Error Modeling Approach Research Articles

Development of a Damage Function Model for Pratylenchus penetrans on Corn.

The lesion nematode Pratylenchus penetrans is a common pest of corn in the north-central United States. There are relatively few studies documenting the impact of Pratylenchus spp. on grain yield even though they are recognized as pests of corn and the target of commercial seed treatments. We adapted a component error modeling approach to develop a damage function for P. penetrans that included the influence of year and site in the yield loss relationship. Field data from six site-years was used to derive panel data consisting of all pairwise comparisons of the difference in nematode population densities and the associated proportional yield difference. Fourteen regression models of the relationship between proportional yield loss and the difference in nematode density were developed from soil and root assays at different corn growth stages. Seven models were significant: four models based on nematode population densities in soil (initial and final samples) and three based on nematode densities in seminal roots (corn growth stages V1 to V2 and V6) and adventitious roots (corn growth stage R1 to R2). The model we consider to be the most important, that based on the initial soil assay, estimated the yield loss caused by each nematode to be 0.0142%. The grand mean of the 118 plots we sampled implied a yield loss of 3.79%. The random effects of year and field did not contribute significantly to any of the models but were close to significance for some, suggesting a benefit from larger data sets. Experimental error was the largest component of the variance for all of the models; therefore, the damage function is more useful for demonstrating impact of P. penetrans rather than for accurately predicting yield loss at the field level. All of the fields in our study were an irrigated loamy sand soil, with grain yields above the county average; therefore, it is possible that our damage function is conservative. The value of soil sampling has been questioned for P. penetrans and this study shows it to be equal to if not better than root assays for predicting yield.

An empirical analysis of the fiscal impacts of changes in tax revenue multiplicity: Focusing on Florida county case

This study investigates the fiscal impacts of changes in tax revenue multiplicity on Florida counties, with a particular focus on number of local consumption taxes. In analyzing fiscal impacts, a robust standard error model approach in fixed-effects model is employed. The major findings are as follows. The number of local consumption taxes, a measure of tax revenue multiplicity, is found to be associated with larger government budget sizes and higher total tax burdens. The research findings contribute a longitudinal perspective to the literature about the fiscal implications of changes in tax revenue multiplicity. This study also has practical implications for tax policy. Financial officials and taxpayers are often interested in having more multiple sourced tax revenue structures to export their tax burdens and increase their budget size. However, taxpayers and financial officials should know that a more multiple sourced tax revenue structure is likely to increase their total tax burdens.

A REDUNDANT GNSS-INS LOW-COST UAV NAVIGATION SOLUTION FOR PROFESSIONAL APPLICATIONS

Abstract. This paper presents the current results for the FP7 GINSEC project. Its goal is to build a pre-commercial prototype of a low-cost, accurate and reliable system for the professional UAV market. Low-cost, in this context, stands for the use of sensors in the most affordable segment of the market, especially MEMS IMUs and GNSS receivers. Reliability applies to the ability of the autopilot to cope with situations where unfavourable GNSS reception conditions or strong electromagnetic fields make the computation of the position and / or attitude of the UAV difficult. Professional and accurate mean that, at least using post-processing techniques as PPP, it will be possible to reach cm-level precisions that open the door to a range of applications demanding high levels of quality in positioning, as precision agriculture or mapping. To achieve such goal, a rigorous sensor error modelling approach, the use of redundant IMUs and a dual-GNSS receiver setup, together with close-coupling techniques and an extended Kalman filter with self-analysis capabilities have been used. Although the project is not yet complete, the results obtained up to now prove the feasibility of the aforementioned goal, especially in those aspects related to position determination. Research work is still undergoing to estimate the heading using a dual-GNNS receiver setup; preliminary results prove the validity of this approach for relatively long baselines, although positive results are expected when these are shorter than 1 m – which is a necessary requisite for small-sized UAVs.

Design of robust minimum variance controller using type-2 fuzzy set

This paper presents a control strategy for achieving a robust minimum variance controller (MVC) by modelling uncertainty using a type-2 fuzzy set and satisfying H∞ norm specifications. In this paper, an MVC is designed considering three types of structural, parametric and algorithmic uncertainties. Thus, an interval type-2 fuzzy set is utilized. This paper utilizes one method to construct interval type-2 fuzzy set models by the uncertain interval data and model error modelling approach. In addition, a sufficient condition is developed that guarantees the stability and robustness of the controller. The design of a robust generalized MVC by the identified model is presented. Finally, an experimental test is used to clarify the effectiveness of the proposed control scheme. The proposed method (omitting uncertainty of the linear model) considerably decreases output variance from 0.15 to 0.0018.

