Calibration Of Complex Research Articles

WPMAVM: Weighted plus-and-minus allowance variance minimization algorithm for solving matching distortion

• A weighted plus-and-minus allowance variance minimization (WPMAVM) algorithm is proposed. • WPMAVM takes the plus-and-minus allowance in the objective function of VMM into account. • The distance function is introduced to suppress the distortion caused by the abnormal allowance. • WPMAVM has better robustness to minus allowance and abnormal point clouds compared to ICP and VMM. • The proposed algorithm is suitable for calibration and measurement of complex curved surfaces. Point cloud matching is widely used in the calibration and measurement of complex curved surfaces, and the matching accuracy affects both the subsequently machined surface quality and measurement results. In the actual application, the existing iterative closest point (ICP) algorithm is prone to matching distortion when the measurement point clouds have inherent defects such as unclosed shape, uneven density and Gaussian noise . Despite this problem can be solved by the variance-minimization matching (VMM) algorithm, but the point clouds match distortedly both for ICP and VMM particularly when the workpieces behave with complex and unknown allowance distribution. For examples, the car bodywork is characterized by remarkable flexibility with local deformation, resulting in abnormal allowance, while the machined engine blade shows over- and less-cutting induced minus and abnormal allowances in high and low curvature areas, respectively. In this paper, a weighted plus-and-minus allowance variance minimization (WPMAVM) algorithm is proposed to overcome these problems. Specifically, the plus-and-minus allowance in the objective function of VMM algorithm is subdivided into the sum of plus-and-minus allowance variance minimization to suppress the distortion caused by the plus-and-minus allowance. The weighted term w , defined as the distance function from the point to the weighted plus-and-minus mean, is introduced to suppress the distortion caused by the abnormal allowance. The simulation and experimental comparisons on three typical workpieces show that the proposed algorithm has better robustness to minus allowance and abnormal point clouds compared to the existing ICP and VMM algorithms.

A derivative-free optimisation method for global ocean biogeochemical models

Abstract. The skill of global ocean biogeochemical models, and the earth system models in which they are embedded, can be improved by systematic calibration of the parameter values against observations. However, such tuning is seldom undertaken as these models are computationally very expensive. Here we investigate the performance of DFO-LS, a local, derivative-free optimisation algorithm which has been designed for computationally expensive models with irregular model–data misfit landscapes typical of biogeochemical models. We use DFO-LS to calibrate six parameters of a relatively complex global ocean biogeochemical model (MOPS) against synthetic dissolved oxygen, phosphate and nitrate “observations” from a reference run of the same model with a known parameter configuration. The performance of DFO-LS is compared with that of CMA-ES, another derivative-free algorithm that was applied in a previous study to the same model in one of the first successful attempts at calibrating a global model of this complexity. We find that DFO-LS successfully recovers five of the six parameters in approximately 40 evaluations of the misfit function (each one requiring a 3000-year run of MOPS to equilibrium), while CMA-ES needs over 1200 evaluations. Moreover, DFO-LS reached a “baseline” misfit, defined by observational noise, in just 11–14 evaluations, whereas CMA-ES required approximately 340 evaluations. We also find that the performance of DFO-LS is not significantly affected by observational sparsity, however fewer parameters were successfully optimised in the presence of observational uncertainty. The results presented here suggest that DFO-LS is sufficiently inexpensive and robust to apply to the calibration of complex, global ocean biogeochemical models.

A Repetitive Parameterization and Optimization Strategy for the Calibration of Complex and Computationally Expensive Process‐Based Models With Application to a 3D Water Quality Model of a Tropical Reservoir

