Error Reduction Method Research Articles

GNSS location error reduction method for microtremor survey system based on EMD-CNN-LSTM

Abstract Large errors exist when the microtremor survey system uses the global navigation satellite system (GNSS) for static localization. Aiming at the problem that the existing methods cannot effectively weaken the random error and multipath error, an error weakening method based on Empirical Mode Decomposition (EMD), Convolutional Neural Networks (CNN), and Long Short-term Memory Networks (LSTM) is proposed. The model first uses EMD to decompose the high-frequency random error, then reconstructs the low-frequency component and extracts the local features using CNN, and finally learns the change rule of multipath error using LSTM and weakens it. The model can remove random errors in the early stage while reducing the interference of noise on the neural network in the later stage and then improve the accuracy of localization. The experimental results show that the model can effectively improve the localization accuracy in the case of short-time measurements so that the localization accuracy in the E, N, and U directions can be improved by 74.57%, 74.76%, and 71.86%, respectively, which is more than 10% higher than the localization accuracy improvement rate of the existing CNN-LSTM model.

Development of multi-sensors fusion monitoring system for shaft wall deformation

Ensuring shaft safety is crucial for underground coal mining. The prevailing method for monitoring shaft deformation involves manual measurements conducted regularly, necessitating numerous surveyors to descend into the shaft. This approach is laborious and time-consuming, and its reliance on human intervention significantly affects accuracy. An emerging alternative employs an enhanced laser slam scanner comprising laser ranging and inertial navigation angle measurement modules. The accuracy of the inertial navigation angle often requires refinement through laser point cloud registration to mitigate errors. However, existing research has not explored the utilization of a single inertial navigation sensor within a shaft, which potentially influences measurement outcomes. To investigate the impact of inertial navigation angle measurement errors from lidar scanners on shaft interior measurements and propose corresponding error reduction methods, this study developed a multisensor fusion system for monitoring shaft wall deformations. Three experiments were conducted to assess the accuracy of this monitoring system: a laboratory experiment, a simulated experiment, and a real shaft experiment. The results demonstrated that the maximum mean absolute errors (MAEs) in the laboratory and simulated shaft experiments were 0.06 cm and 0.30 cm, respectively, validating the reliability of the point cloud processing algorithm. In the real shaft experiment, the maximum MAE was 0.48 cm, confirming the measurement accuracy of the monitoring system. Compared to manual measurements, the system reduced the measurement time by 48% and streamlined the process from 12 to 3 steps. These findings highlight the monitoring system’s centimeter-level accuracy, efficiency in reducing measurement time, simplification of the measurement process, and elimination of the need for human entry into the shaft.

Potential errors during final inspection and certification process of an aircraft component

PurposeAccording to regulations, aircraft must be in an airworthy condition before they can be operated. To ensure airworthiness, they must be maintained by an approved component maintenance organisation. This study is aimed to identify potential errors that may arise during the final inspection and certification process of aircraft components, categorise them, determine their consequences and quantify the associated risks. Any removed aircraft components must be sent to an approved aircraft component maintenance organisation for further maintenance and issuance of European Union Aviation Safety Agency (EASA) Form 1. Thereafter, a final inspection and certification process must be conducted by certifying staff to receive an EASA Form 1. This process is crucial because any errors during this stage can result in the installation of unsafe components in an aircraft.Design/methodology/approachThe Systematic Human Error Reduction and Prediction Approach (SHERPA) method was used to identify potential errors. This method involved a review of the procedures of three maintenance organisations, individual interviews with ten subject matter experts and a consensus group of 14 certifying staff from different maintenance organisations to achieve the desired results.FindingsIn this study, 39 potential errors were identified during the final inspection and certification process. Furthermore, analysis revealed that 48.7% of these issues were attributed to checking errors, making it the most common type of error observed.Originality/valueThis study pinpoints the potential errors in the final inspection and certification of aircraft components. It offers maintenance organisations a roadmap to assess procedures, implement preventive measures and reduce the likelihood of these errors.

