Unexpected Errors Research Articles

Dynamic Simulation-Guided Design of Tumbling Magnetic Microrobots

Abstract The design of robots at the small scale is a trial-and-error based process, which is costly and time-consuming. There are few dynamic simulation tools available to accurately predict the motion or performance of untethered microrobots as they move over a substrate. At smaller length scales, the influence of adhesion and friction, which scales with surface area, becomes more pronounced. Thus, rigid body dynamic simulators, which implicitly assume that contact between two bodies can be modeled as point contact, are not suitable. In this paper, we present techniques for simulating the motion of microrobots where there can be intermittent and non-point contact between the robot and the substrate. We use these techniques to study the motion of tumbling microrobots of different shapes and select shapes that are optimal for improving locomotion performance. Simulation results are verified using experimental data on linear velocity, maximum climbable incline angle, and microrobot trajectory. Microrobots with improved geometry were fabricated, but limitations in the fabrication process resulted in unexpected manufacturing errors and material/size scale adjustments. The developed simulation model can incorporate these limitations and emulate their effect on the microrobot’s motion, reproducing the experimental behavior of the tumbling microrobots, further showcasing the effectiveness of having such a dynamic model.

Robust Hybrid Interval-Probabilistic Approach for the Kidnapped Robot Problem

For a mobile robot to operate in its environment it is crucial to determine its position with respect to an external reference frame using noisy sensor readings. A scenario in which the robot is moved to another position during its operation without being told, known as the kidnapped robot problem, complicates global localisation. In addition to that, sensor malfunction and external influences of the environment can cause unexpected errors, called outliers, that negatively affect the localisation process. This paper proposes a method based on the fusion of a particle filter with bounded-error localisation, which is able to deal with outliers in the measurement data. The application of our algorithm to solve the kidnapped robot problem using simulated data shows an improvement over conventional probabilistic filtering methods.

Feature Preserving Autofocus Algorithm for Phase Error Correction of SAR Images.

Autofocus is an essential technique for airborne synthetic aperture radar (SAR) imaging to correct phase errors mainly due to unexpected motion error. There are several well-known conventional autofocus methods such as phase gradient autofocus (PGA) and minimum entropy (ME). Although these methods are still widely used for various SAR applications, each method has drawbacks such as limited bandwidth of estimation, low convergence rate, huge computation burden, etc. In this paper, feature preserving autofocus (FPA) algorithm is newly proposed. The algorithm is based on the minimization of the cost function containing a regularization term. The algorithm is designed for postprocessing purpose, which is different from the existing regularization-based algorithms such as sparsity-driven autofocus (SDA). This difference makes the proposed method far more straightforward and efficient than those existing algorithms. The experimental results show that the proposed algorithm achieves better performance, convergence, and robustness than the existing postprocessing autofocus algorithms.

Summation pollution of principal component analysis and an improved algorithm for location sensitive data

AbstractPrincipal component analysis (PCA) is widely used for dimensionality reduction and unsupervised learning. The reconstruction error is sometimes large even when a large number of eigenmode is used. In this paper, we show that this unexpected error source is the pollution effect of a summation operation in the objective function of the PCA algorithm. The summation operator brings together unrelated parts of the data into the same optimization and the result is the reduction of the accuracy of the overall algorithm. We introduce a domain decomposed PCA that improves the accuracy, and surprisingly also increases the parallelism of the algorithm. To demonstrate the accuracy and parallel efficiency of the proposed algorithm, we consider three applications including a face recognition problem, a brain tumor detection problem using two‐ and three‐dimensional MRI images.

Analysis of the Cosmic Ray Effects on Sentinel-1 SAR Satellite Data

Ionizing radiation sources such as Solar Energetic Particles and Galactic Cosmic Radiation may cause unexpected errors in imaging and communication systems of satellites in the Space environment, as reported in the previous literature. In this study, the temporal variation of the speckle values on Sentinel 1 satellite images were compared with the cosmic ray intensity/count data, to analyze the effects which may occur in the electromagnetic wave signals or electronic system. Sentinel 1 Synthetic Aperture Radar (SAR) images nearby to the cosmic ray stations and acquired between January 2015 and December 2019 were processed. The median values of the differences between speckle filtered and original image were calculated on Google Earth Engine Platform per month. The monthly median “noise” values were compared with the cosmic ray intensity/count data acquired from the stations. Eight selected stations’ data show that there are significant correlations between cosmic ray intensities and the speckle amounts. The Pearson correlation values vary between 0.62 and 0.78 for the relevant stations.

