Magnitude Of Error Research Articles

All-optical wavelength and format conversion of DQPSK-to-PAM4 using DLI and pump assisted NOLM

An all-optical wavelength and format conversion scheme of 20 Gbps differential quadrature phase shift keying (DQPSK)-to-4-ary pulse amplitude modulation (PAM4) signals based on the delay line interferometer (DLI) and the pump assisted nonlinear optical loop mirror (NOLM) is proposed and numerically simulated. The input DQPSK signal is converted into two star-16-ary quadrature amplitude modulation (star-16QAM) signals (namely, star-16QAM-I and star-16QAM-Q) using DLI with the time delay of 1-symbol and different linear phase shifts of π/4 and −π/4, respectively. The star-16QAM signals are converted into the on-off keying (OOK) signals (namely, OOK-I and OOK-Q) using the pump assisted NOLM based on the self-phase modulation (SPM) and the cross-phase modulation (XPM) in the highly nonlinear fiber (HNLF). The two OOK signals can be aggregated into different types of PAM4 signals by adjusting their power ratio (PR) and relative phase shift (RPS) flexibly. Error vector magnitude (EVM) and bit error rate (BER) for the input and output optical signals are calculated to verify the scheme performance. Compared to the DQPSK signal with an input optical signal-to-noise ratio (OSNR) of 30 dB, there are about 2.5 dB penalty of receiver OSNR for the converted OOK-I and OOK-Q signals. For the corresponding aggregated equidistant PAM4 and equiamplitude PAM4 signals, they have about 6.3 dB and 12.1 dB penalty of receiver OSNR. The proposed scheme of DQPSK-to-PAM4 can be deployed at the all-optical gateway to bridge the long-hual and short-reach optical networks.

Прогноз случайных процессов, при наличии единичных возмущений в интеллектуальных транспортных системах

Forecasting random perturbations allows improving the control quality in intelligent transport systems and ensuring the efficient operation of diagnostic systems. Several works are known where extrapolator models based on Chebyshev polynomials orthogonal on equidistant points are presented. These models use a predictive polynomial whose coefficients are computed using the least squares criterion. Additionally, an analysis of forecast errors for random stationary input signals has been conducted. At the same time, in the case of non-stationary input signals, singular perturbations may occur, the influence of which on the extrapolator leads to significant forecast errors.
 This article presents an example of the occurrence of additive perturbations that arise in automatic train control systems. An analytical expression has been derived, and calculations of forecast error magnitudes in the presence of singular perturbations have been conducted. The analysis of the calculation results allows determining the influence of extrapolator parameters on the forecast error magnitude, highlighting the necessity of detecting singular perturbations, and excluding their influence on the forecast error magnitude.
 The article discusses an algorithm for detecting singular perturbations and their exclusion during the forecasting process. The conclusion is drawn about the effectiveness of using extrapolators for random perturbations with the exclusion of singular perturbations in intelligent systems for automatic train control in subway transportation.

Fitness landscape analysis for assisted seismic history matching problems

Abstract Despite over 20 years of research, assisted seismic history matching (ASHM) remains a challenging problem for the energy industry. ASHM is an optimisation problem to find the best subsurface reservoir model for robust predictions of field performance. The results are typically assessed by a decreasing misfit between simulated and observed data, but the optimised models are often inaccurate, uncertain, and non-unique. In this paper, we take a fresh look at ASHM and view it from the perspective of the fitness landscape, or search space. We propose that characterising the fitness landscape will lead to a deeper understanding of the problem, greater confidence in the optimised models, and a better appreciation of the uncertainties. Fitness landscape analysis (FLA) is established in other fields, but has mostly been applied to combinatorial problems or continuous problems with analytical solutions. In contrast, ASHM is a real-world, ill-posed, inverse problem, which is computationally expensive and contains data errors and model uncertainties. We introduce a new method for FLA that provides intuitive information on the setup of the problem. It uses multidimensional clustering and visualisation to explore the structure of the landscape and detects the presence and relative magnitude of data errors, which are typical of real data. It is applied to a synthetic, full-field, reservoir model and the results are compared with another more-established method. We found that the fitness landscapes of ASHM problems are low-lying plateaus with many minima, which makes it difficult to solve ASHM problems for real-world datasets.

