Derived Error Model Research Articles

Kinematic Calibration Method for Six-Hardpoint Positioning Mechanisms Using Optimal Measurement Pose

In this study, a kinematic calibration method is proposed. The method selects the optimal measurement poses based on the observability index O1, and is used for six-hardpoint positioning mechanisms, which can identify and compensate for kinematic parameter errors of the mechanism. The calibration method is based on the derived error model. In order to improve the problem that measurement noise affects calibration accuracy, the proposed calibration method selects specific poses for calibration based on the observability index O1, effectively improving the calibration accuracy. The reason for using O1 is that we compared the performance of the five observability indices through simulation, and the results show that the calibration based on O1 has the highest accuracy. In order to achieve pose selection based on observability indices, the DETMAX algorithm has been improved to achieve better performance of selection. Finally, an overall evaluation of the proposed calibration method is conducted, and the results show that the method can accurately identify kinematic parameter errors, with a fast error convergence speed. Compared with the traditional method, the proposed method has higher accuracy, reducing the mean position error by 78.4% and the mean attitude error by 70.6%. The proposed method is instrumental in the accurate kinematic calibration of six-hardpoint positioning mechanisms, and can effectively improve the accuracy of the mechanism, thus improving the pose accuracy of the primary mirror of the ground-based large aperture telescope.

Automatic Kinematic Calibration of Multiaxis Gimbal Systems

This article presents an automatic and efficient calibration method for multiaxis gimbal systems. The calibrated kinematic parameters consist of the axis orientation, the joint encoder nonlinearity map, and the endpoint gyro bias. The calibration of these parameters is conducted simultaneously by sequentially and independently moving one gimbal joint back and forth. With such straightforward intuition, the algorithm is formulated based on the product-of-exponential formula with an analytically derived error model. The key and unique contribution of this algorithm is that it produces a closed-form optimal solution, which is computation efficient and easy for embedded implementation. Both numerical and experimental tests are performed to validate the effectiveness of the proposed algorithm in calibrating the kinematic parameters of gimbal systems and compensating the gyro bias.

GRACE gravity field recovery with background model uncertainties

In this article, we present a computationally efficient method to incorporate background model uncertainties into the gravity field recovery process. While the geophysical models typically used during the processing of GRACE data, such as the atmosphere and ocean dealiasing product, have been greatly improved over the last years, they are still a limiting factor of the overall solution quality. Our idea is to use information about the uncertainty of these models to find a more appropriate stochastic model for the GRACE observations within the least squares adjustment, thus potentially improving the gravity field estimates. We used the ESA Earth System Model to derive uncertainty estimates for the atmosphere and ocean dealiasing product in the form of an autoregressive model. To assess our approach, we computed time series of monthly GRACE solutions from L1B data in the time span of 2005 to 2010 with and without the derived error model. Intercomparisons between these time series show that noise is reduced on all spatial scales, with up to 25% RMS reduction for Gaussian filter radii from 250 to 300 km, while preserving the monthly signal. We further observe a better agreement between formal and empirical errors, which supports our conclusion that used uncertainty information does improve the stochastic description of the GRACE observables.

New Theoretical Limit Analysis of LoS and RSS Based Positioning Methods for Ricean Fading Channel in RF Systems

In this paper, bias and CRB analysis for both proposed line-of-sight (LoS) and conventional radio-signal-strength (RSS) based positioning methods are derived. In contrast to the conventional lognormal based analysis, the derived error models take into account the environmental multipath fading effect. The proposed LoS-based scheme improves positioning accuracy by eliminating the effect of non-range dependent portion of powers from the received signal. It suppresses non-line-of-sight (NLoS) disturbance to the positioning result through estimating the LoS component from fading signal. Hence, it is robust to environmental fading disturbance. The derived error models are validated by simulation. Both analytical model and simulation results show that the accuracy and robustness of the proposed LoS-based positioning are superior to that of the conventional RSS-based methods.

