Error Measurement Method Research Articles

A novel SPM error measurement method based on peak detection of nodes centers in a 2D orthogonal lattice as standard

Abstract This paper presented a novel scanning probe microscope (SPM) error measurement method by using a 2D micro-scale orthogonal lattice standard and peak detection (PD) method of the pitch center coordinates based on cross-correlation/convolution (CC) filtering of images raster-scanned by SPMs. The geometric errors of motion and drifting rate in x-y plane are measured. Geometric errors include positional deviations E x x and E y y , straightness deviations E x y and E y x along x- and y-axis respectively, and the orthogonality deviation E o x y between the two axes. The calibration factors C x and C y were calculated based on the number of pixels scanned on the x-axis and y-axis, scanning range, average pitch of standard lattice from metrological verification certification, and average lattice pitch calculated by using PD method. Through case study, the errors of an AFM was measured using an orthogonal lattice standard with a nominal pitch of 10 μm, resulting in C x and C y values of 0.925 and 1.050, respectively, an orthogonal deviation E o x y of 0.015°, and the maximum drift rate of the x-axis and y-axis is −366.21 nm min−1 and −317.38 nm min−1, respectively. The new error measurement method of SPMs provides technical reference for the development and performance improvement of SPM instruments.

Simulation of motion error compensation of CNC with multi-axis linkage

Motion errors can significantly affect the machining accuracy of MACNC. To reduce the motion errors in the operation of CNC, an error compensation algorithm is proposed in the study. Firstly, the motion error measurement and identification method is proposed, and in this way, the MECA based on the third spline fitting is proposed Finally, the effectiveness is verified by simulation experiments. The proposed error measurement method has high measurement accuracy, and the MECA can effectively reduce the machining error value, and its processing time is only 0.012s. The measurement accuracy of the error measurement method proposed in the study reached 98.95%, which was 4.49% and 4.88% higher than the minimum measurement accuracy based on wavelet denoising and genetic algorithm, respectively. When the number of iterations was 5, all three measurement methods achieved the minimum measurement accuracy, and the minimum measurement accuracy of the proposed measurement method was 91.00%, Compared with the minimum measurement accuracy of 93.65% and 94.26% based on wavelet denoising and genetic algorithm, it has decreased by 2.65% and 3.26%, respectively. When disturbed, the accuracy rate of the error compensation algorithm proposed by the research is 93.9%, which is 2%, 2% and 3.5% higher than the minimum values of 91.9%, 91.9% and 90.4% of the three error compensation algorithms based on BP neural network, Particle swarm optimization algorithm and genetic algorithm, respectively. The above results show that the MECA can effectively achieve the error compensation of MACNC, with a fast processing speed, which can effectively workshop Machining efficiency and quality.

Optimizing circumferential assembly angle of rotor parts connected by curvic couplings based on acquired tooth surface error data

Due to the excellent self-centering and load-carrying capability, curvic couplings have been widely used in advanced aero-engine rotors. However, curvic tooth surface errors lead to poor assembly precision. Traditional physical-master-gauge-based indirect tooth surface error measurement and circumferential assembly angle optimization methods have the disadvantages of high cost and weak generality. The unknown tooth surface fitting mechanism is a big barrier to assembly precision prediction and improvement. Therefore, this work puts forward a data-driven assembly simulation and optimization approach for aero-engine rotors connected by curvic couplings. The origin of curvic tooth surface error is deeply investigated. Using 5-axis sweep scan method, a large amount of high-precision curvic tooth surface data are acquired efficiently. Based on geometric models of parts, the fitting mechanism of curvic couplings is uncovered for assembly precision simulation and prediction. A circumferential assembly angle optimization model is developed to decrease axial and radial assembly runouts. Experimental results show that the assembly precision can be predicted accurately and improved dramatically. By uncovering the essential principle of the assembly precision formation and proposing circumferential assembly angle optimization model, this work is meaningful for assembly quality, efficiency and economy improvement of multistage aero-engine rotors connected by curvic couplings.

