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Vibration measurement is a crucial task in industrial systems, where vibration characteristics reflect health conditions and indicate anomalies of the devices. Previous approaches either work in an intrusive manner or fail to capture the micrometer-level vibrations. In this work, we propose mmVib, a practical approach to measure micrometer-level vibrations with mmWave radar. First, we derive a metric called Vibration Signal-to-Noise Ratio (VSNR) that highlights the directions of reducing measurement errors of tiny vibrations. Then, we introduce the design of mmVib based on the concept of Multi-Signal Consolidation (MSC) for the error reduction and multi-object measurement. We implement a prototype of mmVib, and the experiments show that it achieves 3.946% relative amplitude error and 0.02487% relative frequency error in median. Typically, the average amplitude error is only 3.174um when measuring the 100um-amplitude vibration at around 5 meters. Compared to two existing mmWave-based approaches, mmVib reduces the 80th-percentile amplitude error by 69.21% and 97.99% respectively.

The ability to reduce current and future taxable income with prior years' taxable losses is highly relevant for explaining firms' effective tax rates. Compustat data on the tax loss carryforward (TLCF) are, however, often missing. We propose a method to estimate values for the missing TLCF data instead of the common practice in the literature of imputing zero values. In order to assess the accuracy of our method, we compare our estimated TLCFs with both a random selection of 10-K data and Compustat data for firm-years where Compustat data is available. The results show that our estimated values align very closely with the reported data. We re-analyze two existing studies using these estimated values. With the first, we show that imputing our estimated values instead of zeros leads to a large decrease in measurement error. This reduces the risk that firms with missing data and low effective tax rates are incorrectly classified as tax aggressive. The second re-analysis shows that using our estimated TLCFs leads to economically and statistically different conclusions compared to imputing zeros. Using our estimated values thus increases the probability of correct inferences in studies that use Compustat TLCF data. The estimated values are available from https://doi.org/10.34894/N9J1WE.

In this article, an adaptive sensor fault accommodation scheme is proposed for uncertain vehicle active suspensions via output-feedback control where vehicle body displacement is the only measurable output signal corrupted by sensor bias. An adaptive observer with variable gains is constructed to obtain state estimates whose design procedure involves parameter adaption of the uncertain system parameters and sensor bias, and an output-feedback controller is designed to attenuate the vehicle body displacement based on the partial measurement information, estimates of the states, and unknown parameters. Compensation for measurement error is made both in the design process of the adaptive observer and output-feedback controller in order to weaken the influence brought about by sensor bias fault. In order to guarantee system stability, the variable observer gains are determined in real time using a switching strategy where their values can be modified in finite times by monitoring the state estimates generated by the observer itself. It is proved that the vehicle body displacement will converge to a small neighborhood around zero, and all the signals of the closed-loop system are ensured to be bounded through selecting suitable control parameters. Simulation is carried out to show the effectiveness of the proposed method and results indicate that better stabilization of the suspension vertical motion can be achieved through adaptive compensation for sensor bias.

Aiming at the misdiagnosis problem of the inter-turn short-circuit (ITSC) fault detection method caused by the inverter nonlinearity and the current measurement errors, a d-axis residual voltage-based fault diagnostic strategy is proposed for sensorless control of the surface permanent magnet synchronous motor (SPMSM). The model of the 3-phase wye-connected SPMSM with ITSC fault is established, and the expression of the d-axis residual voltage caused by the ITSC fault, the inverter nonlinearity, the current measurement errors and the motor parameter errors is derived. The proposed fault diagnostic strategy can eliminate the current relative gain coefficient error and locate the ITSC fault by using the 8th and 2nd harmonics of the estimated d-axis residual voltage, respectively. The experimental results verify the effectiveness of the proposed fault diagnostic strategy.

Accurate rotor position estimation is important to ensure the high-performance operation of position sensorless permanent magnet synchronous motor (PMSM) drives. To suppress the estimated rotor position harmonic error (ERPHE) caused by inverter nonlinearities, flux spatial harmonics, and the current measurement error in high-frequency signal injection based schemes, an adaptive iterative learning control based online suppression method is proposed in this paper. This method constructs a P-type iterative learning rotor position observer with the forgetting factor to obtain the tracking error and generate the compensation value in real time. The stability, the convergence, and the parameter sensitivity of this observer are analyzed in detail. To improve the suppression effect of the ERPHE for different loads and speeds, an iterative learning gain self-tuning strategy is investigated. The proposed method does not require the offline detection and the discrimination of harmonic frequencies, which has strong suppression ability for different harmonic error components. The effectiveness is verified by experiments on a 2.2-kW interior PMSM drive platform.

