Sources Of Error Research Articles

Driving corporate digitization with reliable data through the A3 approach: an Italian case study

PurposePursuing digital transformation is a valuable strategy to attain companies’ operational excellence and sustainable development. However, installing digital technologies and software is insufficient for achieving a successful digital transformation. Equally vital is providing digital solutions with reliable input data, which is a hard task in companies where information is gathered through manual or non-standardized processes. The lack of reliable data prevents technologies and software from operating at their best, hindering their ability to process information and derive correct insights for improvement. To avoid this, companies should embrace structured problem-solving approaches to evaluate current data retrieval processes, identify error sources and formulate countermeasures. This paper aims to provide an empirical study to substantiate A3 as a winning approach for advancing input data acquisition in companies.Design/methodology/approachA case study research is proposed, investigating the application of A3 in an ink manufacturing company, and checking how A3 improves data collection and company performance.FindingsThe case study corroborates A3 as an effective approach, allowing the removal of inefficiencies that previously went unnoticed and reaching a 10% productivity improvement.Practical implicationsPursuing digital transformation is a valuable strategy to attain companies’ operational excellence and sustainable development. However, installing digital technologies and software is insufficient for achieving a successful digital transformation. Equally vital is providing digital solutions with reliable input data, which is a hard task in companies where information is gathered through manual or non-standardized processes.Originality/valueThe originality of this paper lies in the application scope and aim of the A3 approach. A3 is a mature paradigm but often coined for production and still endowed with unexplored potential. This paper proposes the application of A3 for improving companies’ data retrieval processes, focusing for the first time on information reliability and its importance in ensuring the functioning of digital technologies and software.

A survey of offline precision calibration of industrial robots

Abstract. As the need for precision and efficiency grows in industrial manufacturing, the accuracy of industrial robots assumes paramount importance. Nevertheless, factors, such as mechanical structures and assembly processes often introduce errors, compromising robots' positioning and repeatability accuracy. Consequently, precise calibration and compensation of these errors are paramount for optimizing robots performance. A thorough analysis of error sources and the establishment of error models in industrial robots are conducted in the present study, with special attention paid to offline calibration methods. This paper reviews the robot error model building methods and offline accuracy calibration techniques, summarizes the technical difficulties of the calibration task, and proposes the future development direction for the difficulties such as the complexity of error modeling, the difficulty of non-motor calibration calculation, and the traditional stiffness compensation that does not meet the needs of the robot's operation process. The importance of this research lies in its potential to improve the accuracy and reliability of industrial robots, thus contributing to the development of industrial automation.

Sensitivity of wavelet-based optical flow velocimetry (wOFV) to common experimental error sources

Abstract The influence of several potential error sources and non-ideal experimental effects on the accuracy of a wavelet-based optical flow velocimetry (wOFV) method when applied to tracer particle images is evaluated using data from a series of synthetic flows. Out-of-plane particle displacements, severe image noise, laser sheet thickness reduction, and image intensity non-uniformity are shown to decrease the accuracy of wOFV in a similar manner to correlation-based particle image velocimetry (PIV). For the error sources tested, wOFV displays a similar or slightly increased sensitivity compared to PIV, but the wOFV results are still more accurate than PIV when the magnitude of the non-ideal effects remain within expected experimental bounds. For the majority of test cases, the results are significantly improved by using image pre-processing filters and the magnitude of improvement is consistent between wOFV and PIV. Flow divergence does not appear to have an appreciable effect on the accuracy of wOFV velocity estimation, even though the underlying fluid transport equation on which wOFV is based implicitly assumes that the motion is divergence-free. This is a significant finding for the broader applicability of planar velocimetry measurements using wOFV. Finally, it is noted that the accuracy of wOFV is not reduced notably in regions of the image between tracer particles, as long as the overall seeding density is not too sparse i.e. below 0.02 particles per pixel. This explicitly demonstrates that wOFV (when applied to particle images) yields an accurate whole field measurement, and not only at or adjacent to the discrete particle locations.