Outlier detection based on weighted neighbourhood information network for mixed-valued datasets

Outlier detection is of great importance in industry as unexpected errors or faults, abnormal behaviours or phenomena, etc. can occur due to a variety of human, system, and environmental reasons. To identify and analyse these rare items, events or observations can find either anomalies or novelties and, as a result, can help avoid potential unexpected consequences or improve industrial system performance. The operating data collected from industrial systems in the Industry 4.0 era are characterized as multi-attribute (e.g., both numerical and categorical) compared to previous studies. Therefore, a new outlier detection method for mixed-valued datasets based on the weighted network model is proposed in this paper. Concretely, a weighted neighbourhood information network (WNIN) is constructed by considering the neighbourhood relations and similarities among objects to represent a dataset with mixed-valued attributes (DMA). A tailored Markov random walk method is employed to detect outlier on the predefined network model. After reaching the equilibrium, the inlier score is defined according to the out-degree of nodes in the WNIN to represent the inlier degree of objects. Experiments on two real datasets and a case study illustrate the effectiveness and adaptability of the proposed method.

Robust Uncalibrated Rectification With Low Geometric Distortion Under Unbalanced Field of View Circumstances

Rectification is a standard process in every system that requires multiviews. Existing algorithms largely work on similar field of view (FoV) cases where the two views are mostly identical. Dissimilarities between different FoVs can generate unexpected errors during the optimization process, resulting in a large amount of rectification errors and unwanted geometric distortion. In this study, we present a full pipeline to rectify uncalibrated images captured by cameras that have dissimilar FoVs under the constraints of geometric distortion. The proposed method contains two main parts: Field of View Neutralization and Rectification with Adaptive Geometric Constraints. The Field of View Neutralization module estimates the transformation matrix to compensate for the imbalance between different views. In addition, this module improves the overall quality of correspondences by removing misleading feature matching pairs. Finally, by applying an adaptive optimization process with the geometric constraints involved, our method addresses the overdistortion issue while maintaining small rectification errors. Extensive experiments are conducted to demonstrate the robust performance of the proposed method. Besides the existing datasets, we provide our dissimilar FoVs dataset with multiple baselines to examine the performance. Our method outperforms the existing algorithms in terms of both rectification errors and geometric distortion rates.

A comprehensive strapdown triaxial magnetometer calibration method considering temporal misalignment error

The geomagnetic vector field is always measured by a strapdown triaxial magnetometer on a vehicle. We propose a comprehensive calibration method of strapdown triaxial magnetometers with the aid of external attitude information from a gyro. The temporal misalignment between the two sensors will introduce unexpected measurement errors. The traditional calibration method was expanded to include a temporal misalignment error model for a multi-sensor system, and it was further improved to overcome the ill-posed problem in practical applications. Additionally, the inherent errors of the magnetometer and the vehicle-generated magnetic inferences can be effectively calibrated. Simulations and experimental results demonstrated the success of the method. In comparison with the traditional method, the improvement ratio values of the magnetic field components for the proposed method approximately doubled. This indicated that the algorithm can effectively enhance the measurement accuracy of a strapdown triaxial magnetometer, especially for vector field measurements.

A Study of Prediction Model Improvement for Air-Oxidation Breakaway in a Postulated Spent Nuclear Fuel Pool Complete Loss of Coolant Accident

The temperature of zirconium alloy cladding on the postulated spent nuclear fuel pool complete loss of coolant accident is abruptly increased at a certain time and the cladding is almost fully oxidized to weak ZrO2 in the air. This abrupt temperature escalation phenomenon induced by the air-oxidation breakaway is called a zirconium fire. Although an air-oxidation breakaway kinetic model correlated between time and temperature has been implemented in the MELCOR code, it is likely to bring about unexpected large errors because of many limitations of model derivation. This study suggests an improved time–temperature correlated kinetic model using the Johnson–Mehl equation. It is based on that the air-oxidation breakaway is initiated by the phase transformation from the tetragonal to monoclinic ZrO2 at the oxide–metal interface in the cladding. This new model equation is also evaluated with the Zry-4 air-oxidation literature data. This equation resulted in the almost similar air-oxidation breakaway timing to the actual experimental data at 800 °C. However, at 1000 °C, it showed an error of about 8 min. This could be inferred from the influence of the ZrN phase change due to the nitrogen existing in air.

