Error Prediction Research Articles

Methods for Human Reliability Analysis in Dentistry.

Human error (HE) is one of the main causes of accidents in different organizations and industries. Dentistry is a medical branch with a high risk of error since it involves complex manual tasks that must be performed with a high degree of accuracy. To understand the various aspects of HE in dentistry, which is crucial for developing strategies to mitigate its impact on patients' safety, it is necessary to perform a human reliability analysis (HRA). However, there is scarce data on the use of HRA in dentistry. In this paper, we give a brief description of the main phases of HRA with an emphasis on HRA methods that could be used in dentistry. Since HRA methods have been designed for diverse industrial applications, we discuss their possible application in dentistry. Among the discussed methods, the Systematic Human Error Reduction and Prediction Approach (SHERPA) and the Human Error Assessment and Reduction Technique were identified as the best candidates for performing HRA in dentistry. This is of great importance since understanding and addressing HEs is crucial for improving patient safety and the overall quality of dental care.

Deep learning prediction of error and skill in robotic prostatectomy suturing.

Manual objective assessment of skill and errors in minimally invasive surgery have been validated with correlation to surgical expertise and patient outcomes. However, assessment and error annotation can be subjective and are time-consuming processes, often precluding their use. Recent years have seen the development of artificial intelligence models to work towards automating the process to allow reduction of errors and truly objective assessment. This study aimed to validate surgical skill rating and error annotations in suturing gestures to inform the development and evaluation of AI models. SAR-RARP50 open data set was blindly, independently annotated at the gesture level in Robotic-Assisted Radical Prostatectomy (RARP) suturing. Manual objective assessment tools and error annotation methodology, Objective Clinical Human Reliability Analysis (OCHRA), were used as ground truth to train and test vision-based deep learning methods to estimate skill and errors. Analysis included descriptive statistics plus tool validity and reliability. Fifty-four RARP videos (266min) were analysed. Strong/excellent inter-rater reliability (range r = 0.70-0.89, p < 0.001) and very strong correlation (r = 0.92, p < 0.001) between objective assessment tools was demonstrated. Skill estimation of OSATS and M-GEARS had a Spearman's Correlation Coefficient 0.37 and 0.36, respectively, with normalised mean absolute error representing a prediction error of 17.92% (inverted "accuracy" 82.08%) and 20.6% (inverted "accuracy" 79.4%) respectively. The best performing models in error prediction achieved mean absolute precision of 37.14%, area under the curve 65.10% and Macro-F1 58.97%. This is the first study to employ detailed error detection methodology and deep learning models within real robotic surgical video. This benchmark evaluation of AI models sets a foundation and promising approach for future advancements in automated technical skill assessment.

An Enhanced Seamless Localization Framework Using Spatial-temporal Uncertainty Predictor Under Obscured Indoor and Outdoor Scenes

Abstract. Uncertainty modelling is regarded as one of the core components in the field of urban navigation, that can affect the performance of indoor and outdoor location information acquisition, especially under obscured scenarios. Existing multi-source fusion-based seamless positioning algorithms are subjected to random and highly dynamic human motion characteristics and changeable observation errors caused by the dynamic occlusions of human and buildings under urban scenes, which lead to the insufficient spatiotemporal correlation and poor accuracy of final multi-source fusion structure. To fill in this gap, this paper proposes an enhanced seamless localization framework using spatial-temporal uncertainty predictor under obscured indoor and outdoor scenes (ESL-STUP), that takes into account both temporal correlation and spatial correlation of trajectories provided by Wi-Fi, GNSS, and sensor-originated motion information. An iPDR-based trajectory estimation structure is proposed, using the integration of INS/PDR mechanizations, magnetic observations, and deep-learning based speed estimation to enhance the performance of traditional PDR algorithm. A period of human motion features extracted from hybrid location sources are modelled instead of only one or adjacent location points to realize time-varying measured uncertainty errors prediction, and the predicted uncertainty errors of different indoor and outdoor location sources are integrated with iPDR to realize robust seamless positioning performance. Comprehensive experiments indicate that compared with existing multi-source fusion-based seamless positioning structure, the proposed ESL-STUP realizes much better performance under different scenes.

