Calibration Problem Research Articles

Current Distortion and Sensitivity Calibration Problems of Rogowski Coil

Driven by technology, China's power system has been greatly improved, and has been gradually transformed and upgraded, bringing great improvements to performance in all aspects. Current distortion and sensitivity misalignment of Rogowski coils have always been the key problems to be solved in practical applications. This paper summarizes the methods of calibrating the sensitivity of the Rogowski coil in the shape of the single ring arbitrary skeleton, the Rogowski coil of the double ring skeleton and the Rogowski coil in the shape of the round skeleton, which provides the basis for other improvements of the Rogowski coils. The existing method to solve the current distortion of the Rogowski coil has a single method in the output of the differential current signal, which will reduce the limitation of the measurement range, and the suppression problem still cannot be solved well. This paper summarizes the solutions to current distortion in Rogowski coils, introduces the causes and solutions of current sensor and transistor current distortion, and puts forward certain correlations to provide reference and direction for follow-up research.

Using Zodiacal Light For Spaceborne Calibration Of Polarimetric Imagers.

We propose that spaceborne polarimetric imagers can be calibrated, or self-calibrated using zodiacal light (ZL). ZL is created by a cloud of interplanetary dust particles. It has a significant degree of polarization in a wide field of view. From space, ZL is unaffected by terrestrial disturbances. ZL is insensitive to the camera location, so it is suited for simultaneous cross-calibration of satellite constellations. ZL changes on a scale of months, thus being a quasi-constant target in realistic calibration sessions. We derive a forward model for polarimetric image formation. Based on it, we formulate an inverse problem for polarimetric calibration and self-calibration, as well as an algorithm for the solution. The methods here are demonstrated in simulations. Towards these simulations, we render polarized images of the sky, including ZL from space, polarimetric disturbances, and imaging noise.

A reduced order model to assist welding parameter setup

This article demonstrates the time saving in industrial process setup using numerical reduced order modelling (ROM). The numerical simulations may supply useful information to design manufacturing processes but are often time consuming and then not suited with multi-query study such as inverse problem. ROM aims at replacing the original simulation (so-called high-fidelity (HF)) by a low rank model that will run fast according to the HF simulation time. Multi-query studies will use the ROM instead of the HF simulation to save time and deliver a solution consistent with industrial timeline. To validate the solution, the HF simulation will be used. In this paper, a problem of calibration of TIG welding parameter will illustrate this approach.

Single-Particle Model of Li-ion Battery – Model Calibration and Validation against Real Data in an Electric Vehicular Application

This paper investigates the problem of calibration and validation of a battery electrochemical model as a mandatory step towards accurate estimation of battery important variables, like state of charge (SoC) and state of health (SoH). Here, the Single Particle Model (SPM) is considered, which mathematically describes the battery internal governing phenomena by means of parabolic partial differential equations (PDEs), but whose parameters are notoriously difficult to measure or estimate. After suitable approximation of this model through a linear finite-dimensional model, a systematic procedure of SPM calibration is here proposed and validated against real data issued from battery cycling in an electric vehicular application, i.e., under standard driving cycle scenarios. In a novel approach of SoC estimation, the suitably calibrated SPM, together with measures of voltage and current, allow to analytically connect the internal spatially distributed ions’ concentrations to the equilibrium concentration, which, at its turn, is an image of battery SoC. Results suggest that SPM can reliably predict the battery internal ions’ concentrations and be further used for SoC accurate estimation.

Engine Calibration With Surrogate-Assisted Bilevel Evolutionary Algorithm.

Engine calibration problems are black-box optimization problems which are evaluation costly and most of them are constrained in the objective space. In these problems, decision variables may have different impacts on objectives and constraints, which could be detected by sensitivity analysis. Most existing surrogate-assisted evolutionary algorithms do not analyze variable sensitivity, thus, useless effort may be made on some less sensitive variables. This article proposes a surrogate-assisted bilevel evolutionary algorithm to solve a real-world engine calibration problem. Principal component analysis is performed to investigate the impact of variables on constraints and to divide decision variables into lower-level and upper-level variables. The lower-level aims at optimizing lower-level variables to make candidate solutions feasible, and the upper-level focuses on adjusting upper-level variables to optimize the objective. In addition, an ordinal-regression-based surrogate is adapted to estimate the ordinal landscape of solution feasibility. Computational studies on a gasoline engine model demonstrate that our algorithm is efficient in constraint handling and also achieves a smaller fuel consumption value than other state-of-the-art calibration methods.

