Sources Of Error Research Articles

Arterial input function estimation compensating for inflow and partial voluming in dynamic contrast-enhanced MRI.

Both inflow and the partial volume effect (PVE) are sources of error when measuring the arterial input function (AIF) in dynamic contrast-enhanced (DCE) MRI. This is relevant, as errors in the AIF can propagate into pharmacokinetic parameter estimations from the DCE data. A method was introduced for flow correction by estimating and compensating the number of the perceived pulse of spins during inflow. We hypothesized that the PVE has an impact on concentration-time curves similar to inflow. Therefore, we aimed to study the efficiency of this method to compensate for both effects simultaneously. We first simulated an AIF with different levels of inflow and PVE contamination. The peak, full width at half-maximum (FWHM), and area under curve (AUC) of the reconstructed AIFs were compared with the true (simulated) AIF. In clinical data, the PVE was included in AIFs artificially by averaging the signal in voxels surrounding a manually selected point in an artery. Subsequently, the artificial partial volume AIFs were corrected and compared with the AIF from the selected point. Additionally, corrected AIFs from the internal carotid artery (ICA), the middle cerebral artery (MCA), and the venous output function (VOF) estimated from the superior sagittal sinus (SSS) were compared. As such, we aimed to investigate the effectiveness of the correction method with different levels of inflow and PVE in clinical data. The simulation data demonstrated that the corrected AIFs had only marginal bias in peak value, FWHM, and AUC. Also, the algorithm yielded highly correlated reconstructed curves over increasingly larger neighbourhoods surrounding selected arterial points in clinical data. Furthermore, AIFs measured from the ICA and MCA produced similar peak height and FWHM, whereas a significantly larger peak and lower FWHM was found compared with the VOF. Our findings indicate that the proposed method has high potential to compensate for PVE and inflow simultaneously. The corrected AIFs could thereby provide a stable input source for DCE analysis.

Converged ensemble simulations of climate: possible trends in total solar irradiance cannot explain global warming alone

We address the hypothetical question of whether an increasing total solar irradiance (TSI) trend, without anthropogenic contributions, could be sufficient to explain the ongoing global warming. To this end, the intermediate-complexity climate model PlaSim is used. To consider the total internal variability, we present a set of ensemble simulations, with different forcing histories in TSI and CO2 concentration, that have converged sufficiently tightly to the relevant probability distributions to provide a satisfactory bound on any spurious trend possibly arising from a sampling bias; similar bounds on any other unforced contributions to ensemble mean trends are also estimated. A key point is the consideration, among the forcing histories, the steepest increasing trend in TSI that is still consistent with observations according to a recent study; thereby, we essentially revisit corresponding TSI reconstructions, more than 20 years after their last modeling-based evaluation, by improving the analysis through taking care of all possible sources of error or uncertainty and incorporating data that have become available since then. Without any change in CO2 concentration, our TSI trend (i.e., and upper bound on actual TSI trends) is found to be insufficient to produce outcomes compatible with the observational record in global mean surface temperature (GMST) with a nonnegligible probability. We formalize our statement for quantifiers of GMST trends through evaluating their distributions over the ensemble, and we speculate that the hypothesis about the exclusive role of an increasing TSI remains implausible even beyond our particular model setup. At the same time, if we consider a constant TSI, and the observational record in CO2 concentration is applied as forcing, the simulation results and the recorded GMST match well. While we currently need to leave the question of a precise attribution open, we conclude by pointing out that an attribution of the ongoing global warming to an increasing TSI alone could be made plausible only if a bias in the set of land-based instrumental temperature measurements were increasing more rapidly than commonly estimated; an assessment of the latter possibility is out of the scope of our study, as well as addressing solar forcing mechanisms beyond the effect of TSI.