Piston Error Measurement for Segmented Telescopes Based on a Hybrid Artificial Neural Network.

To address the difficulty and complexity of detecting piston errors for segmented telescopes, this paper proposes a new piston error measurement method based on a hybrid artificial neural network. First, we use the Resnet network to learn the mapping relationship between the focal plane degradation image and signs of the piston error. Then, based on the established theoretical relationship between the modulation transfer function and the piston error, a BP neural network is used to learn the mapping relationship between the MTF and the absolute value of the piston error. After the training of the hybrid network is completed, a wide-range and high-precision detection of the piston error of the sub-mirrors can be achieved using the combined output of the two networks, where only a focal plane image of the point source with broadband illumination is used as the input. The detection range can reach the entire coherent length of the input broadband light, and the detection accuracy can reach 10 nm. The method proposed in this paper has the advantages of high detection accuracy, a wide detection range, low hardware cost, a small network scale, and low training difficulty.

Complex Background Reconstruction for Novelty Detection

Novelty detection aims to detect samples from classes different from the training samples (i.e., the normal class). Existing approaches predominantly make the target reconstruction better and choose the appropriate reconstruction error measurement method but ignore the influence of background information on this process. This paper proposes a novel reconstruction network and mutual information Siamese network. The reconstructed network aims to make the distribution of reconstructed samples consistent with that of original samples, intending to reduce background interference in the reconstruction process. After this, we measure the distance between the original and generated images based on a mutual information Siamese network, which extracts more discriminative features to calculate the similarity between the original images and their reconstructed ones. This part of the network uses global context information to improve the detection accuracy. We conduct extreme experiments to evaluate the proposed solution on two challenging public datasets. The experimental results show that the proposed method significantly outperforms the state-of-the-art methods.

A Customized Deep Sleep Recommender System Using Hybrid Deep Learning.

This paper proposes a recommendation system based on a hybrid learning approach for a personal deep sleep service, called the Customized Deep Sleep Recommender System (CDSRS). Sleep is one of the most important factors for human life in modern society. Optimal sleep contributes to increasing work efficiency and controlling overall well-being. Therefore, a sleep recommendation service is considered a necessary service for modern individuals. Accurate sleep analysis and data are required to provide such a personalized sleep service. However, given the variations in sleep patterns between individuals, there is currently no international standard for sleep. Additionally, service platforms face a cold start problem when dealing with new users. To address these challenges, this study utilizes K-means clustering analysis to define sleep patterns and employs a hybrid learning algorithm to evaluate recommendations by combining user-based and collaborative filtering methods. It also incorporates feedback top-N classification processing for user profile learning and recommendations. The behavior of the study model is as follows. Using personal information received through mobile devices and data, such as snoring, sleep time, movement, and noise collected through AI motion beds, we recommend sleep and receive user evaluations of recommended sleep. This assessment reconstructs the profile and, finally, makes recommendations using top-N classification. The experimental results were evaluated using two absolute error measurement methods: mean squared error (MSE) and mean absolute percentage error (MAPE). The research results regarding the hybrid learning methods show 13.2% fewer errors than collaborative filtering (CF) and 10.2% fewer errors than content-based filtering (CBF) on an MSE basis. According to the MAPE, the methods are 14.7% more accurate than the CF model and 9.2% better than the CBF model. These results demonstrate that CDSRS systems can provide more accurate recommendations and customized sleep services to users than CF, CBF, and combination models. As a result, CDSRS, a hybrid learning method, can better reflect a user's evaluation than traditional methods and can increase the accuracy of recommendations as the number of users increases.

Comparison of dynamic tumor tracking error measurement methods for robotic radiosurgery.

