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Aiming at the shortcomings of the existing machine vision pose measurement technology, a pose measurement method based on monocular vision and a cooperative target is proposed. A planar target designed with circles and rings as the main body is dedicated to object pose measurement, and a feature point coordinate extraction and sorting algorithm is designed for this target to effectively extract image features on the target. The RANSAC algorithm and topology-based fitting for the intersection method are used to optimise data processing, further improving the accuracy of feature point coordinate extraction and ultimately achieving high-precision measurement of object poses. The experimental results show that the measurement accuracy of the roll angle perpendicular to the optical axis can reach 0.02°, and the repeatability can reach 0.0004° after removing the systematic error; the measurement accuracy of the pitch angle can reach 0.03°, and the repeatability can go to 0.002° after removing the systematic error. The measurement range of the pitch angle is [−30°, +30°]; the measurement range of the roll angle is [−179°, +179°]. The experimental results show that the system has high measurement accuracy and meets the requirements of high-precision measurement. [ABSTRACT FROM AUTHOR]

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Deep learning has been recently studied to generate high-quality prediction intervals (PIs) for uncertainty quantification in regression tasks, including recent applications in simulation metamodeling. The high-quality criterion requires PIs to be as narrow as possible, whilst maintaining a pre-specified level of data (marginal) coverage. However, most existing works for high-quality PIs lack accurate information on conditional coverage, which may cause unreliable predictions if it is significantly smaller than the marginal coverage. To address this problem, we propose an end-to-end framework which could output high-quality PIs and simultaneously provide their conditional coverage estimation. In doing so, we design a new loss function that is both easy-to-implement and theoretically justified via an exponential concentration bound. Our evaluation on real-world benchmark datasets and synthetic examples shows that our approach not only achieves competitive results on high-quality PIs in terms of average PI width, but also accurately estimates conditional coverage information that is useful in assessing model uncertainty. [ABSTRACT FROM AUTHOR]

As industrial camera become smarter, due to the increase in supporting algorithms, so the quality of data produced offers higher precision and accuracy of industrial inspection operations. This paper aims to focus on the accurate effect of camera focus on a calibration process under the influence of different sensor exposure and degrees of focus. A computer vision methodology is used to determine the effects of different sensor exposures and at different degrees of camera focus. Endocentric and telecentric lenses are used to acquire images, and a comparative analysis was achieved using the least square method. A sample of 2176 images was used to generate the population for analysis. The analysis showed that the human eye cannot visually determine a hundred percent focus of an image. This means that it is difficult to determine when an image is 100% focused from when it is 95% focused. The result from the experiment showed that as the camera focus tends towards 100% the calibration error decreases, with a minimum calibration error of 0.06 pixels, leading to the conclusion that the developed method achieved a calibration error accuracy with the most effective camera sensor exposure and the camera focus. [ABSTRACT FROM AUTHOR]

Calibration error is one of the primary sources of bias in echo intensity measurements by ground-based radar systems. Calibration errors cause data discontinuity between adjacent radars and reduce the effectiveness of the radar system. The Global Precipitation Measurement Kuband Precipitation Radar (GPM KuPR) has been shown to provide stable long-term observations. In this study, GPM KuPR observations were converted to S-band approximations, which were then matched spatially and temporally with ground-based radar observations. The measurements of stratiform precipitation below the melting layer collected by the KuPR during Typhoon Ampil were compared with those of multiple radar systems in the Yangtze River Delta to determine the deviations in the echo intensity between the KuPR and the ground-based radar systems. The echo intensity data collected by the ground-based radar systems was corrected using the KuPR observations as reference, and the correction results were verified by comparing them with rain gauge observations. It was found that after the correction, the consistency of the echo intensity measurements of the multiple radar systems improved significantly, and the precipitation estimates based on the revised ground-based radar observations were closer to the rain gauge measurements. [ABSTRACT FROM AUTHOR]

