Error Outliers Research Articles

Landslide deformation velocity real-time monitoring based on time-differenced carrier phase and its fault detection method

GNSS technologies such as Real-Time Kinematic (RTK) and Precise Point Positioning (PPP) have become indispensable for landslide monitoring. However, RTK relies on stable reference stations and robust communication network, rendering it unusable without reference station data or communication technology support. Real-time PPP technology provides decimeter level monitoring accuracy and suffers from long convergence times, failing to meet the real-time and reliable monitoring requirements during landslide destabilization phase. This paper presents the first study to investigate the application of time-differenced carrier phase (TDCP) technology for real-time monitoring of landslide deformation velocity. An adaptive fault detection method is proposed, which combines prior closure error outlier detection with the chi-squared residual-based fault detection and exclusion. Unlike traditional methods that require iteratively recalculating solutions with each satellite exclusion, our approach significantly reduces computational time. The effectiveness of the proposed method has been validated through two sets of real measurement data. The results demonstrate that TDCP technology enables real-time monitoring of deformation velocity at the millimeter level, and the proposed adaptive fault detection method effectively identifies measurement anomalies in complex environments. In static complex environments, the standard deviations of the GPS/BDS/GLONASS/Galileo combined solution in the E, N, and U directions are 1.7 mm/s, 1.7 mm/s, and 3.9 mm/s, respectively. During rapid sliding in open environments, the root mean square of the differences between TDCP and RTK monitoring results are 2.2 mm/s, 3.3 mm/s, and 4.6 mm/s, respectively.

An artificial intelligence approach for the estimation of conduction heat transfer using deep neural networks

PurposeThis study aims to use deep neural networks (DNNs) to learn the conduction heat transfer physics and estimate temperature distribution images in a physical domain without using any physical model or mathematical governing equation.Design/methodology/approachTwo novel DNNs capable of learning the conduction heat transfer physics were defined. The first DNN (U-Net autoencoder residual network [UARN]) was designed to extract local and global features simultaneously. In the second DNN, a conditional generative adversarial network (CGAN) was used to enhance the accuracy of UARN, which is referred to as CGUARN. Then, novel loss functions, introduced based on outlier errors, were used to train the DNNs.FindingsA UARN neural network could learn the physics of heat transfer. Within a few epochs, it reached mean and outlier errors that other DNNs could never reach after many epochs. The composite outlier-mean error as a loss function showed excellent performance in training DNNs for physical images. A UARN could excellently capture local and global features of conduction heat transfer, whereas the composite error could accurately guide DNN to extract high-level information by estimating temperature distribution images.Originality/valueThis study offers a unique approach to estimating physical information, moving from traditional mathematical and physical models to machine learning approaches. Developing novel DNNs and loss functions has shown promising results, opening up new avenues in heat transfer physics and potentially other fields.

Low Velocity Impact Monitoring of Composite Tubes Based on FBG Sensors.

Carbon fiber reinforced composites (CFRP) are susceptible to hidden damage from low velocity external impacts during their service life. To ensure the proper monitoring of the state of the composites, it is crucial to predict the location of an impact event. In this paper, fiber Bragg grating (FBG) sensors are affixed to the surface of a carbon fiber composite tube, and an optical sensing interrogator is used to capture the central wavelength shift of the FBG sensors due to low-velocity impacts. A discrete wavelet transform is used for noise reduction in the response signals. Then, the differences in the captured response signals of the FBG sensors at different locations of the impact were analyzed. Moreover, two methods were implemented to predict the location of low-velocity impacts, according to the differences in the captured response signals. The BP neural network-based method utilized three data sets to train the neural network, resulting in an average localization error of 20.68 mm. In contrast, the method based on error outliers selected a specific data set as the reference dataset, achieving an average localization error of 13.98 mm. The comparison of the predicted results shows that the latter approach has a higher predictive accuracy and does not require a significant amount of data.

A robust auxiliary noise power scheduling strategy for online secondary path modeling in active noise control systems

Auxiliary noise injected in active noise control (ANC) systems for online secondary path modeling (SPM) contributes to the residual noise, and thus deteriorates the noise control performance of ANC systems. Moreover, sudden changes in secondary path may lead to easy divergence of the existing online SPM methods. This paper improves the existing methods from three aspects to further mitigate these problems: 1) the noise power is varied on the basis of the convergence status of SPM filter so that large-power noise is injected at the start or when secondary path changes and the power is reduced as the filter converges. 2) a novel ON/OFF controlling strategy is used in the method so that the noise injection is suspended at the optimum point when the modeling accuracy is sufficient. 3) continuous check for error outliers is applied in the method to detect changes in secondary path so that the injection of noise can be reactivated in the case of path perturbation. Comparative simulations show the effectiveness of the improved method both in convergence and noise reduction performance. In addition, the improved method is robust against sudden changes in secondary path.