Error mechanism analysis and accuracy enhancement method for circular gratings angle-measuring system

In order to raise the accuracy of the circular gratings angle-measuring system, a comprehensive error model is established, and a novel compensation method is proposed. Firstly, many error sources, including the rotary errors of axis system, the installation eccentricity and the inclination of the circular grating disc, the graduation line error of the circular grating disc, and the installation error of the multiple readingheads of the high precision circular gratings angle-measuring system are comprehensively taken into consideration. Secondly, the position and attitude errors are propagated to the angular position outputs, thus the mathematical relationships between error sources and the angular position error are established. Aiming at multiple readingheads system, the mechanism of influence of the error sources on the angular position error is unveiled, and the mechanical error identification and reduction method is proposed. After that, a checking system with a 23-mirrored polygon and an autocollimator is set up, and the complete combinatorial method of separating the angular position errors from the readouts of the autocollimator precisely is used. Finally, the angular position error compositions are separated by the harmonic analysis method, and the concerned error coefficients are compensated. The experimental results show that the nominal indication interval value of the angular position errors is reduced to 0.23" by the mechanical error reduction method, and then the nominal indication interval value of the angular position errors is reduced to 0.12" by software measurement method. And the two sets’ repeatability of the separated biases of the 23-mirrored polygon before and after compensating is 95%.

Comparative Study on Predicting Topography from Gravity Anomaly and Gravity Gradient Anomaly

Owing to the dependence of algorithms on the measurement of ship soundings and geophysical parameters, the accuracy and coverage of topography still need to be improved. Previous studies have mostly predicted topography using gravity or gravity gradient, However, there is a relative lack of integrated research combining or comparing gravity and gravity gradient. In this study, we develop observation equations to predict topography based on vertical gravity anomalies (VG; also called gravity anomalies) and vertical gravity gradient (VGG) anomalies generated by a rectangular prism. The sources of interference are divided into medium- to high-frequency errors and low-frequency errors, and these new methods reduce these errors through regularization and error equations. We also use numerical simulations to test the efficiency of the algorithm and error-reduction method. Statistics show that VGG anomalies are more sensitive to topographic fluctuations; however, the linear correlation between VG anomalies and topography is stronger. Additionally, we use the EIGEN-6C4 model of VG and VGG anomalies to predict topography in shallow and deep-sea areas, with maximum depths of 2 km and 5 km, respectively. In the shallow and deep-sea areas, the root mean square (RMS) errors of VGG anomalies prediction are 93.8 m and 233.8 m, and the corresponding accuracies improved by 7.3% and 2.3% compared with those of VG anomaly prediction, respectively. Furthermore, we use cubic spline interpolation to fuse ship soundings and improve the accuracy of the final topography results. We develop a novel analytical algorithm by constructing an observation equation system applicable to VG and VGG anomalies. This will provide new insights and directions to refine topography prediction based on VG and VGG anomalies.

Analysis and compensation for the dominant tool error in ultra-precision diamond ball-end milling

Diamond ball-end milling is an enabling technology to fabricate complex curved surfaces with a nanometer level roughness. However, limited by the errors of tool manufacturing and installation, the cutting edge profile of a single-bladed diamond milling tool cannot form an ideal sphere crown in rotation, which will significantly worsen the machining accuracy. Although a titled milling method and a tool error reduction method based on milling-test have been proposed to improve the machining accuracy, a complete analysis of the effects of multiple tool errors on the machining accuracy have not been performed. Therefore, the present work contributes an analytical model to accurately calculate the tool rotation profile under various position and orientation errors. The results reveal that the horizontal off-center error of tool cutting edge is the dominant error among all the tool errors. According to the results, a novel high precision identification process is developed to evaluate the horizontal off-center error in view of the cutting-mark, and subsequently a tool center shift method is proposed to compensate for such error. Finally, experiments on millimeter scale spherical surface, micro sphere array and micro sinusoidal surface confirm that the proposed tool error compensation method can significantly improve the form accuracy. The average form errors (PV) of three test surfaces are reduced from 2.29 μm, 0.90 μm and 0.88 μm to 0.61 μm, 0.28 μm and 0.18 μm, respectively.

Nonlinear error reduction for phase-shifting profilometry considering periodicity and symmetry of a phase histogram.

Phase-shifting profilometry is extensively utilized for three-dimensional (3D) measurement. However, because of gamma nonlinearity, the image intensities of the captured fringe patterns are regrettably distorted. An effective nonlinear error reduction method without requiring parameter estimation is presented in this paper. Differing from the traditional whole-period phase histogram equalization (PHE) method, our method takes into account not only the periodicity but also the symmetry of the phase histogram. Taking a three-step phase-shifting algorithm as an example, the phase error frequency triples the fringe frequency; thus, we first propose a 1/3-period PHE method. Moreover, since the phase error distribution is sinusoidal with symmetry, we further propose a 1/6-period PHE method. Simulations and experiments both indicate that the 1/6-period PHE method, compared with the whole-period PHE and 1/3-period PHE methods, can further reduce the nonlinear error.