Dynamic tumor motion tracking is used in robotic radiosurgery for targets subject to respiratory motion, such as lung and liver cancers. Different methods of measuring tracking error have been reported, but the differences among these methods have not been studied, and the optimal method is unknown. The purpose of this study was to assess and compare tracking errors encountered with individual patients using different evaluation methods for method optimization. We compared the beam's eye view (BEV), machine learning (ML), log (addition error: AE), and log (root sum square: RSS) methods. Log (AE) and log (RSS) were calculated from log files. These tracking errors were compared, and the optimal evaluation method was ascertained. A t-test was performed to evaluate statistically significant differences. Here, the significance level was set at 5%. The mean values of BEV, log (AE), log (RSS), and ML were 2.87, 3.91, 2.91, and 3.74mm, respectively. The log (AE) and ML were higher than BEV (p<0.001), and log (RSS) was equivalent to the BEV, suggesting that the log (RSS) calculated with the log file method can substitute for the BEV calculated with the BEV method. As RSS error calculation is simpler than BEV calculation, using it may improve clinical practice throughput. This study clarified differences among three tracking error evaluation methods for dynamic tumor tracking radiotherapy using a robotic radiosurgery system. The log (RSS) calculated by the log file method was found to be the best alternative to BEV method, as it can calculate tracking errors more easily than the BEV method.

Establishing minimal clinically important differences for the Quality of Life Instrument of Chronic Gastritis QLICD-CG(V2.0) based on distribution-based methods

BackgroundTo establish the lowest score reflecting meaningful changes from the perspective of patients is very important for explaining the results of patient reports. The measurement scale of quality of life in patients with chronic gastritis has been used in clinical practice, but the minimal clinically important difference (MCID) has not been worked out. In this paper, we use a distribution-based method to calculate the MCID of the scale QLICD-CG (Quality of Life Instruments for Chronic Diseases- Chronic Gastritis) (V2.0).MethodsThe QLICD-CG(V2.0) scale was used to evaluate the quality of life in patients with chronic gastritis. Since the methods for developing MCID were diverse and there was no uniform standard, we took MCID developed by anchor-based method as the gold standard, and compared the MCID of QLICD-CG(V2.0) scale developed by various distribution-based methods for selection. Standard deviation method (SD), effect size method (ES), standardized response mean method (SRM), standard error of measurement method (SEM) and reliable change index method (RCI) are given in the distribution-based methods.ResultsA total of 163 patients, with an average age of (52.37 ± 12.96) years old, were calculated according to the various methods and formulas given by the distribution-based method, and the results were compared with the gold standard. It was suggested that the results of the SEM method at the moderate effect (1.96) should be taken as the preferred MCID of the distribution-based method. And thus the MCID of the physical domain, psychological domain, social domain, general module, specific module and total score of the QLICD-CG(V2.0) scale were 9.29, 13.59, 9.27, 8.29, 13.49 and 7.86, respectively.ConclusionsWith anchor-based method as the gold standard, each method in distribution-based method has its own advantages and disadvantages. In this paper, 1.96SEM was found to have a good effect on the minimum clinically significant difference of the QLICD-CG(V2.0) scale, and it is recommended as the preferred method to establish MCID.

Measuring Antenna Elevation Mechanism Pointing Errors with Multiencoder Information Sources

There are many factors that cause pointing errors in radio telescopes. As one of the motion positioning mechanisms of the radio telescope, the error caused by the elevation mechanism cannot be ignored. The source of error in the elevation mechanism comes mainly from the key transmission components and the support structure. Accurate measurement of the errors caused by them is the key to analyzing their law of change. Aiming at the main error factors in the antenna elevation mechanism, this study builds a scaled-down experimental platform for the elevation mechanism and proposes an error measurement method based on multiencoder information sources. The method compares the error law of change of the antenna elevation mechanism under different driving modes, different centerline deviations of the bearings, and different backlashes and designs error measurement experiments for the abovementioned operating conditions. The results show that the error measurement method based on multiencoder information sources can accurately measure the error of the antenna elevation mechanism under different driving modes. The method can also accurately reflect the law of change in transmission error when the backlash of the elevation mechanism and the centerline deviation of the bearings increase. The final experimental measurement shows that the driving mode of the dual-motor can eliminate about 70% of the mechanism error caused by the backlash. The average value of the error increases by a factor of 1.9 when the backlash increases from 0.1 mm to 1.26 mm. The average value of the error increases by a factor of 5 when the centerline deviation of the bearings increases from 0 to 1.5 mm. This has a good reference value to correct for the pointing error of a radio telescope.