Summary Laryngeal images obtained via high-speed videoendoscopy are an invaluable source of information for the advancement of voice science because they can capture the true cycle-to-cycle vibratory characteristics of the vocal folds in addition to the transient behaviors of the phonatory mechanism, such as onset, offset, and breaks. This information is obtained through relating the spatial and temporal features from acquired images using objective measurements or subjective assessments. While these images are calibrated temporally, a great challenge is the lack of spatial calibration. Recently, a laser-projection system allowing for spatial calibration was developed. However, various sources of optical distortions deviate the images from reflecting the reality. The main purpose of this study was to evaluate the effect of the fiberoptic flexible endoscope distortions on the calibration of images acquired by the laser-projection system. Specifically, it is shown that two sources of nonlinear distortions could deviate captured images from reality. The first distortion stems from the wide-angle lens used in flexible endoscopes. It is shown that endoscopic images have a significantly higher spatial resolution in the center of the field of view than in its periphery. The difference between the two could lead to as high as 26.4% error in calibrated horizontal measurements. The second distortion stems from variation in the imaging angle. It is shown that the disparity between spatial resolution in the center and periphery of endoscopic images increases as the imaging angle deviates from the perpendicular position. Furthermore, it is shown that when the imaging angle varies, the symmetry of the distortion is also affected significantly. The combined distortions could lead to calibrated horizontal measurement errors as high as 65.7%. The implications of the findings on objective measurements and subjective visual assessments are discussed. These findings can contribute to the refinement of the methods for clinical assessment of voice disorders. Considering that the studied phenomena are due to optical principles, the findings of this study, especially those related to the effects of the imaging angle, can provide further insights regarding other endoscopic instruments (eg, distal-chip and rigid endoscopes) and procedures (eg, gastroendoscopy and colonoscopy).

We propose a semiparametric estimator for varying coefficient models when the regressors in the nonparametric components are measured with error. Varying coefficient models are an extension of other popular semiparametric models, including partially linear and nonparametric additive models, and deliver an attractive solution to the curse-of-dimensionality. We use deconvolution kernel estimation in a two-step procedure and show that the estimator is consistent and asymptotically normally distributed. We do not assume that we know the distribution of the measurement error a priori. Instead, we suppose we have access to a repeated measurement of the noisy regressor and present results using the approach of Delaigle, Hall and Meister (2008) and, for cases when the measurement error may be asymmetric, the approach of Li and Vuong (1998) based on Kotlarski’s (1967) identity. We show that the convergence rate of the estimator is significantly reduced when the distribution of the measurement error is assumed unknown and possibly asymmetric. We study the small sample behaviour of our estimator in a simulation study and apply it to a real dataset. In particular, we consider the role of cognitive ability in augmenting the effect of risk preferences on earnings.

Due to the remarkable performance of SiC MOSFET on high temperature, it is promising in the future applications in various of applications. The accurate online junction temperature based on dynamic temperature sensitive electrical parameters (TSEPs) is significant for the protection and condition monitoring, which can prolong or monitor the lifetime of SiC MOSFET devices. In this paper, four different dynamic TSEPs including turn-on delay time, turn-off delay time, maximum current turn-on and turn-off switching rate are theoretical analyzed taking the parasitic parameters into consideration. The experimental analysis of the influence of parasitic parameters dynamic TSEPs are carried on using a buck converter test setup. Based on the theoretical and experiments analysis, a junction temperature correction method is proposed for SiC MOSFET. The online junction temperature monitoring experiments are used to verify the accuracy and effectiveness of the proposed method. The results show that the proposed correction method can largely eliminate the junction temperature monitoring error at different parasitic parameters. The maximum measurement error is reduced from 147 °C to 4.7 °C after correction.

The measurement error evaluation of Power Metering Equipment (PME) is significant for the instrument design and accurate metering of electric energy, especially in extreme environmental stresses. However, actual measurement error prediction is often disturbed by environmental noise and insufficient feature information. To address this problem, an Improved Local Outlier Factor (ILOF) method is first proposed to detect potential outliers. And an optimized distance function and adaptive threshold constraint method based on boxplot are used to improve the outlier detection performance of ILOF. Next, an error prediction method, namely Kernel Support Vector Regression (KSVR), is presented to fuse measurement error and multiple extreme environmental stresses by using the proposed kernel approach. Integrating the ILOF and KSVR, examples from the extreme environmental region demonstrate that the proposed evaluation framework has a higher assessment performance. Compared with several state-of-art prediction methods, our framework has profound outlier identification and error prediction performance under small sample conditions.

This paper presents an online method to monitor the conditions of substation grounding grid. The proposed method utilizes phase-to-ground faults occurring in downstream feeders as test current sources and the neutral of a specially selected feeder as the electrode for GPR (ground potential rise) measurement. The measured fault currents and GPRs are then used to estimate substation grounding grid impedance. The method, once established, can monitor the grounding grid impedance continuously and automatically, and the trend of the impedance change can be observed. As a result, the proposed method can yield much more reliable information on the substation grounding conditions. The method also overcomes many disadvantages of the offline methods. The proposed idea, algorithm, and hardware setup are described in the paper. Factors contributing to measurement errors are investigated in detail.