Trajectory log analysis and cone-beam CT-based daily dose calculation to investigate the dosimetric accuracy of intensity-modulated radiotherapy for gynecologic cancer.

This study evaluated unexpected dosimetric errors caused by machine control accuracy, patient setup errors, and patient weight changes/internal organ deformations. Trajectory log files for 13 gynecologic plans with seven‐ or nine‐beam dynamic multileaf collimator (MLC) intensity‐modulated radiation therapy (IMRT), and differences between expected and actual MLC positions and MUs were evaluated. Effects of patient setup errors on dosimetry were estimated by in‐house software. To simulate residual patient setup errors after image‐guided patient repositioning, planned dose distributions were recalculated (blurred dose) after the positions were randomly moved in three dimensions 0–2 mm (translation) and 0°–2° (rotation) 28 times per patient. Differences between planned and blurred doses in the clinical target volume (CTV) D98% and D2% were evaluated. Daily delivered doses were calculated from cone‐beam computed tomography by the Hounsfield unit‐to‐density conversion method. Fractional and accumulated dose differences between original plans and actual delivery were evaluated by CTV D98% and D2%. The significance of accumulated doses was tested by the paired t test. Trajectory log file analysis showed that MLC positional errors were −0.01 ± 0.02 mm and MU delivery errors were 0.10 ± 0.10 MU. Differences in CTV D98% and D2% were <0.5% for simulated patient setup errors. Differences in CTV D98% and D2% were 2.4% or less between the fractional planned and delivered doses, but were 1.7% or less for the accumulated dose. Dosimetric errors were primarily caused by patient weight changes and internal organ deformation in gynecologic radiation therapy.

Two-stage convolutional encoder-decoder network to improve the performance and reliability of deep learning models for topology optimization

A vital necessity when employing state-of-the-art deep neural networks (DNNs) for topology optimization is to predict near-optimal structures while satisfying pre-defined optimization constraints and objective function. Existing studies, on the other hand, suffer from the structural disconnections which result in unexpected errors in the objective and constraints. In this study, a two-stage network model is proposed using convolutional encoder-decoder networks that incorporate a new way of loss functions to reduce the number of structural disconnection cases as well as to reduce pixel-wise error to enhance the predictive performance of DNNs for topology optimization without any iteration. In the first stage, a single DNN model architecture is proposed and used in two parallel networks using two different loss functions for each called the mean square error (MSE) and mean absolute error (MAE). Once the priori information is generated from the first stage, it is instantly fed into the second stage, which acts as a rectifier network over the priori predictions. Finally, the second stage is trained using the binary cross-entropy (BCE) loss to provide the final predictions. The proposed two-stage network with the proposed loss functions is implemented for both two-dimensional (2D) and three-dimensional (3D) topology optimization datasets to observe its generalization ability. The validation results showed that the proposed two-stage framework could improve network prediction ability compared to a single network while significantly reducing compliance and volume fraction errors.

AN OPEN SOURCE MAIL SERVER MIGRATION EXPERIENCE: IREDMAIL

Abstract. Servers are systems that set up to run for years, but it may be necessary to migrate to a new, better server when they have completed their lifetime or become inadequate. In this study, the open-source iRedMail e-mail server has been successfully migrated to another high-capacity physical server which uses an open-source CentOS operation system. Migration is a process that every step has to be very well planned. However, although planning is well done, unexpected errors may occur. For this reason, it is also essential to choose migration time. The experience gained as a result of the study is a guide for the new ones.

A Novel Robust Observer-Based Nonlinear Trajectory Tracking Control Strategy for Quadrotors

In this article, a novel robust observer-based nonlinear control approach is proposed for quadrotors with unmodeled dynamics and external disturbances, wherein the tracking errors are restricted effectively. Two nonlinear disturbance observers are proposed to deal with uncertainties in the inner and outer loops, respectively. On this basis, a robust observer-based nonlinear control approach is put forward for quadrotor systems. Specifically, in the outer loop subsystem, a robust observer-based control scheme is proposed, which can reduce unexpected tracking errors for quadrotors effectively. Subsequently, based upon the geometric methods, the coordinate-free attitude controller and the nonlinear disturbance observer are integrated as a robust controller in the inner loop subsystem. Based upon full nonlinear dynamics, the solutions of the closed-loop system are proven to be uniformly ultimately bounded by means of rigorous Lyapunov analysis. A series of comparative experiments consisting of the implementation of a nonlinear proportional-integral-derivative (PID)-type controller, an observer-based sliding mode controller, and the proposed controller are conducted to demonstrate the remarkable performance of the proposed method in terms of higher tracking accuracy and stronger robustness.