AbstractParameter calibration is critical for modeling, especially for current process‐based models that are complex with many chemical and biological processes and immeasurable model parameters. This analysis quantifies significant disadvantages of the traditional use of local or global sensitivity analysis (SA) for selecting calibration parameters of nonlinear, expensive models when there are a large number of constituents and parameters. We propose a new Repetitive parameterization and optimization (Rep‐OPT) strategy that uses multiple optimization steps; and between each optimization step, a modeler picks the parameters to be optimized in the next optimization step. The modeler picks the parameters in each iteration following a suggested set of steps that analyze which processes and parameters are related to the poorly fit constituents with the current parameter set. We successfully applied the Rep‐OPT strategy on a complex tropical water quality model with more than 91 parameters using real data. We demonstrate that expert knowledge with assistance of proposed postanalysis techniques (i.e., trade‐off analysis, component analysis, and mass‐balance analysis) can identify the right calibration parameters and obtain excellent model fit. In contrast, the traditional approach using SA with optimization (SA‐OPT) does not find the right calibration parameters for our data. The solution found by Rep‐OPT excellently improves manual solution by 32.7% in goodness‐of‐fit, and all calibrated constituents fit well to observations. The solution found by SA‐OPT using global SA improves manual solution by only 13.3%. Local sensitivity by SA‐OPT performs very poorly being 49.6% worse than manual solution.

MC-Calib: A generic and robust calibration toolbox for multi-camera systems

In this paper, we present MC-Calib, a novel and robust toolbox dedicated to the calibration of complex synchronized multi-camera systems using an arbitrary number of fiducial marker-based patterns. Calibration results are obtained via successive stages of refinement to reliably estimate both the poses of the calibration boards and cameras in the system. Our method is not constrained by the number of cameras, their overlapping field-of-view (FoV), or the number of calibration patterns used. Moreover, neither prior information about the camera system nor the positions of the checkerboards are required. As a result, minimal user interaction is needed to achieve an accurate and robust calibration which makes this toolbox accessible even with limited computer vision expertise. In this work, we put a strong emphasis on the versatility and the robustness of our technique. Specifically, the hierarchical nature of our strategy allows to reliably calibrate complex vision systems even under the presence of noisy measurements. Additionally, we propose a new strategy for best-suited image selection and initial parameters estimation dedicated to non-overlapping FoV cameras. Finally, our calibration toolbox is compatible with both, perspective and fisheye cameras. Our solution has been validated on a large number of real and synthetic sequences including monocular, stereo, multiple overlapping cameras, non-overlapping cameras, and converging camera systems. Project page: https://github.com/rameau-fr/MC-Calib

A pattern-matching method for flow model calibration under training image constraint

Modern geostatistical modeling techniques are developed to simulate complex geologic connectivity patterns (e.g., curvilinear fluvial systems) at the grid-level using a training image (TI) that encodes multiple-point statistical (MPS) information. A challenging aspect of using MPS methods is conditioning the resulting models on nonlinear flow data. We develop a pattern-matching method for calibration of MPS-based facies models subject to the TI constraint. Since the exact statistical information in the TI can only be expressed empirically, flow data conditioning and pattern matching are carried out in two iterative steps, using an alternating-direction algorithm. Flow data integration is formulated through a regularized least-squares by taking advantage of learned k-SVD sparse parametrization and l1-norm sparsity-promoting regularization methods. The TI constraint is enforced through a MPS-based pattern-matching algorithm that uses the identified model calibration solution to generate a corresponding facies model that is consistent with the TI. The pattern-matching algorithm uses a local search template to scan the TI to find facies patterns with smallest distances from the corresponding local patterns in the parameterized approximate solution. The identified patterns for each location in the model are stored and used to estimate local conditional probabilities for assigning the facies types to each grid cell. The resulting solution is passed to the flow data conditioning step as a regularization term to perform the next iteration. The process is repeated until the MPS facies model provides an acceptable match to the data. Numerical experiments are presented to evaluate the performance of the pattern-matching method for calibration of complex facies models.

