Error Uncertainty Research Articles

QC Constellation: a cutting-edge solution for risk and patient-based quality control in clinical laboratories.

Clinical laboratories face limitations in implementing advanced quality control (QC) methods with existing systems. This study aimed to develop a web-based application to addresses this gap, and improve QC practices. QC Constellation, a web application built using Python 3.11, integrates various statistical QC modules. These include Levey-Jennings charts with Westgard rules, sigma-metric calculations, exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts, and method decision charts. Additionally, it offers a risk-based QC section and a patient-based QC module aligning with modern QC practices. The codes and the web application links for QC Constellation were shared at https://github.com/hikmetc/QC_Constellation, and http://qcconstellation.com, respectively. Using synthetic data, QC Constellation demonstrated effective implementation of Levey-Jennings charts with user-friendly features like checkboxes for Westgard rules and customizable moving averages graphs. Sigma-metric calculations for hypothetical performance values of serum total cholesterol were successfully performed using allowable total error and maximum allowable measurement uncertainty goals, and displayed on method decision charts. The utility of the risk-based QC module was exemplified by assessing QC plans for serum total cholesterol, showcasing the application's capability in calculating risk-based QC parameters including maximum unreliable final patient results, risk management index, and maximum run size andoffering risk-based QC recommendations. Similarly, the patient-based QC and optimization modules were demonstrated using simulated sodium results. In conclusion, QC Constellation emerges as a pivotal tool for laboratory professionals, streamlining the management of quality control and analytical performance monitoring, while enhancing patient safety through optimized QC processes.

Validation of the OpenMC Code for Fusion Applications: The FNG-Streaming Benchmark Case

In this work, we benchmark OpenMC against the FNG-ITER streaming experiment. FNG-ITER streaming, a high-quality experiment carried out at the ENEA laboratories in Frascati, Italy, was initially included in SINBAD (Shielding Integral Benchmark Archive and Database). More recently, the benchmark was included in the Compilation of Nuclear Data Experiments for Radiation Characterization as well. It consists of a neutron shielding experiment with a rather complex geometry that constitutes an appropriate validation study for the use of weight windows within OpenMC. Measurements include flux detection via four different types of activation foils divided into three batches and a set of thermoluminescent detectors for nuclear heating. The OpenMC results are in very good agreement with those of MCNP and the experimental measurements, with the majority of the discrepancies within the combined statistical error and experimental uncertainty (less than 10% computed measured discrepancy).

Dynamic uncertainty evaluation of cylindricity error based on Bayesian deep neural network propagation method

Most of the existing methods for measuring and evaluating product geometric tolerances are for static processes, which use lower dimensions, are more complex to calculate, and have poor traceability and dynamic manageability and control of the measurement process. In this paper, a dynamic evaluation model of cylindricity error uncertainty based on the Bayesian deep neural network propagation method is proposed. Firstly, the input random variable sampling sample containing error uncertainty is generated by proposing the improved Monte Carlo sampling method (IMCS), which has the characteristics of homogeneity and validity; then the principle and construction process of a Bayesian neural network model are introduced to generate the propagation model of multidimensional random error variables; further, the Gaussian mixture model is trained by the Monte Carlo method, and the probability density function of the target response is generated by using the Gaussian mixture model. Through numerical experiments, test analysis of bearing outer rings, and comparison with ISO standard methods, the results show that the model based on the dynamic Bayesian deep neural network propagation method can correctly and effectively assess the uncertainty dynamically, and the whole estimation process is stable.

A research on improving the precision of laser tracker in alignment based on Pareto improvement principle.

The accuracy of laser trackers in large-scale dimensional metrology is subject to various influencing factors, with instrument positioning being a primary source of error. To address this, a multi-objective optimization algorithm, grounded in the Pareto improvement principle, is proposed. This algorithm is designed to optimize instrument placement, thereby minimizing measurement errors and enhancing the algorithm's efficacy in error reduction. Thereby, this article proved that positioning the laser tracker consistently on one side of the measurement area and aligning its height with the measuring points can effectively reduce the uncertainty of control point errors by up to 50%.

Reliability-based optimization for concrete-filled steel tubular columns incorporating multi-advanced techniques and model constraints

This work presents an advanced structural reliability analysis of circular concrete-filled steel tubular (CFST) members through machine learning and global optimization, together with numerical Monte Carlo simulations (MCS). The artificial neural network (ANN) model is developed and validated against available experimental data. On the basis of monetary cost of materials, the optimal design of CFST members with reliability constraint is formulated and solved by balancing composite motion optimization (BCMO) algorithm. Based on the MCS, reliability analysis is then carried out to calculate the coefficient of variation of optimal design under different loads. Subsequently, a comprehensive prediction simulation, taking account of the uncertainty of experimental data and potential errors from models (ANN, MCS, and BCMO), is performed to generate the statistics on the axial capacity of CFST members. The critical value for the optimal solution is also investigated as a function of the input variables. The results of this study can be applied to achieve a better reliability-based design optimization with minimum cost for the circular CFST columns.

