scholarly journals BRDF rendering by interpolation of optimised model parameters

2020 ◽  
Vol 2020 (28) ◽  
pp. 162-168
Author(s):  
Tanzima Habib ◽  
Phil Green ◽  
Peter Nussbaum
Keyword(s):  
Interpolation Method ◽  
Model Parameters ◽  
Look Up Table ◽  
Input Sample ◽  
Tristimulus Values

In this paper, we discuss an interpolation method which can be used to create a look up table to map tristimulus values to BRDF parameters. For a given tristimulus value, we interpolate the XYZ lattice formed by eight primaries and secondaries that were printed and measured, and their corresponding optimised BRDF parameters. The BRDF parameters are obtained by careful optimisation of the Ward model and Cook Torrance model with the BRDF measurements of these primaries. The interpolated BRDF parameters of nine test samples from the same printed samples were then evaluated against the optimised BRDF parameters and their reference BRDF measurements. The results show that, this simple and efficient interpolation method produces consistent BRDF parameters that preserves the diffuse colour of the input sample.

scholarly journals Voronoi Diagram and Crowdsourcing-Based Radio Map Interpolation for GRNN Fingerprinting Localization Using WLAN

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3579 ◽  
Author(s):  
Yongliang Sun ◽  
Yu He ◽  
Weixiao Meng ◽  
Xinggan Zhang
Keyword(s):  
Voronoi Diagram ◽  
Wireless Local Area Network ◽  
Local Area Network ◽  
Interpolation Method ◽  
Propagation Model ◽  
General Regression Neural Network ◽  
Model Parameters ◽  
Area Network ◽  
Localization Performance ◽  
Radio Map

In the last decade, fingerprinting localization using wireless local area network (WLAN) has been paid lots of attention. However, this method needs to establish a database called radio map in the off-line stage, which is a labor-intensive and time-consuming process. To save the radio map establishment cost and improve localization performance, in this paper, we first propose a Voronoi diagram and crowdsourcing-based radio map interpolation method. The interpolation method optimizes propagation model parameters for each Voronoi cell using the received signal strength (RSS) and location coordinates of crowdsourcing points and estimates the RSS samples of interpolation points with the optimized propagation model parameters to establish a new radio map. Then a general regression neural network (GRNN) is employed to fuse the new and original radio maps established through interpolation and manual operation, respectively, and also used as a fingerprinting localization algorithm to compute localization coordinates. The experimental results demonstrate that our proposed GRNN fingerprinting localization system with the fused radio map is able to considerably improve the localization performance.

scholarly journals Generalized velocity–density model based on microscopic traffic simulation

Transport ◽  
2017 ◽  
Vol 33 (2) ◽  
pp. 489-501 ◽  
Author(s):  
Oussama Derbel ◽  
Tamás Péter ◽  
Benjamin Mourllion ◽  
Michel Basset
Keyword(s):  
Large Scale ◽  
Interpolation Method ◽  
Function Class ◽  
Density Model ◽  
Driver Model ◽  
Practical Significance ◽  
Model Parameters ◽  
Practical Applications ◽  
Velocity Models ◽  
Traffic Characteristics

In case of the Intelligent Driver Model (IDM) the actual Velocity–Density law V(D) applied by this dynamic system is not defined, only the dynamic behaviour of the vehicles/drivers is determined. Therefore, the logical question is whether the related investigations enhance an existing and known law or reveal a new connection. Specifically, which function class/type is enhanced by the IDM? The publication presents a model analysis, the goal of which was the exploration of a feature of the IDM, which, as yet, ‘remained hidden’. The theoretical model results are useful, this analysis important in the practice in the field of hybrid control as well. The transfer of the IDM groups through large-scale networks has special practical significance. For example, in convoys, groups of special vehicle, safety measures with delegations. In this case, the large-scale network traffic characteristics and the IDM traffic characteristics should be taken into account simultaneously. Important characteristics are the speed–density laws. In case of effective modelling of large networks macroscopic models are used, however the IDMs are microscopic. With careful modelling, we cannot be in contradiction with the application of speed–density law, where there IDM convoy passes. Therefore, in terms of practical applications, it is important to recognize what kind of speed–density law is applied by the IDM convoys in traffic. Therefore, in our case the goal was not the validation of the model, but the exploration of a further feature of the validated model. The separate validation of the model was not necessary, since many validated applications for this model have been demonstrated in practice. In our calculations, also the applied model parameter values remained in the range of the model parameters used in the literature. This paper presents a new approach for Velocity–Density Model (VDM) synthesis. It consists in modelling separately each of the density and the velocity (macroscopic parameter). From this study, safety time headway (microscopic parameter) can be identified from macroscopic data by mean of interpolation method in the developed map of velocity–density. By combining the density and the velocity models, a generalized new VDM is developed. It is shown that from this one, some literature VDMs, as well as their properties, can be derived by fixing some of its parameters.

