Calibration Framework Research Articles

Design and validation of tailored colour reference charts for monitoring cultural heritage degradation

Colour changes of cultural heritage objects can be related with degradation of materials, thus a proper colour monitoring system can be used to detect conservation problems. With this purpose, a monitoring methodology for cultural heritage preventive conservation based on tailored colour reference charts and image analysis is proposed.Reference colour charts have been designed and tested for use in museums. Charts containing 64 colour patches have been printed using high-stability inks on 4 different substrates: Acid-free paper SkyLight, Acid-free paper covered with a propylene film, FOREX® and GlassPack. The stability has been studied by accelerated ageing in an UV chamber, and the harmlessness of the materials by Oddy Test. The final selection of material, laminated paper, is a balance between the colour change upon ageing and the performance in the Oddy Test. Using this material and the proposed design, colour change of copper and silver coupons has been assessed using images that are adjusted and calibrated by an adaptive calibration framework employing a given set of reference colours which homogenises the visual information in the supplied images. Thus, regardless of the camera of origin, any processed picture will deliver reliable information of the state of the colour in the metal surfaces at the moment it was taken.Results demonstrate the adequacy of the approach and the design for colour calibration, so these charts can be used to monitor colour change of sensitive materials –metal coupons– using photographs. As colour change of reference metals is a consequence of corrosion by environmental factors this may be used as a measure of air quality in museum environments. This methodology can be used to design a low-cost preventive conservation tool, where colour change of metal coupons –or other reference materials– can be followed through image analysis of pictures taken periodically by conservators or visitors, introducing citizen science in the conservation strategy.

A Camera to LiDAR calibration approach through the optimization of atomic transformations

This paper proposes a camera-to-3D Light Detection And Ranging calibration framework through the optimization of atomic transformations. The system is able to simultaneously calibrate multiple cameras with Light Detection And Ranging sensors, solving the problem of Bundle. In comparison with the state-of-the-art, this work presents several novelties: the ability to simultaneously calibrate multiple cameras and LiDARs; the support for multiple sensor modalities; the calibration through the optimization of atomic transformations, without changing the topology of the input transformation tree; and the integration of the calibration framework within the Robot Operating System (ROS) framework. The software pipeline allows the user to interactively position the sensors for providing an initial estimate, to label and collect data, and visualize the calibration procedure. To test this framework, an agricultural robot with a stereo camera and a 3D Light Detection And Ranging sensor was used. Pairwise calibrations and a single calibration of the three sensors were tested and evaluated. Results show that the proposed approach produces accurate calibrations when compared to the state-of-the-art, and is robust to harsh conditions such as inaccurate initial guesses or small amount of data used in calibration. Experiments have shown that our optimization process can handle an angular error of approximately 20 degrees and a translation error of 0.5 meters, for each sensor. Moreover, the proposed approach is able to achieve state-of-the-art results even when calibrating the entire system simultaneously.

Catastrophic risks and the pricing of catastrophe equity put options.

In this paper, after a review of the most common financial strategies and products that insurance companies use to hedge catastrophic risks, we study an option pricing model based on processes with jumps where the catastrophic event is captured by a compound Poisson process with negative jumps. Given the importance that catastrophe equity put options (CatEPuts) have in this context, we introduce a pricing approach that provides not only a theoretical contribution whose applicability remains confined to purely numerical examples and experiments, but which can be implemented starting from real data and applied to the evaluation of real CatEPuts. We propose a calibration framework based on historical log-returns, market capitalization and option implied volatilities. The calibrated parameters are then considered to price CatEPuts written on the stock of the main Italian insurance company over the high volatile period from January to April 2020. We show that the ratio between plain-vanilla put options and CatEPuts strictly depends on the shape of the implied volatility smile and it varies over time.

A unified calibration framework for 21 cm cosmology

ABSTRACT Calibration precision is currently a limiting systematic in 21 cm cosmology experiments. While there are innumerable calibration approaches, most can be categorized as either ‘sky-based,’ relying on an extremely accurate model of astronomical foreground emission, or ‘redundant,’ requiring a precisely regular array with near-identical antenna response patterns. Both of these classes of calibration are inflexible to the realities of interferometric measurement. In practice, errors in the foreground model, antenna position offsets, and beam response inhomogeneities degrade calibration performance and contaminate the cosmological signal. Here, we show that sky-based and redundant calibration can be unified into a highly general and physically motivated calibration framework based on a Bayesian statistical formalism. Our new framework includes sky-based and redundant calibration as special cases but can additionally support relaxing the rigid assumptions implicit in those approaches. We present simulation results demonstrating that, in a simple case, working in an intermediate regime between sky-based and redundant calibration improves calibration performance. Our framework is highly general and encompasses novel calibration approaches including techniques for calibrating compact non-redundant arrays, calibrating to incomplete sky models, and constraining calibration solutions across frequency.

