Large Measurement Errors Research Articles

Psychometric properties of the Chinese version of Everyday Cognition Scale (ECog) in Taiwanese older adults.

The Everyday Cognition Scale (ECog) is widely employed for the subjective rating of functional activities of daily living in older adults. This study aimed to examine the psychometric properties of both informant-rated and self-rated ECog in Taiwanese older individuals to assess its applicability in this context. A total of 1166 subjects, including older adults (n = 583) and their primary caregivers or family members (n = 583), were recruited. Both self-rated and informant-rated ECog assessments were administered twice within a one-month period. The study evaluated internal consistency, test-retest reliability, random measurement error, discriminative validity, construct validity, and concurrent validity using the Mini-Mental State Examination (MMSE) as a gold standard. The informant-rated ECog demonstrated high internal consistency, acceptable to good test-retest reliability, small to moderate random measurement error, good discriminative validity, and moderate correlations with the MMSE. Additionally, a seven-factor model of the informant-rated ECog was supported. Conversely, the self-rated ECog exhibited high internal consistency, acceptable test-retest reliability, moderate to large random measurement error, adequate discriminative validity, and small correlations with the MMSE. Notably, only the domains of memory, verbal, planning and organization were supported by a one-factor model. The psychometric properties of the informant-rated ECog surpassed those of the self-rated ECog. However, the self-rated ECog is recommended as a supplementary tool for assessing individuals' awareness of their cognitive function, particularly in the domain of memory.

Large errors in soil carbon measurements attributed to inconsistent sample processing

Large errors in soil carbon measurements attributed to inconsistent sample processing

GPC-LIVO: Point-wise LiDAR-inertial-visual odometry with geometric and photometric composite measurement model

In the pursuit of precision within Simultaneous Localization and Mapping (SLAM), multi-sensor fusion emerges as a validated strategy with vast potential in robotics applications. This work presents GPC-LIVO, an accurate and robust LiDAR-Inertial-Visual Odometry system that integrates geometric and photometric information into one composite measurement model with point-wise updating architecture. GPC-LIVO constructs a belief factor model to assign different weights on geometric and photometric observations in the measurement model and adopts an adaptive error-state Kalman filter state estimation back-end to dynamically estimate the covariance of two observations. Since LiDAR points have larger measurement errors at endpoints and edges, we only fuse photometric information for LiDAR planar features and propose a corresponding validation method based on the associated image plane. Comprehensive experimentation is conducted on GPC-LIVO, encompassing both publicly available data sequences and data collected from our bespoke hardware setup. The results conclusively establish the better performance of our proposed system compare to other state-of-art odometry frameworks, and demonstrate its ability to operate effectively in various challenging environmental conditions. GPC-LIVO outputs states estimation at a high frequency(1-5 kHz, varying based on the processed LiDAR points in a frame) and achieves comparable time consumption for real-time running.

A data-driven intelligent learning algorithm for simultaneous prediction of aerodynamic heat and thermo-physical property parameters

It is of great importance and challenging to simultaneously determine time-varying aerodynamic heat and temperature-dependent thermo-physical property parameters with high accuracy, for the optimization of thermal protection systems of hypersonic vehicles. However, it is difficult to directly measure these parameters under high temperature conditions. It is an effective way to determine thermo-physical property parameters and aerodynamic heat by solving inverse problems, based on measurable or easily measured transient temperatures. However, the prediction error of these parameters may be too large, if the measurement error is large, due to the thermal inertia. To deal with this issue, an intelligent algorithm is proposed to simultaneously predict the aerodynamic heat and thermo-physical property parameters for the thermal protection systems of hypersonic vehicles, based on the temperature measurement information. It combines a genetic algorithm and a machine learning algorithm, and the genetic algorithm is employed to update the relevant parameters in the neural network. By training the neural network, the relationship among the predicted parameters and transient temperatures could be established. Thereafter, the aerodynamic heat subjected to the outer surface of the aircraft and the temperature-dependent non-linear thermo-physical property parameters could be predicted. Examples are given to verify the present algorithm. The results show that this work provides an accurate and efficient method for simultaneously determining the aerodynamic heat and thermo-physical property parameters for the thermal protection system of a hypersonic vehicle. The prediction errors of aerodynamic heat and thermo-physical property parameters are much smaller than the measurement errors, when there are relatively large measurement errors in the input data.

