Reduction Of Systematic Errors Research Articles

Prediction of annual rice imports emphasizes on systematic error reduction with smoothing series and optimal parameter selection techniques

Prediction of annual rice imports emphasizes on systematic error reduction with smoothing series and optimal parameter selection techniques

Evaluating the Ability of the Pre-Launch TanSat-2 Satellite to Quantify Urban CO2 Emissions

TanSat-2, the next-generation Chinese greenhouse gas monitoring satellite for measuring carbon dioxide (CO2), has a new city-scale observing mode. We assess the theoretical capability of TanSat-2 to quantify integrated urban CO2 emissions over the cities of Beijing, Jinan, Los Angeles, and Paris. A high-resolution emission inventory and a column-averaged CO2 (XCO2) transport model are used to build an urban CO2 inversion system. We design a series of numerical experiments describing this observing system to evaluate the impacts of sampling patterns and XCO2 measurement errors on inferring urban CO2 emissions. We find that the correction in systematic and random flux errors is correlated with the signal-to-noise ratio of satellite measurements. The reduction in systematic flux errors for the four cities are sizable, but are subject to unbiased satellite sampling and favorable meteorological conditions (i.e., less cloud cover and lower wind speed). The corresponding correction to the random flux error is 19–28%. Even though clear-sky satellite data from TanSat-2 have the potential to reduce flux errors for cities with high CO2 emissions, quantifying urban emissions by satellite-based measurements is subject to additional limitations and uncertainties.

Denoising Autoencoder Normalization for Large-Scale Untargeted Metabolomics by Gas Chromatography-Mass Spectrometry.

Large-scale metabolomics assays are widely used in epidemiology for biomarker discovery and risk assessments. However, systematic errors introduced by instrumental signal drifting pose a big challenge in large-scale assays, especially for derivatization-based gas chromatography-mass spectrometry (GC-MS). Here, we compare the results of different normalization methods for a study with more than 4000 human plasma samples involved in a type 2 diabetes cohort study, in addition to 413 pooled quality control (QC) samples, 413 commercial pooled plasma samples, and a set of 25 stable isotope-labeled internal standards used for every sample. Data acquisition was conducted across 1.2 years, including seven column changes. In total, 413 pooled QC (training) and 413 BioIVT samples (validation) were used for normalization comparisons. Surprisingly, neither internal standards nor sum-based normalizations yielded median precision of less than 30% across all 563 metabolite annotations. While the machine-learning-based SERRF algorithm gave 19% median precision based on the pooled quality control samples, external cross-validation with BioIVT plasma pools yielded a median 34% relative standard deviation (RSD). We developed a new method: systematic error reduction by denoising autoencoder (SERDA). SERDA lowered the median standard deviations of the training QC samples down to 16% RSD, yielding an overall error of 19% RSD when applied to the independent BioIVT validation QC samples. This is the largest study on GC-MS metabolomics ever reported, demonstrating that technical errors can be normalized and handled effectively for this assay. SERDA was further validated on two additional large-scale GC-MS-based human plasma metabolomics studies, confirming the superior performance of SERDA over SERRF or sum normalizations.

Digital twin-driven virtual commissioning of machine tool

The commissioning of Computerized Numerical Control Machine Tools (CNCMTs) is particularly important and the commissioning quality directly affects its product processing. However, traditional commissioning methods are not suitable for complex and changeable machining conditions during operation, and the derived commissioning results have limited effectiveness. Therefore, this paper proposes a digital twin-driven virtual commissioning method to simulate the machining processes in a virtual environment and perform virtual commissioning to obtain better commissioning results. Firstly, a digital twin model is constructed using a multi-domain unified modeling language combined with a virtual-real mapping strategy to describe the response characteristics of CNCMTs. Secondly, a complex machining scene is simulated based on the twin model, and a virtual commissioning strategy and platform are constructed in this environment. Finally, the effectiveness of the proposed method is verified by taking the spindle system of CNCMTs as an example. The experimental results show a 13% short in response time and a 54% reduction in total systematic error along with a decrease in commissioning time.