A computationally efficient state estimation method for lithium-ion (Li-ion) batteries is proposed based on a degradation-conscious high-fidelity electrochemical-thermal model for advanced battery management systems. The computational burden caused by the high-dimensional nonlinear nature of the battery model is effectively eased by adopting an ensemble-based state estimator using the singular evolutive interpolated Kalman filter (SEIKF). Unlike the existing schemes, it shows that the proposed algorithm intrinsically ensures mass conservation without imposing additional constraints, leading to a battery state estimator simple to tune and fast to converge. The model uncertainty caused by battery degradation and the measurement errors are properly addressed by the proposed scheme as it adaptively adjusts the error covariance matrices of the SEIKF. The performance of the proposed adaptive ensemble-based Li-ion battery state estimator is examined by comparing it with some well-established nonlinear estimation techniques that have been used previously for battery electrochemical state estimation, and the results show that excellent performance can be provided in terms of accuracy, computational speed, as well as robustness.

The height and stagger of the contact wire directly affect the energy supply of high-speed trains. To ensure the operation safety, there is an urgent demand for high-speed railways to measure the static parameters of contact wires all over the line with high precision and efficiency. However, this issue is barely discussed. Concerning the issue, this paper proposes a UAV-LiDAR-based measuring framework for the static height and stagger of high-speed railway contact wire. By mounting LiDAR on the UAV, the framework can efficiently collect data from the lines in service without occupying the train operating-diagrams. It is extremely significant for the high-speed and high-density railways. Then, we present self-adaptive extraction algorithms to extract critical infrastructures (rails, contact wires, masts and other suspensions) based on their specific geometric characteristics as well as the continuity and consistency of the spatial distributions along the line. Finally, the height and stagger are calculated by formulas automatically. To verify the framework in practice, we tested it on Beijing-Shanghai high-speed railway, which is the busiest high-speed railway in China. It is shown that the measurement error is within 9mm and the framework has potential to reform the inspection of high-speed railways.

With the increasing penetration of renewable energy, it is important to analyze the impedance characteristic of renewable energy grid-connected system (REGS) to implement the stability analysis of the grid connection operation. In order to further improve the accuracy of the broadband impedance measurement, this paper proposes a design method for the multi-sine signal considering both the signal-to-noise ratios (SNR) of the perturbation signal and the response signal in the impedance measurement. By quantitatively analyzing the mathematical relationship between the SNRs and the measurement error, the principle to select the minimum SNR requirement of the perturbation and response signal can be obtained so as to guide the amplitude design of the perturbation signal, which can further improve the broadband impedance measurement accuracy. The multi-sine signal is adopted in this paper to realize the proposed design method. In the application of the multi-sine signal, in order to limit the peak value of the multi-sine signal to avoid over-modulation, an improved phase design method for the multi-sine signal based on crest factor reduction algorithm is proposed. Finally, experiments based on Typhoon Control-hardware-in-loop platform (CHIL) are carried out to verify the effectiveness of proposed design method for the multi-sine signal in the impedance measurement.

Industrial processes may incur a significant loss in information under unspecified impacts and data errors. Therefore, robust predictors are required to assure the performance. In this paper, we design an H2 optimal finite impulse response H2-OFIR predictor under persistent disturbances, measurement errors, and initial errors. The H2-OFIR predictor is derived by minimizing the squared weighted Frobenius norms for each error. A suboptimal H2 FIR prediction algorithm is obtained using a linear matrix inequality. The H2-OFIR predictive tracker is tested by simulations assuming Markov disturbances and data errors driven by the Gaussian, uniform, and industrial Cauchy heavy-tailed noise. It is shown experimentally that in predictive tracking of a moving robot using the ultra wide band technology the H2-OFIR predictor operating with full error matrices is more robust than the Kalman and unbiased FIR predictors.

Fault-tolerant attitude tracking control for rigid-flexible coupled spacecraft is studied in this paper. Taking actuator faults, measurement errors of attitude and angular velocity, unmeasured modal displacements, model uncertainties, and external disturbances into consideration, an adaptive fault-tolerant attitude tracking control is designed. Then based on the Sequential Lyapunov Method, the stability of the closed-loop system is analyzed showing that, if the attitude and angular velocity measurement errors are bounded, then successively less conservative bounds on the attitude tracking error and the angular velocity error can be obtained.

As an essential component in power applications, magnetic cores and their design play an important role in achieving high efficiency and high power density. Accurate measurement of the core loss is important for inductor and power converter optimization. Loss measurement depends on exactly determining the phase angle between the voltage and the current. However, measurement errors can be introduced due to the discrepancies in propagation delays in the voltage and current sensors. In addition, the propagation delay is frequency dependent, but has a large influence in the MHz range and above. Previously, several methods have been proposed to compensate for this discrepancy but they are time consuming and can result in large measurement errors. In this paper, a characterization method for core loss based on a vector network analyzer and an impedance converter is proposed to accurately measure the phase discrepancies between voltage and current sensors. The proposed method is experimentally verified up to 15 MHz with a three-coil test setup.

