Calibration Estimation Research Articles

Spatial Distribution of Nutrient Loads Based on Mineral Fertilizers Applied to Crops: Case Study of the Lobo Basin in Côte d’Ivoire (West Africa)

Eutrophication in the Lobo watershed remains a major problem. The work carried out has focused on chemical and biological analyses in the lake or in its immediate environment: they did not sufficiently take into account the diffuse transfer of nutrients over the entire watershed. This study aims to assess the nutrient (N and P) loads in the Lobo watershed, an agricultural area, to understand the spatio-temporal impacts of land management practices on eutrophication. The methodology uses two steps: streamflow calibration and nutrient (N and P) estimation using the Soil and Water Assessment Tool (SWAT) watershed model. Thus, the nutrient inputs were estimated based on the levels of N and P in every kilogram of Nitrogen-phosphorus-Potassium (NPK) type fertilizers applied by farmers. The average quantities of N and P applied to the crops were 47.24 kg ha−1 and 21.25 kg ha−1. Results show a good performance on flow calibration as evidenced using evaluation criteria R2, Nash–Sutcliffe Efficiency (NSE), and Percent Bias (PBIAS) of 0.63, 0.62, and −8.1, respectively. The yields of inorganic N and soluble P varied from 0 to 0.049 kg ha−1 and from 0 to 0.31 kg ha−1. These results show that the crops’ inorganic nitrogen requirements were higher than the demands for soluble phosphorus. Simulations relating to the organic N transfer revealed values ranging from 0.2 to 5 kg ha−1, while the transport of organic phosphorus was estimated to vary from 0.3 to 1.3 kg ha−1.

Optimizing Mean Estimators with Calibrated Minimum Covariance Determinant in Median Ranked Set Sampling

Calibration methods enhance estimates by modifying the initial design weights, for which supplementary information is exploited. This paper first proposes a generalized class of minimum-covariance-determinant (MCD)-based calibration estimators and then presents a novel class of MCD-based calibrated estimators under a stratified median-ranked-set-sampling (MRSS) design. Further, we also present a double MRSS version of generalized and novel classes of estimators. To assess and compare the performance of the generalized and novel classes of estimators, both real and artificial datasets are utilized. In the presented practical scenarios and real-world applications, we utilize information from a dataset comprising 800 individuals in Turkey from 2014. These data include body mass index (BMI) as the primary variable of interest and age values as auxiliary variables. The BMI results shows that the proposed estimators (y¯PMI=581.1897,y¯PaMI=544.8397) have minimum and (y¯PMII=669.1822,y¯PaMII=648.2363) have maximum PREs in the case of single and double MRSS for odd sample sizes. Similarly, (y¯PMI=860.0099,y¯PaMI=844.7803) have minimum and (y¯PMII=974.5859,y¯PaMII=953.7233) have maximum PREs in the case of single and double MRSS for even sample sizes. Additionally, we conduct a simulation study using a symmetric dataset.

Low-light image enhancement based on virtual exposure

Under poor illumination, the image information captured by a camera is partially lost, which seriously affects the visual perception of the human. Inspired by the idea that the fusion of multiexposure images can yield one high-quality image, an adaptive enhancement framework for a single low-light image is proposed based on the strategy of virtual exposure. In this framework, the exposure control parameters are adaptively generated through a statistical analysis of the low-light image, and a virtual exposure enhancer constructed by a quadratic function is applied to generate several image frames from a single input image. Then, on the basis of generating weight maps by three factors, i.e., contrast, saturation and saliency, the image sequences and weight images are transformed by a Laplacian pyramid and Gaussian pyramid, respectively, and multiscale fusion is implemented layer by layer. Finally, the enhanced result is obtained by pyramid reconstruction rule. Compared with the experimental results of several state-of-the-art methods on five datasets, the proposed method shows its superiority on several image quality evaluation metrics. This method requires neither image calibration nor camera response function estimation and has a more flexible application range. It can weaken the possibility of overenhancement, effectively avoid the appearance of a halo in the enhancement results, and adaptively improve the visual information fidelity.

