Accuracy Improvement Method Research Articles

An Optimal SVD Filtering Method for Measurement Accuracy Improvement against Harmonic Disturbance in Grid-Connected Inverters

An Optimal SVD Filtering Method for Measurement Accuracy Improvement against Harmonic Disturbance in Grid-Connected Inverters

A linear compensation method for inference accuracy improvement of memristive in-memory computing

Memristive computing system (MCS), with the feature of in-memory computing capability, for artificial neural networks (ANNs) deployment showing low power and massive parallelism, is a promising alternative for traditional Von-Neumann architecture computing system. However, because of the various non-idealities of both peripheral circuits and memristor array, the performance of the practical MCS tends to be significantly reduced. In this work, a linear compensation method (LCM) is proposed for the performance improvement of MCS under the effect of non-idealities. By considering the effects of various non-ideal states in the MCS as a whole, the output error of the MCS under different conditions is investigated. Then, a mathematic model for the output error is established based on the experimental data. Furthermore, the MCS is researched at the physical circuit level as well, in order to analyze the specific way in which the non-idealities affect the output current. Finally, based on the established mathematical model, the LCM output current is compensated in real time to improve the system performance. The effectiveness of LCM is verified and showing outstanding performance in the residual neural network-34 network architecture, which is easily affected by the non-idealities in hardware. The proposed LCM can be naturally integrated into the operation processes of MCS, paving the way for optimizing the deployment on generic ANN hardware based on the memristor.

Accuracy analysis and improvement methods for revolving parts in counter-rotating electrochemical machining with continuous radial feeding

Counter-rotating electrochemical machining (CRECM) stands out as an efficient and cost-effective method for machining aero-engine casings. To maintain stability in the CRECM process equilibrium state, continuous feeding of the cathode tool is essential. However, this continuous feeding approach with single direction of rotation may result in poor roundness and reduced machining accuracy. This paper aims to analyze the sources and rules of roundness error and asymmetry in CRECM. Based on simulation results, a method involving periodic reverse rotation of electrodes with continuous feeding is proposed for CRECM. This approach aims to mitigate the cumulative machining error, thereby significantly improving machining accuracy. Experimental results validate the efficacy of the proposed method at a rotation speed of 0.2 rpm. The roundness error is markedly reduced from 0.19 mm to 0.04 mm, resulting in a more symmetric convex structure. Additionally, the sidewall taper angle is reduced from 14.5 degrees to 6.4 degrees. This underscores the effectiveness of the proposed method in enhancing machining accuracy.

Research on Improving the Accuracy of SIF Data in Estimating Gross Primary Productivity in Arid Regions

Coupling solar-induced chlorophyll fluorescence (SIF) with gross primary productivity (GPP) for ecological function integration research presents numerous uncertainties, especially in ecologically fragile and climate-sensitive arid regions. Therefore, evaluating the suitability of SIF data for estimating GPP and the feasibility of improving its accuracy in the northern region of Xinjiang is of profound significance for revealing the spatial distribution patterns of GPP and the strong coupling relationship between GPP and SIF in arid regions, achieving the goal of “carbon neutrality” in arid regions. This study is based on multisource SIF satellite data and GPP observation data from sites in three typical ecosystems (cultivated and farmlands, pasture grasslands, and desert vegetation). Two precision improvement methods (canopy and linear) are used to couple multiple indicators to determine the suitability of multisource SIF data for GPP estimation and the operability of accuracy improvement methods in arid regions reveal the spatial characteristics of SIF (GPP). The results indicate the following. (1) The interannual variation of GPP shows an inverted “U” shape, with peaks values in June and July. The cultivated and farmland areas have the highest peak value among the sites (0.35 gC/m2/month). (2) The overall suitability ranking of multisource SIF satellite products for GPP estimation in arid regions is RTSIF > CSIF > SIF_OCO2_005 > GOSIF. RTSIF shows better suitability in the pasture grassland and cultivated and farmland areas (R2 values of 0.85 and 0.84, respectively). (3) The canopy method is suitable for areas with a high leaf area proportion (R2 improvement range: 0.05–0.06), while the linear method is applicable across different surface types (R2 improvement range: 0.01–0.13). However, the improvement effect of the linear method is relatively weaker in areas with high vegetation cover. (4) Combining land use data, the overall improvement of SIF (GPP) is approximately 0.11%, and the peak values of its are mainly distributed in the northern and southern slopes of the Tianshan Mountains, while the low values are primarily found in the Gurbantunggut Desert. The annual mean value of SIF (GPP) is about 0.13 mW/m2/nm/sr. This paper elucidates the applicability of SIF for GPP estimation and the feasibility of improving its accuracy, laying the theoretical foundation for the spatiotemporal coupling study of GPP and SIF in an arid region, and providing practical evidence for achieving carbon neutrality goals.