Modeling of CNC machine tool line rail assembly accuracy and its control method

Aiming at the linear guide assembly process, due to the preload load caused by the assembly deformation mechanism is not clear resulting in the guide assembly straightness is difficult to predict the problem. In this paper, based on the establishment of linear guide assembly deformation model, the ideal and actual conditions of the guide assembly deformation is discussed. Secondly, considering the influence of manufacturing error of the guide rail mounting surface on the guide rail assembly accuracy, it puts forward the integrated assembly deformation and manufacturing error of the guide rail assembly straightness error prediction model, and puts forward the control method of the guide rail assembly accuracy based on the assembly deformation and the inverse repair manufacturing error. Finally, the experimental bench is built and the accuracy of the theoretical model and accuracy control method is verified through experiments , and the results show that the straightness error of the guideway assembly is reduced by .

Comparison of Positioning Error Prediction Results of Industrial Robots Based on Three Different Types of Neural Networks

ABSTRACTWith the increasing development of industry, the market demand for manufacturing has shifted to large‐scale customized production. This poses new challenges to the production flexibility of industrial robots. The offline programming method can perfectly meet this challenge. But its disadvantage is that it relies heavily on the absolute positioning accuracy of industrial robots. In recent years, there has been an increasing number of studies using neural networks (NN) to predict the positioning errors of industrial robots to improve their absolute positioning accuracy. However, most of these studies only focus on the application of NNs, and do not compare the prediction results and performance of different kinds of NNs. This paper selects three typical network models: backpropagation neural network (BPNN), particle swarm algorithm optimization BPNN (PSO‐BPNN), and radial basis function neural network (RBFNN). Through in‐depth experiments and analysis of these networks, the purpose is to reveal their respective prediction effects and characteristics and to summarize their advantages and disadvantages. Experimental results show that BPNN performs poorly in predicting positioning errors. As an optimization method, the particle swarm algorithm can effectively improve the prediction performance of BPNN. In contrast, the RBFNN performs well, which makes it very suitable for predicting the positioning error of industrial robots.

Dead Fuel Moisture Content Reanalysis Dataset for California (2000–2020)

This study presents a novel reanalysis dataset of dead fuel moisture content (DFMC) across California from 2000 to 2020 at a 2 km resolution. Utilizing a data assimilation system that integrates a simplified time-lag fuel moisture model with 10-h fuel moisture observations from remote automated weather stations (RAWS) allowed predictions of 10-h fuel moisture content by our method with a mean absolute error of 0.03 g/g compared to the widely used Nelson model, with a mean absolute error prediction of 0.05 g/g. For context, the values of DFMC in California are commonly between 0.05 g/g and 0.30 g/g. The presented product provides gridded hourly moisture estimates for 1-h, 10-h, 100-h, and 1000-h fuels, essential for analyzing historical fire activity and understanding climatological trends. The methodology presented here demonstrates significant advancements in the accuracy and robustness of fuel moisture estimates, which are critical for fire forecasting and management.

GNSS/IMU/LO integration with a new LO error model and lateral constraint for navigation in urban areas

The quest for reliable vehicle navigation in urban environments has led the integration of Light Detection and Ranging (LiDAR) Odometry (LO) with Global Navigation Satellite Systems (GNSS) and Inertial Measurement Units (IMU). However, the performance of the integrated system is limited by a lack of accurate LO error modeling. In this paper, we propose a weighted GNSS/IMU/LO integration-based navigation system with a novel LO error model. The Squared Exponential Gaussian Progress Regression (SE-GPR) based LO error model is developed by considering the vehicle velocity and number of point cloud features. Based on error prediction for GNSS positioning and LO, a weighting strategy is designed for integration in an Extended Kalman Filter (EKF). Furthermore, error accumulation of the navigation state, especially in GNSS-challenging scenarios, is restrained by the LiDAR-Aided Lateral Constraint (LALC) and Non-Holonomic Constraint (NHC). An experiment was conducted in a deep urban area to test the proposed algorithm. The results show that the proposed algorithm delivers horizontal and three-dimensional (3D) positioning Root Mean Square Errors (RMSEs) of 3.669 m and 5.216 m, respectively. The corresponding accuracy improvements are 35.9% and 50.0% compared to the basic EKF based GNSS/IMU/LO integration.