Equal Noise Resistance of Two Mainstream Iterative Sub-pixel Registration Algorithms in Digital Image Correlation

The inverse compositional Gauss-Newton (IC-GN) algorithm and the forward additive Newton-Raphson (FA-NR) algorithm are two mainstream iterative sub-pixel registration algorithms in digital image correlation. This study compares the accuracy and convergence ability of the two algorithms by theoretical analysis and numerical experiments in the speckle images that have been contaminated with artificial Gaussian noise. Based on the derived error model, the systematic errors of the two algorithms are dominated by interpolation-induced error and are insensitive to noise. The random errors are proportional to the noise level. The noise also reduces the convergence radius and rate in the two algorithms. The two algorithms demonstrate equal noise resistance due to their mathematical equivalence. These conclusions are well supported by the experimental study. The recently reported vulnerability of the FA-NR algorithm to noise is not associated with the inherent flaw of the algorithm but with its implementation. If an inappropriate method is employed to estimate the gradients at sub-pixel locations in the FA-NR algorithm, abnormally large errors may be induced. This problem can be eliminated using the method that is proposed in this study, which has an insignificant extra-computation cost.

Sequencing error correction without a reference genome.

BackgroundNext (second) generation sequencing is an increasingly important tool for many areas of molecular biology, however, care must be taken when interpreting its output. Even a low error rate can cause a large number of errors due to the high number of nucleotides being sequenced. Identifying sequencing errors from true biological variants is a challenging task. For organisms without a reference genome this difficulty is even more challenging.ResultsWe have developed a method for the correction of sequencing errors in data from the Illumina Solexa sequencing platforms. It does not require a reference genome and is of relevance for microRNA studies, unsequenced genomes, variant detection in ultra-deep sequencing and even for RNA-Seq studies of organisms with sequenced genomes where RNA editing is being considered.ConclusionsThe derived error model is novel in that it allows different error probabilities for each position along the read, in conjunction with different error rates depending on the particular nucleotides involved in the substitution, and does not force these effects to behave in a multiplicative manner. The model provides error rates which capture the complex effects and interactions of the three main known causes of sequencing error associated with the Illumina platforms.

STUDY OF A NEW AND LOW-COST MEASUREMENT METHOD OF VOLUMETRIC ERRORS FOR CNC FIVE-AXIS MACHINE TOOLS

An effective and inexpensive volumetric error measurement method is an essential of the software-based error compensation method that can improve the machining accuracy of a CNC machine tool without increasing hardware manufacturing cost. In this paper, a new volumetric-error measurement method incorporating of three derived error models, two-step measurement procedure, and use of telescoping ball-bar was proposed for three major types of five-axis machine tools. Comparing to the methods currently used in industry, the proposed method provides the advantages of low cost, easy setup, and high efficiency. The simulation and experimental results have shown the feasibility and effectiveness of the method.

Modified Allan Variance Analysis on Random Errors of MINS

Allan variance method is a useful tool for analyzing the random errors, but the confidence on the estimate would be lower when the data length became shorter, therefore the modified Allan variance is deduced to analysis the random errors of MEMS inertial sensors (MINS). The definition and limitation of Allan variance are presented first, and then the modified Allan variance is deduced. Allan variance method is compared with modified Allan variance by identifying the simulated 1/f noises, meanwhile the results are illuminated. In the end, the random errors of MEMS inertial sensors were analyzed by the proposed methods. The characteristics of MEMS accelerometers’ and MEMS gyros’ stochastic errors are identified and quantified. The derived error model can be applied further to our attitude and heading reference system of the underwater robot. DOI: http://dx.doi.org/10.11591/telkomnika.v11i3.2190 Full Text: PDF

A Low Cost and Efficient Volumetric-Error Measurement Method for Five-Axis Machine Tools

Error compensation is an effective and inexpensive way that can further enhance the machining accuracy of a multi-axis machine tool. The volumetric error measurement method is an essential of the error compensation method. The measurement of volumetric errors of a 5-axis machine tool is very difficult to be conducted due to its complexity. In this study, a volumetric-error measurement method using telescoping ball-bar was developed for the three major types of 5-axis machines. With the use of the three derived error models and the two-step measurement procedures, the method can quickly determine the volumetric errors of the three types of 5-axis machine tools. Comparing to the measurement methods currently used in industry, the proposed method provides the advantages of low cost, easy setup, and high efficiency.