BDS Dual-Frequency Carrier Phase Multipath Hemispherical Map Model and Its Application in Real-Time Deformation Monitoring.

The BDS multipath delay error is highly related to the surrounding monitoring environment, which cannot be eliminated or mitigated by applying the double difference observation model. In the actual monitoring environment, due to the complexity of the BDS constellation, it is difficult for existing algorithms to consider GEO, IGSO, MEO and other different orbital types of satellites for real-time and efficient multipath error reduction. Therefore, we propose a novel BDS dual-frequency multipath error reduction method for real deformation monitoring for BDS considering various satellite orbit types. This method extracts the single error residual of each satellite based on the assumption of "zero mean" and divides the appropriate grid density of GEO and IGSO/MEO, respectively, to construct a dual-frequency multipath hemispherical map model suitable for BDS satellites with different orbital types. This method can realize the multipath error elimination of the observed values of different orbits and different frequencies. The results of simulation experiments and real deformation monitoring data demonstrate that this method can effectively eliminate low-frequency multipath delay errors in the observation domain and coordinate domain. After multipath correction, the precision of the horizontal coordinates and height coordinates are 1.7 mm and 4.6 mm. The precision of the horizontal coordinate and height coordinate is increased by 50% and 60%, respectively. The fixed rate of ambiguity increased by 5-7%.

A Multipath Error Reduction Method for BDS Using Tikhonov Regularization with Parameter Optimization

Multipath error is an important factor restricting the relative positioning accuracy of the Beidou Navigation Satellite System (BDS). Because of the complexity of the reflection environment, the mathematical modeling of multipath errors is quite difficult. The sidereal filtering algorithm corrects multipath errors by using the feature of period repetition, which can greatly reduce its influence and improve the accuracy of positioning and attitude measurement. In view of the constellation heterogeneity of BDS, it is more complicated to apply sideral filtering. Based on the reconstructed single-difference residual of the carrier phase, the multipath repetition time of the Beidou satellite is estimated using the idea of segmentation. The Tikhonov regularization method and the classical wavelet method are used to extract the multipath of the single-difference residual of the carrier phase, and the “clean” sequence of the single-difference residual is obtained. The experimental results show that it is feasible to extract the multipath error correctly by Tikhonov regularization, and the multipath error is smoother than the original residual measurement. Furthermore, the estimation method of the regularization parameter is further optimized. After using the optimized Tikhonov regularization method with sidereal filtering, the mean RMS improvements of GEO, IGSO, and MEO satellites are 45.9%, 38.2%, and 37.5%, respectively. The positioning accuracy on E, N, and U components is improved by 24.8%, 26.3%, and 42.7%, respectively. The attitude resolution accuracy is improved by 22.9% in the yaw angle and 12.6% in the pitch angle. The proposed method can be an alternative BDS multipath error modeling and mitigation approach.

A feasibility study on AI-controlled closed-loop electrical stimulation implants

Miniaturized electrical stimulation (ES) implants show great promise in practice, but their real-time control by means of biophysical mechanistic algorithms is not feasible due to computational complexity. Here, we study the feasibility of more computationally efficient machine learning methods to control ES implants. For this, we estimate the normalized twitch force of the stimulated extensor digitorum longus muscle on n = 11 Wistar rats with intra- and cross-subject calibration. After 2000 training stimulations, we reach a mean absolute error of 0.03 in an intra-subject setting and 0.2 in a cross-subject setting with a random forest regressor. To the best of our knowledge, this work is the first experiment showing the feasibility of AI to simulate complex ES mechanistic models. However, the results of cross-subject training motivate more research on error reduction methods for this setting.

Prediction and Assessment of Human Errors in Control Room of a Steam Power Plant by SHERPA Method in 2021

Background: Human errors are considered as important factor in occurrence of accidents that can leading to death, injuries and damages. The power plant industry as the most important infrastructure industry plays a significant role in industrial infrastructure. Objectives: The present study was carried out with the aim of prediction and assessment of human errors in a control room of a steam power plant by systematic approach to error prediction and reduction (SHERPA) method. Methods: This descriptive – cross sectional study was conducted in a control room of steam power plant. In this research human errors were identified and analyzed by hierarchical task analysis (HTA) and SHERPA methods. Results: In total 85 errors were identificated that 56 (65.1%) action errors, 24 (27.9%) checking errors, 1 (1.2%) retrieval errors, 2 (2.32%) communication errors and 3 (3.48%) were related to selection errors. Results also indicated that already 51% of risk level due to identification errors in unacceptable and undesirable level. Conclusions: The most important identificated errors in this research were related to action errors. In order to minimize of these errors and limitation of their consequence, we can use checklist and proper instructions according to work and educate control room operators.