A Bayesian model for quantifying errors in citizen science data: application to rainfall observations from Nepal

Abstract. High-quality citizen science data can be instrumental in advancing science toward new discoveries and a deeper understanding of under-observed phenomena. However, the error structure of citizen scientist (CS) data must be well-defined. Within a citizen science program, the errors in submitted observations vary, and their occurrence may depend on CS-specific characteristics. This study develops a graphical Bayesian inference model of error types in CS data. The model assumes that (1) each CS observation is subject to a specific error type, each with its own bias and noise, and (2) an observation's error type depends on the static error community of the CS, which in turn relates to characteristics of the CS submitting the observation. Given a set of CS observations and corresponding ground-truth values, the model can be calibrated for a specific application, yielding (i) number of error types and error communities, (ii) bias and noise for each error type, (iii) error distribution of each error community, and (iv) the single error community to which each CS belongs. The model, applied to Nepal CS rainfall observations, identifies five error types and sorts CSs into four static, model-inferred communities. In the case study, 73 % of CSs submitted data with errors in fewer than 5 % of their observations. The remaining CSs submitted data with unit, meniscus, unknown, and outlier errors. A CS's assigned community, coupled with model-inferred error probabilities, can identify observations that require verification and provides an opportunity for targeted re-training of CSs based on mistake tendencies.

Optimal Scheme to Achieve Energy Conservation in Induced Dipole Models.

Induced dipole models have proven to be effective tools for simulating electronic polarization effects in biochemical processes, yet their potential has been constrained by energy conservation issue, particularly when historical data is utilized for dipole prediction. This study identifies error outliers as the primary factor causing this failure of energy conservation and proposes a comprehensive scheme to overcome this limitation. Leveraging maximum relative errors as a convergence metric, our data demonstrates that energy conservation can be upheld even when using historical information for dipole predictions. Our study introduces the multi-order extrapolation method to quicken induction iteration and optimize the use of historical data, while also developing the preconditioned conjugate gradient with local iterations to refine the iteration process and effectively remove error outliers. This scheme further incorporates a "peek" step via Jacobi under-relaxation for optimal performance. Simulation evidence suggests that our proposed scheme can achieve energy convergence akin to that of point-charge models within a limited number of iterations, thus promising significant improvements in efficiency and accuracy.

Prediction of Pyrolysis Kinetics of Biomass: New Insights from Artificial Intelligence-Based Modeling

The present work introduces a quantitative structure-property relationship (QSPR)-based stochastic gradient boosting (SGB) decision tree framework for simulating and capturing of the thermal decomposition kinetics of biomass considering effective parameters of the ultimate analysis (such as carbon, hydrogen, oxygen, nitrogen, and sulfur content) and process heating rate. Through a total of 149 pyrolysis kinetics, this study developed an artificial model and subjected it to training and testing phases. The proposed model was validated using error analysis, sensitivity, regression, and outlier detection. The coefficient of determination (R2) and mean relative error (%MRE) were calculated to be 0.993 and 4.354%, respectively, suggesting good performance in the estimation of the pyrolysis kinetic parameters. Also, the sensitivity results indicated the process heating rate to have the strongest effect on the model output with a relevancy factor of 0.43. Eventually, the proposed model showed superior performance compared to earlier frameworks.

Robust estimation for partial linear single-index models

In this article, minimum average variance estimation (MAVE) based on local modal regression is proposed for partial linear single-index models, which can be robust to different error distributions or outliers. Asymptotic distributions of the proposed estimators are derived, which have the same convergence rate as the original MAVE based on least squares. A modal EM algorithm is provided to implement our robust estimation. Both simulation studies and a real data example are used to evaluate the finite sample performance of the proposed estimation procedure.