Self-repeat as a multimodal retraction practice

Abstract The paper focuses on a particular practice of self-repeat through which participants retract their prior formulations, and explores its multimodal design and use in the dynamic construction of meaning in Hebrew conversation. Drawing on interactional approaches to language and embodied action, we show that the practice of self-repeat is used to retract a formulation judged by its producer as being inadequate and ill-calibrated in the given interactional context. This function is supported by the multimodal configuration in which the lexical repeat is cast, which involves a stable prosodic component and a variable embodied component. Through its prosodic and embodied design, the repeat is contextualized as a noticeable display of accountability for having made an ill-suited choice of words. While the self-repeat alone is sufficient in proposing a problem of calibration, it can also be followed by a lexical replacement, which makes explicit the adjusted or recalibrated term. The self-repeat practice shows how participants engage in semantic work through online and situated revision of their formulations. This exposed process of meaning construction reveals their understanding of the constitutive link between the conceptual and the normative orders as practiced in actual conversation.

An Informetric View to the Negative Capacitance Phenomenon at Interlayered Metal-Semiconductor Structures and Distinct Electronic Devices

Negative Capacitance (NC) phenomenon, which can be explained as the material exhibiting an inductive behavior, is often referred to as "anomalous" or "abnormal" in the literature. Especially in the forward bias/deposition region, the presence of surface states (Nss) and their relaxation times (τ), series resistance (Rs), minority carrier injection, interface charge loss in occupied states under the Fermi energy level, parasitic inductance, or poor measuring equipment calibration problems can be counted among the causes of this phenomenon. Studies on NC behavior have shown that this behavior can be observed for different frequencies, temperatures, and related parameters at forward biases. However, the NC behavior, which appears as an unidentified peak in admittance spectroscopy data, is not yet fully understood. Ultimately, this study aims to compile and analyze the NC reported in selected scientific studies, investigate the source of this phenomenon, and observe statistics in a general view.

Autonomous method for a strapdown inertial navigation system calibration in operation in operation

Strapdown inertial navigation systems (SINS) are widely used in modern air transport. The service life of such systems is decades. During this time, the characteristics of such systems may deteriorate due to the degradation of inertial sensors - gyroscopes and accelerometers. Under these conditions, in order to maintain the required accuracy of the SINS during the entire period of operation, it is necessary either to periodically carry out routine maintenance with the onboard system, or to continuously adapt the used sensor measurement model in terms of compensating for errors that arise. The second direction, which we will call calibration, has the advantage over the first one that it is carried out automatically by the system itself and does not require any additional equipment or additional maintenance work. The known method of SINS recalibration is based on the integration of information from the SINS and from the receiver of satellite radio navigation signals (SRNS). Such calibration, firstly, significantly depends on the availability and quality of satellite information, and secondly, it is possible only for integrated navigation systems in which the inertial subsystem is supplemented by a SRNS receiver. In contrast to the known method, this article solves the problem of autonomous SINS calibration, which is carried out after each flight. The solution is based on a linearized inertial navigation error model based on the boundary conditions of the state vector (at the beginning and end of the flight). The external information used consists only of the initial values of the coordinates and the current measurements of the baroaltimeter. As a result of the decision at the end of the flight, the errors of gyroscopes and accelerometers are determined, as well as the error in determining the final values of latitude and longitude in the SINS. Sensor errors can be used in subsequent system activations, which improves the accuracy of its operation as a whole. The analysis of the suitability and effectiveness of the developed methodology was carried out on a typical flight simulation program.

Analysis of compositional and moment approaches to solving the calibration problem using a standard example

Moment and compositional approaches to solving the measurement problem of calibration are considered. The use of the moment approach to solving a standard example of thermometer calibration is considered. In this work, this example is solved within the framework of the compositional approach and more accurate and complete results are obtained compared to using the moment approach. This fact is consistent with the assessment of the accuracy of solutions to measurement problems carried out back in 2001 using a more rigorous compositional approach than the moment one: in the problem of calibrating measuring instruments, the moment approach does not fully take into account the inadequacy of functional models, overestimates the accuracy estimates and does not allow obtaining nonlinear models of optimal complexity. To solve the measurement problem within the framework of the compositional approach, this work uses the “MMI-Calibration 3.0” program, which allows taking into account the errors of inadequacy of mathematical models of calibration functions and obtaining tolerance intervals for a given confidence probability. Among polynomials up to the ninth degree inclusive, a three-parameter model of optimal complexity was obtained, the average module of the inadequacy error is 0.002745 °C and the tolerance interval [–0.00828; 0.00761] with a confidence probability of 0.95. Within the framework of the compositional approach, the average module of the error of inadequacy and the kappa criterion used to find a model of optimal complexity are compared. It is shown that the compositional approach fully and strictly describes the solution to the calibration problem without simplifications, allows one to avoid inflated estimates of accuracy, and therefore can serve as an exemplary approach provided that the “MMI-Calibration 3.0” program is used. The simultaneous use of the criteria of the minimum average module of the error of inadequacy and the maximum of the kappa criterion is a necessary and sufficient condition for identifying a model of optimal complexity in the class of polynomials of degree n.