Glueballs at physical pion mass* *Supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDB34030300, XDPB15), the National Key Research and Development Program of China (2020YFA0406400), and the National Natural Science Foundation of China (NNSFC) under Grants ( 11935017, 12070131001) (CRC 110 by DFG and NNSFC). The computations were performed on the CAS ORISE computing environment, the

Glueballs are investigated through gluonic operators on two RBC/UKQCD gauge ensembles at the physical pion mass. The statistical errors of glueball correlation functions are considerably reduced through the cluster decomposition error reduction (CDER) method. The Bethe-Salpeter wave functions are obtained for the scalar, tensor, and pseudoscalar glueballs by using spatially extended glueball operators defined through the gauge potential in the Coulomb gauge. These wave functions exhibit similar features of non-relativistic two-gluon systems and are used to optimize the signals of the related correlation functions at the early time regions, where the ground state masses are extracted. These masses are close to those from the quenched approximation and indicate the possible existence of glueballs at the physical point. The resonance feature of glueballs and the mixing with conventional mesons and multi-hadron systems should be considered in a more systematic lattice study.

Accumulated Error Reduction of Linear Motor Mover Position Measurement Based on SKLM

Cumulative measurement error is the most critical factor affecting the accuracy of long-stroke displacement measurement. Based on the one-dimensional image gradient method and Smooth Kalman filter algorithm (SKLM), this paper proposes a long-stroke linear motor displacement cumulative error reduction and high precision measurement methods. First, according to the motion characteristics of the linear motor and the principle of image measurement, a one-dimensional speckle target image is generated, and the one-dimensional Barron gradient algorithm is used to calculate the displacement of adjacent frames quickly. Second, according to the rea-sons for the accumulated error of long-stroke displacement measurement, the smooth Kalman algorithm for optimized autoregressive data processing is introduced to optimally estimate the measured displacement of adjacent frames to realize the reduction of accumulated error. To improve the robustness of the measurement system, a wavelet soft threshold image filter is introduced to per-form noise reduction and restoration processing on the collected signal, and further realize the high-precision dis-placement measurement of the long-stroke linear motor. Simulations and experiments show that the method in this paper not only improves the measurement accuracy of adjacent frames, but also reduces the cumulative error of long-stroke displacement measurement. And under different working conditions, compared with other methods, it has higher accuracy and anti-interference.

Determination of in-plane residual stress and eigenstrain in laser peened thin sheet using unit pulse function and equilibrium constraint

The residual stress field is a critical physical quantity for the predicting fatigue life and adjusting forming shape in the peening industry. It is an important engineering requirement to accurately obtain the reliable residual stress of treated coupons and components. To improve the spatial resolution and accuracy of stress measurements in laser peened targets, the slitting method via unit pulse function was carried out in the present study. A 4 mm thick rectangular sheet subjected to uniform laser peening was studied thoroughly via theoretical analysis and experimental tests. First, the physical interpretation and basic implementation of the unit pulse function were described. The relevant regularization, error reduction, and analysis methods were expounded. Then, a safe distance of multiple slitting measurements was proposed and further investigated through numerical simulation. The effect of stress relaxation and the positions of the strain gauges employed were also verified. Experimentally, a biaxial through-thickness distribution of residual stress was calculated using the unit pulse approach. Then, a cross-method validation procedure was performed through an incremental X-ray diffraction (XRD). Finally, the distribution patterns of the bending and elongating stress components were analyzed. The unbalanced laser peening-induced stress was obtained through a backward calculation using the equilibrium constraint. The results indicated that general stress field was in a biaxial state, which possessed a roughly equivalent amplitude and uniform distribution. Along the depth direction, both the peened and unpeened faces were under compression, while the sheet center was in tension for global compensation. Further research revealed that the residual stress introduced by laser peening was fundamental to the process, which can provide a test-oriented solution for eigenstrain and reliable data support for deformation prediction.

How do medication errors occur in the nursing communication process? Investigating the relationship between error types and error factors.

