Robust Estimation Approach Research Articles

Examining the effect of economic complexity on energy justice

A clean and healthy environment is necessary to achieve other Sustainable Development Goals. Policymakers must find ways to minimize carbon dioxide emissions and global warming before the Sustainable Development era expires. These ills are compounded by chronic inequalities in access to clean energy and the distribution of energy infrastructure, costs, and benefits. This underscores the importance of examining, addressing, and preventing various inequalities in the energy sector. Thus, this paper endeavours to examine probably for the first time the effect of economic complexity on energy justice in a panel of 62 countries over the past decade. The measure of energy justice in this study is a rarely used indicator constructed from a range of 21 sub indicators. We also use three sub-indices of energy justice: distributive justice, procedural justice, and restorative justice. The empirical analyses based on a range of robust estimation approaches consistently provide strong evidence that, on average, economic complexity improves energy justice and its three sub components. Finally, the results from the mediation analysis show that the positive effect of economic complexity on energy justice mediates through income, and human capital. Based on these results, some policy recommendations are made for the achievement of Sustainable Development Goals 7, 8, and 16.

Sparse Trend Estimation

Abstract The low-frequency movements of economic variables play a prominent role in policy analysis and decision-making. We develop a robust estimation approach for these slow-moving trend processes which is guided by a judicious choice of priors and is characterized by sparsity. We present novel stylized facts from longer-run survey expectations that inform the structure of the estimation procedure. The general version of the proposed Bayesian estimator with a spike-and-slab prior accounts explicitly for cyclical dynamics. We show that it performs well in simulations against relevant benchmarks and report empirical estimates of trend growth for U.S. output and annual mean temperature.

Are stock markets efficient with respect to the Google search volume index? A robustness check of the literature studies

The Efficient Market Hypothesis (EMH) is still a debated subject in the financial area. Particularly, no conclusions are drawn to date in link with the Google Search Volume Index (GSVI). To conclude on this question, our paper takes up the work of Škrinjarić (2019) by proposing robustness tests, various econometric improvements and the inclusion of additional explanatory variables. On a database of ten emerging European indices studied by Škrinjarić (2019), a dynamic panel model was applied. Unlike Škrinjarić (2019) who modeled the time-series separately and thus neglected any possible dependence or homogeneity between countries, our study operates within the framework of panel data. Drawing from a robust estimation approach, our findings indicate that the GSVI has no impact on market returns. In essence, this suggests that internet search queries fail to provide avenues for investors to seize arbitrage opportunities. Such findings support the EMH in the studied markets and underline the exposure of prior studies to robustness challenges.

A novel spatio-temporal characteristic extraction network for bearing remaining useful life prediction

Abstract Remaining useful life (RUL) is an important index indicating the health status of equipment, which has attracted extensive attention. Nevertheless, many existing RUL prediction methods encounter difficulties in effectively capturing comprehensive degradation features hidden in the data. Moreover, within real-world industrial scenarios, noisy signals are inevitably collected in the raw signals, thereby posing a big challenge to the precision of RUL predictions. To address the aforementioned problems, a robust RUL estimation approach based on degradation intrinsic mode functions (IMFs) selection and spatio-temporal feature regression is developed in this paper. The former addresses the issue of deep learning models struggling to extract degradation features of rolling bearings due to interference factors in vibration signals, while the latter resolves the problem of incomplete degradation features extracted by traditional RUL models under complex operating conditions. Firstly, complete ensemble empirical mode decomposition with adaptive noise is adopted to process the raw signals, separating components with degradation features, ineffective components, and noise. Subsequently, an IMFs selection method employing fast dynamic time warping and cosine coefficients is designed to obtain the valuable degradation features. Finally, a spatio-temporal feature extraction network is presented to comprehensively and effectively capture both spatial and temporal features within the chosen degradation IMFs, achieving the prediction of RUL with high accuracy and strong robustness. The experimental part containing two case studies has validated the effectiveness and superiority of the proposed method.

Machine learning-based estimation of patient body weight from radiation dose metrics in computed tomography.