A Computationally Efficient Hybrid Framework for Polarimetric Calibration of Quad‐Pol SAR Data

AbstractPolarimetric Synthetic Aperture Radar (PolSAR) calibration is an essential preprocessing step that must be performed to ensure that the data quality is adequate. This, in turn, helps to minimize the propagation of errors in any further data processing or information extraction. Crosstalk and channel imbalance are two major distortions found to be present in the uncalibrated polarimetric SAR data. The PolSAR calibration mainly aims to reduce these two distortions revealing the true scattering pattern of the targets. In this regard, Quegan's algorithm and Ainsworth algorithm are two widely used algorithms for the PolSAR calibration. In this study, the accuracy and efficiency of these two algorithms have been thoroughly compared using suitable metrics. It has been shown that the Ainsworth algorithm performs better than Quegan's in terms of accuracy at the cost of poor computational efficiency. The data quality metrics also highlight the better calibration accuracy of the Ainsworth algorithm. The issue of higher computational complexity has been effectively addressed by coupling both of these algorithms. Evidently, the computational cost has been reduced in the case of the proposed algorithm. The polarization orientation angle (POA) shift is another distortion caused by the topographic variations present in the target scene. Therefore, correction of POA shift has been incorporated in this research by coupling it with the PolSAR calibration. Subsequently, the improvement in the scattering has been observed. In essence, the proposed algorithm coupled with the correction of POA shift rectifies the major polarimetric distortions with adequate accuracy and computational efficiency.

Calibration Strategies for Detecting Macroscale Patterns in NEON Atmospheric Carbon Isotope Observations

AbstractCarbon fluxes in terrestrial ecosystems and their response to environmental change are a major source of uncertainty in the modern carbon cycle. The National Ecological Observatory Network (NEON) presents the opportunity to merge eddy covariance (EC)‐derived fluxes with CO2 isotope ratio measurements to gain insights into carbon cycle processes. Collected continuously and consistently across >40 sites, NEON EC and isotope data facilitate novel integrative analyses. However, currently provisioned atmospheric isotope data are uncalibrated, greatly limiting ability to perform cross‐site analyses. Here, we present two approaches to calibrating NEON CO2 isotope ratios, along with an R package to calibrate NEON data. We find that calibrating CO2 isotopologues independently yields a lower δ13C bias (<0.05‰) and higher precision (<0.40‰) than directly correcting δ13C with linear regression (bias: <0.11‰, precision: 0.42‰), but with slightly higher error and lower precision in calibrated CO2 mole fraction. The magnitude of the corrections to δ13C and CO2 mole fractions vary substantially by site, underscoring the need for users to apply a consistent calibration framework to data in the NEON archive. Post‐calibration data sets show that site mean annual δ13C correlates negatively with precipitation, temperature, and aridity, but positively with elevation. Forested and agricultural ecosystems exhibit larger gradients in CO2 and δ13C than other sites, particularly during the summer and at night. The overview and analysis tools developed here will facilitate cross‐site analysis using NEON data, provide a model for other continental‐scale observational networks, and enable new advances leveraging the isotope ratios of specific carbon fluxes.

Automatic far‐field camera calibration for construction scene analysis

AbstractThe use of cameras for safety monitoring, progress tracking, and site security has grown significantly on construction and civil infrastructure sites over the past decade. Localization of construction resources is a crucial prerequisite for many applications in automated construction management. However, most existing vision‐based methods perform the analysis in the image plane, overlooking the effect of perspective and depth. The manual and labor‐intensive process of traditional calibration techniques, as well as the busy and restrictive construction environment, makes this a challenging task. This study proposes a framework for automatic camera calibration with no manual intervention. The framework utilizes convolutional neural networks for geometrical scene analysis and object detection, which are used to estimate the location of horizon line, vertical vanishing point, as well as objects with known height distributions. This enables automatic estimation of camera parameters and retrieval of scale. The proposed framework is evaluated on images from two major construction projects in Melbourne, Australia. Results show that the proposed method achieves a minimum accuracy of 90% in estimating proximity of points on the ground and can facilitate further development of vision‐based solutions for safety and productivity analysis.

An automatic calibration framework based on the InfoWorks ICM model: the effect of multiple objectives during multiple water pollutant modeling.