Quadrature Based Neural Network Learning of Stochastic Hamiltonian Systems

Hamiltonian Neural Networks (HNNs) provide structure-preserving learning of Hamiltonian systems. In this paper, we extend HNNs to structure-preserving inversion of stochastic Hamiltonian systems (SHSs) from observational data. We propose the quadrature-based models according to the integral form of the SHSs’ solutions, where we denoise the loss-by-moment calculations of the solutions. The integral pattern of the models transforms the source of the essential learning error from the discrepancy between the modified Hamiltonian and the true Hamiltonian in the classical HNN models into that between the integrals and their quadrature approximations. This transforms the challenging task of deriving the relation between the modified and the true Hamiltonians from the (stochastic) Hamilton–Jacobi PDEs, into the one that only requires invoking results from the numerical quadrature theory. Meanwhile, denoising via moments calculations gives a simpler data fitting method than, e.g., via probability density fitting, which may imply better generalization ability in certain circumstances. Numerical experiments validate the proposed learning strategy on several concrete Hamiltonian systems. The experimental results show that both the learned Hamiltonian function and the predicted solution of our quadrature-based model are more accurate than that of the corrected symplectic HNN method on a harmonic oscillator, and the three-point Gaussian quadrature-based model produces higher accuracy in long-time prediction than the Kramers–Moyal method and the numerics-informed likelihood method on the stochastic Kubo oscillator as well as other two stochastic systems with non-polynomial Hamiltonian functions. Moreover, the Hamiltonian learning error εH arising from the Gaussian quadrature-based model is lower than that from Simpson’s quadrature-based model. These demonstrate the superiority of our approach in learning accuracy and long-time prediction ability compared to certain existing methods and exhibit its potential to improve learning accuracy via applying precise quadrature formulae.

Unusual Multiple and Bilateral Combinations of Arterial and Neural Variations in the Axillae of Single Cadaver

Nerve During the dissection of the axillae and upper limbs of an 80-year-old male donor, multiple vascular and neural variations were encountered. On the right side, the thoracoacromial artery and the lateral thoracic artery were absent. On the left side the subscapular artery was absent, and the posterior cord of the brachial plexus gave branch to two upper subscapular nerves and then divided into radial nerve and a common stem that branched into thoracodorsal nerve, lower subscapular nerve, nerves to teres major and minor muscles and the axillary nerve. Additionally, in the left arm there was a variant communication between the musculocutaneous nerve and the median nerve. Although the existence of these variations is well documented in the literature as individual entities or few combinations, the occurrence of such multiple variations in a single cadaver in a limited area of the body involving vessel and nerves is not common. A multitude of variations in a small area of the body such as this can be a source of diagnostic and imaging interpretation errors. Unpredictable iatrogenic injuries can also occur during various procedures in the area such as during preparation of muscle flap, mastectomy with axillary lymph node dissection, reconstructive and aesthetic plastic surgery of the breast and other clinical procedures including shoulder arthroplasty.

Solving Boltzmann optimization problems with deep learning

Decades of exponential scaling in high-performance computing (HPC) efficiency is coming to an end. Transistor-based logic in complementary metal-oxide semiconductor (CMOS) technology is approaching physical limits beyond which further miniaturization will be impossible. Future HPC efficiency gains will necessarily rely on new technologies and paradigms of computing. The Ising model shows particular promise as a future framework for highly energy-efficient computation. Ising systems are able to operate at energies approaching thermodynamic limits for energy consumption of computation. Ising systems can function as both logic and memory. Thus, they have the potential to significantly reduce energy costs inherent to CMOS computing by eliminating costly data movement. The challenge in creating Ising-based hardware is in optimizing useful circuits that produce correct results on fundamentally nondeterministic hardware. The contribution of this paper is a novel machine learning approach, a combination of deep neural networks and random forests, for efficiently solving optimization problems that minimize sources of error in the Ising model. In addition, we provide a process to express a Boltzmann probability optimization problem as a supervised machine learning problem.

Re-Evaluating the Appropriateness of the "Don't Know" Response Option: Guessing Rate as a Source of Systematic Error on Autism Knowledge Assessments.