Bone Cuts Accuracy of a System for Total Knee Arthroplasty including an Active Robotic Arm

Introduction: This study aimed to assess the bone cuts accuracy of a system for total knee arthroplasty including an active robotic arm. A second objective was to compare the accuracy among orthopaedic surgeons of different levels of experience. Methods: Three orthopaedic surgeons cut 10 sawbone knees each. Planned and actual bone cuts were compared using computed tomography. Difference with respect to the planning was expressed as three position and three orientation errors following the anatomical planes. Statistical tests were performed to detect bias and compare surgeons. Results: None of the 30 knees presented an outlier error, meaning an error ≥3 mm or ≥3°. The root-mean-square values of the 12 error types were below 0.8 mm or 0.8°, except for the femoral proximal–distal errors (1.7 mm) and the tibial anterior-posterior errors (1.4 mm). Biases were observed, particularly in femoral proximal–distal and tibial anterior–posterior positions. Median differences between surgeons were all lower than 0.8 mm and 0.5°, with statistically significant differences among surgeons in the femoral proximal–distal errors and the tibial anterior–posterior errors. Conclusions: The system tested in this study achieved accurate bone cuts independently of the surgeon’s level of experience. Biases were observed, suggesting that there might be options to improve the accuracy, particularly in proximal–distal position for the femur and in anterior–posterior position for the tibia.

Impact localization for composite plate using the modified error-outlier algorithm with Pugh’s concept selection under various temperatures

The previous error outlier based impact localization algorithm has a weak point during the impact localization due to a reciprocity problem coupled with the symmetric boundary conditions of target structures. Thus, the modified error outlier based impact localization algorithm was proposed to overcome the improper impact localization due to this reciprocity problem. The previous impact localization method using fiber Bragg grating sensors did not consider the effect of the operating temperature of structures although they were operated under various temperatures. Thus, this study experimentally verified the error outlier impact localization algorithm by performing impact localization for a carbon fiber-reinforced polymer composite plate under various temperatures. It was found that the total average error of 2.0 mm was estimated under the given temperatures, being only 2.5% of the pre-assigned grid size of 80.0 mm on the plate. Therefore, we concluded that the modified algorithm can overcome the reciprocity problems and be sufficiently used for impact localization using fiber Bragg grating sensors under various temperatures.

Matrix optimization based Euclidean embedding with outliers

Euclidean embedding from noisy observations containing outlier errors is an important and challenging problem in statistics and machine learning. Many existing methods would struggle with outliers due to a lack of detection ability. In this paper, we propose a matrix optimization based embedding model that can produce reliable embeddings and identify the outliers jointly. We show that the estimators obtained by the proposed method satisfy a non-asymptotic risk bound, implying that the model provides a high accuracy estimator with high probability when the order of the sample size is roughly the degree of freedom up to a logarithmic factor. Moreover, we show that under some mild conditions, the proposed model also can identify the outliers without any prior information with high probability. Finally, numerical experiments demonstrate that the matrix optimization-based model can produce configurations of high quality and successfully identify outliers even for large networks.

GAN-Based One-Class Classification for Remote-Sensing Image Change Detection

Currently, most of the supervised change detection approaches require a training data set that contains samples from both the changed and the unchanged data. However, under certain condition, such as natural disaster and military attack, the changed data samples are very few or even not available but the unchanged data are abundant. In this letter, we develop a generative adversarial networks (GANs)-based one-class classification (OCC) technique for time series remote-sensing image change detection. The proposed method is only trained with the unchanged data instead of both the changed and unchanged data. To achieve this purpose, first, spatial-spectral features are extracted from the time series remote-sensing images. Second, a GAN model is trained to detect the changes only with the extracted features of unchanged data. Remarkably, to offset the outlier errors caused by the incomplete supervision information provided by unchanged data alone, changed data, instead of unchanged data, are generated to improve power of discriminator. Finally, testing data are classified by the trained discriminator of GAN to produce a binary change map. Experimental results obtained on two optical time series remote-sensing data sets confirmed the effectiveness of our proposed method.

Enhanced Non-parametric Sequence-based Learning Algorithm for Outlier Detection in the Internet of Things

Although research on outlier detection methods has been an investigation area for long, few of those studies relate to an Internet of Things (IoT) domain. Several critical decisions taken on daily business operations depend on various data collected over time. Therefore, it is mandatory to guarantee its correctness, integrity, and accuracy before any further processing can commence. Outliers are often assumed to be Error by most algorithms in the past, which is always attributed to faulty sensors. Hence, this assumption has been investigated and results show that outliers can be classified into Error and Event types with the support of a Non-parametric sequence-based learning algorithm. The event type outlier is majorly caused by abnormality from sensor readings, which are very important and should not be ignored. However, the non-parametric sequence approach and other existing techniques still find it elusive to detect outliers in the global search space of a large dataset. Therefore, this paper proposes an Enhanced Non-parametric sequence learning algorithm based on Ensemble Clustering Techniques to detect Event and Error outliers in large datasets. Experiments are conducted on six different datasets from the UCL repository, except one collected from a laboratory testbed, to demonstrate the robustness and effectiveness of the proposed approach over the existing techniques. The results show a remarkable performance rate of 96.653% accuracy, 94.284% precision, and 98.112% for Error outlier detection. It also performs better in Event outlier detection with 87.611% accuracy, 71.141% precision and 85.755% specificity with 1291 s execution time.