A comparison of the renal function biomarkers serum creatinine, pro-enkephalin and cystatin C to predict clearance of pemetrexed.

For drugs with a narrow therapeutic window, there is a delicate balance between efficacy and toxicity, thus it is pivotal to administer the right dose from the first administration onwards. Exposure of pemetrexed, a cytotoxic drug used in lung cancer treatment, is dictated by kidney function. To facilitate optimized dosing of pemetrexed, accurate prediction of drug clearance is pivotal. Therefore, the aim of this study was to investigate the performance of the kidney function biomarkers serum creatinine, cystatin C and pro-enkephalin in terms of predicting the elimination of pemetrexed. We performed a population pharmacokinetic analysis using a dataset from two clinical trials containing pharmacokinetic data of pemetrexed and measurements of all three biomarkers. A three-compartment model without covariates was fitted to the data and the obtained individual empirical Bayes estimates for pemetrexed clearance were considered the "true" values (Cltrue). Subsequently, the following algorithms were tested as covariates for pemetrexed clearance: the Chronic Kidney Disease Epidemiology Collaboration equation using creatinine (CKD-EPICR), cystatin C (CKD-EPICYS), a combination of both (CKD-EPICR-CYS), pro-enkephalin as an absolute valueor in a combined algorithm with age and serum creatinine, and lastly, a combination of pro-enkephalin with cystatin C. The dataset consisted of 66 subjects with paired observations for all three kidney function biomarkers. Inclusion of CKD-EPICR-CYS as a covariate on pemetrexed clearance resulted in the best model fit, with the largest decrease in objective function (p < 0.00001) and explaining 35% of the total inter-individual variability in clearance. The predictive performance of the model to containing CKD-EPICR-CYS to predict pemetrexed clearance was good with a normalized root mean squared error and mean prediction error of 19.9% and 1.2%, respectively. In conclusion, this study showed that the combined CKD-EPICR-CYS performs best in terms predicting pharmacokinetics of pemetrexed. Despite the hypothesized disadvantages, creatinine remains to be a suitable and readily available marker to predict pemetrexed clearance in clinical practice.

Rapid Detection of Oil Content in Camellia oleifera Kernels Based on Hyperspectral Imaging and Machine Learning

The oil content (OC) of kernels is one of the primary targets in the breeding of Camellia oleifera. However, the OC determination is labor-consuming and time-costing using traditional methods. In this study, a rapid and efficient OC detecting method was developed based on hyperspectral imaging (HSI). The OCs of 220C. oleifera clones were first determined using the Soxtec extraction method and hyperspectral images of all samples were obtained. Five spectral preprocessing methods and two dimensionality reduction methods was performed to eliminate hyperspectral noise. Based on the preprocessed spectral and OC data, OC predictive models were developed. The optimal OC prediction model was developed based on the characteristic wavelengths selected by competitive adaptive reweighted sampling from the preprocessed data by Savitzky–Golay smoothing and the first derivative method. The determination coefficient of this model was 0.9383, with a root mean squared error prediction of 1.7921% and residual predictive deviation of 4.0271. The further validation of this model by the other samples demonstrated it’s robustness and accuracy. The results reveal the potential of HSI in the rapid OC detection in C. oleifera. This will provide reference and guidance for the phenotype collection of C. oleifera.

