Estimation Error Research Articles

Methods to Adjust for Confounding in Test-Negative Design COVID-19 Effectiveness Studies: Simulation Study.

Real-world COVID-19 vaccine effectiveness (VE) studies are investigating exposures of increasing complexity accounting for time since vaccination. These studies require methods that adjust for the confounding that arises when morbidities and demographics are associated with vaccination and the risk of outcome events. Methods based on propensity scores (PS) are well-suited to this when the exposure is dichotomous, but present challenges when the exposure is multinomial. This simulation study aimed to investigate alternative methods to adjust for confounding in VE studies that have a test-negative design. Adjustment for a disease risk score (DRS) is compared with multivariable logistic regression. Both stratification on the DRS and direct covariate adjustment of the DRS are examined. Multivariable logistic regression with all the covariates and with a limited subset of key covariates is considered. The performance of VE estimators is evaluated across a multinomial vaccination exposure in simulated datasets. Bias in VE estimates from multivariable models ranged from -5.3% to 6.1% across 4 levels of vaccination. Standard errors of VE estimates were unbiased, and 95% coverage probabilities were attained in most scenarios. The lowest coverage in the multivariable scenarios was 93.7% (95% CI 92.2%-95.2%) and occurred in the multivariable model with key covariates, while the highest coverage in the multivariable scenarios was 95.3% (95% CI 94.0%-96.6%) and occurred in the multivariable model with all covariates. Bias in VE estimates from DRS-adjusted models was low, ranging from -2.2% to 4.2%. However, the DRS-adjusted models underestimated the standard errors of VE estimates, with coverage sometimes below the 95% level. The lowest coverage in the DRS scenarios was 87.8% (95% CI 85.8%-89.8%) and occurred in the direct adjustment for the DRS model. The highest coverage in the DRS scenarios was 94.8% (95% CI 93.4%-96.2%) and occurred in the model that stratified on DRS. Although variation in the performance of VE estimates occurred across modeling strategies, variation in performance was also present across exposure groups. Overall, models using a DRS to adjust for confounding performed adequately but not as well as the multivariable models that adjusted for covariates individually.

Solar radiation estimation in West Africa: impact of dust conditions during the 2021 dry season

Abstract. The anticipated increase in solar energy production in West Africa requires high-quality solar irradiance estimates, which are affected by meteorological conditions and in particular the presence of desert dust aerosols. This study examines the impact of incorporating desert dust into solar irradiance and surface temperature estimations. The research focuses on a case study of a dust event in March 2021, which is characteristic of the dry season in West Africa. Significant desert aerosol emissions at the Bodélé Depression are associated with a Harmattan flow that transports the plume westwards. Simulations of this dust event were conducted using the meteorological Weather Research and Forecasting (WRF) Model alone, as well as coupling it with the CHIMERE chemistry transport model, using three different datasets for the dust aerosol initial and boundary conditions (CAMS, GOCART, and MERRA-2). Results show that considering desert dust reduces estimation errors in global horizontal irradiance (GHI) by about 75 %. The dust plume caused an average of 18 % reduction in surface solar irradiance during the event. Additionally, the simulations indicated a positive bias in aerosol optical depth (AOD) and PM10 surface concentrations. The choice of dataset for initial and boundary conditions minimally influenced GHI, surface temperature, and AOD estimates, whereas PM10 concentrations and aerosol size distribution were significantly affected. This study underscores the importance of incorporating dust aerosols into solar forecasting for better accuracy.

Response Surface Optimisation of Carbon Dioxide Adsorption Onto Palm Shell Activated Carbon Functionalised With Natural Amino Acids

