Model Error Estimation Research Articles

An Argument-Principle Based Stability Assessment Method for Grey-Box DFIG Systems

Considerable efforts have been made to analyze the small-signal stability of doubly fed induction generator (DFIG) systems. However, commercial confidentiality and frequency coupling make the DFIG system a grey-box multiple-input-multiple-output (MIMO) system with highly challenging stability analysis. This paper proposes an Argument-principle based stability assessment method to analyze the stability of the grey-box DFIG system. The frequency sweeping technique is first used to acquire the MIMO model of the black-box device, as well as the determinant of the system's return difference matrix. Then a stability criterion based on the determinant trajectory is presented. This criterion applies to the stability analysis of grey-box MIMO systems without detailed system models. Further, a critical-pole estimation method with trajectory information is developed to assess the dominant mode of the target system. The simulation and hardware-in-loop experiment results demonstrate the effectiveness of the proposed method. Finally, some concerns about this method, such as model selection, estimation errors and application potential, are thoroughly analyzed and clarified.

Modeling Northern Highbush Blueberry Cold Hardiness for the Pacific Northwest

Freezing temperatures in fall, winter, and spring can cause damage to multiple perennial fruit crops including northern highbush blueberry (Vaccinium corymbosum). Predictive modeling for lethal temperatures allows producers to make informed decisions about freeze mitigation practices but is lacking for northern highbush blueberry grown in the Pacific Northwest. If buds are hardier than air temperatures, unnecessary use of propane heaters and/or wind machines is costly. In contrast, use of heaters and/or wind machines during freezing, damaging temperatures can minimize crop damage and potential yield loss. The objective of this study was to model cold hardiness across multiple cultivars of northern highbush blueberry grown in various regions in Washington, USA, and to generate predictive cold hardiness models that producers in the Pacific Northwest could use to inform freeze mitigation. Multiple years of experimental cold hardiness data were collected on four cultivars of northern highbush blueberry grown in western and eastern Washington, USA. Freeze chambers were used to reduce bud temperatures systematically, after which buds were dissected and bud survival was assessed. A generalized linear mixed model with a binomial response and logit link was fit to each cultivar to characterize the relationship between bud survival, freezer temperature, recent air temperatures, and growing degree days from fall acclimation to late winter/spring deacclimation. Model simulation was performed to obtain marginal-scale lethal temperature estimates. Model error estimation was performed using cross validation. Results show cultivar-specific cold hardiness models can be generated, and model development and use can help growers make more informed decisions regarding freeze protection that also minimizes costly applications of freeze protection when unnecessary. Furthermore, such models can be adapted to other blueberry growing regions and cultivars experiencing similar climactic conditions.

Effect of feeding history on metabolic rate of largemouth bass (Micropterus nigricans): implications for bioenergetics models

Metabolic rate is a key parameter in fish energy budgets that strongly influences the output of bioenergetics models. In this study, we tested the hypothesis that metabolic rate varies with growth history of age-1 largemouth bass Micropterus nigricans Cuvier, 1828. Two groups of fish were fed alternating maintenance or ad libitum rations of fathead minnow Pimephales promelas Rafinesque, 1820, so that over a 9-week period, initial and ending size of fish was similar. After 9 weeks, oxygen consumption was measured using static, closed respirometry. Although final body weight was similar between the two groups (means, 104–108 g), specific oxygen consumption for fish fed maintenance rations (0.094 mg O2 g−2 h−1) was 38% less than that measured for fish fed ad libitum (0.152 mg O2 g−2 h−1). Bioenergetics estimates of food consumption were similar to observed values for fish fed ad libitum (∼7% error), but for fish fed maintenance rations, the model overestimated food consumption by 65%. By accounting for changes in metabolic rate owing to reduced feeding, error in model estimates of food consumption was reduced. These findings shed new insight into factors associated with consumption-dependent error in bioenergetics models and highlight the importance of feeding history on metabolic rate of fish. Incorporating growth-dependent metabolism into bioenergetics models can improve model accuracy and allow fisheries biologists to make more informed decisions regarding fish growth and energetics.