Global Mean Based nearest Feature object Value Selection with Feature creation Method for Clustering Accuracy Improvement

Global Mean Based nearest Feature object Value Selection with Feature creation Method for Clustering Accuracy Improvement

The vertical accuracy improvement method considering gravitational anomaly for SINS/GNSS

Vertical information (vertical velocity and altitude) is important in three-dimensional navigation. In order to improve the vertical accuracy of strapdown inertial navigation system/global navigation satellite system integrated navigation (SINS/GNSS) while reducing costs, firstly, the propagation laws of vertical error sources in two types of SINS/GNSS (vertical velocity set to 0 and not set to 0) are systematically analyzed. Furthermore, a vertical accuracy improvement method considering the gravitational anomaly is proposed. In this method, the gravitational anomaly is considered as one of the vertical error sources. Then, the processing method of error sources in integrated navigation is referenced, and two processing modes of gravitational anomaly are designed. The first method is to represent the gravitational anomaly as one of the system states of SINS/GNSS. The number of dimensions for the system is expanded from 15 to 16. The corresponding mathematical model is derived to ‘absorb’ the vertical errors caused by the gravitational anomaly. The second is to represent the gravitational anomaly as one of the vertical system noises. Thereby, the Kalman filter is adjusted in real time using the above adaptive method to improve the accuracy of the estimated state. The corresponding errors are then suppressed. Field experiments show that both modes of the proposed method can effectively improve the vertical accuracy.

An improvement method for pancreas CT segmentation using superpixel-based active contour

Objective. Pancreas is one of the most challenging organs for Computed Tomograph (CT) image automatic segmentation due to its complex shapes and fuzzy edges. It is simple and universal to use the traditional segmentation method as a post-processor of deep learning method for segmentation accuracy improvement. As the most suitable traditional segmentation method for pancreatic segmentation, the active contour model (ACM), still suffers from the problems of weak boundary leakage and slow contour evolution speed. Therefore, a convenient post-processor for any deep learning methods using superpixel-based active contour model (SbACM) is proposed to improve the segmentation accuracy. Approach. Firstly, the superpixels with strong adhesion to edges are used to guide the design of narrowband and energy function. A multi-scale evolution strategy is also proposed to reduce the weak boundary leakage and comprehensively improve the evolution speed. Secondly, using the original image and the coarse segmentation results obtained from deep learning methods as inputs, the proposed SbACM method is used as a post-processor for fine segmentation. Finally, the pancreatic segmentation public dataset TCIA from the National Institutes of Health(NIH, USA) is used for evaluation, and the Wilcoxon Test confirmed that the improvement of proposed method is statistically significant. Main results. (1) the superpixel-based narrowband shape and dynamic edge energy of the proposed SbACM work for boundary leakage reduction, as well as the multi-scale evolution strategy and dynamic narrowband width for the evolution speed improvement; (2) as a post-processor, SbACM can increase the Dice similarity coefficients (DSC) of five typical UNet-based models, including UNet, SS-UNet, PBR UNet, ResDSN, and nnUNet, 2.35% in average and 9.04% in maximum. (3) Based on the best backbone nnUNet, the proposed post-processor performs better than either adding edge awareness or adding edge loss in segmentation enhancement without increasing the complexity and training time of deep learning models. Significance. The proposed SbACM can improve segmentation accuracy with the lowest cost, especially in cases of squeezed fuzzy edges with similar neighborhood , and complex edges.

Two-Step Accuracy Improvement for Multitarget Detection in Complex Environment Using UWB Radar

Detecting multiple human targets in indoor scenarios using ultra-wideband (UWB) radar usually involves false detection results caused by the secondary reflections, which might reduce the target detection accuracy and cause a more severe deterioration when the number of targets increases. This article proposed a two-step accuracy improvement method for multitarget detection in environments with multiple human targets of more than three and strong secondary reflections by the surroundings, especially the walls. Based on the rough detection results acquired by the modified CA-CFAR (MCA-CFAR) processing, the first step achieves the primary false alarm suppression using a short-window accumulation in the time domain. Then, the second step applies the decision confidence on the detection results from the first step to assess the reliability of results for improved accuracy. The two-step accuracy improvement could thus have a higher accuracy through cascading false alarm suppression. The effectiveness and accuracy of the proposed algorithm are verified based on the experimental results.

