Estimation Of Flow Parameters Research Articles

Real-Time Bidirectional Traffic Flow Parameter Estimation From Aerial Videos

Unmanned aerial vehicles (UAVs) are gaining popularity in traffic monitoring due to their low cost, high flexibility, and wide view range. Traffic flow parameters such as speed, density, and volume extracted from UAV-based traffic videos are critical for traffic state estimation and traffic control and have recently received much attention from researchers. However, different from stationary surveillance videos, the camera platforms move with UAVs, and the background motion in aerial videos makes it very challenging to process for data extraction. To address this problem, a novel framework for real-time traffic flow parameter estimation from aerial videos is proposed. The proposed system identifies the directions of traffic streams and extracts traffic flow parameters of each traffic stream separately. Our method incorporates four steps that make use of the Kanade-Lucas-Tomasi (KLT) tracker, k-means clustering, connected graphs, and traffic flow theory. The KLT tracker and k-means clustering are used for interest-point-based motion analysis; then, four constraints are proposed to further determine the connectivity of interest points belonging to one traffic stream cluster. Finally, the average speed of a traffic stream as well as density and volume can be estimated using outputs from previous steps and reference markings. Our method was tested on five videos taken in very different scenarios. The experimental results show that in our case studies, the proposed method achieves about 96% and 87% accuracy in estimating average traffic stream speed and vehicle count, respectively. The method also achieves a fast processing speed that enables real-time traffic information estimation.

PEDESTRIAN DETECTION BY LASER SCANNING AND DEPTH IMAGERY

Pedestrian flow is much less regulated and controlled compared to vehicle traffic. Estimating flow parameters would support many safety, security or commercial applications. Current paper discusses a method that enables acquiring information on pedestrian movements without disturbing and changing their motion. Profile laser scanner and depth camera have been applied to capture the geometry of the moving people as time series. Procedures have been developed to derive complex flow parameters, such as count, volume, walking direction and velocity from laser scanned point clouds. Since no images are captured from the faces of pedestrians, no privacy issues raised. The paper includes accuracy analysis of the estimated parameters based on video footage as reference. Due to the dense point clouds, detailed geometry analysis has been conducted to obtain the height and shoulder width of pedestrians and to detect whether luggage has been carried or not. The derived parameters support safety (e.g. detecting critical pedestrian density in mass events), security (e.g. detecting prohibited baggage in endangered areas) and commercial applications (e.g. counting pedestrians at all entrances/exits of a shopping mall).

PEDESTRIAN DETECTION BY LASER SCANNING AND DEPTH IMAGERY

Abstract. Pedestrian flow is much less regulated and controlled compared to vehicle traffic. Estimating flow parameters would support many safety, security or commercial applications. Current paper discusses a method that enables acquiring information on pedestrian movements without disturbing and changing their motion. Profile laser scanner and depth camera have been applied to capture the geometry of the moving people as time series. Procedures have been developed to derive complex flow parameters, such as count, volume, walking direction and velocity from laser scanned point clouds. Since no images are captured from the faces of pedestrians, no privacy issues raised. The paper includes accuracy analysis of the estimated parameters based on video footage as reference. Due to the dense point clouds, detailed geometry analysis has been conducted to obtain the height and shoulder width of pedestrians and to detect whether luggage has been carried or not. The derived parameters support safety (e.g. detecting critical pedestrian density in mass events), security (e.g. detecting prohibited baggage in endangered areas) and commercial applications (e.g. counting pedestrians at all entrances/exits of a shopping mall).

Microparticle image velocimetry approach to flow measurements in isolated contracting lymphatic vessels.