Ultrasonic testing-based method for segmental calibration and quantitative estimation of the electrolyte content in lithium-ion batteries

Nondestructive testing and quantitative estimation of the electrolyte content in seal-packed lithium-ion batteries (LIBs) have remained a major technological challenge. This study developed a non-contact, multi-channel ultrasonic testing solution for nondestructive, in situ testing and quantitative estimation of the electrolyte content in LIBs. First, sample batteries with different electrolyte contents were prepared. Then, the electrolyte contents in different local areas of the sample batteries were calibrated based on the corresponding mass ratios of characteristic elements obtained using energy-dispersive spectroscopy and scanning electron microscopy; thereby, the mapping relationship between the ultrasonic propagation properties and the electrolyte content of the batteries was established. Finally, a method for segmental quantitative electrolyte content estimation with thickness-based corrections was proposed and verified experimentally. The experimental results showed that the magnitude of through-transmission ultrasonic signals and the electrolyte content of the cells were strongly correlated. The error of the electrolyte content estimation method was within 3% for cells with adequate electrolyte content, and the error of electrolyte content was smaller than 6% even for batteries with a low electrolyte content.

Statistical inference with semiparametric nonignorable nonresponse models

AbstractHow to deal with nonignorable response is often a challenging problem encountered in statistical analysis with missing data. Parametric model assumption for the response mechanism is sensitive to model misspecification. We consider a semiparametric response model that relaxes the parametric model assumption in the response mechanism. Two types of efficient estimators, profile maximum likelihood estimator and profile calibration estimator, are proposed, and their asymptotic properties are investigated. Two extensive simulation studies are used to compare with some existing methods. We present an application of our method using data from the Korean Labor and Income Panel Survey.

ARMO: automated and reliable multi-objective model for lymph node metastasis prediction in head and neck cancer

Objective. Accurate diagnosis of lymph node metastasis (LNM) is critical in treatment management for patients with head and neck cancer. Positron emission tomography and computed tomography are routinely used for identifying LNM status. However, for small or less fluorodeoxyglucose (FDG) avid nodes, there are always uncertainties in LNM diagnosis. We are aiming to develop a reliable prediction model is for identifying LNM. Approach. In this study, a new automated and reliable multi-objective learning model (ARMO) is proposed. In ARMO, a multi-objective model is introduced to obtain balanced sensitivity and specificity. Meanwhile, confidence is calibrated by introducing individual reliability, whilst the model uncertainty is estimated by a newly defined overall reliability in ARMO. In the training stage, a Pareto-optimal model set is generated. Then all the Pareto-optimal models are used, and a reliable fusion strategy that introduces individual reliability is developed for calibrating the confidence of each output. The overall reliability is calculated to estimate the model uncertainty for each test sample. Main results. The experimental results demonstrated that ARMO obtained more promising results, which the area under the curve, accuracy, sensitivity and specificity can achieve 0.97, 0.93, 0.88 and 0.94, respectively. Meanwhile, based on calibrated confidence and overall reliability, clinicians could pay particular attention to highly uncertain predictions. Significance. In this study, we developed a unified model that can achieve balanced prediction, confidence calibration and uncertainty estimation simultaneously. The experimental results demonstrated that ARMO can obtain accurate and reliable prediction performance.

Innovative integration of augmented reality and optical surface imaging: A coarse-to-precise system for radiotherapy positioning.