scholarly journals An alternative deterministic method for the spatial interpolation of water retention parameters

2009 ◽  
Vol 13 (4) ◽  
pp. 453-465 ◽  
Author(s):  
H. Saito ◽  
K. Seki ◽  
J. Šimůnek
Keyword(s):  
Spatial Distribution ◽  
Spatial Interpolation ◽  
Water Retention ◽  
Interpolation Method ◽  
Sampling Location ◽  
Model Parameters ◽  
Retention Data ◽  
Retention Models ◽  
Split Sample ◽  
Retention Parameters

Abstract. There are two approaches available for mapping water retention parameters over the study area using a spatial interpolation method. (1) Retention models can be first fitted to retention curves available at sampling locations prior to interpolating model parameters over the study area (the FI approach). (2) Retention data points can first be interpolated over the study area before retention model parameters are fitted (the IF approach). The current study compares the performance of these two approaches in representing the spatial distribution of water retention curves. Standard geostatistical interpolation methods, i.e., ordinary kriging and indicator kriging, were used. The data used in this study were obtained from the Las Cruces trench site database, which contains water retention data for 448 soil samples. Three standard water retention models, i.e., Brooks and Corey (BC), van Genuchten (VG), and Kosugi (KSG), were considered. For each model, standard validation procedures, i.e., leave-one-out cross-validation and split-sample methods were used to estimate the uncertainty of the parameters at each sampling location, allowing for the computation of prediction errors (mean absolute error and mean error). The results show that the IF approach significantly lowered mean absolute errors for the VG model, while also reducing them moderately for the KSG and BC models. In addition, the IF approach resulted in less bias than the FI approach, except when the BC model was used in the split-sample approach. Overall, IF outperforms FI for all three retention models in describing the spatial distribution of retention parameters.

scholarly journals Stationary Ca2+ nanodomains in the presence of buffers with two binding sites

2020 ◽  
Author(s):  
Y. Chen ◽  
V. Matveev
Keyword(s):  
Closed Form ◽  
Binding Sites ◽  
Interpolation Method ◽  
Secretory Vesicle ◽  
Model Parameters ◽  
Relevant Parameter ◽  
Wide Range ◽  
Vesicle Exocytosis ◽  
Cell Processes ◽  
The Impact

ABSTRACTWe examine closed-form approximations for the equilibrium Ca2+ concentration near a point Ca2+ source representing a Ca2+ channel, in the presence of a mobile Ca2+ buffer with 2:1 Ca2+ binding stoichiometry. We consider buffers with two Ca2+ binding sites activated in tandem and possessing distinct binding affinities and kinetics. This allows to model the impact on Ca2+ nanodomains of realistic endogenous Ca2+ buffers characterized by cooperative Ca2+ binding, such as calretinin. The approximations we present involve a combination or rational and exponential functions, whose parameters are constrained using the series interpolation method that we recently introduced for the case of 1:1 Ca2+ buffers. We conduct extensive parameter sensitivity analysis and show that the obtained closed-form approximations achieve reasonable qualitative accuracy for a wide range of buffer’s Ca2+ binding properties and other relevant model parameters. In particular, the accuracy of the newly derived approximants exceeds that of the rapid buffering approximation in large portions of the relevant parameter space.STATEMENT OF SIGNIFICANCEClosed-form approximations describing equilibrium distribution of Ca2+ in the vicinity of an open Ca2+ channel proved useful for the modeling of local Ca2+ signals underlying secretory vesicle exocytosis, muscle contraction and other cell processes. Such approximations provide an efficient method for estimating Ca2+ and buffer concentrations without computationally expensive numerical simulations. However, while most biological buffers have multiple Ca2+ binding sites, much of prior modeling work considered Ca2+ dynamics in the presence of Ca2+ buffers with a single Ca2+ binding site. Here we extend modeling work on equilibrium Ca2+ nanodomains to the case of Ca2+ buffers with two binding sites, allowing to gain deeper insight into the impact of more realistic Ca2+ buffers, including cooperative buffers, on cell Ca2+ dynamics.