ABSTRACTAmino acids have shown promising results for carbon dioxide (CO2) capture when functionalised on solid materials; however, the functionalisation often relies on commercial synthetic amino acids. This study investigated the optimal CO2 adsorption performance of amino acid–functionalised material synthesised from palm shell–based activated carbon and natural amino acids, specifically egg white (EW) solution, in a continuous adsorption column. The process conditions of the column were optimised using response surface methodology. Four parameters, namely, the gas flow rate, adsorption temperature, CO2 concentration and EW concentration in the impregnation solution, were identified as significantly affecting CO2 adsorption performance. Good agreements were obtained between the predicted and experimental data, with the coefficients of determination ranging from 0.9639 to 0.9784. A maximum CO2 adsorption capacity of 1.1793 mmol/g was achieved under optimal process conditions: a gas flow rate of 200 mL/min, an adsorption temperature of 25°C, a CO2 concentration of 25 vol.%, and an EW concentration of 15 wt.%. The validation results further confirmed the reliability of the developed model equation in predicting the maximum CO2 adsorption capacity at a fixed 15 vol.% CO2 concentration, with low estimation error. The comparable results obtained using EW waste in this study represent a significant finding in the potential for waste valorisation, aligning with Sustainable Development Goal (SDG) 12 of the United Nations Sustainable Development Goals, as well as contributing to climate action as outlined in SDG 13.

Estimating effective population size trajectories from time-series identity-by-descent segments.

Long, identical haplotypes shared between pairs of individuals, known as identity-by-descent (IBD) segments, result from recently shared co-ancestry. Various methods have been developed to utilize IBD sharing for demographic inference in contemporary DNA data. Recent methodological advances have extended the screening for IBD segments to ancient DNA (aDNA) data, making demographic inference based on IBD also possible for aDNA. However, aDNA data typically have varying sampling times, but most demographic inference methods for modern data assume that sampling is contemporaneous. Here, we present Ttne (Time-Transect Ne), which models time-transect sampling to infer recent effective population size trajectories. Using simulations, we show that utilizing IBD sharing in time series increased resolution to infer recent fluctuations in effective population sizes compared with methods that only use contemporaneous samples. To account for IBD detection errors common in empirical analyses, we implemented an approach to estimate and model IBD detection errors. Finally, we applied Ttne to two aDNA time transects: individuals associated with the Copper Age Corded Ware Culture and Medieval England. In both cases, we found evidence of a growing population, a signal consistent with archaeological records.

Sustainable and Low-Cost Greenhouse Soil Moisture Monitoring Using Battery-Free RFID Sensors

Intelligent irrigation based on measurements of soil moisture levels in every pot in a greenhouse can not only improve plant productivity and quality but also save water. However, existing soil moisture sensors are too expensive to deploy in every pot. We therefore introduce GreenTag, a low-cost RFID-based soil moisture sensing system whose accuracy is comparable to that of an expensive soil moisture sensor. Our key idea is to attach two RFID tags to a plant’s container so that changes in soil moisture content are reflected in their Differential Minimum Response Threshold (DMRT) metric at the reader. We show that a low-pass filtered DMRT metric is robust to changes both in the RF environment (e.g., from human movement) and in pot locations. In addition, we propose a fast DMRT acquisition algorithm and a time-efficient tag query protocol, which can reduce the sensing latency by 90%. In a realistic setting, GreenTag achieves a 90-percentile moisture estimation errors of 5%, which is comparable to the 4% errors using expensive soil moisture sensors. Moreover, this accuracy is maintained despite changes in the RF environment and container locations. We also show the effectiveness of GreenTag in a real greenhouse.

Rotor Position Estimation Algorithm for Surface-Mounted Permanent Magnet Synchronous Motor Based on Improved Super-Twisting Sliding Mode Observer

In response to the chattering issue inherent in sliding mode observers during rotor position estimation and to enhance the stability and robustness of sensorless control systems for surface-mounted permanent magnet synchronous motors (SPMSM), this study proposes a rotor position estimation algorithm for SPMSM based on an improved super-twisting sliding mode observer (ISTSMO) and a second-order generalized integrator (SOGI) structure. Firstly, the super-twisting algorithm is introduced to design the observer, which effectively attenuates the sliding mode chattering by using continuous control signals. Secondly, SOGI is introduced in the filtering stage, which not only effectively addresses the time delay issues caused by traditional low-pass filters but also enables the observer to extract rotor position information by monitoring only the back electromotive force (back-EMF) signal of the α-phase, thereby simplifying the observer structure. Finally, the proposed scheme is experimentally compared with the traditional sliding mode observer on the YXMBD-TE1000 platform. The experimental results showed that during motor acceleration and deceleration tests, the average speed estimation error was reduced from 141 r/min to 40 r/min, and the maximum position estimation error was reduced from 0.74 rad to 0.29 rad. In load disturbance experiments, the speed variation decreased from 781 r/min to 451 r/min, and the steady-state speed fluctuation was significantly reduced. These results confirm that the proposed observer exhibits superior stability and robustness.