Physically-based urban stormwater quality modelling: An efficient approach for calibration and sensitivity analysis

Physically-based urban stormwater quality modelling is helpful for increasing the understanding of spatial-temporal dynamics of urban pollution, and for designing innovative management technologies. However, because of the high computational cost, calibration and validation of physically-based models is still challenging. In this context, this study aims to develop a new meta-model based framework for efficient calibration and sensitivity analysis of complex and computationally intensive physically-based models. The proposed approach is applied to the FullSWOF-HR model. According to the average rainfall intensity, 21 rainfall events are categorized into three groups, such as 9 light rains, 6 moderate rains and 6 heavy rains. After upscaling the original high-resolution model, 77 parameter nodes are selected by using the adaptive stochastic collocation method with sparse grids algorithm on the lower-resolution surrogate. 77 simulation runs are then performed with the original model for three representative rainfall events, respectively. The interpolating polynomials of the original models are hence generated. Once the meta-model is constructed, we performed the sensitivity analysis with the variance-based Sobol's method, the results of which are consistent with our previous studies. Calibration process of the meta-model is based on the Markov chain Monte Carlo method. The optimized parameters are verified with the original model and then validated for different rainfall events. These promising results show that the proposed meta-model based approach can efficiently perform sensitivity analysis and parameter optimization for complex physical stormwater quality models, and hence will be very helpful for spreading the detailed water quantity and quality modelling for urban water management issues.

A Combined Algorithm of the In-Flight Geometric Calibration Using Unknown Landmarks

Consideration is given to possibility of improving accuracy of in-flight geometric calibration of the spacecraft optical-electronic complex by means of combining two different algorithms intended for calibrations using snapshots of unknown landmarks.

Development of a System for Calibration of a Machine Vision Complex Based on the Use of Color Bands

The problem of scanning the form of raw material in the course of pattern cutting in the fabrication of planar parts is considered. A machine vision system is used to solve the problem. In the context of the problem questions of calibration of the system and the elimination of optical distortions become critical with the use of such technologies. Different methods of calibration of the cameras of computer vision systems are presented. It is proposed that the use of test objects be rejected in favor of marked bands applied along the extreme edges of the working region of the pattern complex as well as the use of an algorithm for searching for marked bands on the images produced by the system.

Experimental and numerical penetration response of laser-welded stiffened panels

Ductile fracture in large structures is often resolved with non-linear finite element (FE) simulations employing structural shell elements which are larger than localization zone. This makes solution element size dependent and calibration of material parameters complex. Therefore, the paper explores the ability of numerical simulations to capture the penetration resistance of stiffened panels after determining steel material fracture ductility at different stress states. The numerical simulations are compared with experiments performed with rigidly fixed 1.2 m square panels penetrated with half-sphere indenter until fracture took place. Response of the panels was measured in terms of indentation force versus indenter displacement. In parallel, tensile tests were performed with four different flat specimens extracted from the face sheet of panels to characterize the material fracture ductility at different stress states. Panel simulations were performed with two fracture criteria: one calibrated based on the test data from dog-bone specimen and other calibrated based on the data from all tensile tests. To evaluate the fracture criteria in terms of their capacity to handle mesh size variations, mesh size was varied from fine to coarse. Results suggest that fracture criterion calibrated based on the range of stress states can handle mesh size variations more effectively as displacement to fracture showed considerably weaker mesh size dependence.

Друге наближення польотного геометричного калібрування знімального комплексу космічного апарата

Друге наближення польотного геометричного калібрування знімального комплексу космічного апарата

Monitoring Forest Dynamics in the Andean Amazon: The Applicability of Breakpoint Detection Methods Using Landsat Time-Series and Genetic Algorithms