A new geometric error modeling approach for multi-axis system based on stream of variation theory

This paper introduces a new geometric error modeling approach for multi axes system (MAS) based on stream of variation (SOV) theory, especially for multi-axis precision stage. SOV is used for measuring product quality for some complicated multi operations system, which is widely used in error propagation in engineering field. This paper introduces SOV concept into geometric error modeling for MAS. Instead of different process in manufacture, the new error modeling approach regards each axis as a station in MAS, and calculates the deviations after each station which is considered as upstream factor to next station. It is clear to observe how geometric errors give influence and how deviations accumulate. Different with conventional methods which are only used for error compensation in machine tools, the new error model is beneficial for sensitive error control and optimal configuration selection in design part. In addition, the new error modeling has some merits such as debugging easily due to observe the deviations after every station. A case study of new error modeling procedure for six-axis stage (SAS) in optoelectronic packaging system (OPS) is developed, and applications related to error reduction order and optimal configuration selection are processed based on the new error model.

Error Modelling Approach based on Artificial Neural Networks for Face Emotion Recognition

In this paper an approach based on modelling of recogni- tion error with Articial Neural Networks is presented to increase face emotion recognition in absence of any pre-processing or enhancement technique for feature extraction. This approach consists of two stages: in the rst stage an ANN structure is dened for the recognition task by means of a Genetic Algorithm (Recognition ANN - ReANN). Then this structure is used to perform recognition on a test set Y to estimate classication error probabilities. In the second stage an additional ANN is dened to associate these error patterns with correct classication pat- terns using the same test set (Corrective ANN - CoANN). The composite ANN system then is tested with a dierent set Z. In recognition tasks performed with the ReANN it was observed that some emotions were more likely to be incorrectly classied than others. This was further corroborated with perceptual data. With the integrated ANN system (ReANN plus CoANN) it was observed that some of these emotions could be recognised more accurately. In general overall recognition was increased from 75% to 85% with this approach.

Error modeling approach to improve time series forecasters

In time series forecasting exercises it has been usual to suppose that the error series generated by the forecasters have a white noise behavior. However, it is possible that such supposition is violated in practice due to model misspecification or disturbances of the phenomenon not captured by the predictive models. It may lead to statistically biased and/or inefficient predictors. The present paper introduces an approach to correct predetermined forecasters by recursively modeling their remaining residuals. Two formalisms are used to illustrate the recursive approach: the well-known (linear) autoregressive integrated moving average (ARIMA) and the (non-linear) Artificial Neural Network (ANN). These models are recursively adjusted to the remaining residuals of a given forecaster until a white noise behavior is achieved. Applications involving ARIMA and ANN forecasters for Dow Jones Industrial Average Index, S&P500 Index, Google Stock Value, Nasdaq Index, Wolf׳s Sunspot, and Canadian Lynx data series indicate the usefulness of the proposed framework.

Robust Black-Box Modeling of Piezoelectric Actuators for Vibration Drilling Control

In this paper, we study a new modeling approach which is experimentally validated on piezoelectric actuators, in order to derive black-box pseudolinear models for the vibration drilling control. A common way is to use physical based approaches. However, sometimes, complex phenomena occur in the system due to atypical changes of the process behavior, output noise or some hard non-linearities. Therefore, identification methods to achieve the modeling task are adopted. The micro-displacements of the piezoelectric systems generate atypical data named observation outliers in the output signal, involving large errors named innovation outliers in the predicted output signal. Since the normal distribution of these estimation errors is disturbed, and present heavy tails, we choose here as model of contaminated distribution the gross error model (GEM) approach. In order to deal with the innovation outliers, we extend the noise interval range of the scaling factor, tuning the robust Huber's ?-function chosen. We propose from this function, a parameterized robust estimation criterion (PREC) and we give the asymptotic covariance matrix of the M-estimator for the Output Error (OE) model structure. A new decisional tool for the models quality, named L 1-contribution function is proposed. Experimental results are presented and discussed.