Comparison of new monofocal innovative and standard monofocal intraocular lens after phacoemulsification.

To evaluate the early findings of new monofocal intraocular lens (Tecnis® Eyhance) in comparison with standard monofocal intraocular lens (Tecnis® 1-piece) in patients who underwent cataract surgery. Sixty-four eyes of thirty-two patients (each intraocular lens model was implanted bilaterally in 16 patients) were included in this retrospective study. Preoperative and postoperative (at one month) manifest refraction, spherical equivalent (SE) values, monocular and binocular uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA), uncorrected intermediate visual acuity (UCIVA), distance-corrected intermediate visual acuity (DCIVA), distance-corrected near visual acuity (DCNVA), binocular defocus curve, photopic contrast-sensitivity, spectacle independence, photic phenomena, tolerance to residual refractive errors and complications were recorded. The mean monocular postoperative UDVA, CDVA, DCNVA, spherical equivalent and cylinder values were comparable between each group (p > 0.05). Monocular and binocular DCIVA and UCIVA were significantly higher in Tecnis® Eyhance group than Tecnis® 1-piece group (p = 0.033, p = 0.038, respectively). No statistically significant difference was detected between groups regarding the photopic contrast sensitivities for any spatial frequency (p > 0.05). Spectacle independence was significantly higher in Tecnis® Eyhance group than Tecnis® 1-piece group. Tecnis® Eyhance IOL revealed a good tolerance to unexpected residual refractive errors than Tecnis® 1-piece IOL. Tecnis® Eyhance appears to be an effective option for both patients and surgeons regarding better intermediate visual acuities and more forgiving for residual refractive errors relative to the standard monofocal intraocular lenses. However, longer follow-up studies with larger group of patients are needed to evaluate the effectiveness of Tecnis® Eyhance intraocular lens.

When actions go awry: Monitoring partner errors and machine malfunctions.

Human actions often aim at triggering certain responses from social interaction partners, but these responses do not always come as expected. Here we show that unexpected partner errors trigger sustained monitoring and that this monitoring exceeds the level that is observed if participants are faced with a machine malfunction rather than an error of an interaction partner (Experiment 1). Critically, this pattern of results emerged even though both types of errors were signaled by physically identical events in an oddball task, ruling out alternative explanations in terms of differential bottom-up factors. Unexpected delays in the action-effect sequence, however, did not trigger increased monitoring for social as compared to nonsocial situations (Experiment 2). These results indicate that mechanisms of performance monitoring might be recruited especially when facing the variability that is inherent in social interactions. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

A technique for quantifying the sensitivity of dosimetric tool gamma with 2D detector array in pretreatment IMRT plans by segment deletion method.

Motivation of this study is to check the sensitivity of dosimetric tool gamma with 2D detector array combination when unexpected errors occur while transferring intensity-modulated radiation therapy treatment plans from planning system to treatment unit. This study consists of 17 head and neck cancer patient's treatment plans. Nine types of verification plans are created for all 17 clinically approved treatment plans by consecutively deleting different segments (up to eight) one by one from each field of the plan. Decrement factor (χ) is introduced in our study which illustrated the degree of decay of gamma passing rate when intentional errors are introduced. We analyzed the data by two different methods-one without selecting the region of interest (ROI) in dose distributions and the other by selecting the region of interest. By linear regression, the absolute value of slopes is 0.025, 0.024 and 0.015 without ROI and 0.030, 0.027 and 0.015 with ROI for 2%/2mm, 3%/3mm and 5%/5mm criteria, respectively. The higher absolute value of the fitted slope indicates the higher sensitivity of this method to identify erroneous plan in treatment unit. The threshold value for 2%/2mm equivalent to 95% passing criteria in 3%/3mm used in clinical practice is obtained as 83.44%. The 2D detector array with dosimetric tool gamma is less sensitive in detecting errors when unprecedented errors of segment deletion occur within the treatment plans.