Code-ifying the Law: How Disciplinary Divides Afflict the Development of Legal Software

Proponents of legal automation believe that translating the law into code can improve the legal system. However, research and reporting suggest that legal software systems often contain flawed translations of the law, resulting in serious harms such as terminating children's healthcare and charging innocent people with fraud. Efforts to identify and contest these mistranslations after they arise treat the symptoms of the problem, but fail to prevent them from emerging. Meanwhile, existing recommendations to improve the development of legal software remain untested, as there is little empirical evidence about the translation process itself. In this paper, we investigate the behavior of fifteen teams---nine composed of only computer scientists and six of computer scientists and legal experts---as they attempt to translate a bankruptcy statute into software. Through an interpretative qualitative analysis, we characterize a significant epistemic divide between computer science and law and demonstrate that this divide contributes to errors, misunderstandings, and policy distortions in the development of legal software. Even when development teams included legal experts, communication breakdowns meant that the resulting tools predominantly presented incorrect legal advice and adopted inappropriately harsh legal standards. Study participants did not recognize the errors in the tools they created. We encourage policymakers and researchers to approach legal software with greater skepticism, as the disciplinary divide between computer science and law creates an endemic source of error and mistranslation in the production of legal software.

Research on Pose Error Modeling and Compensation of Posture Adjustment Mechanism Based on WOA-RBF Neural Network

This paper is aimed to address the issue of decreased accuracy in the ship block docking caused by the structural errors of posture adjustment mechanism. First, inverse kinematic analysis is performed to investigate the sources of static errors in the mechanism. Subsequently, based on the closed-loop vector method, a pose error model for the moving platform is established, which includes eight categories of error terms. The impact of various structural errors on the pose accuracy of the moving platform is then compared and analyzed under both single-limb and multi-limb configurations. Therefore, a compensation method based on the whale optimization algorithm optimized radial basis function neural network is proposed. By transforming pose errors into actuator length errors, it establishes a predictive model between the theoretical pose of the dynamic platform and actuator length errors. After optimizing the network parameters, it yields the actuator length compensation to correct the actual pose of the dynamic platform. Simulation and experimental results validate the effectiveness of this method in enhancing the motion accuracy of the parallel mechanism. The mean pose accuracy of the moving platform is improved by 85.07%, demonstrating a significant compensation effect.

Dropsonde Observations of the Stable Marine Boundary Layer Beneath Atmospheric Rivers

Abstract An important source of errors in predicting landfalling atmospheric rivers (ARs), and their associated extreme precipitation and streamflow, are inaccuracies in model initial conditions in ARs offshore. These inaccuracies are particularly evident in the marine boundary layer (MBL) where the tendency of ARs to transport warm air poleward over progressively cooler SSTs generates a stable MBL (SMBL). The SMBL’s vertical structure and key modulating processes are documented using >1000 dropsondes along 99 transects of ARs from the AR Reconnaissance and CalWater field campaigns. The SMBL depth, modulated by sensible heat loss to the ocean, is typically 300–800 m in the AR core, with vertical wind shears ranging from 5–50 m s−1 km−1, representing a highly variable decoupling of the AR aloft from conditions at the ocean surface. Simulated backward air parcel trajectories originating from dropsonde locations within the AR core are used to calculate the 24-h change in SST experienced by an air parcel (DSST24) beneath each AR. The DSST24 varies from −13°C to +2°C and is directly related to the strength of the AR and its orientation relative to the SST gradient. The DSST24, therefore, distinguishes weak and strong decoupling regimes (WDR, SDR). In SDR cases, relative to WDR cases, the SMBL is characterized by greater sensible heat loss to the ocean, as well as stronger static stability, vertical wind shear, low-level jet, and horizontal water vapor transport. In SDR cases, the SMBL is deeper in the core than in adjacent warm and cold sectors.