Concentration and Model Selection Consistency of the Group Lasso for α$$ \alpha $$‐Mixing Errors

ABSTRACTThe group lasso in linear regression models is studied for ‐mixing subexponential errors. Nonasymptotic guarantees are provided for the estimation error of the sparse coefficient vector and the associated predictions for the high‐dimensional regime where the number of regressors can grow much faster than the sample size. Further, the group lasso is model selection consistent; that is, it picks the right variables with arbitrarily high probability. The results are applied to the index tracking problem in finance and illustrated by analysing stock data from the United States and Asia.

Effects of Sampling Design on Population Abundance Estimation of Ichthyoplankton in Coastal Waters

The abundance, spatial distribution of and dynamic changes in ichthyoplankton species affect the recruitment and fish population dynamics, which are fundamental for stock assessment and fisheries management. An evaluation of alternative sampling designs needs to be carried out to determine the optimal scheme that is cost-effective in collecting high-quality ichthyoplankton data. A simulation study was conducted to evaluate the performances and consistency of six potential sampling designs for an ichthyoplankton survey in the coastal waters of the central and southern Yellow Sea. Relative estimation error (REE) and relative bias (RB) were used to measure the performances in estimating the population abundances of five target ichthyoplankton species in different sampling designs. In general, the two systematic sampling (SYS) designs had high precision and accuracy estimation and remained stable over years for estimating the population abundance of ichthyoplankton species compared with the other four sampling designs. SYS did not result in the overestimation or underestimation of the mean population abundance. Most sampling designs showed relatively high accuracy in abundance estimation when sample sizes were higher than medium levels. This study could improve the performances of sampling designs of ichthyoplankton surveys and provide a reference for the further optimization of sampling designs.

Risky actions: Why and how to estimate variability in motor performance.

We describe the difficulties of measuring variability in performance, a critical but largely ignored problem in studies of risk perception. The problem seems intractable if a large number of successful and unsuccessful trials are infeasible. We offer a solution based on estimates of task-specific variability pooled across the sample. Using a dataset of adult performance in throwing and walking tasks, we show that mischaracterizing the slope leads to unacceptably large errors in estimates of performance levels that undermine analyses of risk perception. We introduce a "pooled-slope" solution that approximates estimates of individual variability in performance and outperforms arbitrary assumptions about performance variability within and across tasks. We discuss the advantages of objectively measuring performance based on the rate of successful attempts-modeled via psychometric functions-for improving comparisons of risk across participants, tasks, and studies.

Decentralized control of interconnected systems with actuator faults and disturbances via cooperative interaction observer approach

This work investigates decentralised control problems of interconnected systems under actuator faults and disturbances via cooperative interaction observers. Firstly, the influence of faults and matched disturbances is described by an unknown signal generated from any unknown input-to-state stable dynamics. Next, by using current and historical state and estimation information, two virtual auxiliary systems are constructed that involve estimation error information of the unknown signal. And then, a robust decentralised observer with a cooperative interaction structure is developed. Through the cooperative interaction between auxiliary systems and estimation error systems, the estimation error information of the unknown signal is fully used to obtain a more accurate estimation signal. Furthermore, based on cyclic-small-gain and H ∞ techniques, a decentralised control scheme with the estimated signal is proposed, which not only effectively eliminates the influence of faults and matched disturbances, but also improves the robustness of systems. Finally, simulation results validate the presented method.

Monte Carlo post-processing for radiation hydro simulations of accreting planets in protoplanetary disks