We describe the development of an optical flow visualization method for resolving the flow velocity vector field in lymphatic vessels in vitro. The aim is to develop an experimental protocol for accurately estimating flow parameters, such as flow rate and shear stresses, with high spatial and temporal resolution. Previous studies in situ have relied on lymphocytes as tracers, but their low density resulted in a reduced spatial resolution whereas the assumption that the flow was fully developed in order to determine the flow parameters of interest may not be valid, especially in the vicinity of the valves, where the flow is undoubtedly more complex. To overcome these issues, we have applied the time-resolved microparticle image velocimetry (μ -PIV) technique, a well-established method that can provide increased spatial and temporal resolution that this transient flow demands. To that end, we have developed a custom light source, utilizing high-power light-emitting diodes, and associated control and image processing software. This paper reports the performance of the system and the results of a series of preliminary experiments performed on vessels isolated from rat mesenteries, demonstrating, for the first time, the successful application of the μ -PIV technique in these vessels.

Optimization Technique for Pedestrian Data Extraction

Data extraction is an essential part of traffic flow parameter estimation. Estimation of traffic flow parameters, such as flow rate, density, and speed are carried out under certain measurement interval. Fundamental relationships among these parameters and capacity analysis are sensitive to the choice of appropriate measurement interval. In this study the impact of different measurement intervals on the results of pedestrian traffic flow parameter estimation have been compared to arrive at an optimized time interval. Analysis has been carried out with measurement intervals of 15 seconds, 30 seconds and 60 seconds. Based on the variation in estimated parameters the optimized measurement interval can be defined to improve pedestrian traffic flow modeling process. From the analysis it was observed that for small interval, extracted data were more scattered in nature and resulted in lesser coefficient of determination. For density data extraction it was observed that density can be estimated using the linear relationship between speed-density rather than extraction from video. In this process data extraction time can be reduced. Analysis proposed in this paper shall be useful in optimizing resources for pedestrian flow analysis.

Pedotransfer in soil physics: trends and outlook — A review —

Parameters governing the retention and movement of water and chemicals in soils are notorious for the difficulties and high labor costs involved in measuring them. Often, there is a need to resort to estimating these parameters from other, more readily available data, using pedotransfer relationships. This work is a mini-review that focuses on trends in pedotransfer development across the World, and considers trends regarding data that are in demand, data we have, and methods to build pedotransfer relationships. Recent hot topics are addressed, including estimating the spatial variability of water contents and soil hydraulic properties, which is needed in sensitivity analysis, evaluation of the model performance, multimodel simulations, data assimilation from soil sensor networks and upscaling using Monte Carlo simulations. Ensembles of pedotransfer functions and temporal stability derived from “big data” as a source of soil parameter variability are also described. Estimating parameter correlation is advocated as the pathway to the improvement of synthetic datasets. Upscaling of pedotransfer relationships is demonstrated for saturated hydraulic conductivity. Pedotransfer at coarse scales requires a different type of input variables as compared with fine scales. Accuracy, reliability, and utility have to be estimated independently. Persistent knowledge gaps in pedotransfer development are outlined, which are related to regional soil degradation, seasonal changes in pedotransfer inputs and outputs, spatial correlations in soil hydraulic properties, and overland flow parameter estimation. Pedotransfer research is an integral part of addressing grand challenges of the twenty-first century, including carbon stock assessments and forecasts, climate change and related hydrological weather extreme event predictions, and deciphering and managing ecosystem services. Overall, pedotransfer functions currently serve as an essential instrument in the science-based toolbox for diagnostics, monitoring, predictions, and management of the changing Earth and soil as a life-supporting Earth system.

Groundwater System Modeling for Simultaneous Identification of Pollution Sources and Parameters with Uncertainty Characterization

Contamination of groundwater poses serious threat to the human health and environment. It is difficult and expensive to clean up contaminated aquifers. Identification of unknown pollution sources is vital for adopting any remediation strategy. Groundwater flow and transport simulation model is used to generate necessary data for Artificial Neural Networks (ANN) model building processes. Breakthrough curves obtained for specified pollution scenario are characterized to reduce the inputs to ANN model. The characterized breakthrough curves parameters serve as inputs to ANN model. Unknown pollution source characteristics, flow parameters and transport parameters are outputs for ANN model. Identification of sources is performed with considerations of three cases—simultaneous estimation of unknown sources and flow parameter; simultaneous estimation of unknown sources, flow and transport parameters; and simultaneous estimation of unknown sources and boundary head. Characterization of uncertainty in source identification due to uncertainty in flow parameter, uncertainty in transport parameter and uncertainty in constant head boundary estimation is performed using fuzzy vertex alpha-cut techniques.

