Initial Error Research Articles

Bayesian Estimation of Potential Performance Improvement Elicited by Robot-Guided Training.

Improving human motor performance via physical guidance by an assist robot device is a major field of interest of the society in many different contexts, such as rehabilitation and sports training. In this study, we propose a Bayesian estimation method to predict whether motor performance of a user can be improved or not by the robot guidance from the user’s initial skill level. We designed a robot-guided motor training procedure in which subjects were asked to generate a desired circular hand movement. We then evaluated the tracking error between the desired and actual subject’s hand movement. Results showed that we were able to predict whether a novel user can reduce the tracking error after the robot-guided training from the user’s initial movement performance by checking whether the initial error was larger than a certain threshold, where the threshold was derived by using the proposed Bayesian estimation method. Our proposed approach can potentially help users to decide if they should try a robot-guided training or not without conducting the time-consuming robot-guided movement training.

Role of initial error growth in the extended range prediction skill of Madden-Julian Oscillation (MJO)

Role of initial error growth in the extended range prediction skill of Madden-Julian Oscillation (MJO)

Automation and evaluation of the interactive homogenization tool HOMER

AbstractThe interactive HOMER is automated by the use of a set of parameters. This approach retains the skill and flexibility of an interactive method, but adds the speed and reproducibility of an automatic method. The automation of the interactive HOMER also enables systematic testing. Its performance is evaluated by the homogenization of the Indecis homogenization benchmark datasets. The overall performance of the interactive HOMER compares well with the methods using the homogenization tools Climatol and ACMANT and surpass the performance of the standard automatic HOMER. All the homogenization methods reduce the initial error. The average residual error and all error percentiles below and including the 99th error percentile do not differ more than 0.3°C between interactive HOMER and the other methods. Interactive HOMER and Climatol report fewer homogeneity breaks than the true number, while standard automatic HOMER and ACMANT report more homogeneity breaks. Across the methods, a higher number of reported homogeneity breaks renders a higher share of the true homogeneity breaks to be detected, but also a higher share of the reported breaks to be false positives. On average the differences in the corrected times series are small between the methods implying that the choice of homogenization method is a matter of preference.

High order dynamical systems approaches for low-thrust station-keeping of libration point orbits

We derive two families of low-thrust station-keeping control procedures based on the dynamical properties of the phase space near libration point orbits. The first family is obtained as the limit of impulsive maneuvers, and the second one by means of dynamically reshaping Floquet modes. Despite following different approaches, the geometrical analysis shows that all these strategies can reshape the dynamical structures about the libration point orbits and stabilize the motion. The numerical results prove their robustness in front of measurement uncertainties and large initial orbit injection errors. Moreover, the long-term behavior is analyzed by means of Poincaré maps, and the results are compared with the Hamiltonian-structure preserving control strategy. Not just restricted to the particular problem considered, the new methodology provides a general framework to analyze the geometric behavior of any low-thrust station-keeping control law and opens the possibility of compact on-board techniques.

Diagnostic Errors in Initial Misdiagnosis of Foreign Body Aspiration in Children: A Retrospective Observational Study in a Tertiary Care Hospital in China.

Background: Foreign body aspiration (FBA) in children is a common emergency that can easily be missed, leading to delays in treatment. Few large cohort studies have focused on errors in diagnostic assessment. The main purpose of this study was to analyze factors contributing to the initial misdiagnosis of FBA in children.Methods: We retrospectively reviewed the charts of 226 children diagnosed with FBA at the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University from January 2018 to November 2020. Cases were divided into two groups according to whether or not patients were initially misdiagnosed. The clinical characteristics of the two groups were then compared. The Diagnosis Error Evaluation and Research (DEER) taxonomy tool was applied to cases with initial misdiagnosis.Results: Of the 226 included children with a final diagnosis of FBA, 153 (67.7%) were boys. Ninety percent of patients were under 3 years old. More than half (61.9%) of the children were referred from primary institutions, and 38.1% visited tertiary hospitals directly. A total of 80 (35.4%) patients were initially misdiagnosed. More than half of misdiagnosed children received an alternative diagnosis of bronchiolitis (51.3%), the most common alternative diagnosis. Test failures (i.e., errors in test ordering, test performance, and clinician processing) were primarily responsible for the majority of initial diagnostic errors (76.3%), followed by failure or delay in eliciting critical case history information (20.0%). Characteristics significantly associated with initial misdiagnosis were: presentation over 24 h (OR 9.2, 95% CI 4.8–17.5), being referred from primary institutions (OR 8.8, 4.1–19.0), no witnessed aspiration crisis (OR 7.8, 3.0–20.3), (4) atypical signs or symptoms (OR 3.2, 1.8–5.7), foreign body not visible on CT (OR 36.2, 2.1–636.8), foreign body located in secondary bronchi (OR 4.8, 1.3–17.2), organic foreign body (OR 6.2, 1.4–27.2), and history of recurrent respiratory infections (OR 2.7, 1.4–5.3). Children with misdiagnosis tended to have a longer time from symptom onset to the definitive diagnosis of FBA (P < 0.001).Conclusions: More than one-third of children with FBA were missed at first presentation. Errors in diagnostic testing and history taking were the main reasons leading to initial misdiagnosis.