An automatic calibration framework of water quality parameters for surface runoff during modeling with InfoWorks ICM was constructed. The framework is based on a genetic algorithm (GA) and fully considers the calibration sequence for multiple water pollutants, namely, total suspended solids (TSS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorous (TP). Meanwhile, four different objective functions including the Nash-Sutcliff efficiency coefficient (NSE), coefficient of determination (R2), percentage error in the peak (PEP), and percentage bias (PBIAS) were selected as fitness evaluators for the GA. The framework was applied successfully to a specific area of Fuzhou in China, and the multi-objective results were compared with the single-objective results. The comprehensive indexes of TSS, COD, TN, and TP by multi-objective calibration were lower than that of the single-objective calibration in both scenarios. Compared with single-objective calibration, the iterations to reach the optimal value were shortened 9, 5, 13, and 15 iterations by multi-objective calibration. Therefore, the findings showed that the multi-objective function GA was more balanced and more efficient than the single-objective function GA. Then, the uncertainty of the model was evaluated by using the samples generated by automatic calibration, which provided a reliable basis for the subsequent application of the model. This framework can be applied to other programs through adjustments of the number and weight of objective functions according to the specific situation, which will make the modeling more efficient and accurate.

Spatiotemporal modeling and optimization for personalized cardiac simulation

Computational modeling of the heart has contributed tremendously in quantitatively understanding the cardiac functions, showing great potential to assist medical doctors in heart-disease diagnosis. However, cardiac simulation is generally subject to uncertainties and variabilities among different individuals. Traditional “one-size-fits-all” simulation is limited in providing individualized optimal diagnosis and treatment for patients with heart disease. Realizing the full potential of cardiac computational modeling in clinical practice requires effective and efficient model personalization. In this paper, we develop a spatiotemporal modeling and optimization framework for cardiac model calibration. The proposed calibration framework not only effectively quantifies the spatiotemporal discrepancy between the simulation model and the physical cardiac system, but also increases the computational efficiency in personalized modeling of cardiac electrophysiology. The model performance is validated and evaluated in the 3D cardiac simulation. Numerical experiments demonstrate that the proposed framework significantly outperforms traditional approaches in calibrating the cardiac simulation.

Online Self-Calibration of Multiple 2D LiDARs Using Line Features With Fuzzy Adaptive Covariance

The calibration techniques for a multi-sensor data fusion need to deal with the high accuracy of each sensor position to the reference frame. The point cloud data generated by low-cost LiDAR sensors possess high uncertainty, giving poor affection to the calibration system. Furthermore, the displacements between the sensors could be adjusted by undesirable situations during operation time. This paper proposes an automatic calibration framework for multiple low-cost 2D lasers on the special Euclidean group SE(2). The key ideas of the proposed method are to perform the line feature-based factor graph optimization and fuzzy inference system to adapt the covariance of the factor graph. Here, to fuse the line feature information, we mimic the weight affection technique using the fuzzy logic rules and then add the adapted covariance to the graph. Next, we suggest an online self-calibration algorithm running in real-time to correct the variation of calibrated parameters. Lastly, the proposed calibration system is evaluated through a real-world dataset. The experiment results show that the proposed method can be widely applied for industrial applications.

Excalibur: A Nonparametric, Hierarchical Wavelength Calibration Method for a Precision Spectrograph

Abstract Excalibur is a nonparametric, hierarchical framework for precision wavelength calibration of spectrographs. It is designed with the needs of extreme-precision radial-velocity (EPRV) instruments in mind, which require calibration or stabilization to better than 10−4 pixels. Instruments vary along only a few dominant degrees of freedom, especially EPRV instruments that feature highly stabilized optical systems and detectors. Excalibur takes advantage of this property by using all calibration data to construct a low-dimensional representation of all accessible calibration states for an instrument. Excalibur also takes advantage of laser-frequency combs or etalons, which generate a dense set of stable calibration points. This density permits the use of a nonparametric wavelength solution that can adapt to any instrument or detector oddities better than parametric models, such as a polynomial. We demonstrate the success of this method with data from the Extreme Precision Spectrograph (EXPRES), which uses a laser-frequency comb. When wavelengths are assigned to laser comb lines using excalibur, the rms of the residuals is about one-fifth that of wavelengths assigned using polynomial fits to individual exposures. Radial-velocity measurements of HD 34411 show a reduction in rms scatter over a 10 month time baseline from 1.17 to 1.05 m s−1.