Robust Discovery of Temporal Structure in Multi-neuron Recordings Using Hopfield Networks

Abstract We present here a novel method for the classical task of extracting reoccurring spatiotemporal patterns from spiking activity of large populations of neurons. In contrast to previous studies that mainly focus on synchrony detection or exactly recurring binary patterns, we perform the search in an approximate way that clusters together nearby, noisy network states in the data. Our approach is to use minimum probability flow (MPF) parameter estimation to determinis- tically fit very large Hopfield networks on windowed spike trains obtained from recordings of spontaneous activity of neurons in cat visual cortex. Examining the structure of the network memories over the spiking activity after training, we find that the networks robustly discover long-range temporal correlations. Specifically, the recurrent network dynamics denoise and group together windowed spike patterns, revealing underlying structure such as cell assemblies. We first demonstrate this by computing an analogy to spike triggered averages that we call memory triggered averages (MTAs). MTAs are obtained by averaging raw spike train windows that converge under the network dynamics to the same memory. The MTAs reveal promi- nent repeating patterns in the data that are difficult to detect with standard methods such as PCA. Additionally, when memories are collected over eight disjoint epochs in 280 seconds of windowed spiking activity from 50 neurons, their counts are nearly identical and the networks store significantly more memories than would be possible if trained on random patterns.

Thermal Design of Missile Airframe with Circumferential Variation in Wall Temperature

Missile airframe experiences large variations in wall temperature along the circumference due to high angle of attack, especially at hypersonic speeds, which leads to large thermal stresses and bending loads. Such a situation with large wall temperature variations occurs due to high angle of attack during flight. Thermal design of the airframe involves the estimation of local flow parameters and heat flux distribution. Kinetic heating analysis has been carried out for the prediction of heat load distribution on missile airframe considering hypersonic flow with high angle of attack for a particular flight trajectory. Out of a set of possible flight trajectories, a trajectory producing minimum circumferential variation in wall temperature, as concluded through kinetic heating analysis, is finalized. Transient three dimensional heat transfer analysis of the airframe is carried out for prediction of wall temperature distribution for proper selection of material of construction of airframe so that it retains its strength at elevated temperatures. Parametric study has been carried out considering various combinations of airframe wall thickness in presence of external thermal protection coating and internal insulation for the finalized trajectory. Based on the present analysis, airframe configuration having axially variable wall thickness corresponding to the selected flight trajectory is finalized. The validation of the methodology adopted for the analysis has been carried out with respect to airframe temperature data acquired during flight.

Facies and flow regimes of sandstone‐hosted columnar intrusions: Insights from the pipes of Kodachrome Basin State Park

AbstractSandstone‐hosted intrusions are found in a wide variety of environments, but remain poorly understood compared with their mudstone‐hosted counterparts. In particular, they remain largely unrecognized in the subsurface, in part because they are problematic to image in seismic data. This study reports on the facies and fluid flow associated with a 20 000 km3 sandstone intrusion province in Utah, USA. Forming a small portion of this intrusion province, the intrusions cropping out in Kodachrome Basin State Park display a very wide array of facies and morphologies, factors which would make their identification in core a significant challenge. Remobilized sediment is shown to have been injected at least 200 m vertically from its source, with flow prolonged enough to concentrate heavy minerals in placer‐style deposits at the pipe margins. Evidence for lateral pipe migration and for associated broader fluidization regions is also presented. A new approach to estimating flow parameters in injectites is implemented herein, and indicates that previous work has overestimated velocities and flow Reynolds numbers by up to two orders of magnitude. Flow modelling suggests turbulent flow in the pipes that is consistent with field observations of erosive margins and chaotic internal structures. Post‐emplacement, these pipes remained as long‐term fluid conduits, as revealed by their diagenetic history, focussing and facilitating flow of extraformational fluids, despite the relatively high porosity and permeability of the aeolian host strata.