A Discrete Quaternion Particle Filter Based on Deterministic Sampling for IMU Attitude Estimation

A discrete quaternion filtering algorithm following high-density uncertainty matching-based deterministic sampling scheme is proposed for IMU attitude estimation. Existing quaternion filters rely on the specific distributions directly or the on-tangent-plane Gaussian distribution indirectly to model the underlying uncertainty. The key idea of this paper is to apply a Dirac mixture at few deterministic weighted samples for non-parametric interpreting the high-density uncertainty of unit quaternion manifold. In contrast to standard Monte Carlo techniques based on random sampling, the proposed approach is deterministic. Furthermore, the deterministic quaternion particles are obtained by projecting on-sphere uniform points to the unit quaternion manifold via an exponential map. The on-manifold quaternions are then exploited in a discrete Bayesian filtering procedure. We compare the new filter with the genetic algorithm embedded quaternion particle filter (GA-QPF) and the unscented quaternion estimator (USQUE) for accuracy and computation cost by using simulated and practical IMU data. The results illustrate that the proposed estimator possesses lower computational cost than the GA-QPF while providing a comparable accuracy, higher accuracy than the USQUE, as well as more effective against large initialization error.

Stacked bidirectional LSTM RNN to evaluate the remaining useful life of supercapacitor

To predict the remaining useful life of supercapacitor, a data-based model is established by using a stacked bidirectional long short-term memory recurrent neural network. On the basis of the traditional long short-term memory recurrent neural network, a reverse recurrent layer with t time and subsequent time values in the input sequence is added. A stacked network can ensure enough capacity space. Simulation results show that the network has superior performance when the number of hidden layers is 2, the predicted RMSE and MAE are 0.0275 and 0.0241, respectively. Meanwhile, simulation compares ordinary and bidirectional recurrent neural networks and the bidirectional recurrent neural networks with different recurrent units. For subsequent ameliorate, this project will add swarm intelligence algorithm to optimize the initial weight of neural network and reduce the initial prediction error.

Viscoelastic biomechanical models to predict inward brain-shift using public benchmark data

Brain-shift during neurosurgery compromises the accuracy of tracking the boundaries of the tumor to be resected. Although several studies have used various finite element models (FEMs) to predict inward brain-shift, evaluation of their accuracy and efficiency based on public benchmark data has been limited. This study evaluates several FEMs proposed in the literature (various boundary conditions, mesh sizes, and material properties) by using intraoperative imaging data (the public REtroSpective Evaluation of Cerebral Tumors [RESECT] database). Four patients with low-grade gliomas were identified as having inward brain-shifts. We computed the accuracy (using target registration error) of several FEM-based brain-shift predictions and compared our findings. Since information on head orientation during craniotomy is not included in this database, we tested various plausible angles of head rotation. We analyzed the effects of brain tissue viscoelastic properties, mesh size, craniotomy position, CSF drainage level, and rigidity of meninges and then quantitatively evaluated the trade-off between accuracy and central processing unit time in predicting inward brain-shift across all models with second-order tetrahedral FEMs. The mean initial target registration error (TRE) was 5.78 ± 3.78 mm with rigid registration. FEM prediction (edge-length, 5 mm) with non-rigid meninges led to a mean TRE correction of 1.84 ± 0.83 mm assuming heterogeneous material. Results show that, for the low-grade glioma patients in the study, including non-rigid modeling of the meninges was significant statistically. In contrast including heterogeneity was not significant. To estimate the optimal head orientation and CSF drainage, an angle step of 5° and an CSF height step of 5 mm were enough leading to <0.26 mm TRE fluctuation.

