Comparison of Prediction-Error-Modelling Criteria
DEFF Research Database (Denmark)
Jørgensen, John Bagterp; Jørgensen, Sten Bay
2007-01-01
Single and multi-step prediction-error-methods based on the maximum likelihood and least squares criteria are compared. The prediction-error methods studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model, which is a r...
Prediction error, ketamine and psychosis: An updated model.
Corlett, Philip R; Honey, Garry D; Fletcher, Paul C
2016-11-01
In 2007, we proposed an explanation of delusion formation as aberrant prediction error-driven associative learning. Further, we argued that the NMDA receptor antagonist ketamine provided a good model for this process. Subsequently, we validated the model in patients with psychosis, relating aberrant prediction error signals to delusion severity. During the ensuing period, we have developed these ideas, drawing on the simple principle that brains build a model of the world and refine it by minimising prediction errors, as well as using it to guide perceptual inferences. While previously we focused on the prediction error signal per se, an updated view takes into account its precision, as well as the precision of prior expectations. With this expanded perspective, we see several possible routes to psychotic symptoms - which may explain the heterogeneity of psychotic illness, as well as the fact that other drugs, with different pharmacological actions, can produce psychotomimetic effects. In this article, we review the basic principles of this model and highlight specific ways in which prediction errors can be perturbed, in particular considering the reliability and uncertainty of predictions. The expanded model explains hallucinations as perturbations of the uncertainty mediated balance between expectation and prediction error. Here, expectations dominate and create perceptions by suppressing or ignoring actual inputs. Negative symptoms may arise due to poor reliability of predictions in service of action. By mapping from biology to belief and perception, the account proffers new explanations of psychosis. However, challenges remain. We attempt to address some of these concerns and suggest future directions, incorporating other symptoms into the model, building towards better understanding of psychosis. © The Author(s) 2016.
DEFF Research Database (Denmark)
Jørgensen, John Bagterp; Jørgensen, Sten Bay
2007-01-01
A Prediction-error-method tailored for model based predictive control is presented. The prediction-error method studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model. The linear discrete-time stochastic state space...... model is realized from a continuous-discrete-time linear stochastic system specified using transfer functions with time-delays. It is argued that the prediction-error criterion should be selected such that it is compatible with the objective function of the predictive controller in which the model...
Error Estimation of An Ensemble Statistical Seasonal Precipitation Prediction Model
Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Gui-Long
2001-01-01
This NASA Technical Memorandum describes an optimal ensemble canonical correlation forecasting model for seasonal precipitation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. Since new CCA scheme is derived for continuous fields of predictor and predictand, an area-factor is automatically included. Thus our model is an improvement of the spectral CCA scheme of Barnett and Preisendorfer. The improvements include (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States (US) precipitation field. The predictor is the sea surface temperature (SST). The US Climate Prediction Center's reconstructed SST is used as the predictor's historical data. The US National Center for Environmental Prediction's optimally interpolated precipitation (1951-2000) is used as the predictand's historical data. Our forecast experiments show that the new ensemble canonical correlation scheme renders a reasonable forecasting skill. For example, when using September-October-November SST to predict the next season December-January-February precipitation, the spatial pattern correlation between the observed and predicted are positive in 46 years among the 50 years of experiments. The positive correlations are close to or greater than 0.4 in 29 years, which indicates excellent performance of the forecasting model. The forecasting skill can be further enhanced when several predictors are used.
Error analysis of short term wind power prediction models
International Nuclear Information System (INIS)
De Giorgi, Maria Grazia; Ficarella, Antonio; Tarantino, Marco
2011-01-01
The integration of wind farms in power networks has become an important problem. This is because the electricity produced cannot be preserved because of the high cost of storage and electricity production must follow market demand. Short-long-range wind forecasting over different lengths/periods of time is becoming an important process for the management of wind farms. Time series modelling of wind speeds is based upon the valid assumption that all the causative factors are implicitly accounted for in the sequence of occurrence of the process itself. Hence time series modelling is equivalent to physical modelling. Auto Regressive Moving Average (ARMA) models, which perform a linear mapping between inputs and outputs, and Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), which perform a non-linear mapping, provide a robust approach to wind power prediction. In this work, these models are developed in order to forecast power production of a wind farm with three wind turbines, using real load data and comparing different time prediction periods. This comparative analysis takes in the first time, various forecasting methods, time horizons and a deep performance analysis focused upon the normalised mean error and the statistical distribution hereof in order to evaluate error distribution within a narrower curve and therefore forecasting methods whereby it is more improbable to make errors in prediction. (author)
A predictive model for dimensional errors in fused deposition modeling
DEFF Research Database (Denmark)
Stolfi, A.
2015-01-01
values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....
A predictive model for dimensional errors in fused deposition modeling
DEFF Research Database (Denmark)
Stolfi, A.
2015-01-01
This work concerns the effect of deposition angle (a) and layer thickness (L) on the dimensional performance of FDM parts using a predictive model based on the geometrical description of the FDM filament profile. An experimental validation over the whole a range from 0° to 177° at 3° steps and two...... values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error.
Faber, Felix A; Hutchison, Luke; Huang, Bing; Gilmer, Justin; Schoenholz, Samuel S; Dahl, George E; Vinyals, Oriol; Kearnes, Steven; Riley, Patrick F; von Lilienfeld, O Anatole
2017-11-14
evidence that ML model predictions deviate from DFT (B3LYP) less than DFT (B3LYP) deviates from experiment for all properties. Furthermore, out-of-sample prediction errors with respect to hybrid DFT reference are on par with, or close to, chemical accuracy. The results suggest that ML models could be more accurate than hybrid DFT if explicitly electron correlated quantum (or experimental) data were available.
A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM.
Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei; Song, Houbing
2018-01-15
Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model's performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM's parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models' performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors.
Chen, Zeng-Ping; Li, Li-Mei; Yu, Ru-Qin; Littlejohn, David; Nordon, Alison; Morris, Julian; Dann, Alison S; Jeffkins, Paul A; Richardson, Mark D; Stimpson, Sarah L
2011-01-07
The development of reliable multivariate calibration models for spectroscopic instruments in on-line/in-line monitoring of chemical and bio-chemical processes is generally difficult, time-consuming and costly. Therefore, it is preferable if calibration models can be used for an extended period, without the need to replace them. However, in many process applications, changes in the instrumental response (e.g. owing to a change of spectrometer) or variations in the measurement conditions (e.g. a change in temperature) can cause a multivariate calibration model to become invalid. In this contribution, a new method, systematic prediction error correction (SPEC), has been developed to maintain the predictive abilities of multivariate calibration models when e.g. the spectrometer or measurement conditions are altered. The performance of the method has been tested on two NIR data sets (one with changes in instrumental responses, the other with variations in experimental conditions) and the outcomes compared with those of some popular methods, i.e. global PLS, univariate slope and bias correction (SBC) and piecewise direct standardization (PDS). The results show that SPEC achieves satisfactory analyte predictions with significantly lower RMSEP values than global PLS and SBC for both data sets, even when only a few standardization samples are used. Furthermore, SPEC is simple to implement and requires less information than PDS, which offers advantages for applications with limited data.
Addressing Conceptual Model Uncertainty in the Evaluation of Model Prediction Errors
Carrera, J.; Pool, M.
2014-12-01
Model predictions are uncertain because of errors in model parameters, future forcing terms, and model concepts. The latter remain the largest and most difficult to assess source of uncertainty in long term model predictions. We first review existing methods to evaluate conceptual model uncertainty. We argue that they are highly sensitive to the ingenuity of the modeler, in the sense that they rely on the modeler's ability to propose alternative model concepts. Worse, we find that the standard practice of stochastic methods leads to poor, potentially biased and often too optimistic, estimation of actual model errors. This is bad news because stochastic methods are purported to properly represent uncertainty. We contend that the problem does not lie on the stochastic approach itself, but on the way it is applied. Specifically, stochastic inversion methodologies, which demand quantitative information, tend to ignore geological understanding, which is conceptually rich. We illustrate some of these problems with the application to Mar del Plata aquifer, where extensive data are available for nearly a century. Geologically based models, where spatial variability is handled through zonation, yield calibration fits similar to geostatiscally based models, but much better predictions. In fact, the appearance of the stochastic T fields is similar to the geologically based models only in areas with high density of data. We take this finding to illustrate the ability of stochastic models to accommodate many data, but also, ironically, their inability to address conceptual model uncertainty. In fact, stochastic model realizations tend to be too close to the "most likely" one (i.e., they do not really realize the full conceptualuncertainty). The second part of the presentation is devoted to argue that acknowledging model uncertainty may lead to qualitatively different decisions than just working with "most likely" model predictions. Therefore, efforts should concentrate on
Evaluation of parametric models by the prediction error in colorectal cancer survival analysis.
Baghestani, Ahmad Reza; Gohari, Mahmood Reza; Orooji, Arezoo; Pourhoseingholi, Mohamad Amin; Zali, Mohammad Reza
2015-01-01
The aim of this study is to determine the factors influencing predicted survival time for patients with colorectal cancer (CRC) using parametric models and select the best model by predicting error's technique. Survival models are statistical techniques to estimate or predict the overall time up to specific events. Prediction is important in medical science and the accuracy of prediction is determined by a measurement, generally based on loss functions, called prediction error. A total of 600 colorectal cancer patients who admitted to the Cancer Registry Center of Gastroenterology and Liver Disease Research Center, Taleghani Hospital, Tehran, were followed at least for 5 years and have completed selected information for this study. Body Mass Index (BMI), Sex, family history of CRC, tumor site, stage of disease and histology of tumor included in the analysis. The survival time was compared by the Log-rank test and multivariate analysis was carried out using parametric models including Log normal, Weibull and Log logistic regression. For selecting the best model, the prediction error by apparent loss was used. Log rank test showed a better survival for females, BMI more than 25, patients with early stage at diagnosis and patients with colon tumor site. Prediction error by apparent loss was estimated and indicated that Weibull model was the best one for multivariate analysis. BMI and Stage were independent prognostic factors, according to Weibull model. In this study, according to prediction error Weibull regression showed a better fit. Prediction error would be a criterion to select the best model with the ability to make predictions of prognostic factors in survival analysis.
Dynamic modeling of predictive uncertainty by regression on absolute errors
Pianosi, F.; Raso, L.
2012-01-01
Uncertainty of hydrological forecasts represents valuable information for water managers and hydrologists. This explains the popularity of probabilistic models, which provide the entire distribution of the hydrological forecast. Nevertheless, many existing hydrological models are deterministic and
Wind Power Prediction Based on LS-SVM Model with Error Correction
Directory of Open Access Journals (Sweden)
ZHANG, Y.
2017-02-01
Full Text Available As conventional energy sources are non-renewable, the world's major countries are investing heavily in renewable energy research. Wind power represents the development trend of future energy, but the intermittent and volatility of wind energy are the main reasons that leads to the poor accuracy of wind power prediction. However, by analyzing the error level at different time points, it can be found that the errors of adjacent time are often approximately the same, the least square support vector machine (LS-SVM model with error correction is used to predict the wind power in this paper. According to the simulation of wind power data of two wind farms, the proposed method can effectively improve the prediction accuracy of wind power, and the error distribution is concentrated almost without deviation. The improved method proposed in this paper takes into account the error correction process of the model, which improved the prediction accuracy of the traditional model (RBF, Elman, LS-SVM. Compared with the single LS-SVM prediction model in this paper, the mean absolute error of the proposed method had decreased by 52 percent. The research work in this paper will be helpful to the reasonable arrangement of dispatching operation plan, the normal operation of the wind farm and the large-scale development as well as fully utilization of renewable energy resources.
Seismic attenuation relationship with homogeneous and heterogeneous prediction-error variance models
Mu, He-Qing; Xu, Rong-Rong; Yuen, Ka-Veng
2014-03-01
Peak ground acceleration (PGA) estimation is an important task in earthquake engineering practice. One of the most well-known models is the Boore-Joyner-Fumal formula, which estimates the PGA using the moment magnitude, the site-to-fault distance and the site foundation properties. In the present study, the complexity for this formula and the homogeneity assumption for the prediction-error variance are investigated and an efficiency-robustness balanced formula is proposed. For this purpose, a reduced-order Monte Carlo simulation algorithm for Bayesian model class selection is presented to obtain the most suitable predictive formula and prediction-error model for the seismic attenuation relationship. In this approach, each model class (a predictive formula with a prediction-error model) is evaluated according to its plausibility given the data. The one with the highest plausibility is robust since it possesses the optimal balance between the data fitting capability and the sensitivity to noise. A database of strong ground motion records in the Tangshan region of China is obtained from the China Earthquake Data Center for the analysis. The optimal predictive formula is proposed based on this database. It is shown that the proposed formula with heterogeneous prediction-error variance is much simpler than the attenuation model suggested by Boore, Joyner and Fumal (1993).
DEFF Research Database (Denmark)
Ashraf, Bilal; Janss, Luc; Jensen, Just
sample). The GBSeq data can be used directly in genomic models in the form of individual SNP allele-frequency estimates (e.g., reference reads/total reads per polymorphic site per individual), but is subject to measurement error due to the low sequencing depth per individual. Due to technical reasons....... In the current work we show how the correction for measurement error in GBSeq can also be applied in whole genome genomic variance and genomic prediction models. Bayesian whole-genome random regression models are proposed to allow implementation of large-scale SNP-based models with a per-SNP correction...... for measurement error. We show correct retrieval of genomic explained variance, and improved genomic prediction when accounting for the measurement error in GBSeq data...
Lu, Dan; Ye, Ming; Meyer, Philip D.; Curtis, Gary P.; Shi, Xiaoqing; Niu, Xu-Feng; Yabusaki, Steve B.
2013-01-01
When conducting model averaging for assessing groundwater conceptual model uncertainty, the averaging weights are often evaluated using model selection criteria such as AIC, AICc, BIC, and KIC (Akaike Information Criterion, Corrected Akaike Information Criterion, Bayesian Information Criterion, and Kashyap Information Criterion, respectively). However, this method often leads to an unrealistic situation in which the best model receives overwhelmingly large averaging weight (close to 100%), which cannot be justified by available data and knowledge. It was found in this study that this problem was caused by using the covariance matrix, CE, of measurement errors for estimating the negative log likelihood function common to all the model selection criteria. This problem can be resolved by using the covariance matrix, Cek, of total errors (including model errors and measurement errors) to account for the correlation between the total errors. An iterative two-stage method was developed in the context of maximum likelihood inverse modeling to iteratively infer the unknown Cek from the residuals during model calibration. The inferred Cek was then used in the evaluation of model selection criteria and model averaging weights. While this method was limited to serial data using time series techniques in this study, it can be extended to spatial data using geostatistical techniques. The method was first evaluated in a synthetic study and then applied to an experimental study, in which alternative surface complexation models were developed to simulate column experiments of uranium reactive transport. It was found that the total errors of the alternative models were temporally correlated due to the model errors. The iterative two-stage method using Cekresolved the problem that the best model receives 100% model averaging weight, and the resulting model averaging weights were supported by the calibration results and physical understanding of the alternative models. Using Cek
Hacker, Joshua; Angevine, Wayne
2013-04-01
Experiments with the single-column implementation of the Weather Research and Forecasting mesoscale model provide a basis for deducing land-atmosphere coupling errors in the model. Coupling occurs both through heat and moisture fluxes through the land-atmosphere interface and roughness sub-layer, and turbulent heat, moisture, and momentum fluxes through the atmospheric surface layer. This work primarily addresses the turbulent fluxes, which are parameterized following Monin-Obukhov similarity theory applied to the atmospheric surface layer. By combining ensemble data assimilation and parameter estimation, the model error can be characterized. Ensemble data assimilation of 2-m temperature and water vapor mixing ratio, and 10-m wind components, forces the model to follow observations during a month-long simulation for a column over the well-instrumented ARM Central Facility near Lamont, OK. One-hour errors in predicted observations are systematically small but non-zero, and the systematic errors measure bias as a function of local time of day. Analysis increments for state elements nearby (15-m AGL) can be too small or have the wrong sign, indicating systematically biased covariances and model error. Experiments using the ensemble filter to objectively estimate a parameter controlling the thermal land-atmosphere coupling show that the parameter adapts to offset the model errors, but that the errors cannot be eliminated. Results suggest either structural error or further parametric error that may be difficult to estimate. Experiments omitting atypical observations such as soil and flux measurements lead to qualitatively similar deductions, showing potential for assimilating common in-situ observations as an inexpensive framework for deducing and isolating model errors. We finish by presenting recent results from a deeper examination of the second-moment ensemble statistics, which demonstrate the effect of assimilation on the coupling through the stability function in
Modeling Input Errors to Improve Uncertainty Estimates for Sediment Transport Model Predictions
Jung, J. Y.; Niemann, J. D.; Greimann, B. P.
2016-12-01
Bayesian methods using Markov chain Monte Carlo algorithms have recently been applied to sediment transport models to assess the uncertainty in the model predictions due to the parameter values. Unfortunately, the existing approaches can only attribute overall uncertainty to the parameters. This limitation is critical because no model can produce accurate forecasts if forced with inaccurate input data, even if the model is well founded in physical theory. In this research, an existing Bayesian method is modified to consider the potential errors in input data during the uncertainty evaluation process. The input error is modeled using Gaussian distributions, and the means and standard deviations are treated as uncertain parameters. The proposed approach is tested by coupling it to the Sedimentation and River Hydraulics - One Dimension (SRH-1D) model and simulating a 23-km reach of the Tachia River in Taiwan. The Wu equation in SRH-1D is used for computing the transport capacity for a bed material load of non-cohesive material. Three types of input data are considered uncertain: (1) the input flowrate at the upstream boundary, (2) the water surface elevation at the downstream boundary, and (3) the water surface elevation at a hydraulic structure in the middle of the reach. The benefits of modeling the input errors in the uncertainty analysis are evaluated by comparing the accuracy of the most likely forecast and the coverage of the observed data by the credible intervals to those of the existing method. The results indicate that the internal boundary condition has the largest uncertainty among those considered. Overall, the uncertainty estimates from the new method are notably different from those of the existing method for both the calibration and forecast periods.
A dynamic model to predict modulation sidebands of a planetary gear set having manufacturing errors
Inalpolat, Murat; Kahraman, Ahmet
2010-02-01
In this study, a nonlinear time-varying dynamic model is proposed to predict modulation sidebands of planetary gear sets. This discrete dynamic model includes periodically time-varying gear mesh stiffnesses and the nonlinearities associated with tooth separations. The model uses forms of gear mesh interface excitations that are amplitude and frequency modulated due to a class of gear manufacturing errors to predict dynamic forces at all sun-planet and ring-planet gear meshes. The predicted gear mesh force spectra are shown to exhibit well-defined modulation sidebands at frequencies associated with the rotational speeds of gears relative to the planet carrier. This model is further combined with a previously developed model that accounts for amplitude modulations due to rotation of the carrier to predict acceleration spectra at a fixed position in the planetary transmission housing. Individual contributions of each gear error in the form of amplitude and frequency modulations are illustrated through an example analysis. Comparisons are made to measured spectra to demonstrate the capability of the model in predicting the sidebands of a planetary gear set with gear manufacturing errors and a rotating carrier.
DEFF Research Database (Denmark)
Del Giudice, Dario; Löwe, Roland; Madsen, Henrik
2015-01-01
In urban rainfall-runoff, commonly applied statistical techniques for uncertainty quantification mostly ignore systematic output errors originating from simplified models and erroneous inputs. Consequently, the resulting predictive uncertainty is often unreliable. Our objective is to present two ....... These properties make it more suitable for off-line applications. The IND can help in diagnosing the causes of output errors and is computationally inexpensive. It produces best results on short forecast horizons that are typical for online applications.......In urban rainfall-runoff, commonly applied statistical techniques for uncertainty quantification mostly ignore systematic output errors originating from simplified models and erroneous inputs. Consequently, the resulting predictive uncertainty is often unreliable. Our objective is to present two...
Modeling coherent errors in quantum error correction
Greenbaum, Daniel; Dutton, Zachary
2018-01-01
Analysis of quantum error correcting codes is typically done using a stochastic, Pauli channel error model for describing the noise on physical qubits. However, it was recently found that coherent errors (systematic rotations) on physical data qubits result in both physical and logical error rates that differ significantly from those predicted by a Pauli model. Here we examine the accuracy of the Pauli approximation for noise containing coherent errors (characterized by a rotation angle ɛ) under the repetition code. We derive an analytic expression for the logical error channel as a function of arbitrary code distance d and concatenation level n, in the small error limit. We find that coherent physical errors result in logical errors that are partially coherent and therefore non-Pauli. However, the coherent part of the logical error is negligible at fewer than {ε }-({dn-1)} error correction cycles when the decoder is optimized for independent Pauli errors, thus providing a regime of validity for the Pauli approximation. Above this number of correction cycles, the persistent coherent logical error will cause logical failure more quickly than the Pauli model would predict, and this may need to be combated with coherent suppression methods at the physical level or larger codes.
DEFF Research Database (Denmark)
Minsley, B. J.; Christensen, Nikolaj Kruse; Christensen, Steen
electromagnetic (AEM) data. Our estimates of model structural uncertainty follow a Bayesian framework that accounts for both the uncertainties in geophysical parameter estimates given AEM data, and the uncertainties in the relationship between lithology and geophysical parameters. Using geostatistical sequential......Model structure, or the spatial arrangement of subsurface lithological units, is fundamental to the hydrological behavior of Earth systems. Knowledge of geological model structure is critically important in order to make informed hydrological predictions and management decisions. Model structure...... is never perfectly known, however, and incorrect assumptions can be a significant source of error when making model predictions. We describe a systematic approach for quantifying model structural uncertainty that is based on the integration of sparse borehole observations and large-scale airborne...
Directory of Open Access Journals (Sweden)
Wenjuan Wei
Full Text Available Mass transfer models are useful in predicting the emissions of volatile organic compounds (VOCs and formaldehyde from building materials in indoor environments. They are also useful for human exposure evaluation and in sustainable building design. The measurement errors in the emission characteristic parameters in these mass transfer models, i.e., the initial emittable concentration (C 0, the diffusion coefficient (D, and the partition coefficient (K, can result in errors in predicting indoor VOC and formaldehyde concentrations. These errors have not yet been quantitatively well analyzed in the literature. This paper addresses this by using modelling to assess these errors for some typical building conditions. The error in C 0, as measured in environmental chambers and applied to a reference living room in Beijing, has the largest influence on the model prediction error in indoor VOC and formaldehyde concentration, while the error in K has the least effect. A correlation between the errors in D, K, and C 0 and the error in the indoor VOC and formaldehyde concentration prediction is then derived for engineering applications. In addition, the influence of temperature on the model prediction of emissions is investigated. It shows the impact of temperature fluctuations on the prediction errors in indoor VOC and formaldehyde concentrations to be less than 7% at 23±0.5°C and less than 30% at 23±2°C.
Wei, Wenjuan; Xiong, Jianyin; Zhang, Yinping
2013-01-01
Mass transfer models are useful in predicting the emissions of volatile organic compounds (VOCs) and formaldehyde from building materials in indoor environments. They are also useful for human exposure evaluation and in sustainable building design. The measurement errors in the emission characteristic parameters in these mass transfer models, i.e., the initial emittable concentration (C 0), the diffusion coefficient (D), and the partition coefficient (K), can result in errors in predicting indoor VOC and formaldehyde concentrations. These errors have not yet been quantitatively well analyzed in the literature. This paper addresses this by using modelling to assess these errors for some typical building conditions. The error in C 0, as measured in environmental chambers and applied to a reference living room in Beijing, has the largest influence on the model prediction error in indoor VOC and formaldehyde concentration, while the error in K has the least effect. A correlation between the errors in D, K, and C 0 and the error in the indoor VOC and formaldehyde concentration prediction is then derived for engineering applications. In addition, the influence of temperature on the model prediction of emissions is investigated. It shows the impact of temperature fluctuations on the prediction errors in indoor VOC and formaldehyde concentrations to be less than 7% at 23±0.5°C and less than 30% at 23±2°C.
MPC-Relevant Prediction-Error Identification
DEFF Research Database (Denmark)
Jørgensen, John Bagterp; Jørgensen, Sten Bay
2007-01-01
A prediction-error-method tailored for model based predictive control is presented. The prediction-error method studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model. The linear discrete-time stochastic state space m...
BANKRUPTCY PREDICTION MODEL WITH ZETAc OPTIMAL CUT-OFF SCORE TO CORRECT TYPE I ERRORS
Directory of Open Access Journals (Sweden)
Mohamad Iwan
2005-06-01
This research has successfully attained the following results: (1 type I error is in fact 59,83 times more costly compared to type II error, (2 22 ratios distinguish between bankrupt and non-bankrupt groups, (3 2 financial ratios proved to be effective in predicting bankruptcy, (4 prediction using ZETAc optimal cut-off score predicts more companies filing for bankruptcy within one year compared to prediction using Hair et al. optimum cutting score, (5 Although prediction using Hair et al. optimum cutting score is more accurate, prediction using ZETAc optimal cut-off score proved to be able to minimize cost incurred from classification errors.
Real-time GPS Satellite Clock Error Prediction Based On No-stationary Time Series Model
Wang, Q.; Xu, G.; Wang, F.