ВОСПРОИЗВЕДЕНИЕ ПРОЦЕССОВ В СТРАТОСФЕРЕ МОДЕЛЬЮ ОБЩЕЙ ЦИРКУЛЯЦИИ АТМОСФЕРЫ ПЛАВ072L96

Dynamics of the stratosphere and ozone layer are among important sources of atmospheric circulation predictability at subseasonal-to-seasonal time scales. The simulation of the stratospheric dynamics with the SL-AV atmospheric general circulation model for the SLAV072L96 seasonal weather prediction configuration is analyzed. The configuration is currently under preoperational testing at the Hydrometcenter of Russia. The model simulates both winter and summer averaged distributions of zonal wind and temperature close to the reanalysis data. The quasi-biennial oscillation of equatorial zonal wind is simulated with a realistic period and amplitude. There is a significant reduction of errors as compared with the previous stratosphere-resolving SL-AV model configuration. It is shown that the stratospheric process simulation enhancement is largely due to the reduction of systematic errors in the simulation of troposphere dynamics. The work on the inclusion of the CHARM photochemical model in the SL-AV model is described. The results of first experiments with the coupled model are given.

A High-Accuracy and Energy-Efficient CORDIC Based Izhikevich Neuron With Error Suppression and Compensation.

Bio-inspired neuron models are the key building blocks of brain-like neural networks for brain-science exploration and neuromorphic engineering applications. The efficient hardware design of bio-inspired neuron models is one of the challenges to implement brain-like neural networks, as the balancing of model accuracy, energy consumption and hardware cost is very challenging. This paper proposes a high-accuracy and energy-efficient Fast-Convergence COordinate Rotation DIgital Computer (FC-CORDIC) based Izhikevich neuron design. For ensuring the model accuracy, an error propagation model of the Izhikevich neuron is presented for systematic error analysis and effective error reduction. Parameter-Tuning Error Compensation (PTEC) method and Bitwidth-Extension Error Suppression (BEES) method are proposed to reduce the error of Izhikevich neuron design effectively. In addition, by utilizing the FC-CORDIC instead of conventional CORDIC for square calculation in the Izhikevich model, the redundant CORDIC iterations are removed and therefore, both the accumulated errors and required computation are effectively reduced, which significantly improve the accuracy and energy efficiency. An optimized fixed-point design of FC-CORDIC is also proposed to save hardware overhead while ensuring the accuracy. FPGA implementation results exhibit that the proposed Izhikevich neuron design can achieve high accuracy and energy efficiency with an acceptable hardware overhead, among the state-of-the-art designs.

Tensor interpolation in virtual manufacturing chains for fiber reinforced composites

Preeminent weight-specific mechanical properties predestine fiber-reinforced plastics for application in structural components. Virtual manufacturing chains (CAE-chains) capture a multi-step process sequence by interlinking models with corresponding constitutive laws for each process step. In common numerical solving techniques, spatial discretization of governing equations yields discrete solutions. Since mesh type and fineness are usually problem-specific, mesh-to-mesh mapping must be embedded in the data interfaces between the individual simulation steps. To receive meaningful data, the underlying averaging and interpolation schemes between non-congruent meshes must be mathematically and physically consistent. In engineering applications, interpolation of tensors is usually carried out component-based, treating each component as an independent scalar-field and thus neglecting the tensorial character. The work at hand gives an overview of sophisticated techniques that preserve specific tensor characteristics in interpolation. Compared to available approaches, an enhanced decomposition-based interpolation method is proposed, allowing for a generalization with more than two basic values. In the context of process simulation for short fiber-reinforced injection molding, the influence of the interpolation techniques is evaluated in a three-stage approach with increasing proximity to application. Firstly, mathematical examples demonstrate that component-based interpolation can result in erroneous, non-monotonous tensor characteristics. Secondly, an analytically resolvable problem is derived, and the solution is compared to reconstructed tensors from different interpolation schemes. Thirdly, a numerical CFD-simulation is conducted. Field recovery via interpolation is performed and systematical errors are statistically evaluated. The results reveal significant biases for certain tensor characteristics induced by conventional component-wise interpolation. Overall, a reduction in systematic interpolation errors is achieved by the proposed decomposition-based interpolation.