Several autism knowledge assessments include "don't know" as a response option. The inclusion of this response option may lead to systematic error, such that participants' guessing rate affects the measurement of their autism knowledge. This study examines both predictors of guessing rate for autism knowledge and predictors of autism knowledge, including guessing rate. School-based professionals (n = 396) completed the Autism Spectrum Knowledge Scale Professional Version-Revised (ASKSP-R; McClain et al, Journal of Autism and Developmental Disorders 50(3):998-1006, 2020). and the Autism Stigma and Knowledge Questionnaire (ASK-Q; Harrison et al, Journal of Autism and Developmental Disorders 47(10):3281-3295, 2017). Both assessments include "don't know" as a response option. Guessing rate was the strongest predictor of autism knowledge across both the ASKSP-R and the ASK-Q assessments. For the ASKSP-R, participants who were school psychologists, practicing for more years, had more autism-related clinical experiences, and who personally knew an autistic person had a higher guessing rate. School psychologists and participants who worked with more autistic students scored higher in autism knowledge. For the ASK-Q, participants with greater self-perceived autism knowledge had a higher guessing rate. Participants with a doctorate degree, who personally knew an autistic person, and who worked with more autistic students scored higher in autism knowledge. Guessing rate can be a source of systematic error on autism knowledge assessments. Potential solutions to correct for guessing rate are examined and recommended for future use.

Kaon mixing beyond the standard model with physical masses

We present nonperturbative results for beyond the standard model kaon mixing matrix elements in the isospin symmetric limit (mu=md) of QCD, including a complete estimate of all dominant sources of systematic error. Our results are obtained from numerical simulations of lattice QCD with Nf=2+1 flavors of dynamical domain wall fermions. For the first time, these quantities are simulated directly at the physical pion mass mπ∼139 MeV for two different lattice spacings. We include data at three lattice spacings in the range a=0.11–0.07 fm and with pion masses ranging from the physical value up to 450 MeV. Compared to our earlier work, we have added both direct calculations at physical quark masses and a third lattice spacing making the removal of discretization effects significantly more precise and eliminating the need for any significant mass extrapolation beyond the range of simulated data. We renormalize the lattice operators nonperturbatively using RI-SMOM off-shell schemes. These schemes eliminate the need to model and subtract nonperturbative pion poles that arises in the RI-MOM scheme and, since the calculations are performed with domain wall fermions, the unphysical mixing between chirality sectors is suppressed. Our results for the bag parameters in the MS¯ scheme at 3 GeV are BK≡B1=0.5240(17)(54), B2=0.4794(25)(35), B3=0.746(13)(17), B4=0.897(02)(10) and B5=0.6882(78)(94), where the first error is from lattice uncertainties and the second is the uncertainty due to the perturbative matching to MS¯. Published by the American Physical Society 2024

All-optical method to directly measure the pressure–volume–temperature equation of state of fluids in the diamond anvil cell

We have developed a new all-optical method to directly measure the pressure–volume–temperature (PVT) equation of state (EOS) of fluids and transparent solids in the diamond anvil high pressure cell by measuring the volume of the sample chamber. Our method combines confocal microscopy and white light interference with a new analysis method, which exploits the mutual dependence of sample density and refractive index: Experimentally, the refractive index determines the measured sample chamber thickness (and therefore the measured sample volume/density), yet the sample density is by far the dominant factor in determining the variation in the refractive index with pressure. Our analysis method allows us to obtain a set of values for the density and refractive index, which are mutually consistent and agree with the experimental data within error. We have conducted proof-of-concept experiments on a variety of samples (H2O, CH4, C2H6, C3H8, KCl, and NaCl) at ambient temperature and at high temperatures up to just above 500 K. Our proof-of-concept data demonstrate that our method is able to reproduce known fluid and solid EOS within error. Furthermore, we demonstrate that our method allows us to directly and routinely measure the PVT EOS of simple fluids at GPa pressures up to, at least, 514 K (the highest temperature reached in our study). A reasonable estimation of the known sources of error in our volume determinations indicates that the error is currently ±2.7% at high temperature and that it is feasible to reduce it to ca. ±1% in future work.

Modeling and characterization of gas coupled ultrasonic transducers at low pressures and temperatures and implications for sonic anemometry on Mars.