An algorithm for traffic flow parameters estimation and prediction using composition of machine learning methods and time series models

A problem of traffic flow analysis and prediction in city transport network is considered in this paper. The proposed algorithm uses GPS / GLONASS data of public transport location as input. Projecting this information on a transport network graph, as well as using additional filtering, we estimate traffic flow parameters. These parameters are used for short-term (up to 1 hour) prediction of road conditions in the city transport network. There is proposed a new method which consists of several steps to construct prediction. First, the transport graph is divided into a number of subgraphs by a territorial basis. Second, we use a dimension reduction method based on principal components analysis to describe the spatio-temporal distribution of traffic flow condition in the subgraphs. Third, an elementary prediction for each of the subgraphs is formed using the potential functions method with the measure of the subgraphs descriptions closeness introduced by analogy with bilateral filtering and support vector machine. Fourth, the additional elementary prediction is calculated using the known scalar and vector Box–Jenkins time series prediction models. Fifth, we construct the result prediction for each of the subgraphs using an adaptive linear composition of elementary predictions. At last, the traffic flow parameters are calculated as a linear combination of predictions for subgraphs of the city transport network. We have also made experimental investigations of transport network in Samara to evaluate the prediction accuracy of the proposed algorithm. The advantages of the proposed solution in comparison with existing ones are provided.

A distributed water balance approach to groundwater recharge estimation for Jeju volcanic island, Korea

Groundwater is a major source of water use in Jeju volcanic island of Korea. Due to highly permeable surface conditions, the island does not have any perennial streams or rivers. Therefore, accurate estimation of groundwater recharge and understanding groundwater system are critical to make a reliable water budget in the island. A distributed quasi steady state water balance model, WetSpass (Water and Energy Transfer between Soil, Plants and Atmosphere under quasi Steady State) was adopted to estimate spatially-varying groundwater recharge considering soil type, land use, topography, and groundwater depth. The results from the study using weather data from October 2006 to September 2007 indicated that the eastern and northern parts of the island showed relatively higher rate of recharge as compared with the western region. A total of 69% of recharge occurred in the middle of the Island where topographic elevation is greater than 200 m from the mean sea level. The total amount of recharge was 1,935.6 million tons, 41.8% of the total precipitation. The higher rate of recharge was observed in the watersheds where forest and bare soil as land uses, and sandy loam and clay loam as soil types are dominant. The total runoff was 1,142.7 million tons (24.7%) and the total evapotranspiration was 1,555.7 million tons (33.5%). The zonal hydraulic conductivities estimated with groundwater flow modeling and parameter estimation process showed a wide range of values from 1.2 m/d to 5,380 m/d. The results of groundwater flow modeling indicated that the crest of groundwater table is off-centered from the peak of Mt. Halla toward south.

System Dynamics Model of Shanghai Passenger Transportation Structure Evolution

Based on the data from a comprehensive transportation survey of Shanghai in 2004 and 2009, this paper analyzed the evolution of urban passenger transportation structure using the system dynamics approach. A system dynamics model of Shanghai passenger transportation structure evolution is proposed, which consists of setting modeling targets, establishing transportation system boundaries, causality analysis, establishing flow diagram, parameter estimation and model validation.

Modeling Residual-Geometric Flow Sampling

Traffic monitoring and estimation of flow parameters in high-speed routers have recently become challenging as the Internet grew in both scale and complexity. In this paper, we focus on a family of flow-size estimation algorithms we call Residual-Geometric Sampling (RGS), which generates a random point within each flow according to a geometric random variable and records all remaining packets in a flow counter. Our analytical investigation shows that previous estimation algorithms based on this method exhibit bias in recovering flow statistics from the sampled measurements. To address this problem, we derive a novel set of unbiased estimators for RGS, validate them using real Internet traces, and show that they provide an accurate and scalable solution to Internet traffic monitoring.