River suspended-sand flux computation with uncertainty estimation using water samples and high-resolution ADCP measurements

Abstract. Measuring suspended-sand fluxes in rivers remains a scientific challenge due to their high spatial and temporal variability. To capture the vertical and lateral gradients of concentration in the cross-section, measurements with point samples are performed. However, the uncertainty related to these measurements is rarely evaluated, as few studies of the major sources of error exist. Therefore, the aim of this study is to develop a method to determine the cross-sectional sand flux and estimate its uncertainty. This SDC (for sand discharge computing) method combines suspended-sand concentrations from point samples with ADCP (acoustic Doppler current profiler) high-resolution depth and velocity measurements. The MAP (for multitransect averaged profile) method allows obtaining an average of several ADCP transects on a regular grid, including the unmeasured areas. The suspended-sand concentrations are integrated vertically by fitting a theoretical exponential suspended-sand profile to the data using Bayesian modeling. The lateral integration is based on the water depth as a proxy for the local bed shear stress to evaluate the bed concentration and sediment diffusion along the river cross-section. The estimation of uncertainty combines ISO standards and semi-empirical methods with a Bayesian approach to estimate the uncertainty due to the vertical integration. The new method is applied to data collected in four rivers under various hydro-sedimentary conditions: the Colorado, Rhône, Isère, and Amazon rivers, with computed flux uncertainties ranging between 18 % and 32 %. The relative difference between the suspended-sand flux in 21 cases calculated with the proposed SDC method compared to the International Organization for Standardization (ISO) 4363 standard method ranges between −40 % and +23 %. This method that comes with a flexible, open-source code is the first to propose an applicable uncertainty estimation that could be adapted to other flux computation methods.

Controller Area Network (CAN) Bus Transceiver with Authentication Support and Enhanced Rail Converters

This paper presents an advanced Controller Area Network (CAN) bus transceiver designed to enhance security using frame-level authentication with the concept of a nonphysical virtual auxiliary data channel. We describe the newly conceived transceiver security features and provide results concerning the design, implementation, fabrication and test of the transceiver to validate its functionality and robust operation in the presence of systemic error sources including Process, Voltage, and Temperature (PVT) variations. The virtual auxiliary channel integrates CAN frame authentication signatures into the primary data payload via phase modulation while also providing compatibility with existing CAN protocols, interoperability with non-enhanced systems and requiring no network or software modifications. Enhanced rail converters are designed to facilitate single-rail to dual-rail data conversion and vice versa, preserving phase information and minimizing phase errors across various nonideal effects such as frequency drift, Process, Voltage, and Temperature (PVT) variations, and cable phase mismatch. This ensures reliable data transmission and robust authentication in the presence of adversarial cyberattacks such as packet injection. The receiver recovers both the CAN frame data and the security signature, comparing the latter with an authorized signature to provide a real-time “GO/NO_GO” signal for verifying packet authenticity and without exceeding the CAN clock jitter specifications.

Machine learning-based modelling of zenith wet delay using terrestrial meteorological data in the Brazilian territory

Abstract. The Zenith Total Delay (ZTD) is one of the primary error sources derived from the neutral atmosphere associated with the GNSS (Global Navigation Satellite Systems) technique. Zenith Wet Delay (ZWD) is the smallest part of the ZTD, but the high variability is caused by spatial-temporal variation, making the modelling of this component a challenging task. Although ZWD is considered an error in GNSS positioning, it is also a variable composed mainly of water vapour and can, therefore, be used for atmospheric investigations, and assists in climate studies for precipitation events. In this work, a model was trained to estimate the delay wet component from surface atmospheric parameters. The training data comes from 29 radiosonde stations around Brazil, for a six-year period (2017 to 2022), with data collected at 12 h UTC (Universal Time Coordinated). The model was validated using the holdout technique, with 70% of the data used in training and 30% for validation (cross-validation analysis). The generated model achieved a RMSE (Root Mean Squared Error) of approximately 38 mm, with an 81% of determination coefficient.

Strategies to Overcome Erroneous Outcomes in Reverse Transcription-Polymerase Chain Reaction (RT-PCR) Testing: Insights From the COVID-19 Pandemic.