The Andean Amazon is an endangered biodiversity hot spot but its forest dynamics are less studied than those of the Amazon lowland and forests from middle or high latitudes. This is because its landscape variability, complex topography and cloudy conditions constitute a challenging environment for any remote-sensing assessment. Breakpoint detection with Landsat time-series data is an established robust approach for monitoring forest dynamics around the globe but has not been properly evaluated for implementation in the Andean Amazon. We analyzed breakpoint detection-generated forest dynamics in order to determine its limitations when applied to three different study areas located along an altitude gradient in the Andean Amazon in Ecuador. Using all available Landsat imagery for the period 1997–2016, we evaluated different pre-processing approaches, noise reduction techniques, and breakpoint detection algorithms. These procedures were integrated into a complex function called the processing chain generator. Calibration was not straightforward since it required us to define values for 24 parameters. To solve this problem, we implemented a novel approach using genetic algorithms. We calibrated the processing chain generator by applying a stratified training sampling and a reference dataset based on high resolution imagery. After the best calibration solution was found and the processing chain generator executed, we assessed accuracy and found that data gaps, inaccurate co-registration, radiometric variability in sensor calibration, unmasked cloud, and shadows can drastically affect the results, compromising the application of breakpoint detection in mountainous areas of the Andean Amazon. Moreover, since breakpoint detection analysis of landscape variability in the Andean Amazon requires a unique calibration of algorithms, the time required to optimize analysis could complicate its proper implementation and undermine its application for large-scale projects. In exceptional cases when data quality and quantity were adequate, we recommend the pre-processing approaches, noise reduction algorithms and breakpoint detection algorithms procedures that can enhance results. Finally, we include recommendations for achieving a faster and more accurate calibration of complex functions applied to remote sensing using genetic algorithms.

In-flight calibration of imaging complex of a spacecraft using unknown landmarks without GPS messages attracting

In-flight calibration of imaging complex of a spacecraft using unknown landmarks without GPS messages attracting

Modernized active spectroscopic diagnostics (CXRS) of the T-10 tokamak

This work presents the results of modernization of the CXRS (charge exchange recombination spectroscopy) diagnostics [1] at the T-10 tokamak. The relevance of this work is due to the importance of measurements of the ion temperature and nuclei density of the working gas and impurities for analysis of transport processes in the plasma ion component. Measurements of radial profiles of the ion temperature are extremely important for investigating the geodesic acoustic mode behavior which is conducted at the T-10 [2]. The modernized scheme of CXRS measurements, as well as the design and operational features of the spectrometer created for the new diagnostics, is described. Principles of data recording and further processing are considered in detail; attention is given to the problem of calibration of the whole complex of equipment. The performed changes in diagnostics allow the measurements to be taken simultaneously in three spectral intervals: in the region of the beam line Hα, the CXRS line of carbon ion C5+, and the CXRS line of one of the hydrogen-like ions: He1+, Li2+, N6+, O7+ or Ne9+. This makes it possible to measure the density profiles of two plasma impurities simultaneously, as well as the ion temperature from CXRS lines of different elements. The modernized diagnostics significantly broadens the possibilities of studying the physics of transport processes and quasi-coherent modes of plasma oscillations at the T-10.

Assessment of Model Configuration Effect by Alternative Evapotranspiration, Runoff, and Water Routing Functions on Watershed Modeling Using SWAT

Abstract. The choice of different model structures and the subsequent parameter identification are relevant and usually have a strong subjective component. The impacts of various decisions associated with the internal configuration of a given watershed model need greater exploration to ensure confidence in hydrologic and water quality modeling results, yet they are often overlooked. In this study, a non-subjective approach to selecting alternative methods is developed. The goal is to assess the impact of different configurations of the Soil and Water Assessment Tool (SWAT) model on consequential hydrologic and water quality behavior. A total of 12 SWAT configurations, each containing a unique combination of alternative algorithms in estimating surface runoff, potential evapotranspiration, and water routing, were calibrated against flow and nutrient data using the Dynamically Dimensioned Search (DDS) optimization algorithm within the Integrated Parameter Estimation and Uncertainty Analysis Tool (IPEAT). The assessment ensures that non-subjective decisions are formed by fully exploring different model configurations and consequences in the optimization process. The accuracy of calibrated outputs was sensitive to the choice of alternative methods, especially for ammonia-N predictions: NSE values varied from 0.23 to 0.67 for streamflow, from 0.59 to 0.82 for sediment, and from -0.15 to 0.66 for ammonia-N. Similarly, uncertainty in the calibrated outputs varied among alternative methods: inclusion rate varied from 31% to 58% for streamflow, from 46% to 71% sediment, and from 25% to 87% for ammonia-N. The results highlight the significance of non-subjectivity in selecting alternative methods in the calibration of complex watershed models. The potential impact of selecting alternative methods should be fully explored in advance before further applications of complex watershed simulation models.