Traditional methods of radiotherapy positioning have shortcomings such as fragile skin-markers, additional doses, and lack of information integration. Emerging technologies may provide alternatives for the relevant clinical practice. To propose a noninvasive radiotherapy positioning system integrating augmented reality (AR) and optical surface, and to evaluate its feasibility in clinical workflow. AR and structured light-based surface were integrated to implement the coarse-to-precise positioning through two coherent steps, the AR-based coarse guidance and the optical surface-based precise verification. To implement quality assurance, recognition of face and pattern was used for patient authentication, case association, and accessory validation in AR scenes. The holographic images reconstructed from simulation computed tomography (CT) images, guided the initial posture correction by virtual-real alignment. The point clouds of body surface were fused, with the calibration and pose estimation of structured light cameras, and segmented according to the preset regions of interest (ROIs). The global-to-local registration for cross-source point clouds was achieved to calculate couch shifts in six degrees-of-freedom (DoF), which were ultimately transmitted to AR scenes. The evaluation based on phantom and human-body (4 volunteers) included, (i) quality assurance workflow, (ii) errors of both steps and correlation analysis, (iii) receiver operating characteristic (ROC), (iv) distance characteristics of accuracy, and (v) clinical positioning efficiency. The maximum errors in phantom evaluation were 3.4±2.5mm in Vrt and 1.4±1.0° in Pitch for the coarse guidance step, while 1.6±0.9mm in Vrt and 0.6±0.4° in Pitch for the precise verification step. The Pearson correlation coefficients between precise verification and cone beam CT (CBCT) results were distributed in the interval [0.81, 0.85]. In ROC analysis, the areas under the curve (AUC) were 0.87 and 0.89 for translation and rotation, respectively. In human body-based evaluation, the errors of thorax and abdomen (T&A) were significantly greater than those of head and neck (H&N) in Vrt (2.6±1.1vs. 1.7±0.8, p<0.01), Lng (2.3±1.1vs. 1.4±0.9, p<0.01), and Rtn (0.8±0.4vs. 0.6±0.3, p=0.01) while relatively similar in Lat (1.8±0.9vs. 1.7±0.8, p=0.07). The translation displacement range, after coarse guidance step, required for high accuracy of the optical surface component of the integrated system was 0-42mm, and the average positioning duration of the integrated system was significantly less than that of conventional workflow (355.7±21.7 vs. 387.7±26.6 s, p<0.01). The combination of AR and optical surface has utility and feasibility for patient positioning, in terms of both safety and accuracy.

Calibration-Based Mean Estimators under Stratified Median Ranked Set Sampling

Using auxiliary information, the calibration approach modifies the original design weights to enhance the mean estimates. This paper initially proposes two families of estimators based on an adaptation of the estimators presented by recent researchers, and then, it presents a new family of calibration estimators with the set of some calibration constraints under stratified median ranked set sampling (MRSS). The result has also been implemented to the situation of two-stage stratified median ranked set sampling (MRSS). To best of our knowledge, we are presenting for the first time calibration-based mean estimators under stratified MRSS, so the performance evaluation is made between adapted and proposed estimators on behalf of the simulation study with real and artificial datasets. For real-world data or applications, we use information on the body mass index (BMI) of 800 people in Turkey in 2014 as a research variable and age as an auxiliary variable.

The mean residual life model for the right-censored data in the presence of covariate measurement errors.

In this article, we consider the mean residual life regression model in the presence of covariate measurement errors. In the whole cohort, the surrogate variable of the error-prone covariate is available for each subject, while the instrumental variable (IV), which is related to the underlying true covariates, is measured only for some subjects, the calibration sample. Without specifying distributions of measurement errors but assuming that the IV is missing at random, we develop two estimation methods, the IV calibration and cohort estimators, for the regression parameters by solving estimation equations (EEs) based on the calibration sample and cohort sample, respectively. To improve estimation efficiency, a synthetic estimator is derived by applying the generalized method of moment for all EEs. The large sample properties of the proposed estimators are established and their finite sample performance are evaluated via simulation studies. Simulation results show that the cohort and synthetic estimators outperform the IV calibration estimator and the relative efficiency of the cohort and synthetic estimators mainly depends on the missing rate of IV. In the case of low missing rate, the synthetic estimator is more efficient than the cohort estimator, while the result can be reversed when the missing rate is high. We illustrate the proposed method by application to data from the patients with stage 5 chronic kidney disease in Taiwan.