The reverse transcription-polymerase chain reaction (RT-PCR) test to detect SARS-CoV-2, the virus causing COVID-19, has been regarded as the diagnostic gold standard. However, the excessive sensitivity of RT-PCR may cause false-positive outcomes from contamination. Again, its technical complexity increases the chances of false-negatives due to pre-analytical and analytical errors. This narrative review explores the elements contributing to inaccurate results during the COVID-19 pandemic and offers strategies to minimize these errors. False-positive results may occur due to specimen contamination, non-specific primer binding, residual viral RNA, and false-negatives, which may arise from improper sampling, timing, labeling, storage, low viral loads, mutations, and faulty test kits. Proposed mitigation strategies to enhance the accuracy of RT-PCR testing include comprehensive staff training in specimen collection, optimizing the timing of tests, analyzing multiple gene targets, incorporating clinical findings, workflow automation, and implementing stringent contamination control measures. Identifying and rectifying sources of error in RT-PCR diagnosis through quality control and standardized protocols is imperative for ensuring quality patient care and effective epidemic control.

On the accuracy of distance estimates by propagation time of sound signals on the arctic shelf

As part of numerical modeling, estimates are made of the accuracy of determining the distance between underwater sources and sound receivers located at a distance of several kilometers from each other in the Kara Sea in the autumn. It is assumed that the main source of possible errors in determining the distance is the lack of accurate data on the vertical profile of the sound speed along the propagation path of acoustic signals. Data from September and November were analyzed, in the interval between which significant changes in the profile take place, when the vertical gradient of sound speed changes from negative to positive values. Characteristic values of sound speed variations were obtained by statistical processing of hydrological data taken from the World Ocean Database. The results obtained are important for analyzing the capabilities of underwater acoustic navigation.

Local fuzzy rough attribute reduction for large-scale mixed data with limited missing labels based on local fuzzy self information

The advent of the era of big data is accompanied by the generation of large-scale data of various types. Extracting the potential value and rules from such data has always been a challenge. Due to various external and internal factors, it is commonplace for large-scale data to exhibit the phenomenon of missing limited labels. In addressing a large-scale mixed information system with limited label missing (LSMDISLML), local neighborhood rough set model (LNRS-model) is typically employed. However, the identical neighborhood radius is often used by such model when confronted with numerical attributes, which could potentially attenuate the classification capability of the data. Local fuzzy rough set model (LFRS-model) can overcomes this point. This paper studies local fuzzy rough attribute reduction for large-scale mixed data with limited missing labels based on LFRS-model via local fuzzy self information and overlap degree function. First, leveraging the statistical distribution of data as a foundation, fuzzy relations on the entire sample set are established, which has the advantage of being able to use different fuzzy similarity radii to calculate similarity, thereby adapting to different data distributions. Subsequently, the samples with missing labels are discarded as they constitute a small proportion of the entire sample set and have little impact on overall performance of dataset. The limited computing resources and storage space are focused on the sample set with complete labels (denoted as target set). Thereafter, based on the target set, local fuzzy λ-upper and lower approximations are defined, and LFRS-model is constructed. This model not only reduces processing time and sources of error in large-scale data but also improves data quality and enhances the reliability of the experimental results. Then, local fuzzy λ-self information is introduced and used to design a local fuzzy rough attribute reduction algorithm in a LSMDISLML. Furthermore, a overlap degree function is introduced to evaluate and reorder the attributes based on their importance, prioritizing the elimination of redundant attributes with high overlap and low importance from the preordered attribute set. This strategy effectively improves the efficiency of obtaining the optimal subset. Finally, a series of experiments are carried out. The experiment results demonstrate that the designed algorithm exhibits excellent performance in classification tasks and outlier detection tasks, surpassing existing four algorithms.

Accounting for intensity variation within pixels of Shack-Hartmann wavefront sensors

The accuracy of centroiding algorithms in Shack-Hartmann wavefront sensing is limited by the implicit approximation of uniform pixel illumination. Iterative centroiding algorithms are further limited by the consideration of full pixels to define the image domain for centroiding. Here, we demonstrate two practical and complementary approaches to mitigate both these sources of error. First, we consider partial or ‘fractional’ pixels to maintain centroiding area symmetry around the center of mass. Secondly, we propose methods to perform piece-wise polynomial interpolation to calculate intensity distribution within pixels, which is then used to estimate the centroid within each pixel area. This approach that accounts for intensity non-uniformity across pixels notably reduces centroid errors up to a factor of 5 across lenslet image widths ranging from 1.33 to 3.10 pixels full-width-half-maximum (FWHM). Consequently, wavefront sensing errors decrease from 14% to 4%, on average, for FWHM = 1.35 pixels, demonstrating a substantial benefit when the number of pixels per lenslet is minimized to enhance the signal-to-noise ratio or increase frame rate.