Drifted Uncertainty Evaluation of a Compact Machine Tool Spindle Error Measurement System

The accurate measurement of spindle errors, especially quasi-static errors, is one of the key issues for the analysis and compensation of machine tool thermal errors in machining accuracy. To quantitatively analyze the influence of the measurement system’s own drift on the measurement results, a drifted uncertainty evaluation method of the precision instrument considering the time drift coefficient is proposed. This study also produced a high-precision compact spindle error measurement device (with a displacement measurement error of less than ±1.33 μm and an angular measurement error of less than ±1.42 arcsecs) as the research object to verify the proposed drift uncertainty evaluation method. A method for evaluating the drift uncertainty of the measurement system is proposed to quantitatively evaluate the system error and drift uncertainty of the measurement device. Experiments show that the drift uncertainty evaluation method proposed in this paper is more suitable for evaluating the uncertainty changes in measurement instruments during long-term measurements compared to traditional methods.

Quantifying the effect of scanning parameters on digital volume correlation analysis for in situ X-ray imaging of concrete

The potential effects of X-ray Computed Tomography (XCT) scan settings on the kinematic field computed via Digital Volume Correlation (DVC) are discussed in this paper. A parametric sensitivity study was conducted, including a series of acquired XCT scans, probing the same volume of interest (VOI) of a concrete specimen, and applying various combinations of XCT scan settings. Different values were adopted for the number of projections acquired, the exposure time and the pixel size. The DVC results revealed a linear correlation between the mean normalised correlation residuals, designating the quality of the correlation and the reliability of the computed kinematic field and the scanning parameters. Gaussian and median filters were applied to the raw data to explore the effect of image noise smoothness in the DVC analysis. The significant reduction noted for the mean normalised correlation residuals was not accompanied by similar levels of change for the corresponding error uncertainties, as defined by the displacement magnitude. The concept was tested against a realistic loading scenario, where the DVC kinematic fields, computed for various XCT “recipes”, were compared. Slim differences in the distribution of the mean minimum principal strain were observed.

A multi-point leakage prediction statistical method using Bayesian inference in water distribution networks

ABSTRACT Leakage in water distribution networks (WDNs) not only leads to serious water loss and pipe contamination but also affects residents’ daily water. Accurate localization of leaks in WDN is significant to conserve water resources and reduce economic losses. However, in traditional optimization and verification methods for leak detection, factors such as modeling and pressure monitoring point errors are not taken into account, resulting in the deviation from the actual location of leaks. To address the mentioned issues, this article presents a WDN leakage probability prediction method based on Bayesian inference. The method converts the prior probability of leakage events into posterior probability. Then, by utilizing the posterior probability density function, the uncertainty of modeling and measurement errors in the hydraulic simulation model are quantified. The probability distribution of leakage pipes and leakage quantity within the leakage area is calculated, allowing for the location prediction and corresponding magnitude of leakage. The experimental results indicate that the prediction model can detect unknown quantities of leakage events. By collecting multiple sets of leakage data, it is possible to accurately predict the location and quantity of leaks, enhancing the efficiency of leakage detection in large-scale water supply networks and providing decision-making assistance for water utilities.

Joint segmentation and image reconstruction with error prediction in photoacoustic imaging using deep learning

Deep learning has been used to improve photoacoustic (PA) image reconstruction. One major challenge is that errors cannot be quantified to validate predictions when ground truth is unknown. Validation is key to quantitative applications, especially using limited-bandwidth ultrasonic linear detector arrays. Here, we propose a hybrid Bayesian convolutional neural network (Hybrid-BCNN) to jointly predict PA image and segmentation with error (uncertainty) predictions. Each output pixel represents a probability distribution where error can be quantified. The Hybrid-BCNN was trained with simulated PA data and applied to both simulations and experiments. Due to the sparsity of PA images, segmentation focuses Hybrid-BCNN on minimizing the loss function in regions with PA signals for better predictions. The results show that accurate PA segmentations and images are obtained, and error predictions are highly statistically correlated to actual errors. To leverage error predictions, confidence processing created PA images above a specific confidence level.