Model Error (or Ambiguity) and Its Estimation, with Particular Application to Loss Reserving

This paper is concerned with the estimation of forecast error, particularly in relation to insurance loss reserving. Forecast error is generally regarded as consisting of three components, namely parameter, process and model errors. The first two of these components, and their estimation, are well understood, but less so model error. Model error itself is considered in two parts: one part that is capable of estimation from past data (internal model error), and another part that is not (external model error). Attention is focused here on internal model error. Estimation of this error component is approached by means of Bayesian model averaging, using the Bayesian interpretation of the LASSO. This is used to generate a set of admissible models, each with its prior probability and likelihood of observed data. A posterior on the model set, conditional on the data, may then be calculated. An estimate of model error (for a loss reserve estimate) is obtained as the variance of the loss reserve according to this posterior. The population of models entering materially into the support of the posterior may turn out to be “thinner” than desired, and bootstrapping of the LASSO is used to increase this population. This also provides the bonus of an estimate of parameter error. It turns out that the estimates of parameter and model errors are entangled, and dissociation of them is at least difficult, and possibly not even meaningful. These matters are discussed. The majority of the discussion applies to forecasting generally, but numerical illustration of the concepts is given in relation to insurance data and the problem of insurance loss reserving.

Online verification and management scheme of gateway meter flow in the power system by machine learning

Currently, the calibration of electric energy meters often involves manual meter reading, dismantling inspection, or regular sampling inspection conducted by professionals. To improve work efficiency and verification accuracy, this research integrates machine learning into the scheme of online verification and management of gateway meter flow in the power system. The approach begins by applying the Faster Region Convolutional Neural Network (Faster-RCNN) model and the Single Shot MultiBox Detector (SSD) model to the recognition system for dial readings. Then, the collected measurement data is pre-processed, excluding data collected under light load conditions. Next, an estimation error model and a solution equation for the electricity meter are established based on the pre-processed data. The operation error of the electricity meter is estimated, and the estimation accuracy is verified using the limited memory recursive least squares algorithm (LMRLSA). Furthermore, business assistant decision-making is carried out by combining the remote verification results with the estimation outcomes. The proposed dial reading recognition system is tested using 528 images of meter readings, achieving an accuracy of 98.49%. In addition, the influence of various parameters on the error results of the electricity meter is also explored. The results demonstrate that a memory length ranging from 600 to 1,200 and a line loss error of less than 5% yield the most suitable accuracy for estimating the electricity meter error. Meanwhile, it is advisable to remove measurement data collected under light load to avoid unnecessary checks. The experiments manifest that the proposed algorithm can properly eliminate the influence of old measurement data on the error parameter estimation, thereby enhancing the accuracy of the estimation. The adjustment of the memory length ensures real-time performance in estimating meter errors and enables online monitoring. This research has certain reference significance for achieving the online verification and management of gateway meter flow in the power system.

Relation-balanced graph convolutional network for 3D human pose estimation

Graph convolutional networks (GCNs) have been applied to 2D-to-3D human pose estimation (HPE) and have shown encouraging performance. However, existing GCNs model the relations between joints via individual kernels, which can be overly flexible and fail to capture common relational patterns due to the symmetric nature of the human body. Although some GCNs share kernels to capture common relations, the unified way for all neighbors limits relational diversity to some extent. In order to balance the diversity and commonality of relations, we conduct a comprehensive study of existing kernel-sharing strategies and propose a Relation-balanced Graph Convolutional Network (RbGC-Net). RbGC-Net introduces the Part-Specific Kernel-Sharing strategy (PSKS) that assigns kernels based on the semantic meanings of neighbors to establish specific relational patterns for different types of neighborhoods. Furthermore, RbGC-Net incorporates a Local–Global Feature Fusion module (LGFF) that extracts the local relations among joints and balances them with the final global relations to improve the interactions between joints. Compared with state-of-the-art methods for 3D HPE, our RbGC-Net achieves the optimal balance between model size and estimation errors. Results on two benchmark Human3.6 M and MPI-INF-3DHP datasets demonstrate the excellent performance and strong generalization ability of our pure GCN-based method.