A novel approach for tool-narayanaswamy-moynihan model parameter extraction using multi-scale neural model

The accurate determination of parameters in the Tool-Narayanaswamy-Moynihan (TNM) model, which describes the viscoelastic behavior of glass-forming materials, is crucial for predicting material responses through various thermal histories.Traditional methods rely heavily on curve-fitting techniques; however, these often fail due to noise in the data. Furthermore, traditional methods are computationally intensive and prone to inaccuracies, particularly when dealing with complex datasets or when the initial parameter guesses are far from optimal; also, they require a skilled personnel.In this study, we propose the application of a multi-scale convolutional neural network (MCNN) as a machine learning approach to address these challenges. The MCNN model is trained on a comprehensive simulated dataset encompassing a wide range of TNM parameters, allowing it to learn intricate patterns and dependencies within the data that are difficult to capture with conventional methods. Our results show that the MCNN significantly improves the accuracy of the parameter estimations for β and x across the entire spectrum of tested conditions, achieving performance that is not only comparable to, but often surpasses, traditional curve-fitting methods. Furthermore, the MCNN demonstrates superior robustness when initial parameter estimates are suboptimal or when the dataset exhibits significant noise. Although the prediction accuracy for the activation energy Δh∗ and the pre-exponential factor log(A) was somewhat lower, the method still provides valuable estimates that can be refined with supplementary techniques.This work highlights the potential of machine learning approaches like MCNN to revolutionize the parameter extraction process in complex physical models, reducing the reliance on manual curve-fitting and providing a more automated, scalable solution. We also analyze the primary sources of prediction errors in the MCNN outputs and offer insights into future improvements, including model architecture refinements and the integration of additional physical constraints. Our findings suggest that this approach can be extended to other domains where similar models are employed, paving the way for broader applications of machine learning in materials science.

Correcting on-chip distortion of control pulses with silicon spin qubits

Abstract In semiconductor quantum dot systems, pulse distortion is a significant source of coherent errors, which impedes qubit characterization and control. Here, we demonstrate two calibration methods using a two-qubit system as a detector to correct distortion and calibrate the transfer function of the control line. Both methods are straightforward to implement, robust against noise, and applicable to a wide range of qubit types. The two methods differ in correction accuracy and complexity. The first, the Coarse Predistortion (CPD) method, partially mitigates distortion. The second, the All Predistortion (APD) method, measures the transfer function and significantly enhances exchange oscillation uniformity. Both methods use exchange oscillation homogeneity as the metric and are suitable for any qubit driven by a diabatic pulse. We believe these methods will enhance qubit characterization accuracy and operation quality in future applications.

Revisiting the assessment of magnetic texture of Rare-Earth Magnets: The role of coercivity

A crucial figure of merit of rare-earth magnets is their magnetic texture, because it is closely related to the maximum remanence a magnet can achieve, hence assessing the magnetic texture is pivotal in terms of magnet’s performance. Fernengel et al. (1996) developed a methodology based on magnetometry, which allows for a quick determination of the texture by calculating the degree of alignment 〈cosθ〉 from the remanent magnetization in directions parallel and perpendicular to the texture axis of the magnet. Although this method provided reliable values, its application was limited to magnets with high degrees of alignment (〈cosθ〉 > 95 %), a restriction that was corrected by Quispe et al. (2020). Nevertheless, recent experiments have indicated that the magnetometry technique can introduce errors in texture assessment when the magnet presents high levels of coercivity. This work presents an approach that incorporates a correction factor into the magnetometry technique, accounting for the effect of the coercivity and circumventing this error source in texture assessment. The proposed methodology has been successfully applied to magnets with varying levels of coercivity.