Magnetic Comparison Between Experimental Flake Powder and Spherical Powder for Inductor Cores at High Frequency

This article is the first work to carefully investigate the characteristics of iron loss per weight and relative permeability between the flake and spherical powders with a sinusoidal excitation up to a high frequency of 3.5 MHz in experiments using three separate measurement systems. The composition of the considered powder samples contains 0.001 wt% C, 0.012 wt% Si, 0.09 wt% Mn, 0.0004 wt% P, 0.008 wt% S, and 0.06 wt% O. The flake particle has the preferred orientation that the easy axes are normal to the platelet face, and the water atomized iron powder was processed with ball milling with the lubrication. Key objectives of the utilization of the three independent measurement systems with different specialized equipment and probes are to cross-check and overcome unexpected measurement errors of the related devices in use for validating measured results thoroughly. Experimental results demonstrate the much better performances on the iron loss per weight and relative permeability of the flake powder than that of the spherical powder at high frequencies. Physics-based explanations for the measured results are also presented. Furthermore, two estimation functions based on a six-parameter Steinmetz equation for the measured iron loss of the powders are developed by using MATLAB.

Continual Learning for Addressing Optimization Problems with a Snake-Like Robot Controlled by a Self-Organizing Model

We have entered a new era, “Industry 4.0”, that sees the overall industry marching toward an epoch of man–machine symbiosis and intelligent production. The developers of so-called “intelligent” systems must attempt to seriously take into account all possible situations that might occur in the real world, to minimize unexpected errors. By contrast, biological systems possess comparatively better “adaptability” than man-made machines, as they possess a self-organizing learning that plays an indispensable role. The objective of this study was to apply a malleable learning system to the movement control of a snake-like robot, to investigate issues related to self-organizing dynamics. An artificial neuromolecular (ANM) system previously developed in our laboratory was used to control the movements of an eight-joint snake-like robot (called Snaky). The neuromolecular model is a multilevel neural network that abstracts biological structure–function relationships into the system’s structure, in particular into its intraneuronal structure. With this feature, the system possesses structure richness in generating a broad range of dynamics that allows it to learn how to complete the assigned tasks in a self-organizing manner. The activation and rotation angle of each motor are dependent on the firing activity of neurons that control the motor. An evolutionary learning algorithm is used to train the system to complete the assigned tasks. The key issues addressed include the self-organizing learning capability of the ANM system in a physical environment. The experimental results show that Snaky was capable of learning in a continuous manner. We also examined how the ANM system controlled the angle of each of Snaky’s joints, to complete each assigned task. The result might provide us with another dimension of information on how to design the movement of a snake-like robot.

Data-Driven Motion Compensation for Airborne Bistatic SAR Imagery Under Fast Factorized Back Projection Framework

Due to the independence of azimuth invariance and high implementing efficiency, a fast time-domain algorithm has significant advantages for airborne bistatic synthetic aperture radar (BiSAR) data process with general geometric configuration. In this article, the practical problem of unexpected motion errors of the airborne platform is carefully analyzed under a fast factorized back-projection (FFBP) framework for a general BiSAR process and a coherent data-driven motion compensation (MOCO) algorithm integrated with FFBP is proposed. By utilizing wavenumber decomposition, the analytical spectrum of a polar grid image is obtained where the motion error can be conveniently investigated in image spectrum domain and the coherence between azimuthal phase error (APE) and motion-induced nonsystematic range cell migration (NsRCM) can be perfectly revealed. Then, a new data-driven MOCO method for both APE and NsRCM correction is developed with the FFBP process. Different from the data-driven MOCO in most frequency-domain algorithms, the residual NsRCM introduced by the FFBP process is particularly analyzed and addressed in the MOCO, which significantly improves the image quality in focusing. Promising results from both simulation and raw data experiments are presented and analyzed to validate the advantages of the proposed algorithm for the airborne BiSAR process.

Error analysis of reconstructed wave force on a circular cylinder by using wave elevation data

This paper presents a study on reconstruction errors of wave force around a circular cylinder using measured wave elevation data through theoretical analysis and experimental validation. Since the accuracy of reconstructed results is directly subjected to the collected data of wave elevation, some unexpected errors can be injected into the results as the data are not informative enough. A mathematical analysis is presented first, which considers the wave gauges uniformly distributed around the cylinder with the number of three, five, seven and nine. Amplitude error and phase error of the reconstructed wave force are defined to quantify the effect of distribution and number of wave gauges. An experiment was conducted to validate the theoretical results for both amplitude and phase errors. It shows that experimental data with three wave gauges can be well predicted by using theoretical analysis. Based on the theoretical results, an amended method is proposed to improve the reconstructed results with three wave gauges. The amended method is proved by the test data to be an efficient method in some specific situations. Finally, a comprehensive discussion on the difference between two kinds of processing method of the reconstructed wave force is performed.