2009-04-01
Analysis Centers of the IGS provide precise satellite ephemeris for GPS data post-processing. The accuracy of orbit products is better than 5cm, and that of the satellite clock errors (SCE) approaches 0.1ns (igscb.jpl.nasa.gov), which can meet with the requirements of precise point positioning (PPP). Due to the 13 day-latency of the IGS final products, only the broadcast ephemeris and IGS ultra rapid products (predicted) are applicable for real time PPP (RT-PPP). Therefore, development of an approach to estimate high precise GPS SCE in real time is of particular importance for RT-PPP. Many studies have been carried out for forecasting the corrections using models, such as Linear Model (LM), Quadratic Polynomial Model (QPM), Quadratic Polynomial Model with Cyclic corrected Terms (QPM+CT), Grey Model (GM) and Kalman Filter Model (KFM), etc. However, the precisions of these models are generally in nanosecond level. The purpose of this study is to develop a method using which SCE forecasting for RT-PPP can be reached with a precision of sub-nanosecond. Analysis of the last 8 years IGS SCE data shown that predicted precision depend on the stability of the individual satellite clock. The clocks of the most recent GPS satellites (BLOCK IIR and BLOCK IIR-M) are more stable than that of the former GPS satellites (BLOCK IIA). For the stable satellite clock, the next 6 hours SCE can be easily predict with LM. The residuals of unstable satellite clocks are periodic ones with noise components. Dominant periods of residuals are found by using Fourier Transform and Spectrum Analysis. For the rest part of the residuals, an auto-regression model is used to determine their systematic trends. Summarized from this study, a no-stationary time series model can be proposed to predict GPS SCE in real time. This prediction model includes: linear term, cyclic corrected terms and auto-regression term, which are used to represent SCE trend, cyclic parts and rest of the errors, respectively
Potter, Gail E; Smieszek, Timo; Sailer, Kerstin
2015-09-01
Face-to-face social contacts are potentially important transmission routes for acute respiratory infections, and understanding the contact network can improve our ability to predict, contain, and control epidemics. Although workplaces are important settings for infectious disease transmission, few studies have collected workplace contact data and estimated workplace contact networks. We use contact diaries, architectural distance measures, and institutional structures to estimate social contact networks within a Swiss research institute. Some contact reports were inconsistent, indicating reporting errors. We adjust for this with a latent variable model, jointly estimating the true (unobserved) network of contacts and duration-specific reporting probabilities. We find that contact probability decreases with distance, and that research group membership, role, and shared projects are strongly predictive of contact patterns. Estimated reporting probabilities were low only for 0-5 min contacts. Adjusting for reporting error changed the estimate of the duration distribution, but did not change the estimates of covariate effects and had little effect on epidemic predictions. Our epidemic simulation study indicates that inclusion of network structure based on architectural and organizational structure data can improve the accuracy of epidemic forecasting models.
Recursive prediction error methods for online estimation in nonlinear state-space models
Directory of Open Access Journals (Sweden)
Dag Ljungquist
1994-04-01
Full Text Available Several recursive algorithms for online, combined state and parameter estimation in nonlinear state-space models are discussed in this paper. Well-known algorithms such as the extended Kalman filter and alternative formulations of the recursive prediction error method are included, as well as a new method based on a line-search strategy. A comparison of the algorithms illustrates that they are very similar although the differences can be important for the online tracking capabilities and robustness. Simulation experiments on a simple nonlinear process show that the performance under certain conditions can be improved by including a line-search strategy.
Analysts forecast error : A robust prediction model and its short term trading
Boudt, Kris; de Goeij, Peter; Thewissen, James; Van Campenhout, Geert
We examine the profitability of implementing a short term trading strategy based on predicting the error in analysts' earnings per share forecasts using publicly available information. Since large earnings surprises may lead to extreme values in the forecast error series that disrupt their smooth
Improved model predictive control of resistive wall modes by error field estimator in EXTRAP T2R
Setiadi, A. C.; Brunsell, P. R.; Frassinetti, L.
2016-12-01
Many implementations of a model-based approach for toroidal plasma have shown better control performance compared to the conventional type of feedback controller. One prerequisite of model-based control is the availability of a control oriented model. This model can be obtained empirically through a systematic procedure called system identification. Such a model is used in this work to design a model predictive controller to stabilize multiple resistive wall modes in EXTRAP T2R reversed-field pinch. Model predictive control is an advanced control method that can optimize the future behaviour of a system. Furthermore, this paper will discuss an additional use of the empirical model which is to estimate the error field in EXTRAP T2R. Two potential methods are discussed that can estimate the error field. The error field estimator is then combined with the model predictive control and yields better radial magnetic field suppression.
A Java simulator of Rescorla and Wagner's prediction error model and configural cue extensions.
Alonso, Eduardo; Mondragón, Esther; Fernández, Alberto
2012-10-01
In this paper we present the "R&W Simulator" (version 3.0), a Java simulator of Rescorla and Wagner's prediction error model of learning. It is able to run whole experimental designs, and compute and display the associative values of elemental and compound stimuli simultaneously, as well as use extra configural cues in generating compound values; it also permits change of the US parameters across phases. The simulator produces both numerical and graphical outputs, and includes a functionality to export the results to a data processor spreadsheet. It is user-friendly, and built with a graphical interface designed to allow neuroscience researchers to input the data in their own "language". It is a cross-platform simulator, so it does not require any special equipment, operative system or support program, and does not need installation. The "R&W Simulator" (version 3.0) is available free. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.
Murray, J. R.
2017-12-01
Earth surface displacements measured at Global Navigation Satellite System (GNSS) sites record crustal deformation due, for example, to slip on faults underground. A primary objective in designing geodetic networks to study crustal deformation is to maximize the ability to recover parameters of interest like fault slip. Given Green's functions (GFs) relating observed displacement to motion on buried dislocations representing a fault, one can use various methods to estimate spatially variable slip. However, assumptions embodied in the GFs, e.g., use of a simplified elastic structure, introduce spatially correlated model prediction errors (MPE) not reflected in measurement uncertainties (Duputel et al., 2014). In theory, selection algorithms should incorporate inter-site correlations to identify measurement locations that give unique information. I assess the impact of MPE on site selection by expanding existing methods (Klein et al., 2017; Reeves and Zhe, 1999) to incorporate this effect. Reeves and Zhe's algorithm sequentially adds or removes a predetermined number of data according to a criterion that minimizes the sum of squared errors (SSE) on parameter estimates. Adapting this method to GNSS network design, Klein et al. select new sites that maximize model resolution, using trade-off curves to determine when additional resolution gain is small. Their analysis uses uncorrelated data errors and GFs for a uniform elastic half space. I compare results using GFs for spatially variable strike slip on a discretized dislocation in a uniform elastic half space, a layered elastic half space, and a layered half space with inclusion of MPE. I define an objective criterion to terminate the algorithm once the next site removal would increase SSE more than the expected incremental SSE increase if all sites had equal impact. Using a grid of candidate sites with 8 km spacing, I find the relative value of the selected sites (defined by the percent increase in SSE that further
Error associated with model predictions of wildland fire rate of spread
Miguel G. Cruz; Martin E. Alexander
2015-01-01
How well can we expect to predict the spread rate of wildfires and prescribed fires? The degree of accuracy in model predictions of wildland fire behaviour characteristics are dependent on the model's applicability to a given situation, the validity of the model's relationships, and the reliability of the model input data (Alexander and Cruz 2013b#. We...
Schoups, G.; Vrugt, J.A.
2010-01-01
Estimation of parameter and predictive uncertainty of hydrologic models has traditionally relied on several simplifying assumptions. Residual errors are often assumed to be independent and to be adequately described by a Gaussian probability distribution with a mean of zero and a constant variance.
Reward positivity: Reward prediction error or salience prediction error?
Heydari, Sepideh; Holroyd, Clay B
2016-08-01
The reward positivity is a component of the human ERP elicited by feedback stimuli in trial-and-error learning and guessing tasks. A prominent theory holds that the reward positivity reflects a reward prediction error signal that is sensitive to outcome valence, being larger for unexpected positive events relative to unexpected negative events (Holroyd & Coles, 2002). Although the theory has found substantial empirical support, most of these studies have utilized either monetary or performance feedback to test the hypothesis. However, in apparent contradiction to the theory, a recent study found that unexpected physical punishments also elicit the reward positivity (Talmi, Atkinson, & El-Deredy, 2013). The authors of this report argued that the reward positivity reflects a salience prediction error rather than a reward prediction error. To investigate this finding further, in the present study participants navigated a virtual T maze and received feedback on each trial under two conditions. In a reward condition, the feedback indicated that they would either receive a monetary reward or not and in a punishment condition the feedback indicated that they would receive a small shock or not. We found that the feedback stimuli elicited a typical reward positivity in the reward condition and an apparently delayed reward positivity in the punishment condition. Importantly, this signal was more positive to the stimuli that predicted the omission of a possible punishment relative to stimuli that predicted a forthcoming punishment, which is inconsistent with the salience hypothesis. © 2016 Society for Psychophysiological Research.
Energy Technology Data Exchange (ETDEWEB)
Grimm, Lars J., E-mail: Lars.grimm@duke.edu; Ghate, Sujata V.; Yoon, Sora C.; Kim, Connie [Department of Radiology, Duke University Medical Center, Box 3808, Durham, North Carolina 27710 (United States); Kuzmiak, Cherie M. [Department of Radiology, University of North Carolina School of Medicine, 2006 Old Clinic, CB No. 7510, Chapel Hill, North Carolina 27599 (United States); Mazurowski, Maciej A. [Duke University Medical Center, Box 2731 Medical Center, Durham, North Carolina 27710 (United States)
2014-03-15
Purpose: The purpose of this study is to explore Breast Imaging-Reporting and Data System (BI-RADS) features as predictors of individual errors made by trainees when detecting masses in mammograms. Methods: Ten radiology trainees and three expert breast imagers reviewed 100 mammograms comprised of bilateral medial lateral oblique and craniocaudal views on a research workstation. The cases consisted of normal and biopsy proven benign and malignant masses. For cases with actionable abnormalities, the experts recorded breast (density and axillary lymph nodes) and mass (shape, margin, and density) features according to the BI-RADS lexicon, as well as the abnormality location (depth and clock face). For each trainee, a user-specific multivariate model was constructed to predict the trainee's likelihood of error based on BI-RADS features. The performance of the models was assessed using area under the receive operating characteristic curves (AUC). Results: Despite the variability in errors between different trainees, the individual models were able to predict the likelihood of error for the trainees with a mean AUC of 0.611 (range: 0.502–0.739, 95% Confidence Interval: 0.543–0.680,p < 0.002). Conclusions: Patterns in detection errors for mammographic masses made by radiology trainees can be modeled using BI-RADS features. These findings may have potential implications for the development of future educational materials that are personalized to individual trainees.
Grimm, Lars J; Ghate, Sujata V; Yoon, Sora C; Kuzmiak, Cherie M; Kim, Connie; Mazurowski, Maciej A
2014-03-01
The purpose of this study is to explore Breast Imaging-Reporting and Data System (BI-RADS) features as predictors of individual errors made by trainees when detecting masses in mammograms. Ten radiology trainees and three expert breast imagers reviewed 100 mammograms comprised of bilateral medial lateral oblique and craniocaudal views on a research workstation. The cases consisted of normal and biopsy proven benign and malignant masses. For cases with actionable abnormalities, the experts recorded breast (density and axillary lymph nodes) and mass (shape, margin, and density) features according to the BI-RADS lexicon, as well as the abnormality location (depth and clock face). For each trainee, a user-specific multivariate model was constructed to predict the trainee's likelihood of error based on BI-RADS features. The performance of the models was assessed using area under the receive operating characteristic curves (AUC). Despite the variability in errors between different trainees, the individual models were able to predict the likelihood of error for the trainees with a mean AUC of 0.611 (range: 0.502-0.739, 95% Confidence Interval: 0.543-0.680,p errors for mammographic masses made by radiology trainees can be modeled using BI-RADS features. These findings may have potential implications for the development of future educational materials that are personalized to individual trainees.
International Nuclear Information System (INIS)
Grimm, Lars J.; Ghate, Sujata V.; Yoon, Sora C.; Kim, Connie; Kuzmiak, Cherie M.; Mazurowski, Maciej A.
2014-01-01
Purpose: The purpose of this study is to explore Breast Imaging-Reporting and Data System (BI-RADS) features as predictors of individual errors made by trainees when detecting masses in mammograms. Methods: Ten radiology trainees and three expert breast imagers reviewed 100 mammograms comprised of bilateral medial lateral oblique and craniocaudal views on a research workstation. The cases consisted of normal and biopsy proven benign and malignant masses. For cases with actionable abnormalities, the experts recorded breast (density and axillary lymph nodes) and mass (shape, margin, and density) features according to the BI-RADS lexicon, as well as the abnormality location (depth and clock face). For each trainee, a user-specific multivariate model was constructed to predict the trainee's likelihood of error based on BI-RADS features. The performance of the models was assessed using area under the receive operating characteristic curves (AUC). Results: Despite the variability in errors between different trainees, the individual models were able to predict the likelihood of error for the trainees with a mean AUC of 0.611 (range: 0.502–0.739, 95% Confidence Interval: 0.543–0.680,p < 0.002). Conclusions: Patterns in detection errors for mammographic masses made by radiology trainees can be modeled using BI-RADS features. These findings may have potential implications for the development of future educational materials that are personalized to individual trainees
Christiansen, Bo
2015-04-01
Linear regression methods are without doubt the most used approaches to describe and predict data in the physical sciences. They are often good first order approximations and they are in general easier to apply and interpret than more advanced methods. However, even the properties of univariate regression can lead to debate over the appropriateness of various models as witnessed by the recent discussion about climate reconstruction methods. Before linear regression is applied important choices have to be made regarding the origins of the noise terms and regarding which of the two variables under consideration that should be treated as the independent variable. These decisions are often not easy to make but they may have a considerable impact on the results. We seek to give a unified probabilistic - Bayesian with flat priors - treatment of univariate linear regression and prediction by taking, as starting point, the general errors-in-variables model (Christiansen, J. Clim., 27, 2014-2031, 2014). Other versions of linear regression can be obtained as limits of this model. We derive the likelihood of the model parameters and predictands of the general errors-in-variables model by marginalizing over the nuisance parameters. The resulting likelihood is relatively simple and easy to analyze and calculate. The well known unidentifiability of the errors-in-variables model is manifested as the absence of a well-defined maximum in the likelihood. However, this does not mean that probabilistic inference can not be made; the marginal likelihoods of model parameters and the predictands have, in general, well-defined maxima. We also include a probabilistic version of classical calibration and show how it is related to the errors-in-variables model. The results are illustrated by an example from the coupling between the lower stratosphere and the troposphere in the Northern Hemisphere winter.
Bergen, Silas; Sheppard, Lianne; Sampson, Paul D; Kim, Sun-Young; Richards, Mark; Vedal, Sverre; Kaufman, Joel D; Szpiro, Adam A
2013-09-01
Studies estimating health effects of long-term air pollution exposure often use a two-stage approach: building exposure models to assign individual-level exposures, which are then used in regression analyses. This requires accurate exposure modeling and careful treatment of exposure measurement error. To illustrate the importance of accounting for exposure model characteristics in two-stage air pollution studies, we considered a case study based on data from the Multi-Ethnic Study of Atherosclerosis (MESA). We built national spatial exposure models that used partial least squares and universal kriging to estimate annual average concentrations of four PM2.5 components: elemental carbon (EC), organic carbon (OC), silicon (Si), and sulfur (S). We predicted PM2.5 component exposures for the MESA cohort and estimated cross-sectional associations with carotid intima-media thickness (CIMT), adjusting for subject-specific covariates. We corrected for measurement error using recently developed methods that account for the spatial structure of predicted exposures. Our models performed well, with cross-validated R2 values ranging from 0.62 to 0.95. Naïve analyses that did not account for measurement error indicated statistically significant associations between CIMT and exposure to OC, Si, and S. EC and OC exhibited little spatial correlation, and the corrected inference was unchanged from the naïve analysis. The Si and S exposure surfaces displayed notable spatial correlation, resulting in corrected confidence intervals (CIs) that were 50% wider than the naïve CIs, but that were still statistically significant. The impact of correcting for measurement error on health effect inference is concordant with the degree of spatial correlation in the exposure surfaces. Exposure model characteristics must be considered when performing two-stage air pollution epidemiologic analyses because naïve health effect inference may be inappropriate.
Addressing Common Cloud-Radiation Errors from 4-hour to 4-week Model Prediction
Benjamin, S.; Sun, S.; Grell, G. A.; Green, B.; Olson, J.; Kenyon, J.; James, E.; Smirnova, T. G.; Brown, J. M.
2017-12-01
Cloud-radiation representation in models for subgrid-scale clouds is a known gap from subseasonal-to-seasonal models down to storm-scale models applied for forecast duration of only a few hours. NOAA/ESRL has been applying common physical parameterizations for scale-aware deep/shallow convection and boundary-layer mixing over this wide range of time and spatial scales, with some progress to be reported in this presentation. The Grell-Freitas scheme (2014, Atmos. Chem. Phys.) and MYNN boundary-layer EDMF scheme (Olson / Benjamin et al. 2016 Mon. Wea. Rev.) have been applied and tested extensively for the NOAA hourly updated 3-km High-Resolution Rapid Refresh (HRRR) and 13-km Rapid Refresh (RAP) model/assimilation systems over the United States and North America, with targeting toward improvement to boundary-layer evolution and cloud-radiation representation in all seasons. This representation is critical for both warm-season severe convective storm forecasting and for winter-storm prediction of snow and mixed precipitation. At the same time the Grell-Freitas scheme has been applied also as an option for subseasonal forecasting toward improved US week 3-4 prediction with the FIM-HYCOM coupled model (Green et al 2017, MWR). Cloud/radiation evaluation using CERES satellite-based estimates have been applied to both 12-h RAP (13km) and also during Weeks 1-4 from 32-day FIM-HYCOM (60km) forecasts. Initial results reveal that improved cloud representation is needed for both resolutions and now is guiding further refinement for cloud representation including with the Grell-Freitas scheme and with the updated MYNN-EDMF scheme (both now also in global testing as well as with the 3km HRRR and 13km RAP models).
The disparity mutagenesis model predicts rescue of living things from catastrophic errors
Directory of Open Access Journals (Sweden)
Mitsuru eFurusawa
2014-12-01
Full Text Available In animals including humans, mutation rates per generation will exceed a perceived threshold, which should result in an excessive genetic load. Despite this, they have survived without extinction. This is a perplexing problem for human genetics, arising at the end of the last century, and to date still does not have a fully satisfactory explanation. Shortly after we proposed the disparity theory of evolution in 1992, the disparity mutagenesis model was proposed, which forms the basis for an explanation for an acceleration of evolution and species survival. This model predicts a significant increase of the mutation threshold values if there is a high enough fidelity difference in replication between the lagging and leading strands. When applied to biological evolution, the model predicts that living things, including humans, might overcome the lethal effect of accumulated deleterious mutations and be able to survive. Artificially-prepared mutator strains of microorganisms, in which an enhanced lagging-strand-biased mutagenesis was introduced, showed unexpectedly high adaptability to severe environments. The implications of the striking behaviors shown by these disparity mutators will be discussed in relation to how living things with high mutation rates can avoid the self-defeating risk of excess mutations.
The disparity mutagenesis model predicts rescue of living things from catastrophic errors
Furusawa, Mitsuru
2014-01-01
In animals including humans, mutation rates per generation exceed a perceived threshold, and excess mutations increase genetic load. Despite this, animals have survived without extinction. This is a perplexing problem for animal and human genetics, arising at the end of the last century, and to date still does not have a fully satisfactory explanation. Shortly after we proposed the disparity theory of evolution in 1992, the disparity mutagenesis model was proposed, which forms the basis for an explanation for an acceleration of evolution and species survival. This model predicts a significant increase of the mutation threshold values if the fidelity difference in replication between the lagging and leading strands is high enough. When applied to biological evolution, the model predicts that living things, including humans, might overcome the lethal effect of accumulated deleterious mutations and be able to survive. Artificially derived mutator strains of microorganisms, in which an enhanced lagging-strand-biased mutagenesis was introduced, showed unexpectedly high adaptability to severe environments. The implications of the striking behaviors shown by these disparity mutators will be discussed in relation to how living things with high mutation rates can avoid the self-defeating risk of excess mutations. PMID:25538731
Christensen, Nikolaj K; Minsley, Burke J.; Christensen, Steen
2017-01-01
We present a new methodology to combine spatially dense high-resolution airborne electromagnetic (AEM) data and sparse borehole information to construct multiple plausible geological structures using a stochastic approach. The method developed allows for quantification of the performance of groundwater models built from different geological realizations of structure. Multiple structural realizations are generated using geostatistical Monte Carlo simulations that treat sparse borehole lithological observations as hard data and dense geophysically derived structural probabilities as soft data. Each structural model is used to define 3-D hydrostratigraphical zones of a groundwater model, and the hydraulic parameter values of the zones are estimated by using nonlinear regression to fit hydrological data (hydraulic head and river discharge measurements). Use of the methodology is demonstrated for a synthetic domain having structures of categorical deposits consisting of sand, silt, or clay. It is shown that using dense AEM data with the methodology can significantly improve the estimated accuracy of the sediment distribution as compared to when borehole data are used alone. It is also shown that this use of AEM data can improve the predictive capability of a calibrated groundwater model that uses the geological structures as zones. However, such structural models will always contain errors because even with dense AEM data it is not possible to perfectly resolve the structures of a groundwater system. It is shown that when using such erroneous structures in a groundwater model, they can lead to biased parameter estimates and biased model predictions, therefore impairing the model's predictive capability.
2013-09-01
and M1 using Latin Hypercube sampling . For each of those samples , a Markov Chain Monte Carlo ( MCMC ) algorithm called slice sampling is employed using...104 samples to calculate the posterior distribution. Figures 6 and 7 show the integrated posterior distributions for the uncertain inputs and errors...include errors from sampling , discretization, coupled solution procedures, and other mathematical approximations. In the presence of these various
Directory of Open Access Journals (Sweden)
Francisco Javier Bucio
2017-10-01
Full Text Available Due to its nutritional and economic value, the tomato is considered one of the main vegetables in terms of production and consumption in the world. For this reason, an important case study is the fruit maturation parametrized by its mass loss in this study. This process develops in the fruit mainly after harvest. Since that parameter affects the economic value of the crop, the scientific community has been progressively approaching the issue. However, there is no a state-of-the-art practical model allowing the prediction of the tomato fruit mass loss yet. This study proposes a prediction model for tomato mass loss in a continuous and definite time-frame using regression methods. The model is based on a combination of adjustment methods such as least squares polynomial regression leading to error estimation, and cross validation techniques. Experimental results from a 50 fruit of tomato sample studied over a 54 days period were compared to results from the model using a second-order polynomial approach found to provide optimal data fit with a resulting efficiency of ~97%. The model also allows the design of precise logistic strategies centered on post-harvest tomato mass loss prediction usable by producers, distributors, and consumers.
Hajibozorgi, M; Arjmand, N
2016-04-11
Range of motion (ROM) of the thoracic spine has implications in patient discrimination for diagnostic purposes and in biomechanical models for predictions of spinal loads. Few previous studies have reported quite different thoracic ROMs. Total (T1-T12), lower (T5-T12) and upper (T1-T5) thoracic, lumbar (T12-S1), pelvis, and entire trunk (T1) ROMs were measured using an inertial tracking device as asymptomatic subjects flexed forward from their neutral upright position to full forward flexion. Correlations between body height and the ROMs were conducted. An effect of measurement errors of the trunk flexion (T1) on the model-predicted spinal loads was investigated. Mean of peak voluntary total flexion of trunk (T1) was 118.4 ± 13.9°, of which 20.5 ± 6.5° was generated by flexion of the T1 to T12 (thoracic ROM), and the remaining by flexion of the T12 to S1 (lumbar ROM) (50.2 ± 7.0°) and pelvis (47.8 ± 6.9°). Lower thoracic ROM was significantly larger than upper thoracic ROM (14.8 ± 5.4° versus 5.8 ± 3.1°). There were non-significant weak correlations between body height and the ROMs. Contribution of the pelvis to generate the total trunk flexion increased from ~20% to 40% and that of the lumbar decreased from ~60% to 42% as subjects flexed forward from upright to maximal flexion while that of the thoracic spine remained almost constant (~16% to 20%) during the entire movement. Small uncertainties (±5°) in the measurement of trunk flexion angle resulted in considerable errors (~27%) in the model-predicted spinal loads only in activities involving small trunk flexion. Copyright © 2015 Elsevier Ltd. All rights reserved.
Aircraft system modeling error and control error
Kulkarni, Nilesh V. (Inventor); Kaneshige, John T. (Inventor); Krishnakumar, Kalmanje S. (Inventor); Burken, John J. (Inventor)
2012-01-01
A method for modeling error-driven adaptive control of an aircraft. Normal aircraft plant dynamics is modeled, using an original plant description in which a controller responds to a tracking error e(k) to drive the component to a normal reference value according to an asymptote curve. Where the system senses that (1) at least one aircraft plant component is experiencing an excursion and (2) the return of this component value toward its reference value is not proceeding according to the expected controller characteristics, neural network (NN) modeling of aircraft plant operation may be changed. However, if (1) is satisfied but the error component is returning toward its reference value according to expected controller characteristics, the NN will continue to model operation of the aircraft plant according to an original description.
DEFF Research Database (Denmark)
Christensen, Steen; Doherty, John
2008-01-01
over the model area. Singular value decomposition (SVD) of the (possibly weighted) sensitivity matrix of the pilot point based model produces eigenvectors of which we pick a small number corresponding to significant eigenvalues. Super parameters are defined as factors through which parameter...... nonlinear functions. Recommendations concerning the use of pilot points and singular value decomposition in real-world groundwater model calibration are finally given. (c) 2008 Elsevier Ltd. All rights reserved....