Hybrid optimization strategy for lithium-ion battery's State of Charge/Health using joint of dual Kalman filter and Modified Sine-cosine Algorithm

In order to improve the accuracy of State of Charge / Health (SOC/SOH) estimation, Modified-Sine-cosine Algorithm-based dual Extend Kalman filter (MSCA-DEKF) is proposed. Second-order RC model is applied, the model parameters are obtained by Pulse discharge test and Open circuit voltage test (OCV). DEKF is divided into state filter and parameter filter. Nonlinear decrement of transformation parameter r1 in Sine-cosine Algorithm (SCA) is proposed. Modified SCA (MSCA) is applied to optimizing the covariance noise matrix in the state filter. Parameter filter is applied to estimating the Ohm internal resistance and capacity online, Meanwhile, the time scale of parameters’ online update is adjusted to 60 time steps for reducing computing costs. SOH is also obtained by Ohm internal resistance and capacity. Simulation results show that the proposed method improves the accuracy of SOC estimation, and the initial errors of SOC and Ro can be corrected in the first parameter estimation. Ro-based SOH has a better robustness and precision than capacity-based SOH.

Orthokeratology Lenses for Myopia Control. Factors Affecting Efficacy (Literature Review)

This review focuses on the major factors affecting the efficacy of myopia control in patients wearing orthokeratology lenses (OKLs). Orthokeratology lenses have been proven to be effective for myopia control, however further efficacy comparisons between OKLs of standard and custom designs revealed that customization of parameters resulted in an increase of relative peripheral myopia in myopic patients regardless of their initial refractive error. Purpose: to systematize the data of the literature on the effectiveness of orthoceratological correction to control the progression of myopia. Methods. The analysis of publications on the resources PubMed, eLibrary, Cochrane, Cyberleninka is carried out. Results. According to the search results, the main factor saffecting the degree of control of myopia in patients using orthokeratological correction were highlighted. Conclusions. Evaluation of the anatomical and physiological parameters of patients, as well as development of customizable designs of OKLs for myopia control will allow for greater control over myopia progression in its early stages.

A novel method for state of energy estimation of lithium-ion batteries using particle filter and extended Kalman filter

State-of-energy (SOE) estimation of lithium-ion batteries (LIBs) is one of the core functions of battery management systems in electric vehicles. In this study, to improve the accuracy and robustness of SOE estimation, a novel SOE method using a particle filter (PF) and extended Kalman filter (EKF) insensitive to uncertain total available energy loss and ambient temperatures is proposed. First, a battery model is established, and then the model parameters at different temperatures in the whole SOE range are identified. Second, the PF algorithm is chosen to estimate the SOE, whereas the EKF algorithm is used to update the total available energy online. Finally, the effectiveness of the proposed SOE method is verified by experiments under dynamic conditions. The experimental results indicate that the maximum error of the SOE estimation with the proposed PF–EKF algorithm is less than 3% under the dynamic stress test at 0, 25, and 40 ℃. Moreover, even if there are large initial SOE and total available energy errors, the SOE estimation by the proposed algorithm would be able to quickly converge to it is reference trajectory with high accuracy and robustness.

Enhancing Stability and Robustness of State-of-Charge Estimation for Lithium-Ion Batteries by Using Improved Adaptive Kalman Filter Algorithms

The traditional Kalman filter algorithms have disadvantages of poor stability (the program cannot converge or crash), robustness (sensitive to the initial errors) and accuracy, partially resulted from the fact that noise covariance matrices in the algorithms need to be set artificially. To overcome the above problems, some adaptive Kalman filter (AKF) algorithms are studied, but the problems still remain unsolved. In this study, two improved AKF algorithms, the improved Sage-Husa and innovation-based adaptive estimation (IAE) algorithms, are proposed. Under the different operating conditions, the estimation accuracy, filter stability, and robustness of the two proposed algorithms are analyzed. Results show that the state of charge (SOC) Max error based on the improved Sage-Husa and the improved IAE is less than 3% and 1.5%, respectively, while the Max errors of the original algorithms is larger than 16% and 4% The two proposed algorithms have higher filter stability than the traditional algorithms. In addition, analyses of the robustness of the two proposed algorithms are carried out by changing the initial parameters, proving that neither are sensitive to the initial errors.