The Minimum-Cost Bounded-Error Calibration Tree problem (MBCT) is a wireless network optimization problem that arises from the sensors' need of periodical calibration. The MBCT takes into account two objectives. The first is to minimize the communication distance between the network sensors, while the second is to reduce the maximum post-calibration error in the network. In this paper, we propose a mathematical formulation for the MBCT. Furthermore, we solve the problem using two different implementations of an Augmented ϵ -Constraint Method ( a u g ϵ - C M ) that incorporate the proposed formulation. We also employed the Pareto-fronts obtained by a u g ϵ - C M to evaluate the Node-depth Phylogenetic-based Non-dominated Sorting Artificial Immune System (NPE-NSAIS), the most recent heuristic in the literature for MBCT. Computational experiments showed that a u g ϵ - C M can solve MBCT instances up to 50 nodes. Furthermore, a statistical test demonstrated that the running times of one of the a u g ϵ - C M implementations was significantly smaller than those of the other. Finally, we show that NPE-NSAIS solutions are very close to the Pareto-fronts given by a u g ϵ - C M , achieving good results on the evaluated metrics. [ABSTRACT FROM AUTHOR]

Summary: This research is part of a larger effort to better understand and quantify the epistemic model uncertainty in dynamic response‐history simulations. This paper focuses on how calibration methods influence model uncertainty. Structural models in earthquake engineering are typically built up from independently calibrated component models. During component calibration, engineers often use experimental component response under quasi‐static loading to find parameters that minimize the error in structural response under dynamic loading. Since the calibration and the simulation environments are different, if a calibration method wants to provide optimal parameters for simulation, it has to focus on features of the component response that are important from the perspective of global structural behavior. Relevance describes how efficiently a calibration method can focus on such important features. A framework of virtual experiments and a methodology is proposed to evaluate the influence of calibration relevance on model error in simulations. The evaluation is demonstrated through a case study with buckling‐restrained braced frames (BRBF). Two calibration methods are compared in the case study. The first, highly relevant calibration method is based on stiffness and hardening characteristics of braces; the second, less relevant calibration method is based on the axial force response of braces. The highly relevant calibration method consistently identified the preferable parameter sets. In contrast, the less relevant calibration method showed poor to mediocre performance. The framework and methodology presented here are not limited to BRBF. They have the potential to facilitate and systematize the improvement of component‐model calibration methods for any structural system. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper introduces a new automatic drilling and riveting system for aircraft panel assembly which is composed of two five-axis machines in coordination. Kinematic modeling and base frame calibration are fundamental problems for the dual-machine system to improve its positioning accuracy. In this paper, a comprehensive kinematic model for the dual-machine system is established based on the modularization concept, and a new base frame calibration method is proposed which consists of the direct calibration based on laser tracker and calibration error compensation based on a specially designed instrument. In the kinematic modeling stage, the kinematic model of the dual-machine system is divided into three parts and three corresponding sub-models are established. With the pose constraints of coordinated machines, a comprehensive kinematic model of the dual-machine system is built. In the base frame calibration stage, the base frames of dual machines are firstly established by laser tracker. Due to many error sources in the calibration process, the relative pose errors of dual machines exist with the relationship between the base frames of coordinated machines. Then, the calibration error is measured by a specially designed instrument and used to modify the primary base frame calibration results. To verify the validity of the proposed method, experiments have been performed and the results have shown great improvement in base frame calibration accuracy. With the advantage of easy operation and high accuracy, this method can be extended to other multi-machine and multi-robot systems. [ABSTRACT FROM AUTHOR]

In this letter, we propose a robust direction-of-arrival (DOA) estimation algorithm in the context of sparse reconstruction, where some array sensors are miscalibrated. In this case, conventional DOA estimation algorithms suffer from degraded performance or even failed operations. In the proposed approach, the miscalibrated sensor observations are treated as outliers, and a weighting factor is adaptively optimized and applied to each sensor in order to effectively mitigate the effect of the outliers. An algorithm based on the maximum correntropy criterion is then developed to yield robust DOA estimation. The simulation results are presented to verify the effectiveness and superiority of the proposed approach compared with conventional DOA estimation algorithms. [ABSTRACT FROM AUTHOR]

Continuous glucose monitors (CGMs) are prone to inaccuracy due to time lags, sensor drift, calibration errors, and measurement noise. The aim of this study is to derive the model of the error of the second generation Medtronic Paradigm Veo Enlite (ENL) sensor and compare it with the Dexcom SEVEN PLUS (7P), G4 PLATINUM (G4P), and advanced G4 for Artificial Pancreas studies (G4AP) systems. An enhanced methodology to a previously employed technique was utilized to dissect the sensor error into several components. The dataset used included 37 inpatient sessions in 10 subjects with type 1 diabetes (T1D), in which CGMs were worn in parallel and blood glucose (BG) samples were analyzed every 15 ± 5 min Calibration error and sensor drift of the ENL sensor was best described by a linear relationship related to the gain and offset. The mean time lag estimated by the model is 9.4 ± 6.5 min. The overall average mean absolute relative difference (MARD) of the ENL sensor was 11.68 ± 5.07% Calibration error had the highest contribution to total error in the ENL sensor. This was also reported in the 7P, G4P, and G4AP. The model of the ENL sensor error will be useful to test the in silico performance of CGM-based applications, i.e., the artificial pancreas, employing this kind of sensor. [ABSTRACT FROM AUTHOR]