DEFF Research Database (Denmark)
Christensen, Steen; Doherty, John
2008-01-01
A significant practical problem with the pilot point method is to choose the location of the pilot points. We present a method that is intended to relieve the modeler from much of this responsibility. The basic idea is that a very large number of pilot points are distributed more or less uniformly...... over the model area. Singular value decomposition (SVD) of the (possibly weighted) sensitivity matrix of the pilot point based model produces eigenvectors of which we pick a small number corresponding to significant eigenvalues. Super parameters are defined as factors through which parameter...... combinations corresponding to the chosen eigenvectors are multiplied to obtain the pilot point values. The model can thus be transformed from having many-pilot-point parameters to having a few super parameters that can be estimated by nonlinear regression on the basis of the available observations. (This...
Brunius, Carl; Pedersen, Anders; Malmodin, Daniel; Karlsson, B Göran; Andersson, Lars I; Tybring, Gunnel; Landberg, Rikard
2017-11-15
Biobanks are important infrastructures for life science research. Optimal sample handling regarding e.g. collection and processing of biological samples is highly complex, with many variables that could alter sample integrity and even more complex when considering multiple study centers or using legacy samples with limited documentation on sample management. Novel means to understand and take into account such variability would enable high-quality research on archived samples. This study investigated whether pre-analytical sample variability could be predicted and reduced by modeling alterations in the plasma metabolome, measured by NMR, as a function of pre-centrifugation conditions (1-36 h pre-centrifugation delay time at 4 °C and 22 °C) in 16 individuals. Pre-centrifugation temperature and delay times were predicted using random forest modeling and performance was validated on independent samples. Alterations in the metabolome were modeled at each temperature using a cluster-based approach, revealing reproducible effects of delay time on energy metabolism intermediates at both temperatures, but more pronounced at 22 °C. Moreover, pre-centrifugation delay at 4 °C resulted in large, specific variability at 3 h, predominantly of lipids. Pre-analytical sample handling error correction resulted in significant improvement of data quality, particularly at 22 °C. This approach offers the possibility to predict pre-centrifugation delay temperature and time in biobanked samples before use in costly downstream applications. Moreover, the results suggest potential to decrease the impact of undesired, delay-induced variability. However, these findings need to be validated in multiple, large sample sets and with analytical techniques covering a wider range of the metabolome, such as LC-MS. The sampleDrift R package is available at https://gitlab.com/CarlBrunius/sampleDrift. carl.brunius@chalmers.se. Supplementary data are available at Bioinformatics online
SHERPA: A systematic human error reduction and prediction approach
International Nuclear Information System (INIS)
Embrey, D.E.
1986-01-01
This paper describes a Systematic Human Error Reduction and Prediction Approach (SHERPA) which is intended to provide guidelines for human error reduction and quantification in a wide range of human-machine systems. The approach utilizes as its basic current cognitive models of human performance. The first module in SHERPA performs task and human error analyses, which identify likely error modes, together with guidelines for the reduction of these errors by training, procedures and equipment redesign. The second module uses a SARAH approach to quantify the probability of occurrence of the errors identified earlier, and provides cost benefit analyses to assist in choosing the appropriate error reduction approaches in the third module
Critical evidence for the prediction error theory in associative learning.
Terao, Kanta; Matsumoto, Yukihisa; Mizunami, Makoto
2015-03-10
In associative learning in mammals, it is widely accepted that the discrepancy, or error, between actual and predicted reward determines whether learning occurs. Complete evidence for the prediction error theory, however, has not been obtained in any learning systems: Prediction error theory stems from the finding of a blocking phenomenon, but blocking can also be accounted for by other theories, such as the attentional theory. We demonstrated blocking in classical conditioning in crickets and obtained evidence to reject the attentional theory. To obtain further evidence supporting the prediction error theory and rejecting alternative theories, we constructed a neural model to match the prediction error theory, by modifying our previous model of learning in crickets, and we tested a prediction from the model: the model predicts that pharmacological intervention of octopaminergic transmission during appetitive conditioning impairs learning but not formation of reward prediction itself, and it thus predicts no learning in subsequent training. We observed such an "auto-blocking", which could be accounted for by the prediction error theory but not by other competitive theories to account for blocking. This study unambiguously demonstrates validity of the prediction error theory in associative learning.
Spontaneous prediction error generation in schizophrenia.
Directory of Open Access Journals (Sweden)
Yuichi Yamashita
Full Text Available Goal-directed human behavior is enabled by hierarchically-organized neural systems that process executive commands associated with higher brain areas in response to sensory and motor signals from lower brain areas. Psychiatric diseases and psychotic conditions are postulated to involve disturbances in these hierarchical network interactions, but the mechanism for how aberrant disease signals are generated in networks, and a systems-level framework linking disease signals to specific psychiatric symptoms remains undetermined. In this study, we show that neural networks containing schizophrenia-like deficits can spontaneously generate uncompensated error signals with properties that explain psychiatric disease symptoms, including fictive perception, altered sense of self, and unpredictable behavior. To distinguish dysfunction at the behavioral versus network level, we monitored the interactive behavior of a humanoid robot driven by the network. Mild perturbations in network connectivity resulted in the spontaneous appearance of uncompensated prediction errors and altered interactions within the network without external changes in behavior, correlating to the fictive sensations and agency experienced by episodic disease patients. In contrast, more severe deficits resulted in unstable network dynamics resulting in overt changes in behavior similar to those observed in chronic disease patients. These findings demonstrate that prediction error disequilibrium may represent an intrinsic property of schizophrenic brain networks reporting the severity and variability of disease symptoms. Moreover, these results support a systems-level model for psychiatric disease that features the spontaneous generation of maladaptive signals in hierarchical neural networks.
Transition Models with Measurement Errors
Magnac, Thierry; Visser, Michael
1999-01-01
In this paper, we estimate a transition model that allows for measurement errors in the data. The measurement errors arise because the survey design is partly retrospective, so that individuals sometimes forget or misclassify their past labor market transitions. The observed data are adjusted for errors via a measurement-error mechanism. The parameters of the distribution of the true data, and those of the measurement-error mechanism are estimated by a two-stage method. The results, based on ...
Ryu, Y. H.; Hodzic, A.; Barré, J.; Descombes, G.; Minnis, P.
2017-12-01
Clouds play a key role in radiation and hence O3 photochemistry by modulating photolysis rates and light-dependent emissions of biogenic volatile organic compounds (BVOCs). It is not well known, however, how much of the bias in O3 predictions is caused by inaccurate cloud predictions. This study quantifies the errors in surface O3 predictions associated with clouds in summertime over CONUS using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. Cloud fields used for photochemistry are corrected based on satellite cloud retrievals in sensitivity simulations. It is found that the WRF-Chem model is able to detect about 60% of clouds in the right locations and generally underpredicts cloud optical depths. The errors in hourly O3 due to the errors in cloud predictions can be up to 60 ppb. On average in summertime over CONUS, the errors in 8-h average O3 of 1-6 ppb are found to be attributable to those in cloud predictions under cloudy sky conditions. The contribution of changes in photolysis rates due to clouds is found to be larger ( 80 % on average) than that of light-dependent BVOC emissions. The effects of cloud corrections on O3 are about 2 times larger in VOC-limited than NOx-limited regimes, suggesting that the benefits of accurate cloud predictions would be greater in VOC-limited than NOx-limited regimes.
Lock, Jacobus C.; Smit, Willie J.; Treurnicht, Johann
2016-05-01
The Solar Thermal Energy Research Group (STERG) is investigating ways to make heliostats cheaper to reduce the total cost of a concentrating solar power (CSP) plant. One avenue of research is to use unmanned aerial vehicles (UAVs) to automate and assist with the heliostat calibration process. To do this, the pose estimation error of each UAV must be determined and integrated into a calibration procedure. A computer vision (CV) system is used to measure the pose of a quadcopter UAV. However, this CV system contains considerable measurement errors. Since this is a high-dimensional problem, a sophisticated prediction model must be used to estimate the measurement error of the CV system for any given pose measurement vector. This paper attempts to train and validate such a model with the aim of using it to determine the pose error of a quadcopter in a CSP plant setting.
Notes on human error analysis and prediction
International Nuclear Information System (INIS)
Rasmussen, J.
1978-11-01
The notes comprise an introductory discussion of the role of human error analysis and prediction in industrial risk analysis. Following this introduction, different classes of human errors and role in industrial systems are mentioned. Problems related to the prediction of human behaviour in reliability and safety analysis are formulated and ''criteria for analyzability'' which must be met by industrial systems so that a systematic analysis can be performed are suggested. The appendices contain illustrative case stories and a review of human error reports for the task of equipment calibration and testing as found in the US Licensee Event Reports. (author)
Working Memory Load Strengthens Reward Prediction Errors.
Collins, Anne G E; Ciullo, Brittany; Frank, Michael J; Badre, David
2017-04-19
Reinforcement learning (RL) in simple instrumental tasks is usually modeled as a monolithic process in which reward prediction errors (RPEs) are used to update expected values of choice options. This modeling ignores the different contributions of different memory and decision-making systems thought to contribute even to simple learning. In an fMRI experiment, we investigated how working memory (WM) and incremental RL processes interact to guide human learning. WM load was manipulated by varying the number of stimuli to be learned across blocks. Behavioral results and computational modeling confirmed that learning was best explained as a mixture of two mechanisms: a fast, capacity-limited, and delay-sensitive WM process together with slower RL. Model-based analysis of fMRI data showed that striatum and lateral prefrontal cortex were sensitive to RPE, as shown previously, but, critically, these signals were reduced when the learning problem was within capacity of WM. The degree of this neural interaction related to individual differences in the use of WM to guide behavioral learning. These results indicate that the two systems do not process information independently, but rather interact during learning. SIGNIFICANCE STATEMENT Reinforcement learning (RL) theory has been remarkably productive at improving our understanding of instrumental learning as well as dopaminergic and striatal network function across many mammalian species. However, this neural network is only one contributor to human learning and other mechanisms such as prefrontal cortex working memory also play a key role. Our results also show that these other players interact with the dopaminergic RL system, interfering with its key computation of reward prediction errors. Copyright © 2017 the authors 0270-6474/17/374332-11$15.00/0.
An error prediction framework for interferometric SAR data
DEFF Research Database (Denmark)
Mohr, Johan Jacob; Merryman Boncori, John Peter
2008-01-01
points (GCPs) (or an external digital elevation model). In this paper, a simple framework for the prediction of error standard deviation is outlined and investigated. Inputs are GCP position, a priori GCP accuracy, baseline calibration method along with a closed-form model for the covariance......Three of the major error sources in interferometric synthetic aperture radar measurements of terrain elevation and displacement are baseline errors, atmospheric path length errors, and phase unwrapping errors. In many processing schemes, these errors are calibrated out by using ground control...... of atmospheric path length disturbances, and a model for phase unwrapping errors. The procedure can be implemented as a stand-alone add-on to standard interferometric processors. It is validated by using a set of single-frame interferograms acquired over Rome, Italy, and a double difference data set over...
A Geomagnetic Reference Error Model
Maus, S.; Woods, A. J.; Nair, M. C.
2011-12-01
The accuracy of geomagnetic field models, such as the International Geomagnetic Reference Field (IGRF) and the World Magnetic Model (WMM), has benefitted tremendously from the ongoing series of satellite magnetic missions. However, what do we mean by accuracy? When comparing a geomagnetic reference model with a magnetic field measurement (for example of an electronic compass), three contributions play a role: (1) The instrument error, which is not subject of this discussion, (2) the error of commission, namely the error of the model coefficients themselves in representing the geomagnetic main field, and (3) the error of omission, comprising contributions to the geomagnetic field which are not represented in the reference model. The latter can further be subdivided into the omission of the crustal field and the omission of the disturbance field. Several factors have a strong influence on these errors: The error of commission primarily depends on the time elapsed since the last update of the reference model. The omission error for the crustal field depends on altitude of the measurement, while the omission error for the disturbance field has a strong latitudinal dependence, peaking under the auroral electrojets. A further complication arises for the uncertainty in magnetic declination, which is directly dependent on the strength of the horizontal field. Here, we present an error model which takes all of these factors into account. This error model will be implemented as an online-calculator, providing the uncertainty of the magnetic elements at the entered location and time.
Berthet, Lionel; Marty, Renaud; Bourgin, François; Viatgé, Julie; Piotte, Olivier; Perrin, Charles
2017-04-01
An increasing number of operational flood forecasting centres assess the predictive uncertainty associated with their forecasts and communicate it to the end users. This information can match the end-users needs (i.e. prove to be useful for an efficient crisis management) only if it is reliable: reliability is therefore a key quality for operational flood forecasts. In 2015, the French flood forecasting national and regional services (Vigicrues network; www.vigicrues.gouv.fr) implemented a framework to compute quantitative discharge and water level forecasts and to assess the predictive uncertainty. Among the possible technical options to achieve this goal, a statistical analysis of past forecasting errors of deterministic models has been selected (QUOIQUE method, Bourgin, 2014). It is a data-based and non-parametric approach based on as few assumptions as possible about the forecasting error mathematical structure. In particular, a very simple assumption is made regarding the predictive uncertainty distributions for large events outside the range of the calibration data: the multiplicative error distribution is assumed to be constant, whatever the magnitude of the flood. Indeed, the predictive distributions may not be reliable in extrapolation. However, estimating the predictive uncertainty for these rare events is crucial when major floods are of concern. In order to improve the forecasts reliability for major floods, an attempt at combining the operational strength of the empirical statistical analysis and a simple error modelling is done. Since the heteroscedasticity of forecast errors can considerably weaken the predictive reliability for large floods, this error modelling is based on the log-sinh transformation which proved to reduce significantly the heteroscedasticity of the transformed error in a simulation context, even for flood peaks (Wang et al., 2012). Exploratory tests on some operational forecasts issued during the recent floods experienced in
Aberg, Kristoffer C; Müller, Julia; Schwartz, Sophie
2017-01-01
Anticipation and delivery of rewards improves memory formation, but little effort has been made to disentangle their respective contributions to memory enhancement. Moreover, it has been suggested that the effects of reward on memory are mediated by dopaminergic influences on hippocampal plasticity. Yet, evidence linking memory improvements to actual reward computations reflected in the activity of the dopaminergic system, i.e., prediction errors and expected values, is scarce and inconclusive. For example, different previous studies reported that the magnitude of prediction errors during a reinforcement learning task was a positive, negative, or non-significant predictor of successfully encoding simultaneously presented images. Individual sensitivities to reward and punishment have been found to influence the activation of the dopaminergic reward system and could therefore help explain these seemingly discrepant results. Here, we used a novel associative memory task combined with computational modeling and showed independent effects of reward-delivery and reward-anticipation on memory. Strikingly, the computational approach revealed positive influences from both reward delivery, as mediated by prediction error magnitude, and reward anticipation, as mediated by magnitude of expected value, even in the absence of behavioral effects when analyzed using standard methods, i.e., by collapsing memory performance across trials within conditions. We additionally measured trait estimates of reward and punishment sensitivity and found that individuals with increased reward (vs. punishment) sensitivity had better memory for associations encoded during positive (vs. negative) prediction errors when tested after 20 min, but a negative trend when tested after 24 h. In conclusion, modeling trial-by-trial fluctuations in the magnitude of reward, as we did here for prediction errors and expected value computations, provides a comprehensive and biologically plausible description of
Prediction-error of Prediction Error (PPE)-based Reversible Data Hiding
Wu, Han-Zhou; Wang, Hong-Xia; Shi, Yun-Qing
2016-01-01
This paper presents a novel reversible data hiding (RDH) algorithm for gray-scaled images, in which the prediction-error of prediction error (PPE) of a pixel is used to carry the secret data. In the proposed method, the pixels to be embedded are firstly predicted with their neighboring pixels to obtain the corresponding prediction errors (PEs). Then, by exploiting the PEs of the neighboring pixels, the prediction of the PEs of the pixels can be determined. And, a sorting technique based on th...
Scaling prediction errors to reward variability benefits error-driven learning in humans.
Diederen, Kelly M J; Schultz, Wolfram
2015-09-01
Effective error-driven learning requires individuals to adapt learning to environmental reward variability. The adaptive mechanism may involve decays in learning rate across subsequent trials, as shown previously, and rescaling of reward prediction errors. The present study investigated the influence of prediction error scaling and, in particular, the consequences for learning performance. Participants explicitly predicted reward magnitudes that were drawn from different probability distributions with specific standard deviations. By fitting the data with reinforcement learning models, we found scaling of prediction errors, in addition to the learning rate decay shown previously. Importantly, the prediction error scaling was closely related to learning performance, defined as accuracy in predicting the mean of reward distributions, across individual participants. In addition, participants who scaled prediction errors relative to standard deviation also presented with more similar performance for different standard deviations, indicating that increases in standard deviation did not substantially decrease "adapters'" accuracy in predicting the means of reward distributions. However, exaggerated scaling beyond the standard deviation resulted in impaired performance. Thus efficient adaptation makes learning more robust to changing variability. Copyright © 2015 the American Physiological Society.
Owens, P. R.; Libohova, Z.; Seybold, C. A.; Wills, S. A.; Peaslee, S.; Beaudette, D.; Lindbo, D. L.
2017-12-01
The measurement errors and spatial prediction uncertainties of soil properties in the modeling community are usually assessed against measured values when available. However, of equal importance is the assessment of errors and uncertainty impacts on cost benefit analysis and risk assessments. Soil pH was selected as one of the most commonly measured soil properties used for liming recommendations. The objective of this study was to assess the error size from different sources and their implications with respect to management decisions. Error sources include measurement methods, laboratory sources, pedotransfer functions, database transections, spatial aggregations, etc. Several databases of measured and predicted soil pH were used for this study including the United States National Cooperative Soil Survey Characterization Database (NCSS-SCDB), the US Soil Survey Geographic (SSURGO) Database. The distribution of errors among different sources from measurement methods to spatial aggregation showed a wide range of values. The greatest RMSE of 0.79 pH units was from spatial aggregation (SSURGO vs Kriging), while the measurement methods had the lowest RMSE of 0.06 pH units. Assuming the order of data acquisition based on the transaction distance i.e. from measurement method to spatial aggregation the RMSE increased from 0.06 to 0.8 pH units suggesting an "error propagation". This has major implications for practitioners and modeling community. Most soil liming rate recommendations are based on 0.1 pH unit increments, while the desired soil pH level increments are based on 0.4 to 0.5 pH units. Thus, even when the measured and desired target soil pH are the same most guidelines recommend 1 ton ha-1 lime, which translates in 111 ha-1 that the farmer has to factor in the cost-benefit analysis. However, this analysis need to be based on uncertainty predictions (0.5-1.0 pH units) rather than measurement errors (0.1 pH units) which would translate in 555-1,111 investment that
Prediction with measurement errors in finite populations.
Singer, Julio M; Stanek, Edward J; Lencina, Viviana B; González, Luz Mery; Li, Wenjun; Martino, Silvina San
2012-02-01
We address the problem of selecting the best linear unbiased predictor (BLUP) of the latent value (e.g., serum glucose fasting level) of sample subjects with heteroskedastic measurement errors. Using a simple example, we compare the usual mixed model BLUP to a similar predictor based on a mixed model framed in a finite population (FPMM) setup with two sources of variability, the first of which corresponds to simple random sampling and the second, to heteroskedastic measurement errors. Under this last approach, we show that when measurement errors are subject-specific, the BLUP shrinkage constants are based on a pooled measurement error variance as opposed to the individual ones generally considered for the usual mixed model BLUP. In contrast, when the heteroskedastic measurement errors are measurement condition-specific, the FPMM BLUP involves different shrinkage constants. We also show that in this setup, when measurement errors are subject-specific, the usual mixed model predictor is biased but has a smaller mean squared error than the FPMM BLUP which point to some difficulties in the interpretation of such predictors.
Signed reward prediction errors drive declarative learning
De Loof, E.; Ergo, K.; Naert, L.; Janssens, C.; Talsma, D.; van Opstal, F.; Verguts, T.
2018-01-01
Reward prediction errors (RPEs) are thought to drive learning. This has been established in procedural learning (e.g., classical and operant conditioning). However, empirical evidence on whether RPEs drive declarative learning–a quintessentially human form of learning–remains surprisingly absent. We
The feedback-related negativity signals salience prediction errors, not reward prediction errors.
Talmi, Deborah; Atkinson, Ryan; El-Deredy, Wael
2013-05-08
Modulations of the feedback-related negativity (FRN) event-related potential (ERP) have been suggested as a potential biomarker in psychopathology. A dominant theory about this signal contends that it reflects the operation of the neural system underlying reinforcement learning in humans. The theory suggests that this frontocentral negative deflection in the ERP 230-270 ms after the delivery of a probabilistic reward expresses a prediction error signal derived from midbrain dopaminergic projections to the anterior cingulate cortex. We tested this theory by investigating whether FRN will also be observed for an inherently aversive outcome: physical pain. In another session, the outcome was monetary reward instead of pain. As predicted, unexpected reward omissions (a negative reward prediction error) yielded a more negative deflection relative to unexpected reward delivery. Surprisingly, unexpected pain omission (a positive reward prediction error) also yielded a negative deflection relative to unexpected pain delivery. Our data challenge the theory by showing that the FRN expresses aversive prediction errors with the same sign as reward prediction errors. Both FRNs were spatiotemporally and functionally equivalent. We suggest that FRN expresses salience prediction errors rather than reward prediction errors.
Prediction and error of baldcypress stem volume from stump diameter
Bernard R. Parresol
1998-01-01
The need to estimate the volume of removals occurs for many reasons, such as in trespass cases, severance tax reports, and post-harvest assessments. A logarithmic model is presented for prediction of baldcypress total stem cubic foot volume using stump diameter as the independent variable. Because the error of prediction is as important as the volume estimate, the...
Directory of Open Access Journals (Sweden)
Pierre Berthet
Full Text Available Optogenetic stimulation of specific types of medium spiny neurons (MSNs in the striatum has been shown to bias the selection of mice in a two choices task. This shift is dependent on the localisation and on the intensity of the stimulation but also on the recent reward history. We have implemented a way to simulate this increased activity produced by the optical flash in our computational model of the basal ganglia (BG. This abstract model features the direct and indirect pathways commonly described in biology, and a reward prediction pathway (RP. The framework is similar to Actor-Critic methods and to the ventral/dorsal distinction in the striatum. We thus investigated the impact on the selection caused by an added stimulation in each of the three pathways. We were able to reproduce in our model the bias in action selection observed in mice. Our results also showed that biasing the reward prediction is sufficient to create a modification in the action selection. However, we had to increase the percentage of trials with stimulation relative to that in experiments in order to impact the selection. We found that increasing only the reward prediction had a different effect if the stimulation in RP was action dependent (only for a specific action or not. We further looked at the evolution of the change in the weights depending on the stage of learning within a block. A bias in RP impacts the plasticity differently depending on that stage but also on the outcome. It remains to experimentally test how the dopaminergic neurons are affected by specific stimulations of neurons in the striatum and to relate data to predictions of our model.
Error Resilient Video Compression Using Behavior Models
Directory of Open Access Journals (Sweden)
Jacco R. Taal
2004-03-01
Full Text Available Wireless and Internet video applications are inherently subjected to bit errors and packet errors, respectively. This is especially so if constraints on the end-to-end compression and transmission latencies are imposed. Therefore, it is necessary to develop methods to optimize the video compression parameters and the rate allocation of these applications that take into account residual channel bit errors. In this paper, we study the behavior of a predictive (interframe video encoder and model the encoders behavior using only the statistics of the original input data and of the underlying channel prone to bit errors. The resulting data-driven behavior models are then used to carry out group-of-pictures partitioning and to control the rate of the video encoder in such a way that the overall quality of the decoded video with compression and channel errors is optimized.
Stochastic Models of Human Errors
Elshamy, Maged; Elliott, Dawn M. (Technical Monitor)
2002-01-01
Humans play an important role in the overall reliability of engineering systems. More often accidents and systems failure are traced to human errors. Therefore, in order to have meaningful system risk analysis, the reliability of the human element must be taken into consideration. Describing the human error process by mathematical models is a key to analyzing contributing factors. Therefore, the objective of this research effort is to establish stochastic models substantiated by sound theoretic foundation to address the occurrence of human errors in the processing of the space shuttle.
A Prediction Error Estimator for Nonlinear Stochastic Systems
Leontaritis, I.J.; Billings, S.A.
1986-01-01
A prediction error estimation algorithm incorporating model selection and validation techniques is developed for a class of multivariable discrete time stochastic nonlinear systems which can be represented by the NARMAX (Nonlinear AutoRegressive Moving Average Model with eXogenous inputs)
Functional Error Models to Accelerate Nested Sampling
Josset, L.; Elsheikh, A. H.; Demyanov, V.; Lunati, I.