New calibration estimation procedure in the presence of unit non response

In the present work, we have proposed the calibrated estimator of population mean in the presence of unit non response. The non response has been adjusted using the non response adjustment factor depending on different models (which are inverse linear, logistic and exponential) based on auxiliary information, incorporated in calibration estimation. We have studied the case of unit non response through calibration technique under three sampling designs named as simple random sampling (SRS), probability proportional to size sampling (PPS) and stratified random sampling. A broad simulation study has been conducted to evaluate the performances of the calibrated estimators in each sampling scheme.

On representativeness, informative sampling, nonignorable nonresponse, semiparametric prediction and calibration

Informative sampling refers to a sampling design for which the sample selection probabilities depend on the values of the model outcome variable. In such cases the model holding for the sample data is different from the model holding for the population data. Similarly, nonignorable nonresponse refers to a nonresponse mechanism in which the response probability depends on the value of a missing outcome variable. For such a nonresponse mechanism the model holding for the response data is different from the model holding for the population data. In this paper, we study, within a modelling framework, the semi-parametric prediction of a finite population total by specifying the probability distribution of the response units under informative sampling and nonignorable nonresponse. This is the most general situation in surveys and other combinations of sampling informativeness and response mechanisms can be considered as special cases. Furthermore, based on the relationship between response distribution and population distribution, we introduce a new measure of the representativeness of a response set and a new test of nonignorable nonresponse and informative sampling, jointly. Finally, a calibration estimator is obtained when the sampling design is informative and the nonresponse mechanism is nonignorable.

A unified approach based on multidimensional scaling for calibration estimation in survey sampling with qualitative auxiliary information.

Survey calibration is a widely used method to estimate the population mean or total score of a target variable, particularly in medical research. In this procedure, auxiliary information related to the variable of interest is used to recalibrate the estimation weights. However, when the auxiliary information includes qualitative variables, traditional calibration techniques may be not feasible or the optimisation procedure may fail. In this article, we propose the use of linear calibration in conjunction with a multidimensional scaling-based set of continuous, uncorrelated auxiliary variables along with a suitable metric in a distance-based regression framework. The calibration weights are estimated using a projection of the auxiliary information on a low-dimensional Euclidean space. The approach becomes one of the linear calibration with quantitative variables avoiding the usual computational problems in the presence of qualitative auxiliary information. The new variables preserve the underlying assumption in linear calibration of a linear relationship between the auxiliary and target variables, and therefore the optimal properties of the linear calibration method remain true. The behaviour of this approach is examined using a Monte Carlo procedure and its value is illustrated by analysing real data sets and by comparing its performance with that of traditional calibration procedures.

Bias-aware thermoacoustic data assimilation

Ensemble data assimilation algorithms combine experimental data and numerical models to estimate the state and parameters of a system. If the model is unbiased, the estimation concentrates around the true state. Thermoacoustic instabilities are, however, commonly modelled with low-order models, which are biased by definition. We propose the introduction of reservoir computing to represent the model bias. We combine the ensemble square-root Kalman filter with an echo state network to perform, in real time, (1) the estimation of the state of the system, (2) parameter calibration, and (3) model bias estimation. The proposed methodology is tested in a Rijke tube system, with synthetic experimental data from a high order model.

Self-Supervised Domain Calibration and Uncertainty Estimation for Place Recognition

Visual place recognition techniques based on deep learning, which have imposed themselves as the state-of-the-art in recent years, do not generalize well to environments visually different from the training set. Thus, to achieve top performance, it is sometimes necessary to fine-tune the networks to the target environment. To this end, we propose a self-supervised domain calibration procedure based on robust pose graph optimization from Simultaneous Localization and Mapping (SLAM) as the supervision signal without requiring GPS or manual labeling. Moreover, we leverage the procedure to improve uncertainty estimation for place recognition matches which is important in safety critical applications. We show that our approach can improve the performance of a state-of-the-art technique on a target environment dissimilar from its training set and that we can obtain uncertainty estimates. We believe that this approach will help practitioners to deploy robust place recognition solutions in real-world applications.