A sonic anemometer targeted at wind speed measurements on the surface of Mars is described. This environment requires transducer operation in 4-10 mbar CO2 at temperatures between 143 and 293 K (-130 °C and 20 °C, respectively). Over these ranges, transducer pressure and temperature sensitivity could be a source of measurement error. To investigate this, four candidate transducers were tested using transmission mode ultrasonic testing and impedance measurements: two narrowband piezoelectric transducers, a broadband capacitive transducer, and a micromachined capacitive ultrasound transducer. A system model was used for comparison and interpretation, and implications for a sonic anemometer were examined. Variation of transducer characteristics, including diffraction effects, across 2-10 mbar in CO2 and 190-293 K (-83 °C-20 °C) result in ±2.3% error in wind speed measurement and ±1.1% error in speed of sound measurement for the worst case but only ±0.14% error in wind and ±0.07% error in speed of sound for the best transducer operated off resonance. The acoustic conditions on Mars are similar to those in Earth's stratosphere at 30-42 km of altitude. Hence, testing was also conducted in dry air over the same range of pressures and temperatures with relevance to a secondary application of the instrument as a stratospheric anemometer for high altitude balloon missions on Earth.

Correcting for measurement errors in a long‐term aerial survey with auxiliary photographic data

AbstractLong‐term, large‐scale monitoring of wildlife populations is an integral part of conservation research and management. However, some traditional monitoring protocols lack the information needed to account for sources of measurement error in data analyses. Ignoring measurement error, such as partial availability, imperfect detection, and species misidentification, can lead to mischaracterizations of population states and processes. Accounting for measurement error is key to robust monitoring of populations, which can inform a wide variety of decisions, including harvest, habitat restoration, and determination of the legal status of species. We undertook an effort to retroactively minimize bias in a large‐scale, long‐term monitoring program for marine birds in the Salish Sea, Washington, USA, by conducting an auxiliary study to jointly estimate components of measurement error. We built a novel model in a Bayesian framework that simultaneously harnessed human observer and photographic data types to produce estimates necessary to correct for the effects of partial availability, imperfect detection, and species misidentification. Across all 31 species identified in photographs, both observers had instances of undercounting and overcounting birds but tended to undercount (observers undercounted totals across all species on 69.3%–78.9% of transects). We estimated species‐specific correction factors that can be used to correct both historical and future counts from the Salish Sea survey, which has been running since 1992. Our novel modeling framework can be applied in other multispecies monitoring contexts where minimal photographic data can be collected for the purposes of correcting for measurement error in large‐scale, long‐term datasets.

Assessment of hepatic transporter function in rats using dynamic gadoxetate-enhanced MRI: a reproducibility study

ObjectivePrevious studies have revealed a substantial between-centre variability in DCE-MRI biomarkers of hepatocellular function in rats. This study aims to identify the main sources of variability by comparing data measured at different centres and field strengths, at different days in the same subjects, and over the course of several months in the same centre.Materials and methods13 substudies were conducted across three facilities on two 4.7 T and two 7 T scanners using a 3D spoiled gradient echo acquisition. All substudies included 3–6 male Wistar-Han rats each, either scanned once with vehicle (n = 76) or twice with either vehicle (n = 19) or 10 mg/kg of rifampicin (n = 13) at follow-up. Absolute values, between-centre reproducibility, within-subject repeatability, detection limits, and effect sizes were derived for hepatocellular uptake rate (Ktrans) and biliary excretion rate (kbh). Sources of variability were identified using analysis of variance and stratification by centre, field strength, and time period.ResultsData showed significant differences between substudies of 31% for Ktrans (p = 0.013) and 43% for kbh (p < 0.001). Within-subject differences were substantially smaller for kbh (8%) but less so for Ktrans (25%). Rifampicin-induced inhibition was safely above the detection limits, with an effect size of 75 ± 3% in Ktrans and 67 ± 8% in kbh. Most of the variability in individual data was accounted for by between-subject (Ktrans = 23.5%; kbh = 42.5%) and between-centre (Ktrans = 44.9%; kbh = 50.9%) variability, substantially more than the between-day variation (Ktrans = 0.1%; kbh = 5.6%). Significant differences in kbh were found between field strengths at the same centre, between centres at the same field strength, and between repeat experiments over 2 months apart in the same centre.DiscussionBetween-centre bias caused by factors such as hardware differences, subject preparations, and operator dependence is the main source of variability in DCE-MRI of liver function in rats, closely followed by biological between-subject differences. Future method development should focus on reducing these sources of error to minimise the sample sizes needed to detect more subtle levels of inhibition.