Analysis of the Distance between the Measured and Assumed Location of a Point Source of Pollution in Groundwater as a Function of the Variance of the Estimation Error

The localization of pollution sources is one of the main tasks in environmental engineering. For this paper, models of spatial distribution of nitrate concentration in groundwater were created, and the point of highest concentration was determined. This point represents the assumed location of the pollution source and differs from the actual location, so there is a certain distance between the measured and assumed location. This paper puts forward a new hypothesis that the distance between the measured and the assumed location is a function of the variance of the estimation error. The scientific contribution of this paper is based on the fact that the interaction of statistical and geostatistical methods can locate the dominant point source of pollution or narrow down the search area. The above hypothesis is confirmed by the example of the Varaždin wellfield, which was closed due to an excessively high groundwater nitrate concentration. Seven different interpolation methods were used to create spatial distribution models. Each method provides a different model, a different variance of the estimation error, and estimates of the location of the pollution source. The smallest value of variance of the estimation error of 1.65 was obtained for the minimum curvature interpolation method and the largest value of variance (24.49) was obtained for the kriging with logarithmic variogram. Our results show a nonlinear and monotonic relationship between the distance and the variance of the estimation error, so logarithmic and rational quadratic models were fitted to the scatter point data. The models were linearized, a t-test was performed, and the results show that the models can be considered reliable, which is confirmed by the values of the coefficients of determination of the linearized models, which are around 0.50. The obtained results can be used in planning additional research work to determine the measured location of the pollution source. The research methodology we used is universal and can be applied to other locations where high concentrations of certain contaminants have been detected in groundwater in alluvial aquifers.

Local minima and factor rotations in exploratory factor analysis.

In exploratory factor analysis, factor rotation algorithms can converge to local solutions (i.e., local minima) when they are initiated from different starting points. To better understand this problem, we performed three studies that investigated the prevalence and correlates of local solutions with five factor rotation algorithms: varimax, oblimin, entropy, and geomin (orthogonal and oblique). In total, we simulated 16,000 data sets and performed more than 57 million factor rotations to examine the influence of (a) factor loading size, (b) number of factor indicators, (c) factor cross loadings, (d) factor correlation size, (e) factor loading standardization, (f) sample size, and (g) model approximation error on the frequency of local solutions in factor rotation. We also examined local solutions in an exploratory factor analysis of an open source data set that included 54 personality items. Across three studies, all five algorithms converged to local solutions under some conditions with geomin (orthogonal and oblique) producing the highest number of local solutions. Follow-up analyses showed that, when factor rotations produced multiple solutions, the factor pattern with the maximum hyperplane count (rather than the lowest complexity value) was typically closest in mean squared error to the population factor pattern. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Eco-driving strategy for connected vehicles at signalized intersections considering human driver error

In recent years, eco-driving strategies based on connected vehicle (CV) technologies have been studied to assist human drivers to reduce fuel consumption and pollutant emissions. In this paper, a real-time eco-driving strategy for CVs that considers human driver error is proposed to improve both traffic and fuel efficiency at signalized intersections where CVs and human-driven vehicles (HDVs) coexist. Firstly, a human driver error estimation model is established using real-world driving data. Then, based on the signal phase and timing information, vehicle state information, and the estimated human driver errors, a constrained nonlinear optimal control problem (OCP) is proposed to calculate the optimal advisory speed of each CV. The trajectory of HDV is estimated by utilizing the Gipps’ car-following model. Fast stochastic model predictive control (SMPC) is employed to solve the proposed OCP effectively. At last, simulation studies and real-vehicle experiments are conducted in various scenarios to verify the performance of the proposed strategy. Simulation and experiment results indicate that compared with the baseline strategies, the proposed eco-driving strategy can significantly reduce travel time and fuel consumption while ensuring the real-time performance.

Careful feedback active noise and vibration control algorithm robust to large secondary path changes

Feedback active noise and vibration control (ANVC) reduces narrowband noise and vibration when there is no reference signal. However, most ANVC algorithms require a secondary path (plant) model and can become unstable when this model is inaccurate. This work proposes an algorithm less sensitive to plant modeling errors and can cope with large and fast plant changes. This is achieved by modeling the plant and the noise using an autoregressive exogenous input (ARX) model. This model allows obtaining the expected values of the future residual noise and determining the optimal control signal. Since the model coefficient uncertainty (variance) is considered when calculating the expected value, the resulting controller is a careful controller. This allows solving problems due to significant model estimation errors when information is insufficient to estimate the full ARX model.