Predicting the Height of Individuals with Machine Learning Methods by Considering Non-Genetic Factors

As many parents want to know how many centimeters their child will be in the future, many people in their developmental years want to know how many centimeters their future height will be. In addition, the development of children in terms of height and weight is medically controlled from the moment they are born. As a result, height development is important for both individuals and medical professionals. In this study, it is aimed to predict the height of individuals using personal and family information and factors affecting height. In the study, the 10 most known characteristics among the factors affecting height were selected. These attributes, mother's height, father's height, economic status, jumping and weight sports status, gender, information about the child's age, history of chronic illness in the individual, the longest living region, and the individual's height were taken as input values in machine learning methods. Using these input values, the length of the individual was predicted using Linear Regression (LR) and Artificial Neural Network (ANN) from machine learning methods. In addition, three error measurement methods were used to evaluate the success of the model: mean absolute error (MAE), mean square error (MSE) and R-Square (R^2). In the R^2 evaluation metric, the method was 84.48% in LR and 81.74% in ANN.

A geometric error measurement method for five-axis ultra-precision machine tools

A geometric error measurement method for five-axis ultra-precision machine tools

A deep neural network-based smart error measurement method for fiscal accounting data.

The error measurement of fiscal accounting data can effectively slow down the change of financial assets. Based on deep neural network theory, we constructed an error measurement model for fiscal and tax accounting data, and we analyzed the relevant theories of fiscal and tax performance evaluation. By establishing a batch evaluation index of finance and tax accounting, the model can monitor the changing trend of the error of urban finance and tax benchmark data scientifically and accurately, as well as solve the problem of high cost and delay in predicting the error of finance and tax benchmark data. In the simulation process, based on the panel data of credit unions, the entropy method and a deep neural network were used to measure the fiscal and tax performance of regional credit unions. In the example application, the model, combined with MATLAB programming, calculated the contribution rate of regional higher fiscal and tax accounting input to economic growth. The data show that the contribution rates of some fiscal and tax accounting input, commodity and service expenditure, other capital expenditure and capital construction expenditure to regional economic growth are 0.0060, 0.0924, 0.1696 and -0.0822, respectively. The results show that the proposed method can effectively map the relationships between variables.

Analytical review of studies in the field of stabilization error measurement methods

In this article, an analytical review of the literature on methods for measuring the stabilization error of stabilization systems (SS) is carried out. The relevance of further research and development in the field of ways to improve the accuracy of measurements of the stabilization error of the SS is substantiated.&#x0D; The modern method for determining the median error of the SS has distinctive features and has a number of advantages: it requires much less time and the use of manual labor; performed in digital form of information processing in numerical and graphical form; does not require the need to install a movie camera (it was replaced by an optoelectronic module, information from which is fed to the input of a video monitor, to which it is connected via the Pinnacle Movie Box Plus USB matching device, through which the video image is transmitted for recording to a laptop). After performing the research, information according to a specially developed program is automatically processed on a computer, which, based on the results of calculations, gives a more accurate (compared to the new method) value of the median stabilization error along the horizontal and vertical guidance channels.&#x0D; The model of construction of the line of domestic SS corresponds to the principle of "dependent stabilization" rigid connection of vertical and horizontal channels of the stabilizer with the device of supervision over objects.