Study on new thermal error compensation method for complex surface five-axis machining based on new experimental strategy

Five-axis machine tools are well-suited for processing complex surface parts. However, during the machining process, thermal error can have a significant impact on the machining accuracy. To propose an accountable on-line compensation method for five-axis milling, this paper developed a new experimental device for in-situ measurements of thermal-induced displacements in both X- and Y-axis directions. With the help of this self-developed device, a five-axis milling experiment was carried out to collect the temperature change of key thermal points and thermal-induced error during continuous impeller milling. A thermal error prediction model was established with collected data and a thermal error compensation system was developed for the five-axis milling accordingly. The following validation experiment showed that the surface profile error of the blades of milled impeller can be stabilized within the range of 0.1 mm with the help of proposed the thermal error compensation system. The results of the validation experiment proved the effectiveness of the proposed method for the reduction of thermal error during the five-axis milling.

A temperature-sensitive points selection method for machine tool based on rough set and multi-objective adaptive hybrid evolutionary algorithm

Reasonable deployment of temperature sensors is the key to accurately monitoring the temperature field of machine tools and improving the accuracy of thermal error prediction and compensation models. To determine the optimal deployment location of sensors, this paper proposes a temperature-sensitive points selection method tightly coupled with rough set and multi-objective optimization. Firstly, the importance of each temperature measurement point to the thermal error is calculated based on the rough set, and information entropy is introduced to amplify the importance difference among adjacent measurement points at the same heat source. Then, with the temperature measurement points groups as the variables, the number of temperature measurement points in the group, and the information importance of the group as the objectives, a multi-objective attribute reduction model is established, which transforms the temperature-sensitive points selection problem into a discrete multi-objective optimization problem. Finally, a multi-objective adaptive hybrid evolutionary algorithm is proposed, which designs a population initialization method based on mutual information and interval probability, and dynamic adaptive evolutionary parameters to achieve optimal temperature-sensitive points selection. Experiments on the high-speed dry hobbing machine verify the superiority and effectiveness of the proposed method.

Semi-analytic approximate time-optimal asteroid landing with dimensionality reduction shooting

Pinpoint landing on asteroid is very challenging due to the uncertainties of the gravitational field, which highlights the urgent need for autonomous landing algorithms under such uncertainties. Some well-known guidance algorithms, such as APDG and real-time convex programming, either ignore thrust boundary constraints or are severely time consuming. In addition, these algorithms have the same difficulty in analyzing landing errors due to model uncertainty propagation, making the reliability of the algorithms in actual landings questionable. To address these challenges, we propose a landing framework that combines model identification, trajectory analytical solution and closed-loop corrections to achieve near-optimal real-time landing control. Firstly, we simplified the landing scenario, focusing on reducing the dimension of shooting variables to enable rapid trajectory calculation. Secondly, we derived error propagation equations and established criteria for trajectory replanning based on landing error prediction. Finally, we validated our approach with practical examples of the Shoemaker probe landing on Eros 433. Through real-time landing error estimation, our proposed framework enables spacecraft to achieve near time-optimal land within a given error threshold.

A deep learning approach for pose error prediction in parallel robots

Parallel robots are increasingly favored in industrial automation due to their high precision, stability, and rigidity in high-speed and heavy-load tasks. However, factors such as manufacturing tolerances, assembly deviations, loads, and environmental conditions can lead to pose inaccuracies, manifesting as both geometric and non-geometric errors. Traditional model-driven approaches effectively predict geometric errors but often neglect non-geometric factors, potentially impacting the overall accuracy. This paper presents a deep learning-based method for comprehensive pose error prediction in parallel robots, addressing both geometric and non-geometric errors. The proposed approach employs an encoder-decoder network that takes target pose inputs and outputs predicted pose errors. The encoder integrates multi-layer convolutional sparse coding (CSC) blocks and efficient channel attention (ECA) modules to extract features from the network inputs. Monte Carlo dropout is utilized to estimate prediction uncertainties, enhancing the reliability of the network predictions. Experimental validation on a Stewart platform demonstrates the high accuracy and robustness of the method under various loading conditions. Comparative analyses with several typical models affirm the superior performance of the proposed network, indicating its potential to improve the operational accuracy and reliability of parallel robots in high-precision applications.