Dynamic risk assessment of Uncertain Random System considering operator's simple emergency-stop action in short time window

Human-machine systems can be considered as typical Uncertain Random Systems (URS). In human-machine systems, operator's simple emergency-stop actions act as a vital soft barrier. Conventional Human Reliability Analyses face challenges in evaluating operator's simple emergency-stop action due to their static characteristics, incompatibility with short time windows, and problems posed by small samples. This paper provides a novel solution to risk assessment of human-machine systems involving operator's simple emergency-stop mechanism. Specifically, an evaluation method of operator's simple emergency-stop action is established, where human cognition is decomposed according to its temporal and logical dimensions, key indicators of human sub-cognition are evaluated, and uncertainty theory is used to integrate these indicators. The output of proposed method is uncertainty of operator's behavior (UOB), which can offer more information than traditional Human Error Probability (HEP) and can be readily converted into uncertainty of human error. Further, a precise simulation method for dynamic risk assessment of Uncertain Random Systems is developed, in which the integration of the UOB is achieved. A case study demonstrates the effectiveness of the proposed methods, and its results show that the proposed methods can clearly reflect the differences in system risk under short time window conditions that have minor distinctions.

Crucial time of emergency monitoring for reliable numerical pollution source identification

The Pollution source identification (PSI) is an important issue on river water quality management especially for urban receiving water. Numerical inversion method is theoretically an effective PSI technique, which employs monitored downstream pollutant breakthrough curves to identify the pollution source. In practice, it is important to know how much monitoring data should be accumulated to provide PSI results with acceptable accuracy and uncertainty. However, no literature reports on this key point and it seriously handers the numerical PSI technology to mature practical applications. To seek a monitoring guideline for PSI, we conducted extensively numerical experiments for single-point source instantaneous release taking Bayesian-MCMC method as the baseline inversion technique. The crucial time (Tc) phenomenon was found during the data accumulation process for Bayesian source inversion. After Tc, estimated source parameters subsequent sustained low error levels and uncertainty convergence. Results shown the presence of Tc impacted by the number and location of monitoring sections, while monitoring frequency and data error do not. Under different river hydrodynamic conditions, relative crucial time (Λ) is determined by the river's Peclet number, and minimum effective Λ was controlled by dispersion coefficient (Dx). Analytic spatial structure of Λ(U, Dx) was uncovered and this relationship successfully explained by the information entropy theory. Based on these findings, a novel design method of PSI emergency monitoring network for preparedness plan and a practical framework of PSI for emergency response were established. These findings fill the important knowledge gap in PSI applications and the guidelines provide valuable references for river water quality management.

Seasonal solar irradiance forecasting using artificial intelligence techniques with uncertainty analysis

Renewable integration in utility grid is crucial in the current energy scenario. Optimized utilization of renewable energy can minimize the energy consumption from the grid. This demands accurate forecasting of renewable contribution and planning. Most of the researches aim to find a suitable forecasting model in terms of accuracy and error metrics. However, the uncertainty and variability in these forecasts are also significant. This work combines point forecast with interval forecast to provide comprehensive information about the forecast uncertainty. In this work, solar irradiance forecasting is carried out using artificial intelligence (AI) techniques. Forecasting is done using seasonal auto-regressive moving average with exogenous factors (SARIMAX), support vector regression (SVR), long short term memory (LSTM) techniques and performance is evaluated. SVR model exhibited the best performance with R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document} values of 0.97 and 0.96 for winter and summer respectively and 0.85 for monsoon and post-monsoon seasons. This is followed by forecast error distribution studies and uncertainty analysis. For this, SVR forecast error data is fitted using laplace distribution. Uncertainty study is carried out using confidence intervals and coverage rates. Excellent coverage rates are obtained for various confidence levels for all seasons, indicating the appropriate fitting of error distribution. For the narrow 85% confidence band, coverage rates of 89%, 95%, 90%, and 88% are obtained for winter, summer, monsoon and post-monsoon respectively. The work emphasizes the need for error-distribution studies, modeling of forecast errors and their application in providing reliable forecast intervals with the perspective of enhancing system reliability.

An Improved Voltage Sag Monitor Placement Method by using Simple Probability Reach Area Matrix

Aim: A novel optimization model is introduced in this paper to achieve monitor placements of voltage sag. Background: Considering the uncertainty of system parameters, calculation errors and measurement errors, the method of MRA is not suitable. Objective: The contradiction between the number of monitors and observation areas can be coordinated to achieve voltage sag observation with fewer monitor quantities. Method: A new generalized approach defined as a simple probability reach area matrix (SPMRA) is proposed. Result: SPMRA method is simulated by the IEEE-39 bus system, which reveals that monitor placement quantities can be reduced effectively under probabilistic observability. Conclusion: SPMRA method is a general method for monitor placements of voltage sag. This method not only performs the functions of conventional MRA methods, but also addresses the challenges of errors and conflicts between observation requirements and installation costs.