Characterizing Errors Using Satellite Metadata for Eco‐Hydrological Model Calibration

AbstractUnderstanding the origins of errors between model predictions and catchment observations is a critical element in hydrologic model calibration and uncertainty estimation. Difficulties arise because there are a variety of error sources but only one measure of the total residual error between model predictions and catchment observations. One promising approach is to collect extra information a priori to characterize the data error before calibration. We implement here a new model calibration strategy for an ecohydrological model, using the satellite metadata information as a means to inform the model priors, to decompose data error from total residual error. This approach, referred to as Bayesian ecohydrological error model (BEEM), is first examined in a synthetic setting to establish its validity, and then applied to three real catchments across Australia. Results show that (a) BEEM is valid in a synthetic setting, as it can perfectly ascertain the true underlying error; (b) in real catchments the model error is reduced when utilizing the observation error variance as added error contributing to total error variance, while the magnitude of total residual error is more robust when utilizing metadata about the data quality proportionality as the basis for assigning total error variance; (c) BEEM improves model calibration by estimating the model error appropriately and estimating the uncertainty interval more precisely. Overall, our work demonstrates a new approach to collect prior error information in satellite metadata and reveals the potential for fully utilizing metadata about error sources in uncertainty estimation.

Improving UAV‐SfM photogrammetry for modelling high‐relief terrain: Image collection strategies and ground control quantity

AbstractImage collection strategies and ground control points (GCPs) are of particular importance for uncrewed aerial vehicle combined with Structure‐from‐Motion (UAV–SfM) photogrammetry, and the generalization of their effects has proved elusive. This study designed various photogrammetric scenarios to investigate the effects of image collection strategies, ground control quantity, and their interaction on digital elevation model (DEM) errors and their spatial structure in high‐relief terrain. The results of 1.77 × 105 UAV–SfM scenarios provide insights for improving UAV–SfM practices. A high image capture angle (20–40°) enhances camera calibration quality decreasing the magnitude and spatial correlation of errors. High camera inclination reduces the sensitivity of mean and standard deviation of error to flying height but not the spatial correlation of error. Including additional data (e.g. supplemented convergent images; images captured at multiple flying heights) has only a minor effect if imagery is highly inclined. GCPs provide more effective constraints than image collection strategies. The mean error and standard error decline quickly with a small number of GCPs and then become stable in all scenarios, but the spatial correlation of error can be further improved with increasing GCPs. However, the effects of GCP quantity do interact with image collection strategies. High camera inclination reduces requirements for GCPs, whilst strategies combining different flying heights and image orientations have little effect on necessary GCP quantity. The distribution of GCPs still affects the errors, but the effect of GCP distribution becomes less important with an increase in the number of GCPs. Finally, we show that UAV–SfM photogrammetric quality assessment should routinely assess the spatial dependence of error using a statistic like Moran's I.

Intrastromal Corneal Ring Segments: Effect of Depth of Implantation in Visual, Refractive and Topographic Outcomes in Keratoconus Patients.

To evaluate visual, refractive and topographic outcomes of intracorneal ring segment implantation in relation to the achieved segment depth, using the manual technique. Ophthalmology Department, Hospital de Braga, Braga, Portugal. Retrospective cohort study. We obtained 104 eyes of 93 keratoconus patients submitted to Ferrara intracorneal ring segment (ICRS) implantation, using a manual technique. Subjects were divided into 3 groups according to the achieved depth of implantation: 40-70% (Group 1), 70-80% (Group 2), and 80-100% (Group 3). Visual, refractive and topographic variables were evaluated at baseline and 6-months. Topographic measurement was performed using Pentacam. Thibos-Horner and Alpins methods were used to analyze the vectorial change of refractive and topographic astigmatism, respectively. We found a significant improvement of UDVA and CDVA in all groups at 6-months (P<0.005); no differences were observed regarding safety and efficacy indexes in the three groups (p>0.05). Manifest cylinder and spherical equivalent significantly reduced in all groups (P<0.05). Topographic evaluation showed a significant improvement of all parameters in the three groups (P<0.05). A shallower (group 1) or deeper (group 3) implantation was associated with topographic cylinder overcorrection, a higher magnitude of error and a higher mean centroid postoperative corneal astigmatism. ICRS implantation with manual technique showed to be equally effective in terms of visual and refractive outcomes despite the depth of implantation; however, shallower or deeper implants were associated with topographic overcorrection and a higher mean centroid postoperative astigmatism, which explain the lower topographic predictability associated with manual surgery for ICRS implantation.