Currently, precise patient body weight (BW) at the time of diagnostic imaging cannot always be used for radiation dose management. Various methods have been explored to address this issue, including the application of deep learning to medical imaging and BW estimation using scan parameters. This study develops and evaluates machine learning-based BW prediction models using 11 features related to radiation dose obtained from computed tomography (CT) scans. A dataset was obtained from 3996 patients who underwent positron emission tomography CT scans, and training and test sets were established. Dose metrics and descriptive data were automatically calculated from the CT images or obtained from Digital Imaging and Communications in Medicine metadata. Seven machine-learning models and three simple regression models were employed to predict BW using features such as effective diameter (ED), water equivalent diameter (WED), and mean milliampere-seconds. The mean absolute error (MAE) and correlation coefficient between the estimated BW and the actual BW obtained from each BW prediction model were calculated. Our results found that the highest accuracy was obtained using a light gradient-boosting machine model, which had an MAE of 1.99kg and a strong positive correlation between estimated and actual BW (ρ=0.972). The model demonstrated significant predictive power, with 73% of patients falling within a±5% error range. WED emerged as the most relevant dose metric for BW estimation, followed by ED and sex. The proposed machine-learning approach is superior to existing methods, with high accuracy and applicability to radiation dose management. The model's reliance on universal dose metrics that are accessible through radiation dose management software enhances its practicality. In conclusion, this study presents a robust approach for BW estimation based on CT imaging that can potentially improve radiation dose management practices in clinical settings.

Effects of unbalanced fertilizer use on system productivity and profitability under rice‐based cropping systems: Evidence from Eastern Gangetic Plain

AbstractUnbalanced nutrient supply is one of the key causes of yield gaps and low farming profitability which impact food insecurity globally. However, the effects of an unbalanced nutrient supply on rice yields and farming profitability have not been quantified for diverse rice‐based cropping systems. To explore the effects, 412 respondents (mostly smallholder farmers) were surveyed in 2019 for the rice crops in three fully rice‐based cropping systems in four agro‐ecological zones of Bangladesh. Two robust estimation approaches namely propensity score matching and endogenous switching regression approaches were used to quantify the effects of unbalanced fertilizer use. The results show that about 68% of farmers in the study areas used unbalanced (over‐doses or under‐doses) nutrient rates relative to government‐endorsed recommendations. Adoption of recommended fertilizer rates was influenced by education, amount of organic manure used, off‐farm income, crop farming‐related training, organizational membership and lack of soil testing tendency. The findings also indicate that adopters of recommended nutrient rates received almost 16% and 46% higher system rice yield and system net return, respectively, relative to non‐adopters. However, based on our research findings, formulation and implementation of effective agricultural policies like effective extension services, strengthening soil testing facilities, increasing organizational membership and targeted awareness programmes could motivate smallholder rice farmers towards adoption of recommended nutrient rates. Adoption of recommended fertilizer rates can significantly enhance crop yields and farming profitability which can boost food security and rural livelihoods in the Eastern Gangetic Plain.

Exploring AI approaches for predicting groundwater levels in coastal agro-climatic zones: a case study in Cuttack District, Odisha

Groundwater level (GWL) prediction across various time scales is essential for efficient management and governance of water resources especially in regions characterized by arid and semi-arid climates, and it holds great significance. Within certain coastal regions, agro-climatic zones give rise to challenges like water scarcity in summer and waterlogging during the rainy season, resulting in reduced GWL during scarcity periods and saltwater intrusion that contaminates groundwater. This study emphasizes on application of diverse AI methodologies, encompassing Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Wavelet Transform-based ANN (W-ANN), ANFIS (W-ANFIS), SVR (W-SVR), and LSTM (W-LSTM) models for quantitative assessment of groundwater in Odisha's Cuttack District, aiming to comprehend GWL fluctuations across the region. The investigation leverages historical groundwater data from monitoring wells, incorporating monthly datasets of rainfall, temperature, relative humidity, and GWLs. Through comparative assessment using statistical methods namely Pearson’s R (R), co-efficient of determination (R2), Root Mean Squared Error (RMSE), and Sum of Squared Error (SSE), the most precise and robust AI approach for groundwater estimation in the area is identified. The W-LSTM (R2-0.78196, RMSE- 0.09254, R-0.88428 and SSE-2.66357) and W-ANFIS (R2-0.74068, RMSE-0.08229, R-0.86063 and SSE-2.10596) hybrid algorithms consistently achieved the most accurate predictions for GWLs followed by W-SVR, W-ANN hybrid models and LSTM and ANN for all stations. Overall, this study demonstrated promising outcomes, offering a dependable foundation for water resources planners to guide future investigations into groundwater resources.

Robust multiple fault detection and estimation approaches based on adaptive finite‐time observer for nonlinear systems

AbstractThis article investigates robust multiple fault detection (FD) and fault estimation (FE) schemes, based on adaptive finite‐time observer (AFTO), applied to nonlinear systems with disturbances, actuator faults, and sensor faults. The AFTO is designed, and a parameter adaptive law is constructed to continuously optimize the residual based performance function to improve the AFTO robustness. The schemes are able to simultaneously detect and estimate unknown states, disturbances, and faults. The AFTO's design, finite‐time convergence and stability conditions are guaranteed in terms of linear matrix inequalities. Finally, simulation results, based on two typical practical systems, are given to illustrate the effectiveness of the proposed approaches. The simulation results also show that the designed AFTO can offer faster convergence speed, higher estimation accuracy, and stronger robustness than conventional observer and sliding mode observer.