Hybrid-MVS: Robust Multi-View Reconstruction With Hybrid Optimization of Visual and Depth Cues

Consumer-level RGB-D cameras have been widely used for dense 3D reconstruction of scenes. Especially for textureless or non-lambertian surfaces, consumer RGB-D cameras can ensure completeness of the reconstructed models at a low cost. However, the reconstruction quality relies heavily on the accuracy of the depth sensors. Digital cameras are also used popularly for capturing high-resolution pictures to achieve high-quality dense reconstruction of the scenes, but cannot handle textureless or non-lambertian regions well due to the visual ambiguity problem. To ensure both completeness and accuracy of the reconstructed 3D models, we propose a hybrid multi-view reconstruction pipeline named Hybrid-MVS, which combines the high-resolution images taken by a digital camera and the low-resolution RGB-D frames captured by a consumer RGB-D camera for robust reconstruction of complicated scenes with challenging textureless and non-lambertian surfaces. Unlike most existing multi-sensor systems which require explicit hardware calibration and synchronization of various sensors, the calibration and synchronization problems between the digital camera and RGB-D camera are implicitly solved for compositing reliable depth prior of the digital images in our pipeline. Especially, we propose a hybrid MVS framework for robust PatchMatch stereo and Delaunay meshing, which tightly couples both visual cues given by the digital images and depth cues from the RGB-D frames to maximize the complementary advantages. The experiments with quantitative and qualitative evaluations demonstrate the effectiveness of the proposed Hybrid-MVS framework, which can successfully achieve high-quality 3D reconstruction of complicated natural scenes with robustness to weakly textured and non-lambertian areas.

Determination of load combination factors for code calibration

Semi-probabilistic methods are widely used for structural engineering design and assessment. In such methods, the safety and performance adequacy of structures are typically tied to partial safety factors for load and resistance, and load combination factors. There exist some heuristic strategies for estimating these partial factors, such as the design value method and first order reliability coefficient method. However, when adopted for the estimation of load combination factors, these strategies are either inaccurate or have non-unique estimates. Excessive conservatism in factor estimates is not desirable, particularly for the performance assessment of existing structures. In this study we propose a method for estimating load combination factors using a matrix linear algebra approach. Specifically, we develop a closed-form analytical expression to estimate unique load combination factors for typical code calibration problems. A comparative study of the existing heuristic approaches with the new approach is presented and demonstrated on two case studies. It is shown that the proposed method offers a valuable means of deriving load combination factors: one that avoids hueristics, and conservatism.

Evaluating Landsat-9 TIRS-2 calibrations and land surface temperature retrievals against ground measurements using multi-instrument spatial and temporal sampling along transects

High-resolution TIR data are essential for a wide range of studies, e.g., in environmental monitoring, urban effects, water stress, agricultural productivity, and land/water resources. Landsat series provides high-resolution TIR data, but data accuracy must be ensured to contribute to these fields. After the calibration problems of Landsat 8 (L8) TIRS due to a stray light effect, efforts were made in the design of Landsat 9 (L9) TIRS-2 to mitigate this effect. In any case, field calibration and validation activities are necessary to evaluate the accuracy of recently launched sensors. These activities require high-quality ground TIR radiance data that are representative at satellite spatial resolution and that quantify spatial–temporal fluctuations at the experimental sites concurrently to satellite overpasses. However, these requirements are not always accomplished in the literature.This paper provides vicarious calibrations of the L9 TIRS-2 data with accurate ground measurements acquired using multi-instrument spatial and temporal sampling in a rice paddy area from December 2021 to August 2023, in order to contribute to the calibration and validation of L9 TIRS-data.In March 1, 2023 L9 TIRS-2 scenes started to be reprocessed and calibration was updated. Both the original calibration and that after reprocessing were evaluated. Close results were obtained for band 10 (b10). When comparing satellite brightness temperatures and those determined from in situ LSTs, systematic differences of −0.3 K were shown, with root-mean-square differences (RMSDs) of 0.5 K. Results for band 11 showed a bias of −0.1 K for the original data and of 0.2 K after the reprocessing, with RMSDs of 0.6 K.The L2 LST product generated operationally from b10 data was also evaluated, together with alternative single-channel (SC) algorithms. A bias (RMSD) of 0.4 K (1.1 K) was obtained for the product. However, negative biases were shown both with the radiative transfer equation used on b10 radiances and the alternative SC algorithm.Finally, we validated six types of split-window (SW) algorithms applied to L9 TIRS-2 data before and after the reprocessing. A systematic difference of about −0.6 K between SW LST biases when using the reprocessed and original data was observed. In any case, for most of the algorithms with original and reprocessed data, LST biases and standard deviations were within the recommended threshold of 1 K, which proves the good performance of L9 TIRS-2 data to monitor LST at high spatial resolution.