This paper is part of a series investigating the observational appearance of planets accreting from their nascent protoplanetary disk (PPD). We evaluate the differences between gas temperature distributions determined in our radiation hydrodynamical (RHD) simulations and those recalculated via post-processing with a Monte Carlo (MC) radiative transport (RT) scheme. Our MCRT simulations were performed for global PPD models, each composed of a local 3D high-resolution RHD model embedded in an axisymmetric global disk simulation. We report the level of agreement between the two approaches and point out several caveats that prevent a perfect match between the temperature distributions with our respective methods of choice. Overall, the level of agreement is high, with a typical discrepancy between the RHD and MCRT temperatures of the high-resolution region of only about 10 percent. The largest differences were found close to the disk photosphere, at the transition layer between optically dense and thin regions, as well as in the far-out regions of the PPD, occasionally exceeding values of 40 percent. We identify several reasons for these discrepancies, which are mostly related to general features of typical radiative transfer solvers used in hydrodynamical simulations (angle- and frequency-averaging and ignored scattering) and MCRT methods (ignored internal energy advection and compression and expansion work). This provides a clear pathway to reduce systematic temperature inaccuracies in future works. Based on MCRT simulations, we finally determined the expected error in flux estimates, both for the entire PPD and for planets accreting gas from their ambient disk, independently of the amount of gas piling up in the Hill sphere and the used model resolution.

FMCW-based contactless heart rate monitoring

Heart disease is a significant global health issue. Traditional methods for heart rate monitoring typically require close physical contact, which limits the continuity and convenience of monitoring. To achieve real-time, non-contact heartbeat monitoring, researchers have introduced millimeter-wave radar technology. The technology’s penetration and privacy offer a potential solution for heart condition monitoring. Therefore, this study utilized frequency-modulated continuous wave (FMCW) radar for heart rate monitoring. Firstly, the collected millimeter-wave radar signals were preprocessed to accurately locate the area of cardiac activity in the human body. Secondly, an adaptive variational mode decomposition (A-VMD) algorithm was designed to extract the heartbeat signal, considering signal variations caused by random body movements and respiration and their harmonics, to obtain an accurate heartbeat signal. Finally, the accurate heart rate is obtained by weighted estimation based on the harmonic relationship of the heartbeat signal. The study invited ten subjects to participate in the experiment to verify the effectiveness of this method. The results show that this method can reduce the influence of and random body movements and respiration and harmonics on heart rate monitoring, the average absolute error of heart rate estimation is less than four bpm.

Multigas Identification by Temperature-Modulated Operation of a Single Anodic Aluminum Oxide Gas Sensor Platform and Deep Learning Algorithm.

Semiconductor metal oxide (SMO) gas sensors are gaining prominence owing to their high sensitivity, rapid response, and cost-effectiveness. These sensors detect changes in resistance resulting from oxidation-reduction reactions with target gases, responding to a variety of gases simultaneously. However, their inherent limitations lie in selectivity. Despite attempts to address this through new sensing materials and filters, achieving perfect selectivity remains challenging. This study addresses the selectivity issue by implementing temperature-modulated operation of a single SMO gas sensor utilizing an anodic aluminum oxide (AAO) microheater platform. The AAO-based sensor ensures a high thermal and mechanical stability during prolonged temperature modulation. A staircase waveform featuring six temperature conditions was applied to the microheater platform, and gas response data were collected for acetone, ammonia, ethanol, and nitrogen dioxide. Leveraging a convolutional neural network (CNN), gas patterns were trained and used to predict gas types and concentrations. The results demonstrated a high classification accuracy of 97.0%, with mean absolute percentage errors (MAPE) for concentration estimation of acetone, ammonia, ethanol, and nitrogen dioxide at 13.7, 19.2, 19.8, and 19.4%, respectively. The proposed method effectively classified four spices and accurately distinguished similar odors, which are difficult for human olfaction to differentiate. The results highlight that the combination of temperature modulation and deep learning algorithms proves to be highly effective in precisely determining gas types and concentrations.

V̇O2peak estimation in people with overweight and obesity before and after a 14-week lifestyle intervention.