2014-12-01
The main challenge in groundwater problems is the reliance on large numbers of unknown parameters with wide rage of associated uncertainties. To translate this uncertainty to quantities of interest (for instance the concentration of pollutant in a drinking well), a large number of forward flow simulations is required. To make the problem computationally tractable, Josset et al. (2013, 2014) introduced the concept of functional error models. It consists in two elements: a proxy model that is cheaper to evaluate than the full physics flow solver and an error model to account for the missing physics. The coupling of the proxy model and the error models provides reliable predictions that approximate the full physics model's responses. The error model is tailored to the problem at hand by building it for the question of interest. It follows a typical approach in machine learning where both the full physics and proxy models are evaluated for a training set (subset of realizations) and the set of responses is used to construct the error model using functional data analysis. Once the error model is devised, a prediction of the full physics response for a new geostatistical realization can be obtained by computing the proxy response and applying the error model. We propose the use of functional error models in a Bayesian inference context by combining it to the Nested Sampling (Skilling 2006; El Sheikh et al. 2013, 2014). Nested Sampling offers a mean to compute the Bayesian Evidence by transforming the multidimensional integral into a 1D integral. The algorithm is simple: starting with an active set of samples, at each iteration, the sample with the lowest likelihood is kept aside and replaced by a sample of higher likelihood. The main challenge is to find this sample of higher likelihood. We suggest a new approach: first the active set is sampled, both proxy and full physics models are run and the functional error model is build. Then, at each iteration of the Nested
Relationships of Measurement Error and Prediction Error in Observed-Score Regression
Moses, Tim
2012-01-01
The focus of this paper is assessing the impact of measurement errors on the prediction error of an observed-score regression. Measures are presented and described for decomposing the linear regression's prediction error variance into parts attributable to the true score variance and the error variances of the dependent variable and the predictor…
Relative Effects of Trajectory Prediction Errors on the AAC Autoresolver
Lauderdale, Todd
2011-01-01
Trajectory prediction is fundamental to automated separation assurance. Every missed alert, false alert and loss of separation can be traced to one or more errors in trajectory prediction. These errors are a product of many different sources including wind prediction errors, inferred pilot intent errors, surveillance errors, navigation errors and aircraft weight estimation errors. This study analyzes the impact of six different types of errors on the performance of an automated separation assurance system composed of a geometric conflict detection algorithm and the Advanced Airspace Concept Autoresolver resolution algorithm. Results show that, of the error sources considered in this study, top-of-descent errors were the leading contributor to missed alerts and failed resolution maneuvers. Descent-speed errors were another significant contributor, as were cruise-speed errors in certain situations. The results further suggest that increasing horizontal detection and resolution standards are not effective strategies for mitigating these types of error sources.
Prospective detection of large prediction errors: a hypothesis testing approach
International Nuclear Information System (INIS)
Ruan, Dan
2010-01-01
Real-time motion management is important in radiotherapy. In addition to effective monitoring schemes, prediction is required to compensate for system latency, so that treatment can be synchronized with tumor motion. However, it is difficult to predict tumor motion at all times, and it is critical to determine when large prediction errors may occur. Such information can be used to pause the treatment beam or adjust monitoring/prediction schemes. In this study, we propose a hypothesis testing approach for detecting instants corresponding to potentially large prediction errors in real time. We treat the future tumor location as a random variable, and obtain its empirical probability distribution with the kernel density estimation-based method. Under the null hypothesis, the model probability is assumed to be a concentrated Gaussian centered at the prediction output. Under the alternative hypothesis, the model distribution is assumed to be non-informative uniform, which reflects the situation that the future position cannot be inferred reliably. We derive the likelihood ratio test (LRT) for this hypothesis testing problem and show that with the method of moments for estimating the null hypothesis Gaussian parameters, the LRT reduces to a simple test on the empirical variance of the predictive random variable. This conforms to the intuition to expect a (potentially) large prediction error when the estimate is associated with high uncertainty, and to expect an accurate prediction when the uncertainty level is low. We tested the proposed method on patient-derived respiratory traces. The 'ground-truth' prediction error was evaluated by comparing the prediction values with retrospective observations, and the large prediction regions were subsequently delineated by thresholding the prediction errors. The receiver operating characteristic curve was used to describe the performance of the proposed hypothesis testing method. Clinical implication was represented by miss
Modeling Errors in Daily Precipitation Measurements: Additive or Multiplicative?
Tian, Yudong; Huffman, George J.; Adler, Robert F.; Tang, Ling; Sapiano, Matthew; Maggioni, Viviana; Wu, Huan
2013-01-01
The definition and quantification of uncertainty depend on the error model used. For uncertainties in precipitation measurements, two types of error models have been widely adopted: the additive error model and the multiplicative error model. This leads to incompatible specifications of uncertainties and impedes intercomparison and application.In this letter, we assess the suitability of both models for satellite-based daily precipitation measurements in an effort to clarify the uncertainty representation. Three criteria were employed to evaluate the applicability of either model: (1) better separation of the systematic and random errors; (2) applicability to the large range of variability in daily precipitation; and (3) better predictive skills. It is found that the multiplicative error model is a much better choice under all three criteria. It extracted the systematic errors more cleanly, was more consistent with the large variability of precipitation measurements, and produced superior predictions of the error characteristics. The additive error model had several weaknesses, such as non constant variance resulting from systematic errors leaking into random errors, and the lack of prediction capability. Therefore, the multiplicative error model is a better choice.
Signed reward prediction errors drive declarative learning.
Directory of Open Access Journals (Sweden)
Esther De Loof
Full Text Available Reward prediction errors (RPEs are thought to drive learning. This has been established in procedural learning (e.g., classical and operant conditioning. However, empirical evidence on whether RPEs drive declarative learning-a quintessentially human form of learning-remains surprisingly absent. We therefore coupled RPEs to the acquisition of Dutch-Swahili word pairs in a declarative learning paradigm. Signed RPEs (SRPEs; "better-than-expected" signals during declarative learning improved recognition in a follow-up test, with increasingly positive RPEs leading to better recognition. In addition, classic declarative memory mechanisms such as time-on-task failed to explain recognition performance. The beneficial effect of SRPEs on recognition was subsequently affirmed in a replication study with visual stimuli.
Signed reward prediction errors drive declarative learning.
De Loof, Esther; Ergo, Kate; Naert, Lien; Janssens, Clio; Talsma, Durk; Van Opstal, Filip; Verguts, Tom
2018-01-01
Reward prediction errors (RPEs) are thought to drive learning. This has been established in procedural learning (e.g., classical and operant conditioning). However, empirical evidence on whether RPEs drive declarative learning-a quintessentially human form of learning-remains surprisingly absent. We therefore coupled RPEs to the acquisition of Dutch-Swahili word pairs in a declarative learning paradigm. Signed RPEs (SRPEs; "better-than-expected" signals) during declarative learning improved recognition in a follow-up test, with increasingly positive RPEs leading to better recognition. In addition, classic declarative memory mechanisms such as time-on-task failed to explain recognition performance. The beneficial effect of SRPEs on recognition was subsequently affirmed in a replication study with visual stimuli.
Measurement error models with interactions
Midthune, Douglas; Carroll, Raymond J.; Freedman, Laurence S.; Kipnis, Victor
2016-01-01
An important use of measurement error models is to correct regression models for bias due to covariate measurement error. Most measurement error models assume that the observed error-prone covariate (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$W$\\end{document}) is a linear function of the unobserved true covariate (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$X$\\end{document}) plus other covariates (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$Z$\\end{document}) in the regression model. In this paper, we consider models for \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$W$\\end{document} that include interactions between \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$X$\\end{document} and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$Z$\\end{document}. We derive the conditional distribution of
Positioning Errors Predicting Method of Strapdown Inertial Navigation Systems Based on PSO-SVM
Directory of Open Access Journals (Sweden)
Xunyuan Yin
2013-01-01
Full Text Available The strapdown inertial navigation systems (SINS have been widely used for many vehicles, such as commercial airplanes, Unmanned Aerial Vehicles (UAVs, and other types of aircrafts. In order to evaluate the navigation errors precisely and efficiently, a prediction method based on support vector machine (SVM is proposed for positioning error assessment. Firstly, SINS error models that are used for error calculation are established considering several error resources with respect to inertial units. Secondly, flight paths for simulation are designed. Thirdly, the -SVR based prediction method is proposed to predict the positioning errors of navigation systems, and particle swarm optimization (PSO is used for the SVM parameters optimization. Finally, 600 sets of error parameters of SINS are utilized to train the SVM model, which is used for the performance prediction of new navigation systems. By comparing the predicting results with the real errors, the latitudinal predicting accuracy is 92.73%, while the longitudinal predicting accuracy is 91.64%, and PSO is effective to increase the prediction accuracy compared with traditional SVM with fixed parameters. This method is also demonstrated to be effective for error prediction for an entire flight process. Moreover, the prediction method can save 75% of calculation time compared with analyses based on error models.
Error modeling for surrogates of dynamical systems using machine learning
Trehan, Sumeet; Carlberg, Kevin T.; Durlofsky, Louis J.
2017-12-01
A machine-learning-based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (e.g., random forests, LASSO) to map a large set of inexpensively computed `error indicators' (i.e., features) produced by the surrogate model at a given time instance to a prediction of the surrogate-model error in a quantity of interest (QoI). This eliminates the need for the user to hand-select a small number of informative features. The methodology requires a training set of parameter instances at which the time-dependent surrogate-model error is computed by simulating both the high-fidelity and surrogate models. Using these training data, the method first determines regression-model locality (via classification or clustering), and subsequently constructs a `local' regression model to predict the time-instantaneous error within each identified region of feature space. We consider two uses for the resulting error model: (1) as a correction to the surrogate-model QoI prediction at each time instance, and (2) as a way to statistically model arbitrary functions of the time-dependent surrogate-model error (e.g., time-integrated errors). We apply the proposed framework to model errors in reduced-order models of nonlinear oil--water subsurface flow simulations. The reduced-order models used in this work entail application of trajectory piecewise linearization with proper orthogonal decomposition. When the first use of the method is considered, numerical experiments demonstrate consistent improvement in accuracy in the time-instantaneous QoI prediction relative to the original surrogate model, across a large number of test cases. When the second use is considered, results show that the proposed method provides accurate statistical predictions of the time- and well-averaged errors.
LÉVY-BASED ERROR PREDICTION IN CIRCULAR SYSTEMATIC SAMPLING
Directory of Open Access Journals (Sweden)
Kristjana Ýr Jónsdóttir
2013-06-01
Full Text Available In the present paper, Lévy-based error prediction in circular systematic sampling is developed. A model-based statistical setting as in Hobolth and Jensen (2002 is used, but the assumption that the measurement function is Gaussian is relaxed. The measurement function is represented as a periodic stationary stochastic process X obtained by a kernel smoothing of a Lévy basis. The process X may have an arbitrary covariance function. The distribution of the error predictor, based on measurements in n systematic directions is derived. Statistical inference is developed for the model parameters in the case where the covariance function follows the celebrated p-order covariance model.
Learning from sensory and reward prediction errors during motor adaptation.
Izawa, Jun; Shadmehr, Reza
2011-03-01
Voluntary motor commands produce two kinds of consequences. Initially, a sensory consequence is observed in terms of activity in our primary sensory organs (e.g., vision, proprioception). Subsequently, the brain evaluates the sensory feedback and produces a subjective measure of utility or usefulness of the motor commands (e.g., reward). As a result, comparisons between predicted and observed consequences of motor commands produce two forms of prediction error. How do these errors contribute to changes in motor commands? Here, we considered a reach adaptation protocol and found that when high quality sensory feedback was available, adaptation of motor commands was driven almost exclusively by sensory prediction errors. This form of learning had a distinct signature: as motor commands adapted, the subjects altered their predictions regarding sensory consequences of motor commands, and generalized this learning broadly to neighboring motor commands. In contrast, as the quality of the sensory feedback degraded, adaptation of motor commands became more dependent on reward prediction errors. Reward prediction errors produced comparable changes in the motor commands, but produced no change in the predicted sensory consequences of motor commands, and generalized only locally. Because we found that there was a within subject correlation between generalization patterns and sensory remapping, it is plausible that during adaptation an individual's relative reliance on sensory vs. reward prediction errors could be inferred. We suggest that while motor commands change because of sensory and reward prediction errors, only sensory prediction errors produce a change in the neural system that predicts sensory consequences of motor commands.
Cashaback, Joshua G A; McGregor, Heather R; Pun, Henry C H; Buckingham, Gavin; Gribble, Paul L
2017-01-01
The human sensorimotor system is routinely capable of making accurate predictions about an object's weight, which allows for energetically efficient lifts and prevents objects from being dropped. Often, however, poor predictions arise when the weight of an object can vary and sensory cues about object weight are sparse (e.g., picking up an opaque water bottle). The question arises, what strategies does the sensorimotor system use to make weight predictions when one is dealing with an object whose weight may vary? For example, does the sensorimotor system use a strategy that minimizes prediction error (minimal squared error) or one that selects the weight that is most likely to be correct (maximum a posteriori)? In this study we dissociated the predictions of these two strategies by having participants lift an object whose weight varied according to a skewed probability distribution. We found, using a small range of weight uncertainty, that four indexes of sensorimotor prediction (grip force rate, grip force, load force rate, and load force) were consistent with a feedforward strategy that minimizes the square of prediction errors. These findings match research in the visuomotor system, suggesting parallels in underlying processes. We interpret our findings within a Bayesian framework and discuss the potential benefits of using a minimal squared error strategy. Using a novel experimental model of object lifting, we tested whether the sensorimotor system models the weight of objects by minimizing lifting errors or by selecting the statistically most likely weight. We found that the sensorimotor system minimizes the square of prediction errors for object lifting. This parallels the results of studies that investigated visually guided reaching, suggesting an overlap in the underlying mechanisms between tasks that involve different sensory systems. Copyright © 2017 the American Physiological Society.
A vector model for error propagation
Energy Technology Data Exchange (ETDEWEB)
Smith, D.L.; Geraldo, L.P.
1989-03-01
A simple vector model for error propagation, which is entirely equivalent to the conventional statistical approach, is discussed. It offers considerable insight into the nature of error propagation while, at the same time, readily demonstrating the significance of uncertainty correlations. This model is well suited to the analysis of error for sets of neutron-induced reaction cross sections. 7 refs., 1 fig.
Evolutionary modeling-based approach for model errors correction
Directory of Open Access Journals (Sweden)
S. Q. Wan
2012-08-01
Full Text Available The inverse problem of using the information of historical data to estimate model errors is one of the science frontier research topics. In this study, we investigate such a problem using the classic Lorenz (1963 equation as a prediction model and the Lorenz equation with a periodic evolutionary function as an accurate representation of reality to generate "observational data."
On the basis of the intelligent features of evolutionary modeling (EM, including self-organization, self-adaptive and self-learning, the dynamic information contained in the historical data can be identified and extracted by computer automatically. Thereby, a new approach is proposed to estimate model errors based on EM in the present paper. Numerical tests demonstrate the ability of the new approach to correct model structural errors. In fact, it can actualize the combination of the statistics and dynamics to certain extent.
Development of a prototype system for prediction of the group error at the maintenance work
International Nuclear Information System (INIS)
Yoshino, Kenji; Hirotsu, Yuuko
2001-01-01
This paper described on development and performance evaluation of a prototype system for prediction of the group error at the maintenance work. The results so far are as follows. (1) When a user inputs the existence and the grade of the feature factor of the maintenance work as a prediction object, an organization and an organization factor and a group PSF put into the system. The maintenance group error to target can be predicted through the prediction model which consists of a class of seven stages. (2) This system by utilizing the information on a prediction result database, it can be use not only for prediction of a maintenance group but for various safe Activity, such as KYT(Kiken Yochi Training) and TBM(Tool Box Meeting). (3) This system predicts a cooperation error at highest rate, and subsequently. Predicts the detection error at a high rate. and to the decision-making. Error, the transfer error and the state cognitive error, and state error, it has the characteristics predicted at almost same rate. (4) if it has full knowledge even if the feature, such as the enforcement conditions of maintenance work, and organization, even if the user has neither the knowledge about a human factor, users experience, anyone of this system is slight about the extent, generating of a maintenance group error made difficult from the former logically and systematically, it can predict with business time for about 15 minutes. (author)
A Prediction Error and Stepwise Regression Estimation Algorithm for Nonlinear Systems
Billings, S.A.; Voon, W.S.F.
1985-01-01
The identification of nonlinear systems based on a NARMAX (Nonlinear Autoregressive Moving Average model with exogenous inputs)model representation is considered and a combined stepwise regression/prediction error estimation algorithm is derived.
Steen Magnussen; Ronald E. McRoberts; Erkki O. Tomppo
2009-01-01
New model-based estimators of the uncertainty of pixel-level and areal k-nearest neighbour (knn) predictions of attribute Y from remotely-sensed ancillary data X are presented. Non-parametric functions predict Y from scalar 'Single Index Model' transformations of X. Variance functions generated...
Human medial frontal cortex activity predicts learning from errors.
Hester, Robert; Barre, Natalie; Murphy, Kevin; Silk, Tim J; Mattingley, Jason B
2008-08-01
Learning from errors is a critical feature of human cognition. It underlies our ability to adapt to changing environmental demands and to tune behavior for optimal performance. The posterior medial frontal cortex (pMFC) has been implicated in the evaluation of errors to control behavior, although it has not previously been shown that activity in this region predicts learning from errors. Using functional magnetic resonance imaging, we examined activity in the pMFC during an associative learning task in which participants had to recall the spatial locations of 2-digit targets and were provided with immediate feedback regarding accuracy. Activity within the pMFC was significantly greater for errors that were subsequently corrected than for errors that were repeated. Moreover, pMFC activity during recall errors predicted future responses (correct vs. incorrect), despite a sizeable interval (on average 70 s) between an error and the next presentation of the same recall probe. Activity within the hippocampus also predicted future performance and correlated with error-feedback-related pMFC activity. A relationship between performance expectations and pMFC activity, in the absence of differing reinforcement value for errors, is consistent with the idea that error-related pMFC activity reflects the extent to which an outcome is "worse than expected."
A causal link between prediction errors, dopamine neurons and learning.
Steinberg, Elizabeth E; Keiflin, Ronald; Boivin, Josiah R; Witten, Ilana B; Deisseroth, Karl; Janak, Patricia H
2013-07-01
Situations in which rewards are unexpectedly obtained or withheld represent opportunities for new learning. Often, this learning includes identifying cues that predict reward availability. Unexpected rewards strongly activate midbrain dopamine neurons. This phasic signal is proposed to support learning about antecedent cues by signaling discrepancies between actual and expected outcomes, termed a reward prediction error. However, it is unknown whether dopamine neuron prediction error signaling and cue-reward learning are causally linked. To test this hypothesis, we manipulated dopamine neuron activity in rats in two behavioral procedures, associative blocking and extinction, that illustrate the essential function of prediction errors in learning. We observed that optogenetic activation of dopamine neurons concurrent with reward delivery, mimicking a prediction error, was sufficient to cause long-lasting increases in cue-elicited reward-seeking behavior. Our findings establish a causal role for temporally precise dopamine neuron signaling in cue-reward learning, bridging a critical gap between experimental evidence and influential theoretical frameworks.
Measurement error models, methods, and applications
Buonaccorsi, John P
2010-01-01
Over the last 20 years, comprehensive strategies for treating measurement error in complex models and accounting for the use of extra data to estimate measurement error parameters have emerged. Focusing on both established and novel approaches, ""Measurement Error: Models, Methods, and Applications"" provides an overview of the main techniques and illustrates their application in various models. It describes the impacts of measurement errors on naive analyses that ignore them and presents ways to correct for them across a variety of statistical models, from simple one-sample problems to regres
Berner, J; Doblas-Reyes, F J; Palmer, T N; Shutts, G; Weisheimer, A
2008-07-28
The impact of a nonlinear dynamic cellular automaton (CA) model, as a representation of the partially stochastic aspects of unresolved scales in global climate models, is studied in the European Centre for Medium Range Weather Forecasts coupled ocean-atmosphere model. Two separate aspects are discussed: impact on the systematic error of the model, and impact on the skill of seasonal forecasts. Significant reductions of systematic error are found both in the tropics and in the extratropics. Such reductions can be understood in terms of the inherently nonlinear nature of climate, in particular how energy injected by the CA at the near-grid scale can backscatter nonlinearly to larger scales. In addition, significant improvements in the probabilistic skill of seasonal forecasts are found in terms of a number of different variables such as temperature, precipitation and sea-level pressure. Such increases in skill can be understood both in terms of the reduction of systematic error as mentioned above, and in terms of the impact on ensemble spread of the CA's representation of inherent model uncertainty.
Bayesian Total Error Analysis - An Error Sensitive Approach to Model Calibration
Franks, S. W.; Kavetski, D.; Kuczera, G.
2002-12-01
The majority of environmental models require calibration of their parameters before meaningful predictions of catchment behaviour can be made. Despite the importance of reliable parameter estimates, there are growing concerns about the ability of objective-based inference methods to adequately calibrate environmental models. The problem lies with the formulation of the objective or likelihood function, which is currently implemented using essentially ad-hoc methods. We outline limitations of current calibration methodologies and introduce a more systematic Bayesian Total Error Analysis (BATEA) framework for environmental model calibration and validation, which imposes a hitherto missing rigour in environmental modelling by requiring the specification of physically realistic model and data uncertainty models with explicit assumptions that can and must be tested against available evidence. The BATEA formalism enables inference of the hydrological parameters and also of any latent variables of the uncertainty models, e.g., precipitation depth errors. The latter could be useful for improving data sampling and measurement methodologies. In addition, distinguishing between the various sources of errors will reduce the current ambiguity about parameter and predictive uncertainty and enable rational testing of environmental models' hypotheses. Monte Carlo Markov Chain methods are employed to manage the increased computational requirements of BATEA. A case study using synthetic data demonstrates that explicitly accounting for forcing errors leads to immediate advantages over traditional regression (e.g., standard least squares calibration) that ignore rainfall history corruption and pseudo-likelihood methods (e.g., GLUE) do not explicitly characterise data and model errors. It is precisely data and model errors that are responsible for the need for calibration in the first place; we expect that understanding these errors will force fundamental shifts in the model
Error estimation and adaptive chemical transport modeling
Directory of Open Access Journals (Sweden)
Malte Braack
2014-09-01
Full Text Available We present a numerical method to use several chemical transport models of increasing accuracy and complexity in an adaptive way. In largest parts of the domain, a simplified chemical model may be used, whereas in certain regions a more complex model is needed for accuracy reasons. A mathematically derived error estimator measures the modeling error and provides information where to use more accurate models. The error is measured in terms of output functionals. Therefore, one has to consider adjoint problems which carry sensitivity information. This concept is demonstrated by means of ozone formation and pollution emission.
Pokhrel, Samir; Saha, Subodh Kumar; Dhakate, Ashish; Rahman, Hasibur; Chaudhari, Hemantkumar S.; Salunke, Kiran; Hazra, Anupam; Sujith, K.; Sikka, D. R.
2016-04-01
A detailed analysis of sensitivity to the initial condition for the simulation of the Indian summer monsoon using retrospective forecast by the latest version of the Climate Forecast System version-2 (CFSv2) is carried out. This study primarily focuses on the tropical region of Indian and Pacific Ocean basin, with special emphasis on the Indian land region. The simulated seasonal mean and the inter-annual standard deviations of rainfall, upper and lower level atmospheric circulations and Sea Surface Temperature (SST) tend to be more skillful as the lead forecast time decreases (5 month lead to 0 month lead time i.e. L5-L0). In general spatial correlation (bias) increases (decreases) as forecast lead time decreases. This is further substantiated by their averaged value over the selected study regions over the Indian and Pacific Ocean basins. The tendency of increase (decrease) of model bias with increasing (decreasing) forecast lead time also indicates the dynamical drift of the model. Large scale lower level circulation (850 hPa) shows enhancement of anomalous westerlies (easterlies) over the tropical region of the Indian Ocean (Western Pacific Ocean), which indicates the enhancement of model error with the decrease in lead time. At the upper level circulation (200 hPa) biases in both tropical easterly jet and subtropical westerlies jet tend to decrease as the lead time decreases. Despite enhancement of the prediction skill, mean SST bias seems to be insensitive to the initialization. All these biases are significant and together they make CFSv2 vulnerable to seasonal uncertainties in all the lead times. Overall the zeroth lead (L0) seems to have the best skill, however, in case of Indian summer monsoon rainfall (ISMR), the 3 month lead forecast time (L3) has the maximum ISMR prediction skill. This is valid using different independent datasets, wherein these maximum skill scores are 0.64, 0.42 and 0.57 with respect to the Global Precipitation Climatology Project
The Pupillary Orienting Response Predicts Adaptive Behavioral Adjustment after Errors.
Directory of Open Access Journals (Sweden)
Peter R Murphy
Full Text Available Reaction time (RT is commonly observed to slow down after an error. This post-error slowing (PES has been thought to arise from the strategic adoption of a more cautious response mode following deployment of cognitive control. Recently, an alternative account has suggested that PES results from interference due to an error-evoked orienting response. We investigated whether error-related orienting may in fact be a pre-cursor to adaptive post-error behavioral adjustment when the orienting response resolves before subsequent trial onset. We measured pupil dilation, a prototypical measure of autonomic orienting, during performance of a choice RT task with long inter-stimulus intervals, and found that the trial-by-trial magnitude of the error-evoked pupil response positively predicted both PES magnitude and the likelihood that the following response would be correct. These combined findings suggest that the magnitude of the error-related orienting response predicts an adaptive change of response strategy following errors, and thereby promote a reconciliation of the orienting and adaptive control accounts of PES.
Structural Model Error and Decision Relevancy
Goldsby, M.; Lusk, G.