The effect of back muscle fatigue on EMG and kinematics based estimation of low-back loads and active moments during manual lifting tasks

This study investigated the effects of back muscle fatigue on the estimation of low-back loads and active low-back moments during lifting, using an EMG and kinematics based model calibrated with data from an unfatigued state. Fourteen participants performed lifting tasks in unfatigued and fatigued states. Fatigue was induced through semi-static forward bending. EMG, kinematics, and ground reaction forces were measured, and low-back loads were estimated using inverse dynamics and EMG-driven muscle model. A regression model was developed using data from a set of calibration lifts, and its accuracy was evaluated for unfatigued and fatigued lifts. During the fatigue-inducing task, the EMG amplitude increased by 2.8 %MVC, representing a 38% increase relative to the initial value. However, during the fatigued lifts, the peak EMG amplitude was found to be 1.6 %MVC higher than that observed during the unfatigued lifts, representing a mere 4% increase relative to the baseline unfatigued peak EMG amplitude. Kinematics and low-back load estimates remained unaffected. Regression model estimation errors remained unaffected for 5 kg lifts, but increased by no more than 5% of the peak active low-back moment for 15 kg lifts. We conclude that the regression-based estimation quality of active low-back moments can be maintained during periods of muscle fatigue, although errors may slightly increase for heavier loads.

System error estimation for sensor network with integrated sensing and communication application

The integrated sensing and communication base station can perform both communication and sensing tasks simultaneously. The key premise of accurate localization and tracking of targets is sensor registration process. If system errors of multi-sensor system are not corrected properly, it can lead to significant sensing errors and reduce the overall system performance. In this paper, we propose a system error estimation method for a 3-dimensional asynchronous base station network to achieve satisfactory localization and tracking performance. Specifically, by making use of the priori information of the cooperative targets, we propose to minimize the sum of squares of the position mismatch errors of the targets, this leads to a nonlinear error estimation model for the system errors. To handle the high nonlinearity and ensure fast convergence of online registration, an efficient inexact block coordinate descent optimization algorithm incorporating the proximal method is proposed. The algorithm divides the estimation problem into a linear least square subproblem and several quadratic constrained quadratic program subproblems, enabling different kinds of system errors to be alternatively updated. Finally, the effectiveness and feasibility of the proposed estimation algorithm are demonstrated through numerical simulation.

Predicting rice phenology and optimal sowing dates in temperate regions using machine learning

AbstractCrop phenology modeling often involves determining variety‐specific growing degree day thresholds, or parameterizing mechanistic crop models. In this work, we used machine learning methods to develop models that provide daily predictions of the probability that rice (Oryza sativa) crops had reached the panicle initiation and flowering growth stages. These per‐date classifications were summarized into per‐paddock growth stage transition dates, which were then compared with field‐sampled reference data, encompassing 15 rice varieties, 10 years, and 380 sites. Leave‐one‐year‐out cross validation was used to provide realistic estimates of model errors. Compared with more complex and computationally intensive algorithms, logistic regression produced competitive results (mean cross‐season validation RMSE 3.9 and 5.2 days for panicle initiation and flowering, respectively). Logistic regression had additional advantages: providing confidence of growth stage predictions at each date (as it is a probabilistic algorithm), and straightforward explainability (as model parameters directly indicated how the various input variables contributed to growth stage predictions). Input variables included accumulated weather, rice variety, and sowing methods. The models were applied to forecasting phenology transition dates of the rice crops planted throughout the Murray and Murrumbidgee valleys. In addition, recommendations for optimal sowing dates were developed, using simulations involving more than 40 years of weather data, with the goal of minimizing the risk of cold‐temperatures during the microspore growth phase, which can severely degrade yield in temperate rice growing regions.