In general, target localization in distributed multiple-input multiple-output (MIMO) radar systems requires synchronization among the sensors, which may not be feasible when the sensors are widely separated. In this paper, the target localization problem is addressed under asynchronous distributed MIMO radar systems in the presence of clock and frequency offsets with the aid of a cooperative target, whose position and velocity are known with measurement errors. Specifically, the influence of the cooperative target position and velocity errors on the unknown target localization accuracy is analyzed through the Cramer-Rao lower bound (CRLB) derivation firstly. Then two methods, namely, the offset estimation based localization method and the offset elimination based localization method, are proposed to estimate the unknown target location. The former estimates the clock and frequency offsets firstly and then locates the unknown target based on the offset estimates, and the latter achieves the unknown target location estimate based on the differential measurements of the two targets. Through the theoretical derivation and numerical simulations, the two proposed methods are demonstrated to be approximately unbiased and can attain the CRLB under mild noise. Moreover, the simulation results show that the proposed methods enjoy higher localization accuracy than the state-of-the-art algorithms.

This article considers the practical fixed-time self-triggered consensus tracking problem of delayed multiagent networks (MANs) subject to external disturbances under undirected topology and directed topology. The fixed-time consensus implies that the consensus is reached in a finite time and the convergence time is independent of initial conditions under the nonlinear consensus protocols. A self-triggered control (STC) strategy is developed based on the event-triggered control (ETC) strategy. For the ETC strategy, the nonlinear controllers and the measurement errors are designed based on the hyperbolic tangent function to avoid a nondifferential problem and Zeno behavior. To avoid continuous monitoring, the STC strategy is presented. Furthermore, the minimal interevent interval is strictly positive, which implies that no Zeno behavior occurs in the STC strategy. Finally, a numerical example is presented to verify the availability of the algorithms.

Mobile robots are often subject to multiplicative noise in the target tracking tasks, where the multiplicative measurement noise is correlated with additive measurement noise. In this paper, first, a correlation multiplicative measurement noise model is established. It is able to more accurately represent the measurement error caused by the distance sensor dependence state. Then, the estimated performance mismatch problem of Cubature Kalman Filter (CKF) under multiplicative noise is analyzed. An improved Gaussian filter algorithm is introduced to help obtain the CKF algorithm with correlated multiplicative noise. In practice, the model parameters are unknown or inaccurate, especially the correlation of noise is difficult to obtain, which can lead to a decrease in filtering accuracy or even divergence. To address this, an adaptive CKF algorithm is further provided to achieve reliable state estimation for the unknown noise correlation coefficient and thus the application of the CKF algorithm is extended. Finally, the estimated performance is analyzed theoretically, and the simulation study is conducted to validate the effectiveness of the proposed algorithm.

Background The purpose of the present study was to evaluate the accuracy of the measurements associated with leg alignment on a teleoroentgenogram and an orthoroentgenogram. Methods The models being irradiated were manufactured and represented 10° varus and 15° varus leg alignment, in which the true values of hip-knee-ankle angle (HKAA), mechanical axis (MA) length, and percentage of MA (%MA) were already known. HKAA, MA length, and %MA were measured in various radiographic conditions of the teleoroentgenogram and orthoroentgenogram. Then, the differences between the radiographic measurement values and the corresponding true values were analyzed. Results Regarding HKAA, the teleoroentgenogram provided accurate angular measurements with minimal differences between the measurement and true value in both the 10° and 15° varus models, irrespective of the radiographic condition. In the orthoroentgenogram, the modified method measured accurate HKAA; however, the standard method showed significant angular measurement errors with a 0.6° and 1.0° difference from the true value in the 10° and 15° varus models, respectively. This angular measurement error of HKAA in the standard orthoroentgenogram became significantly larger with object-to-image distance. Regarding MA length, the orthoroentgenogram exhibited accurate length measurements. In contrast, the teleoroentgenogram showed a significant length measurement error for the MA length. Regarding %MA, significant differences from the true values of 2.0% and 2.4% were observed in the modified orthoroentgenograms of the 10° and 15° varus models, respectively. The teleoroentgenogram and standard orthoroentgenogram reproduced the accurate measurement value of %MA in the 10° and 15° varus models. Conclusion A teleoroentgenogram is a reliable modality for accurate angular measurements such as HKAA and %MA. An orthoroentgenogram has the potential to measure both HKAA and length accurately if the radiographic condition was modified; however, measurement error in %MA may occur.