Suppression of cross-interference in the absorption spectra of gas mixtures

In the quantitative analysis of mixed gases by tunable diode laser absorption spectroscopy, the overlapping of absorption spectra and mutual interference of multi-component gases can lead to problems of large measurement errors and low analysis accuracy. In this paper, an improved firefly algorithm is proposed and applied to the support vector machine regression model to solve this problem. The specific method includes introducing an adaptive step size to balance the local and global searches and using the gradient descent method to accelerate the parameter optimization process so as to improve the model’s generalization ability and prediction accuracy. The experimental results show that the maximum errors of the improved algorithm in the prediction of CH4 and CO gas concentrations are no more than 0.0443 % and 2 ppm, with coefficients of determination, R2, of 0.9994 and 0.99815. The promising results obtained by the system provide theoretical support for the realization of high-precision detection of multicomponent gases with a single source of light, and also demonstrate the high efficiency and feasibility of the method in practical detection.

The New Method for Ground Insulation Monitoring of the Secondary Circuit in Substations

When two-or-more-point grounding faults occur in the secondary circuit of a substation, they will lead to large data measurement errors in the protection, measurement, and control devices, which can easily cause protection malfunction. At present, there is a lack of an online panoramic monitoring system for the secondary circuits of substations, and it is difficult to accurately identify the faults in these secondary circuits. In order to solve this problem, this paper proposes a cloud–edge collaboration framework for the power Internet of Things (IoT), which can accurately identify faults by monitoring the secondary circuit of power grid substations with panoramic online status, reducing maintenance costs and improving overall operational efficiency. Firstly, the collection requirements of current transformer (CT) and potential transformer (PT) fault characteristics in the secondary circuit of the substation are analyzed. The platform architecture of the secondary loop panoramic monitoring system of the cloud-side cooperative substation is proposed, along with the algorithm and data wireless acquisition scheme of the edge computing, cloud computing, and cloud-side system. Furthermore, the edge computing platform and the current and voltage wireless sensors are developed. The maximum error in the accuracy test of the voltage sensor is 1.9180%, while the maximum error in the accuracy test of the current sensor is 1.3406%.

Suppression of Rayleigh fading induced errors in φOTDR by different pulse widths for improving the reliability of civil infrastructure monitoring

Rayleigh fading is a widely observed phenomenon in the many fields, such as wireless communication and optical imaging. It is also the main factor limiting the performance of the phase-sensitive optical time domain reflectometry (φOTDR). The low SNR at the fading points results in a large measurement error, severely affecting the reliability of civil infrastructure monitoring. The proposed method involves changing the pulse width during measurements to suppress the impact of fading. Experimental result shows that the number of fading points is greatly reduced by ∼ 96 % and the measurement error is reduced by more than 5 times. Unlike existing methods, this approach requires no hardware modifications, making it applicable to almost all current phase-based φOTDR systems. The versatility and effectiveness of this method make it an excellent candidate for infrastructure monitoring and related fields.

A robust iterative algorithm for SINS/USBL integrated navigation based on dual hydrophone differential model

Aiming at the complex underwater environment which leads to large measurement errors and outliers in ultrashort baseline, a robust iterative Kalman filtering algorithm based on the differential model of dual hydrophones is proposed in this paper. Firstly, to achieve accurate navigation under large initial misalignment angles, the algorithm builds an inertial navigation error equation based on the right invariant error definition of Lie groups. Then a dual hydrophone differential model is proposed based on SINS/USBL tightly coupled. It successfully improves the accuracy of integrated navigation and significantly mitigating the issue of SINS/USBL positioning accuracy degradation caused by USBL slant distance noise that grows with distance. Finally, a robust iterative Kalman filter based on the dual hydrophone differential model is proposed to counter the threat to the vehicle’s safe operation and the accuracy loss caused by outliers. The algorithm’s superiority and effectiveness are verified through simulation and sea trial.

Accurate surface profile measurement using CMM without estimating tip correction vectors

Detailed measurement of the curved surface of an industrial product with a radius of curvature of less than a few millimeters is a challenging task for tactile coordinate measuring machines. To estimate a surface profile, tip radius correction is typically performed by estimating the tip correction vector direction. However, a substantial measurement error is introduced by the error in estimating the tip correction vector direction under measurement conditions such as a large position measurement error of an indicated measured point or a short sampling interval, In this study, a method that can estimate a surface profile by calculating the envelope of a probe tip path was proposed. The proposed method was experimentally and numerically confirmed to be able to estimate surface profiles with sub-micrometer accuracy under such measurement conditions.