Statistical Error Estimation Methods for Engineering-Relevant Quantities From Scale-Resolving Simulations

Abstract Scale-resolving simulations, such as large eddy simulations, have become affordable tools to investigate the flow in turbomachinery components. The resulting time-resolved flow field is typically analyzed using first- and second-order statistical moments. However, two sources of uncertainty are present when statistical moments from scale-resolving simulations are computed: the influence of initial transients and statistical errors due to the finite number of samples. In this paper, both are systematically analyzed for several quantities of engineering interest using time series from a long-time large eddy simulation of the low-pressure turbine cascade T106C. A set of statistical tools to either remove or quantify these sources of uncertainty is assessed. First, the Marginal Standard Error Rule is used to detect the end of the initial transient. The method is validated for integral and local quantities and guidelines on how to handle spatially varying initial transients are formulated. With the initial transient reliably removed, the statistical error is estimated based on standard error relations considering correlations in the time series. The resulting confidence intervals are carefully verified for quantities of engineering interest utilizing cumulative and simple moving averages. Furthermore, the influence of periodic content from large scale vortex shedding on the error estimation is studied. Based on the confidence intervals, the required averaging interval to reduce the statistical uncertainty to a specific level is indicated for each considered quantity.

Theoretical model of bending moment for the penetrated mortise-tenon joint involving gaps in traditional timber structure

Mortise-tenon joints often have neighboring gaps due to initial manufacturing errors and accumulated damage over thousands of years. The looseness of joints can cause degradation of mechanical performance of joint, which might lead to the collapse or damage of the entire structure. In this study, the mechanical performances of penetrated mortise-tenon joint involving gaps were investigated. Working states and judging criteria for different working states were produced under positive and negative loading. A theoretical model of the bending moment for a loose mortise-tenon joint was proposed. Full-scale experiments were conducted to verify the proposed theoretical model. Transverse four-column wooden frames, characterizing the overall mechanical properties of a structure, were chosen to clarify the applicability of the proposed model. A simplified finite element model (FEM) with bar units and rotation springs was established and verified using solid FEM. The deformation characteristics and lateral resistance of wooden frame were then analyzed. The results showed that the theoretical model was in good agreement with test results and could be used to estimate the joint bending moment. Simplified models of joints were then applied in the establishment of a timber structure. The lateral behavior of the wood frame mainly contributed by column foot joints at the beginning of rotation and penetrated mortise-tenon joints at later rotation. This study provided important references on the protection and repair of existing traditional timber structures.

Bayesian Joint State-Parameter-Input Estimation of Flexible-Base Buildings from Sparse Measurements Using Timoshenko Beam Models

This paper presents a computationally efficient framework for the Bayesian identification of sparsely instrumented building structures that is amenable to rapid postearthquake condition assessment. Flexible-base Timoshenko beam models are employed within a Bayesian framework, which uses the extended Kalman filter (EKF) as a joint state-parameter-input estimation tool. Highly sparse and noisy measurements are utilized to identify the properties of the superstructure, the soil–foundation substructure, and the foundation input motion simultaneously under strong nonstationary shaking. The proposed framework is verified and its robustness is examined through synthetic problems featuring wide-ranging random initial errors. A validation study is also carried out on an instrumented building, namely, Caltech’s Millikan Library. The results show that the proposed framework is capable of estimating the unknown parameters of the soil-foundation-structure system together with the input excitation using as few as three measurement channels. Representing the superstructure by a model that offers an analytical solution to system dynamics and determining the analytical derivatives for EKF using direct differentiation has led to a computationally efficient and accurate tool that robustly identifies the system from a minimal set of measurements.

An improved minimal error model for the robotic kinematic calibration based on the POE formula

SummaryThe conventional product of exponentials $\left(\rm POE\right)$ -based methods dissatisfy the parametric minimality for the kinematic calibration of serial robots due to overlooking the magnitude and pitch constraints. Thus, the minimal kinematic model is presented to solve this problem, which can be developed further. This paper puts forward an improved algorithm for the minimal parameter calibration. An actual kinematic model with the minimal parameters $\left(\rm MP\right)$ is constructed according to the geometric properties of actual joint twists in the auxiliary frames established on the basis of the nominal joint axes. Then, the initial pose error is defined in the tool coordinate frame, which is expressed as the exponential map of the twist, and all twist descriptions are unified, so as to give a unified kinematic model in mathematics. By differentiating the kinematic model, a minimal error model is derived in explicit form. Subsequently, we propose a novel parameter identification method, which identifies the orientation error and position error parameters separately by the iterative least-squares method and updates the MP uniformly. Finally, the simulations and experiments on the different serial robots are conducted to verify the correctness and effectiveness of the proposed algorithm. The simulation results show our calibration algorithm outperforms the existing ones in the accuracy aspect, and the experiment result shows that the absolute pose accuracy of the UR5 industrial robot is upgraded about 9 times under a statistics sense after the calibration.