Bridging Differences in Cohort Analyses of the Relationship between Secondhand Smoke Exposure during Pregnancy and Birth Weight: The Transportability Framework in the ECHO Program.

Estimates for the effects of environmental exposures on health outcomes, including secondhand smoke (SHS) exposure, often present considerable variability across studies. Knowledge of the reasons behind these differences can aid our understanding of effects in specific populations as well as inform practices of combining data from multiple studies. This study aimed to assess the presence of effect modification by measured sociodemographic characteristics on the effect of SHS exposure during pregnancy on birth weights that may drive differences observed across cohorts. We also aimed to quantify the extent to which differences in the cohort mean effects observed across cohorts in the Environmental influences on Child Health Outcomes (ECHO) consortium are due to differing distributions of these characteristics. We assessed the presence of effect modification and transportability of effect estimates across five ECHO cohorts in a total of 6,771 mother-offspring dyads. We assessed the presence of effect modification via gradient boosting of regression trees based on the H-statistic. We estimated individual cohort effects using linear models and targeted maximum likelihood estimation (TMLE). We then estimated transported effects from one cohort to each of the remaining cohorts using a robust nonparametric estimation approach relying on TMLE estimators and compared them to the original effect estimates for these cohorts. Observed effect estimates varied across the five cohorts, ranging from significantly lower birth weight associated with exposure [; 95% confidence interval (CI): , ] to higher birth weight with wide CIs, including the null (; 95% CI: , 99.8). Transported effect estimates only minimally explained differences in the point estimates for two out of the four cohort pairs. Our findings of weak to moderate evidence of effect modification and transportability indicate that unmeasured individual-level and contextual factors and sources of bias may be responsible for differences in the effect estimates observed across ECHO cohorts. https://doi.org/10.1289/EHP13961.

Efficient parameter extraction of photovoltaic models with a novel enhanced prairie dog optimization algorithm

The growing demand for solar energy conversion underscores the need for precise parameter extraction methods in photovoltaic (PV) plants. This study focuses on enhancing accuracy in PV system parameter extraction, essential for optimizing PV models under diverse environmental conditions. Utilizing primary PV models (single diode, double diode, and three diode) and PV module models, the research emphasizes the importance of accurate parameter identification. In response to the limitations of existing metaheuristic algorithms, the study introduces the enhanced prairie dog optimizer (En-PDO). This novel algorithm integrates the strengths of the prairie dog optimizer (PDO) with random learning and logarithmic spiral search mechanisms. Evaluation against the PDO, and a comprehensive comparison with eighteen recent algorithms, spanning diverse optimization techniques, highlight En-PDO’s exceptional performance across different solar cell models and CEC2020 functions. Application of En-PDO to single diode, double diode, three diode, and PV module models, using experimental datasets (R.T.C. France silicon and Photowatt-PWP201 solar cells) and CEC2020 test functions, demonstrates its consistent superiority. En-PDO achieves competitive or superior root mean square error values, showcasing its efficacy in accurately modeling the behavior of diverse solar cells and performing optimally on CEC2020 test functions. These findings position En-PDO as a robust and reliable approach for precise parameter estimation in solar cell models, emphasizing its potential and advancements compared to existing algorithms.

A new robust dynamic state estimation approach for power systems with non-Gaussian noise

The Gaussian noise distribution is typically used in dynamic state estimation (DSE) but it is not always true in practice because of abnormal system inputs, impulsive noise and measurement outliers. In this paper, a new robust DSE approach based on a new robust Lp norm based estimator and the cubature Kalman filter (CKF) is developed for power systems with non-Gaussian noise statistics. The Lp norm based estimator is derived from the Lp norm formula and the quadratic formula in order to alleviate the impacts from bad data and outliers. The proposed Lp-CKF DSE approach exhibits good accuracy because a new estimation error covariance is obtained by using the influence function. The robustness of the proposed Lp-CKF DSE approach is verified by performing simulations on a generator in the IEEE 39-bus system.