Model-Based Estimation of Flow Characteristics Using an Ionic Polymer–Metal Composite Beam

An ionic polymer-metal composite (IPMC) beam is capable of producing an electric signal closely correlated with its mechanical movement, due to the redistribution of mobile ions inside the IPMC material. Motivated by the potential application of this intrinsic sensing characteristic to flow property measurements in automotive engines, this paper investigates the feasibility of detecting the start and end of a pulsating flow and its fluid characteristics using an IPMC-beam-based sensor. A dynamic model is developed for the IPMC beam under the flow. The model consists of multiple rigid elements connected by rotational springs and, under suitable conditions, has a closed-form solution that enables efficient estimation of fluid properties and flow parameters with the least-squares minimization approach. The proposed fluid estimation scheme is validated using experimental results with different fluid media, and it is found that the estimated fluid drag coefficients (highly correlated with fluid viscosity) have good agreement with their actual values. This is very important for automotive applications where the characteristics of the fuel blend (such as gasoline and ethanol) need to be identified in real time.

Simultaneous calculation of metamorphic fluid fluxes, reaction rates and fluid–rock interaction timescales using a novel inverse modeling framework

To study metamorphic carbonation at greenschist facies conditions in the SW Scottish Highlands, a novel inverse modeling framework, which combines solutions of the transport equation with a global optimization method of differential evolution, was developed. Using this framework, we calculated simultaneously time-integrated and time-averaged metamorphic fluid fluxes of 83.4±35.4m3m−2 and 10–10.1±0.5m3m−2s−1, respectively, a time-averaged reaction rate constant of 10–12.7–−10.2ms−1 and comparable timescales for fluid flow and fluid-driven reaction of 104.3±0.5yr and 102.7–5.2yr, respectively. These parameters were calculated using an empirical estimate of the coefficient of molecular diffusion and a calculated value for metamorphic porosity. Our estimates are (1) consistent with single pass flow of fluid released by metamorphic devolatilization, (2) within the range where heat is transported by conduction and matter is transported by advection, (3) in agreement with an emerging consensus that metamorphic events are relatively short-lived, and (4) supportive of applying laboratory-based estimates of kinetic parameters to metamorphic systems. Based on a sensitivity analysis, we show that (1) selecting the diffusion coefficient (rather than fluid velocity, reaction rate or flow duration) as an input parameter yields more robust estimates of metamorphic fluid flow parameters, and (2) ignoring reaction-dependent porosity and reaction rates can result in an order-of-magnitude uncertainty in best-fit flow parameters, evaluated from concentration profiles. Finally, similarity between our calculated time-averaged metamorphic fluid fluxes which were obtained numerically and those which were obtained analytically confirms the validity of using the ‘quasi-stationary state’ assumption to quantify metamorphic fluid flow parameters.

Boosting iterative stochastic ensemble method for nonlinear calibration of subsurface flow models

A novel parameter estimation algorithm is proposed. The inverse problem is formulated as a sequential data integration problem in which Gaussian process regression (GPR) is used to integrate the prior knowledge (static data). The search space is further parameterized using Karhunen–Loève expansion to build a set of basis functions that spans the search space. Optimal weights of the reduced basis functions are estimated by an iterative stochastic ensemble method (ISEM). ISEM employs directional derivatives within a Gauss–Newton iteration for efficient gradient estimation. The resulting update equation relies on the inverse of the output covariance matrix which is rank deficient.In the proposed algorithm we use an iterative regularization based on the ℓ2 Boosting algorithm. ℓ2 Boosting iteratively fits the residual and the amount of regularization is controlled by the number of iterations. A termination criteria based on Akaike information criterion (AIC) is utilized. This regularization method is very attractive in terms of performance and simplicity of implementation. The proposed algorithm combining ISEM and ℓ2 Boosting is evaluated on several nonlinear subsurface flow parameter estimation problems. The efficiency of the proposed algorithm is demonstrated by the small size of utilized ensembles and in terms of error convergence rates.