P526 A prediction model for successful increase of adalimumab dose intervals: analysis of the pragmatic open-label randomised controlled non-inferiority LADI trial

Abstract Background We showed in the pragmatic open-label randomised controlled non-inferiority LADI trial that increasing adalimumab (ADA) dose intervals was non-inferior to conventional dosing for persistent flares in CD patients in stable remission, while infection-related adverse events (AE) were reduced. This was counterbalanced by lower rates of clinical remission and more gastro-intestinal AEs after 48 weeks. In the current study we aimed to develop a prediction model to identify patients who could successfully increase their ADA dose interval. Methods This is a secondary analysis of the intervention group of the LADI trial. In this group, patients in steroid-free clinical remission for ≥ 9 months (Harvey-Bradshaw Index (HBI) &amp;lt; 5, CRP &amp;lt; 10 mg/L and faecal calprotectin (FCP) &amp;lt; 150 µg/g), on conventional ADA dosing increased ADA intervals to 3 and then to 4 weeks. A successful dose interval increase was defined as: no persistent flare (&amp;gt;8 weeks), no intervention-related severe AE, no rescue medication use, and an increased dose interval while in clinical and biochemical remission at week 48. Candidate baseline predictors were selected after a study group consensus meeting (Table 1). Prediction models were based on logistic regression. Four variable selection strategies were used: inclusion of all variables as a naïve reference model, stepwise backwards regression, LASSO with minimal lambda, and LASSO using the ‘one standard error’ rule. Models were evaluated on discrimination and calibration. Missing data were multiply imputed and models were internally validated using bootstrap optimism correction. Results The four models were developed on 109 patients, of which 59.1% experienced the outcome of successful dose interval increase (Table 1). Apparent performance of the models was adequate, with areas under the receiver operating characteristic curves (AUC) between 0.60 and 0.67 (Table 2 and Figure 1). Predicted probabilities from LASSO-based models were too modest (Table 2 and Figure 2). Internal validation showed optimism-corrected AUCs around 0.5 (Table 2), meaning that models could not identify patients who successfully increased their ADA dose interval. Optimism-corrected calibration estimates for the naïve model and backwards selection showed overfitting on the data. Internally validated predicted probabilities from LASSO-based models were too modest. Conclusion After 48 weeks, approximately 60% of patients could successfully increase their ADA dose interval without major negative clinical impact, but these could not be identified with a series of different prediction models. Risks and benefits of this strategy should be discussed with individual patients based on their risk perception and medication preferences.

Robust calibration for estimating the population mean using stratified random sampling

Estimating the population mean is of prime concern in many studies, and calibrations are popular choices. A robust calibration estimator estimates the mean using the minimum covariance determinant (MCD) and the minimum volume ellipsoid (MVE) estimations under stratified random sampling. Efficiency comparisons have been made between the robust calibration estimator and classical calibration estimator. Simulations and empirical results show that the proposed robust calibration estimator has a lower mean square error than the calibration estimators. When the relative efficiency and computation times are considered together, it is seen that the proposed robust calibration estimators based on MCD estimates are more efficient.

Towards High Performance Low Complexity Calibration in Appearance Based Gaze Estimation.