A general approach for error modeling of machine tools

This paper presents a general and systematic approach for geometric error modeling of machine tools due to the geometric errors arising from manufacturing and assembly. The approach can be implemented in three steps: (1) development of a linear map between the pose error twist and source errors within machine tool kinematic chains using homogeneous transformation matrix method; (2) formulation of a linear map between the pose error twist and the error intensities of a machine tool; (3) combination of these two models for error separation. The merit of this approach lies in that it enables the source errors affecting the compensatable and uncompensatable pose accuracy of the machine tool to be explicitly separated, thereby providing designers and/or field engineers with an informative guideline for the accuracy improvement by suitable measures, i.e. component tolerancing in design, manufacturing and assembly processes, and error compensation. Two typical multi-axis machine tools are taken as examples to illustrate the generality and effectiveness of this approach.

Validation of a previous-day recall measure of active and sedentary behaviors.

A previous-day recall (PDR) may be a less error-prone alternative to traditional questionnaire-based estimates of physical activity and sedentary behavior (e.g., past year), but the validity of the method is not established. We evaluated the validity of an interviewer administered PDR in adolescents (12-17 yr) and adults (18-71 yr). In a 7-d study, participants completed three PDR, wore two activity monitors, and completed measures of social desirability and body mass index. PDR measures of active and sedentary time was contrasted against an accelerometer (ActiGraph) by comparing both to a valid reference measure (activPAL) using measurement error modeling and traditional validation approaches. Age- and sex-specific mixed models comparing PDR to activPAL indicated the following: 1) there was a strong linear relationship between measures for sedentary (regression slope, β1 = 0.80-1.13) and active time (β1 = 0.64-1.09), 2) person-specific bias was lower than random error, and 3) correlations were high (sedentary: r = 0.60-0.81; active: r = 0.52-0.80). Reporting errors were not associated with body mass index or social desirability. Models comparing ActiGraph to activPAL indicated the following: 1) there was a weaker linear relationship between measures for sedentary (β1 = 0.63-0.73) and active time (β1 = 0.61-0.72), (2) person-specific bias was slightly larger than random error, and (3) correlations were high (sedentary: r = 0.68-0.77; active: r = 0.57-0.79). Correlations between the PDR and the activPAL were high, systematic reporting errors were low, and the validity of the PDR was comparable with the ActiGraph. PDR may have value in studies of physical activity and health, particularly those interested in measuring the specific type, location, and purpose of activity-related behaviors.

An Enhanced MEMS Error Modeling Approach Based on Nu-Support Vector Regression

Micro Electro Mechanical System (MEMS)-based inertial sensors have made possible the development of a civilian land vehicle navigation system by offering a low-cost solution. However, the accurate modeling of the MEMS sensor errors is one of the most challenging tasks in the design of low-cost navigation systems. These sensors exhibit significant errors like biases, drift, noises; which are negligible for higher grade units. Different conventional techniques utilizing the Gauss Markov model and neural network method have been previously utilized to model the errors. However, Gauss Markov model works unsatisfactorily in the case of MEMS units due to the presence of high inherent sensor errors. On the other hand, modeling the random drift utilizing Neural Network (NN) is time consuming, thereby affecting its real-time implementation. We overcome these existing drawbacks by developing an enhanced Support Vector Machine (SVM) based error model. Unlike NN, SVMs do not suffer from local minimisation or over-fitting problems and delivers a reliable global solution. Experimental results proved that the proposed SVM approach reduced the noise standard deviation by 10–35% for gyroscopes and 61–76% for accelerometers. Further, positional error drifts under static conditions improved by 41% and 80% in comparison to NN and GM approaches.

Land use and transit ridership connections: Implications for state-level planning agencies

In this article we attempt to establish the connections between transit ridership and land use and socio-economic variables, and project future ridership under different scenarios. We subdivided the state of Maryland, USA into 1151 Statewide Modeling Zones and developed a set of variables for the base year (2000). We estimated multiple models of transit ridership – using ordinary least squares and spatial error modeling approaches – for the entire state. We also test for the determinants of ridership within urban, suburban and rural typologies. We find that land use type, transit accessibility, income, and density are strongly significant and robust predictors of transit ridership for the statewide and urban areas datasets. We also find that the determinants and their coefficients vary across urban, suburban and rural areas. Next we used a suite of econometric, land use and other models to generate two sets of future transit ridership scenarios under conditions of – (a) business as usual and (b) high energy price – for a 30-year horizon. We analyze these scenarios to demonstrate the value of our approach for state-level decision-making.