Fraser-Suzuki function as an essential tool for mathematical modeling of crystallization in glasses

The performance of the Fraser-Suzuki function during mathematic deconvolution of crystal growth kinetic processes was extensively analyzed based on theoretical simulations. Regarding pure imitation, the Fraser-Suzuki function well describes processes with moderate negative asymmetry of a3 ≈ <−0.6; −0.2>. Considering the ability of the Fraser-Suzuki function to transfer the kinetic information during the mathematic deconvolution (i.e., performance in the procedure: kinetic signal → fit by Fraser-Suzuki function → kinetic analysis of the Fraser-Suzuki data-curve), it is very well suited for separating processes following single-exponent kinetic models such as the nucleation-growth Johnson-Mehl-Avrami-Kolmogorov model or the nth order reaction model. For the nth order autocatalytic model, the magnitude of errors depends directly on the exponent nNC. Reliable performance of the Fraser-Suzuki function is achieved when resulting nNC falls in <0; 1.2> interval. Combining the Fraser-Suzuki mathematic deconvolution with the consequent kinetic analysis utilizing the nth order autocatalytic model is highly recommended.

Enhancing UAV-SfM Photogrammetry for Terrain Modeling from the Perspective of Spatial Structure of Errors

UAV-SfM photogrammetry is widely used in remote sensing and geoscience communities. Scholars have tried to optimize UAV-SfM for terrain modeling based on analysis of error statistics like root mean squared error (RMSE), mean error (ME), and standard deviation (STD). However, the errors of terrain modeling tend to be spatially distributed. Although the error statistic can represent the magnitude of errors, revealing spatial structures of errors is still challenging. The “best practice” of UAV-SfM is lacking in research communities from the perspective of spatial structure of errors. Thus, this study designed various UAV-SfM photogrammetric scenarios and investigated the effects of image collection strategies and GCPs on terrain modeling. The error maps of different photogrammetric scenarios were calculated and quantitatively analyzed by ME, STD, and Moran’s I. The results show that: (1) A high camera inclination (20–40°) enhances UAV-SfM photogrammetry. This not only decreases the magnitude of errors, but also mitigates its spatial correlation (Moran’s I). Supplementing convergent images is valuable for reducing errors in a nadir camera block, but it is unnecessary when the image block is with a high camera angle. (2) Flying height increases the magnitude of errors (ME and STD) but does not affect the spatial structure (Moran’s I). By contrast, the camera angle is more important than the flying height for improving the spatial structure of errors. (3) A small number of GCPs rapidly reduce the magnitude of errors (ME and STD), and a further increase in GCPs has a marginal effect. However, the structure of errors (Moran’s I) can be further improved with increasing GCPs. (4) With the same number, the distribution of GCPs is critical for UAV-SfM photogrammetry. The edge distribution should be first considered, followed by the even distribution. The research findings contribute to understanding how different image collection scenarios and GCPs can influence subsequent terrain modeling accuracy, precision, and spatial structure of errors. The latter (spatial structure of errors) should be routinely assessed in evaluations of the quality of UAV-SfM photogrammetry.

Incorporating statistical and machine learning techniques into the optimization of correction factors for software development effort estimation

AbstractAccurate effort estimation is necessary for efficient management of software development projects, as it relates to human resource management. Ensemble methods, which employ multiple statistical and machine learning techniques, are more robust, reliable, and accurate effort estimation techniques. This study develops a stacking ensemble model based on optimization correction factors by integrating seven statistical and machine learning techniques (K‐nearest neighbor, random forest, support vector regression, multilayer perception, gradient boosting, linear regression, and decision tree). The grid search optimization method is used to obtain valid search ranges and optimal configuration values, allowing more accurate estimation. We conducted experiments to compare the proposed method with related methods, such as use case points‐based single methods, optimization correction factors‐based single methods, and ensemble methods. The estimation accuracies of the methods were evaluated using statistical tests and unbiased performance measures on a total of four datasets, thus demonstrating the effectiveness of the proposed method more clearly. The proposed method successfully maintained its estimation accuracy across the four experimental datasets and gave the best results in terms of the sum of squares errors, mean absolute error, root mean square error, mean balance relative error, mean inverted balance relative error, median of magnitude of relative error, and percentage of prediction (0.25). The p‐value for the t‐test showed that the proposed method is statistically superior to other methods in terms of estimation accuracy. The results show that the proposed method is a comprehensive approach for improving estimation accuracy and minimizing project risks in the early stages of software development.

