Error Analysis Model Research Articles

Error analysis and visualization of 3D geological models of mineral deposits

The accuracy of 3D geological models of mineral deposits has a significant impact on the precision and reliability of mining production decisions. High-precision 3D geological models can provide more accurate geological information of mineral deposits. A scientific error analysis method is proposed to quantify the errors in geological models and visualize these errors by assigning them to geological model entities. The errors in 3D geological models of mineral deposits mainly originate from modeling data and interpolation methods. By analyzing the generation and processing of modeling data, an error model for the modeling data is established. The Kriging interpolation method is used to quantitatively describe the errors in the modeling methods, and these errors are integrated into the 3D geological model. By using Boolean operations, the 3D geological model is combined with the open-pit mine entity model to generate an error-containing mining site model and mined rock model, and to calculate their mining and stripping volumes and errors, achieving error visualization. Using an open-pit coal mine in Inner Mongolia as a case study, the construction of its 3D mineral deposit and mining field models demonstrates the effectiveness and superiority of the proposed method in practical applications, highlighting its importance in improving the accuracy and reliability of mining production decisions.

An Analysis of Common Errors Made by Grade 7 Students in Writing English Paragraphs at a Secondary School in Hanoi, Vietnam

Error analysis is a technique that identifies and classifies the inappropriate forms that foreign language learners produce (Crystal, 1987), and it is considered a useful tool for helping them improve their writing skills. The study investigated the secondary students' common errors in writing English paragraphs. During the first term of the school year 2023-2024, 43 grade 7 students from a secondary school in Hanoi participated in a descriptive quantitative research project. The participants were asked to complete three writing tests at three different times to generate their written errors based on Ferris’ (2014) model of error analysis. The findings indicate that the types of errors that grade 7 students made are lexical errors, syntactic errors, morphological errors, orthographical errors, and paragraph errors. Lexical, syntactic, and orthographical errors are greatly committed by the students. Accordingly, it comes to the conclusion that secondary students in general and 7th graders in particular struggle with writing English paragraphs. The results of the research would also give the secondary teachers of English some implications, such as focusing on grammatical patterns, emphasizing irregular instances, offering adequate practice opportunities, implementing explicit instructions, and engaging students in interactive exercises, visual aids, collaborative activities, authentic materials, etc. to mitigate the students' written errors.

Investigating pre-tertiary students’ mistakes in solving algebraic word problems: Insights from Asutifi North District, Ghana

Algebra word problems inculcate in learners the skills and ability to think critically and look for answers to problems in society and the world. Using an explanatory sequential mixed design, the study investigated the errors made by pre-tertiary students in algebraic word problems in a selected school in the Asutifi North District of the Ahafo Region of Ghana. Whereas the Newman Error Analysis Model was used to find the types of errors students make when translating and solving algebraic word problems, protocols from topics relating to the study were carefully selected and used as interview guides alongside inspiration from the Newman Error Analysis Model. Quota and simple random sampling techniques were used to select ninety-eight (98) respondents. The Algebraic Word Problem Achievement Test (AWPAT) was given to the ninety-eight students who had studied several topics on word problems, followed by a structured interview to elicit more information from the respondents. The scores gained from the marked test items illustrated that students made more transformation errors, which happened indirectly from a lack of understanding of the concept. A careful analysis of the written responses of the five (5) students interviewed also revealed that, lack of comprehension directly translated into students’ inability to transform algebraic word problems into mathematics equations. In the same vein, the study showed that the main cause of students’ failure to translate and solve algebraic word problems was students’ lack of understanding of the concept of “word problem” among others. The study recommends that students be motivated by encouragement and praise to arouse their interest in translating and solving algebraic word problems.

System Structural Error Analysis in Binocular Vision Measurement Systems

A binocular stereo vision measurement system is widely used in fields such as industrial inspection and marine engineering due to its high accuracy, low cost, and ease of deployment. An unreasonable structural design can lead to difficulties in image matching and inaccuracies in depth computation during subsequent processing, thereby limiting the system’s performance and applicability. This paper establishes a systemic error analysis model to enable the validation of changes in structural parameters on the performance of the binocular vision measurement. Specifically, the impact of structural parameters such as baseline distance and object distance on measurement error is analyzed. Extensive experiments reveal that when the ratio of baseline length to object distance is between 1 and 1.5, and the angle between the baseline and the optical axis is between 30 and 40 degrees, the system measurement error is minimized. The experimental conclusions provide guidance for subsequent measurement system research and parameter design.