2017-12-01
The extent to which climate models can underwrite specific climate policies has long been a contentious issue. Skeptics frequently deny that climate models are trustworthy in an attempt to undermine climate action, whereas policy makers often desire information that exceeds the capabilities of extant models. While not skeptics, a group of mathematicians and philosophers [Frigg et al. (2014)] recently argued that even tiny differences between the structure of a complex dynamical model and its target system can lead to dramatic predictive errors, possibly resulting in disastrous consequences when policy decisions are based upon those predictions. They call this result the Hawkmoth effect (HME), and seemingly use it to rebuke rightwing proposals to forgo mitigation in favor of adaptation. However, a vigorous debate has emerged between Frigg et al. on one side and another philosopher-mathematician pair [Winsberg and Goodwin (2016)] on the other. On one hand, Frigg et al. argue that their result shifts the burden to climate scientists to demonstrate that their models do not fall prey to the HME. On the other hand, Winsberg and Goodwin suggest that arguments like those asserted by Frigg et al. can be, if taken seriously, "dangerous": they fail to consider the variety of purposes for which models can be used, and thus too hastily undermine large swaths of climate science. They put the burden back on Frigg et al. to show their result has any effect on climate science. This paper seeks to attenuate this debate by establishing an irenic middle position; we find that there is more agreement between sides than it first seems. We distinguish a `decision standard' from a `burden of proof', which helps clarify the contributions to the debate from both sides. In making this distinction, we argue that scientists bear the burden of assessing the consequences of HME, but that the standard Frigg et al. adopt for decision relevancy is too strict.
Winham, Stacey J; Motsinger-Reif, Alison A
2011-01-01
The standard in genetic association studies of complex diseases is replication and validation of positive results, with an emphasis on assessing the predictive value of associations. In response to this need, a number of analytical approaches have been developed to identify predictive models that account for complex genetic etiologies. Multifactor Dimensionality Reduction (MDR) is a commonly used, highly successful method designed to evaluate potential gene-gene interactions. MDR relies on classification error in a cross-validation framework to rank and evaluate potentially predictive models. Previous work has demonstrated the high power of MDR, but has not considered the accuracy and variance of the MDR prediction error estimate. Currently, we evaluate the bias and variance of the MDR error estimate as both a retrospective and prospective estimator and show that MDR can both underestimate and overestimate error. We argue that a prospective error estimate is necessary if MDR models are used for prediction, and propose a bootstrap resampling estimate, integrating population prevalence, to accurately estimate prospective error. We demonstrate that this bootstrap estimate is preferable for prediction to the error estimate currently produced by MDR. While demonstrated with MDR, the proposed estimation is applicable to all data-mining methods that use similar estimates. © 2010 The Authors Annals of Human Genetics © 2010 Blackwell Publishing Ltd/University College London.
DEFF Research Database (Denmark)
Wu, Guanglei; Bai, Shaoping; Kepler, Jørgen Asbøl
2012-01-01
This paper deals with the error modelling and analysis of a 3-PPR planar parallel manipulator with joint clearances. The kinematics and the Cartesian workspace of the manipulator are analyzed. An error model is established with considerations of both configuration errors and joint clearances. Using...... this model, the upper bounds and distributions of the pose errors for this manipulator are established. The results are compared with experimental measurements and show the effectiveness of the error prediction model....
Mesoscale Predictability and Error Growth in Short Range Ensemble Forecasts
Gingrich, Mark
Although it was originally suggested that small-scale, unresolved errors corrupt forecasts at all scales through an inverse error cascade, some authors have proposed that those mesoscale circulations resulting from stationary forcing on the larger scale may inherit the predictability of the large-scale motions. Further, the relative contributions of large- and small-scale uncertainties in producing error growth in the mesoscales remain largely unknown. Here, 100 member ensemble forecasts are initialized from an ensemble Kalman filter (EnKF) to simulate two winter storms impacting the East Coast of the United States in 2010. Four verification metrics are considered: the local snow water equivalence, total liquid water, and 850 hPa temperatures representing mesoscale features; and the sea level pressure field representing a synoptic feature. It is found that while the predictability of the mesoscale features can be tied to the synoptic forecast, significant uncertainty existed on the synoptic scale at lead times as short as 18 hours. Therefore, mesoscale details remained uncertain in both storms due to uncertainties at the large scale. Additionally, the ensemble perturbation kinetic energy did not show an appreciable upscale propagation of error for either case. Instead, the initial condition perturbations from the cycling EnKF were maximized at large scales and immediately amplified at all scales without requiring initial upscale propagation. This suggests that relatively small errors in the synoptic-scale initialization may have more importance in limiting predictability than errors in the unresolved, small-scale initial conditions.
Mathematical Model of the Laser Gyro Errors
Directory of Open Access Journals (Sweden)
V. N. Enin
2017-01-01
Full Text Available The paper presents the analysed and systemised results of the experimental study of laser gyro (LG errors. Determines a structure of the resulting LG error, as a linear combination of the random processes, characterizing natural and technical fluctuations of difference frequency of the counter-propagating waves, with a random constant zero shift available in the sensor readings. Formulates the requirements for the structure and form of the analytic description of the error model. Shows a generalized model of the LG fluctuation processes, on the basis of which a mathematical model of LG errors was developed as an inertial sensor.The model is represented by a system of the stochastic differential equations and functional relationships to characterize a resulting error of the sensor. The paper provides a correlation analysis of the model equations and final equations obtained for the mean-square values of the particular components, which allow us to identify the resulting error parameters. The model parameters are presented through the values of the power spectral density of the particular components. The discrete form of the model is considered, the convergence of continuous and difference equations is shown in fulfilling conditions of the limiting transition. Further research activities are defined.
Parameters and error of a theoretical model
International Nuclear Information System (INIS)
Moeller, P.; Nix, J.R.; Swiatecki, W.
1986-09-01
We propose a definition for the error of a theoretical model of the type whose parameters are determined from adjustment to experimental data. By applying a standard statistical method, the maximum-likelihoodlmethod, we derive expressions for both the parameters of the theoretical model and its error. We investigate the derived equations by solving them for simulated experimental and theoretical quantities generated by use of random number generators. 2 refs., 4 tabs
Prediction Error During Functional and Non-Functional Action Sequences
DEFF Research Database (Denmark)
Nielbo, Kristoffer Laigaard; Sørensen, Jesper
2013-01-01
behavioral study has shown that human subjects segment non-functional behavior in a more fine-grained way than functional behavior. This increase in segmentation rate implies that non-functionality elicits a stronger error signal. To further explore the implications, two computer simulations using simple...... error. Non-functionality in this proximal sense is a feature of many socio-cultural practices, such as those found in religious rituals private and social, as well as pathological practices, such as ritualized behavior found among people suffering from Obsessive Compulsory Disorder (OCD). A recent...... recurrent networks were made and the results are presented in this article. The simulations show that non-functional action sequences do indeed increase prediction error, but that context representations, such as abstract goal information, can modulate the error signal considerably. It is also shown...
Mini-review: Prediction errors, attention and associative learning.
Holland, Peter C; Schiffino, Felipe L
2016-05-01
Most modern theories of associative learning emphasize a critical role for prediction error (PE, the difference between received and expected events). One class of theories, exemplified by the Rescorla-Wagner (1972) model, asserts that PE determines the effectiveness of the reinforcer or unconditioned stimulus (US): surprising reinforcers are more effective than expected ones. A second class, represented by the Pearce-Hall (1980) model, argues that PE determines the associability of conditioned stimuli (CSs), the rate at which they may enter into new learning: the surprising delivery or omission of a reinforcer enhances subsequent processing of the CSs that were present when PE was induced. In this mini-review we describe evidence, mostly from our laboratory, for PE-induced changes in the associability of both CSs and USs, and the brain systems involved in the coding, storage and retrieval of these altered associability values. This evidence favors a number of modifications to behavioral models of how PE influences event processing, and suggests the involvement of widespread brain systems in animals' responses to PE. Copyright © 2016 Elsevier Inc. All rights reserved.
Differing Air Traffic Controller Responses to Similar Trajectory Prediction Errors
Mercer, Joey; Hunt-Espinosa, Sarah; Bienert, Nancy; Laraway, Sean
2016-01-01
A Human-In-The-Loop simulation was conducted in January of 2013 in the Airspace Operations Laboratory at NASA's Ames Research Center. The simulation airspace included two en route sectors feeding the northwest corner of Atlanta's Terminal Radar Approach Control. The focus of this paper is on how uncertainties in the study's trajectory predictions impacted the controllers ability to perform their duties. Of particular interest is how the controllers interacted with the delay information displayed in the meter list and data block while managing the arrival flows. Due to wind forecasts with 30-knot over-predictions and 30-knot under-predictions, delay value computations included errors of similar magnitude, albeit in opposite directions. However, when performing their duties in the presence of these errors, did the controllers issue clearances of similar magnitude, albeit in opposite directions?
Soft error mechanisms, modeling and mitigation
Sayil, Selahattin
2016-01-01
This book introduces readers to various radiation soft-error mechanisms such as soft delays, radiation induced clock jitter and pulses, and single event (SE) coupling induced effects. In addition to discussing various radiation hardening techniques for combinational logic, the author also describes new mitigation strategies targeting commercial designs. Coverage includes novel soft error mitigation techniques such as the Dynamic Threshold Technique and Soft Error Filtering based on Transmission gate with varied gate and body bias. The discussion also includes modeling of SE crosstalk noise, delay and speed-up effects. Various mitigation strategies to eliminate SE coupling effects are also introduced. Coverage also includes the reliability of low power energy-efficient designs and the impact of leakage power consumption optimizations on soft error robustness. The author presents an analysis of various power optimization techniques, enabling readers to make design choices that reduce static power consumption an...
Multiple indicators, multiple causes measurement error models.
Tekwe, Carmen D; Carter, Randy L; Cullings, Harry M; Carroll, Raymond J
2014-11-10
Multiple indicators, multiple causes (MIMIC) models are often employed by researchers studying the effects of an unobservable latent variable on a set of outcomes, when causes of the latent variable are observed. There are times, however, when the causes of the latent variable are not observed because measurements of the causal variable are contaminated by measurement error. The objectives of this paper are as follows: (i) to develop a novel model by extending the classical linear MIMIC model to allow both Berkson and classical measurement errors, defining the MIMIC measurement error (MIMIC ME) model; (ii) to develop likelihood-based estimation methods for the MIMIC ME model; and (iii) to apply the newly defined MIMIC ME model to atomic bomb survivor data to study the impact of dyslipidemia and radiation dose on the physical manifestations of dyslipidemia. As a by-product of our work, we also obtain a data-driven estimate of the variance of the classical measurement error associated with an estimate of the amount of radiation dose received by atomic bomb survivors at the time of their exposure. Copyright © 2014 John Wiley & Sons, Ltd.
Prediction error in reinforcement learning: a meta-analysis of neuroimaging studies.
Garrison, Jane; Erdeniz, Burak; Done, John
2013-08-01
Activation likelihood estimation (ALE) meta-analyses were used to examine the neural correlates of prediction error in reinforcement learning. The findings are interpreted in the light of current computational models of learning and action selection. In this context, particular consideration is given to the comparison of activation patterns from studies using instrumental and Pavlovian conditioning, and where reinforcement involved rewarding or punishing feedback. The striatum was the key brain area encoding for prediction error, with activity encompassing dorsal and ventral regions for instrumental and Pavlovian reinforcement alike, a finding which challenges the functional separation of the striatum into a dorsal 'actor' and a ventral 'critic'. Prediction error activity was further observed in diverse areas of predominantly anterior cerebral cortex including medial prefrontal cortex and anterior cingulate cortex. Distinct patterns of prediction error activity were found for studies using rewarding and aversive reinforcers; reward prediction errors were observed primarily in the striatum while aversive prediction errors were found more widely including insula and habenula. Copyright © 2013 Elsevier Ltd. All rights reserved.
Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time.
Limongi, Roberto; Silva, Angélica M
2016-11-01
The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production - where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.
QUANTIFIERS UNDONE: REVERSING PREDICTABLE SPEECH ERRORS IN COMPREHENSION.
Frazier, Lyn; Clifton, Charles
2011-03-01
Speakers predictably make errors during spontaneous speech. Listeners may identify such errors and repair the input, or their analysis of the input, accordingly. Two written questionnaire studies investigated error compensation mechanisms in sentences with doubled quantifiers such as Many students often turn in their assignments late . Results show a considerable number of undoubled interpretations for all items tested (though fewer for sentences containing doubled negation than for sentences containing many-often, every-always or few-seldom.) This evidence shows that the compositional form-meaning pairing supplied by the grammar is not the only systematic mapping between form and meaning. Implicit knowledge of the workings of the performance systems provides an additional mechanism for pairing sentence form and meaning. Alternate accounts of the data based on either a concord interpretation or an emphatic interpretation of the doubled quantifier don't explain why listeners fail to apprehend the 'extra meaning' added by the potentially redundant material only in limited circumstances.
Artificial neural network implementation of a near-ideal error prediction controller
Mcvey, Eugene S.; Taylor, Lynore Denise
1992-01-01
responses be known for a particular input and modeled plant. These responses are used in the error prediction controller. An analysis was done on the general dynamic behavior that results from including a digital error predictor in a control loop and these were compared to those including the near-ideal Neural Network error predictor. This analysis was done for a second and third order system.
Hierarchical prediction errors in midbrain and septum during social learning.
Diaconescu, Andreea O; Mathys, Christoph; Weber, Lilian A E; Kasper, Lars; Mauer, Jan; Stephan, Klaas E
2017-04-01
Social learning is fundamental to human interactions, yet its computational and physiological mechanisms are not well understood. One prominent open question concerns the role of neuromodulatory transmitters. We combined fMRI, computational modelling and genetics to address this question in two separate samples (N = 35, N = 47). Participants played a game requiring inference on an adviser's intentions whose motivation to help or mislead changed over time. Our analyses suggest that hierarchically structured belief updates about current advice validity and the adviser's trustworthiness, respectively, depend on different neuromodulatory systems. Low-level prediction errors (PEs) about advice accuracy not only activated regions known to support 'theory of mind', but also the dopaminergic midbrain. Furthermore, PE responses in ventral striatum were influenced by the Met/Val polymorphism of the Catechol-O-Methyltransferase (COMT) gene. By contrast, high-level PEs ('expected uncertainty') about the adviser's fidelity activated the cholinergic septum. These findings, replicated in both samples, have important implications: They suggest that social learning rests on hierarchically related PEs encoded by midbrain and septum activity, respectively, in the same manner as other forms of learning under volatility. Furthermore, these hierarchical PEs may be broadcast by dopaminergic and cholinergic projections to induce plasticity specifically in cortical areas known to represent beliefs about others. © The Author (2017). Published by Oxford University Press.
The Sensitivity of Evapotranspiration Models to Errors in Model ...
African Journals Online (AJOL)
Three levels of sensitivity, herein termed sensitivity, ratings, were established, namely: Highly Sensitive (Rating:1); Moderately sensitive' (Rating:2); and 'not too sensitive'(Rating: 3). The ratings were based on the amount of error in the measured parameter to introduce + 10% relative error in the predicted Et. The level of ...
León Blanco, José M; González-R, Pedro L; Arroyo García, Carmen Martina; Cózar-Bernal, María José; Calle Suárez, Marcos; Canca Ortiz, David; Rabasco Álvarez, Antonio María; González Rodríguez, María Luisa
2018-01-01
This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10 000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.
Understanding error generation in fused deposition modeling
International Nuclear Information System (INIS)
Bochmann, Lennart; Transchel, Robert; Wegener, Konrad; Bayley, Cindy; Helu, Moneer; Dornfeld, David
2015-01-01
Additive manufacturing offers completely new possibilities for the manufacturing of parts. The advantages of flexibility and convenience of additive manufacturing have had a significant impact on many industries, and optimizing part quality is crucial for expanding its utilization. This research aims to determine the sources of imprecision in fused deposition modeling (FDM). Process errors in terms of surface quality, accuracy and precision are identified and quantified, and an error-budget approach is used to characterize errors of the machine tool. It was determined that accuracy and precision in the y direction (0.08–0.30 mm) are generally greater than in the x direction (0.12–0.62 mm) and the z direction (0.21–0.57 mm). Furthermore, accuracy and precision tend to decrease at increasing axis positions. The results of this work can be used to identify possible process improvements in the design and control of FDM technology. (paper)
Understanding error generation in fused deposition modeling
Bochmann, Lennart; Bayley, Cindy; Helu, Moneer; Transchel, Robert; Wegener, Konrad; Dornfeld, David
2015-03-01
Additive manufacturing offers completely new possibilities for the manufacturing of parts. The advantages of flexibility and convenience of additive manufacturing have had a significant impact on many industries, and optimizing part quality is crucial for expanding its utilization. This research aims to determine the sources of imprecision in fused deposition modeling (FDM). Process errors in terms of surface quality, accuracy and precision are identified and quantified, and an error-budget approach is used to characterize errors of the machine tool. It was determined that accuracy and precision in the y direction (0.08-0.30 mm) are generally greater than in the x direction (0.12-0.62 mm) and the z direction (0.21-0.57 mm). Furthermore, accuracy and precision tend to decrease at increasing axis positions. The results of this work can be used to identify possible process improvements in the design and control of FDM technology.
Nonclassical measurements errors in nonlinear models
DEFF Research Database (Denmark)
Madsen, Edith; Mulalic, Ismir
that contains very detailed information about incomes. This gives a unique opportunity to learn about the magnitude and nature of the measurement error in income reported by the respondents in the Danish NTS compared to income from the administrative register (correct measure). We find that the classical...... of a households face. In this case an important policy parameter is the effect of income (reflecting the household budget) on the choice of travel mode. This paper deals with the consequences of measurement error in income (an explanatory variable) in discrete choice models. Since it is likely to give misleading...... estimates of the income effect it is of interest to investigate the magnitude of the estimation bias and if possible use estimation techniques that take the measurement error problem into account. We use data from the Danish National Travel Survey (NTS) and merge it with administrative register data...
International Nuclear Information System (INIS)
Trehan, Sumeet; Carlberg, Kevin T.; Durlofsky, Louis J.
2017-01-01
A machine learning–based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (eg, random forests, and LASSO) to map a large set of inexpensively computed “error indicators” (ie, features) produced by the surrogate model at a given time instance to a prediction of the surrogate-model error in a quantity of interest (QoI). This eliminates the need for the user to hand-select a small number of informative features. The methodology requires a training set of parameter instances at which the time-dependent surrogate-model error is computed by simulating both the high-fidelity and surrogate models. Using these training data, the method first determines regression-model locality (via classification or clustering) and subsequently constructs a “local” regression model to predict the time-instantaneous error within each identified region of feature space. We consider 2 uses for the resulting error model: (1) as a correction to the surrogate-model QoI prediction at each time instance and (2) as a way to statistically model arbitrary functions of the time-dependent surrogate-model error (eg, time-integrated errors). We then apply the proposed framework to model errors in reduced-order models of nonlinear oil-water subsurface flow simulations, with time-varying well-control (bottom-hole pressure) parameters. The reduced-order models used in this work entail application of trajectory piecewise linearization in conjunction with proper orthogonal decomposition. Moreover, when the first use of the method is considered, numerical experiments demonstrate consistent improvement in accuracy in the time-instantaneous QoI prediction relative to the original surrogate model, across a large number of test cases. When the second use is considered, results show that the proposed method provides accurate statistical predictions of the time- and well
Probabilistic modeling of systematic errors in two-hybrid experiments.
Sontag, David; Singh, Rohit; Berger, Bonnie
2007-01-01
We describe a novel probabilistic approach to estimating errors in two-hybrid (2H) experiments. Such experiments are frequently used to elucidate protein-protein interaction networks in a high-throughput fashion; however, a significant challenge with these is their relatively high error rate, specifically, a high false-positive rate. We describe a comprehensive error model for 2H data, accounting for both random and systematic errors. The latter arise from limitations of the 2H experimental protocol: in theory, the reporting mechanism of a 2H experiment should be activated if and only if the two proteins being tested truly interact; in practice, even in the absence of a true interaction, it may be activated by some proteins - either by themselves or through promiscuous interaction with other proteins. We describe a probabilistic relational model that explicitly models the above phenomenon and use Markov Chain Monte Carlo (MCMC) algorithms to compute both the probability of an observed 2H interaction being true as well as the probability of individual proteins being self-activating/promiscuous. This is the first approach that explicitly models systematic errors in protein-protein interaction data; in contrast, previous work on this topic has modeled errors as being independent and random. By explicitly modeling the sources of noise in 2H systems, we find that we are better able to make use of the available experimental data. In comparison with Bader et al.'s method for estimating confidence in 2H predicted interactions, the proposed method performed 5-10% better overall, and in particular regimes improved prediction accuracy by as much as 76%. http://theory.csail.mit.edu/probmod2H
Effect of GPS errors on Emission model
DEFF Research Database (Denmark)
Lehmann, Anders; Gross, Allan
n this paper we will show how Global Positioning Services (GPS) data obtained from smartphones can be used to model air quality in urban settings. The paper examines the uncertainty of smartphone location utilising GPS, and ties this location uncertainty to air quality models. The results presented...... in this paper indicates that the location error from using smartphones is within the accuracy needed to use the location data in air quality modelling. The nature of smartphone location data enables more accurate and near real time air quality modelling and monitoring. The location data is harvested from user...
Temporal dissociation of salience and prediction error responses to appetitive and aversive taste.
Hird, E J; El-Deredy, W; Jones, A; Talmi, D
2018-02-01
The feedback-related negativity (FRN), a frontocentral ERP occurring 200-350 ms after emotionally valued outcomes, has been posited as the neural correlate of reward prediction error, a key component of associative learning. Recent evidence challenged this interpretation and has led to the suggestion that this ERP expresses salience instead. Here, we distinguish between utility prediction error and salience by delivering or withholding hedonistically matched appetitive and aversive tastes, and measure ERPs to cues signaling each taste. We observed a typical FRN (computed as the loss-minus-gain difference wave) to appetitive taste, but a reverse FRN to aversive taste. When tested axiomatically, frontocentral ERPs showed a salience response across tastes, with a particularly early response to outcome delivery, supporting recent propositions of a fast, unsigned, and unspecific response to salient stimuli. ERPs also expressed aversive prediction error peaking at 285 ms, which conformed to the logic of an axiomatic model of prediction error. With stimuli that most resemble those used in animal models, we did not detect any frontocentral ERP signal for utility prediction error, in contrast with dominant views of the functional role of the FRN ERP. We link the animal and human literature and present a challenge for current perspectives on associative learning research using ERPs. © 2017 Society for Psychophysiological Research.
Mulej Bratec, Satja; Xie, Xiyao; Schmid, Gabriele; Doll, Anselm; Schilbach, Leonhard; Zimmer, Claus; Wohlschläger, Afra; Riedl, Valentin; Sorg, Christian
2015-12-01
Cognitive emotion regulation is a powerful way of modulating emotional responses. However, despite the vital role of emotions in learning, it is unknown whether the effect of cognitive emotion regulation also extends to the modulation of learning. Computational models indicate prediction error activity, typically observed in the striatum and ventral tegmental area, as a critical neural mechanism involved in associative learning. We used model-based fMRI during aversive conditioning with and without cognitive emotion regulation to test the hypothesis that emotion regulation would affect prediction error-related neural activity in the striatum and ventral tegmental area, reflecting an emotion regulation-related modulation of learning. Our results show that cognitive emotion regulation reduced emotion-related brain activity, but increased prediction error-related activity in a network involving ventral tegmental area, hippocampus, insula and ventral striatum. While the reduction of response activity was related to behavioral measures of emotion regulation success, the enhancement of prediction error-related neural activity was related to learning performance. Furthermore, functional connectivity between the ventral tegmental area and ventrolateral prefrontal cortex, an area involved in regulation, was specifically increased during emotion regulation and likewise related to learning performance. Our data, therefore, provide first-time evidence that beyond reducing emotional responses, cognitive emotion regulation affects learning by enhancing prediction error-related activity, potentially via tegmental dopaminergic pathways. Copyright © 2015 Elsevier Inc. All rights reserved.
Fernandez Pierna, J.A.; Lin, L.; Wahl, F.; Faber, N.M.; Massart, D.L.
2003-01-01
The prediction uncertainty is studied when using a multivariate partial least squares regression (PLSR) model constructed with reference values that contain a sizeable measurement error. Several approximate expressions for calculating a sample-specific standard error of prediction have been proposed
The Representation of Prediction Error in Auditory Cortex
Rubin, Jonathan; Ulanovsky, Nachum; Tishby, Naftali
2016-01-01
To survive, organisms must extract information from the past that is relevant for their future. How this process is expressed at the neural level remains unclear. We address this problem by developing a novel approach from first principles. We show here how to generate low-complexity representations of the past that produce optimal predictions of future events. We then illustrate this framework by studying the coding of ‘oddball’ sequences in auditory cortex. We find that for many neurons in primary auditory cortex, trial-by-trial fluctuations of neuronal responses correlate with the theoretical prediction error calculated from the short-term past of the stimulation sequence, under constraints on the complexity of the representation of this past sequence. In some neurons, the effect of prediction error accounted for more than 50% of response variability. Reliable predictions often depended on a representation of the sequence of the last ten or more stimuli, although the representation kept only few details of that sequence. PMID:27490251
Error propagation in energetic carrying capacity models
Pearse, Aaron T.; Stafford, Joshua D.