Isolated UIPC-intertied AC-DC microgrids operation with novel dynamic flexible loads model

This work discusses the islanding operation of hybrid microgrids (HµGs) interconnected by a unified interphase power controller (UIPC). It is illustrated that considering islanding operation of HµG, the problem of power dispatch among intertied AC and DC µGs, as well as voltage and frequency control, can be handled by UIPC. The power dispatch among various AC and DC µGs using UIPC is analyzed and formulated through defining a simple demand condition function (DCF). A new control structure is proposed for the series power converter of the UIPC which enjoys the robustness of sliding mode control (SMC) concept and adopts a new energy management system (EMS) with embedded decision-making. This provides a fast, robust, and simple power flow control unit which acts as a general supervisory system for the UIPC and islanding operation of HµG. Also, a novel generalized dynamic model for flexible loads has been proposed. These loads have been directly controlled through UIPC supervisory system here. The analytical response of the proposed flexible load model is discussed and further developed using the nonlinear autoregressive-moving-average artificial neural network (ANN) model. The Levenberg-Marquardt learning algorithm is used to train the network based on the model estimation error. Besides, a new model predictive-based optimal control scheme is proposed for the flexible load. Simulation results using MATLAB support the usefulness of the proposed UIPC-based islanded HµG platform.

Aemulus ν: precise predictions for matter and biased tracer power spectra in the presence of neutrinos

We present the Aemulus ν simulations: a suite of 150 (1.05 h-1 Gpc)3 N-body simulations with a mass resolution of 3.51 × 1010 Ω cb /0.3 h-1 M ⊙ in a wνCDM cosmological parameter space. The simulations have been explicitly designed to span a broad range in σ 8 to facilitate investigations of tension between large scale structure and cosmic microwave background cosmological probes. Neutrinos are treated as a second particle species to ensure accuracy to 0.5 eV, the maximum neutrino mass that we have simulated. By employing Zel'dovich control variates, we increase the effective volume of our simulations by factors of 10-105 depending on the statistic in question. As a first application of these simulations, we build new hybrid effective field theory and matter power spectrum surrogate models, demonstrating that they achieve ≤ 1% accuracy for k ≤ 1 hMpc-1 and 0 ≤ z ≤ 3, and ≤ 2% accuracy for k ≤ 4 hMpc-1 for the matter power spectrum. We publicly release the trained surrogate models, and estimates of the surrogate model errors in the hope that they will be broadly applicable to a range of cosmological analyses for many years to come.

Application of power electronic circuit design based on BP artificial neural network in environmental monitoring

Abstract Our natural resources have played an important role in building our economy. In addition to the number one energy resource, oil, electric energy is also the best quality and much in demand by society in the history of human civilization. It is because electrical energy can be fully developed and applied by society that the construction of human spiritual civilization has entered industrialization and high-tech information technology. The transmission and application of electric energy has been recognized by society, but how can we make it more reasonable and efficient for easy utilization. In order to solve the problem of how complex power electronic circuit devices can be more widely used with improved technology. This paper establishes the error estimation model of intelligent circuit from data collection and data prediction preprocessing, then proposes to optimize the number of nodes in the hidden layer by using particle swarm optimization algorithm to address the limitations of the traditional BP neural network, and uses the optimized number of nodes in the hidden layer to build a BP neural network structure to train the training sample data, and calculates the error data of the intelligent circuit based on the trained BP neural network to test the sample data. The method established in this paper is used to perform the evaluation of intelligent circuit operation errors. The simulation example shows that the established model can effectively evaluate the smart circuit operation error, and the evaluation accuracy is significantly improved compared with the traditional evaluation method.

Dynamic Event-Based Distributed Piecewise Filtering Over Sensor Networks Under Periodic DoS Attacks

In this article, the problem of distributed filtering for a class of linear discrete-time systems under periodic denial-of-service (DoS) attack is investigated. First, a cyclical and repeatable variable is introduced to characterize the behavior of a periodic DoS attack. Based on the introduced variable, a novel dynamic event-triggered scheme is designed to reduce the network transmission burden. Second, a cyclic switching estimation error model, which includes stable subsystems in the absence of a DoS attack and unstable subsystems in the presence of a DoS attack, is constructed. The global exponential stability with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> performance is synthesized by utilizing multiple piecewise Lyapunov functions, and the gain matrix of the distributed filtering is derived in piecewise form. Third, the codesign algorithm of the dynamic event-triggered scheme and distributed piecewise filtering is proposed to make a tradeoff between network transmission burden and the performance of distributed filtering. Finally, an example is applied to verify the effectiveness of the proposed distributed piecewise filtering.