A Nonstationary Uncertainty Model and Bayesian Calibration of Strain-Life Models

Abstract The Coffin–Manson–Basquin–Haford (CMBH) model is a well-accepted strain-life relationship to model fatigue life as a function of applied strain. In this paper, we propose a nonstationary uncertainty model for the CMBH model, alongside a Bayesian framework for model calibration and estimation of confidence and prediction intervals. Using Inconel 617 coupon test data, we compared our approach to traditional stationary variance models. The proposed uncertainty model successfully captures the fact that the variance of strain amplitude decreases with increasing fatigue life. Additionally, a discussion on how to use the proposed Bayesian framework to compensate for the lack of data by using prior information coming from similar alloys is also presented considering Hastealloy-X and Inconel 617 coupon data.

Analysis of the stress and deformation states in the vertical flat-jack test

Performing a realistic assessment of unreinforced masonry structures involves designing and executing appropriate experimental tests on masonry components for determining the material model parameters to be used in structural analysis. Considering recent developments in which inverse analysis was used as calibration framework, a vertical flat-jack test is investigated in this paper. Two simplified models are described for the analysis of the stress state in the masonry due to the pressure transferred by the flat-jack. Furthermore, with the aim of designing the sensor setup for the test, a POD analysis on the deformation state of the structure is carried out, highlighting the basic deformation modes which govern the response. The results show that high stresses and local modes can occur in the proximity of the flat-jack, and thus local use of FRP reinforcement is recommended to avoid undesired brittle crack propagation which may prevent accurate calibration of mortar joint mechanical characteristics.

Calibration of local-stochastic and path-dependent volatility models to vanilla and no-touch options

We propose a generic calibration framework to both vanilla and no-touch options for a large class of continuous semi-martingale models. The method builds upon the forward partial integro-differential equation (PIDE) derived in Hambly et al. (2016), which allows fast computation of up-and-out call prices for the complete set of strikes, barriers and maturities. It also utilises a novel two-states particle method to estimate the Markovian projection of the variance onto the spot and running maximum. We detail a step-by-step procedure for a Heston-type local-stochastic volatility model with local vol-of-vol, as well as two path-dependent volatility models where the local volatility component depends on the running maximum. In numerical tests, we benchmark these new models against standard models for a set of EURUSD market data, all three models are seen to calibrate well within the market no-touch bid--ask.

Self-Designed Colour Chart and a Multi-Dimensional Calibration Approach for Cultural Heritage Preventive Preservation

In this paper we present a colour calibration framework for cultural heritage preventive conservation. It includes a custom colour chart based on a regular sRGB division and a multi-dimensional calibration approach based on hyper-surfaces that map the original colours to the calibrated space taking information from all the channels. As shown, our work outperforms past proposals. This solution has been designed to be automatic and match the requirements of our cultural heritage surveillance framework needs, such as non-invasiveness and easiness to deploy. Its performance has been compared to publicly available calibration proposals and charts, and their results judged basing on the context and restrictions of the intended goal, such as illumination. Conclusions on the need for adapting technological solutions to the context of the goal, its cases of use when valid and self-designed methods are drawn. This work is part of the MIPAC-CM project.

A Hybrid Hand–Eye Calibration Method for Multilink Cable-Driven Hyper-Redundant Manipulators

Multilink cable-driven hyper-redundant manipulators (MCDHMs) have a slender body and flexible movement characteristics, which are very suitable for complex unstructured environments. Since MCDHMs have multiple coupling between active cables, linkage cables, joints, and the end-effector, the mathematical modeling of the segmented linkage MCDHM becomes more complicated, and the uncertainty of its model makes the motion accuracy of the end-effector become low. Traditional calibration methods that rely solely on eye-in-hand or eye-to-hand calibration method to calibrate the MCDHM model will produce relatively large errors. This article proposes a hybrid hand–eye calibration method, which eliminates the errors introduced by inaccurate kinematics models through the fusion modeling of eye-in-hand and eye-to-hand. First, for unstructured application scenarios, the hybrid calibration framework under the condition of large model errors is introduced, and then the multiple kinematics model of the MCDHM is derived. Then, two different types of hybrid hand–eye calibration equations are derived and solved by the recursive iteration method. Furthermore, according to the hand–eye calibration result, the MCDHM kinematic parameters are calibrated by the particle swarm optimization (PSO) method. Finally, in order to demonstrate the feasibility and superiority of the hybrid calibration method, experiments are carried out on the real MCDHM to further verify the proposed method. The experimental results show that the proposed hybrid calibration method has higher calibration accuracy.

Utilizing True Wireless Stereo Earbuds in Automated Pure-Tone Audiometry.