Day-Ahead Electric Load Forecast for a Ghanaian Health Facility Using Different Algorithms

Ghana suffers from frequent power outages, which can be compensated by off-grid energy solutions. Photovoltaic-hybrid systems become more and more important for rural electrification due to their potential to offer a clean and cost-effective energy supply. However, uncertainties related to the prediction of electrical loads and solar irradiance result in inefficient system control and can lead to an unstable electricity supply, which is vital for the high reliability required for applications within the health sector. Model predictive control (MPC) algorithms present a viable option to tackle those uncertainties compared to rule-based methods, but strongly rely on the quality of the forecasts. This study tests and evaluates (a) a seasonal autoregressive integrated moving average (SARIMA) algorithm, (b) an incremental linear regression (ILR) algorithm, (c) a long short-term memory (LSTM) model, and (d) a customized statistical approach for electrical load forecasting on real load data of a Ghanaian health facility, considering initially limited knowledge of load and pattern changes through the implementation of incremental learning. The correlation of the electrical load with exogenous variables was determined to map out possible enhancements within the algorithms. Results show that all algorithms show high accuracies with a median normalized root mean square error (nRMSE) <0.1 and differing robustness towards load-shifting events, gradients, and noise. While the SARIMA algorithm and the linear regression model show extreme error outliers of nRMSE >1, methods via the LSTM model and the customized statistical approaches perform better with a median nRMSE of 0.061 and stable error distribution with a maximum nRMSE of <0.255. The conclusion of this study is a favoring towards the LSTM model and the statistical approach, with regard to MPC applications within photovoltaic-hybrid system solutions in the Ghanaian health sector.

A Hierarchical Bayes Unit-Level Small Area Estimation Model for Normal Mixture Populations

National statistical agencies are regularly required to produce estimates about various subpopulations, formed by demographic and/or geographic classifications, based on a limited number of samples. Traditional direct estimates computed using only sampled data from individual subpopulations are usually unreliable due to small sample sizes. Subpopulations with small samples are termed small areas or small domains. To improve on the less reliable direct estimates, model-based estimates, which borrow information from suitable auxiliary variables, have been extensively proposed in the literature. However, standard model-based estimates rely on the normality assumptions of the error terms. In this research we propose a hierarchical Bayesian (HB) method for the unit-level nested error regression model based on a normal mixture for the unit-level error distribution. Our method proposed here is applicable to model cases with unit-level error outliers as well as cases where each small area population is comprised of two subgroups, neither of which can be treated as an outlier. Our proposed method is more robust than the normality based standard HB method (Datta and Ghosh, Annals Stat. 19, 1748–1770, 1991) to handle outliers or multiple subgroups in the population. Our proposal assumes two subgroups and the two-component mixture model that has been recently proposed by Chakraborty et al. (Int. Stat. Rev. 87, 158–176, 2019) to address outliers. To implement our proposal we use a uniform prior for the regression parameters, random effects variance parameter, and the mixing proportion, and we use a partially proper non-informative prior distribution for the two unit-level error variance components in the mixture. We apply our method to two examples to predict summary characteristics of farm products at the small area level. One of the examples is prediction of twelve county-level crop areas cultivated for corn in some Iowa counties. The other example involves total cash associated in farm operations in twenty-seven farming regions in Australia. We compare predictions of small area characteristics based on the proposed method with those obtained by applying the Datta and Ghosh (Annals Stat. 19, 1748–1770, 1991) and the Chakraborty et al. (Int. Stat. Rev. 87, 158–176, 2019) HB methods. Our simulation study comparing these three Bayesian methods, when the unit-level error distribution is normal, or t, or two-component normal mixture, showed the superiority of our proposed method, measured by prediction mean squared error, coverage probabilities and lengths of credible intervals for the small area means.

Hierarchical Template Matching for 3D Myocardial Tracking and Cardiac Strain Estimation

Myocardial tracking and strain estimation can non-invasively assess cardiac functioning using subject-specific MRI. As the left-ventricle does not have a uniform shape and functioning from base to apex, the development of 3D MRI has provided opportunities for simultaneous 3D tracking, and 3D strain estimation. We have extended a Local Weighted Mean (LWM) transformation function for 3D, and incorporated in a Hierarchical Template Matching model to solve 3D myocardial tracking and strain estimation problem. The LWM does not need to solve a large system of equations, provides smooth displacement of myocardial points, and adapt local geometric differences in images. Hence, 3D myocardial tracking can be performed with 1.49 mm median error, and without large error outliers. The maximum error of tracking is up to 24% reduced compared to benchmark methods. Moreover, the estimated strain can be insightful to improve 3D imaging protocols, and the computer code of LWM could also be useful for geo-spatial and manufacturing image analysis researchers.