A Combined Approach of Systematic Error Reduction with Smoothing Series and Optimal Parameter Selection Techniques for Prediction of Annual Rice Imports

A Combined Approach of Systematic Error Reduction with Smoothing Series and Optimal Parameter Selection Techniques for Prediction of Annual Rice Imports

Characterization of an Instrument Model for Exoplanet Transit Spectrum Estimation through Wide-scale Analysis on HST Data

Abstract Instrument models (IMs) enable the reduction of systematic error in transit spectroscopy light-curve data, but, since the model formulation can influence the estimation of science model parameters, characterization of the instrument model effects is crucial to the interpretation of the reduced data. We analyze a simple instrument model and assess its validity and performance across Hubble WFC3 and STIS instruments. Over a large, n = 63, sample of observed targets, a Markov chain Monte Carlo sampler computes the parent distribution of each instrument model parameter. Possible parent distribution functions are then fit and tested against the empirical IM distribution. Correlation and other analyses are then performed to find IM relationships. The model is shown to perform well across the two instruments and three filters analyzed and, further, the Student’s t distribution is shown to closely fit the empirical parent distribution of IM parameters and the Gaussian distribution is shown to poorly model the observed distribution. This parent distribution can be used in the MCMC prior fitting and demonstrates IM consistency for wide-scale atmospheric analysis using this model. Finally, we propose a simple metric based on light-curve residuals to determine model performance, and we demonstrate its ability to determine whether a derived spectrum under this IM is high quality and robust.

Camera motion-induced systematic errors in stereo-DIC and speckle-based compensation method

Stereo-digital image correlation (stereo-DIC) is now a standard technique for determination of the mechanical properties of materials and structures. In stereo-DIC, cameras are assumed to be motionless after camera calibration, so the three-dimensional (3D) reconstruction can be implemented using pre-calibrated parameters. However, this assumption is not true in some situations, such as drop test, seismic shaking table test and non-laboratory environment. Due to the presence of ground shaking or wind blowing, it's almost impossible to avoid camera motion in these experiments even if mechanical fixing is adopted. Camera motion during the experiment can undoubtedly introduce significant errors on measured results. Generally, camera motion can be divided into pair motion and relative motion. The influence of these two kinds of motion on stereo-DIC measurement is different and worth of separate study. Keep this in mind, in this paper, the influence of camera motion on stereo-DIC is investigated and the effects of pair motion and relative motion are examined separately using precisely controllable simulated experiments. Specifically, by integrating into the stereo matching, the speckle-based compensation method for relative motion is proposed and the accuracy of the compensation methods is analyzed in detail. The reduction of camera motion-induced systematic errors will be helpful for the further applications of stereo-DIC in non-laboratory environments and engineering fields.

Estimating the forcing function in a mechanical system by an inverse calibration method

This article proposes and demonstrates a calibration-based integral formulation for resolving the forcing function in a mass–spring–damper system, given either displacement or acceleration data. The proposed method is novel in the context of vibrations, being thoroughly studied in the field of heat transfer. The approach can be expanded and generalized further to multi-variable systems associated with machine parts, vehicle suspensions, translational and rotational systems, gear systems, etc. when mathematically described by a system of constant property, linear, time-invariant ordinary differential equations. The analytic approach and subsequent numerical reconstruction of the forcing function is based on resolving a parameter-free inverse formulation for the equation(s) of motion. The calibration approach is formulated in the frequency domain and takes advantage of several observations produced by the dimensionality reduction leading to an algebratized system involving an input–output relationship and a transfer function possessing all the system parameters. The transfer function is eliminated in lieu of experimental data, from a calibration effort, thus leading to a reduction of systematic errors. These parameter-free, reduced systematic error aspects are the distinct and novel advantages of the proposed method. A first-kind Volterra integral equation is formed containing only the unknown forcing function and experimental data. As with all ill-posed problems, regularization must be introduced for system stabilization. A future-time technique is instituted for forming a family of predictions based on the chosen regularization parameter. The optimal regularization parameter is estimated using a combination of phase–plane analysis and cross-correlation principles. Finally, a numerical simulation is performed verifying the proposed approach.