Prediction of human error probability in helicopter to ship transfer operation under an evidential reasoning extended CREAM approach.

Although the helicopter to ship transfer operation is rarely applied in ports around the world, some of the hub ports have encouraged it. The operational process poses challenges to safety due to the nature of the work to be completed by the ship crew. This article performs a systematic human error prediction to ensure safe helicopter to ship transfer operations under different circumstances since the human factor is becoming a significant contributor to maritime accidents. To accomplish this, the article addresses a cognitive reliability and error analysis method (CREAM) under the evidential reasoning (ER) approach. In the proposed approach, the CREAM provides a comprehensive human error prediction tool and ER is capable of supporting experts' judgment in decision-making. The findings of the research show that 'Secure the loose objects within or adjacent to the operating area' poses a high human error probability (4.50E-02) during helicopter to ship transfer operations.

Prediction of plasma rotation velocity and ion temperature profiles in EAST Tokamak using artificial neural network models

Abstract Artificial neural network models have been developed to predict rotation velocity and ion temperature profiles on the EAST tokamak based on spectral measurements from the X-ray crystal spectrometer. Both Deep Neural Network (DNN) and Convolutional Neural Network (CNN) models have been employed to infer line-integrated ion temperatures. The predicted results from these two models exhibit a strong correlation with the target values, providing an opportunity for cross-validation to enhance prediction accuracy. Notably, the computational speed of these models has been significantly increased, surpassing traditional methods by over tenfold. Furthermore, the investigation of input data range and error prediction serves as the foundation for future automated calculation process. Finally, CNNs have also been employed to predict line-integrated rotation velocity profiles and inverted ion temperature profiles for their robustness in the training process. It is noted that these algorithms are not restricted to any specific physics model and can be readily adapted to various fusion devices.

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.

Determination of leaf nitrogen content in apple and jujube by near-infrared spectroscopy

The nitrogen content of apple leaves and jujube leaves is an important index to judge the growth and development of apple trees and jujube trees to a certain extent. The prediction performance of the two samples was compared between different models for leaf nitrogen content, respectively. The near-infrared absorption spectra of 287 apple leaf samples and 192 jujube leaf samples were collected. After eliminating the outliers by Mahalanobis distance method, the remaining spectral data were processed by six different preprocessing methods. BP neural network (BP), random forest regression (RF), least partial squares (PLS), K-Nearest Neighbor (KNN), and support vector regression (SVR) were compared to establish prediction models of nitrogen content in apple leaves and jujube leaves. The results showed that the determination coefficient (R2), root mean square error (RMSE) and residual prediction deviation (RPD) of the models established by different combined pretreatment methods were compared among the five methods. Compared with the performance of the other four models, the modeling method of SG + SD + CARS + RF was suitable for the prediction of nitrogen content in apple leaves, and its modeling set R2 was 0.85408, RMSE was 0.082188, and RPD was 2.5864. The validation set R2 is 0.75527, RMSE is 0.099028, RPD is 2.1956. The modeling method of FD + CARS + PLS was suitable for the prediction of nitrogen content in jujube leaves. The modeling set R2 was 0.7954, RMSE was 0.14558, and RPD was 2.4264; the validation set R2 is 0.81348, RMSE is 0.089217, and RPD is 2.4552.In the prediction modeling of apple leaf nitrogen content in the characteristic band, the model quality of RF was better than the other four prediction models. The model quality of PLS in predictive modeling of nitrogen content of jujube leaves in characteristic bands is superior to the other four predictive models, These results provide a reference for the use of near-infrared spectroscopy to determine whether apple trees and jujube trees are deficient in nutrients.

Understanding post-operative refractive outcome in pediatrics after IOL implementation: factors and predictors.