Error Modeling and Compensation Approach for Three-dimensional Surface Scanning Robot

For the three-dimensional surface scanning robotic system,the system measuring accuracy mainly depends on the following aspects: Robot positioning accuracy,line structured light vision sensor measuring accuracy,hand to eye calibration accuracy.The three aspects mentioned above can be considered as part ones.Whatever how much error will these three aspects be,the final error will be reflected in the obtained measuring data.However,the error model of the system is difficult to be established via mathematical models.As to enhance the system measuring accuracy,the error model is constructed via particle swarm optimization-radial basis function neural network(PSO-RBFNN).The input of PSO-RBFNN is chosen as the image coordinates of laser stripes,while the output of PSO-RBFNN is chosen as the errors between measured points and closest points after iterative closest point technique.The centers and widths of RBFNN are optimized by particle swarm optimization(PSO) to find more suitable parameters and thus reducing the number of neurons in the hidden layer.Experimental results demonstrate that the system measuring accuracy is improved after the error compensation scheme via constructed error network model.

The Effect of Satellite Rainfall Error Modeling on Soil Moisture Prediction Uncertainty

Abstract This study assesses the impact of satellite rainfall error structure on soil moisture simulations with the NASA Catchment land surface model. Specifically, the study contrasts a complex satellite rainfall error model (SREM2D) with the standard rainfall error model used to generate ensembles of rainfall fields as part of the Land Data Assimilation System (LDAS) developed at the NASA Global Modeling and Assimilation Office. The study is conducted in the Oklahoma region, which offers good coverage by weather radars and in situ meteorological and soil moisture measurement stations. The authors used high-resolution (25 km, 3-hourly) satellite rainfall fields derived from the NOAA/Climate Prediction Center morphing (CMORPH) global satellite product and rain gauge–calibrated radar rainfall fields (considered as the reference rainfall). The LDAS simulations are evaluated in terms of rainfall and soil moisture error. Comparisons of rainfall ensembles generated by SREM2D and LDAS against reference rainfall show that both rainfall error models preserve the satellite rainfall error characteristics across a range of spatial scales. The error structure in SREM2D is shown to generate rainfall replicates with higher variability that better envelop the reference rainfall than those generated by the LDAS error model. Likewise, the SREM2D-generated soil moisture ensemble shows slightly higher spread than the LDAS-generated ensemble and thus better encapsulates the reference soil moisture. Soil moisture errors, however, are less sensitive than precipitation errors to the complexity of the precipitation error modeling approach because soil moisture dynamics are dissipative and nonlinear.

Quantization and Colored Noises Error Modeling for Inertial Sensors for GPS/INS Integration

In this paper, modeling approaches for quantization and colored noises have been proposed. To accommodate the quantization noise, a modified inertial navigation system (INS) error dynamics is developed in this paper, and the quantization noise is incorporated into the modified INS error dynamics as augmenting driving noise. The three kinds of colored noises are modeled by using an equivalent differential equation driven by a unit white noise, and a technique is developed in this paper to augment the Kalman Filter of GPS/INS integration using this equivalent differential equation. Experimental test results show that the proposed stochastic error modeling approaches for quantization and colored noises significantly improves the accuracies of the estimated inertial drifts and the navigation solutions.

A General Approach for Geometric Error Modeling of Lower Mobility Parallel Manipulators

This paper presents a general and systematic approach for geometric error modeling of lower mobility manipulators. The approach can be implemented in three steps: (1) development of a linear map between the pose error twist and source errors within an individual limb using the homogeneous transformation matrix method; (2) formulation of a linear map between the pose error twist and the joint error intensities of a lower mobility parallel manipulator; and (3) combination of these two models. The merit of this approach lies in that it enables the source errors affecting the compensatable and uncompensatable pose accuracy of the platform to be explicitly separated, thereby providing designers and/or field engineers with an informative guideline for the accuracy improvement achievable by suitable measures, i.e., component tolerancing in design, manufacturing and assembly processes, and kinematic calibration. Three typical and well-known parallel manipulators are taken as examples to illustrate the generality and effectiveness of this approach.

Modeling nonlinear errors in surface electromyography due to baseline noise: A new methodology