P.69 Minimum clinically important difference in magnetic resonance biomarkers in DMD

Magnetic resonance (MR) biomarkers that quantify increases in intramuscular fat are highly sensitive to disease progression and predictive of meaningful functional milestones in DMD. However, the minimum clinically important difference (MCID), defined as "the smallest change that is important to patients" has not been established. The purpose of this investigation was to estimate the MCID for both muscle fat fraction measured using MR spectroscopy and bulk muscle T2 measured using MRI. As part of the multicenter ImagingDMD study, localized 1H-MR spectra (TE=108 ms; TR=3000 ms) and multi-echo T2 weighted images were collected longitudinally in 180 males with DMD, with 0-8 annual follow up visits. Spectra from the vastus lateralis and soleus muscles were integrated and relaxation-corrected then used to estimate fat fraction (fat:(fat+water)). Spin-echo images were used to calculate T2 maps, which were manually segmented to obtain T2 measurements for 7 leg muscles. Ambulatory function groups were defined as: able to rise from the floor - Group I; able to walk - Group II; nonambulatory - Group III. MCID was estimated using 1) the standard error of measurement method, incorporating the group variance and day-to-day reproducibility of the measurement from 111 study participants, 2) the 1/3 standard deviation method, and 3) a survey of experts. Combining results from the three estimation methods, ranges for MCID for fat fraction were 0.02-0.05 in the vastus lateralis and 0.01-0.05 in the soleus. For MRI T2, MCID ranged between 1.9 and 3.0 ms for the vastus lateralis and 0.9 and 2.4 ms for the soleus. The average annual change in fat fraction or T2 exceeds the MCID in Groups I, II, and III for vastus lateralis fat fraction, Groups I and II for vastus lateralis T2, Groups II and III for soleus fat fraction, and Group II for soleus T2. Expected change, expected variability, and estimated MCID in the target population should all be considered when selecting a primary MR measure to use in clinical trials.

Error analysis and measurement methods of curved optical element surface defects dark-field imaging inspection system based on multi-axis kinematics

In surface defect dark-field inspection of the curved optical element, the camera field of view (FOV) is much smaller than the aperture of the element, so the images of different positions on the element surface, which are also called sub-aperture images, need to be collected and stitched to obtain the whole surface image of the element. It is usually achieved by controlling multi-axis motion guide rails to make relative motion between camera and optical element. During sub-aperture images collection, there is not only relative translation between camera and optical element, but also optical element spin and swing with its optical axis for curved optical element. The motion errors of each motion axis will make sub-aperture scanning path deviate its predetermined path. Hence, it will lead to the dislocation of the stitching image, and will cause defects break and dislocation in stitching image, which will largely affect the optical element surface quality quantitative evaluation. Since multi-axis kinematics (MAK) is a mathematical model which is often used to describe the motion of objects located in multi-axis system, such as numerical control machine and mechanical arm. The multi-axis kinematics error analysis (MAKEA) model is put forward in this paper and applied in the optical element surface defects dark-field imaging inspection system. The effect of all axis motion errors including optical axis motion errors to sub-aperture scanning path has been specifically simulated and analyzed. The simulation result indicates that the optical axis swing error caused by assembly is the main error of dark-field imaging system, whereupon a swing angle assembly error measurement (SAAEM) method by measuring the optical axis positions of different thickness spherical optical elements is proposed. By adjusting the motor of the element optical axis swing, the optical axis swing error can be compensated. The sub-aperture images stitching experiment is carried out to verify the validity of optical axis swing error compensation. Results show the situation of sub-aperture images stitching dislocation is improved. The root-mean-square-error (RMSE) of sub-aperture images scanning path after error compensation is nearly 1/20 of the RMSE before compensation.