Deep learning-based detection and classification of multi-leaf collimator modeling errors in volumetric modulated radiation therapy.

The purpose of this study was to create and evaluate deep learning-based models to detect and classify errors of multi-leaf collimator (MLC) modeling parameters in volumetric modulated radiation therapy (VMAT), namely the transmission factor (TF) and the dosimetric leaf gap (DLG). A total of 33 clinical VMAT plans for prostate and head-and-neck cancer were used, assuming a cylindrical and homogeneous phantom, and error plans were created by altering the original value of the TF and the DLG by±10, 20, and 30% in the treatment planning system (TPS). The Gaussian filters of and 1.0 were applied to the planar dose maps of the error-free plan to mimic the measurement dose map, and thus dose difference maps between the error-free and error plans were obtained. We evaluated 3 deep learning-based models, created to perform the following detections/classifications: (1) error-free versus TF error, (2) error-free versus DLG error, and (3) TF versus DLG error. Models to classify the sign of the errors were also created and evaluated. A gamma analysis was performed for comparison. The detection and classification of TF and DLG error were feasible for ; however, a considerable reduction of accuracy was observed for depending on the magnitude of error and treatment site. The sign of errors was detectable by the specifically trained models for and 1.0. The gamma analysis could not detect errors. We demonstrated that the deep learning-based models could feasibly detect and classify TF and DLG errors in VMAT dose distributions, depending on the magnitude of the error, treatment site, and the degree of mimicked measurement doses.

Chat GPT as a Neuro-Score Calculator: Analysis of a Large Language Model’s Performance on Various Neurological Exam Grading Scales

ChatGPT is a large language model artificial intelligence chatbot that has been applied to different aspects of the medical field. Our study aims to assess the quality of chatGPT to evaluate patients based on their exams for different scores including Glasgow Coma Scale (GCS), intracranial hemorrhage score (ICH), and Hunt & Hess (H&H) classification. We created batches of patient test cases with detailed neurological exams, totaling 20 cases and created variants of increasing complex phrasing of the test cases. Using ChatGPT, we assessed repeatability and quantified the errors, including the average error rate (AER) and magnitude of errors (AME). We repeated this process for the H&H and the ICH score using base cases. Specific prompts were created for each calculator. The GCS calculator on 10 base test cases had an AER/AME of 10%/0.150. The accuracy of ChatGPT decreased with increasing complexity; for example, in a variation where crucial information was missing, the AER was 45% for 20 cases. For H&H, AER/AME was 13%/0.13 and for ICH, AER/AME was 27.5%/0.325. Using a simple prompt resulted in a significantly higher error rate of 70%. ChatGPT demonstrates ability in this proof-of-concept experiment in evaluating neuroexams using established assessment scales including GCS, ICH, and H&H. However, it has limitations in accuracy and may "hallucinate" with complex or vague descriptions. Nonetheless, ChatGPT, has promising potential in medicine.

A Novel Approach for Improving Gait Speed Estimation Using a Single Inertial Measurement Unit Embedded in a Smartphone: Validity and Reliability Study.

Gait speed is a valuable biomarker for mobility and overall health assessment. Existing methods to measure gait speed require expensive equipment or personnel assistance, limiting their use in unsupervised, daily-life conditions. The availability of smartphones equipped with a single inertial measurement unit (IMU) presents a viable and convenient method for measuring gait speed outside of laboratory and clinical settings. Previous works have used the inverted pendulum model to estimate gait speed using a non-smartphone-based IMU attached to the trunk. However, it is unclear whether and how this approach can estimate gait speed using the IMU embedded in a smartphone while being carried in a pants pocket during walking, especially under various walking conditions. This study aimed to validate and test the reliability of a smartphone IMU-based gait speed measurement placed in the user's front pants pocket in both healthy young and older adults while walking quietly (ie, normal walking) and walking while conducting a cognitive task (ie, dual-task walking). A custom-developed smartphone application (app) was used to record gait data from 12 young adults and 12 older adults during normal and dual-task walking. The validity and reliability of gait speed and step length estimations from the smartphone were compared with the gold standard GAITRite mat. A coefficient-based adjustment based upon a coefficient relative to the original estimation of step length was applied to improve the accuracy of gait speed estimation. The magnitude of error (ie, bias and limits of agreement) between the gait data from the smartphone and the GAITRite mat was calculated for each stride. The Passing-Bablok orthogonal regression model was used to provide agreement (ie, slopes and intercepts) between the smartphone and the GAITRite mat. The gait speed measured by the smartphone was valid when compared to the GAITRite mat. The original limits of agreement were 0.50 m/s (an ideal value of 0 m/s), and the orthogonal regression analysis indicated a slope of 1.68 (an ideal value of 1) and an intercept of -0.70 (an ideal value of 0). After adjustment, the accuracy of the smartphone-derived gait speed estimation improved, with limits of agreement reduced to 0.34 m/s. The adjusted slope improved to 1.00, with an intercept of 0.03. The test-retest reliability of smartphone-derived gait speed was good to excellent within supervised laboratory settings and unsupervised home conditions. The adjustment coefficients were applicable to a wide range of step lengths and gait speeds. The inverted pendulum approach is a valid and reliable method for estimating gait speed from a smartphone IMU placed in the pockets of younger and older adults. Adjusting step length by a coefficient derived from the original estimation of step length successfully removed bias and improved the accuracy of gait speed estimation. This novel method has potential applications in various settings and populations, though fine-tuning may be necessary for specific data sets.