Improving large angle flow measurement accuracy of five-hole probe using novel calibration coefficient and accuracy progressive neural network

Novel calibration coefficients and data-fitting techniques based on a two-stage accuracy progressive neural network were developed to improve the accuracy of a five-hole probe in measuring large-angle flows. By modifying the denominator and numerator of traditional calibration coefficients, the novel coefficients can solve the singularity and multi-value problems of large-angle flow measurement. By training the neural network using both global calibration data and the calibration data of large measurement error points, the two-stage accuracy progressive neural network can effectively improve the measurement accuracy of a five-hole probe when flow separation occurs around the probe head at large flow angles. The experimental results demonstrate that applying the novel calibration coefficients and accuracy progressive neural network ensures that the calibration error of the flow angle is less than 0.8°, and the flow pressure error is less than 0.1 % when the flow angle reaches 50° at low subsonic speeds.

Accurate Ultrasonic Thickness Measurement for Arbitrary Time-Variant Thermal Profile.

Ultrasonic thickness measurement of mechanical structures is one of the most popular and commonly used nondestructive methods for various kinds of process control and corrosion monitoring. With ultrasonic propagation speed being temperature-dependent, the thickness measurement can be performed reliably only when the thermal profile is completely known. Most conventional techniques assume the temperature of the test structure is uniform and at room temperature across its thickness. Such assumptions may lead to large errors in the thickness measurement, especially when there are significant temperature variations across the thickness. State-of-the-art techniques use external temperature measurements or implement iterative methods to compensate for the unknown thermal profiles. However, such techniques produce unsatisfactory results when the heat distribution is complex or varies rapidly with time. In this work, we propose a two-sensors technique, using both compressive and shear excitations, with a non-iterative rapid data processing method for accurate thickness measurement under arbitrary time-variant thermal profile. The independent behavior of shear and compressive waves is used to formulate a real-time thickness estimation technique. The developed technique is experimentally validated on a steel plate with fixed acoustic sensors. Test results show that the error in thickness estimation can be reduced by up to 98% compared to conventional thickness gauging methods.

Impact of surface conductivity on the zeta potential determination of concentrated aqueous polymer dispersions using electroacoustics and electrokinetic standard models

Surface conductivity can have a significant impact on the determination of the zeta potential, but it is normally not accounted for when applying the Helmholtz-Smoluchowski or Henry models. In this study, we investigate concentrated polymer dispersions using electroacoustics and both standard models. We also pay particular attention to the influence of surface conductivity, which is characterized by conductivity measurements of the dispersion and dispersion medium. The Dukhin number as a measure of surface conductivity is calculated according to Maxwell–Wagner-O’Konski theory. Zeta potentials were determined by means of colloid vibration current (CVI) and electrophoretic light scattering (ELS) methods. It has been found that neglecting surface conductivity in standard electrokinetic models can lead to large measurement errors of up to 100% with increasing particle volume fraction. In this study, the surface conductivity is now correctly taken into account by using the conductivities of the dispersion and the dispersion medium. Alternatively, this influence can also be considered using the Dukhin number. The zeta potentials resulting from the CVI measurement are then in excellent agreement with ELS reference measurements over a wide volume fraction range.Graphical

Dynamometer card generation for pumping units based on CNN and electrical parameters

In the actual production process of oil fields, the real-time and accurate acquisition of dynamic recorder data from the pumping unit is of great significance for the diagnosis of well failures. The traditional method of obtaining the card of the dynamometer usually includes installing a load sensor on the auxiliary head of the pumping unit. However, due to the harsh environment of the oil field production site, these load sensors often suffer from damage, distortion, and aging, resulting in large measurement errors and low reliability. This paper proposes a mixed model of pumping based on motor electrical parameter data and CNN convolutional neural network, which has good consistency with actual data in terms of predictive performance. Thus, the highlights of this paper can be summed up in two points: (1) Based on the mathematical model of the AC motor, the speed of the motor and the torque output of the motor are accurately estimated. (2) The convolutional neural network is introduced to compensate for the errors caused by the defects of the pumping unit mechanism model.