Measurement method of spindle motion error based on composite laser target

The primary component of ultra-precision machining machines and measuring instruments is a high-precision spindle, and spindle error affects the accuracy of these machines. Traditional spindle error measurement methods often require a standard device as a reference, which inevitably reduces the accuracy of spindle error measurement, while the error separation technique is tedious and time-consuming. This study proposed a spindle error measurement method based on a composite laser target, which can measure the motion error of the spindle, including axial, radial, and tilt error in terms of five degrees of freedom. In this method, a composite laser target containing a laser point source and laser collimated beam was constructed and mounted on the spindle as a reference frame to mark the position of the spindle during rotation. Consequently, the error of the spindle was obtained via measurements of the position and angle of the composite laser target. Differential confocal microscopy technique was employed for measuring the axial position of the laser point source to obtain the axial error of the spindle, whereas the radial position was measured using a conventional microscope optical path to obtain the radial error of the spindle. Further, the angle of laser collimation was measured using a collimation measurement optical path to obtain the tilt error of the spindle. The measurement resolution of the proposed method for spindle error was verified through suitable experiments, yielding resolutions of 4 and 2 nm for axial and radial errors, respectively, and 0.2 μrad in case of tilt error. In addition, the method was tested on an air spindle to demonstrate the feasibility of using the method on machines. Thus, it was demonstrated that the proposed method can be used to obtain the spindle error by optical standard devices without additional error separation techniques, particularly for a spindle in ultra-high precision measurement equipment. Further, it facilitates real time monitoring or evaluation and calibration of the spindle error. This method is expected to serve several important practical applications in the field of ultra-precision machining and measurement. • Spindle error measurement method based on a composite laser target proposed. • Method eliminates reliance of accuracy on standard device used. • Motion error of spindle: axial, radial, tilt in terms of five degrees of freedom. • Resolution of proposed method verified through suitable experiments. • Practical applications in the field of ultra-precision machining and measurement.

Identification of Geometrical Error on Multi-Axis Machine Tools Based on a Laser Tracker

The geometrical errors are affected by many factors for a multi-axis machine tool, such as materials, manufacturing, assembly, measurement, control, and environmental. The geometric error will eventually be reflected in the accuracy of the workpiece; therefore, for each part of the machine tool, the measurement of geometric error is essential. Most geometrical errors are measured separately for each axis. The single geometrical error measurement method is time-consuming. The multiple geometric error measurement methods have some limitations based on different instruments. Laser tracker based on GPS (Global Positioning System) positioning principle can measure the dimensional coordinate. Thus, the laser tracker measured geometric errors in high efficiency, high precision, wide range. This paper introduces the method of measuring the multi-axis machine geometrical error by using a laser tracker with a 1280mm×1280mm×240mm range and compares the measurement result from the traditional method. The results show the laser tracker method has high measurement accuracy, and rapid measurement and compensation of geometrical errors are achievable on a large-stroke machine tools.

Alignment error calculation and measurement method of a compact spaceborne focal plane

The thermal and mechanical stabilization of the focal plane assembly (FPA) naturally has a magnificent impact on the performance of space camera. Thermal and mechanical features of the proposed FPA are described first. Special attention is paid to the thermo-structure design of the FPA. Then the fitting methods based on singular value decomposition and least square are proposed to use the finite-element analysis results to predict the relative position changes between charge coupled devices under thermal and mechanical load cases. A comprehensive finite-element model of the FPA has been built, and the alignment errors, which were induced thermal and gravity loads, are predicted based on the proposed fitting method. To further prove that the design of FPA meets the mechanical and thermal stability requirements, an optical bench was developed. Meanwhile thermal cycling and vibration test of the three FPAs were performed; the alignment error measurements of the FPAs after each test were also performed. Both the simulation and tests show a good stability of the FPA design under operational and test environments.

A new single probe scanning method for on-machine measurement of roundness error

A new single probe scanning method for on-machine measurement of roundness error

ESTIMATION OF ERRORS OF MEASUREMENT METHODS ON A UNIVERSAL MEASURING MICROSCOPE

The article presents the results of evaluating the errors of measurement methods on a universal measuring microscope, determines the causes of their occurrence and their impact on the measurement result, and also provides a comparative analysis of the resulting errors in comparison with other measuring microscopes.