Evolution in pulsating variable stars: Long term, and inter-cycle

Abstract We used machine learning techniques to predict period changes in variable stars using noisy and sparse time series data, while inferring underlying physics and generalizing predictions about cycle-to-cycle variations. Our focus was on Mira variables, a well-known class of pulsating stars. Pre-processing data from Mira, R Andromedae, U Orionis, and Chi Cygni, obtained from the American Association of Variable Star Observers (AAVSO), we predicted luminosity magnitude uncertainty and classified pulsation states. Employing various classification and regression algorithms, along with feature engineering, we aimed to generalize predictions. We created a generalized dataset with collective averaged data points, limiting our analysis to a common time duration. Linear regression models yielded no successful predictions, but Decision Tree and KNN regressors accurately predicted luminosity magnitude errors, indicative of variation over time. Feature engineering successfully aided regression and classification of pulsating star states. After hyper parameter tuning using Bayesian Neural networks, we achieved a classification accuracy of 0.8 and 0.94 for the KNN classifier, respectively, in classifying pulsation states of Mira variables. The regression model achieved an R2 score of 0.98. Our work provides a foundation for developing tools to analyze various pulsating star variables, including Cepheids, RR Lyrae, and Delta Scuti variables, as well as other astrophysical data. These techniques demonstrate impressive performance with time series datasets.

Endoscopic measurement of the size of gastrointestinal polyps using an electromagnetic tracking system and computer vision-based algorithm

PurposePolyp size is an important factor that may influence diagnosis and clinical management decision, but estimation by visual inspection during endoscopy is often difficult and subject to error. The purpose of this study is to develop a quantitative approach that enables an accurate and objective measurement of polyp size and to study the feasibility of the method.MethodsWe attempted to estimate polyp size and location relative to the gastro-oesophageal junction by integrating data from an electromagnetic tracking sensor and endoscopic images. This method is based on estimation of the three-dimensional coordinates of the borders of the polyp by combining the endoscope camera position and the corresponding points along the polyp border in endoscopic images using a computer vision-based algorithm. We evaluated the proposed method using a simulated upper gastrointestinal endoscopy model.ResultsThe difference between the mean of ten measurements of one artificial polyp and its actual size (10 mm in diameter) was 0.86 mm. Similarly, the difference between the mean of ten measurements of the polyp distance from the gastroesophageal junction and its actual distance (~ 22 cm) was 1.28 mm. Our results show that the changes in camera positions in which the images were taken and the quality of the polyp segmentation have the most impact on the accuracy of polyp size estimation.ConclusionThis study demonstrated an innovative approach to endoscopic measurements using motion tracking technologies and computer vision and demonstrated its accuracy in determining the size and location of the polyp. The observed magnitude of error is clinically acceptable, and the measurements are available immediately after the images captured. To enhance accuracy, it is recommended to avoid identical images and instead utilise control wheels on the endoscope for capturing different views. Future work should further evaluate this innovative method during clinical endoscopic procedures.

Deep Brain Stimulation Lead Localization Variability Comparing Intraoperative MRI Versus Postoperative Computed Tomography.

