ground Control Points." Water 15, no. 1 (December 29, 2022): 123. http://dx.doi.org/10.3390/w15010123.

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Large-scale particle image velocimetry (LSPIV) provides a cost-effective, rapid, and secure monitoring tool for streamflow measurements. However, surveys of ground control points (GCPs) might affect the camera parameters through the solution of collinearity equations and then impose uncertainty on the measurement results. In this paper, we explore and present an uncertainty analysis for image-based streamflow measurements with the main focus on the ground control points. The study area was Yufeng Creek, which is upstream of the Shimen Reservoir in Northern Taiwan. A monitoring system with dual cameras was set up on the platform of a gauge station to measure the surface velocity. To evaluate the feasibility and accuracy of image-based LSPIV, a comparison with the conventional measurement using a flow meter was conducted. Furthermore, the degree of uncertainty in LSPIV streamflow measurements influenced by the ground control points was quantified using Monte Carlo simulation (MCS). Different operations (with survey times from one to nine) and standard errors (30 mm, 10 mm, and 3 mm) during GCP measurements were considered. Overall, the impacts in the case of single GCP measurement are apparent, i.e., a shifted and wider confidence interval. This uncertainty can be alleviated if the coordinates of the control points are measured and averaged with three repetitions. In terms of the standard errors, the degrees of uncertainty (i.e., normalized confidence intervals) in the streamflow measurement were 20.7%, 12.8%, and 10.7%. Given a smaller SE in GCPs, less uncertain estimations of the river surface velocity and streamflow from LSPIV could be obtained.

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Fischer, Andreas. "Limiting Uncertainty Relations in Laser-Based Measurements of Position and Velocity Due to Quantum Shot Noise." Entropy 21, no. 3 (March 8, 2019): 264. http://dx.doi.org/10.3390/e21030264.

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With the ongoing progress of optoelectronic components, laser-based measurement systems allow measurements of position as well as displacement, strain and velocity with unbeatable speed and low measurement uncertainty. The performance limit is often studied for a single measurement setup, but a fundamental comparison of different measurement principles with respect to the ultimate limit due to quantum shot noise is rare. For this purpose, the Cramér-Rao bound is described as a universal information theoretic tool to calculate the minimal achievable measurement uncertainty for different measurement techniques, and a review of the respective lower bounds for laser-based measurements of position, displacement, strain and velocity at particles and surfaces is presented. As a result, the calculated Cramér-Rao bounds of different measurement principles have similar forms for each measurand including an indirect proportionality with respect to the number of photons and, in case of the position measurement for instance, the wave number squared. Furthermore, an uncertainty principle between the position uncertainty and the wave vector uncertainty was identified, i.e., the measurement uncertainty is minimized by maximizing the wave vector uncertainty. Additionally, physically complementary measurement approaches such as interferometry and time-of-flight positions measurements as well as time-of-flight and Doppler particle velocity measurements are shown to attain the same fundamental limit. Since most of the laser-based measurements perform similar with respect to the quantum shot noise, the realized measurement systems behave differently only due to the available optoelectronic components for the concrete measurement task.

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Rozos, Evangelos, Panayiotis Dimitriadis, Katerina Mazi, Spyridon Lykoudis, and Antonis Koussis. "On the Uncertainty of the Image Velocimetry Method Parameters." Hydrology 7, no. 3 (September 8, 2020): 65. http://dx.doi.org/10.3390/hydrology7030065.

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Image velocimetry is a popular remote sensing method mainly because of the very modest cost of the necessary equipment. However, image velocimetry methods employ parameters that require high expertise to select appropriate values in order to obtain accurate surface flow velocity estimations. This introduces considerations regarding the subjectivity introduced in the definition of the parameter values and its impact on the estimated surface velocity. Alternatively, a statistical approach can be employed instead of directly selecting a value for each image velocimetry parameter. First, probability distribution should be defined for each model parameter, and then Monte Carlo simulations should be employed. In this paper, we demonstrate how this statistical approach can be used to simultaneously produce the confidence intervals of the estimated surface velocity, reduce the uncertainty of some parameters (more specifically, the size of the interrogation area), and reduce the subjectivity. Since image velocimetry algorithms are CPU-intensive, an alternative random number generator that allows obtaining the confidence intervals with a limited number of iterations is suggested. The case study indicated that if the statistical approach is applied diligently, one can achieve the previously mentioned threefold objective.

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Journal articles on the topic 'Velocity Uncertainty'

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