Mistakes in Personal Identification Attempts

The following study is meant to present the most frequent mistakes (dangers) of identity parades (line-up, recognition) and their possible consequences, such as for instance the worst outcome: justizmord, (miscarriage of justice). The author analyses sources of criminal tactical and criminal technical major errors through real international and Hungarian homicide cases and suggests legislative changes “de lege ferenda” as well as preventive methods and tactics for law enforcers and criminalists as well.

Challenges of high-fidelity air quality modeling in urban environments – PALM sensitivity study during stable conditions

Abstract. Urban air quality is an important part of human well-being, and its detailed and precise modeling is important for efficient urban planning. In this study the potential sources of errors in large eddy simulation (LES) runs of the PALM model in stable conditions for a high-traffic residential area in Prague, Czech Republic, with a focus on street canyon ventilation, are investigated. The evaluation of the PALM model simulations against observations obtained during a dedicated campaign revealed unrealistically high concentrations of modeled air pollutants for a short period during a winter inversion episode. To identify potential reasons, the sensitivities of the model to changes in meteorological boundary conditions and adjustments of model parameters were tested. The model adaptations included adding the anthropogenic heat from cars, setting a bottom limit of the subgrid-scale turbulent kinetic energy (TKE), adjusting the profiles of parameters of the synthetic turbulence generator in PALM, and limiting the model time step. The study confirmed the crucial role of the correct meteorological boundary conditions for realistic air quality modeling during stable conditions. Besides this, the studied adjustments of the model parameters proved to have a significant impact in these stable conditions, resulting in a decrease in concentration overestimation in the range 30 %–66 % while exhibiting a negligible influence on model results during the rest of the episode. This suggested that the inclusion or improvement of these processes in PALM is desirable despite their negligible impact in most other conditions. Moreover, the time step limitation test revealed numerical inaccuracies caused by discretization errors which occurred during such extremely stable conditions.

Optimized Noise Suppression for Quantum Circuits

Quantum computation promises to advance a wide range of computational tasks. However, current quantum hardware suffers from noise and is too small for error correction. Thus, accurately utilizing noisy quantum computers strongly relies on noise characterization, mitigation, and suppression. Crucially, these methods must also be efficient in terms of their classical and quantum overhead. Here, we efficiently characterize and mitigate crosstalk noise, which is a severe error source in, for example, cross-resonance based superconducting quantum processors. For crosstalk characterization, we develop a simplified measurement experiment. Furthermore, we analyze the problem of optimal experiment scheduling and solve it for common hardware architectures. After characterization, we mitigate noise in quantum circuits by a noise-aware qubit routing algorithm. Our integer programming algorithm extends previous work on optimized qubit routing by swap insertion. We incorporate the measured crosstalk errors in addition to other, more easily accessible noise data in the objective function. Furthermore, we strengthen the underlying integer linear model by proving a convex hull result about an associated class of polytopes, which has applications beyond this work. We evaluate the proposed method by characterizing crosstalk noise for two chips with up to 127 qubits and leverage the resulting data to improve the approximation ratio of the Quantum Approximate Optimization Algorithm by up to 10% compared with other established noise-aware routing methods. Our work clearly demonstrates the gains of including noise data when mapping abstract quantum circuits to hardware native ones. History: Accepted by Giacomo Nannicini, Area Editor for Quantum Computing and Operations Research. Accepted for Special Issue on Quantum Computing. Funding: This work was supported by Bavarian state government; Bayerische Staatsministerium für Wirtschaft, Landesentwicklung und Energie. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0551 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0551 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Focused Ion Beam Milling as a Method for Machining Explosive Crystals**

AbstractExperimental pore collapse studies have traditionally relied on machining methods that are mechanically active and result in rough surfaces, added stresses, and premature material fractures which are a source of experimental error. This work focuses on the method development and the results of adapting the focused ion beam to mill micro and nano‐scale pores with increased geometrical freedom into explosive crystals. Results include developing a simplified mathematical relationship between the electron beam energy and mill rate, assessments of pore quality, and material and machine‐specific optimal milling parameters.