Toward Assistive Technologies for Focus Adjustment in Teleoperated Robotic Non-Contact Laser Surgery

In the last decade, focused laser radiation has become an alternative instrumentation for soft tissue surgery due to precise ablation with simultaneous coagulation. However, adjustment of the focal position with respect to the tissue surface is crucial for optimal energy input and minimised trauma. Advantageous laser delivery without tissue contact poses challenges and demands for assistance. This contribution introduces three novel concepts for user assistance during focal adaptation in noncontact laser surgery with robotic teleoperation. Visual, haptic, and visuo-haptic feedback modalities are designed based on real-time scene reconstruction and laser-to-camera registration. The performance of proposed methods was assessed in a phantom study with positioning tasks under different assistance conditions using a teleoperated extensible continuum robot. Focal position error and task execution time were selected as performance metrics. In total, 15 subjects conducted trials under four different conditions ( $N=240$ ). A mean error reduction from 1.2 mm for nonassisted to 0.25 mm for visuo-haptic feedback was revealed and error outliers were eliminated efficiently. Mean execution times of below 13 s were achieved for applied conditions. Presented work decreased the error to one-fifth in comparison to absence of assistance in teleoperation. The results strongly motivate prospective integration to surgical devices for noncontact laser delivery.

Patient-specific guides improve hip arthroplasty surgical accuracy

The role of patient-specific (PS) technology in total hip arthroplasty remains relatively unexplored. We asked whether PS guides: (1) Reduced average surgical errors? (2) Reduced outlier error frequencies? (3) Could predict the size of implants used? A single surgeon implanted femurs using either standard or PS guides and was blinded to the pre-operative plans. There were significant differences in median leg length errors between standard (3.3 mm) and PS groups (1.4 mm), U = 110, z = –2.3, p = 0.02. In contrast to the PS group, the standard group had significantly more outlier errors and frequently undersized implants. PS guides improve hip arthroplasty surgical accuracy.Abbreviations: PS: patient specific; THA: total hip arthroplasty; LLD: leg length discrepancies; HRA: hip resurfacing arthroplasty

Impact localization on a composite plate based on error outliers with Pugh’s concept selection

Many researchers have performed impact localization using multiplexed fiber Bragg grating (FBG) sensors for a unique estimation of impact position. However, the previous impact localization did not show the methodology to choose the location with the lowest error among candidate impact points, estimated from sensors. We propose an error-outlier-based algorithm with Pugh’s concept selection that can choose the impact point with the lowest error among several candidate impact points. The proposed impact localization algorithm was implemented for 30 arbitrary points on a carbon-fiber-reinforced polymer composite plate with two bonded FBG sensors. The results showed that the proposed algorithm could efficiently localize all impact points. Additionally, it could successfully choose the impact point with lower error among two candidate impact points estimated from the two sensors. The accuracy of selection was 93.33% and the totally estimated averaged-error for the selected sensors by Pugh’s concept selection for all tests was 6.24 mm. Therefore, we conclude that the proposed impact localization algorithm is effective in localizing the impact points with the lowest errors when multiplexed sensors are used, regardless of the type of sensor. This technique will contribute to reducing measurement errors when localizing impact points on composite structures.

Error outlier with weighted Median Absolute Deviation threshold algorithm and FBG sensor based impact localization on composite wing structure

In this paper, a low velocity impact localization algorithm based on an error outlier assessment technique with weighted Median Absolute Deviation (MAD) threshold was developed to localize impact on complex composite structures. The error outlier with MAD threshold algorithm was used to localize impacts on a 1000mm×1400mm area of the upper and lower surfaces of a Jabiru UL-D composite wing. Impact monitoring was performed with three multiplexed FBG sensors and the data acquisition was done using SFI-710 high speed interrogator to obtain the impact response signal at a frequency of 100kHz. The weighted MAD threshold used to filter the likely impact locations from a set of detected locations, and the proposed algorithm was able to localize impacts with consistent overall performance for a varying number of allowed detected locations. A parametric study was also performed to determine the possibility of localizing impact using a single sensor. The feasibility of utilizing the error outlier with weighted MAD threshold algorithm to localize impact with a single sensor was effectively demonstrated. Overall, impacts on the upper and lower surface of the composite wing were predicted and the localization results for three and one FBG sensors were comparable.