This study aimed to investigate the validity and applicability of a non-exercise estimation of cardiorespiratory fitness using resting seismocardiography (SCG eV̇O2peak) in people with overweight and obesity before and after a 14-week lifestyle intervention. The study was carried out at a Folk high school that offers 14-week courses on lifestyle changes where participants live at the school and voluntarily participate in daily lectures and activities. Sixty-seven men and women with age and body mass index between 18 and 70 years and 25-50 kg·m-2 were tested at baseline, and 52 had a follow-up test after 14 weeks. Testing included the determination of anthropometric variables, an SCG eV̇O2peak at supine rest, and a gold standard V̇O2peak test on a cycle ergometer until voluntary exhaustion. Agreement analysis for V̇O2peak at baseline (n = 67, SCG eV̇O2peak: 26.9 ± 1.9 ml·min-1·kg-1, V̇O2peak: 26.6 ± 1.6 ml·min-1·kg-1, mean ± 95% confidence interval) showed a bias of 0.3 ± 1.0 ml·min-1·kg-1 with 95% limits of agreement (LoA) ranging ± 9.8 ml·min-1·kg-1. A Pearson's correlation of r = 0.78 (p < 0.0001) and a standard error of estimate (SEE) of 5.0 ml·min-1·kg-1 were found between methods. At follow-up (n = 52), body mass was reduced by 6.6 ± 1.4 kg (p < 0.0001). V̇O2peak increased by 3.3 ± 0.9 ml·min-1·kg-1 and 175 ± 78 ml·min-1 and SCG eV̇O2peak by 2.6 ± 0.8 ml·min-1·kg-1 and 93 ± 76 ml·min-1 (two-way ANOVA repeated measure: intervention p < 0.0001, method p = 0.939 and interaction p = 0.125, relative V̇O2peak). A Pearson's correlation of r = 0.37 (p < 0.05) was found between changes in relative V̇O2peak but not for absolute V̇O2peak r = 0.10 (p = 0.402). The SCG method is accurate for estimating V̇O2peak and appropriate for detecting group changes in both relative and absolute V̇O2peak following a lifestyle intervention in people with overweight and obesity. Furthermore, the method can detect individual changes in V̇O2peak but not independently of body mass changes. Yet, the applicability is still limited by the relatively large variation in LoA and SEE.

Sensorless Junction Temperature Estimation of Onboard SiC MOSFETs Using Dual-Gate-Bias-Triggered Third-Quadrant Characteristics.

Silicon carbide (SiC) metal oxide semiconductor field-effect transistors (MOSFETs) are a future trend in traction inverters in electric vehicles (EVs), and their thermal safety is crucial. Temperature-sensitive electrical parameters' (TSEPs) indirect detection normally requires additional circuits, which can interfere with the system and increase costs, thereby limiting applications. Therefore, there is still a lack of cost-effective and sensorless thermal monitoring techniques. This paper proposes a high-efficiency datasheet-driven method for sensorless estimation utilizing the third-quadrant characteristics of MOSFETs. Without changing the existing hardware, the closure degree of MOS channels is controlled through a dual-gate bias (DGB) strategy to achieve reverse conduction in different patterns with body diodes. This method introduces a MOSFET operating current that TSEPs are equally sensitive to into the two-argument function, improving the complexity and accuracy. A two-stage current pulse is used to decouple the motor effect in various conduction modes, and the TSEP-combined temperature function is built dynamically by substituting the currents. Then, the junction temperature is estimated by the measured bus voltage and current. Its effectiveness was verified through spice model simulation and a test bench with a three-phase inverter. The average relative estimation error of the proposed method is below 7.2% in centigrade.

Asynchronous l 2-gain estimator design of discrete-time cyclic switched systems under a cyclic switching strategy

In this paper, the l 2 -gain estimator design problem is investigated for discrete-time cyclic switched systems under asynchronous switching. Based on interval segmentation technique, an asynchronous convex Lyapunov function (CLF) is put forth following estimator mode switching. Slow/fast cycle-dependent average dwell time (CDADT) switching is designed to derive the sufficient condition for ensuring the stability of estimation error system with unstable and stable error subsystems. On the basis of the stability and l 2 -gain analysis, the asynchronous estimator is deduced. Finally, two numerical examples and a boost converter circuit are used to manifest the effectiveness and benefits of the proposed theoretical results.

Clamping Force Estimation for Electro-Mechanical Brake Based on Friction Torque Fusion Approach

Electromechanical brakes (EMB) are anticipated to become the standard brake system in the future, gaining favor among researchers and automobile manufacturers due to their numerous advantages. However, to achieve cost competitiveness, expensive load cells used to measure the clamping force on the disc must be replaced with a clamping force estimation algorithm. To do this, an algorithm is first developed to estimate the pad contact point, which represents the point of contact between the pad and the disc, to determine where the clamping force occurs. Subsequently, this paper proposes a novel Kalman filter approach utilizing friction torque fusion for clamping force estimation. Specifically, the estimation performance is enhanced by incorporating both dynamic and static friction torque models. The proposed estimation algorithm is validated by comparing its results with the actual clamping force measured using a load cell sensor. Furthermore, experimental tests are conducted to confirm whether the proposed estimation algorithm maintains its performance under various control reference conditions, and overall, the estimation error was within about 5% in this paper.