Identifying Students’ Problems in Solving Quadratic Equation Using Newman Error Analysis Model

Identifying Students’ Problems in Solving Quadratic Equation Using Newman Error Analysis Model

Analysis of model error in forecast errors of extended atmospheric Lorenz 05 systems and the ECMWF system

Analysis of model error in forecast errors of extended atmospheric Lorenz 05 systems and the ECMWF system

A method for assessing guide layout and error on implant accuracy

Functional and esthetic results require accurate implant placement. We aimed to develop a predictive method for assessing guide layout and error on implant accuracy. A mathematical model for position error analysis was constructed based on triangular mesh data. This model examines the relationship between the spatial shifts of multiple surfaces and the spatial shifts of specific points. It involves encasing these surfaces in a cuboid bounding box and expressing them in a local coordinate system. The influence of positioning surface error and layout of surgical guide were researched with a simulation test. The result shows that error in the implant site position is directly related to the error in the guide locating surface under the same layout. When the guide locating surface layout varies, as the length, width, and height of the minimum cuboid envelope increase, the maximum deviation in the implant site position decreases.

Error Identities for Parabolic Equations with Monotone Spatial Operators

Abstract The article studies quantitative relations (error identities) that characterize distances between exact solutions of nonlinear evolutionary problems and functions considered as approximations. The restrictions imposed on such a function are minimal and actually come down to the condition that it belongs to the same functional class as the generalized solution of the problem under consideration. Functional identities of this type reflect the most general relations between deviations from exact solutions of parabolic initial boundary value problems and those data that can be observed in a numerical experiment. The identities contain no mesh dependent constants and are valid for any function in the admissible (energy) class regardless of the method by which it was constructed. Therefore, they can serve as basic tools for deriving fully reliable a posteriori estimates of approximation errors as well as for analysis of modeling errors. The corresponding examples are discussed in the paper.

The value of Sigma-metrics in laboratory medicine.

While Six Sigma is used in different disciplines to improve quality, Tony Badric and Elvar Theodorsson in a recent paper in CCLM have questioned Six Sigma application in medical laboratory concluding Six Sigma has provided no value to medical laboratory. In addition, the authors have expanded their criticism to Total Analytical Error (TAE) model and statistical quality control. To address their arguments, we have explained the basics of TAE model and Six Sigma and have shown the value of Six Sigma to medical laboratory.

Uncovering the Common Linguistic Errors in Student Journalists’ Unedited News Articles: A Comprehensive Analysis

English language learners encounter difficulties in mastering grammar and vocabulary, which can significantly influence their writing skills. This study analyzes the most common writing errors and their linguistic classification in student journalists’ unedited articles; it also utilizes a qualitative-descriptive design and Corder’s Error Analysis Model for data analysis. Ten selected unedited news articles written by student journalists were examined. Findings revealed that the student writers have common linguistic errors: mechanical, morpho-syntactic, and lexico-semantic. The findings imply that student journalists are not exempted from committing linguistic errors that may affect information decoding despite being trained and exposed to writing activities. The findings have broader implications as student journalists may serve as representatives of English language learners in their institution or community. Schools may develop remedial programs to provide ample practice opportunities using interactive, technology-based tools for grammar and vocabulary. English teachers may create instructional materials and develop community extension and outreach programs to address grammatical learning gaps.

Human error probability evaluation based on reference task using intuitionistic fuzzy theory

Human Reliability Analysis (HRA) is a critical issue for addressing human error in system reliability. There are numerous tasks for which human factors-related data are not available, rendering expert knowledge the only basis for assessing such tasks. However, the knowledge obtained from experts is subject to ambiguity and vagueness, which affects the usability of the assessment results. To overcome this challenge, in this paper a reference task based HRA method is proposed and the intuitionistic fuzzy set (IFS) is adopted because of its advantage of being able to handle ambiguous information. Firstly, to analyze the human error probability (HEP) of the target task, a reference task-based human error analysis model is introduced. Two solutions are provided: calculating the performance shaping factors (PSFs) distance between the reference task and the target task and establishing a quantitative relationship between PSFs and HEP. Secondly, the PSFs evaluation and inference methods based on triangular intuitionistic fuzzy numbers (TIFNs) are developed. Finally, the effectiveness and consistency of the two solutions of TIFN-HRA are demonstrated through a spaceflight refueling mission analysis. The distances between the results of the two solutions and the expert linguistic are compared and both results have the shortest distance to “Low”. However, solution I is simpler and the result is clearer.

Rethinking the applicability domain analysis in QSAR models.