An analytical model for photomultiplier tube calibration

The purpose of the present article is to offer an analytical model that can be used for the calibration of photomultiplier tubes (PMTs). The derivation of the mathematical formulae is discussed extensively and we apply this machinery to a real problem; the gain determination of the R7081 Hamamatsu PMT. We hope that our work can provide a third, powerful alternative to the task of gain determination. We also believe that the techniques explained in this article can be applied to other problems in PMT calibration.

State of health estimation for lithium-ion batteries based on current interrupt method and genetic algorithm optimized back propagation neural network

Accurate state-of-health (SOH) estimation is an essential prerequisite for a battery management system (BMS) to improve battery utilization efficiency. The impedance information can be utilized to reflect the SOH. However, the traditional electrochemical impedance spectroscopy (EIS) method suffers from time-consuming measurements and specialized equipment. This study aims to establish a connection between EIS and the current interrupt method, which reduces the difficulty of obtaining impedance information through its utilization. This method can be applied in real time during charging, enabling it to be incorporated into a BMS. A genetic algorithm optimized back propagation neural network (GA-BPNN) is developed to estimate the SOH based on the impedance information obtained in the current interrupt method as inputs. The genetic algorithm improves the weights and thresholds of the neural network, which solves the parameter calibration problem. In this study, besides utilizing measurement data from different aging conditions as training set data, hybrid tests comparable to the actual usage environment are employed as validation set data. The experimental results show that a combination of the proposed current interrupt method and GA-BPNN can estimate SOH accurately with the root mean square error (RMSE) as low as 0.77 % in a complex hybrid test environment.

Active learning sample selection based on multicriteria

In multivariate calibration problems, model performance is affected significantly by the calibration samples used during model building. In recent years, active learning methods have become one of the best methods for sample selection. However, most active learning methods only select instances from prediction uncertainty or sample space distance, and these single-criteria methods tend to select undesired samples. In addition, sample density characterizes the spatial information carried by the sample, but few studies in quantitative analysis utilize sample density alone to select calibration samples. Considering these issues, based on the k-means clustering algorithm, this paper proposes an active learning sample selection method (Diversity Informativeness Density Active Learning, DIDAL), which combines the three criteria of diversity, informativeness and sample density. The most representative sample is iteratively selected for - addition to the calibration set for modeling and estimating the chemical concentration of analytes. Soybean meal and soy sauce samples were analyzed by DIDAL and compared with existing sample selection methods. The prediction results show that the DIDAL algorithm significantly outperforms several existing algorithms and is close to the performance of full-sample modeling. A model with high prediction accuracy can be constructed by selecting only a few samples using the DIDAL method.

Solving the inverse cosmological calibration problem of gamma-ray bursts

ABSTRACT We have received a new physical characteristics fitting based on actual observational data from the Swift mission’s long-duration gamma-ray bursts (LGRBs). We considered such characteristics as the Amati parameters for linear correlation (Eiso–Ep,i) and the k-correction for gravitational lensing and Malmquist bias (GLMB) effect. We used the Pantheon SN Ia catalogue and the standard Lambda cold dark matter model with a fixed Hubble constant of H0 = 70 km s−1 Mpc−1 as the baseline for the Hubble function μ(z). In our paper, we formulated the inverse cosmological calibration problem (ICCP) in the non-parametric statistics framework. The ICCP involves fitting non-observable physical characteristics while assuming a fixed cosmological model. To solve this problem, we developed a new method that is resistant to non-Gaussian processes. This method is based on error propagation through the Monte Carlo method and the Theil–Sen method for linear regression estimate. We have demonstrated the stability and robustness of this assessment method. The parameter estimates are as follows: $a=0.92^{+0.12}_{-0.12}$, $b=50.32^{+0.33}_{-0.32}$ without considering the GLMB effect, and $a=0.63^{+0.13}_{-0.14}$, $b=50.12^{+0.33}_{-0.31}$, and $k=1.98^{+0.25}_{-0.24}$ with the effect included. The proposed method can be applied to any other calibration sample of known standard candles, a calibrated sample of LGRBs, and the Hubble function μ(z). In the future, the ICCP idea can be used as an alternative cosmological test for estimating cosmological parameters, including the GLMB effect, or even for the selection of models, providing new information about the Universe. This can be done by analysing the residual values of observational data within the Bayesian statistics paradigm.

Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Parameter Calibration in Hydrological Simulation

ABSTRACT Parameter calibration is an important part of hydrological simulation and affects the final simulation results. In this paper, we introduce heuristic optimization algorithms, genetic algorithm (GA) to cope with the complexity of the parameter calibration problem, and use particle swarm optimization algorithm (PSO) as a comparison. For large-scale hydrological simulations, we use a multilevel parallel parameter calibration framework to make full use of processor resources, and accelerate the process of solving high-dimensional parameter calibration. Further, we test and apply the experiments on domestic supercomputers. The results of parameter calibration with GA and PSO can basically reach the ideal value of 0.65 and above, with PSO achieving a speedup of 58.52 on TianHe-2 supercomputer. The experimental results indicate that using a parallel implementation on multicore CPUs makes high-dimensional parameter calibration in large-scale hydrological simulation possible. Moreover, our comparison of the two algorithms shows that the GA obtains better calibration results, and the PSO has a more pronounced acceleration effect.

A systematic comparison of different machine learning models for the spatial estimation of air pollution

Air pollutants harm human health and the environment. Nowadays, deploying an air pollution monitoring network in many urban areas could provide real-time air quality assessment. However, these networks are usually sparsely distributed and the sensor calibration problems that may appear over time lead to missing and wrong measurements. There is an increasing interest in developing air quality modelling methods to minimize measurement errors, predict spatial and temporal air quality, and support more spatially-resolved health effect analysis. This research aims to evaluate the ability of three feed-forward neural network architectures for the spatial prediction of air pollutant concentrations using the measures of an air quality monitoring network. In addition to these architectures, Support Vector Machines and geostatistical methods (Inverse Distance Weighting and Ordinary Kriging) were also implemented to compare the performance of neural network models. The evaluation of the methods was performed using the historical values of seven air pollutants (Nitrogen monoxide, Nitrogen dioxide, Sulphur dioxide, Carbon monoxide, Ozone, and particulate matters with size less than or equal to 2.5 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu $$\\end{document}m and to 10 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu $$\\end{document}m) from an urban air quality monitoring network located at the metropolitan area of Madrid (Spain). To assess and compare the predictive ability of the models, three estimation accuracy indicators were calculated: the Root Mean Squared Error, the Mean Absolute Error, and the coefficient of determination. FFNN-based models are superior to geostatistical methods and slightly better than Support Vector Machines for fitting the spatial correlation of air pollutant measurements.Graphical abstract

Supernova calibration by gravitational waves

Hubble tension is one of the most important problems in cosmology. Although the local measurements on the Hubble constant with Type Ia supernovae (SNe Ia) are independent of cosmological models, they suffer the problem of zero-point calibration of the luminosity distance. The observations of gravitational waves (GWs) with space-based GW detectors can measure the luminosity distance of the GW source with high precision. By assuming that massive binary black hole mergers and SNe Ia occur in the same host galaxy, we study the possibility of re-calibrating the luminosity distances of SNe Ia by GWs. Then we use low-redshift re-calibrated SNe Ia to determine the local Hubble constant. We find that we need at least 7 SNe Ia with their luminosity distances re-calibrated by GWs to reach a 2% precision of the local Hubble constant. The value of the local Hubble constant is free from the problems of zero-point calibration and model dependence, so the result can shed light on the Hubble tension.

Calibration in the “real world” of a partially specified stochastic volatility model

AbstractWe study the “real world” calibration of a partially specified stochastic volatility model, where the analytic expressions of the asset price drift rate and of the stochastic variance drift are not specified. The model is calibrated matching the observed asset log returns and the priors assigned by the investor. No option price data are used in the calibration. The priors chosen for the asset price drift rate and for the stochastic variance drift are those suggested by the Heston model. For this reason, the model presented can be considered as an “enhanced” Heston model. The calibration problem is formulated as a stochastic optimal control problem and solved using the dynamic programming principle. The model presented and the Heston model are calibrated using synthetic and Standard & Poor 500 (S&P500) data. The calibrated models are used to produce 6, 12, and 24 months in the future synthetic and S&P500 forecasts.