The safety monitoring of lithium-ion batteries (LIBs) is of great significance for realizing all-climate and full-lifespan battery management. In-situ measurement of anode potential with implanted reference electrodes (REs) has proven to be effective to monitor and avoid the occurrence of severe side reactions like Li plating to ensure the safe and fast charging. However, the intrinsic measurement errors caused by local blocking effects, which also can be referred to as potential artefacts, are seldom taken into consideration in existing studies, yet they highly dominate the correctness of conclusions inferred from REs. In this study, aiming at exploring the physical origin of the measurement errors and ensure reliable potential monitoring, electrochemical and post-mortem tests are conducted using commercial pouch cells with implanted REs. Corresponding electrochemical model which describes the blocking effects, is established to validate the abnormal absence of lithium plating that predicted by measured anode potentials under various charging rates. Theoretical derivation is further presented to explain the error sources, which can be attributed to increased local liquid potential of the RE position. Most importantly, with the guidance of error analysis, a novel parameter-independent error correction method for RE measurements is proposed for the first time, which is proven to be adequate to estimate the real anode potentials and deduce the critical C-rate of Li plating with extra safety margin. After error correction, the resulting critical C-rates are all within the range of 0.55 ± 0.03C, which is close to the C-rate of 0.6–0.7C obtained from experiments. In addition, this error correction method can be performed conveniently with only some simple RE measurements of polarization voltages, totally independent of battery electrochemical and geometric parameters. This study provides highly practical error correction method for RE measurements in real LIBs, substantially facilitating the fast diagnosis and safety evaluation of Li plating during charging of LIBs.

Rationale and Objectives A critical performance metric for any quantitative imaging biomarker is its ability to reliably generate similar values on repeat testing. This is known as the repeatability of the biomarker, and it is used to determine the minimum detectable change needed in order to show that a change over time is real change and not just due to measurement error. Test-retest studies are the classic approach for estimating repeatability; however, these studies can be infeasible when the imaging is expensive, time-consuming, invasive, or requires contrast agents. The objective of this study was to develop and test a method for estimating repeatability without a test-retest study. Materials and Methods We present a statistical method for estimating repeatability and testing whether an imaging method meets a specified criterion for repeatability in the absence of a test-retest study. The new method is applicable for the particular situation where a reference standard is available. A Monte Carlo simulation study was conducted to evaluate the performance of the new method. Results The proposed estimator is unbiased, and hypothesis tests with the new estimator have nominal type I error rate and power similar to a test-retest study. We considered the situation where the reference standard provides the true value, as well as when the reference standard itself has various magnitudes of measurement error. An example from CT imaging biomarkers of atherosclerosis illustrates the new method. Conclusion Precision of a QIB can be measured without a test-retest study in the situation where a reference standard is available.

This paper proposes a systematic framework of AC/DC grid state estimation with higher accuracy, computational tractability, and robustness against measurement and control input errors. First, tractable lossy models of bidirectional isolated DC/DC voltage source converters (VSCs), bidirectional DC/AC VSCs, and DC lines for state estimation are developed. Then, the state variables of the DC grid are selected, and measurement equations are derived. Finally, combined with the state estimation model of AC grids, a general framework for the robust AC/DC transmission grid state estimation against both measurement errors and control input errors is presented. Simulation results show that the proposed framework can effectively capture the operating state of AC/DC grid, and can suppress the impact of simultaneous measurement errors and control input errors.

This paper presents a simple and nonintrusive method to identify and compensate for current measurement errors in interior permanent magnet synchronous motor (IPMSM) drives. Each IPMSM has a distinct steady-state short-circuit current characteristic. By controlling the motor to operate at its steady-state short-circuit current during coasting down, the inverter can be used to smoothly switch the IPMSM into three-phase short-circuit state. Then the d-q axis measured currents are compared with the true short-circuit characteristic of the IPMSM. The distortion pattern in the d-q axis currents contains all the needed information to correct for current sensor errors in IPMSM drives. For a three-phase motor, the proposed method can correct for single-current-sensor error or dual-current-sensor error with good accuracy. The effectiveness of the method is validated by both simulation and experiment.

The proliferation of vehicle-to-vehicle (V2V) communication techniques has resulted in collaborative vehicle localization (CVL) approaches that localize a target vehicle by leveraging the state information of nearby vehicles. However, CVL approaches typically require a large search space to locate the real position of a target vehicle and assume small measurement errors of nearby vehicle information, which limit the efficiency and robustness of the existing methods. In this paper, we propose an efficient and error-tolerant CVL approach (referred to as ET-CVL) that increases localization efficiency and accuracy even in the case of large measurement errors of nearby vehicle information. Unlike the existing CVL approaches, our approach prunes the search space for the position of the target vehicle through a pruning-based strategy that considers the relative positions of nearby vehicles. To determine the position of the target vehicle from the search space, we propose a displacement-based selection method to reduce the influence of the measurement errors of nearby vehicle information. The localization accuracy and efficiency of the proposed approach are then evaluated using simulated GPS trajectories in a large road network in New York City. The experimental results show that the proposed approach achieves higher localization efficiency and greater accuracy even with large measurement errors compared with state-of-the-art CVL approaches.