The Investigation of Temperature Effect on Dynamic Characteristics of Rubber Isolator

2011 ◽  
Vol 138-139 ◽  
pp. 575-580
Author(s):  
De Bao Han
Keyword(s):  
Interpolation Method ◽  
Dynamic Properties ◽  
Low Frequency ◽  
Model Parameters ◽  
Force Model ◽  
Third Order ◽  
Restoring Force ◽  
First Order ◽  
The Third ◽  
Rubber Isolator

This article focuses on the temperature dependent dynamic properties of rubber isolator. First, a set of experimental device was designed to conduct the experimental investigation. Then, a polynomial model of hysteretic used as an isolator restoring force model was proposed and the model parameters were identified using the displacement-restoring force loop from experiment by the optimal least-squares arithmetic. Finally, the Hermite interpolation method was utilized to add the number of identified parameters, such that curvatures that represent the first order stiffness, the third order stiffness and damping varied with frequency, amplitude under different temperature were obtained. The analysis results indicated that the first order stiffness varies weakly with the temperature increasing, and there is an area of the first order stiffness varied drastically. The third order stiffness have a strong nonlinear area within the low frequency and little amplitude, the third order stiffness magnitude increases with the temperature increasing firstly, then decreases while the temperature over 50°C. There is a sensitive area as the amplitude less than 1.5mm, the damping decreases rapidly with the augmenting of vibration amplitude, and the rate of decreasing is less gradually with the temperature rising.

Consistent look-up table interpolation method for real-gas flow simulations

2015 ◽  
Vol 107 ◽  
pp. 178-188 ◽  
Author(s):  
M. Pini ◽  
A. Spinelli ◽  
G. Persico ◽  
S. Rebay
Keyword(s):  
Gas Flow ◽  
Interpolation Method ◽  
Real Gas ◽  
Flow Simulations ◽  
Look Up Table

scholarly journals Modeling and Prediction of Momentum Wheel Speed Data

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Jichao Li ◽  
Xiaxia Wang ◽  
Chaobo Chen ◽  
Song Gao
Keyword(s):  
Model Simulation ◽  
Interpolation Method ◽  
Least Square Method ◽  
Interval Data ◽  
Least Square ◽  
Ar Model ◽  
Prediction Algorithm ◽  
Model Parameters ◽  
Stable Operation ◽  
Equal Interval

To solve the problems of data loss and unequal interval of momentum wheel (MW) speed during a satellite stable operation, this paper presents a multidimensional AR model. A Lagrange interpolation method is used to convert measurements to equal interval data, and the FFT algorithm is adopted to calculate the period of MW speed variation. The long data sequence is converted into multidimensional time series, based on the equal interval data and the period. A multidimensional AR model is established, and the least square method is used to estimate the model parameters. The future data trend is predicted by the proposed model. Simulation results show that the prediction algorithm can achieve the across cycle prediction of the MW speed data.