In pediatric ophthalmology, calculating intra-ocular lens (IOL) power can be challenging. It is important to predict if the post-surgery refractive error (RE) will meet the intended refractive goal. In this study, we aimed to investigate the factors and predictors influencing RE outcomes in children undergoing IOL implantation. This was a retrospective cross-sectional cohort study that involved 47 eyes with congenital cataracts underwent IOL implantation. Each patient underwent follow-up visits at two months and two years' post-surgery. The IOL power calculations were conducted using the Holladay 1 formula, and both the prediction error (PE) and absolute prediction error (APE) were calculated. The mean age was 6.52 ± 4.61years, with an age range of 1-15years. The mean IOL power was 20.31 ± 6.57 D, and the mean post-operative refraction was 1.31 ± 2.65 D. The mean of PE and APE were 0.67 ± 1.77 and 1.55 ± 1.06 D, respectively. Whereas PE was correlated to axial length with an R-value of - 0.29 (P = 0.04). The calculation method had a significant negative relationship with APE and PE, with coefficients of - 1.05 (P = 0.009) and - 1.81 (P = 0.009), respectively. High astigmatism was associated with greater errors in the refractive outcome. The calculation methods had the most considerable impact on the post-operative RE. The customization of surgical approaches to accommodate individual characteristics is crucial. Further research with diverse subgroups is needed to comprehensively understand the influence of each factor.

A reliable ensemble forecasting modeling approach for complex time series with distributionally robust optimization

Accurate ensemble forecast results provide strong support for management decisions. While the existing ensemble forecasting model ignores the requirement of directional accuracy making its prediction direction is usually error-prone. To address this issue, we develop a convex directional prediction error measure and subsequently construct a reliable ensemble forecasting model that minimizes the horizontal prediction error with the bounded directional prediction error. Mathematically, we provided the proper range of the required directional error level. Furthermore, we find the classical ensemble forecasting model is a special case of our study when the directional error level is larger than the upper bound we gave in this study. To deal with the possible deterioration of the individual model over time, we also considered its worst possible prediction performance by introducing the distributionally robust optimization (DRO) technique into the proposed reliable ensemble forecasting model. Technically, we showed that the DRO-based reliable ensemble forecasting model is convex and can be reformulated into a second-order cone problem which can be readily solved by off-the-shelf solvers. Finally, the effectiveness of the proposed reliable ensemble forecasting model and the DRO-based reliable ensemble forecasting model were validated on three different datasets, e.g., the exchange rate, the oil price, and the country risk index. To sum up, we construct a reliable ensemble forecasting model to simultaneously control the horizontal prediction error and directional prediction error of ensemble forecasting, and thus enhance the reliability of ensemble forecasting.

Bias correction and ensemble techniques in statistical downscaling model for rainfall prediction using Tweedie-LASSO in West Java, Indonesia

Rainfall is a climate element with high variations in space and time scales, so it is not easy to predict. One way to predict rainfall is statistical downscaling (SD). SD can predict local rainfall based on Global Circulation Model (GCM) data. The Decadal Climate Prediction Project (DCPP), one of the GCMs, originates from adjacent grids and experiences multicollinearity problems. Rainfall as a response variable is Tweedie Compound Poisson Gamma (TCPG) distribution data because it has a discrete component (rainfall events) and a continuous component (rainfall intensity), so SD modelling will be carried out using Tweedie-LASSO. This research aims to compare the performance of bias correction and ensemble methods in SD in predicting rainfall in West Java, Indonesia. Bias correction uses Empirical Quantile Mapping (EQM) with CHIRPS data, and the ensemble method uses a stacking technique with Random Forest (Stacking-RF) due to the varied characteristics of DCPP model sources. Evaluation results using Root Mean Square Error Prediction (RMSEP) and correlation coefficient show that bias correction improves single-model performance but not ensemble models. Besides that, ensemble models outperform single models both before and after bias correction. The combination of bias correction and ensemble modelling can be recommended when conducting SD to enhance the prediction capability of rainfall at stations and other areas.