An average-phase calibration method for the sensing matrix of a Modulated Wideband Converter

As a sub-Nyquist sampling structure based on compressed sensing (CS), a modulated wideband converter (MWC) is used for spectral blind sampling and reconstruction of multiband sparse signals. Sinusoidal signals with known phases have been theoretically used to study the calibration of the sensing matrix of MWC, but in practice, the initial phase of sinusoidal signals is unfortunately difficult to know in advance. Consequently, phase error will exist in the calibrated sensing matrix, which reduces the reconstruction signal-to-noise ratio (RSNR) of the reconstructed signal.In this paper, we first analyze the phase error uncertainty caused by the DC component of the periodic pseudo-random sequence. In addition, an average-phase calibration method based on a 0/1 mixing sequence is proposed to calibrate the sensing matrix. Each column of the sensing matrix is calibrated according to its average phase, which avoids introducing phase error when using sinusoidal signals with arbitrary unknown phases. Comparative experiments are conducted to evaluate the performance of the proposed method in terms of the RSNR and the reconstruction mean squared error (RMSE). The results show that the RSNR obtained by the proposed method is 40 dB, which is close to the signal-to-noise (SNR) of the input RF signal.

Development of a prediction tool for the compressive strength of ternary blended ultra-high performance concrete using machine learning techniques

ABSTRACT This study addresses the challenge of quantifying the complex and non-linear correlations between the technical properties of ultra-high-Performance concrete (UHPC) and its mixture composition through conventional statistical methods. To tackle this, this study employs five advanced machine learning methods: Random Forest (RF), Long-Short-Term Memory, Convolutional Neural Network, K-Nearest Neighbor, and Adaboost. A dataset comprising 308 UHPC compressive strength tests with varying amounts of cement, silica fumes, superplasticizers, ground granulated blast furnace slag, fine aggregates, coarse aggregates, age, and temperature was used to train these models. The models’ performance was validated using statistical metrics, regression error characteristic curve, SHAP analysis, and uncertainty analysis. Among the models, the RF model achieved the highest prediction accuracy with an R2 of 0.9877 and an RMSE of 0.0323. This study also introduces an open-source graphical user interface based on the RF model, providing engineers with a practical tool for estimating the compressive strength of UHPC and facilitating mix proportioning decisions. This interface aims to bridge the gap between machine learning predictions and practical applications in UHPC mix design.

Prediction of compressive strength of high-volume fly ash self-compacting concrete with silica fume using machine learning techniques

The quality and composition of the components in Self-Compacting Concrete (SCC) determine its compressive strength; however, determining these complex relationships through traditional statistical methods is difficult. This study uses five state-of-the-art machine learning techniques, namely, Random Forest (RF), Adaboost, Convolutional Neural Network (CNN), K-Nearest Neighbor (KNN), and Bidirectional Long Short-Term Memory (BI-LSTM), to simulate the strength properties of high-volume fly ash self-compacting concrete (HVFA-SCC) with silica fume. A database with 240 SCC compressive strength tests that included a range of material proportions was put together in order to train these models. The primary constituents that were selected and taken into consideration as input variables were cement, fly ash, super-plasticizer, silica fume, coarse and fine aggregate, and age. Various statistical metrics, regression error characteristics curve, SHAP analysis, and uncertainty analysis were used to validate the models' effectiveness in predicting the compressive strength of HVFA-SCC with varying material compositions. The recommended RF model demonstrated the highest predictive accuracy of all the models that were analysed. While machine learning can predict compressive strengths, its opaque 'black-box' nature limits its use for SCC mix engineers. This study introduces an open-source Random Forest-based GUI to close this gap. This interface helps engineers make mix proportion decisions by accurately estimating SCC compressive strength under various test conditions.

Analysis of Long-Distance Geometric Error Measurement and Uncertainty Based on PSD Laser Collimation Principle

Due to the limitations of traditional geometric error measurement, the measurement accuracy of long-stroke geometric errors is generally not high and the operation is complicated. In response to the above situation, in this study, a geometric error measurement system is built with a laser beam as the reference line and 2D position sensitive detector as the photoelectric conversion device. The single measurement range is 40 m, and the measurement range is further expanded through the principle of segmented splicing. Using an ultra-long guide rail as the measurement object for straightness measurement, the experimental results are similar to those of a laser interferometer. The uncertainty analysis model was obtained through the analysis of quantity characteristics, and based on this, the variance synthesis theorem and probability distribution propagation principle were studied to form two uncertainty synthesis methods. The measurement evaluation results showed that the two methods were basically consistent. The work provided a reference method for the uncertainty evaluation of position-sensitive detector measurement systems in the future.