T → 0 extrapolation function in SFtX method for the energy–momentum tensor

Abstract We theoretically clarify the functional form to be used in t → 0 extrapolation in the small flow time expansion (SFtX) method for the energy–momentum tensor (EMT), which facilitates lattice simulation of the EMT based on the gradient flow. We argue that in the t → 0 extrapolation analysis, lattice data should be fitted by a power function in g(μ(t)), the flow time dependent running coupling, where the power is determined by the perturbation order we consider. From actual lattice data, we confirm the validity of the extrapolation function. Using the new extrapolation function, we present updated lattice results for thermodynamics quantities in quenched QCD; our results are consistent with the previous study [arXiv:1812.06444] but we obtain smaller errors due to reduction of systematic errors.

Accuracy of an MR-only workflow for prostate radiotherapy using semi-automatically burned-in fiducial markers

BackgroundThe benefit of MR-only workflow compared to current CT-based workflow for prostate radiotherapy is reduction of systematic errors in the radiotherapy chain by 2–3 mm. Nowadays, MRI is used for target delineation while CT is needed for position verification. In MR-only workflows, MRI based synthetic CT (sCT) replaces CT. Intraprostatic fiducial markers (FMs) are used as a surrogate for the position of the prostate improving targeting. However, FMs are not visible on sCT. Therefore, a semi-automatic method for burning-in FMs on sCT was developed. Accuracy of MR-only workflow using semi-automatically burned-in FMs was assessed and compared to CT/MR workflow.MethodsThirty-one prostate cancer patients receiving radiotherapy, underwent an additional MR sequence (mDIXON) to create an sCT for MR-only workflow simulation. Three sources of accuracy in the CT/MR- and MR-only workflow were investigated. To compare image registrations for target delineation, the inter-observer error (IOE) of FM-based CT-to-MR image registrations and soft-tissue-based MR-to-MR image registrations were determined on twenty patients. Secondly, the inter-observer variation of the resulting FM positions was determined on twenty patients. Thirdly, on 26 patients CBCTs were retrospectively registered on sCT with burned-in FMs and compared to CT-CBCT registrations.ResultsImage registration for target delineation shows a three times smaller IOE for MR-only workflow compared to CT/MR workflow. All observers agreed in correctly identifying all FMs for 18 out of 20 patients (90%). The IOE in CC direction of the center of mass (COM) position of the markers was within the CT slice thickness (2.5 mm), the IOE in AP and RL direction were below 1.0 mm and 1.5 mm, respectively. Registrations for IGRT position verification in MR-only workflow compared to CT/MR workflow were equivalent in RL-, CC- and AP-direction, except for a significant difference for random error in rotation.ConclusionsMR-only workflow using sCT with burned-in FMs is an improvement compared to the current CT/MR workflow, with a three times smaller inter observer error in CT-MR registration and comparable CBCT registration results between CT and sCT reference scans.Trial registry Medical Research Involving Human Subjects Act (WMO) does apply to this study and was approved by the Medical Ethics review Committee of the Academic Medical Center. Registration number: NL65414.018.18. Date of registration: 21–08-2018.

Quantifying multinucleon effect in Argon using high-pressure TPC

Neutrino oscillation experiments use heavy nuclear targets to achieve sufficient interaction rates. Nuclear effects are introduced in the experimental environment by the use of these targets and need to be quantified as they add to the systematic errors. In the low energy region (around 1 GeV) multinucleon events are also present along with Quasi Elastic (QE) and Delta interactions. Therefore if these multinucleon events are not incorporated in the data set properly, we end up with an inaccurate reconstruction of neutrino energy. In our work, we have illustrated the importance of incorporation of multinucleon events for the reduction of systematic errors in physics predictions by DUNE-Near Detector (ND). To achieve this we have presented the event distribution ratio of Ar/C, Ar/Ar, and C/C as a function of squared four-momentum transfer by employing different nuclear models. This analysis recommends the addition of 2p2h or multinucleon events in the event sample and promotes model with Random Phase Approximations (RPA) effect for the analysis of the event sample to overcome or reduce the systematic uncertainties.