This article designs the static and dynamic event-triggered control (ETC) and self-triggered control (STC) algorithms to achieve the semiglobal stabilization of discrete-time systems with input constraints. First, a novel static ETC algorithm based on the discrete-time parametric Lyapunov equation (DPLE) is designed. In order to further increase the interevent times (IETs), the corresponding dynamic ETC is designed. Next, both static and dynamic STC, where the next control law updates depend on the previous triggered states, are proposed to avoid monitoring the measurement errors. The proposed algorithms are not only capable of reducing the number of transmissions significantly but also build a very simple and clear relationship between the only design parameter and the nontrivial IET (NIET). This allows us to change regularly IETs by adjusting the design parameter so that the nontriviality of static and dynamic ETC and STC is guaranteed and a tradeoff between the IETs and the control performance can be easily found. Specifically, by exploring the properties of DPLE, the designed algorithms avoid the complex relationship between the nontrivial condition and the system matrices. Finally, the designed static and dynamic ETC and STC algorithms are applied to the design of the spacecraft rendezvous control system and their effectiveness is verified by simulation results.

This paper provides sufficient conditions for identification of a nonparametric regression model with an unobserved continuous regressor subject to nonclassical measurement error. The measurement error may be directly correlated with the latent regressor in the model. Our identification strategy does not require the availability of additional data information, such as a secondary measurement, an instrumental variable, or an auxiliary sample. Our main assumptions for nonparametric identification include monotonicity of the regression function, independence of the regression error, and completeness of the measurement error distribution. We also propose a sieve maximum likelihood estimator and investigate its finite sample property through Monte Carlo simulations.

PURPOSE To compare the accuracy of printed to digital casts produced from various intraoral and extraoral scanners with different scanning technologies. MATERIALS AND METHODS A conventional stone cast was fabricated from the reference typodont cast and scanned with two intraoral scanners (TRIOS 3 version 1.4.7.5, and Dental Wings version 2.1.0.421), and two extraoral scanners (S600 Arti, Zirkonzahn, and Ceramill map 600, Amann Girrbach GmbH). All digital scans were saved in the form of STL files and measurements were calculated using Geomagic analysis software. Two types of measurements were assessed on the casts: tooth- and arch-level measurements. Absolute errors were calculated by subtracting the measurements on 3D-printed, digital, and conventional stone casts from the measurements on the reference typodont cast. One-way ANOVA was used for comparing different measurement errors between groups. Linear regression was performed to determine the association between different explanatory variables, and the average measurement errors (dependent variable) adjusted to reference cast measurements. Regression coefficients (B) and 95% confidence intervals (CI) were calculated. RESULTS For both 3D printed, and digital casts, Dental Wings showed significantly greater error compared to other scanners and to the conventional stone cast at all measurements except AL (in the 3D printed modality only), while conventional casts showed the lowest error. Error was significantly higher in intraoral than extraoral scanners (B = 0.009, 95% CI = 0.005, 0.02), and in arch level measurements than tooth level measurements (B = 0.03, 95% CI = 0.01, 0.04), and significantly lower in 3D printed than digital casts (B = -0.04, 95% CI = -0.05, -0.04). There were no statistically significant differences between measurement errors of both arches (maxillary and mandibular arches). CONCLUSIONS Extraoral scanners showed higher accuracy than intraoral scanners, and 3D-printed casts showed higher accuracy than their digital counterparts. Dental Wings scanner had the greatest measurement error. This article is protected by copyright. All rights reserved.

We address the phase retrieval problem with errors in the sensing vectors. A number of recent methods for phase retrieval are based on least squares (LS) formulations which assume errors in the quadratic measurements. We extend this approach to handle errors in the sensing vectors by adopting the total least squares (TLS) framework that is used in linear inverse problems with operator errors. We show how gradient descent and the specific geometry of the phase retrieval problem can be used to obtain a simple and efficient TLS solution. Additionally, we derive the gradients of the TLS and LS solutions with respect to the sensing vectors and measurements which enables us to calculate the solution errors. By analyzing these error expressions we determine conditions under which each method should outperform the other. We run simulations to demonstrate that our method can lead to more accurate solutions. We further demonstrate the effectiveness of our approach by performing phase retrieval experiments on real optical hardware which naturally contains both sensing vector and measurement errors.

Power inversion algorithms (PI) are widely used by ant-jamming receivers in harsh environment, but it also induces pseudo-range measurement error which is fatal to high precision receivers. In this paper, it proposes a more accurate method based on the S Curve Bias(SCB) to evaluate the pseudo-range bias which can actually affect the positioning results, as it excludes a fixed time delay contained in the directly differing method. When using this method to assess the bias, it is found that pseudo-range errors can be influenced by many factors including the type of interference. Simulation as well as experiment based on a 4-element array antenna are designed and carried out. The simulation has shown that both SOP-PI and STAP-PI will bring bias to pseudo-range measurement. Specifically, the maximum bias caused by SOP-PI is 1m while the bias caused by STAP-PI is up to 10m. Moreover, comparing to the method which directly differs the pseudo-range of output signal and the reference signal,the method proposed in this paper has better performance. Furthermore, the experimental results verify the effectiveness of the proposed evaluating method.