Copyright of Piezoelectrics & Acoustooptics is the property of Piezoelectric & Acoustooptic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

OBJECTIVES Noninvasive brachial systolic blood pressure (nSBP-B) usually approaches invasive central systolic blood pressure (iSBP-C) with a high correlation. Whether nSBP-B is an accurate estimate of iSBP-C remained to be investigated. Thus, this study aimed to compare the errors of nSBP-B and noninvasive central systolic blood pressure (nSBP-C) with different techniques in estimating iSBP-C. METHODS Simultaneous invasive high-fidelity central aortic pressure waveforms and the noninvasive left brachial pulse volume recording (PVR) waveform were recorded in a Generation group (N = 40) and a Validation group (N = 100). The accuracy of the noninvasive estimates of iSBP-C obtained from analysis of the calibrated PVR waveform using the generalized transfer function (GTF), pulse waveform analysis (PWA), and N-point moving average (NPMA) methods was examined in the Validation group by calculating the mean absolute error (MAE). RESULTS In Generation group, the MAE was 4.6±4.1mm Hg between nSBP-B and invasive brachial SBP, and 6.8±5.5mm Hg between nSBP-B and iSBP-C. In comparison, the MAE of between iSBP-C and nSBP-C with PWA, NPMA, and GTF were 5.5±4.5, 5.8±4.9, and 5.9±5.0mm Hg, respectively. In Validation group, the MAE of nSBP-B (6.9±4.6mm Hg) for estimating iSBP-C was significantly greater than that of PWA (5.0±3.4mm Hg) and NPMA (6.1±4.4mm Hg), and GTF (6.1±4.9mm Hg). The percentage of absolute band error ≤5mm Hg was 62% for nSBP-B, 69% for GTF, 83% for PWA, and 69% for NPMA. CONCLUSIONS The accuracy of nSBP-B was inferior to the n SBP-C measures in estimating iSBP-C. [ABSTRACT FROM AUTHOR]

Purpose: Goldmann applanation tonometer (GAT) is the current Gold standard tonometer. However, its calibration error is common and can go unnoticed in clinics. Its company repair has limitations. The purpose of this report is to describe a self-taught technique of rectifying calibration error of GAT. Materials and Methods: Twenty-nine slit-lamp-mounted Haag-Streit Goldmann tonometers (Model AT 900 C/M; Haag-Streit, Switeerland) were included in this cross-sectional interventional pilot study. The technique of rectification of calibration error of the tonometer involved cleaning and lubrication of the instrument followed by alignment of weights when lubrication alone didn't suffice. We followed the South East Asia Glaucoma Interest Group's definition of calibration error tolerance (acceptable GAT calibration error within ±2, ±3 and ±4 mm Hg at the 0, 20 and 60-mm Hg testing levels, respectively). Results: Twelve out of 29 (41.3%) GATs were out of calibration. The range of positive and negative calibration error at the clinically most important 20-mm Hg testing level was 0.5 to 20 mm Hg and -0.5 to -18 mm Hg, respectively. Cleaning and lubrication alone sufficed to rectify calibration error of 11 (91.6%) faulty instruments. Only one (8.3%) faulty GAT required alignment of the counter-weight. Conclusions: Rectification of calibration error of GAT is possible in-house. Cleaning and lubrication of GAT can be carried out even by eye care professionals and may suffice to rectify calibration error in the majority of faulty instruments. Such an exercise may drastically reduce the downtime of the Gold standard tonometer. [ABSTRACT FROM AUTHOR]