Multi-Objective Support Vector Regression Reduces Systematic Error in Moderate Resolution Maps of Tree Species Abundance

When forest conditions are mapped from empirical models, uncertainty in remotely sensed predictor variables can cause the systematic overestimation of low values, underestimation of high values, and suppression of variability. This regression dilution or attenuation bias is a well-recognized problem in remote sensing applications, with few practical solutions. Attenuation is of particular concern for applications that are responsive to prediction patterns at the high end of observed data ranges, where systematic error is typically greatest. We addressed attenuation bias in models of tree species relative abundance (percent of total aboveground live biomass) based on multitemporal Landsat and topoclimatic predictor data. We developed a multi-objective support vector regression (MOSVR) algorithm that simultaneously minimizes total prediction error and systematic error caused by attenuation bias. Applied to 13 tree species in the Acadian Forest Region of the northeastern U.S., MOSVR performed well compared to other prediction methods including single-objective SVR (SOSVR) minimizing total error, Random Forest (RF), gradient nearest neighbor (GNN), and Random Forest nearest neighbor (RFNN) algorithms. SOSVR and RF yielded the lowest total prediction error but produced the greatest systematic error, consistent with strong attenuation bias. Underestimation at high relative abundance caused strong deviations between predicted patterns of species dominance/codominance and those observed at field plots. In contrast, GNN and RFNN produced dominance/codominance patterns that deviated little from observed patterns, but predicted species relative abundance with lower accuracy and substantial systematic error. MOSVR produced the least systematic error for all species with total error often comparable to SOSVR or RF. Predicted patterns of dominance/codominance matched observations well, though not quite as well as GNN or RFNN. Overall, MOSVR provides an effective machine learning approach to the reduction of systematic prediction error and should be fully generalizable to other remote sensing applications and prediction problems.

ICBM-OCEAN: Processing Ultrahigh-Resolution Mass Spectrometry Data of Complex Molecular Mixtures.

Untargeted molecular analyses of complex mixtures are relevant for many fields of research, including geochemistry, pharmacology, and medicine. Ultrahigh-resolution mass spectrometry is one of the most powerful tools in this context. The availability of open scripts and online tools for specific data processing steps such as noise removal or molecular formula assignment is growing, but an integrative tool where all crucial steps are reproducibly evaluated and documented is lacking. We developed a novel, server-based tool (ICBM-OCEAN, Institute for Chemistry and Biology of the Marine Environment, Oldenburg-complex molecular mixtures, evaluation & analysis) that integrates published and novel approaches for standardized processing of ultrahigh-resolution mass spectrometry data of complex molecular mixtures. Different from published approaches, we offer diagnostic and validation tools for all relevant steps. Among other features, we included objective and reproducible reduction of noise and systematic errors, spectra recalibration and alignment, and identification of likeliest molecular formulas. With 15 chemical elements, the tool offers high flexibility in formula attribution. Alignment of mass spectra among different samples prior to molecular formula assignment improves mass error and facilitates molecular formula confirmation with the help of isotopologues. The online tool and the detailed instruction manual are freely accessible at www.icbm.de/icbm-ocean.

Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data

BackgroundThe ability to confidently predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics. Yet, the goal of developing actionable, robust, and reproducible predictive signatures of phenotypes such as clinical outcome has not been attained in almost any disease area. Here, we report a comprehensive analysis spanning prediction tasks from ulcerative colitis, atopic dermatitis, diabetes, to many cancer subtypes for a total of 24 binary and multiclass prediction problems and 26 survival analysis tasks. We systematically investigate the influence of gene subsets, normalization methods and prediction algorithms. Crucially, we also explore the novel use of deep representation learning methods on large transcriptomics compendia, such as GTEx and TCGA, to boost the performance of state-of-the-art methods. The resources and findings in this work should serve as both an up-to-date reference on attainable performance, and as a benchmarking resource for further research.ResultsApproaches that combine large numbers of genes outperformed single gene methods consistently and with a significant margin, but neither unsupervised nor semi-supervised representation learning techniques yielded consistent improvements in out-of-sample performance across datasets. Our findings suggest that using l2-regularized regression methods applied to centered log-ratio transformed transcript abundances provide the best predictive analyses overall.ConclusionsTranscriptomics-based phenotype prediction benefits from proper normalization techniques and state-of-the-art regularized regression approaches. In our view, breakthrough performance is likely contingent on factors which are independent of normalization and general modeling techniques; these factors might include reduction of systematic errors in sequencing data, incorporation of other data types such as single-cell sequencing and proteomics, and improved use of prior knowledge.

ANNIE: Neutron multiplicity in neutrino interactions and new technologies

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) aims at measuring the neutron abundance in the final state of neutrino-nucleus interactions. This measurement will have a direct impact on our understanding of neutrino interactions and will lead to a better reduction of systematic errors and an improvement of signal-background discrimination in future large neutrino detectors, thus impacting long baseline oscillation experiments as well as proton decay searches and supernova detection. With a volume of about 26 tons of pure water doped with gadolinium to enhance neutron tagging efficiency, ANNIE will provide a measurement of the neutron yield of neutrino interactions as a function of the neutrino energy in the well-characterized Booster Neutrino Beam at Fermilab. The modularity of ANNIE will allow it to perform several fundamental tests of new technologies to be used in neutrino detectors, such as a novel kind of photodetectors called Large Area Picosecond Photodetectors and Water-based Liquid Scintillator, a newly-developed detection medium. The technology behind the ANNIE detector will have a noticeable impact on the development of future large water Cherenkov detectors as well as on detection techniques for neutrino physics.

Simultaneous removal of nonrotationally symmetric errors in tilted wave interferometry

The tilted wave interferometer has been developed as a fast and accurate instrument for the measurement of aspheric and freeform surfaces. We present a method for increasing its robustness and flexibility. Tilted wave interferometry crucially depends on accurate calibration and any changes to a calibrated setup require, in general, a recalibration. Therefore, we propose a method for simultaneous topography reconstruction and elimination of errors arising from such changes. An approach to identify trends in systematic errors for the complex non-null setup with a large number of blackbox model parameters is worked out. The procedure allows deriving an error removal scheme for nonrotationally symmetric components based on measurements in different rotational positions. The feasibility and benefit of the error elimination method are shown both by simulation methods and dedicated experiments. A significant reduction of systematic errors even in a miscalibrated state is achieved. Hence, recalibrations are avoided and measurement time and flexibility are improved.

Biobased carbon content quantification through AMS radiocarbon analysis of liquid fuels

Increased production and use of renewable hydrocarbons has created a need for accurate quantification of the biobased carbon content of hydrocarbon fuels. The blend percentages of conventional biofuels (such as biodiesel and ethanol) can be quantified by numerous methods such as gas chromatography and infrared spectroscopy. Distinguishing the biobased content of a purely hydrocarbon fuel, however, is more difficult. In this study, a single-blind trial was conducted to assess the accuracy and precision of accelerator mass spectrometry (AMS) radiocarbon analysis of the biobased carbon content of hydrocarbon fuels. Test samples with known biobased carbon contents ranging from 0 to 10 wt% were sent to four labs for analysis by ASTM D6866-16 Method B. Blank correction of the test results was found to improve measurement accuracy through the reduction of lab-specific systematic error. Favorable precision, accuracy, and a limit of detection were determined. This study strongly supports radiocarbon analysis by AMS as an accurate and reliable method for quantifying the biobased carbon content of hydrocarbon transportation fuels.