Small-sample linear profile error uncertainty assessment based on grey system

The uncertainty assessment of the profile error of the cam profile, as defined in the national standard method, is difficult to carry out under conditions of small sample size and absence of probability distribution assumptions. This paper proposes a small-sample assessment model for the uncertainty of the profile error based on grey system. Firstly, the coordinate transformation is conducted using Vector Alignment Method to reduce systematic errors, and the non-uniform rational B-splines curve interpolation is utilized to fit the cam profile curve and perform error assessment. Subsequently, based on the error assessment results, Grey Information Measurement Model (GIMM) for the uncertainty of the profile error in small samples is established. This model employs Grey Relational Analysis to eliminate outliers and evaluates the uncertainty of the profile error by solving grey correlation coefficients. Maximum-Minimum Information Measure Method is used to assess the optimal sample size. Finally, numerical experiments and experimental tests were conducted on the uncertainty of camshaft profile error in automobiles. A total of 15 sets of profile data were compared with Guide to the Representation of Uncertainty in Measurement (GUM) and Monte Carlo Method (MCM) under different sample sizes. The results showed that GIMM achieved evaluation with only 8 sets of data samples under small sample and poor information conditions, with an uncertainty of 0.6338 μm, compared to 0.6346 μm for GUM and 0.6391 μm for MCM. The acceptance rate of GIMM reached 95.2%. This model outperforms other methods, providing a simplified and reliable assessment of cam profile error uncertainty.

A computational efficient approach for distributionally robust unit commitment with enhanced disjointed layered ambiguity set

AbstractTo achieve the sustainable development of the society, renewable energy dominated power systems are gradually formed. However, the uncertainty of renewable power poses challenges for power system operations, such as balancing the load and generation in day‐ahead unit commitment problems. To address this issue, an enhanced disjointed layered ambiguity set is proposed in this paper to effectively capture the uncertainty of renewable generation forecast errors. On this basis, a two‐stage distributionally robust unit commitment optimization problem is formulated, which is computational challenge. To this end, a novel reformation approach and a worst‐case support set identification method are proposed to derive the optimal solution in a computationally efficient way. Simulation results demonstrate that the proposed ambiguity set brings lower operation costs of unit commitment while maintaining similar reliability compared to existing methods. In detail, the proposed ambiguity set achieves a reduction in operating costs of approximately 10.01%. The proposed novel reformation approach and solution method are also shown to have higher computation efficiency than the existing Benders decomposition method, resulting in a reduction in computation time to less than 3.78% of the original duration.

A complexity efficient penta-diagonal quantum smoothing filter for bio-medical signal denoising: a study on ECG

Extracting information-bearing signal from a noisy environment has been a practical challenge in both classical and quantum computing formalism, especially in critical signal processing applications. To filter out the effect of noise, we propose a quantum smoothing filter built upon quantum formalism-based circuits applied for electrocardiogram signal denoising. The proposed quantum filter is a conceptually novel framework with an advantage in computational complexity as compared to the existing classical filters, such as discrete wavelet transform and empirical mode decomposition, whereas it achieves similar performance metrics for the accuracy of the filter. Further, we exploit the penta-diagonal Toeplitz structure of the smoothing filter, which gives approximately 48%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$48\\%$$\\end{document} gate cost reduction for 10 qubit circuit compared to the standard Hamiltonian simulation without structure. The run-time complexity using the quantum matrix inversion technique for the structured matrix is given by O~κ2poly(logN)εP\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ilde{{\\mathscr {O}}}\\left( \\frac{\\kappa ^2 \ ext {poly}(\\log {N})}{\\varepsilon _P}\\right)$$\\end{document} for condition number κ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\kappa$$\\end{document} of the N×N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N\ imes N$$\\end{document} filter matrix within precision εP\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varepsilon _P$$\\end{document}. Embedding fixed sparsity of the banded matrix, the quantum filter shows potentially better run-time complexity than classical filtering techniques. For the quantifiable research results of our work, we have shown several performance metrics, such as mean-square error and peak signal-to-noise ratio analysis, with a bound of error due to observation noise, simulation error and quantum measurement uncertainty.