Human error types and error factors are two important elements of error analysis. Understanding the relationship between them can contribute to new case analyses, the tendency of error occurrence statistics, error factor identification, and prevention of error recurrence. To provide evidence and guidance for the prevention and improvement of medication communication errors by quantitatively exploring the relationship between error types and error factors. Data were collected on self-reported errors in the medication administration process by nurses in all departments of three cooperative medical institutions, and an error sheet of specified style was adopted. Error types were determined by the systematic human error reduction and prediction approach method and human cognition processes. Error factors were extracted using the root cause analysis combined with Berlo's communication model, and the relationship between error types and error factors was quantitatively studied using the partial least-squares regression method. After a one-by-one analysis of 303 error cases, the communication errors occurring in the nursing medication process could be explained by six error types and 12 error factors. In addition, 20 correlation patterns between the error types and error factors were quantitatively obtained, and their path coefficient distributions ranged from 0.088 to 0.467. The results of this study may provide reference to understand errors and establish countermeasures from the statistics of error occurrence trends, extract error factors related to error types and determine key error factors.

МЕТОД ИЗМЕРЕНИЯ ЧАСТОТЫ ПО ТРЕМ ОТСЧЕТАМ ВХОДНОГО СИГНАЛА

The paper deals with the method of signal frequency measurement based on the analysis of three consecutive samples of the input signal. The ad-vantages and disadvantages of the method are considered. The method of error re-duction, based on the use of a low-frequency filter, is proposed. The simulation model is built with the help of Simulink package. An estimate of the error for the case of sinusoidal, polyharmonic and sinusoidal distorted by noise signals is obtained.

Convolution Error Reduction for a Fabry–Pérot-Based Linewidth Measurement: A Theoretical and Experimental Study

Linewidth measurement of a short pulse single-longitudinal mode laser with a low repetition rate has been a big challenge. Although the Fabry–Pérot (FP) etalon in combination with a beam profiler is an effective approach to measure the linewidth, the convolution error introduced by the inherent transmission spectrum width of an FP restricts the measurement accuracy. Here, the source of convolutional errors of the FP etalon-based linewidth measurement is analyzed, and the convolutional fitting method is proposed to reduce the errors. The results show that the linewidth measurement using the FP cavity with low reflectance (95%) can achieve the same resolution as that with high reflectance (99.5%) based on this convolution error reduction method. The study provides a simple approach to accurately measuring the linewidth of pulsed lasers, even with low energy.

On connections between Amplitude Flow and Error Reduction for phase retrieval and ptychography

In this paper, we consider two iterative algorithms for the phase retrieval problem: the well-known Error Reduction method and the Amplitude Flow algorithm, which performs minimization of the amplitude-based squared loss via the gradient descent. We show that Error Reduction can be interpreted as a scaled gradient method applied to minimize the same amplitude-based squared loss, which allows to establish its convergence properties. Moreover, we show that for a class of measurement scenarios, such as ptychography, both methods have the same computational complexity and sometimes even coincide.

An enhanced method to reduce reconstruction error of compressed sensing for structure vibration signals

Compressed sensing (CS) utilizes the signal’s sparsity to reconstruct signals from far less linear measurements. However, ambient vibration response in structural dynamics typically lacks sparsity on a regular transform basis. Hence, when the vibration signals are reconstructed through the CS, significant errors are unavoidably induced, especially at high compression ratios, limiting the CS applicability in structural health monitoring and damage detection. To address these issues, this paper proposes an enhanced error reduction method, exploited as a post-processing scheme for signal reconstruction. The suggested method constructs an autoregression model, whose residuum increases to correspond to the reconstruction error based on empirical observations. Through minimizing the residuum under the constraint of compressed measurements, the reconstruction error is then minimized, which leads to an optimized result of the reconstructed signals. The suggested method is validated using ambient vibration data collected from a laboratory-scale shear frame model and a full-scale cable-stayed bridge.

Deep learning-based method for non-uniform motion-induced error reduction in dynamic microscopic 3D shape measurement.

The non-uniform motion-induced error reduction in dynamic fringe projection profilometry is complex and challenging. Recently, deep learning (DL) has been successfully applied to many complex optical problems with strong nonlinearity and exhibits excellent performance. Inspired by this, a deep learning-based method is developed for non-uniform motion-induced error reduction by taking advantage of the powerful ability of nonlinear fitting. First, a specially designed dataset of motion-induced error reduction is generated for network training by incorporating complex nonlinearity. Then, the corresponding DL-based architecture is proposed and it contains two parts: in the first part, a fringe compensation module is developed as network pre-processing to reduce the phase error caused by fringe discontinuity; in the second part, a deep neural network is employed to extract the high-level features of error distribution and establish a pixel-wise hidden nonlinear mapping between the phase with motion-induced error and the ideal one. Both simulations and real experiments demonstrate the feasibility of the proposed method in dynamic macroscopic measurement.