Commercially available lead localization software for deep brain stimulation (DBS) often relies on postoperative computed tomography (CT) scans to define electrode positions. When cases are performed with intraoperative MRI, another imaging set exists with which to perform these localizations. To compare DBS localization error between postoperative CT scans and intraoperative MRI. A retrospective cohort of patients who underwent MRI-guided placement of DBS electrodes using the ClearPoint platform was identified. Using Brainlab Elements, postoperative CT scans were coregistered to intraoperative magnetic resonance images visualizing the ClearPoint guidance sheaths and ceramic stylets. DBS electrodes were identified in CT scans using Brainlab's lead localization tool. Trajectory and vector errors were quantified between scans for each lead in each patient. Eighty patients with a total of 157 implanted DBS electrodes were included. We observed mean trajectory and vector errors of 0.78 ± 0.44 mm (range 0.1-2.0 mm) and 1.57 ± 0.79 mm (range 0.2-4.2 mm), respectively, between postoperative CT and intraoperative MRI. There were 7 patients with CT scans collected at multiple time points. Trajectory error increased by 0.15 ± 0.42 mm ( P = .31), and vector error increased by 0.22 ± 0.53 mm ( P = .13) in the later scans. Across all scans, there was no significant association between trajectory ( P = .053) or vector ( P = .98) error and the date of CT acquisition. DBS electrodes targeting the subthalamic nucleus had significantly greater trajectory errors ( P = .02) than those targeting the globus pallidus pars internus nucleus. Commercially available software produced largely concordant lead localizations when comparing intraoperative MRIs with postoperative CT scans, with trajectory errors on average <1 mm. CT scans tend to be more comparable with intraoperative MRI in the immediate postoperative period, with increased time intervals associated with a greater magnitude of error between modalities.

Adaptive tuning of uncertain parameters in a numerical weather prediction model based upon data assimilation

AbstractIn numerical weather prediction models, near‐surface quantities like 10‐m wind speed (FF10M) or 2‐m temperature (T2M) tend to exhibit significantly larger forecast errors than the related variables in the free troposphere. Besides representativeness issues of surface stations, this is primarily related to parametrization errors and insufficient knowledge of relevant physical properties of the soil and the surface layer. For instance, the vegetation roughness length is usually derived from land‐cover classifications that may contain errors and reflect only part of the natural variability. This article describes a methodology implemented into Deutscher Wetterdienst's operational numerical weather prediction model ICON in order to improve the estimate of such parameters by using information from the data assimilation system. Building upon the condition that FF10M, T2M, and 2‐m relative humidity are assimilated, time‐filtered assimilation increments are calculated for the respective fields at the lowest model level. These are taken as proxies for the related model biases. For T2M, an additional weighted increment field is computed that indicates the model bias in the diurnal temperature amplitude. Based on these increment fields, several physical parameter fields and a few model tuning parameters are varied around their base values. This adaptive parameter adjustment is used operationally in the global and regional forecasting systems of Deutscher Wetterdienst. The ensuing reduction of the FF10M, T2M, and 2‐m relative humidity errors typically lies on the order of 5% on a hemispheric average but has substantial regional and seasonal variability that depends on the original magnitude of the model error. A weaker but still statistically significant positive impact is seen in the radiosonde verification of wind speed, humidity, and temperature in the lower troposphere, giving confidence that the adaptive tuning indeed reduces model errors rather than pushing the model towards unrepresentative station observations.

EEG error-related potentials encode magnitude of errors and individual perceptual thresholds

Error-related potentials (ErrPs) are a prominent electroencephalogram (EEG) correlate of performance monitoring, and so crucial for learning and adapting our behavior. It is poorly understood whether ErrPs encode further information beyond error awareness. We report an experiment with sixteen participants over three sessions in which occasional visual rotations of varying magnitude occurred during a cursor reaching task. We designed a brain-computer interface (BCI) to detect ErrPs that provided real-time feedback. The individual ErrP-BCI decoders exhibited good transfer across sessions and scalability over the magnitude of errors. A non-linear relationship between the ErrP-BCI output and the magnitude of errors predicts individual perceptual thresholds to detect errors. We also revealtheta-gamma oscillatory coupling that co-varied with the magnitude of the required adjustment. Our findings open new avenues to probe and extend current theories of performance monitoring by incorporating continuous human interaction tasks and analysis of the ErrP complex rather than individual peaks.

Refractive outcomes of toric intra-ocular lens implantation in cases of high posterior corneal astigmatism.