A novel subgrid-scale stress model considering the influence of combustion on turbulence: A priori and a posteriori assessment

Most classical turbulence models were proposed and developed based on non-reacting flows without considering the effects of combustion on turbulence. The flow structure in turbulent combustion is more complex, creating challenges to the applicability of traditional turbulence models. Given this, a novel flame surface and k-equation-based gradient model (FKGM) considering combustion effects is proposed for the modeling of the subgrid-scale (SGS) stress in large eddy simulation (LES). The SGS stress is calculated based on the SGS kinetic energy (kSGS) and normalized velocity gradient. The velocity gradient incorporates first-order gradients at multiple stencil locations and considers the anisotropy of the stress near the flame surface. The FKGM model is first validated by the a priori analysis based on the direct numerical simulation (DNS) database of a premixed swirling flame. The closure terms of the kSGS equation are well validated, and the predicted SGS stress using the FKGM model achieves good agreement with the filtered DNS results. In the a posteriori LES study, the FKGM model demonstrates better performance compared with the traditional dynamic Smagorinsky model and velocity gradient model, providing more accurate predictions for mean and fluctuation velocities. The error analysis for SGS kinetic energy is further conducted by comparing the LES results with the DNS data, and the results indicate that the underestimation of the generation term of the kSGS equation is the main source of error.

Errors in estimating reproductive parameters with macroscopic methods: a case study on the protogynous blacktip grouper Epinephelus fasciatus (Forsskål 1775).

A size-based, histological analysis of the reproductive life history of the blacktip grouper, Epinephelus fasciatus (Forsskål 1775), was conducted in Indonesia to evaluate the error rate associated with macroscopic reproductive analysis. Histological results indicated that E. fasciatus was protogynous with female L50 at 13.4 cm total length (LT) and a size at sexual transition of 22.0 cm LT. The weight-length relationship for the species was W = 0.011 L3.13. Overall sex ratios were significantly female biased, operational sex ratios were significantly male biased, and sex ratios of mature individuals varied predictably with length from female to male dominance as size increased. No significant relationship between length and batch fecundity was found. The population has a spawning period from February to August. Overall, 54.4% of macroscopic evaluations were incorrect compared to histological results. Of the errors, 14.8% were a failure to detect ovotestes, 12.7% were classifying non-gonadal tissue as ovary or testis, 12.2% were misclassifying sex, and 12.7% were misclassifying maturity status. However, the largest source of error (47.7%) was from misclassifying both sex and maturity status. Of these, 92.9% were macroscopically classified as immature females, but were histologically confirmed to be mature males. Compared to histological results, the only accurate macroscopic results were the absence of a sex-based difference in weight-length relationship and spawning seasonality estimated by a gonadosomatic index (February-June). The use of macroscopic methods to estimate reproductive life-history parameters for sex-changing reefes fish may introduce significant inaccuracies and misinterpretations. Of the parameters estimated by histological methods, size at maturity, size-specific sex ratios, and spawning seasonality have the greatest potential to inform local fishery management policy.