A robust estimation based on penalised regularisation for the varying-coefficient additive model

To effectively handle functional data and longitudinal data, we propose a robust estimation approach based on penalised regularisation with the framework of the varying-coefficient additive model. Our method utilises an iterative backfitting algorithm that leverages splines to estimate the component functions. The proposed algorithm showcases both stability and effectiveness in model fitting, particularly when dealing with scenarios involving high-dimensional covariates and high levels of noise. We establish a comprehensive theoretical framework for the proposed method. Furthermore, we investigate the asymptotic normality of the estimators and construct asymptotic confidence bands to quantify the uncertainty of the estimates. To validate the performance of our method, we conduct extensive simulation studies and compare the results with those obtained using alternative approaches. Additionally, we apply our proposed method to a real dataset focussed on young high school dropouts in the US, demonstrating its practical applicability.

A Curve Relocation Approach for Robust Battery Open Circuit Voltage Reconstruction and Capacity Estimation Based on Partial Charging Data

A Curve Relocation Approach for Robust Battery Open Circuit Voltage Reconstruction and Capacity Estimation Based on Partial Charging Data

Research on Robust Adaptive RTK Positioning of Low-Cost Smart Terminals.

The performance of low-cost smart terminals is limited by the performance of their low-cost Global Navigation Satellite System (GNSS) hardware and chips, as well as by the impact of complex urban environments, which affect the positioning accuracy and stability of GNSS services. To this end, this paper proposes a robust adaptive Kalman filter for different environments that can be applied after data preprocessing. Based on the Kalman filter algorithm, a robust estimation approach is introduced into real-time kinematic (RTK) positioning to make judgments on the abnormal observation values of low-cost smart terminals, which amplifies the variance and covariance of the outlier observation equation, and reduces the impact of outliers on positioning performance. The Institute of Geodesy and Geophysics III (IGG III) function is used for regulation purposes, where prior information is modified and refreshed using the equivalent weight matrix and adaptive factors, thus reducing the impact of system model errors on system state estimation results. In addition, a robust factor is defined to adjust positioning deviation weighting between the pre- and post-test robust estimates. The experimental results show that after robust RTK positioning in the static experiments, the overall improvement in positioning accuracies of the Xiaomi 8, Huawei P40, Huawei mate40, and low-cost M8 receiver reached 29.6%, 31.3%, 32.1%, and 30.7%, respectively. Similarly, after applying the proposed robust method in the dynamic experiments, the overall positioning accuracies of the Xiaomi 8, Huawei P40, Huawei mate40, and the low-cost M8 receiver improved by 28.3%, 32.9%, 35.4%, and 26.2%, respectively. The experimental results reveal that an excellent positioning effect of a smartphone is positively correlated with robust RTK positioning performance. However, it is worth noting that when the positioning accuracy reaches a high level, such as the positioning results achieved using low-cost receivers, the robustness performance shows a relatively decreasing trend. This finding suggests that under the condition of high positioning accuracy, the sensitivity of specific positioning equipment to interference sources may increase, resulting in a decline in the effect of robust RTK positioning.

Strategies for optimal sky subtraction in the low surface brightness regime

ABSTRACT The low surface brightness (LSB) regime (μg ≳ 26 mag arcsec−2) comprises a vast, mostly unexplored discovery space, from dwarf galaxies to the diffuse interstellar medium. Accessing this regime requires precisely removing instrumental signatures and light contamination, including, most critically, night sky emission. This is not trivial, as faint astrophysical and instrumental contamination can bias sky models at the precision needed to characterize LSB structures. Using idealized synthetic images, we assess how this bias impacts two common LSB-oriented sky-estimation algorithms: (1) masking and parametric modelling, and (2) stacking and smoothing dithered exposures. Undetected flux limits both methods by imposing a pedestal offset to all derived sky models. Careful, deep masking of fixed sources can mitigate this, but source density always imposes a fundamental limit. Stellar scattered light can contribute ∼28–29 mag arcsec−2 of background flux even in low-density fields; its removal is critical prior to sky estimation. For complex skies, image combining is an effective non-parametric approach, although it strongly depends on observing strategy and adds noise to images on the smoothing kernel scale. Preemptive subtraction of fixed sources may be the only practical approach for robust sky estimation. We thus tested a third algorithm, subtracting a preliminary sky-subtracted coadd from exposures to isolate sky emission. Unfortunately, initial errors in sky estimation propagate through all subsequent sky models, making the method impractical. For large-scale surveys like Legacy Survey of Space and Time, where key science goals constrain observing strategy, masking and modelling remain the optimal sky estimation approach, assuming stellar scattered light is removed first.

A Robust Gaze Estimation Approach via Exploring Relevant Electrooculogram Features and Optimal Electrodes Placements

A Robust Gaze Estimation Approach via Exploring Relevant Electrooculogram Features and Optimal Electrodes Placements

A gradient-based approach to fast and accurate head motion compensation in cone-beam CT.