2014-01-01
Conservation objectives derived from carrying capacity models have been used to inform management of landscapes for wildlife populations. Energetic carrying capacity models are particularly useful in conservation planning for wildlife; these models use estimates of food abundance and energetic requirements of wildlife to target conservation actions. We provide a general method for incorporating a foraging threshold (i.e., density of food at which foraging becomes unprofitable) when estimating food availability with energetic carrying capacity models. We use a hypothetical example to describe how past methods for adjustment of foraging thresholds biased results of energetic carrying capacity models in certain instances. Adjusting foraging thresholds at the patch level of the species of interest provides results consistent with ecological foraging theory. Presentation of two case studies suggest variation in bias which, in certain instances, created large errors in conservation objectives and may have led to inefficient allocation of limited resources. Our results also illustrate how small errors or biases in application of input parameters, when extrapolated to large spatial extents, propagate errors in conservation planning and can have negative implications for target populations.
Uncertainties of predictions from parton distributions 1, experimental errors
Martin, A D; Stirling, William James; Thorne, R S; CERN. Geneva
2003-01-01
We determine the uncertainties on observables arising from the errors on the experimental data that are fitted in the global MRST2001 parton analysis. By diagonalizing the error matrix we produce sets of partons suitable for use within the framework of linear propagation of errors, which is the most convenient method for calculating the uncertainties. Despite the potential limitations of this approach we find that it can be made to work well in practice. This is confirmed by our alternative approach of using the more rigorous Lagrange multiplier method to determine the errors on physical quantities directly. As particular examples we determine the uncertainties on the predictions of the charged-current deep-inelastic structure functions, on the cross-sections for W production and for Higgs boson production via gluon--gluon fusion at the Tevatron and the LHC, on the ratio of W-minus to W-plus production at the LHC and on the moments of the non-singlet quark distributions. We discuss the corresponding uncertain...
DeGuzman, Marisa; Shott, Megan E; Yang, Tony T; Riederer, Justin; Frank, Guido K W
2017-06-01
Anorexia nervosa is a psychiatric disorder of unknown etiology. Understanding associations between behavior and neurobiology is important in treatment development. Using a novel monetary reward task during functional magnetic resonance brain imaging, the authors tested how brain reward learning in adolescent anorexia nervosa changes with weight restoration. Female adolescents with anorexia nervosa (N=21; mean age, 16.4 years [SD=1.9]) underwent functional MRI (fMRI) before and after treatment; similarly, healthy female control adolescents (N=21; mean age, 15.2 years [SD=2.4]) underwent fMRI on two occasions. Brain function was tested using the reward prediction error construct, a computational model for reward receipt and omission related to motivation and neural dopamine responsiveness. Compared with the control group, the anorexia nervosa group exhibited greater brain response 1) for prediction error regression within the caudate, ventral caudate/nucleus accumbens, and anterior and posterior insula, 2) to unexpected reward receipt in the anterior and posterior insula, and 3) to unexpected reward omission in the caudate body. Prediction error and unexpected reward omission response tended to normalize with treatment, while unexpected reward receipt response remained significantly elevated. Greater caudate prediction error response when underweight was associated with lower weight gain during treatment. Punishment sensitivity correlated positively with ventral caudate prediction error response. Reward system responsiveness is elevated in adolescent anorexia nervosa when underweight and after weight restoration. Heightened prediction error activity in brain reward regions may represent a phenotype of adolescent anorexia nervosa that does not respond well to treatment. Prediction error response could be a neurobiological marker of illness severity that can indicate individual treatment needs.
Semiparametric analysis of linear transformation models with covariate measurement errors.
Sinha, Samiran; Ma, Yanyuan
2014-03-01
We take a semiparametric approach in fitting a linear transformation model to a right censored data when predictive variables are subject to measurement errors. We construct consistent estimating equations when repeated measurements of a surrogate of the unobserved true predictor are available. The proposed approach applies under minimal assumptions on the distributions of the true covariate or the measurement errors. We derive the asymptotic properties of the estimator and illustrate the characteristics of the estimator in finite sample performance via simulation studies. We apply the method to analyze an AIDS clinical trial data set that motivated the work. © 2013, The International Biometric Society.
Directory of Open Access Journals (Sweden)
Jensen Just
2002-05-01
Full Text Available Abstract In this paper, we consider selection based on the best predictor of animal additive genetic values in Gaussian linear mixed models, threshold models, Poisson mixed models, and log normal frailty models for survival data (including models with time-dependent covariates with associated fixed or random effects. In the different models, expressions are given (when these can be found – otherwise unbiased estimates are given for prediction error variance, accuracy of selection and expected response to selection on the additive genetic scale and on the observed scale. The expressions given for non Gaussian traits are generalisations of the well-known formulas for Gaussian traits – and reflect, for Poisson mixed models and frailty models for survival data, the hierarchal structure of the models. In general the ratio of the additive genetic variance to the total variance in the Gaussian part of the model (heritability on the normally distributed level of the model or a generalised version of heritability plays a central role in these formulas.
International Nuclear Information System (INIS)
Yoshino, Kenji; Hirotsu, Yuko
2000-01-01
In order to attain zero-izing of much more error rather than it can set to a nuclear power plant, Authors development and its system-izing of the error prediction causal model which predicts group error action at the time of maintenance work were performed. This prototype system has the following feature. (1) When a user inputs the existence and the grade of the existence of the 'feature factor of the maintenance work' as a prediction object, 'an organization and an organization factor', and a 'group PSF (Performance Shaping Factor) factor' into this system. The maintenance group error to target can be predicted through the prediction model which consists of a class of seven stages. (2) This system by utilizing the information on a prediction result database, it can use not only for prediction of a maintenance group error but for various safe activity, such as KYT (dangerous forecast training) and TBM (Tool Box Meeting). (3) This system predicts a cooperation error' at highest rate, and, subsequently predicts the detection error' at a high rate. And to the 'decision-making error', the transfer error' and the 'state cognitive error', it has the characteristic predicted at almost same rate. (4) If it has full knowledge even of the features, such as the enforcement conditions of maintenance work, and organization, even if the user has neither the knowledge about a human factor, nor experience, anyone of this system is slight about the existence, its extent, etc. of generating of a maintenance group error made difficult from the former logically and systematically easily, it can predict in business time for about 15 minutes. (author)
Chasing probabilities — Signaling negative and positive prediction errors across domains
DEFF Research Database (Denmark)
Meder, David; Madsen, Kristoffer H; Hulme, Oliver
2016-01-01
Adaptive actions build on internal probabilistic models of possible outcomes that are tuned according to the errors of their predictions when experiencing an actual outcome. Prediction errors (PEs) inform choice behavior across a diversity of outcome domains and dimensions, yet neuroimaging studies...... of the two. We acquired functional MRI data while volunteers performed four probabilistic reversal learning tasks which differed in terms of outcome valence (reward-seeking versus punishment-avoidance) and domain (abstract symbols versus facial expressions) of outcomes. We found that ventral striatum...
Modeling error distributions of growth curve models through Bayesian methods.
Zhang, Zhiyong
2016-06-01
Growth curve models are widely used in social and behavioral sciences. However, typical growth curve models often assume that the errors are normally distributed although non-normal data may be even more common than normal data. In order to avoid possible statistical inference problems in blindly assuming normality, a general Bayesian framework is proposed to flexibly model normal and non-normal data through the explicit specification of the error distributions. A simulation study shows when the distribution of the error is correctly specified, one can avoid the loss in the efficiency of standard error estimates. A real example on the analysis of mathematical ability growth data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 is used to show the application of the proposed methods. Instructions and code on how to conduct growth curve analysis with both normal and non-normal error distributions using the the MCMC procedure of SAS are provided.
Krigolson, Olav E; Hassall, Cameron D; Handy, Todd C
2014-03-01
Our ability to make decisions is predicated upon our knowledge of the outcomes of the actions available to us. Reinforcement learning theory posits that actions followed by a reward or punishment acquire value through the computation of prediction errors-discrepancies between the predicted and the actual reward. A multitude of neuroimaging studies have demonstrated that rewards and punishments evoke neural responses that appear to reflect reinforcement learning prediction errors [e.g., Krigolson, O. E., Pierce, L. J., Holroyd, C. B., & Tanaka, J. W. Learning to become an expert: Reinforcement learning and the acquisition of perceptual expertise. Journal of Cognitive Neuroscience, 21, 1833-1840, 2009; Bayer, H. M., & Glimcher, P. W. Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47, 129-141, 2005; O'Doherty, J. P. Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology, 14, 769-776, 2004; Holroyd, C. B., & Coles, M. G. H. The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679-709, 2002]. Here, we used the brain ERP technique to demonstrate that not only do rewards elicit a neural response akin to a prediction error but also that this signal rapidly diminished and propagated to the time of choice presentation with learning. Specifically, in a simple, learnable gambling task, we show that novel rewards elicited a feedback error-related negativity that rapidly decreased in amplitude with learning. Furthermore, we demonstrate the existence of a reward positivity at choice presentation, a previously unreported ERP component that has a similar timing and topography as the feedback error-related negativity that increased in amplitude with learning. The pattern of results we observed mirrored the output of a computational model that we implemented to compute reward
Varying coefficients model with measurement error.
Li, Liang; Greene, Tom
2008-06-01
We propose a semiparametric partially varying coefficient model to study the relationship between serum creatinine concentration and the glomerular filtration rate (GFR) among kidney donors and patients with chronic kidney disease. A regression model is used to relate serum creatinine to GFR and demographic factors in which coefficient of GFR is expressed as a function of age to allow its effect to be age dependent. GFR measurements obtained from the clearance of a radioactively labeled isotope are assumed to be a surrogate for the true GFR, with the relationship between measured and true GFR expressed using an additive error model. We use locally corrected score equations to estimate parameters and coefficient functions, and propose an expected generalized cross-validation (EGCV) method to select the kernel bandwidth. The performance of the proposed methods, which avoid distributional assumptions on the true GFR and residuals, is investigated by simulation. Accounting for measurement error using the proposed model reduced apparent inconsistencies in the relationship between serum creatinine and GFR among different clinical data sets derived from kidney donor and chronic kidney disease source populations.
Energy Technology Data Exchange (ETDEWEB)
Voisin, Sophie [ORNL; Pinto, Frank M [ORNL; Morin-Ducote, Garnetta [University of Tennessee, Knoxville (UTK); Hudson, Kathy [University of Tennessee, Knoxville (UTK); Tourassi, Georgia [ORNL
2013-01-01
Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels. Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from 4 Radiology residents and 2 breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADs images features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated. Results: Diagnostic error can be predicted reliably by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model (AUC=0.79). Personalized user modeling was far more accurate for the more experienced readers (average AUC of 0.837 0.029) than for the less experienced ones (average AUC of 0.667 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features. Conclusions: Diagnostic errors in mammography can be predicted reliably by leveraging the radiologists gaze behavior and image content.
International Nuclear Information System (INIS)
Voisin, Sophie; Tourassi, Georgia D.; Pinto, Frank; Morin-Ducote, Garnetta; Hudson, Kathleen B.
2013-01-01
Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists’ gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels.Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from four Radiology residents and two breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADS images features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated.Results: Machine learning can be used to predict diagnostic error by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model [area under the ROC curve (AUC) = 0.792 ± 0.030]. Personalized user modeling was far more accurate for the more experienced readers (AUC = 0.837 ± 0.029) than for the less experienced ones (AUC = 0.667 ± 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features.Conclusions: Diagnostic errors in mammography can be predicted to a good extent by leveraging the radiologists’ gaze behavior and image content
Energy Technology Data Exchange (ETDEWEB)
Voisin, Sophie; Tourassi, Georgia D. [Biomedical Science and Engineering Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States); Pinto, Frank [School of Engineering, Science, and Technology, Virginia State University, Petersburg, Virginia 23806 (United States); Morin-Ducote, Garnetta; Hudson, Kathleen B. [Department of Radiology, University of Tennessee Medical Center at Knoxville, Knoxville, Tennessee 37920 (United States)
2013-10-15
Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists’ gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels.Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from four Radiology residents and two breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADS images features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated.Results: Machine learning can be used to predict diagnostic error by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model [area under the ROC curve (AUC) = 0.792 ± 0.030]. Personalized user modeling was far more accurate for the more experienced readers (AUC = 0.837 ± 0.029) than for the less experienced ones (AUC = 0.667 ± 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features.Conclusions: Diagnostic errors in mammography can be predicted to a good extent by leveraging the radiologists’ gaze behavior and image content.
Yan, Ying; Yi, Grace Y
2016-07-01
Covariate measurement error occurs commonly in survival analysis. Under the proportional hazards model, measurement error effects have been well studied, and various inference methods have been developed to correct for error effects under such a model. In contrast, error-contaminated survival data under the additive hazards model have received relatively less attention. In this paper, we investigate this problem by exploring measurement error effects on parameter estimation and the change of the hazard function. New insights of measurement error effects are revealed, as opposed to well-documented results for the Cox proportional hazards model. We propose a class of bias correction estimators that embraces certain existing estimators as special cases. In addition, we exploit the regression calibration method to reduce measurement error effects. Theoretical results for the developed methods are established, and numerical assessments are conducted to illustrate the finite sample performance of our methods.
Modeling human response errors in synthetic flight simulator domain
Ntuen, Celestine A.
1992-01-01
This paper presents a control theoretic approach to modeling human response errors (HRE) in the flight simulation domain. The human pilot is modeled as a supervisor of a highly automated system. The synthesis uses the theory of optimal control pilot modeling for integrating the pilot's observation error and the error due to the simulation model (experimental error). Methods for solving the HRE problem are suggested. Experimental verification of the models will be tested in a flight quality handling simulation.
Spindle Thermal Error Optimization Modeling of a Five-axis Machine Tool
Guo, Qianjian; Fan, Shuo; Xu, Rufeng; Cheng, Xiang; Zhao, Guoyong; Yang, Jianguo
2017-05-01
Aiming at the problem of low machining accuracy and uncontrollable thermal errors of NC machine tools, spindle thermal error measurement, modeling and compensation of a two turntable five-axis machine tool are researched. Measurement experiment of heat sources and thermal errors are carried out, and GRA(grey relational analysis) method is introduced into the selection of temperature variables used for thermal error modeling. In order to analyze the influence of different heat sources on spindle thermal errors, an ANN (artificial neural network) model is presented, and ABC(artificial bee colony) algorithm is introduced to train the link weights of ANN, a new ABC-NN(Artificial bee colony-based neural network) modeling method is proposed and used in the prediction of spindle thermal errors. In order to test the prediction performance of ABC-NN model, an experiment system is developed, the prediction results of LSR (least squares regression), ANN and ABC-NN are compared with the measurement results of spindle thermal errors. Experiment results show that the prediction accuracy of ABC-NN model is higher than LSR and ANN, and the residual error is smaller than 3 μm, the new modeling method is feasible. The proposed research provides instruction to compensate thermal errors and improve machining accuracy of NC machine tools.
Cultural Resource Predictive Modeling
2017-10-01
refining formal, inductive predictive models is the quality of the archaeological and environmental data. To build models efficiently, relevant...geomorphology, and historic information . Lessons Learned: The original model was focused on the identification of prehistoric resources. This...system but uses predictive modeling informally . For example, there is no probability for buried archaeological deposits on the Burton Mesa, but there is
Temporal prediction errors modulate task-switching performance
Directory of Open Access Journals (Sweden)
Roberto eLimongi
2015-08-01
Full Text Available We have previously shown that temporal prediction errors (PEs, the differences between the expected and the actual stimulus’ onset times modulate the effective connectivity between the anterior cingulate cortex and the right anterior insular cortex (rAI, causing the activity of the rAI to decrease. The activity of the rAI is associated with efficient performance under uncertainty (e.g., changing a prepared behavior when a change demand is not expected, which leads to hypothesize that temporal PEs might disrupt behavior-change performance under uncertainty. This hypothesis has not been tested at a behavioral level. In this work, we evaluated this hypothesis within the context of task switching and concurrent temporal predictions. Our participants performed temporal predictions while observing one moving ball striking a stationary ball which bounced off with a variable temporal gap. Simultaneously, they performed a simple color comparison task. In some trials, a change signal made the participants change their behaviors. Performance accuracy decreased as a function of both the temporal PE and the delay. Explaining these results without appealing to ad-hoc concepts such as executive control is a challenge for cognitive neuroscience. We provide a predictive coding explanation. We hypothesize that exteroceptive and proprioceptive minimization of PEs would converge in a fronto-basal ganglia network which would include the rAI. Both temporal gaps (or uncertainty and temporal PEs would drive and modulate this network respectively. Whereas the temporal gaps would drive the activity of the rAI, the temporal PEs would modulate the endogenous excitatory connections of the fronto-striatal network. We conclude that in the context of perceptual uncertainty, the system is not able to minimize perceptual PE, causing the ongoing behavior to finalize and, in consequence, disrupting task switching.
Error Propagation in Equations for Geochemical Modeling of ...
Indian Academy of Sciences (India)
This paper presents error propagation equations for modeling of radiogenic isotopes during mixing of two components or end-members. These equations can be used to estimate errors on an isotopic ratio in the mixture of two components, as a function of the analytical errors or the total errors of geological field sampling ...
the sensitivity of evapotranspiration models to errors in model ...
African Journals Online (AJOL)
Dr Obe
ABSTRACT. Five evapotranspiration (Et) model-the penman, Blaney - Criddel, Thornthwaite, the Blaney –. Morin-Nigeria, and the Jensen and Haise models – were analyzed for parameter sensitivity under Nigerian Climatic conditions. The sensitivity of each model to errors in any of its measured parameters (variables) was ...
Measurement Model Specification Error in LISREL Structural Equation Models.
Baldwin, Beatrice; Lomax, Richard
This LISREL study examines the robustness of the maximum likelihood estimates under varying degrees of measurement model misspecification. A true model containing five latent variables (two endogenous and three exogenous) and two indicator variables per latent variable was used. Measurement model misspecification considered included errors of…
A machine learning approach to the accurate prediction of multi-leaf collimator positional errors
Carlson, Joel N. K.; Park, Jong Min; Park, So-Yeon; In Park, Jong; Choi, Yunseok; Ye, Sung-Joon
2016-03-01
Discrepancies between planned and delivered movements of multi-leaf collimators (MLCs) are an important source of errors in dose distributions during radiotherapy. In this work we used machine learning techniques to train models to predict these discrepancies, assessed the accuracy of the model predictions, and examined the impact these errors have on quality assurance (QA) procedures and dosimetry. Predictive leaf motion parameters for the models were calculated from the plan files, such as leaf position and velocity, whether the leaf was moving towards or away from the isocenter of the MLC, and many others. Differences in positions between synchronized DICOM-RT planning files and DynaLog files reported during QA delivery were used as a target response for training of the models. The final model is capable of predicting MLC positions during delivery to a high degree of accuracy. For moving MLC leaves, predicted positions were shown to be significantly closer to delivered positions than were planned positions. By incorporating predicted positions into dose calculations in the TPS, increases were shown in gamma passing rates against measured dose distributions recorded during QA delivery. For instance, head and neck plans with 1%/2 mm gamma criteria had an average increase in passing rate of 4.17% (SD = 1.54%). This indicates that the inclusion of predictions during dose calculation leads to a more realistic representation of plan delivery. To assess impact on the patient, dose volumetric histograms (DVH) using delivered positions were calculated for comparison with planned and predicted DVHs. In all cases, predicted dose volumetric parameters were in closer agreement to the delivered parameters than were the planned parameters, particularly for organs at risk on the periphery of the treatment area. By incorporating the predicted positions into the TPS, the treatment planner is given a more realistic view of the dose distribution as it will truly be
Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do.
Zhao, Linlin; Wang, Wenyi; Sedykh, Alexander; Zhu, Hao
2017-06-30
Numerous chemical data sets have become available for quantitative structure-activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting.
Principal components analysis of reward prediction errors in a reinforcement learning task.
Sambrook, Thomas D; Goslin, Jeremy
2016-01-01
Models of reinforcement learning represent reward and punishment in terms of reward prediction errors (RPEs), quantitative signed terms describing the degree to which outcomes are better than expected (positive RPEs) or worse (negative RPEs). An electrophysiological component known as feedback related negativity (FRN) occurs at frontocentral sites 240-340ms after feedback on whether a reward or punishment is obtained, and has been claimed to neurally encode an RPE. An outstanding question however, is whether the FRN is sensitive to the size of both positive RPEs and negative RPEs. Previous attempts to answer this question have examined the simple effects of RPE size for positive RPEs and negative RPEs separately. However, this methodology can be compromised by overlap from components coding for unsigned prediction error size, or "salience", which are sensitive to the absolute size of a prediction error but not its valence. In our study, positive and negative RPEs were parametrically modulated using both reward likelihood and magnitude, with principal components analysis used to separate out overlying components. This revealed a single RPE encoding component responsive to the size of positive RPEs, peaking at ~330ms, and occupying the delta frequency band. Other components responsive to unsigned prediction error size were shown, but no component sensitive to negative RPE size was found. Copyright © 2015 Elsevier Inc. All rights reserved.
Uncertainty and error in complex plasma chemistry models
Turner, Miles M.
2015-06-01
Chemistry models that include dozens of species and hundreds to thousands of reactions are common in low-temperature plasma physics. The rate constants used in such models are uncertain, because they are obtained from some combination of experiments and approximate theories. Since the predictions of these models are a function of the rate constants, these predictions must also be uncertain. However, systematic investigations of the influence of uncertain rate constants on model predictions are rare to non-existent. In this work we examine a particular chemistry model, for helium-oxygen plasmas. This chemistry is of topical interest because of its relevance to biomedical applications of atmospheric pressure plasmas. We trace the primary sources for every rate constant in the model, and hence associate an error bar (or equivalently, an uncertainty) with each. We then use a Monte Carlo procedure to quantify the uncertainty in predicted plasma species densities caused by the uncertainty in the rate constants. Under the conditions investigated, the range of uncertainty in most species densities is a factor of two to five. However, the uncertainty can vary strongly for different species, over time, and with other plasma conditions. There are extreme (pathological) cases where the uncertainty is more than a factor of ten. One should therefore be cautious in drawing any conclusion from plasma chemistry modelling, without first ensuring that the conclusion in question survives an examination of the related uncertainty.
Social learning through prediction error in the brain
Joiner, Jessica; Piva, Matthew; Turrin, Courtney; Chang, Steve W. C.
2017-06-01
Learning about the world is critical to survival and success. In social animals, learning about others is a necessary component of navigating the social world, ultimately contributing to increasing evolutionary fitness. How humans and nonhuman animals represent the internal states and experiences of others has long been a subject of intense interest in the developmental psychology tradition, and, more recently, in studies of learning and decision making involving self and other. In this review, we explore how psychology conceptualizes the process of representing others, and how neuroscience has uncovered correlates of reinforcement learning signals to explore the neural mechanisms underlying social learning from the perspective of representing reward-related information about self and other. In particular, we discuss self-referenced and other-referenced types of reward prediction errors across multiple brain structures that effectively allow reinforcement learning algorithms to mediate social learning. Prediction-based computational principles in the brain may be strikingly conserved between self-referenced and other-referenced information.
Predictive modeling of complications.
Osorio, Joseph A; Scheer, Justin K; Ames, Christopher P
2016-09-01
Predictive analytic algorithms are designed to identify patterns in the data that allow for accurate predictions without the need for a hypothesis. Therefore, predictive modeling can provide detailed and patient-specific information that can be readily applied when discussing the risks of surgery with a patient. There are few studies using predictive modeling techniques in the adult spine surgery literature. These types of studies represent the beginning of the use of predictive analytics in spine surgery outcomes. We will discuss the advancements in the field of spine surgery with respect to predictive analytics, the controversies surrounding the technique, and the future directions.
Dopamine Prediction Errors in Reward Learning and Addiction: From Theory to Neural Circuitry.
Keiflin, Ronald; Janak, Patricia H
2015-10-21
Midbrain dopamine (DA) neurons are proposed to signal reward prediction error (RPE), a fundamental parameter in associative learning models. This RPE hypothesis provides a compelling theoretical framework for understanding DA function in reward learning and addiction. New studies support a causal role for DA-mediated RPE activity in promoting learning about natural reward; however, this question has not been explicitly tested in the context of drug addiction. In this review, we integrate theoretical models with experimental findings on the activity of DA systems, and on the causal role of specific neuronal projections and cell types, to provide a circuit-based framework for probing DA-RPE function in addiction. By examining error-encoding DA neurons in the neural network in which they are embedded, hypotheses regarding circuit-level adaptations that possibly contribute to pathological error signaling and addiction can be formulated and tested. Copyright © 2015 Elsevier Inc. All rights reserved.
Radiation risk estimation based on measurement error models
Masiuk, Sergii; Shklyar, Sergiy; Chepurny, Mykola; Likhtarov, Illya
2017-01-01
This monograph discusses statistics and risk estimates applied to radiation damage under the presence of measurement errors. The first part covers nonlinear measurement error models, with a particular emphasis on efficiency of regression parameter estimators. In the second part, risk estimation in models with measurement errors is considered. Efficiency of the methods presented is verified using data from radio-epidemiological studies.
Modeling measurement error in tumor characterization studies
Directory of Open Access Journals (Sweden)
Marjoram Paul
2011-07-01
Full Text Available Abstract Background Etiologic studies of cancer increasingly use molecular features such as gene expression, DNA methylation and sequence mutation to subclassify the cancer type. In large population-based studies, the tumor tissues available for study are archival specimens that provide variable amounts of amplifiable DNA for molecular analysis. As molecular features measured from small amounts of tumor DNA are inherently noisy, we propose a novel approach to improve statistical efficiency when comparing groups of samples. We illustrate the phenomenon using the MethyLight technology, applying our proposed analysis to compare MLH1 DNA methylation levels in males and females studied in the Colon Cancer Family Registry. Results We introduce two methods for computing empirical weights to model heteroscedasticity that is caused by sampling variable quantities of DNA for molecular analysis. In a simulation study, we show that using these weights in a linear regression model is more powerful for identifying differentially methylated loci than standard regression analysis. The increase in power depends on the underlying relationship between variation in outcome measure and input DNA quantity in the study samples. Conclusions Tumor characteristics measured from small amounts of tumor DNA are inherently noisy. We propose a statistical analysis that accounts for the measurement error due to sampling variation of the molecular feature and show how it can improve the power to detect differential characteristics between patient groups.