Dynamical‐statistical long‐term prediction for tropical cyclone landfalls in East Asia

AbstractThis study develops a statistical‐dynamical seasonal typhoon forecast model (SDTFM) that utilizes the statistical correlation between East Asia (EA) tropical cyclone (TC) landfall and atmospheric circulation predicted by a coupled general circulation model for seasonal prediction and its predictability is verified. A total of 40 ensemble members produced through different data assimilation and time‐lag methods introduced as a way to reduce the initial condition error and model uncertainty enabled the development of the new SDTFM. According to the results, the SDTFM developed in this study showed significant predictability in TC landfall prediction when using the month of May for the initial conditions for the entire East Asia (EEA) and its three sub‐domains: Northern East Asia (NEA), Middle East Asia (MEA), and Southern East Asia (SEA). The predicted TC season is July–September (JAS), and only for SEA, including South China, the Philippines, and Vietnam, it is July–November (JASON) considering the relatively long landfall period. The models developed for each domain significantly predict the interannual variability of TC landfall at the 99% confidence level. The cross‐validated results are still significant at the 99% confidence level in NEA and SEA and the 95% confidence level in MEA and EEA.

The most sensitive initial error modes modulating intensities of CP- and EP- El Niño events

The two types of El Niño events simulated by the Geophysical Fluid Dynamics Laboratory Climate Model version 2p1 (GFDL CM2p1) model and its “spring predictability barrier” (SPB) associations are examined. By conducting the ensemble hindcast experiments related to the sea temperature on the whole Pacific, both of the predictions for CP- and EP-El Niño show a significant SPB phenomenon, but the CP-El Niño features a much weaker SPB compared to the EP-El Niño. Further analyses revealed that, for CP-El Niño events, the initial sea temperature errors of the North Pacific with triple-like shape, referred to as negative Victoria Mode (VM) induces the largest prediction errors in Niño4 areas and modulates the intensities of CP-El Niño events. While for EP-El Niño events, the initial sea temperature errors in the subsurface layer of the western equatorial Pacific and the upper layer of the Southeast Pacific (15–30ºS) with the meridional mode induce the largest prediction errors in Niño3 areas and modulates the intensities of EP-El Niño events. Obviously, results stress that, in order to reduce final prediction errors and obtain better predictions in terms of intensity on the two types of El Niño events, we should mainly focus on initial sea temperature accuracy in not only the subsurface layer of the west equatorial Pacific but also the surface layer of southeast Pacific and the region covered by the VM-like mode in the North Pacific.

Modified dual extended Kalman filters for SOC estimation and online parameter identification of lithium-ion battery via modified gray wolf optimizer

In order to achieve accurate state of charge (SOC) estimation of Lithium-Ion Battery, A method that dual Extended Kalman filters (DEKF) optimized by PSO-based Gray Wolf optimizer (MGWO) is proposed. A second-order equivalent circuit model with two resistor-capacitor branches is applied. The battery parameters are determined by battery test. Dual Extended Kalman filters are divided into state filter and parameter filter. Parameter filter is applied to adjust battery parameters online, state filter is applied to SOC estimation. Meanwhile, MGWO is applied to optimize the noise covariance matrix to improve the state estimation accuracy of SOC which reduces the linearization error from EKF. The results shows that the accuracy of algorithm is improved by adding online parameter identification and the optimization of the noise covariance matrix, meanwhile, the proposed method can adapt to the initial error well.

Fixed-time convergent sliding-modes-based differentiators

Conventional sliding-modes based differentiators make it possible to estimate successive derivatives of a given time-varying signal in finite-time and with exact convergence in noise free case. In general, the convergence time is an unbounded increasing function of initial estimation errors. Most already proposed solutions guarantee a convergence in a maximum time independent of initial conditions. In this paper, novel sliding mode differentiators with a prescribed convergence time are proposed. The convergence time can be chosen arbitrary whatever large initial estimation errors. The proposed key solution is based on a time-dependent transformation using modulating functions which make it possible to cancel the effect of initial conditions on the convergence time. New arbitrary order differentiators including the super-twisting algorithm based on modulating functions are introduced. Lyapunov functions and homogeneity tools are used to prove the convergence of the proposed first-order and arbitrary order differentiators, respectively. Robustness with respect to measurement noise is also addressed.