DEVELOPMENT AND RESEARCH OF SOFTWARE SOLUTION FOR BUSINESS PROCESS MODEL CORRECTNESS ANALYSIS USING MACHINE LEARNING

Poorly designed business process models are a source of errors and the subsequent costs associated with these errors, such as monetary costs, lost time, or even some harmful impact on people or the environment if the erroneous business process models are associated with critical industries. The BPM (Business Process Management) lifecycle usually consists of designing, implementing, monitoring, and controlling the business process execution, but it lacks continuous quality control of the created BPMN (Business Process Model and Notation) models. Thus, this paper considers the problem of business process models classification based on their correctness, which solution will ensure quality control of the designed business process models. Thus, this study aims to improve the quality of business process models by developing a software solution for business process models classification based on their correctness. The subject of the study is the process of business process models classification based on their correctness, which uses quality measures and thresholds, usually, complexity measures. The subject of the study is a software solution for business process models classification based on their correctness. Therefore, in this study, the algorithm to solve the problem of BPMN models classification using logistic regression, interface complexity, and modularity measures is proposed, the software requirements are determined, the software development tools are selected, the software for business process models classification based on their correctness is designed, the corresponding software components are developed, the use of a software solution for solving the problem of business process models classification based on their correctness is demonstrated, the obtained results are analyzed and discussed. The developed software indicates high performance of BPMN models classification based on their correctness, achieving high accuracy (99.14 %), precision (99.88 %), recall (99.23 %), and F-score (99.56 %), highlighting the high performance of modeling errors detection.

RESEARCH ON ERROR PROBABILITY ASSESSMENT IN USER PERSONAL DATA PROCESSING IN GDPR-COMPLIANT BUSINESS PROCESS MODELS

The only right strategy for businesses and government organizations in Ukraine and other countries that may face aggression is to recognize themselves as a potential target for cyberattacks by the aggressor (both by its government agencies and related cybercriminal groups) and take appropriate measures in accordance with the European Union’s General Data Protection Regulation (GDPR). The main purpose of the GDPR is to regulate the rights to personal data protection and to protect EU citizens from data leaks and breaches of confidentiality, which is especially important in today’s digital world, where the processing and exchange of personal data are integral parts of almost every business process. Therefore, the GDPR encourages organizations to transform their day-to-day business processes that are involved in managing, storing, and sharing customers’ personal data during execution. Thus, business process models created in accordance with the GDPR regulations must be of high quality, just like any other business process models, and the probability of errors in them must be minimal. This is especially important with regard to the observance of human rights to personal data protection, since low-quality models can become sources of errors, which, in turn, can lead to a breach of confidentiality and data leakage of business process participants. This paper analyzes recent research and publications, proposes a method for analyzing business process models that ensure compliance with the GDPR regulations, and tests its performance based on the analysis of BPMN models of business processes for obtaining consent to data processing and withdrawal of consent to user data processing. As a result, the probability of errors in the considered business process models was obtained, which suggests the possibility of confidentiality violations and data leaks of the participants of the considered business processes associated with these errors, and appropriate recommendations were made.

Mach band effect: A source of diagnostic error in intensive care unit

Abstract This is a case of pseudopneumothorax caused by skin fold artifact in chest X-ray. This is important to diagnose as it can lead to inappropriate intervention or radiation exposure as in this case. Human eye’s ability to perceive margin by contrast difference is Mach band effect.

A scoping review of rule-based clinical decision support malfunctions.

Conduct a scoping review of research studies that describe rule-based clinical decision support (CDS) malfunctions. In April 2022, we searched three bibliographic databases (MEDLINE, CINAHL, and Embase) for literature referencing CDS malfunctions. We coded the identified malfunctions according to an existing CDS malfunction taxonomy and added new categories for factors not already captured. We also extracted and summarized information related to the CDS system, such as architecture, data source, and data format. Twenty-eight articles met inclusion criteria, capturing 130 malfunctions. Architectures used included stand-alone systems (eg, web-based calculator), integrated systems (eg, best practices alerts), and service-oriented architectures (eg, distributed systems like SMART or CDS Hooks). No standards-based CDS malfunctions were identified. The "Cause" category of the original taxonomy includes three new types (organizational policy, hardware error, and data source) and two existing causes were expanded to include additional layers. Only 29 malfunctions (22%) described the potential impact of the malfunction on patient care. While a substantial amount of research on CDS exists, our review indicates there is a limited focus on CDS malfunctions, with even less attention on malfunctions associated with modern delivery architectures such as SMART and CDS Hooks. CDS malfunctions can and do occur across several different care delivery architectures. To account for advances in health information technology, existing taxonomies of CDS malfunctions must be continually updated. This will be especially important for service-oriented architectures, which connect several disparate systems, and are increasing in use.