True wireless stereo (TWS) earbuds have become popular and widespread in recent years, and numerous automated pure-tone audiometer applications have been developed for portable devices. However, most of these applications require specifically designed earphones to which the public may not have access. Therefore, the present study investigates the accuracy of automated pure-tone audiometry based on TWS earbuds (Honor FlyPods). The procedure for developing an automated pure-tone audiometer is reported. Calibration of the TWS earbuds was accomplished by electroacoustic measurements and establishing corrected reference equivalent threshold sound pressure levels. The developed audiometer was then compared with a clinical audiometer using 20 hearing-impaired participants. The average signed and absolute deviations between hearing thresholds measured using the two audiometers were 3.1 dB and 6.7 dB, respectively. The overall accuracy rate in determining the presence/absence of hearing loss was 81%. The results show that the proposed procedure for an automated air-conduction audiometer based on TWS earbuds is feasible, and the system gives accurate hearing level estimation using the reported calibration framework.

Stochastic Inverse Modeling and Parametric Uncertainty of Sediment Deposition Processes Across Geologic Time Scales

In this work an integrated methodological and operational framework for diagnosis and calibration of Stratigraphic Forward Models (SFMs) which are typically employed for the characterization of sedimentary basins is presented. Model diagnosis rests on local and global sensitivity analysis tools and leads to quantification of the relative importance of uncertain model parameters on modeling goals of interest. Model calibration is performed in a stochastic framework, leading to estimates of distributions of model parameters (and ensuing spatial distributions of model outputs) conditional on available information. Starting from a considerable number of uncertain model parameters, which is typically associated with SFMs of the kind analyzed, the approach leads to the identification of a reduced set of parameters which are most influential to drive stratigraphic modeling results. Probability distributions of these model parameters conditional on available data are then evaluated through stochastic inverse modeling. To alleviate computational efforts, this step is performed through a combination of a surrogate model constructed through the Polynomial Chaos Expansion approach and a machine learning algorithm for efficient search of the parameter space during model inversion. As a test bed for the workflow, focus is on a realistic synthetic three-dimensional scenario which is modeled through a widely used SFM that enables one to perform three-dimensional numerical simulations of the accumulation of siliciclastic and carbonate sediments across geologic time scales. These results constitute a robust basis upon which further deployment of the approach to industrial field settings can be designed.

Towards a more consistent eco-hydrological modelling through multi-objective calibration: a case study in the Andean Vilcanota River basin, Peru

ABSTRACT Most hydrological studies rely on a model calibrated using discharge alone. However, judging the model reliability based on such calibration is problematic, as it does not guarantee the correct representation of internal hydrological processes. This study aims (a) to develop a comprehensive multi-objective calibration framework using remote sensing vegetation data and hydrological signatures (flow duration curve – FDC, and baseflow index) in addition to discharge, and (b) to apply this framework for calibration of the Soil and Water Assessment Tool (SWAT) in a typical Andean catchment. Overall, our calibration approach outperformed traditional discharge-based and FDC signature-based calibration strategies in terms of vegetation, streamflow, and flow partitioning simulation. New hydrological insights for the region are the following: baseflow is the main component of the streamflow sustaining the long dry-season flow, and pasture areas offer higher water yield and baseflow than other land-cover types. The proposed approach could be used in other data-scarce regions with complex topography.

Identification of spatially distributed parameters of hydrological models using the dimension-adaptive key grid calibration strategy

Distributed hydrological models have been developed to describe temporal and spatial variations. However, it is difficult to calibrate the spatially heterogeneous parameters because of dimensional issues and spatial uncertainty. This study aims to propose a dimension-adaptive key grid (DAKG) calibration strategy to identify spatial parameters of distributed hydrological models. The method uses sensitivity analyses to find important model parameters, and significantly fewer grid points are then selected as representative grids to reduce the dimensionality of parameters. Apart from the observed outlet streamflow, grid actual evaporation, and grid runoff are used for the multi-objective calibration framework. The Xiangjiang basin, the Yuanjiang basin, and the source region of the Yellow River in China are used as case studies, where the Variable Infiltration Capacity (VIC) land surface models have been calibrated. The results indicate that: (1) The proposed DAKG calibration strategy improves performance in terms of accuracy and model complexity for hydrological simulation. (2) The spatial distributions of the shape parameter B and the thickness of the second soil layer Depth2 are easy to be identified, where the spatial distribution of Depth2 is the most positively correlated with that of elevation. (3) The fusion of the runoff and evaporation products helps to reduce the uncertainty of the distributed parameters. Overall, the DAKG calibration strategy is an effective technique for parameter identification in distributed hydrological models.