Calibration methodology of low-cost sensors for high-quality monitoring of fine particulate matter

Low concentrations of pollutants may already be associated with significant health effects. An accurate assessment of individual exposure to pollutants therefore requires measuring pollutant concentrations at the finest possible spatial and temporal scales. Low-cost sensors (LCS) of particulate matter (PM) meet this need so well that their use is constantly growing worldwide. However, everyone agrees that LCS must be calibrated before use. Several calibration studies have already been published, but there is not yet a standardized and well-established methodology for PM sensors. In this work, we develop a method combining an adaptation of an approach developed for gas-phase pollutants with a dust event preprocessing to calibrate PM LCS (PMS7003) commonly used in urban environments. From the selection of outliers to model tuning and error estimation, the developed protocol allows to analyze, process and calibrate LCS data using multilinear (MLR) and random forest (RFR) regressions for comparison with a reference instrument. We demonstrate that the calibration performance was very good for PM1 and PM2.5 but turns out less good for PM10 (R2 = 0.94, RMSE = 0.55 μg/m3, NRMSE = 12 % for PM1 with MLR, R2 = 0.92, RMSE = 0.70 μg/m3, NRMSE = 12 % for PM2.5 with RFR and R2 = 0.54, RMSE = 2.98 μg/m3, NRMSE = 27 % for PM10 with RFR). Dust events removal significantly improved LCS accuracy for PM2.5 (11 % increase of R2 and 49 % decrease of RMSE) but no significant changes for PM1. Best calibration models included internal relative humidity and temperature for PM2.5 and only internal relative humidity for PM1. It turns out that PM10 cannot be properly measured and calibrated because of technical limitations of the PMS7003 sensor. This work therefore provides guidelines for PM LCS calibration. This represents a first step toward standardizing calibration protocols and facilitating collaborative research.

Secure state estimation for complex networks with multi-channel oriented round robin protocol

This paper focuses on the secure state estimation problem for complex networks (CNs) which are compromised by deception attack and constrained with limited communication resource. Firstly, a multi-channel oriented round robin (RR) protocol is proposed to schedule the data transferred over the communication network consisting of multiple transmission channels. The extended RR protocol can not only avoid the data collision caused by limited communication resource, but also fully utilize the sliced network bandwidth. Then, a state estimation error model is constructed by further considering the influence of deception attack. Following the model, efficient state estimators are designed based on analyzing the sufficient conditions that assuring the stability of the formulated state estimation error system. Finally, numerical results are presented to validate the theoretical outcomes.

A spectral method for the depth-separated solution of a wavenumber integration model for horizontally stratified fluid acoustic waveguides

The wavenumber integration model is the most precise approach for assessing arbitrary horizontally stratified media within the sphere of computational ocean acoustics. Unlike the normal-mode approach, it considers not only discrete spectra but also continuous spectral components, resulting in fewer model approximation errors for horizontally stratified media. Traditionally, the depth-separated wave equation in the wavenumber integration model has been solved using analytical and semianalytical methods, and numerical solutions have been primarily based on the finite difference and finite element methods. This paper proposes an algorithm for solving the depth equation via the Chebyshev–Tau spectral method, combined with a domain decomposition strategy, resulting in the development of a numerical program named WISpec. The algorithm can simulate the sound field excitation not only from a point source but also from an infinite line source. To that end, the depth equations for each layer are first discretized through the Chebyshev–Tau spectral method and subsequently solved simultaneously by incorporating boundary and interface conditions. Representative numerical experiments are presented to validate the accuracy and speed of WISpec. The high degree of consistency of results obtained from different software tools running the same configuration provides ample evidence that the numerical algorithm described in this paper is accurate, reliable, and numerically stable.