Prediction of arterial failure based on a microstructural bi-layer fiber–matrix model with softening

Two approaches to predict failure of soft tissue are available. The first is based on a pointwise criticality condition, e.g. von Mises maximum stress, which is restrictive because only local state of deformation is considered to be critical and the failure criterion is separated from stress analysis. The second is based on damage mechanics where internal (unobservable) variables are introduced which make the experimental calibration of the theory complex. As an alternative to the local failure criteria and damage mechanics we present a softening hyperelasticity approach, where the constitutive description of soft tissue is enhanced with strain softening, which is controlled by material constants. This approach is attractive because the new material constants can be readily calibrated in experiments on the one hand and the failure criteria are global on the other hand. We illustrate the efficiency of the softening hyperelasticity approach on the problem of prediction of arterial failure. For this purpose, we enhance a bi-layer fiber–matrix microstructural arterial model with softening and analyze the arterial failure under internal pressure. We show that the overall arterial strength is (a) dominated by the media layer, (b) controlled by microfibers and (c) increased by residual stresses.

Multiobjective sensitivity analysis of sediment and nitrogen processes with a watershed model

This paper presents a computational analysis for evaluating critical non‐point‐source sediment and nutrient (specifically nitrogen) processes and management actions at the watershed scale. In the analysis, model parameters that bear key uncertainties were presumed to reflect the importance of natural processes and/or management actions that they represent. The multiobjective generalized sensitivity analysis and the tree‐structured density estimation procedures were used in combination to investigate correlation structure in the parameter space while accounting for multiple objectives. Using the Soil and Water Assessment Tool, this framework was applied to the Dreisbach watershed in Indiana in the Midwestern portion of the United States. Results showed that incorporation of parameter interactions and multiple objectives is essential to obtaining conclusive information about critical system processes and management actions. Interactions between surface runoff volume and within‐channel processes were critical to describe transport of sediments in the study watershed. Key management actions for total nitrogen control were found to be nitrogen fertilizer application and upland farming practices. The sensitivity analysis reported herein could be used to derive a list of key non‐point‐source best management practices for development of watershed management plans. Implications of the analysis that are relevant to calibration of complex watershed models are discussed.

Precise measurement of 1H 90° pulse in solid-state NMR spectroscopy for complex and heterogeneous molecular systems

The 90 degrees pulse calibration is essential in NMR spectroscopy to prevent artefacts in the liquid state or to enhance cross-polarization efficiency in the solid state. We verified pulse-angle (PA) errors due to circuit impedances in solid-state NMR and suggested a possible solution to prevent the inconvenience of PA errors. The classic pulse sequences used to calibrate (1)H 90 degrees pulse lengths by direct detection of protons or by cross-polarization were modified in order to replace single (1)H pulses with (1)H pulse trains. Pulse trains were found to decrease the effect of PA imperfections in the calibration of basic pulses (i.e. 90 degrees and 180 degrees ) for a number of organic substrates. The modified sequences are especially important to rapidly obtain pulse calibration of complex and heterogeneous molecular systems such as humic substances, which usually require a long time when using single (1)H pulses.

Patuxent landscape model: integrated ecological economic modeling of a watershed

The Patuxent Landscape Model (PLM) is designed to simulate fundamental ecological processes on the watershed scale, in interaction with an economic component that predicts the land use patterns. The paper focuses on the ecological component of the PLM and describes how the spatial and structural rescaling can be instrumental for calibration of complex spatially distributed models. The PLM is based on a modified General Ecosystem Model (GEM) that is replicated across a grid of cells that compose the rasterized landscape. Different habitats and land use types translate into different parameter sets to be fed into GEM. Cells are linked by horizontal fluxes of material and information, driven mostly by the hydrologic flows. This approach provides additional flexibility in scaling up and down over a range of spatial resolutions and is essential to track the land use change patterns generated by the economic component. Structural modularity is another important feature that is implemented in the general purpose software packages (Spatial Modeling Environment and Collaborative Modeling Environment), that the PLM employs.