An efficient accuracy improvement method for cable defect location based on instantaneous filtering in time-frequency domain

Time-frequency domain reflectometry is a highly sensitive method to locate cable defect. However, locating a potential defect far from the cable head may be less accurate because of the signal amplitude attenuation and the interference parts. To solve the problem, this paper proposes an accuracy improvement method based on instantaneous filtering in time-frequency domain. The method extracts the frequency spectrum of the time-frequency signal, and then filters the spectrum and reconstructs the signal based on the difference between the signal and interference spectra, which can eliminate the interference and improve the location accuracy. In simulation, the location error of this method improves from 5.47 % to 0.57 %. In experiment, the proposed method reduces the location errors and magnifies the location peaks of the breakage defect, thermal aging defect, and ground faults. For the defects studied in this paper, the method improves the defect location accuracy and defect amplitude detectability of TFDR.

A risk-aware reference trajectory resampling method for quadrotor tracking accuracy improvement

PurposeMany existing trajectory optimization algorithms use parameters like maximum velocity or acceleration to formulate constraints. Due to the ignoring of the quadrotor actual tracking capability, the generated trajectories may not be suitable for tracking control. The purpose of this paper is to design an online adjustment algorithm to improve the overall quadrotor trajectory tracking performance.Design/methodology/approachThe authors propose a reference trajectory resampling layer (RTRL) to dynamically adjust the reference signals according to the current tracking status and future tracking risks. First, the authors design a risk-aware tracking monitor that uses the Frenét tracking errors and the curvature and torsion of the reference trajectory to evaluate tracking risks. Then, the authors propose an online adjusting algorithm by using the time scaling method.FindingsThe proposed RTRL is shown to be effective in improving the quadrotor trajectory tracking accuracy by both simulation and experiment results.Originality/valueInfeasible reference trajectories may cause serious accidents for autonomous quadrotors. The results of this paper can improve the safety of autonomous quadrotor in application.

Hybrid physics-guided data-driven modeling for generalizable geometric accuracy prediction and improvement in two-photon lithography

Two-photon lithography (TPL) is an additive manufacturing technique to produce three-dimensional (3D) micro- and nano-scale structures. Geometric accuracy is of vital importance to ensure the quality and functionality of additively manufactured 3D structures. However, there exists limited research on modeling and improving the geometric accuracy of TPL. Moreover, there is a lack of understanding of the influence of TPL process parameters (e.g., scanning rate, laser power) and structure design on the resulting geometric accuracy. This paper presents a new hybrid physics-guided data-driven modeling framework for generalizable cross-design and cross-parameter geometric accuracy prediction and improvement. The proposed model decomposes TPL-produced geometric features into two levels: a global trend and a spatial trend. Guided by physical laws that govern voxel dimensions, we construct the global trend as a function of TPL process parameters. Our new findings reveal strong linear relationships among the parameters of the global trend model across structure design dimensions, which enable generalizable predictive modeling. Moreover, the spatial trend captures the spatially varying patterns in geometric features. A large-scale experimental design consisting of six hemisphere sizes and six parameter combinations is carried out to thoroughly test the effectiveness of the proposed method. Our modeling approach demonstrates high accuracy in predicting the geometric error of a target design with knowledge derived from other designs and parameters. The average prediction errors for radius and height are 5.23% and 4.66%, respectively. Furthermore, the proposed compensation strategy is shown to reduce the geometric errors from 22.19% to 3.21% for radius, and from 12.18% to 4.96% for height. Meanwhile, the within-sample variations are greatly reduced, indicating improved process consistency. To the best of our knowledge, this study is among the first to develop a generalizable method for cross-design and cross-parameter geometric accuracy modeling and improvement in TPL.

Dispersion Compensation Method for Accuracy Improvement of Optical Frequency Domain Polarimetry

Dispersion Compensation Method for Accuracy Improvement of Optical Frequency Domain Polarimetry

Geometric Positioning Accuracy Improvement Method of Micro SAR Satellites With Large Positioning Errors

Geometric Positioning Accuracy Improvement Method of Micro SAR Satellites With Large Positioning Errors

A Novel Positioning Accuracy Improvement Method for Polishing Robot Based on Levenberg–Marquardt and Opposition-based Learning Squirrel Search Algorithm

A Novel Positioning Accuracy Improvement Method for Polishing Robot Based on Levenberg–Marquardt and Opposition-based Learning Squirrel Search Algorithm

Accuracy improvement of multi-view 3D laser scanning measurements based on point cloud error correction and global calibration optimization.

Multi-camera laser scanning measurement is emerging as a pivotal element in three-dimensional (3D) optical measurements. It reduces occlusion and enables the gathering of more 3D data. However, it also increases the difficulty of system algorithms in obtaining high measurement accuracy. To improve the measurement accuracy, there is an urgent need to address global calibration and error correction issues caused by the employment of multi-view systems. An accuracy improvement method for multi-view 3D laser scanning measurements based on point cloud error correction and global calibration optimization is then proposed. First, a planar asymmetric circular grid target is designed to calibrate the cameras, laser planes, and initial global transformation matrices of the multi-view 3D laser scanning probe simultaneously. The influence of the position of the laser plane on the measurement error is analyzed and what we believe to be novel mathematical error influencing factors are then modelled for point accuracy. Furthermore, a believed to be novel error model based on the backpropagation (BP) neural network is established for the regression analysis of the mathematical error influencing factors and measurement deviations for each point based on the standard sphere plate measurement. The final measurement is optimized by the correction of point cloud for each camera of the multi-view system and the global calibration optimization based on the error model. The proposed method is reliable and easy to implement, since it only requires a standard sphere plate and a planar target. Several experiments show that the method can effectively improve the measurement accuracy of multi-view 3D laser scanning probe through point cloud error correction and calibration optimization.