The wireless electromagnetic method (WEM) can be used to detect resources at the distances of up to 10 km underground, monitor and forecast earthquakes and fires, and study ionosphere and space weather. Based on the extremely low frequency (ELF) provided by a high-power artificial source, WEM has the advantages of strong anti-interference ability, stable signal, small measurement error, large transmitting dipole moment, and wide signal coverage. To apply the WEM to geophysical electromagnetic exploration and improve the reliability of calculation results, we study the analytical solution of the electromagnetic field in a spherical multilayer ``earth-ionosphere'' model, considering the displacement current in the air layer and the polarization and conductivity of the underground medium. We used Pade approximation to accelerate the numerical convergence and compare with the previous analytical and numerical results to verify the accuracy of our method. The characteristics of electromagnetic fields in different frequencies of the ``earth-ionosphere'' three-layer model excited by a vertical electric dipole and the influence of the earth's conductivity on electromagnetic fields are simulated. The reasonable range of air conductivity is discussed, providing a basis for other numerical calculation methods. In contrast to the conventional electromagnetic detection methods, the electromagnetic field will vibrate in the ELF range. With decreasing underground conductivity, the electromagnetic field at the surface will gradually weaken, and the resonance phenomenon will gradually disappear, indicating that this method can be applied for the detection of underground conductivity and will further improve the detection accuracy of WEM.

Hypothesis Interactions between droplets are of fundamental importance for understanding phenomena involving droplet collision and coalescence that determine multiphase flow behavior. The quantitative understanding of these interactions is essential for the manipulation and control of emulsions or complex fluids. The existing methods for interaction force determination are typically based on expensive mechanical probes and fine distance control. Therefore, further development of new techniques for interaction force determination is expected to be beneficial for research on surface force. Experiments In this study, droplet deformation during the interaction between two droplets was captured and analyzed to determine the interaction force. The approach speed of the two droplets was controlled by the injection rate of the fluid. The dynamic interaction force between two tetradecane droplets in various aqueous solutions was determined using the newly developed method, and the effects of two-phase physical properties and operating conditions on the measurement errors were investigated. Findings The droplet profile deformation was first applied as a probe to detect the interaction force. The measurement results were in good agreement with those obtained using the precise weighing sensor of a commercial interfacial tensiometer (K100, Kruss, Germany). The newly developed method was reliable, simple, and did not require the use of expensive devices. Furthermore, droplet deformability was found to be the key parameter in determining the total interaction force between the droplets.

Silicon carbide mixed electrical discharge machining (SCM-EDM), an unconventional machining process that utilizes an electrolyte mix additive, combines the advantage of various energies (abrasive, heat). The aim of these articles is to investigate the impact of control variables on the machining performance of SCM-EDM using LM-25/SiC MMC. The major variable of SCM-EDM is identified as current intensity, pulse duration. The experimental run has conducted as per Box- Behnken design which produces statistical data solely based on the experiment run and estimates the measurement error. Artificial neural network (ANN) technique implemented to estimate experimental inputs and modelling of measure response such as material deletion rate (MRR), tool wear (TW), and surface roughness (SR). It is observed that predicted results from ANN model are compared with experimental result are quite satisfactory. The chemical composition and analysis of variance show the percentage contribution of each parameter on machining performance.

Linear-array multiple-input-multiple-output (LA-MIMO) synthetic aperture radar (SAR) can obtain 3-D radar images by only one pass. However, it is sensitive to time-variant measurement errors of curved track and time-variant attitude angles, meaning that autofocus processing for the LA-MIMO SAR tomography is necessary. The existing autofocus methods cannot be used to estimate the time-variant and 3-D space-variant motion errors (3-D SVME) of the LA-MIMO SAR. To solve this problem, a new autofocus approach based on multiple local autofocusing and the LA-MIMO SAR time-variant motion error estimation is proposed. First, the local motion error estimation based on the fast local spectral analysis (SPECAN) 3-D imaging and the maximum contrast optimization 2-D local autofocusing is performed to estimate the local time-variant motion errors. Then, based on the linear-array motion error model, the time-variant 3-D trajectory deviations of the array center and attitude angles are estimated by the weighted least square estimation (WLSE) to solve the 3-D SVMEs. Last, the 3-D fast factorized backprojection (FFBP) is performed to obtain the well-focused 3-D image of the whole beam. The proposed approach has been applied for the tomography of a new crawler-type unmanned-ground-vehicle (UGV) LA-MIMO SAR. Both the simulation and real data experiments verify the effectiveness of the proposed approach.