State estimation of two-time-scale Markov jump complex networks subject to general probabilities information and sensor nonlinearities

This paper investigates the passive state estimation issue for two-time-scale Markov jump complex networks subject to sensors nonlinearities, in which the Markov chain with the general probability information is considered. The singular perturbation parameter in Markov jump complex networks characterises the system's two-time-scale behaviour. A state estimator is devised that utilises nonlinear measurement output to estimate the states of two-time-scale Markov jump complex networks with general probability information. The estimation error system is guaranteed to be stochastic stable under the passive performance criterion by establishing sufficient conditions using Lyapunov stability theory. For the purpose of reducing conservatism in the process of obtaining estimator gains, an improved decoupling approach is presented. Finally, the superiority of the proposed decoupling approach and the efficiency of the designed estimator are showcased through simulations.

Deep learning-based 6-DoF visual relocalization assisted simultaneous localization and mapping (SLAM)

Purpose Visual simultaneous localization and mapping (SLAM) methods suffer from accumulated errors, especially in challenging environments without loop closure. By constructing lightweight offline maps and using deep learning (DL)-based technology in the two stages, i.e. image retrieval and feature matching, the goal is to reconstruct the six-degree-of-freedom (6-DoF) relationship between SLAM sequences and map sequences. This study aims to propose a comprehensive coarse-to-fine 6-DoF long-term visual relocalization assisted SLAM method specifically designed for challenging environments, aiming to achieve more accurate pose estimation. Design/methodology/approach First, image global feature matching and patch-level global feature matching are conducted to achieve optimal frame-to-frame matching. Second, a DL network is introduced to extract and match features between the most similar frames, enabling point-to-point motion estimation. Finally, a fast pose graph optimization method is proposed to achieve real-time optimization of the pose in the SLAM sequence. Findings The proposed method has been successfully validated on the real-world FinnForest Dataset and UZH-FPV Drone Racing Dataset. The accuracy of the proposed method is evaluated using absolute positional error and absolute rotational error. Experimental results show that in most cases, there are significant improvements in the root mean square error and the standard deviation of the error in pose estimation, and it performs better than loop closure in terms of accuracy. This indicates that the method has strong generalizability and robustness. Originality/value The main contribution of this study is the proposal of a complete DL-based coarse-to-fine 6-DoF long-term visual relocalization method to assist vSLAM, which demonstrates enhanced robustness and generalizability and can eliminate cumulative errors in pose estimation under challenging environments.

A Purely Real-Valued Fast Estimator of Dynamic Harmonics for Application in Embedded Monitoring Devices in Power-Electronic Grids

Dynamic harmonic estimation is important for the monitoring and control of power-electronic grids. But the high-precision dynamic harmonic estimation algorithms usually have a heavy computational burden and occupy a large memory space, making them difficult to implement in the embedded platform. Thus, the motivation of this paper lies in providing an estimator with low computational complexity and less storage space consumption. A purely real-valued fast dynamic harmonics estimator is proposed. Firstly, a purely real-valued estimation model is established based on the Taylor series expansion on the time-varying amplitude and phase angle. Secondly, the estimation filter bank is computed in the least-squares sense, and the corresponding estimation error is theoretically analyzed. Finally, the purely real-valued fast dynamic harmonics estimator is designed. The advantage includes significantly reducing the computational complexity and memory space consumption while maintaining high-precision estimation. The testing results show that the proposed estimator can achieve the highest harmonics estimation precision under dynamic conditions. The frequency error, magnitude error, and phase angle error are less than 5 × 10−2 Hz, 7 × 10−1%, and 8 × 10−2 degrees, respectively, which verifies the advantage of high-precision estimation. The proposed estimator achieves a computational speed-up of approximately 430, 396, and 330 times compared to the Prony method, ESPRIT method, and iterative Taylor Fourier transform method, respectively. The computational load rate for executing the proposed estimator on the embedded prototype using C6748 DSP for estimating 50 harmonics is approximately only 2.05%, which verifies the advantage of a low computational load rate.