Notwithstanding the wide adoption of the OECD principles (or best practices) for QSAR modeling, disparities between in silico predictions and experimental results are frequent, suggesting that model predictions are often too optimistic. Of these OECD principles, the applicability domain (AD) estimation has been recognized in several reports in the literature to be one of the most challenging, implying that the actual reliability measures of model predictions are often unreliable. Applying tree-based error analysis workflows on 5 QSAR models reported in the literature and available in the QsarDB repository, i.e., androgen receptor bioactivity (agonists, antagonists, and binders, respectively) and membrane permeability (highest membrane permeability and the intrinsic permeability), we demonstrate that predictions erroneously tagged as reliable (AD prediction errors) overwhelmingly correspond to instances in subspaces (cohorts) with the highest prediction error rates, highlighting the inhomogeneity of the AD space. In this sense, we call for more stringent AD analysis guidelines which require the incorporation of model error analysis schemes, to provide critical insight on the reliability of underlying AD algorithms. Additionally, any selected AD method should be rigorously validated to demonstrate its suitability for the model space over which it is applied. These steps will ultimately contribute to more accurate estimations of the reliability of model predictions. Finally, error analysis may also be useful in "rational" model refinement in that data expansion efforts and model retraining are focused on cohorts with the highest error rates.

Electromagnetic performance of generalized deployable log periodic dipole antenna with mechanical errors

The primary contribution of this paper is to investigate the effect of mechanical errors on the electromagnetic (EM) performance of the generalized deployable log-period dipole (GDLPDA) antenna. The concept of the “GDLPDA” is introduced first. Additionally, an electromechanical error analysis model is developed to assess the effects of dipole machining errors and deformations on the EM performance of GDLPDA. Based on this model, positional errors in two directions along the feedline are considered. Furthermore, an electromechanical error analysis model considering dipole rotational errors on cross-polarized electric fields is established. The actual positional and rotational errors are simulated to analyze the EM performance of GDLPDA. The simulation results show that the dipole positional and rotational errors can degrade the EM performance. Specifically, positional errors reduce gain and pointing accuracy, along with an increase in the 3 dB beamwidth. Rotational errors generate cross-polarization fields, decreasing the level of the co-polarization amplitude. This insight can help antenna design engineers to optimize GDLPDA to minimize the adverse effects of error on performance.

Multitask learning to predict successful weaning in critically ill ventilated patients: A retrospective analysis of the MIMIC-IV database.

Weaning is an essential issue in critical care. This study explores the efficacy of multitask learning models in predicting successful weaning in critically ill ventilated patients using the Medical Information Mart for Intensive Care (MIMIC) IV database. We employed a multitask learning framework with a shared bottom network to facilitate common knowledge extraction across all tasks. We used the Shapley additive explanations (SHAP) plot and partial dependence plot (PDP) for model explainability. Furthermore, we conducted an error analysis to assess the strength and limitation of the model. Area under receiver operating characteristic curve (AUROC), calibration plot and decision curve analysis were used to determine the performance of the model. A total of 7758 critically ill patients were included in the analyses, and 78.5% of them were successfully weaned. Multitask learning combined with spontaneous breath trial achieved a higher performance to predict successful weaning compared with multitask learning combined with shock and mortality (area under receiver operating characteristic curve, AUROC, 0.820 ± 0.002 vs 0.817 ± 0.001, p < 0.001). We assessed the performance of the model using calibration and decision curve analyses and further interpreted the model through SHAP and PDP plots. The error analysis identified a relatively high error rate among those with low disease severities, including low mean airway pressure and high enteral feeding. We demonstrated that multitask machine learning increased predictive accuracy for successful weaning through combining tasks with a high inter-task relationship. The model explainability and error analysis should enhance trust in the model.

How much Romanian does Google Translate know?

To compensate, even if on a small scale, for the scarcity of investigations of English-into-Romanian machine translations from a corpus-based genre-specific perspective, this case study concerns the quality of such translation outcomes in the particular case of two different, but closely related, text genres – everyday and newspaper/ news releases language, that have been translated with the help of Google Translate. The paper starts from Keshavarz’s (1999) very general model of error analysis to create a linguistic error profile in the two genres taken into consideration. The errors identified and discussed are illustrated with small-scale corpus examples. Since they reflect the capabilities of the Google translation platform, the findings of this paper may be relevant for its developers to get a clearer picture of its strengths and limitations and suggest ways of improving it so that it can ultimately provide higher quality translations. It may also contribute to raising translators’ attention to which areas are potentially problematic when they work, in the post-editing stage, with the English-Romanian language pair, with the particular text genres considered here.

Comprehensive Analysis of Model Errors in Blueberry Detection and Maturity Classification: Identifying Limitations and Proposing Future Improvements in Agricultural Monitoring

In blueberry farming, accurately assessing maturity is critical to efficient harvesting. Deep Learning solutions, which are increasingly popular in this area, often undergo evaluation through metrics like mean average precision (mAP). However, these metrics may only partially capture the actual performance of the models, especially in settings with limited resources like those in agricultural drones or robots. To address this, our study evaluates Deep Learning models, such as YOLOv7, RT-DETR, and Mask-RCNN, for detecting and classifying blueberries. We perform these evaluations on both powerful computers and embedded systems. Using Type-Influence Detector Error (TIDE) analysis, we closely examine the accuracy of these models. Our research reveals that partial occlusions commonly cause errors, and optimizing these models for embedded devices can increase their speed without losing precision. This work improves the understanding of object detection models for blueberry detection and maturity estimation.