The surface electromyographic (EMG) signal is often contaminated by some degree of baseline noise. It is customary for scientists to subtract baseline noise from the measured EMG signal prior to further analyses based on the assumption that baseline noise adds linearly to the observed EMG signal. The stochastic nature of both the baseline and EMG signal, however, may invalidate this assumption. Alternately, “true” EMG signals may be either minimally or nonlinearly affected by baseline noise. This information is particularly relevant at low contraction intensities when signal-to-noise ratios (SNR) may be lowest. Thus, the purpose of this simulation study was to investigate the influence of varying levels of baseline noise (approximately 2–40% maximum EMG amplitude) on mean EMG burst amplitude and to assess the best means to account for signal noise. The simulations indicated baseline noise had minimal effects on mean EMG activity for maximum contractions, but increased nonlinearly with increasing noise levels and decreasing signal amplitudes. Thus, the simple baseline noise subtraction resulted in substantial error when estimating mean activity during low intensity EMG bursts. Conversely, correcting EMG signal as a nonlinear function of both baseline and measured signal amplitude provided highly accurate estimates of EMG amplitude. This novel nonlinear error modeling approach has potential implications for EMG signal processing, particularly when assessing co-activation of antagonist muscles or small amplitude contractions where the SNR can be low.

Nodal predictive error model and Bayesian approach for thermal diffusivity and heat source mapping

This article aims at solving a two-dimensional inverse heat conduction problem in order to retrieve both the thermal diffusivity and heat source field in a thin plate. A spatial random heat pulse is applied to the plate and the thermal response is analysed. The inverse approach is based on the minimisation of a nodal predictive error model, which yields a linear estimation problem. As a result of this approach, the sensitivity matrix is directly filled with experimental data, and thus is partially noisy. Bayesian estimators, such as the Maximum A Posteriori and a Markov Chain Monte Carlo approach (Metropolis–Hastings), are implemented and compared with the Ordinary Least Squares solution. Simulated temperature measurements are used in the inverse analysis. The nodal strategy relies on the availability of temperature measurements with fine spatial resolution and high frequency, typical of nowadays infrared cameras. The effects of both the measurement errors and of the model errors on the inverse problem solution are also analysed.

A MEM-Based Analysis of Volatility Spillovers in East Asian Financial Markets

Transmission mechanisms in financial markets reflect the degree of integration of capital markets, as well as the relative importance of real economies. Market volatility has components which may behave differently across quiet and turbulent periods, but appear to behave in similar ways from market to market. In this paper we suggest a Multiplicative Error Model (MEM) approach to study volatility spillovers among a set of markets, using as a proxy, the market daily range. We model the dynamics of the expected volatility of one market including interactions with the past daily ranges of other markets, building a fully interdependent model. We analyze eight East Asian markets in the period 1995-2006, devoting particular attention to the treatment of the 1997-1998 turbulent period. We find no evidence of independent markets while several interdependence relationships can be stressed. Hong Kong turns out to be the most important market while Taiwan seems to have suffered quite limited effects from the crisis. Impulse response functions and multiperiod forecast profiles are developed and suggest a build-up in the spillover effects.

On uncertainties in carbon flux modelling and remotely sensed data assimilation: The Brasschaat pixel case

Uncertainty on carbon fluxes is determined by the uncertainties of ecosystem model structure, data and model parameter uncertainties and the temporal and spatial inaccuracy of the input data retrieval. The objective of this paper is to understand the error propagation and uncertainty of evaporative fraction (EF), soil moisture content (SMC) and water limited net ecosystem productivity (NEP). In this respect, C-Fix and spaceborne remote sensing are used for the ‘Brasschaat’ pixel. A simple model based on error theory and a Monte-Carlo approach are used. Different error scenarios are implemented to assess input uncertainty on EF, SMC and NEP as estimated with C-Fix. The minimum and maximum relative errors on the time averaged EF values from the simple error modelling approach amount 11% and 54%. Applying the Monte-Carlo approach, the minimum and maximum relative errors on EF are 8% and 34%, respectively. The minimum and maximum relative errors on the averaged SMC from the simple error approach are 4% and 18%, respectively. From the Monte-Carlo approach, the minimum and maximum relative errors on SMC are 4% and 12%. The minimum and maximum absolute errors on daily NEP (of 0.22 gC m −2 d −1) estimated from the simple error approach are 1.28 and 4.51 gC m −2 d −1. From the Monte-Carlo approach, the minimum and maximum absolute errors are 0.86 and 1.85 gC m −2 d −1. The simple error modelling and Monte-Carlo approach lead to error estimates of the same order of magnitude, though the error values derived from the Monte-Carlo approach are lower. For ecosystem carbon fluxes, both error assessment approaches lead to large differences. The complexity of the model and hence the correlation between model parameters might be responsible for this. Conclusively, the contribution of the error on soil respiration produces the largest uncertainty on NEP. This carbon flux is the most difficult one to measure and model. Improvement in NEP flux estimation can only be expected when more insight is gained in the process of soil respiration and hence, better (sub-)models are obtained.