Accurate and Robust Static Hydrophobic Contact Angle Measurements Using Machine Learning.

We present a machine learning (ML) approach to static contact angle measurement, trained on a large data set (>7.2 million) of half drop contours based on solutions to the Young-Laplace equation where the contact angle is known a priori (removing all sources of error from human input). The data set included the effects of surface roughness, gravity, the size of drop relative to the image, and reflections of the drop on the surface. The presented ML model (valid for contact angles >110°), in combination with a new automated image and contour processing approach, is shown to be more accurate than other methods when benchmarked against an experimental data set, with an estimated error of 1°. The ML model is also 2 orders of magnitude faster at predicting contact angles than Young-Laplace fitting (the current best practice approach). The accuracy and speed of the presented approach provides a viable pathway toward robust and reproducible high-throughput contact angle analysis. This approach, Conan-ML, is open-source and provided for the use and development of new approaches to goniometry.

Constellation design and performance of future quantum satellite gravity missions

Temporal aliasing is currently the largest error contributor to time-variable satellite gravity field models. Therefore, the evolution of sensor technologies has to be complemented by strategies to reduce temporal aliasing errors. The most straightforward way to improve temporal aliasing is through extended satellite constellations because they improve the observation geometry and increase the achievable temporal resolution. Therefore, strategies to optimize the design of larger satellite constellations are investigated in this contribution. A complete constellation modeling procedure is presented, starting from primary design variables (such as the required targeted resolutions) and concluding with concrete orbital elements for the individual satellites. In parallel, it is evaluated if improved instrument sensitivities based on quantum technologies (cold atom interferometry) can be fully exploited in the case of larger constellations. For this, future quantum satellite gravity missions adopting the gradiometry concept (similar to the GOCE mission) and the low-low satellite-to-satellite tracking concept (similar to GRACE/-FO) are simulated on optimized constellations with up to 6 satellites/pairs. The retrieval performance of a 6-pair mission in terms of the global equivalent water height RMS can be improved by a factor of roughly 3 compared to an inclined double-pair mission. 3D-gradiometry intrinsically has a better de-aliasing behavior but has extremely high accuracy requirements for the gradiometer (about 10 µEotvos) and the attitude reconstruction to be of any benefit. All simulations show that when incorporating improved sensor technologies, such as future quantum sensing instruments in extended constellations, temporal aliasing will remain the dominant error source by far, up to five orders of magnitude larger than the instrument errors. Therefore, improving sensor technologies has to go hand in hand with larger satellite constellations and improved space–time parameterization strategies to further reduce temporal aliasing effects.Graphical

Online motion accuracy compensation of industrial servomechanisms using machine learning approaches

This paper addresses the crucial aspect of position error modeling and compensation in industrial servomechanisms with the aim to achieve accurate control and high-performance operation in industrial robots and automated production systems. The inherent complexity and nonlinear behavior of these modules, usually consisting of a servomotor and a speed reducer, often challenge traditional analytical modeling approaches. In response, the study extensively explores the design and implementation of Machine Learning (ML) algorithms to obtain a comprehensive model of the Transmission Error (TE) in rotating vector reducers, which is a main source of robot motion accuracy errors. The ML models are trained with experimental data obtained from a special purpose test rig, where the reducer is tested under different combinations of input speed, applied load and oil temperature. In the second part of the work, the resulting predictive model, tailored to capture the intricate dynamics of the analyzed reducer, is imported into a programmable logic controller to enable online compensation strategies during the execution of custom motion profiles. Experimental tests are conducted using two distinct motion profiles: one generated with a cycloidal law, typical of industrial machinery, and the other extrapolated from the joints of an industrial robot during a pick-and-place task. The results demonstrate the effectiveness of the proposed approach, enabling accurate prediction and substantial reductions (over 90%) in the overall reducer TE through the implemented predictive model.