Functional test of pedotransfer functions to predict water flow and solute transport with the dual-permeability model MACRO

Abstract. Estimating pesticide leaching risks at the regional scale requires the ability to completely parameterise a pesticide fate model using only survey data, such as soil and land-use maps. Such parameterisations usually rely on a set of lookup tables and (pedo)transfer functions, relating elementary soil and site properties to model parameters. The aim of this paper is to describe and test a complete set of parameter estimation algorithms developed for the pesticide fate model MACRO, which accounts for preferential flow in soil macropores. We used tracer monitoring data from 16 lysimeter studies, carried out in three European countries, to evaluate the ability of MACRO and this "blind parameterisation" scheme to reproduce measured solute leaching at the base of each lysimeter. We focused on the prediction of early tracer breakthrough due to preferential flow, because this is critical for pesticide leaching. We then calibrated a selected number of parameters in order to assess to what extent the prediction of water and solute leaching could be improved. Our results show that water flow was generally reasonably well predicted (median model efficiency, ME, of 0.42). Although the general pattern of solute leaching was reproduced well by the model, the overall model efficiency was low (median ME = −0.26) due to errors in the timing and magnitude of some peaks. Preferential solute leaching at early pore volumes was also systematically underestimated. Nonetheless, the ranking of soils according to solute loads at early pore volumes was reasonably well estimated (concordance correlation coefficient, CCC, between 0.54 and 0.72). Moreover, we also found that ignoring macropore flow leads to a significant deterioration in the ability of the model to reproduce the observed leaching pattern, and especially the early breakthrough in some soils. Finally, the calibration procedure showed that improving the estimation of solute transport parameters is probably more important than the estimation of water flow parameters. Overall, the results are encouraging for the use of this modelling set-up to estimate pesticide leaching risks at the regional-scale, especially where the objective is to identify vulnerable soils and "source" areas of contamination.

Modeling and experimental investigation on water-driven steam injector for waste heat recovery

Abstract This paper discusses a proposed model of the water-driven steam injector that can be used to recover waste heat in many industries. A mathematical model was developed for evaluating the injector recovery performance in this study; the results indicated that a higher inlet steam pressure, lower inlet-driven water temperature or lower inlet-driven water pressure improved the entrainment ratio, whereas the back pressure of the discharge water had little effect on the injector performance. Moreover, a one-dimensional two-fluid analytical model of two-phase flow was presented with a set of closure equations for estimating flow parameters of the whole injector. The one-dimensional model involved a single distinct flow regime, inverted annular flow in the mixing nozzle and throat tube of the injector. The condensation heat transfer and interfacial shear stress were found to be two main factors affecting the static pressure profiles along the injector. A series of experiments were conducted to investigate the performance of the injector and the pressure distribution along the injector under different operating conditions. The entrainment ratio and flow parameters along the condensing section of the models were reasonably consistent with the experimental data.

Inferring spatial distribution of the radially integrated transmissivity from pumping tests in heterogeneous confined aquifers

Hydrologists routinely analyze pumping test data using conventional interpretation methods that are based on the assumption of homogeneity and that, consequently, yield single estimates of representative flow parameters. However, natural subsurface formations are intrinsically heterogeneous, and hence, the flow parameters influencing the drawdown vary as the cone of depression expands in time. In this paper a novel procedure for the analysis of pumping tests in heterogeneous confined aquifers is developed. We assume that a given heterogeneous aquifer can be represented by a homogeneous system whose flow parameters evolve in time as the pumping test progresses. At any point in time, the interpreted flow parameters are estimated using the ratio of the drawdown and its derivative observed at that particular time. The procedure is repeated for all times, yielding time‐dependent estimates of transmissivity Ti(t) and storativity, Si(t). Based on the analysis of the sensitivity of drawdown to inhomogeneities in the T field, the time‐dependent interpreted transmissivity values are found to be a good estimate of Tg(r), the geometric mean of the transmissivity values encompassed within a progressively increasing radius r from the well. The procedure is illustrated for Gaussian heterogeneous fields with ln(T) variances up to a value of 2. The impact of the separation distance between the pumping well and observation point on data interpretation is discussed. The results show that information about the spatial variability of the transmissivity field can be inferred from time‐drawdown data collected at a single observation point.