Solving quadratic equations by completing the square: Applying Newman’s Error Analysis Model to analyse Grade 11 errors

Error analysis is an instructional strategy that can assist teachers to identify learners’ areas of weakness in mathematics and that can point to remediation of those errors. This article explores the errors learners exhibit when solving quadratic equations by completing the square using Newman’s Error Analysis Model. A research study explored the errors learners exhibit when solving quadratic equations by completing the square. Newman’s Error Analysis Model provided the analytic framework for the qualitative approach that was used to explore those errors. A diagnostic test with five test items was administered to 35 learners in one secondary school in Limpopo province of South Africa. Subsequently, 12 learners whose scripts featured common mistakes were identified; these learners participated in a semi-structured interview to diagnose the errors. The findings revealed that learners commit comprehension, transformation and process errors. The findings suggest that if the errors that learners make are exposed and made explicit, the errors can be remediated and thereby enhance understanding and learning. The findings of this study indicate that for teachers to understand the types of errors learners commit when solving quadratic equations by completing the square it is vital for them (errors) to be addressed. Mathematics teachers should also consider diagnosing why learners commit those errors, as they would know the strategies to be employed to teach this topic and subsequent topics.Contribution: The findings of this article add value to the current literature by providing empirical knowledge on learner challenges when solving quadratic equations by completing the square. This study provides opportunities for mathematics teachers to focus more on the strategies that would assist learners to understand this topic.

Комплексный подход к анализу аргументативных отношений в текстах научной коммуникации

The problem of automatic analysis of argumentation in scientific communication texts is considered. Argumentation is understood as an ordered set of arguments used to support a certain thesis. An argument includes at least one premise and one conclusion, connected by an argumentative relation. The purpose of the work is an experimental study of neu-ral network approaches to solving the problem of searching and extracting argumentative relations between statements located closely in the text. The study was conducted on a corpus of texts with argumentative markup created using the previously developed web platform. The corpus included texts of scientific news, analytical articles from the Habr web-site, scientific articles and reviews. Datasets for machine learning were built based on these texts. To improve the quali-ty of neural network models training, these sets were supplemented with new data by using automatic paraphrasing and double translation methods. Two approaches to training models were considered: the first one with labeling of indi-cators in texts and the second one with preliminary training of a language model on the task of predicting indicators. To evaluate the models performance, an approach was proposed based on estimates of agreement between experts, usu-ally used to compare markups of manually created texts. A comparison of agreement coefficients between experts and trained models showed that the quality threshold for extracting argumentative relations was almost reached on the model with labeled indicators. A manual analysis of model errors was carried out by visualizing the obtained results. Thus, the novelty of the work lies in the application of an integrated approach to creating data sets, training models and evaluating the results obtained from the automatic extraction of argumentative relations.

Error propagation and attribution in simulation-based capital models

AbstractCalculation of loss scenarios is a fundamental requirement of simulation-based capital models and these are commonly approximated. Within a life insurance setting, a loss scenario may involve an asset-liability optimization. When cashflows and asset values are dependent on only a small number of risk factor components, low-dimensional approximations may be used as inputs into the optimization and resulting in loss approximation. By considering these loss approximations as perturbations of linear optimization problems, approximation errors in loss scenarios can be bounded to first order and attributed to specific proxies. This attribution creates a mechanism for approximation improvements and for the eventual elimination of approximation errors in capital estimates through targeted exact computation. The results are demonstrated through a stylized worked example and corresponding numerical study. Advances in error analysis of proxy models enhance confidence in capital estimates. Beyond error analysis, the presented methods can be applied to general sensitivity analysis and the calculation of risk.

Analysis of influencing factors on numerical simulation of transverse jet in supersonic flow

Numerical simulation is becoming an important research method in the field of supersonic flow. Its accuracy and reliability have always been the key to its further application and the focus of current researches. In this study, a series of numerical simulations of transverse supersonic jet in supersonic free flow are carried out based on Reynolds averaged (RANS) solver. Firstly, the flow field structure obtained by different calculation cases is studied, and the mechanism of the difference of flow field structure is described. Furthermore, by changing the accuracy of difference scheme for the convection term and the turbulence model, the different simulation results are compared with the experimental data. The deviation from numerical simulation compared with the experiment is further analyzed to clarify the critical factors affecting the calculation reliability. The results show that improving the accuracy of the scheme cannot effectively improve the calculation results, while choosing an appropriate turbulence model can be helpful to improve the calculation accuracy. When the numerical simulation of jet flow mixing in supersonic flow field is carried out, the conclusions of this study can provide a support for determining the numerical model method and error analysis.