Comparisons between In-Check DIAL® and PF810® in evaluation and training inspiratory capacity for using dry powder inhalers

BackgroundDry powder inhalers (DPIs) rely on both internal resistance and patients’ inspiratory capacity for effective operation. Optimal inspiratory technique is crucial for DPI users. This study assessed the accuracy and repeatability of two available devices, PF810® and In-Check DIAL®, and analyzed their measurement errors and consistency in detecting inspiratory capacity.MethodsThe accuracy and repeatability of peak inspiratory flow (PIF) and forced inspiratory vital capacity (FIVC) against various internal resistances of the two devices were assessed using standard waveforms generated by a breathing simulator. The agreement of PIF measurements between the two devices in healthy volunteers and chronic obstructive pulmonary disease (COPD) patients was analyzed with the intraclass correlation coefficient and Bland–Altman graphical analysis.ResultsPF810® showed great accuracy and repeatability in measuring PIF, except for square waveforms at the lowest flow rate (20 L/min). In-Check DIAL® exhibited poor accuracy against high resistance levels. In scenarios with no resistance, In-Check DIAL® had significantly smaller measurement errors than PF810®, but larger errors against high resistance levels. The two devices showed excellent agreement (ICC > 0.80, P < 0.05), except for healthy volunteers against medium to high resistance (R3-R5) where the ICC was insignificant. Bland–Altman plots indicated small disagreements between the two devices for both healthy volunteers and COPD patients.ConclusionsIn-Check DIAL® exhibited poor accuracy and larger measurement errors than PF810® when detecting PIFs against higher internal resistances. However, its good performance against lower internal resistances, along with its cost-effectiveness and convenience made it appropriate for primary care. PF810® showed good accuracy and repeatability and could detect additional parameters of inspiratory capacity beyond PIF, though required further studies to confirm its clinical benefits.

Evaluation of the errors of measuring residual stresses by the hole drilling method

A sample configuration with a specified distribution of residual stresses in depth was developed to elaborate modes for studying residual stresses using mechanical and physical methods. The sample formation technique is based on non-uniform plastic deformation of an aluminum beam of rectangular cross section according to the pure bending scheme. The control of the deformed state during loading was performed on the end surface of the sample by the field of normal deformations obtained using a digital image correlation system. The depth of the plastically deformed layer was 1.3 mm. The theoretical distribution of residual stresses obtained as a result of unloading of a plastically deformed sample was determined from the results of a numerical calculation of a finite element model, with allowance for the physical and mechanical characteristics, elastic-plastic hardening, as well as the deformation curve in true coordinates obtained as a result of uniaxial tension on elementary samples. A study of residual stresses inhomogeneous in depth was carried out by drilling holes in accordance with ASTM E837 on two opposite sides of the sample: the region of tension under loading and the area of compression, respectively. The control of the deformation response resulted from drilling was recorded using three-axis strain gauge rosettes. The results of the actual measurement of the longitudinal component of residual stresses were compared with their theoretical distribution obtained by a numerical method. The root-mean-square error in measuring residual stresses, relative to their theoretical distribution, for an aluminum alloy sample reaches 18.7 MPa. It should be noted that largest measurement errors were recorded at small depths, since they are characterized by small values of deformations comparable to the value of shot noise.

A model of regional housing markets in England and Wales

PurposeWe propose an economic model of housing markets. The model incorporates the macroeconomic relationships between prices, demand and supply. Since vacancy rates are not observable, the demand-supply mismatches are identified using a microeconomic model of search, matching and price formation. The model is applied to data on regional housing markets in England and Wales.Design/methodology/approachEconomic theory combining macroeconomics and microeconomics together with new generation econometric methods for empirical analysis.FindingsThe empirical model, estimated for the ten government office regions of England and Wales, validates the economic model. We find that there is substantial heterogeneity across the regions, which is useful in informing housing and land-use policies. In addition to heterogeneity, the model enables us to better understand unrestricted inter-regional spatial relationships. The estimated spatial autocorrelations imply different drivers of spatial diffusion in different regions.Research limitations/implicationsIn the nature of other empirical work, the findings are subject to specificities of the data considered here. The understanding of spatial diffusion can also be further developed in future work.Practical implicationsThis paper develops a nice way of closing macroeconomic models of housing markets when complete demand, supply and pricing data are not available. The model may also be useful when data are available but with large measurement errors. The model comes together with corresponding empirical methods.Social implicationsImplications for the housing market and other regional policies are important. These are context-specific, but some implications for housing policy in the UK are provided in the paper as an example.Originality/valueUnique housing market paper combining both macroeconomic and microeconomic theory as well as both theory and empirics. The rich framework so developed can be extended to much future work.