Simple functions of radar backscatter coefficients have been proposed as indices of soil moisture and vegetation, such as the radar vegetation index, i.e., RVI, and the soil saturation index, i.e., ms. These indices are ratios of noisy and potentially miscalibrated radar measurements and are therefore particularly susceptible to estimation errors. In this study, we consider uncertainty in satellite estimates of RVI and ms arising from two radar error sources: noise and miscalibration. We derive expressions for the variance and bias in estimates of RVI and ms due to noise. We also derive expressions for the sensitivity of RVI and ms to calibration errors. We use one year (September 1, 2011 to August 31, 2012) of Aquarius scatterometer observations at three polarizations ( σHH, σVV, and σHV) to map predicted error estimates globally, using parameters relevant to the National Aeronautics and Space Administration Soil Moisture Active and Passive satellite mission. We find that RVI is particularly vulnerable to errors in the calibration offset term over lightly vegetated regions, resulting in overestimates of RVI in some arid regions. ms is most sensitive to calibration errors over regions where the dynamic range of the backscatter coefficient is small, including deserts and forests. Noise induces biases in both indices, but they are negligible in both cases; however, it also induces variance, which is large for highly vegetated regions (for RVI) and areas with low dynamic range in backscatter values (for ms). We find that, with appropriate temporal and spatial averaging, noise errors in both indices can be reduced to acceptable levels. Areas sensitive to calibration errors will require masking. [ABSTRACT FROM PUBLISHER]

In recognition of the evolving body of knowledge in the accounting profession, the American Institute of Certified Public Accountants (AICPA 2010) highlights the importance of viewing learning as a lifelong process that requires selfawareness and extends beyond the academic setting. Metacognition, the assessment and regulation of one's own learning, is a crucial element in lifelong learning. We draw upon judgment of learning research and introduce mindset theory to explore the relationship among (1) exam performance, (2) calibration error, measured as expected minus actual exam scores, and (3) mindset, a person's basic beliefs about learning and ability (Dweck 2000, 2006) in the accounting classroom. We find strong evidence that exam performance is inversely related to calibration error (Kruger and Dunning 1999). We also find modest evidence that a growth mindset is associated with improved performance and decreased calibration error. While the mindset results were not entirely consistent with prior research in educational psychology, we explore possible reasons and future directions for accounting education research. [ABSTRACT FROM AUTHOR]

This paper presents an research of the length comparator's warm sources surrounding temperature fields. Comparator operates in dynamic automatic mode, so, it contains electric and electronic devices, which get warm over time and radiate this warm. Every heat source creates its own temperature field, which affects calibration conditions. Priori information is insufficient for establishing theoretical model of this process, it was therefore necessary to carry out experimental research. A set of temperature sensors was used for measuring the temperatures of every heat source and near field temperatures. After data logging the thermal graphs were created, analyzed temperatures variation in calibration mode and estimated the weight of every heat source. Finally the presumptions how to minimize influence of heat sources to calibration error were discussed. [ABSTRACT FROM AUTHOR]

Purely rotation-based self-calibration receives the most attention among various self-calibration methods, owing to its algorithmic simplicity. However, it is actually impossible to ensure that the camera motion for this kind of self-calibration is a pure rotation, since the rotation center should--but unfortunately may never--completely coincide with the optical center with an unknown position. Almost all conventional pure rotation methods tend to ignore any translation, so the significant errors of calibration results could be inevitably introduced. We propose a practical and effective approach to improve the purely rotation-based self-calibration approach. The proposed approach accounts for translational offsets. According to the fact that the rotational angles between camera orientations have a very strong impact on the calibration errors from the translations, we compute the relative camera orientations prior to calibrating, then use different and very appropriate strategies for self-calibration in different angle circumstances, to achieve better calibration results. Both synthetic data and real data are used to test the proposed approach and very good results are obtained. Comparing the experimental results with those of conventional methods, the proposed approach significantly yields higher accuracy, and therefore reduces the calibration errors by the translations. [ABSTRACT FROM AUTHOR]

Methods are described of calibrating measurement transducers. [ABSTRACT FROM AUTHOR]

We propose and demonstrate a method that uses four-wave mixing to estimate the error in the measurement of optical power in an optical spectrum analyzer. We detail the underlying theory, measurement technique and measurement automation for the estimation of the optical power-calibration error. The experimental results of the estimation of the power-calibration error of an optical spectrum analyzer are presented. [ABSTRACT FROM AUTHOR]