Accurate human microsatellite genotypes from high-throughput resequencing data using informed error profiles

Repetitive sequences are biologically and clinically important because they can influence traits and disease, but repeats are challenging to analyse using short-read sequencing technology. We present a tool for genotyping microsatellite repeats called RepeatSeq, which uses Bayesian model selection guided by an empirically derived error model that incorporates sequence and read properties. Next, we apply RepeatSeq to high-coverage genomes from the 1000 Genomes Project to evaluate performance and accuracy. The software uses common formats, such as VCF, for compatibility with existing genome analysis pipelines. Source code and binaries are available at http://github.com/adaptivegenome/repeatseq.

A Ball-Bar Based Error Measurement Method for a RRTTT-Type Five-Axis Machine Tool

Measurement method using telescoping ball-bar that can directly determine the volumetric errors of three main types of five-axis machine tools was developed. Adopting Single Socket method, and the method following the defined two-step measurements sequence and incorporating with derived error models, can quickly determine the five degrees-of-freedom (DOF) volumetric errors of five-axis machine tools. Comparing to most of the current used measurement methods, the proposed method provides the advantages of low cost, high efficiency, easy setup, and high accuracy.

Robust multi-tissue gene panel for cancer detection

BackgroundWe have identified a set of genes whose relative mRNA expression levels in various solid tumors can be used to robustly distinguish cancer from matching normal tissue. Our current feature set consists of 113 gene probes for 104 unique genes, originally identified as differentially expressed in solid primary tumors in microarray data on Affymetrix HG-U133A platform in five tissue types: breast, colon, lung, prostate and ovary. For each dataset, we first identified a set of genes significantly differentially expressed in tumor vs. normal tissue at p-value = 0.05 using an experimentally derived error model. Our common cancer gene panel is the intersection of these sets of significantly dysregulated genes and can distinguish tumors from normal tissue on all these five tissue types.MethodsFrozen tumor specimens were obtained from two commercial vendors Clinomics (Pittsfield, MA) and Asterand (Detroit, MI). Biotinylated targets were prepared using published methods (Affymetrix, CA) and hybridized to Affymetrix U133A GeneChips (Affymetrix, CA). Expression values for each gene were calculated using Affymetrix GeneChip analysis software MAS 5.0. We then used a software package called Genes@Work for differential expression discovery, and SVM light linear kernel for building classification models.ResultsWe validate the predictability of this gene list on several publicly available data sets generated on the same platform. Of note, when analysing the lung cancer data set of Spira et al, using an SVM linear kernel classifier, our gene panel had 94.7% leave-one-out accuracy compared to 87.8% using the gene panel in the original paper. In addition, we performed high-throughput validation on the Dana Farber Cancer Institute GCOD database and several GEO datasets.ConclusionsOur result showed the potential for this panel as a robust classification tool for multiple tumor types on the Affymetrix platform, as well as other whole genome arrays. Apart from possible use in diagnosis of early tumorigenesis, some other potential uses of our methodology and gene panel would be in assisting pathologists in diagnosis of pre-cancerous lesions, determining tumor boundaries, assessing levels of contamination in cell populations in vitro and identifying transformations in cell cultures after multiple passages. Moreover, based on the robustness of this gene panel in identifying normal vs. tumor, mislabelled or misinterpreted samples can be pinpointed with high confidence.