Six-axis force/torque sensors are transducers capable of measuring three dimensions of force and three dimensions of torque, and have a wide range of applications in robotic systems. The sensing principle behind the greatest majority of these is based on detecting strains in an elastic structure, and there are multiple modalities for strain measurement, including resistive strain gauge, piezoelectric, capacitive, and optoelectronic techniques. Developed sensors can be evaluated based on a range of parameters, including decoupling error, nonlinearity error, and measurement isotropy, where the primary design challenge is to maximise the allowable sensing range while minimising measurement errors. This literature review describes the state of the art of the field, and includes all relevant research associated with the search term "six-axis force/torque sensor" spanning 2015 - June 2021. The published literature for each sensing technique is summarised and evaluated, and a generalised sensor selection framework is proposed to assist the reader with the selection procedure for a state of the art sensor. In the period considered, there have been no significant improvements with the sensors documented in terms of measurement errors. Researchers have instead focused on features such as fault tolerance, alternative sensing techniques, cost reduction, and miniaturisation. These trends are expected to continue, and moving forward, the most significant challenge in the field is expected to be the lowering of manufacturing costs, as trends indicate measurement performance has reached maturity.

A novel photonics-based technique of microwave frequency measurement (MFM) is proposed and verified. The method is implemented by introducing a parameter called Brillouin phase-gain ratio (BPGR) which combines Brillouin phase shift and Brillouin gain, where BPGR features a linear response to the unknown signal frequency. Moreover, it enables to enlarge the channel bandwidth of frequency measurement relative to the method of applying only the phase shift spectrum slope or gain spectrum slope, and the important problem of pump-power-dependency affecting system performance can be alleviated. The experiment is established to verify its feasibility and the results show that this method is competent for frequency measurement with very high accuracy. In the end, within two frequency bands of linear slope 0.12-0.17 and 10.12-10.17 GHz, the measurement error below 5 MHz is successfully achieved.

Detailed measurements of changes in aeolian landscapes help researchers better understand aeolian processes and monitor the effects of climate change in these systems. This study focuses on a photographic method to measure sand surface changes. It introduces custom erosion pins (photoscale pins) designed to allow proper scaling of measurements taken from digital images or high resolution (gigapixel) panoramas. Furthermore, this study discusses errors associated with measuring photoscale pins photographically. Resolution errors arise when marking object boundaries on digital images. These typically affect measurement precision, but even so, differences between repeated measurements of the same pin average 0.58 mm. Perspective errors arise when pins are tilted relative to the camera’s line of sight and introduce bias, affecting measurement accuracy. Photographic measurement can allow remote surveillance, reduce site disturbance, increase the frequency of observation, and reduce measurement error compared to manual measurement of erosion pins. A study of sand movement in a dune complex on the southeastern shoreline of Lake Michigan provides an example of the method’s application. Photoscale pin measurements that bracket a wind event associated with Hurricane Sandy show average surface elevation changes of 3.97 cm/pin. Regional wind data, pin measurements, and surface changes recorded in gigapixel panoramas suggest that vegetation baffling, recirculation eddies, and topographic steering played roles in sand deposition and erosion at the site. Photographic measurement of erosion pins can complement wind instrumentation and other tools as aeolian research increasingly incorporates multiple techniques to study surface changes in greater detail both in time and space.

To better characterize the off-axis performance of fiber reinforced composite materials, a general model is developed in this paper. One coefficient was added to this model to help control the shape, and it could be calculated by corresponding off-axis performance data at any angle. After demonstrating the influence of the coefficients and material anisotropic characteristics on the shape for the proposed model, the collected off-axis performance data of fiber reinforced composite materials (e.g., wood, engineered bamboo products, and fiber/epoxy composites) were used for proving the validity of the model. Furthermore, three optimization methods were presented to improve the accuracy of the model to allow for material characteristics and measurement errors. The results showed that the general model could accurately capture the off-axis performance characteristics of anisotropic fiber reinforced composite materials.

In this paper, a novel method for real-time pressure measurement based on rapid phase demodulation of multi-cavities Fabry-Perot (F-P) sensor is proposed. In the demodulation system, according to the principle of low-coherence interference, birefringent crystals, polarizers and analyzers are used to obtain orthogonal signals, which can significantly reduce the influence of irrelevant F-P cavities on the demodulation result, indicating that the method is suitable for rapid demodulation of F-P sensors containing multiple cavities. Experimental results also confirm that the method is able to demodulate the signal of F-P MEMS sensor in real-time. By using the four-quadrant inverse tangent operation instead of differential cross multiplier algorithm to calculate the phase of the F-P sensor, pressure in the range of 110 kPa -200 kPa is measured in real-time, with the demodulation rate of 5 kHz. The measurement errors are less than 0.41 kPa, and the standard deviations are less than 0.25 kPa. The proposed method provides a new idea for the rapid demodulation of F-P sensors containing multiple cavities, and has great potential in promotion and applications of real-time pressure measurement, such as dynamic pressure of aero-engines, shock waves and so on.