Factory-calibrated continuous glucose monitoring (FC-CGM) sensors are new devices used in type 1 diabetes (T1D) therapy to measure the glucose concentration almost continuously for 10–14 days without requiring any in vivo calibration. Understanding and modelling CGM errors is important when designing new tools for T1D therapy. Available literature CGM error models are not suitable to describe the FC-CGM sensor error, since their domain of validity is limited to 12-h time windows, i.e., the time between two consecutive in vivo calibrations. The aim of this paper is to develop a model of the error of FC-CGM sensors. The dataset used contains 79 FC-CGM traces collected by the Dexcom G6 sensor. The model is designed to dissect the error into its three main components: effect of plasma-interstitium kinetics, calibration error, and random measurement noise. The main novelties are the model extension to cover the entire sensor lifetime and the use of a new single-step identification procedure. The final error model, which combines a first-order linear dynamic model to describe plasma-interstitium kinetics, a second-order polynomial model to describe calibration error, and an autoregressive model to describe measurement noise, proved to be suitable to describe FC-CGM sensor errors, in particular improving the estimation of the physiological time-delay. [ABSTRACT FROM AUTHOR]

Coprime array with M + N sensors can achieve an increased degrees-of-freedom (DOF) of O (M N) for direction-of-arrival (DOA) estimation. Utilizing the compressive sensing (CS)-based DOA estimation methods, the increased DOF offered by the coprime array can be fully exploited. However, when some sensors in the array are miscalibrated, these DOA estimation methods suffer from degraded performance or even failed operation. Besides, the key to the success of CS-based DOA estimation is that every target falls on the predefined grid. Thus, a coarse grid may cause the mismatch problem, whereas a fine grid requires great computational cost. In this paper, a robust CS-based DOA estimation algorithm is proposed for coprime array with miscalibrated sensors. In the proposed algorithm, signals received by the miscalibrated sensors are viewed as outliers, and correntropy is introduced as the similarity measurement to distinguish these outliers. Incorporated with maximum correntropy criterion (MCC), an iterative sparse reconstruction-based algorithm is then developed to give the DOA estimation while mitigating the influence of the outliers. A multiresolution grid refinement strategy is also incorporated to reconcile the contradiction between computational cost and the mismatch problem. The numerical simulation results verify the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

To improve the accuracy of calibration of C-arm, and overcome the space limitation in surgery, we proposed a new calibrator for online calibration of C-arm. After the image rectification by a polynomial fitting-based global correction method, the C-arm was assumed as an ideal pinhole model. The relationships between two kinds of spatial calibration errors and the distribution of fiducial points were studied: the performance of FRE (Fiducial Registration Error) and TRE (Target Registration Error) were not consistent, but both were best at the 12 marked points; the TRE decreased with the increase of the uniformity of calibration points distribution, and with the decrease of the distance between the target point and the center of calibration points. A calibrator with 12 fiducial points conical helically distributed, which could be placed on the knee, was an attractive option. A total of 10 experiments on C-arm calibration accuracy were conducted and the mean value of mapping error was 0.41 mm. We designed an ACL reconstruction navigation system and carried out specimen experiments on 4 pairs of dry femur and tibia. The mean accuracy of navigation system was 0.85 mm, which is important to the tunnel positioning for ACL reconstruction. [ABSTRACT FROM AUTHOR]

Aim: Assess current practice and views regarding checking Goldmann applanation tonometers for calibration errors in the United Kingdom. Design: Questionnaire survey. Methods: A total of 100 ophthalmology residents from England, Wales, and Scotland attending the 2004 Congress of the Royal College of Ophthalmologists, UK, responded to a structured questionnaire. They were asked the following: how often they used different tonometers between clinical sessions; how often they checked their tonometers for errors; and who they felt was responsible for checking tonometers for calibration errors. Results: All respondents were using a different Goldmann tonometer for each clinic: 85% never check tonometers for errors; only 7% perform checks at the start of each clinical session; and 8% would only check the tonometer for calibration errors if they had suspicious or unexpected measurements. A total of 70% of respondents felt that calibration checks are not part of their responsibility. They believe that either nursing staff or other hospital staff should carry out calibration checks and ensure that tonometers are accurate. The remaining 30% felt that calibration checks should be carried out by the doctor using the tonometer. Conclusion: Despite evidence that Goldmann tonometers lose accuracy during routine use in clinical practice, only a minority are checking the tonometers for calibration errors. There is no consensus as to who should be responsible for ensuring that tonometer calibration is maintained. We recommend that tonometers should be checked for calibration errors at least on a monthly basis by individuals identified by departmental protocols. [ABSTRACT FROM AUTHOR]