Zhu, Lusha; Mathewson, Kyle E; Hsu, Ming
2012-01-31
Decision-making in the presence of other competitive intelligent agents is fundamental for social and economic behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions of others competing for the same rewards. However, whereas we know much about strategic learning at both theoretical and behavioral levels, we know relatively little about the underlying neural mechanisms. Here, we show using a multi-strategy competitive learning paradigm that strategic choices can be characterized by extending the reinforcement learning (RL) framework to incorporate agents' beliefs about the actions of their opponents. Furthermore, using this characterization to generate putative internal values, we used model-based functional magnetic resonance imaging to investigate neural computations underlying strategic learning. We found that the distinct notions of prediction errors derived from our computational model are processed in a partially overlapping but distinct set of brain regions. Specifically, we found that the RL prediction error was correlated with activity in the ventral striatum. In contrast, activity in the ventral striatum, as well as the rostral anterior cingulate (rACC), was correlated with a previously uncharacterized belief-based prediction error. Furthermore, activity in rACC reflected individual differences in degree of engagement in belief learning. These results suggest a model of strategic behavior where learning arises from interaction of dissociable reinforcement and belief-based inputs.
Belief about nicotine selectively modulates value and reward prediction error signals in smokers.
Gu, Xiaosi; Lohrenz, Terry; Salas, Ramiro; Baldwin, Philip R; Soltani, Alireza; Kirk, Ulrich; Cinciripini, Paul M; Montague, P Read
2015-02-24
Little is known about how prior beliefs impact biophysically described processes in the presence of neuroactive drugs, which presents a profound challenge to the understanding of the mechanisms and treatments of addiction. We engineered smokers' prior beliefs about the presence of nicotine in a cigarette smoked before a functional magnetic resonance imaging session where subjects carried out a sequential choice task. Using a model-based approach, we show that smokers' beliefs about nicotine specifically modulated learning signals (value and reward prediction error) defined by a computational model of mesolimbic dopamine systems. Belief of "no nicotine in cigarette" (compared with "nicotine in cigarette") strongly diminished neural responses in the striatum to value and reward prediction errors and reduced the impact of both on smokers' choices. These effects of belief could not be explained by global changes in visual attention and were specific to value and reward prediction errors. Thus, by modulating the expression of computationally explicit signals important for valuation and choice, beliefs can override the physical presence of a potent neuroactive compound like nicotine. These selective effects of belief demonstrate that belief can modulate model-based parameters important for learning. The implications of these findings may be far ranging because belief-dependent effects on learning signals could impact a host of other behaviors in addiction as well as in other mental health problems.
Archaeological predictive model set.
2015-03-01
This report is the documentation for Task 7 of the Statewide Archaeological Predictive Model Set. The goal of this project is to : develop a set of statewide predictive models to assist the planning of transportation projects. PennDOT is developing t...
Relative prediction error and protection from attentional blink in human associative learning.
Glautier, Steven; Shih, Shui-L
2015-01-01
The relationship between predictive learning and attentional processing was investigated in two experiments. During a learning procedure participants viewed rapid serial visual presentation (RSVP) of stimuli in the context of a choice-reaction-time (CRT) task. Salient stimuli in the RSVP streams were either predictive or non-predictive for the outcome of the CRT task. Following this procedure we measured attentional blink (AB) to the predictive and non-predictive stimuli. In Experiment 1, despite the use of a large sample and checks demonstrating the validity of the learning procedure and the AB measure, we did not observe reduced AB for predictive stimuli. In contrast, in Experiment 2, where the predictive stimuli occurred alongside salient non-predictive comparison stimuli, we did find less AB for predictive than for non-predictive stimuli. Our results support an attentional model of learning in which relative prediction error is used to increase learning rates for good predictors and reduce learning rates for poor predictors and provide confirmation of the AB learning effect.
Directory of Open Access Journals (Sweden)
Pooyan Vahidi Pashsaki
2016-06-01
Full Text Available Accuracy of a five-axis CNC machine tool is affected by a vast number of error sources. This paper investigates volumetric error modeling and its compensation to the basis for creation of new tool path for improvement of work pieces accuracy. The volumetric error model of a five-axis machine tool with the configuration RTTTR (tilting head B-axis and rotary table in work piece side A΄ was set up taking into consideration rigid body kinematics and homogeneous transformation matrix, in which 43 error components are included. Volumetric error comprises 43 error components that can separately reduce geometrical and dimensional accuracy of work pieces. The machining accuracy of work piece is guaranteed due to the position of the cutting tool center point (TCP relative to the work piece. The cutting tool is deviated from its ideal position relative to the work piece and machining error is experienced. For compensation process detection of the present tool path and analysis of the RTTTR five-axis CNC machine tools geometrical error, translating current position of component to compensated positions using the Kinematics error model, converting newly created component to new tool paths using the compensation algorithms and finally editing old G-codes using G-code generator algorithm have been employed.
Levy, R.; Mcginness, H.
1976-01-01
Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed.
Directory of Open Access Journals (Sweden)
Bruni Vanida
2010-01-01
Full Text Available Abstract Background Drug prescribing errors are frequent in the hospital setting and pharmacists play an important role in detection of these errors. The objectives of this study are (1 to describe the drug prescribing errors rate during the patient's stay, (2 to find which characteristics for a prescribing error are the most predictive of their reproduction the next day despite pharmacist's alert (i.e. override the alert. Methods We prospectively collected all medication order lines and prescribing errors during 18 days in 7 medical wards' using computerized physician order entry. We described and modelled the errors rate according to the chronology of hospital stay. We performed a classification and regression tree analysis to find which characteristics of alerts were predictive of their overriding (i.e. prescribing error repeated. Results 12 533 order lines were reviewed, 117 errors (errors rate 0.9% were observed and 51% of these errors occurred on the first day of the hospital stay. The risk of a prescribing error decreased over time. 52% of the alerts were overridden (i.e error uncorrected by prescribers on the following day. Drug omissions were the most frequently taken into account by prescribers. The classification and regression tree analysis showed that overriding pharmacist's alerts is first related to the ward of the prescriber and then to either Anatomical Therapeutic Chemical class of the drug or the type of error. Conclusions Since 51% of prescribing errors occurred on the first day of stay, pharmacist should concentrate his analysis of drug prescriptions on this day. The difference of overriding behavior between wards and according drug Anatomical Therapeutic Chemical class or type of error could also guide the validation tasks and programming of electronic alerts.
Zephyr - the prediction models
DEFF Research Database (Denmark)
Nielsen, Torben Skov; Madsen, Henrik; Nielsen, Henrik Aalborg
2001-01-01
utilities as partners and users. The new models are evaluated for five wind farms in Denmark as well as one wind farm in Spain. It is shown that the predictions based on conditional parametric models are superior to the predictions obatined by state-of-the-art parametric models.......This paper briefly describes new models and methods for predicationg the wind power output from wind farms. The system is being developed in a project which has the research organization Risø and the department of Informatics and Mathematical Modelling (IMM) as the modelling team and all the Danish...
Directory of Open Access Journals (Sweden)
Ulrich eKirk
2015-02-01
Full Text Available Reinforcement learning models have demonstrated that phasic activity of dopamine neurons during reward expectation encodes information about the predictability of rewards and cues that predict reward. Evidence indicates that mindfulness-based approaches reduce reward anticipation signal in the striatum to negative and positive incentives suggesting the hypothesis that such training influence basic reward processing. Using a passive conditioning task and fMRI in a group of experienced mindfulness meditators and age-matched controls, we tested the hypothesis that mindfulness meditation influence reward and reward prediction error signals. We found diminished positive and negative prediction error-related blood-oxygen level-dependent (BOLD responses in the putamen in meditators compared with controls. In the meditators, this decrease in striatal BOLD responses to reward prediction was paralleled by increased activity in posterior insula, a primary interoceptive region. Critically, responses in the putamen during early trials of the conditioning procedure (run 1 were elevated in both meditators and controls. These results provide evidence that experienced mindfulness meditators show attenuated reward prediction signals to valenced stimuli, which may be related to interoceptive processes encoded in the posterior insula.
Quasi-eccentricity error modeling and compensation in vision metrology
Shen, Yijun; Zhang, Xu; Cheng, Wei; Zhu, Limin
2018-04-01
Circular targets are commonly used in vision applications for its detection accuracy and robustness. The eccentricity error of the circular target caused by perspective projection is one of the main factors of measurement error which needs to be compensated in high-accuracy measurement. In this study, the impact of the lens distortion on the eccentricity error is comprehensively investigated. The traditional eccentricity error turns to a quasi-eccentricity error in the non-linear camera model. The quasi-eccentricity error model is established by comparing the quasi-center of the distorted ellipse with the true projection of the object circle center. Then, an eccentricity error compensation framework is proposed which compensates the error by iteratively refining the image point to the true projection of the circle center. Both simulation and real experiment confirm the effectiveness of the proposed method in several vision applications.
Functional multiple indicators, multiple causes measurement error models.
Tekwe, Carmen D; Zoh, Roger S; Bazer, Fuller W; Wu, Guoyao; Carroll, Raymond J
2017-05-08
Objective measures of oxygen consumption and carbon dioxide production by mammals are used to predict their energy expenditure. Since energy expenditure is not directly observable, it can be viewed as a latent construct with multiple physical indirect measures such as respiratory quotient, volumetric oxygen consumption, and volumetric carbon dioxide production. Metabolic rate is defined as the rate at which metabolism occurs in the body. Metabolic rate is also not directly observable. However, heat is produced as a result of metabolic processes within the body. Therefore, metabolic rate can be approximated by heat production plus some errors. While energy expenditure and metabolic rates are correlated, they are not equivalent. Energy expenditure results from physical function, while metabolism can occur within the body without the occurrence of physical activities. In this manuscript, we present a novel approach for studying the relationship between metabolic rate and indicators of energy expenditure. We do so by extending our previous work on MIMIC ME models to allow responses that are sparsely observed functional data, defining the sparse functional multiple indicators, multiple cause measurement error (FMIMIC ME) models. The mean curves in our proposed methodology are modeled using basis splines. A novel approach for estimating the variance of the classical measurement error based on functional principal components is presented. The model parameters are estimated using the EM algorithm and a discussion of the model's identifiability is provided. We show that the defined model is not a trivial extension of longitudinal or functional data methods, due to the presence of the latent construct. Results from its application to data collected on Zucker diabetic fatty rats are provided. Simulation results investigating the properties of our approach are also presented. © 2017, The International Biometric Society.
Thermal Error Test and Intelligent Modeling Research on the Spindle of High Speed CNC Machine Tools
Luo, Zhonghui; Peng, Bin; Xiao, Qijun; Bai, Lu
2018-03-01
Thermal error is the main factor affecting the accuracy of precision machining. Through experiments, this paper studies the thermal error test and intelligent modeling for the spindle of vertical high speed CNC machine tools in respect of current research focuses on thermal error of machine tool. Several testing devices for thermal error are designed, of which 7 temperature sensors are used to measure the temperature of machine tool spindle system and 2 displacement sensors are used to detect the thermal error displacement. A thermal error compensation model, which has a good ability in inversion prediction, is established by applying the principal component analysis technology, optimizing the temperature measuring points, extracting the characteristic values closely associated with the thermal error displacement, and using the artificial neural network technology.
Measurement Error in Designed Experiments for Second Order Models
McMahan, Angela Renee
1997-01-01
Measurement error (ME) in the factor levels of designed experiments is often overlooked in the planning and analysis of experimental designs. A familiar model for this type of ME, called the Berkson error model, is discussed at length. Previous research has examined the effect of Berkson error on two-level factorial and fractional factorial designs. This dissertation extends the examination to designs for second order models. The results are used to suggest ...
Inverse and Predictive Modeling
Energy Technology Data Exchange (ETDEWEB)
Syracuse, Ellen Marie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-09-27
The LANL Seismo-Acoustic team has a strong capability in developing data-driven models that accurately predict a variety of observations. These models range from the simple – one-dimensional models that are constrained by a single dataset and can be used for quick and efficient predictions – to the complex – multidimensional models that are constrained by several types of data and result in more accurate predictions. Team members typically build models of geophysical characteristics of Earth and source distributions at scales of 1 to 1000s of km, the techniques used are applicable for other types of physical characteristics at an even greater range of scales. The following cases provide a snapshot of some of the modeling work done by the Seismo- Acoustic team at LANL.
Allegrini, Franco; Braga, Jez W B; Moreira, Alessandro C O; Olivieri, Alejandro C
2018-06-29
A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS). Copyright © 2018 Elsevier B.V. All rights reserved.
D'Astolfo, Lisa; Rief, Winfried
2017-01-01
Modifying patients' expectations by exposing them to expectation violation situations (thus maximizing the difference between the expected and the actual situational outcome) is proposed to be a crucial mechanism for therapeutic success for a variety of different mental disorders. However, clinical observations suggest that patients often maintain their expectations regardless of experiences contradicting their expectations. It remains unclear which information processing mechanisms lead to modification or persistence of patients' expectations. Insight in the processing could be provided by Neuroimaging studies investigating prediction error (PE, i.e., neuronal reactions to non-expected stimuli). Two methods are often used to investigate the PE: (1) paradigms, in which participants passively observe PEs ("passive" paradigms) and (2) paradigms, which encourage a behavioral adaptation following a PE ("active" paradigms). These paradigms are similar to the methods used to induce expectation violations in clinical settings: (1) the confrontation with an expectation violation situation and (2) an enhanced confrontation in which the patient actively challenges his expectation. We used this similarity to gain insight in the different neuronal processing of the two PE paradigms. We performed a meta-analysis contrasting neuronal activity of PE paradigms encouraging a behavioral adaptation following a PE and paradigms enforcing passiveness following a PE. We found more neuronal activity in the striatum, the insula and the fusiform gyrus in studies encouraging behavioral adaptation following a PE. Due to the involvement of reward assessment and avoidance learning associated with the striatum and the insula we propose that the deliberate execution of action alternatives following a PE is associated with the integration of new information into previously existing expectations, therefore leading to an expectation change. While further research is needed to directly assess
On the Correspondence between Mean Forecast Errors and Climate Errors in CMIP5 Models
Energy Technology Data Exchange (ETDEWEB)
Ma, H. -Y.; Xie, S.; Klein, S. A.; Williams, K. D.; Boyle, J. S.; Bony, S.; Douville, H.; Fermepin, S.; Medeiros, B.; Tyteca, S.; Watanabe, M.; Williamson, D.
2014-02-01
The present study examines the correspondence between short- and long-term systematic errors in five atmospheric models by comparing the 16 five-day hindcast ensembles from the Transpose Atmospheric Model Intercomparison Project II (Transpose-AMIP II) for July–August 2009 (short term) to the climate simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and AMIP for the June–August mean conditions of the years of 1979–2008 (long term). Because the short-term hindcasts were conducted with identical climate models used in the CMIP5/AMIP simulations, one can diagnose over what time scale systematic errors in these climate simulations develop, thus yielding insights into their origin through a seamless modeling approach. The analysis suggests that most systematic errors of precipitation, clouds, and radiation processes in the long-term climate runs are present by day 5 in ensemble average hindcasts in all models. Errors typically saturate after few days of hindcasts with amplitudes comparable to the climate errors, and the impacts of initial conditions on the simulated ensemble mean errors are relatively small. This robust bias correspondence suggests that these systematic errors across different models likely are initiated by model parameterizations since the atmospheric large-scale states remain close to observations in the first 2–3 days. However, biases associated with model physics can have impacts on the large-scale states by day 5, such as zonal winds, 2-m temperature, and sea level pressure, and the analysis further indicates a good correspondence between short- and long-term biases for these large-scale states. Therefore, improving individual model parameterizations in the hindcast mode could lead to the improvement of most climate models in simulating their climate mean state and potentially their future projections.
Study of error modeling in kinematic calibration of parallel manipulators
Directory of Open Access Journals (Sweden)
Liping Wang
2016-10-01
Full Text Available Error modeling is the foundation of a kinematic calibration which is a main approach to assure the accuracy of parallel manipulators. This article investigates the influence of error model on the kinematic calibration of parallel manipulators. Based on the coupling analysis between error parameters, an identifiability index for evaluating the error model is proposed. Taking a 3PRS parallel manipulator as an example, three error models with different values of identifiability index are given. With the same parameter identification, measurement, and compensation method, the computer simulations and prototype experiments of the kinematic calibration with each error model are performed. The simulation and experiment results show that the kinematic calibration using the error model with a bigger value of identifiability index can lead to a better accuracy of the manipulator. Then, an approach of error modeling is proposed to obtain a bigger value of identifiability index. The study of this article is useful for error modeling in kinematic calibration of other parallel manipulators.
Interferometric GPS Attitude: A Stochastic Error Model
1993-02-01
i’attitude SPG ainsi que les erreurs d’attitude observdes durant ressai en mer. II caracterise ces erreurs come 6tant des procedures stochastiques. ..-’II...the multipath error, which changes with satellite geometry. The satellites, in 12 hour orbits , move through about 33 degrees in 4,000 seconds, which
Putting Reward in Art: A Tentative Prediction Error Account of Visual Art
Directory of Open Access Journals (Sweden)
Sander Van de Cruys
2011-12-01
Full Text Available The predictive coding model is increasingly and fruitfully used to explain a wide range of findings in perception. Here we discuss the potential of this model in explaining the mechanisms underlying aesthetic experiences. Traditionally art appreciation has been associated with concepts such as harmony, perceptual fluency, and the so-called good Gestalt. We observe that more often than not great artworks blatantly violate these characteristics. Using the concept of prediction error from the predictive coding approach, we attempt to resolve this contradiction. We argue that artists often destroy predictions that they have first carefully built up in their viewers, and thus highlight the importance of negative affect in aesthetic experience. However, the viewer often succeeds in recovering the predictable pattern, sometimes on a different level. The ensuing rewarding effect is derived from this transition from a state of uncertainty to a state of increased predictability. We illustrate our account with several example paintings and with a discussion of art movements and individual differences in preference. On a more fundamental level, our theorizing leads us to consider the affective implications of prediction confirmation and violation. We compare our proposal to other influential theories on aesthetics and explore its advantages and limitations.
Putting reward in art: A tentative prediction error account of visual art.
Van de Cruys, Sander; Wagemans, Johan
2011-01-01
The predictive coding model is increasingly and fruitfully used to explain a wide range of findings in perception. Here we discuss the potential of this model in explaining the mechanisms underlying aesthetic experiences. Traditionally art appreciation has been associated with concepts such as harmony, perceptual fluency, and the so-called good Gestalt. We observe that more often than not great artworks blatantly violate these characteristics. Using the concept of prediction error from the predictive coding approach, we attempt to resolve this contradiction. We argue that artists often destroy predictions that they have first carefully built up in their viewers, and thus highlight the importance of negative affect in aesthetic experience. However, the viewer often succeeds in recovering the predictable pattern, sometimes on a different level. The ensuing rewarding effect is derived from this transition from a state of uncertainty to a state of increased predictability. We illustrate our account with several example paintings and with a discussion of art movements and individual differences in preference. On a more fundamental level, our theorizing leads us to consider the affective implications of prediction confirmation and violation. We compare our proposal to other influential theories on aesthetics and explore its advantages and limitations.
Dual Numbers Approach in Multiaxis Machines Error Modeling
Directory of Open Access Journals (Sweden)
Jaroslav Hrdina
2014-01-01
Full Text Available Multiaxis machines error modeling is set in the context of modern differential geometry and linear algebra. We apply special classes of matrices over dual numbers and propose a generalization of such concept by means of general Weil algebras. We show that the classification of the geometric errors follows directly from the algebraic properties of the matrices over dual numbers and thus the calculus over the dual numbers is the proper tool for the methodology of multiaxis machines error modeling.
Dopamine Modulates Adaptive Prediction Error Coding in the Human Midbrain and Striatum.
Diederen, Kelly M J; Ziauddeen, Hisham; Vestergaard, Martin D; Spencer, Tom; Schultz, Wolfram; Fletcher, Paul C
2017-02-15
Learning to optimally predict rewards requires agents to account for fluctuations in reward value. Recent work suggests that individuals can efficiently learn about variable rewards through adaptation of the learning rate, and coding of prediction errors relative to reward variability. Such adaptive coding has been linked to midbrain dopamine neurons in nonhuman primates, and evidence in support for a similar role of the dopaminergic system in humans is emerging from fMRI data. Here, we sought to investigate the effect of dopaminergic perturbations on adaptive prediction error coding in humans, using a between-subject, placebo-controlled pharmacological fMRI study with a dopaminergic agonist (bromocriptine) and antagonist (sulpiride). Participants performed a previously validated task in which they predicted the magnitude of upcoming rewards drawn from distributions with varying SDs. After each prediction, participants received a reward, yielding trial-by-trial prediction errors. Under placebo, we replicated previous observations of adaptive coding in the midbrain and ventral striatum. Treatment with sulpiride attenuated adaptive coding in both midbrain and ventral striatum, and was associated with a decrease in performance, whereas bromocriptine did not have a significant impact. Although we observed no differential effect of SD on performance between the groups, computational modeling suggested decreased behavioral adaptation in the sulpiride group. These results suggest that normal dopaminergic function is critical for adaptive prediction error coding, a key property of the brain thought to facilitate efficient learning in variable environments. Crucially, these results also offer potential insights for understanding the impact of disrupted dopamine function in mental illness. SIGNIFICANCE STATEMENT To choose optimally, we have to learn what to expect. Humans dampen learning when there is a great deal of variability in reward outcome, and two brain regions that
Predicting positional error of MLC using volumetric analysis
International Nuclear Information System (INIS)
Hareram, E.S.
2008-01-01
IMRT normally using multiple beamlets (small width of the beam) for a particular field to deliver so that it is imperative to maintain the positional accuracy of the MLC in order to deliver integrated computed dose accurately. Different manufacturers have reported high precession on MLC devices with leaf positional accuracy nearing 0.1 mm but measuring and rectifying the error in this accuracy is very difficult. Various methods are used to check MLC position and among this volumetric analysis is one of the technique. Volumetric approach was adapted in our method using primus machine and 0.6cc chamber at 5 cm depth In perspex. MLC of 1 mm error introduces an error of 20%, more sensitive to other methods
Diuk, Carlos; Tsai, Karin; Wallis, Jonathan; Botvinick, Matthew; Niv, Yael
2013-03-27
Studies suggest that dopaminergic neurons report a unitary, global reward prediction error signal. However, learning in complex real-life tasks, in particular tasks that show hierarchical structure, requires multiple prediction errors that may coincide in time. We used functional neuroimaging to measure prediction error signals in humans performing such a hierarchical task involving simultaneous, uncorrelated prediction errors. Analysis of signals in a priori anatomical regions of interest in the ventral striatum and the ventral tegmental area indeed evidenced two simultaneous, but separable, prediction error signals corresponding to the two levels of hierarchy in the task. This result suggests that suitably designed tasks may reveal a more intricate pattern of firing in dopaminergic neurons. Moreover, the need for downstream separation of these signals implies possible limitations on the number of different task levels that we can learn about simultaneously.
Optical linear algebra processors - Noise and error-source modeling
Casasent, D.; Ghosh, A.
1985-01-01
The modeling of system and component noise and error sources in optical linear algebra processors (OLAPs) are considered, with attention to the frequency-multiplexed OLAP. General expressions are obtained for the output produced as a function of various component errors and noise. A digital simulator for this model is discussed.
Optical linear algebra processors: noise and error-source modeling.
Casasent, D; Ghosh, A
1985-06-01
The modeling of system and component noise and error sources in optical linear algebra processors (OLAP's) are considered, with attention to the frequency-multiplexed OLAP. General expressions are obtained for the output produced as a function of various component errors and noise. A digital simulator for this model is discussed.
Reward expectation and prediction error in human medial frontal cortex: an EEG study.
Silvetti, Massimo; Nuñez Castellar, Elena; Roger, Clémence; Verguts, Tom
2014-01-01
The mammalian medial frontal cortex (MFC) is involved in reward-based decision making. In particular, in nonhuman primates this area constructs expectations about upcoming rewards, given an environmental state or a choice planned by the animal. At the same time, in both humans and nonhuman primates, the MFC computes the difference between such predictions and actual environmental outcomes (reward prediction errors). However, there is a paucity of evidence about the time course of MFC-related activity during reward prediction and prediction error in humans. Here we experimentally investigated this by recording the EEG during a reinforcement learning task. Our results support the hypothesis that human MFC codes for reward prediction during the cue period and for prediction error during the outcome period. Further, reward expectation (cue period) was positively correlated with prediction error (outcome period) in error trials but negatively in correct trials, consistent with updating of reward expectation by prediction error. This demonstrates in humans, like in nonhuman primates, a role of the MFC in the rapid updating of reward expectations through prediction errors. © 2013.
Role-modeling and medical error disclosure: a national survey of trainees.