Appearance-based gaze estimation from RGB images provides relatively unconstrained gaze tracking from commonly available hardware. The accuracy of subject-independent models is limited partly by small intra-subject and large inter-subject variations in appearance, and partly by a latent subject-dependent bias. To improve estimation accuracy, we have previously proposed a gaze decomposition method that decomposes the gaze angle into the sum of a subject-independent gaze estimate from the image and a subject-dependent bias. Estimating the bias from images outperforms previously proposed calibration algorithms, unless the amount of calibration data is prohibitively large. This paper extends that work with a more complete characterization of the interplay between the complexity of the calibration dataset and estimation accuracy. In particular, we analyze the effect of the number of gaze targets, the number of images used per gaze target and the number of head positions in calibration data using a new NISLGaze dataset, which is well suited for analyzing these effects as it includes more diversity in head positions and orientations for each subject than other datasets. A better understanding of these factors enables low complexity high performance calibration. Our results indicate that using only a single gaze target and single head position is sufficient to achieve high quality calibration. However, it is useful to include variability in head orientation as the subject is gazing at the target. Our proposed estimator based on these studies (GEDDNet) outperforms state-of-the-art methods by more than 6.3%. One of the surprising findings of our work is that the same estimator yields the best performance both with and without calibration. This is convenient, as the estimator works well "straight out of the box," but can be improved if needed by calibration. However, this seems to violate the conventional wisdom that train and test conditions must be matched. To better understand the reasons, we provide a new theoretical analysis that specifies the conditions under which this can be expected. The dataset is available at http://nislgaze.ust.hk. Source code is available at https://github.com/HKUST-NISL/GEDDnet.

Analytical and Experimental Investigation of Electromagnetic Tracking System for Location and Orientation Estimation

This paper presents the development of an electromagnetic (EM) tracking system that consists of a pyramid field generator (PFG) of five excitation coils and a sensing coil for location and orientation estimation. The system measures the induced electromotive forces (EMF) of the sensing coil with the change of the magnetic field contributed by the PFG. The proposed modeling distributed magnetic flux (DMF) method that divides the multi-turns sensing coil into multiple layers and concentric circles for the distributed elements is utilized to determine EMF induced in the sensing coils for investigating the effects of translational and angular displacement. The finite element analysis numerically verifies the DMF method and validates the Artificial Neural Network (ANN)-based estimation models. The calibration and ANN-based estimation process are applied to inversely estimate the location and orientation of the sensing coil based on the DMF method and the experimental EM tracking system. The proposed design and a prototype of the EM tracking system have experimentally verified the DMF method and showed the validity of the location and orientation estimation.

Calibration Ratio Estimators of Population Mean Using Median of Auxiliary Variable

In this study we propose a calibration ratio estimator and a calibration separate ratio-product estimator of population mean of study variable under stratified sampling using the median of auxiliary variable. The calibration estimator used calibrated weight determined to minimize a chi-square distance measure subject to a set of constraint related to the auxiliary variable in other to increase precision of the estimators. The median of the auxiliary variable was used in defining the calibration constraints. The variances of the proposed estimators were also obtained. An empirical study to ascertain the performance of these estimators using simulated data under underlying distribution assumption of Student-T distribution, Cauchy distribution, Lognormal distribution, and Standard normal distribution with varying sample sizes of 10%, 20%, and 25% were carried out. The result of simulation reveals that when the underlying distribution is Student-T, at 10% sample size, the efficiency performance of the proposed calibration separate ratio-product estimator is better than other competing estimators. As the sample size is increased to 20% and 25%, the efficiency performance of the existing stratified ratio estimator and existing calibration ratio estimator respectively become better than the other estimators. Under the skewed distributions (Cauchy and Lognormal) and the standard normal distribution, it is observed that the proposed calibration ratio estimator is better than other competing estimators in terms of efficiency, consistency and reliability. The result also reveals that under the lognormal distribution, the conventional stratified ratio estimator and the conventional calibration ratio estimator give the same result.

ArcOLITIRS: A Toolbox for Radiometric Calibration and Surface Temperature Estimation from Landsat 8 Products in ArcGIS Environment

ArcOLITIRS: A Toolbox for Radiometric Calibration and Surface Temperature Estimation from Landsat 8 Products in ArcGIS Environment