A single-axis force sensor based on a 3D-printed elastic ring

Force measurement is indispensable in vast engineering applications but conventional force sensors usually encounter large measurement errors or high costs. To address this problem, we develop a force sensor based on a 3D-printed elastic ring structure, which measures the external force by utilizing the displacement variation of the elastic ring. The nonlinear theory based on the minimum potential energy principle is proposed to describe the nonlinear relationship between the external force and the ring top displacement. The feasibility of the proposed force sensor scheme is validated through FEM simulations and experiments. The range of the force sensor can be adjusted by tuning the geometric parameters and the material parameters of the elastic ring, while the force sensor shows good measurement error at different measurement ranges. This force sensor possesses simple fabrication and low cost, showing good potential for various applications such as industrial automation, mechanical testing, and medical devices.

Detecting New Visual Binaries in Gaia DR3 with Gaia and Two Micron All Sky Survey (2MASS) Photometry. II. Speckle Observations of 16 Low-separation Systems

Here we present speckle observations of 16 low-separation (s < 30 au) high-probability candidate binaries from the catalog by Medan et al., where secondaries typically lack astrometric solutions in Gaia. From these speckle observations, we find a second component is always detected within the field of view. To determine if the detection is consistent with a physical companion or a chance alignment with a background source, we utilize a statistic from Tokovinin & Kiyaeva that compares the apparent motion of the systems to the expected orbital motion ( μ′ ). Using simulated binary orbits, we construct likelihood distributions of μ′ assuming various total errors on the measurements. With the hypothesis that the system is a true binary, we show that large measurement errors can result in μ′ values higher than expected for bound systems. Using simulated chance alignments, we also create similar likelihoods to test this alternative hypothesis. By combining likelihoods of both true binaries and chance alignments, we find that 15 of the 16 candidates are physical systems regardless of the level of measurement error. Our findings also accommodate all 16 as physical systems if the average, relative measurement error on the binary separations and position angles is ∼4.3%, which is consistent with our knowledge of the Gaia and Gemini speckle pipelines. Importantly, beyond assessing the likelihood of a true binary versus chance alignment, this quantitative assessment of the true average measurement error will allow more robust error estimates of mass determinations from short separation binaries with Gaia and/or Gemini speckle data.

High-speed and high-precision measurement of biaxial in-plane displacements: tens of nanometers principle error suppression in microprobe sensors

When the microprobe sensor is faced with the demand of high-speed biaxial displacement measurement, due to the characteristics of phase generated carrier (PGC) technology, accompanying optical intensity modulation (AOIM) and unfavorable phase modulation depth (PMD) will bring about the tens of nanometer cyclic nonlinear errors, further hindering high-speed and high-precision measurement. Herein, a light source intensity stabilization system based on semiconductor optical amplifier (SOA) feedback control is achieved to eliminate the error caused by AOIM in the presence of high-frequency and large-amplitude laser modulation. Based on this, the reasons for large nonlinear errors in biaxial measurements and the inability to ensure the stability of the accuracy of multiple measurement axes are methodically examined, and an effective nonlinear error elimination methodology based on the normalized amplitude correction of active temperature scanning is proposed. The continuity and linearity of the temperature scanning are also discussed. The performed experiments show that the above approach is capable of reducing the displacement demodulation error from the nanometer scale to the sub-nanometer scale. Further, the nonlinear error is reduced to within 0.1 nm for both measurement axes and the performance becomes consistent. The dual-axis measurement resolution of the microprobe sensor reaches 0.4 nm and the measurement speed is better than 1.2 m/s with the standard deviation of lower than 0.5 nm.

Earthquake forecasting from paleoseismic records

Forecasting large earthquakes along active faults is of critical importance for seismic hazard assessment. Statistical models of recurrence intervals based on compilations of paleoseismic data provide a forecasting tool. Here we compare five models and use Bayesian model-averaging to produce time-dependent, probabilistic forecasts of large earthquakes along 93 fault segments worldwide. This approach allows better use of the measurement errors associated with paleoseismic records and accounts for the uncertainty around model choice. Our results indicate that although the majority of fault segments (65/93) in the catalogue favour a single best model, 28 benefit from a model-averaging approach. We provide earthquake rupture probabilities for the next 50 years and forecast the occurrence times of the next rupture for all the fault segments. Our findings suggest that there is no universal model for large earthquake recurrence, and an ensemble forecasting approach is desirable when dealing with paleoseismic records with few data points and large measurement errors.