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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.)

A measurement method and system based on conventional radars has been proposed to address the problem of radar cross-section (RCS) measurement of ship targets in open seas. Calibration errors for RCS measurement on ground plane range (GPR) are analysed, and the error elimination method and accurate RCS calculation formula are given. An RCS measurement system is designed by improving conventional radars. These systems measure two Luneburg lens reflectors of different sizes on the lake. The average RCS of the big Luneburg lens reflector is obtained, and the error is calculated to be 2.73% compared with its actual value, which verifies the rationality of the proposed measurement method and system. The system measures the RCS of a ship in the open sea and obtains its circumferential RCS distribution. The effect of the error in the ship's position calculation on the RCS measurement results is analysed, and the uncertainty range of the error is obtained.

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]

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.

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]

Coprime arrays have been widely adopted for direction-of-arrival (DOA) estimation since it can achieve an increased number of degrees of freedom (DOF). To utilize all information received by the coprime array, array interpolation methods are developed, which construct a virtual uniform linear array (ULA) with the same aperture from the non-uniform coprime array. However, the conventional non-robust DOA estimation algorithms for coprime arrays, including the interpolation based methods, suffer from degraded performance or even failed operation when some sensors are miscalibrated. In this paper, a novel maximum correntropy criterion (MCC) based virtual array interpolation algorithm for robust DOA estimation is developed to address this problem. The proposed approach treats the miscalibrated sensor observations as outliers, and by exploiting the property of MCC, the interpolated virtual array covariance matrix is reconstructed via nuclear norm minimization (NNM) with less influence of these outliers. In this manner, the robust DOA estimation is enabled by the robustly reconstructed covariance matrix. Simulation results demonstrate that the proposed algorithm can effectively the mitigate effect of the miscalibrated sensors while maintaining the enhanced DOF offered by coprime arrays.

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]

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, Switzerland) 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.

Publishing manuscripts is the only way by which scientists communicate with each other. In recent times, there is an increasing desire to publish manuscripts from the developing world for a variety of reasons. Though, performing a research study is challenging in itself, writing it for publication is the final frontier that can be daunting, especially for the novice. Work that remains unpublished in one form or the other is essentially incomplete or undone. Hence, it is critically important for one to publish one′s findings in a reputed journal. The purpose of this paper is to alleviate the mystique involved in manuscript writing and provide a blueprint where the subheadings given under each of the sections of introduction, methods, results and discussion can be expanded as per the particular study and the manuscript can be constructed in a stepwise manner. We hope that by following this approach, potential researchers and practicing ophthalmologists will develop the skill and aptitude for medical writing, and that the developing world shall do justice to its tremendous intellectual capital by making meaningful contributions to global scientific literature.

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.

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.

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.)

We report a case of a 2-month-old baby with bilateral nonsyndromic partial cryptophthalmos presenting with upper eyelid incomplete development and fusion to the cornea with resultant inability to close the eyes. He was managed successfully with bilateral upper lid reconstruction with composite graft using maternal skin and oral mucous membrane, amniotic membrane, and donor scleral graft. After this one stage surgery, lids were well-formed, and the patient was able to close both eyes, thus achieving good anatomical, functional, and cosmetic outcome.

Intravitreal application of Ozurdex ® (Allergan, Inc., Irvine, CA, USA) which is a biodegradable, sustained-release dexamethasone implant has been reported to be effective in the treatment of macular edema. Migration of such implant into the anterior chamber has been recently described in cases without perfect zonular or the posterior capsular integrity. Herein, we report the first case with anterior migration of Ozurdex ® implant that mislocated just behind the intraocular lens (IOL) in an intact capsular bag. It is thought that such implant migrated anteriorly towards into the posterior chamber through weak zonules as the present case had a medical history of uneventful phacoemulsification surgery with the implantation of posterior chamber IOL. However, the migrated implant was well tolerated since there was no sign of the corneal complication, rise in intraocular pressure, and anterior chamber reaction. Close follow-up was scheduled to find out any signs of anterior segment pathology. Meanwhile dexamethasone implant completely degraded at the 4 th month of postoperative follow-up.

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]

The analytical calibration of an instrumental method is very important, being considered as a key point in method validation. There are different validation guidelines; showing that analytical calibration process variety prevails in terms of nomenclature, methodology employed and acceptance criteria. Very common mistakes in the analytical calibration process are the use of correlation and/or determination coefficients as a test for linearity, the negligence in the heteroscedasticity of the experimental data and selection of appropriate weighting factor, misunderstanding about the regression through the origin and using zero-point calibration. Once the calibration function is established, their linearity can be confirmed by using different procedures such as graphical plots, statistical significance tests and numerical parameters. In particular, deviation from back-calculated concentrations expressed in the form of percentage of relative error ( %RE ) can be considered very useful for unambiguous linearity evaluation. Some case studies were included to explain the linearity assessment from a practical viewpoint. [ABSTRACT FROM AUTHOR]