Cone-beam computed tomography (CBCT) systems, with their flexibility, present a promising avenue for direct point-of-care medical imaging, particularly in critical scenarios such as acute stroke assessment. However, the integration of CBCT into clinical workflows faces challenges, primarily linked to long scan duration resulting in patient motion during scanning and leading to image quality degradation in the reconstructed volumes. This paper introduces a novel approach to CBCT motion estimation using a gradient-based optimization algorithm, which leverages generalized derivatives of the backprojection operator for cone-beam CT geometries. Building on that, a fully differentiable target function is formulated which grades the quality of the current motion estimate in reconstruction space. We drastically accelerate motion estimation yielding a 19-fold speed-up compared to existing methods. Additionally, we investigate the architecture of networks used for quality metric regression and propose predicting voxel-wise quality maps, favoring autoencoder-like architectures over contracting ones. This modification improves gradient flow, leading to more accurate motion estimation. The presented method is evaluated through realistic experiments on head anatomy. It achieves a reduction in reprojection error from an initial average of 3 mm to 0.61 mm after motion compensation and consistently demonstrates superior performance compared to existing approaches. The analytic Jacobian for the backprojection operation, which is at the core of the proposed method, is made publicly available. In summary, this paper contributes to the advancement of CBCT integration into clinical workflows by proposing a robust motion estimation approach that enhances efficiency and accuracy, addressing critical challenges in time-sensitive scenarios.

A Robust Approach Assisted by Signal Quality Assessment for Fetal Heart Rate Estimation from Doppler Ultrasound Signal.

Fetal heart rate (FHR) monitoring, typically using Doppler ultrasound (DUS) signals, is an important technique for assessing fetal health. In this work, we develop a robust DUS-based FHR estimation approach complemented by DUS signal quality assessment (SQA) based on unsupervised representation learning in response to the drawbacks of previous DUS-based FHR estimation and DUS SQA methods. We improve the existing FHR estimation algorithm based on the autocorrelation function (ACF), which is the most widely used method for estimating FHR from DUS signals. Short-time Fourier transform (STFT) serves as a signal pre-processing technique that allows the extraction of both temporal and spectral information. In addition, we utilize double ACF calculations, employing the first one to determine an appropriate window size and the second one to estimate the FHR within changing windows. This approach enhances the robustness and adaptability of the algorithm. Furthermore, we tackle the challenge of low-quality signals impacting FHR estimation by introducing a DUS SQA method based on unsupervised representation learning. We employ a variational autoencoder (VAE) to train representations of pre-processed fetal DUS data and aggregate them into a signal quality index (SQI) using a self-organizing map (SOM). By incorporating the SQI and Kalman filter (KF), we refine the estimated FHRs, minimizing errors in the estimation process. Experimental results demonstrate that our proposed approach outperforms conventional methods in terms of accuracy and robustness.

A Robust Model-Based Radius Estimation Approach for Helical Climbing Motion of Snake Robots

In this article, a radius estimation approach for snake robot's helical climbing motion is proposed, which enables a snake robot successfully implementing compliant helical climbing along pipes/trees with varying radius. More specifically, a mathematical model is first set up to describe the relationship between the helical rolling gait and the radius information. Based on the model, a robust radius estimation approach, consisting of two important stages of multistep calculation and curve fitting, is then proposed, which utilizes the measurements of joint angles as feedback to generate an accurate estimation for the radius of pipes/trees. Utilizing the estimation result, a control algorithm is then employed to make the robot climb along pipes/trees successfully, even when the radius changes over time. Some experimental results are collected and then analyzed to show the superior performance of the proposed robust model-based radius estimation approach.

Robust Observer-Based Parameter Identification and State Estimation for COVID-19 Modeling: Analyzing the Omicron Variant in Mexico using Fractional-Order SIR and SEIR Models

This work presents a robust observer-based approach for parameter identification and state estimation in fractional-order SIR and SEIR models, applied to analyze the spread of the SARS-CoV-2 Omicron variant in Mexico from December 26, 2021, to March 26, 2022. By utilizing measurements of changes in the infected population, a robust observer is proposed to reconstruct the transmission rate within the fractional model. The method accounts for noisy measurement cases and incorporates parametric reconstruction and state observation. The proposed approach is demonstrated through numerical results, showcasing its capabilities in effectively estimating parameters and states. The findings of this study have implications for formulating strategies to prevent, slow, and halt the spread of infectious diseases, offering valuable insights for combating future disease outbreaks.