Strapdown Inertial Navigation System Accuracy Improvement Methods Based on Inertial Measuring Unit Rotation: Analytical Review

Strapdown Inertial Navigation System Accuracy Improvement Methods Based on Inertial Measuring Unit Rotation: Analytical Review

Research on Hand–Eye Calibration Accuracy Improvement Method Based on Iterative Closest Point Algorithm

In the functioning of the hand–eye collaboration of an apple picking robot, the accuracy of the hand–eye relationship is a key factor affecting the efficiency and accuracy of the robot’s operation. In order to enhance the low accuracy of traditional hand–eye calibration methods, linear and nonlinear solving methods based on mathematical tools such as quaternions are commonly adopted. To solve the loss of accuracy in decoupling during the linearization solution and to reduce the cumulative error that occurs during nonlinear solutions, a hand–eye calibration method, based on the ICP algorithm, is proposed in this paper. The method initializes the ICP matching algorithm with a solution derived from Tsai–Lenz, and substitutes it for iterative computation, thereby ascertaining a precise hand–eye conversion relationship by optimizing the error threshold and iteration count in the ICP matching process. Experimental results demonstrate that the ICP-based hand–eye calibration optimization algorithm not only circumvents the issues pertaining to accuracy loss and significant errors during solving, but also enhances the rotation accuracy by 13.6% and the translation accuracy by 2.47% compared with the work presented by Tsai–Lenz.

TOF-assisted binocular vision accuracy improvement method for underwater fish size inspection.

To address problems such as the lack of accuracy in acquiring depth maps for dynamic fish 3D measurements by usual binocular vision or a time-of-flight (TOF) depth camera, a TOF-assisted binocular vision depth acquisition algorithm is used to obtain high-quality depth maps. The TOF depth energy function is designed to guide the binocular stereo matching process, which improves the correct matching rate of binocular matching in low-texture regions; the TOF and binocular stereo matching confidence weighting functions are designed to achieve the fusion of the two at pixel level to improve the matching quality of fish in the occluded overlapping regions. The experimental results show that the TOF-assisted binocular vision system improves the accuracy of fish size measurement compared to single binocular vision while reducing the measurement error when the fish body has a significant inclination along the depth axis.

Direct learning-based deep spiking neural networks: a review.

The spiking neural network (SNN), as a promising brain-inspired computational model with binary spike information transmission mechanism, rich spatially-temporal dynamics, and event-driven characteristics, has received extensive attention. However, its intricately discontinuous spike mechanism brings difficulty to the optimization of the deep SNN. Since the surrogate gradient method can greatly mitigate the optimization difficulty and shows great potential in directly training deep SNNs, a variety of direct learning-based deep SNN works have been proposed and achieved satisfying progress in recent years. In this paper, we present a comprehensive survey of these direct learning-based deep SNN works, mainly categorized into accuracy improvement methods, efficiency improvement methods, and temporal dynamics utilization methods. In addition, we also divide these categorizations into finer granularities further to better organize and introduce them. Finally, the challenges and trends that may be faced in future research are prospected.

Small Signal Modeling of LLC Converter with LED Load and Quasi-Resonant Controller Based Active Ripple Rejection

This paper has analyzed the defects of the traditional extended description function (EDF) based LLC small signal modeling method when driving light emitting diode (LED) load and proposed an accuracy improvement method. Detailed small signal model modeling methods have been deduced, and the accuracy of different modeling methods has been compared thoroughly. To suppress the second-order harmonic ripple in the direct current link-induced output current ripple, a quasi-resonant controller (QRC) is adopted to realize active ripple rejection (ARR). Considering the frequency of the second-order harmonic ripple changes with the grid voltage frequency, a single-phase software phase lock loop (SPLL) is adopted to extract the frequency of the second-order harmonic. By dynamically regulating the control parameter of the QRC according to the locked second-order frequency, output voltage ripple active rejection ability immune to grid voltage frequency deviation is obtained. Based on the deduced accurate small signal model, the digital controller is designed with stability, steady state and dynamics performance guaranteed. The accuracy of the proposed small signal model, the SPLL and the QRC-based ARR method has been verified at last.