To evaluate whether the toric intra-ocular lens (IOL) power calculation based on total corneal astigmatism (TCA) in eyes with high posterior corneal astigmatism (PCA) could result in a systematic over-correction or under-correction after operation. The present study included a mono-centric retrospective study design. The data were collected from 62 consecutive eyes during uncomplicated cataract surgery by a single surgeon with a measured PCA of 0.50 diopters (D) or higher. Toric IOL calculations were made using TCA measurements. The eyes were grouped as either "with-the-rule" (WTR) or "against-the-rule" (ATR) on the basis of the steep anterior corneal meridian. The post-operative refractive astigmatic prediction error was analyzed 1 month post-operatively using the vector analysis by the Alpins method and double-angle plots method. The correction indexes were 1.14 ± 0.29 in the ATR eyes and 1.25 ± 0.18 for the WTR eyes, indicating a tendency toward over-correction. The mean over-correction was 0.22 ± 0.52D in the ATR group and 0.65 ± 0.60D in the WTR group. The magnitude of error (ME) values were significantly different from the ideal value of zero in both groups (ATR: P = 0.03; WTR: P = 0.00). No significant difference in mean absolute error (MAE) in predicted residual astigmatism was found between ATR and WTR groups (0.61 ± 0.42 D versus 0.64 ± 0.39 D; P = 0.54). The ATR group yielded better results, with 48% <0.50D prediction error in the main analysis. The results suggested that in cases of high PCA, the toric IOL calculation, which was performed using TCA, may cause a potential over-correction in the ATR and WTR eyes. For ATR eyes, over-correction led to slight disruption of post-operative visual quality because of the "with-the-rule" residual astigmatism after operation. Therefore, we suggested using TCA for toric IOL calculation in ATR eyes.

On the Measurement of Ocean Near-Surface Current from a Moving Buoy

This paper studies the error that occurs when measuring surface currents with a current meter mounted on a buoy or a mooring line whose horizontal and vertical motions respond to the presence of waves. The error is defined with respect to an Eulerian reference measurement where the sensor does not move. First, we present the subject with a theoretical analysis in the case of a monochromatic wave. That idealized model allows us to study particular sensor or mooring line motions. Second, a realistic numerical model is implemented to reconstruct the current field with a high resolution near the surface. Wave orbital velocities are generated with a random phase model. An Ekman-type current, uniform in the horizontal but with a vertical shear, is also incorporated. The results indicate that the error in the current measurement is highly dependent on the sensor motion induced by waves. The error magnitude is proportional to the wave momentum or Stokes drift and depends on the wave development state and the wind-generated current’s magnitude. The error obtained in the current measurement is analyzed by considering that the buoy only responds to low-frequency waves up to a maximum frequency. That maximum frequency is referenced concerning the peak frequency of the third moment of the spectrum (i.e., the Stokes drift spectrum). It allows us to classify the current time average into three ranges with respect to the maximum frequency: (1) Eulerian average, (2) wave-following average, and (3) intermediate case of undulating average where results cannot be generalized. The measurement error is most important in the region above the wave troughs. However, the error is also considerable in the region confined below the wave troughs and down to the Stokes drift e-folding depth. The error is particularly relevant in conditions of developed and energetic waves (Hs&gt;3 m), where the surface Stokes drift can reach values above 0.1 m/s. It should be noted that measurement error can exceed the value of the Stokes drift at the sensor depth for certain mooring line motions. Those results should help better interpret in situ near-surface current measurements obtained from various devices.

Simple and low-cost motion-sensorless control approach for the IPMSM with elimination of DC offset and steady-state error

Voltage type flux observer has attracted much attention because of its less mathematical calculations and ease of implementation. The only issue with the flux estimation approach is the integration mechanism; using a pure integrator may cause integrator saturation and a DC bias issue, whereas using the low pass filter (LPF) and a self-adaptive band-pass filter (SABPF) would result in magnitude and phase angle errors. This work offers an improved flux linkage estimation approach with a proportional integral (PI) DC offset compensator, resolving the DC drift issue associated with the observer’s pure integrator. Compared to the standard voltage type flux observer, the DC offset is removed, the LPF and phase adjustment are eliminated, and low-speed armature position estimate accuracy is improved. The proposed method can reduce calculation. As a result, the control period is reduced. The Matlab Simulink framework findings are presented to validate the performance and effectiveness of the proposed strategy.