Seismic data interpolation by greedy local Radon transform

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. WB225-WB234 ◽  
Author(s):  
Juefu Wang ◽  
Mark Ng ◽  
Mike Perz
Keyword(s):  
Radon Transform ◽  
Interpolation Method ◽  
Synthetic Data ◽  
Inversion Method ◽  
Model Parameters ◽  
Weighted Norm ◽  
Data Interpolation ◽  
Data Set ◽  
Time Space ◽  
Model Spaces

We propose a greedy inversion method for a spatially localized, high-resolution Radon transform. The kernel of the method is based on a conventional iterative algorithm, conjugate gradient (CG), but is utilized adaptively in amplitude-prioritized local model spaces. The adaptive inversion introduces a coherence-oriented mechanism to enhance focusing of significant model parameters, and hence increases the model resolution and convergence rate. We adopt the idea in a time-space domain local linear Radon transform for data interpolation. We find that the local Radon transform involves iteratively applying spatially localized forward and adjoint Radon operators to fit the input data. Optimal local Radon panels can be found via a subspace algorithm which promotes sparsity in the model, and the missing data can be predicted using the resulting local Radon panels. The subspacing strategy greatly reduces the cost of computing local Radon coefficients, thereby reducing the total cost for inversion. The method can handle irregular and regular geometries and significant spatial aliasing. We compare the performance of our method using three simple synthetic data sets with a popular interpolation method known as minimum weighted norm Fourier interpolation, and show the advantage of the new algorithm in interpolating spatially aliased data. We also test the algorithm on the 2D synthetic data and a field data set. Both tests show that the algorithm is a robust antialiasing tool, although it cannot completely recover missing strongly curved events.

Accounting for Parametric Model Uncertainty in Collision Avoidance for Unmanned Vehicles Using Sparse Grid Interpolation

2013 ◽  
Author(s):  
Sarah L. Noble ◽  
Joel M. Esposito ◽  
Jason Case
Keyword(s):  
Collision Avoidance ◽  
Ad Hoc ◽  
Initial Conditions ◽  
Interpolation Method ◽  
Unmanned Vehicles ◽  
Sparse Grid ◽  
Model Parameters ◽  
Ground Vehicles ◽  
Computationally Efficient ◽  
Grid Interpolation

In this paper we present an enhancement of model-based trajectory selection algorithms — a popular class of collision avoidance techniques for autonomous ground vehicles. Rather than dilate a set of individual candidate trajectories in an ad hoc way to account for uncertainty, we generate a set of trajectory clouds — sets of states that represent possible future poses over a product of intervals representing uncertainty in the model parameters, initial conditions and actuator commands. The clouds are generated using the sparse-grid interpolation method which is both error-controlled and computationally efficient. The approach is implemented on a differential drive vehicle.

Structural geologic modeling as an inference problem: A Bayesian perspective

2016 ◽  
Vol 4 (3) ◽  
pp. SM1-SM16 ◽  
Author(s):  
Miguel de la Varga ◽  
J. Florian Wellmann
Keyword(s):  
Potential Field ◽  
Interpolation Method ◽  
Future Research ◽  
Model Parameters ◽  
Conceptual Approach ◽  
Inference Problem ◽  
Potential Field Data ◽  
Additional Information ◽  
Likelihood Functions ◽  
Insight Into

Structural geologic models are widely used to represent the spatial distribution of relevant geologic features. Several techniques exist to construct these models on the basis of different assumptions and different types of geologic observations. However, two problems are prevalent when constructing models: (1) observations and assumptions, and therefore also the constructed model, are subject to uncertainties and (2) additional information is often available, but it cannot be considered directly in the geologic modeling step — although it could be used to reduce model uncertainties. The first problem has been addressed in recent work. Here we develop a conceptual approach to consider the second aspect: We combine uncertain prior information with geologically motivated likelihood functions in a Bayesian inference framework. The result is that we not only reduce uncertainties in the ensemble of generated models, but we also gain the potential to learn additional features about the model parameters. We develop an implementation of this concept in a probabilistic programming framework, in which we extend the functionality of a 3D implicit potential-field interpolation method with geologic likelihood functions. With schematic examples, we show how this combination leads to suites of models with reduced uncertainties and how it provides a deeper insight into parameter correlations. Furthermore, the integration into a hierarchical Bayesian model provides an insight into potential extensions of the method, for example, the interpolation functional itself, and other types of information, such as gravity or magnetic potential-field data. These aspects constitute promising paths for future research.

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