Martinez, William; Hickson, Gerald B; Miller, Bonnie M; Doukas, David J; Buckley, John D; Song, John; Sehgal, Niraj L; Deitz, Jennifer; Braddock, Clarence H; Lehmann, Lisa Soleymani
2014-03-01
To measure trainees' exposure to negative and positive role-modeling for responding to medical errors and to examine the association between that exposure and trainees' attitudes and behaviors regarding error disclosure. Between May 2011 and June 2012, 435 residents at two large academic medical centers and 1,187 medical students from seven U.S. medical schools received anonymous, electronic questionnaires. The questionnaire asked respondents about (1) experiences with errors, (2) training for responding to errors, (3) behaviors related to error disclosure, (4) exposure to role-modeling for responding to errors, and (5) attitudes regarding disclosure. Using multivariate regression, the authors analyzed whether frequency of exposure to negative and positive role-modeling independently predicted two primary outcomes: (1) attitudes regarding disclosure and (2) nontransparent behavior in response to a harmful error. The response rate was 55% (884/1,622). Training on how to respond to errors had the largest independent, positive effect on attitudes (standardized effect estimate, 0.32, P error (OR 1.37, 95% CI 1.15-1.64; P errors. Negative role models may be a significant impediment to disclosure among trainees.
NASA Model of "Threat and Error" in Pediatric Cardiac Surgery: Patterns of Error Chains.
Hickey, Edward; Pham-Hung, Eric; Nosikova, Yaroslavna; Halvorsen, Fredrik; Gritti, Michael; Schwartz, Steven; Caldarone, Christopher A; Van Arsdell, Glen
2017-04-01
We introduced the National Aeronautics and Space Association threat-and-error model to our surgical unit. All admissions are considered flights, which should pass through stepwise deescalations in risk during surgical recovery. We hypothesized that errors significantly influence risk deescalation and contribute to poor outcomes. Patient flights (524) were tracked in real time for threats, errors, and unintended states by full-time performance personnel. Expected risk deescalation was wean from mechanical support, sternal closure, extubation, intensive care unit (ICU) discharge, and discharge home. Data were accrued from clinical charts, bedside data, reporting mechanisms, and staff interviews. Infographics of flights were openly discussed weekly for consensus. In 12% (64 of 524) of flights, the child failed to deescalate sequentially through expected risk levels; unintended increments instead occurred. Failed deescalations were highly associated with errors (426; 257 flights; p associated with a 29% rate of failed deescalation versus 4% in flights with no consequential error (p < 0.0001). The most dangerous errors were apical errors typically (84%) occurring in the operating room, which caused chains of propagating unintended states (n = 110): these had a 43% (47 of 110) rate of failed deescalation (versus 4%; p < 0.0001). Chains of unintended state were often (46%) amplified by additional (up to 7) errors in the ICU that would worsen clinical deviation. Overall, failed deescalations in risk were extremely closely linked to brain injury (n = 13; p < 0.0001) or death (n = 7; p < 0.0001). Deaths and brain injury after pediatric cardiac surgery almost always occur from propagating error chains that originate in the operating room and are often amplified by additional ICU errors. Copyright © 2017 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.
How well can we forecast future model error and uncertainty by mining past model performance data
Solomatine, Dimitri
2016-04-01
Consider a hydrological model Y(t) = M(X(t), P), where X=vector of inputs; P=vector of parameters; Y=model output (typically flow); t=time. In cases when there is enough past data on the model M performance, it is possible to use this data to build a (data-driven) model EC of model M error. This model EC will be able to forecast error E when a new input X is fed into model M; then subtracting E from the model prediction Y a better estimate of Y can be obtained. Model EC is usually called the error corrector (in meteorology - a bias corrector). However, we may go further in characterizing model deficiencies, and instead of using the error (a real value) we may consider a more sophisticated characterization, namely a probabilistic one. So instead of rather a model EC of the model M error it is also possible to build a model U of model M uncertainty; if uncertainty is described as the model error distribution D this model will calculate its properties - mean, variance, other moments, and quantiles. The general form of this model could be: D = U (RV), where RV=vector of relevant variables having influence on model uncertainty (to be identified e.g. by mutual information analysis); D=vector of variables characterizing the error distribution (typically, two or more quantiles). There is one aspect which is not always explicitly mentioned in uncertainty analysis work. In our view it is important to distinguish the following main types of model uncertainty: 1. The residual uncertainty of models. In this case the model parameters and/or model inputs are considered to be fixed (deterministic), i.e. the model is considered to be optimal (calibrated) and deterministic. Model error is considered as the manifestation of uncertainty. If there is enough past data about the model errors (i.e. its uncertainty), it is possible to build a statistical or machine learning model of uncertainty trained on this data. Here the following methods can be mentioned: (a) quantile regression (QR
Uehara, Shintaro; Mawase, Firas; Celnik, Pablo
2017-09-14
Humans can acquire knowledge of new motor behavior via different forms of learning. The two forms most commonly studied have been the development of internal models based on sensory-prediction errors (error-based learning) and success-based feedback (reinforcement learning). Human behavioral studies suggest these are distinct learning processes, though the neurophysiological mechanisms that are involved have not been characterized. Here, we evaluated physiological markers from the cerebellum and the primary motor cortex (M1) using noninvasive brain stimulations while healthy participants trained finger-reaching tasks. We manipulated the extent to which subjects rely on error-based or reinforcement by providing either vector or binary feedback about task performance. Our results demonstrated a double dissociation where learning the task mainly via error-based mechanisms leads to cerebellar plasticity modifications but not long-term potentiation (LTP)-like plasticity changes in M1; while learning a similar action via reinforcement mechanisms elicited M1 LTP-like plasticity but not cerebellar plasticity changes. Our findings indicate that learning complex motor behavior is mediated by the interplay of different forms of learning, weighing distinct neural mechanisms in M1 and the cerebellum. Our study provides insights for designing effective interventions to enhance human motor learning. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Visual Prediction Error Spreads Across Object Features in Human Visual Cortex.
Jiang, Jiefeng; Summerfield, Christopher; Egner, Tobias
2016-12-14
Visual cognition is thought to rely heavily on contextual expectations. Accordingly, previous studies have revealed distinct neural signatures for expected versus unexpected stimuli in visual cortex. However, it is presently unknown how the brain combines multiple concurrent stimulus expectations such as those we have for different features of a familiar object. To understand how an unexpected object feature affects the simultaneous processing of other expected feature(s), we combined human fMRI with a task that independently manipulated expectations for color and motion features of moving-dot stimuli. Behavioral data and neural signals from visual cortex were then interrogated to adjudicate between three possible ways in which prediction error (surprise) in the processing of one feature might affect the concurrent processing of another, expected feature: (1) feature processing may be independent; (2) surprise might "spread" from the unexpected to the expected feature, rendering the entire object unexpected; or (3) pairing a surprising feature with an expected feature might promote the inference that the two features are not in fact part of the same object. To formalize these rival hypotheses, we implemented them in a simple computational model of multifeature expectations. Across a range of analyses, behavior and visual neural signals consistently supported a model that assumes a mixing of prediction error signals across features: surprise in one object feature spreads to its other feature(s), thus rendering the entire object unexpected. These results reveal neurocomputational principles of multifeature expectations and indicate that objects are the unit of selection for predictive vision. We address a key question in predictive visual cognition: how does the brain combine multiple concurrent expectations for different features of a single object such as its color and motion trajectory? By combining a behavioral protocol that independently varies expectation of
Measurement Error and Bias in Value-Added Models. Research Report. ETS RR-17-25
Kane, Michael T.
2017-01-01
By aggregating residual gain scores (the differences between each student's current score and a predicted score based on prior performance) for a school or a teacher, value-added models (VAMs) can be used to generate estimates of school or teacher effects. It is known that random errors in the prior scores will introduce bias into predictions of…
Directory of Open Access Journals (Sweden)
Jun Yang
2014-01-01
Full Text Available To improve the CNC machine tools precision, a thermal error modeling for the motorized spindle was proposed based on time series analysis, considering the length of cutting tools and thermal declined angles, and the real-time error compensation was implemented. A five-point method was applied to measure radial thermal declinations and axial expansion of the spindle with eddy current sensors, solving the problem that the three-point measurement cannot obtain the radial thermal angle errors. Then the stationarity of the thermal error sequences was determined by the Augmented Dickey-Fuller Test Algorithm, and the autocorrelation/partial autocorrelation function was applied to identify the model pattern. By combining both Yule-Walker equations and information criteria, the order and parameters of the models were solved effectively, which improved the prediction accuracy and generalization ability. The results indicated that the prediction accuracy of the time series model could reach up to 90%. In addition, the axial maximum error decreased from 39.6 μm to 7 μm after error compensation, and the machining accuracy was improved by 89.7%. Moreover, the X/Y-direction accuracy can reach up to 77.4% and 86%, respectively, which demonstrated that the proposed methods of measurement, modeling, and compensation were effective.
Hand-eye calibration using a target registration error model.
Chen, Elvis C S; Morgan, Isabella; Jayarathne, Uditha; Ma, Burton; Peters, Terry M
2017-10-01
Surgical cameras are prevalent in modern operating theatres and are often used as a surrogate for direct vision. Visualisation techniques (e.g. image fusion) made possible by tracking the camera require accurate hand-eye calibration between the camera and the tracking system. The authors introduce the concept of 'guided hand-eye calibration', where calibration measurements are facilitated by a target registration error (TRE) model. They formulate hand-eye calibration as a registration problem between homologous point-line pairs. For each measurement, the position of a monochromatic ball-tip stylus (a point) and its projection onto the image (a line) is recorded, and the TRE of the resulting calibration is predicted using a TRE model. The TRE model is then used to guide the placement of the calibration tool, so that the subsequent measurement minimises the predicted TRE. Assessing TRE after each measurement produces accurate calibration using a minimal number of measurements. As a proof of principle, they evaluated guided calibration using a webcam and an endoscopic camera. Their endoscopic camera results suggest that millimetre TRE is achievable when at least 15 measurements are acquired with the tracker sensor ∼80 cm away on the laparoscope handle for a target ∼20 cm away from the camera.
Schiffer, Anne-Marike; Ahlheim, Christiane; Wurm, Moritz F; Schubotz, Ricarda I
2012-01-01
Influential concepts in neuroscientific research cast the brain a predictive machine that revises its predictions when they are violated by sensory input. This relates to the predictive coding account of perception, but also to learning. Learning from prediction errors has been suggested for take place in the hippocampal memory system as well as in the basal ganglia. The present fMRI study used an action-observation paradigm to investigate the contributions of the hippocampus, caudate nucleus and midbrain dopaminergic system to different types of learning: learning in the absence of prediction errors, learning from prediction errors, and responding to the accumulation of prediction errors in unpredictable stimulus configurations. We conducted analyses of the regions of interests' BOLD response towards these different types of learning, implementing a bootstrapping procedure to correct for false positives. We found both, caudate nucleus and the hippocampus to be activated by perceptual prediction errors. The hippocampal responses seemed to relate to the associative mismatch between a stored representation and current sensory input. Moreover, its response was significantly influenced by the average information, or Shannon entropy of the stimulus material. In accordance with earlier results, the habenula was activated by perceptual prediction errors. Lastly, we found that the substantia nigra was activated by the novelty of sensory input. In sum, we established that the midbrain dopaminergic system, the hippocampus, and the caudate nucleus were to different degrees significantly involved in the three different types of learning: acquisition of new information, learning from prediction errors and responding to unpredictable stimulus developments. We relate learning from perceptual prediction errors to the concept of predictive coding and related information theoretic accounts.
Managing errors in radiology: a working model
International Nuclear Information System (INIS)
Melvin, C.; Bodley, R.; Booth, A.; Meagher, T.; Record, C.; Savage, P.
2004-01-01
AIM: To develop a practical mechanism for reviewing reporting discrepancies as addressed in the Royal College of Radiologists publication 'To err is human. The case for review of reporting discrepancies'. MATERIALS AND METHODS: A regular meeting was developed, and has evolved, within the department to review discrepancies. Standard forms were devised for submission of cases as well as recording and classification of discrepancies. This has resulted in availability of figures that can be audited annually. RESULTS: Eighty-one cases involving error were reviewed over a 12-month period. Seven further cases flagged as discrepancies were not identified on peer review. Twenty-four reports were amended subsequent to the meeting. Nineteen additional cases were brought to the meeting as illustrative of teaching points or for discussion. CONCLUSION: We have evolved a successful process of reviewing reporting errors, which enjoys the confidence and support of all clinical radiologists, and is perceived as a method of improving patient care through an increasing awareness of lapses in performance
The error model and experiment of measuring angular position error based on laser collimation
Cai, Yangyang; Yang, Jing; Li, Jiakun; Feng, Qibo
2018-01-01
Rotary axis is the reference component of rotation motion. Angular position error is the most critical factor which impair the machining precision among the six degree-of-freedom (DOF) geometric errors of rotary axis. In this paper, the measuring method of angular position error of rotary axis based on laser collimation is thoroughly researched, the error model is established and 360 ° full range measurement is realized by using the high precision servo turntable. The change of space attitude of each moving part is described accurately by the 3×3 transformation matrices and the influences of various factors on the measurement results is analyzed in detail. Experiments results show that the measurement method can achieve high measurement accuracy and large measurement range.
Neural prediction errors reveal a risk-sensitive reinforcement-learning process in the human brain.
Niv, Yael; Edlund, Jeffrey A; Dayan, Peter; O'Doherty, John P
2012-01-11
Humans and animals are exquisitely, though idiosyncratically, sensitive to risk or variance in the outcomes of their actions. Economic, psychological, and neural aspects of this are well studied when information about risk is provided explicitly. However, we must normally learn about outcomes from experience, through trial and error. Traditional models of such reinforcement learning focus on learning about the mean reward value of cues and ignore higher order moments such as variance. We used fMRI to test whether the neural correlates of human reinforcement learning are sensitive to experienced risk. Our analysis focused on anatomically delineated regions of a priori interest in the nucleus accumbens, where blood oxygenation level-dependent (BOLD) signals have been suggested as correlating with quantities derived from reinforcement learning. We first provide unbiased evidence that the raw BOLD signal in these regions corresponds closely to a reward prediction error. We then derive from this signal the learned values of cues that predict rewards of equal mean but different variance and show that these values are indeed modulated by experienced risk. Moreover, a close neurometric-psychometric coupling exists between the fluctuations of the experience-based evaluations of risky options that we measured neurally and the fluctuations in behavioral risk aversion. This suggests that risk sensitivity is integral to human learning, illuminating economic models of choice, neuroscientific models of affective learning, and the workings of the underlying neural mechanisms.
Dorazio, Robert M.
2012-01-01
Several models have been developed to predict the geographic distribution of a species by combining measurements of covariates of occurrence at locations where the species is known to be present with measurements of the same covariates at other locations where species occurrence status (presence or absence) is unknown. In the absence of species detection errors, spatial point-process models and binary-regression models for case-augmented surveys provide consistent estimators of a species’ geographic distribution without prior knowledge of species prevalence. In addition, these regression models can be modified to produce estimators of species abundance that are asymptotically equivalent to those of the spatial point-process models. However, if species presence locations are subject to detection errors, neither class of models provides a consistent estimator of covariate effects unless the covariates of species abundance are distinct and independently distributed from the covariates of species detection probability. These analytical results are illustrated using simulation studies of data sets that contain a wide range of presence-only sample sizes. Analyses of presence-only data of three avian species observed in a survey of landbirds in western Montana and northern Idaho are compared with site-occupancy analyses of detections and nondetections of these species.
Simulation Model for Correction and Modeling of Probe Head Errors in Five-Axis Coordinate Systems
Directory of Open Access Journals (Sweden)
Adam Gąska
2016-05-01
Full Text Available Simulative methods are nowadays frequently used in metrology for the simulation of measurement uncertainty and the prediction of errors that may occur during measurements. In coordinate metrology, such methods are primarily used with the typical three-axis Coordinate Measuring Machines (CMMs, and lately, also with mobile measuring systems. However, no similar simulative models have been developed for five-axis systems in spite of their growing popularity in recent years. This paper presents the numerical model of probe head errors for probe heads that are used in five-axis coordinate systems. The model is based on measurements of material standards (standard ring and the use of the Monte Carlo method combined with select interpolation methods. The developed model may be used in conjunction with one of the known models of CMM kinematic errors to form a virtual model of a five-axis coordinate system. In addition, the developed methodology allows for the correction of identified probe head errors, thus improving measurement accuracy. Subsequent verification tests prove the correct functioning of the presented model.
Modeling and Prediction Using Stochastic Differential Equations
DEFF Research Database (Denmark)
Juhl, Rune; Møller, Jan Kloppenborg; Jørgensen, John Bagterp
2016-01-01
deterministic and can predict the future perfectly. A more realistic approach would be to allow for randomness in the model due to e.g., the model be too simple or errors in input. We describe a modeling and prediction setup which better reflects reality and suggests stochastic differential equations (SDEs......) for modeling and forecasting. It is argued that this gives models and predictions which better reflect reality. The SDE approach also offers a more adequate framework for modeling and a number of efficient tools for model building. A software package (CTSM-R) for SDE-based modeling is briefly described....... that describes the variation between subjects. The ODE setup implies that the variation for a single subject is described by a single parameter (or vector), namely the variance (covariance) of the residuals. Furthermore the prediction of the states is given as the solution to the ODEs and hence assumed...
Incorporating measurement error in n = 1 psychological autoregressive modeling
Schuurman, Noémi K.; Houtveen, Jan H.; Hamaker, Ellen L.
2015-01-01
Measurement error is omnipresent in psychological data. However, the vast majority of applications of autoregressive time series analyses in psychology do not take measurement error into account. Disregarding measurement error when it is present in the data results in a bias of the autoregressive parameters. We discuss two models that take measurement error into account: An autoregressive model with a white noise term (AR+WN), and an autoregressive moving average (ARMA) model. In a simulation study we compare the parameter recovery performance of these models, and compare this performance for both a Bayesian and frequentist approach. We find that overall, the AR+WN model performs better. Furthermore, we find that for realistic (i.e., small) sample sizes, psychological research would benefit from a Bayesian approach in fitting these models. Finally, we illustrate the effect of disregarding measurement error in an AR(1) model by means of an empirical application on mood data in women. We find that, depending on the person, approximately 30–50% of the total variance was due to measurement error, and that disregarding this measurement error results in a substantial underestimation of the autoregressive parameters. PMID:26283988
Empirical study of the GARCH model with rational errors
International Nuclear Information System (INIS)
Chen, Ting Ting; Takaishi, Tetsuya
2013-01-01
We use the GARCH model with a fat-tailed error distribution described by a rational function and apply it to stock price data on the Tokyo Stock Exchange. To determine the model parameters we perform Bayesian inference to the model. Bayesian inference is implemented by the Metropolis-Hastings algorithm with an adaptive multi-dimensional Student's t-proposal density. In order to compare our model with the GARCH model with the standard normal errors, we calculate the information criteria AIC and DIC, and find that both criteria favor the GARCH model with a rational error distribution. We also calculate the accuracy of the volatility by using the realized volatility and find that a good accuracy is obtained for the GARCH model with a rational error distribution. Thus we conclude that the GARCH model with a rational error distribution is superior to the GARCH model with the normal errors and it can be used as an alternative GARCH model to those with other fat-tailed distributions
International Nuclear Information System (INIS)
Nijsten, S. M. J. J. G.; Elmpt, W. J. C. van; Mijnheer, B. J.; Minken, A. W. H.; Persoon, L. C. G. G.; Lambin, P.; Dekker, A. L. A. J.
2009-01-01
Electronic portal imaging devices (EPIDs) are increasingly used for portal dosimetry applications. In our department, EPIDs are clinically used for two-dimensional (2D) transit dosimetry. Predicted and measured portal dose images are compared to detect dose delivery errors caused for instance by setup errors or organ motion. The aim of this work is to develop a model to predict dose-volume histogram (DVH) changes due to setup errors during breast cancer treatment using 2D transit dosimetry. First, correlations between DVH parameter changes and 2D gamma parameters are investigated for different simulated setup errors, which are described by a binomial logistic regression model. The model calculates the probability that a DVH parameter changes more than a specific tolerance level and uses several gamma evaluation parameters for the planning target volume (PTV) projection in the EPID plane as input. Second, the predictive model is applied to clinically measured portal images. Predicted DVH parameter changes are compared to calculated DVH parameter changes using the measured setup error resulting from a dosimetric registration procedure. Statistical accuracy is investigated by using receiver operating characteristic (ROC) curves and values for the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values. Changes in the mean PTV dose larger than 5%, and changes in V 90 and V 95 larger than 10% are accurately predicted based on a set of 2D gamma parameters. Most pronounced changes in the three DVH parameters are found for setup errors in the lateral-medial direction. AUC, sensitivity, specificity, and negative predictive values were between 85% and 100% while the positive predictive values were lower but still higher than 54%. Clinical predictive value is decreased due to the occurrence of patient rotations or breast deformations during treatment, but the overall reliability of the predictive model remains high. Based on our
Deconvolution Estimation in Measurement Error Models: The R Package decon
Wang, Xiao-Feng; Wang, Bin
2011-01-01
Data from many scientific areas often come with measurement error. Density or distribution function estimation from contaminated data and nonparametric regression with errors-in-variables are two important topics in measurement error models. In this paper, we present a new software package decon for R, which contains a collection of functions that use the deconvolution kernel methods to deal with the measurement error problems. The functions allow the errors to be either homoscedastic or heteroscedastic. To make the deconvolution estimators computationally more efficient in R, we adapt the fast Fourier transform algorithm for density estimation with error-free data to the deconvolution kernel estimation. We discuss the practical selection of the smoothing parameter in deconvolution methods and illustrate the use of the package through both simulated and real examples. PMID:21614139
Uncertainties of predictions from parton distributions 2, theoretical errors
Martin, A D; Stirling, W J; Thorne, R S; 10.1140/epjc/s2004-01825-2
2004-01-01
We study the uncertainties in parton distributions, determined in global fits to deep inelastic and related hard scattering data, due to so-called theoretical errors. Amongst these, we include potential errors due to the change of perturbative order (NLO to NNLO), ln(1/x) and ln(1 - x) effects, absorptive corrections and higher-twist contributions. We investigate these uncertainties both by including explicit corrections to our standard global analysis and by examining the sensitivity to changes of the x, Q/sup 2/, W/sup 2/ cuts on the data that are fitted. In this way we expose those kinematic regions where the conventional DGLAP description is inadequate. As a consequence we obtain a set of NLO, and of NNLO, conservative partons where the data are fully consistent with DGLAP evolution, but over a restricted kinematic domain. We also examine the potential effects of such issues as the choice of input parametrisation, heavy target corrections, assumptions about. the strange quark sea and isospin violation. He...
Bayesian approach to errors-in-variables in regression models
Rozliman, Nur Aainaa; Ibrahim, Adriana Irawati Nur; Yunus, Rossita Mohammad
2017-05-01
In many applications and experiments, data sets are often contaminated with error or mismeasured covariates. When at least one of the covariates in a model is measured with error, Errors-in-Variables (EIV) model can be used. Measurement error, when not corrected, would cause misleading statistical inferences and analysis. Therefore, our goal is to examine the relationship of the outcome variable and the unobserved exposure variable given the observed mismeasured surrogate by applying the Bayesian formulation to the EIV model. We shall extend the flexible parametric method proposed by Hossain and Gustafson (2009) to another nonlinear regression model which is the Poisson regression model. We shall then illustrate the application of this approach via a simulation study using Markov chain Monte Carlo sampling methods.
Which forcing data errors matter most when modeling seasonal snowpacks?
Raleigh, M. S.; Lundquist, J. D.; Clark, M. P.
2014-12-01
High quality forcing data are critical when modeling seasonal snowpacks and snowmelt, but their quality is often compromised due to measurement errors or deficiencies in gridded data products (e.g., spatio-temporal interpolation, empirical parameterizations, or numerical weather model outputs). To assess the relative impact of errors in different meteorological forcings, many studies have conducted sensitivity analyses where errors (e.g., bias) are imposed on one forcing at a time and changes in model output are compared. Although straightforward, this approach only considers simplistic error structures and cannot quantify interactions in different meteorological forcing errors (i.e., it assumes a linear system). Here we employ the Sobol' method of global sensitivity analysis, which allows us to test how co-existing errors in six meteorological forcings (i.e., air temperature, precipitation, wind speed, humidity, incoming shortwave and longwave radiation) impact specific modeled snow variables (i.e., peak snow water equivalent, snowmelt rates, and snow disappearance timing). Using the Sobol' framework across a large number of realizations (>100000 simulations annually at each site), we test how (1) the type (e.g., bias vs. random errors), (2) distribution (e.g., uniform vs. normal), and (3) magnitude (e.g., instrument uncertainty vs. field uncertainty) of forcing errors impact key outputs from a physically based snow model (the Utah Energy Balance). We also assess the role of climate by conducting the analysis at sites in maritime, intermountain, continental, and tundra snow zones. For all outputs considered, results show that (1) biases in forcing data are more important than random errors, (2) the choice of error distribution can enhance the importance of specific forcings, and (3) the level of uncertainty considered dictates the relative importance of forcings. While the relative importance of forcings varied with snow variable and climate, the results broadly
Spatial Linear Mixed Models with Covariate Measurement Errors.
Li, Yi; Tang, Haicheng; Lin, Xihong
2009-01-01
Spatial data with covariate measurement errors have been commonly observed in public health studies. Existing work mainly concentrates on parameter estimation using Gibbs sampling, and no work has been conducted to understand and quantify the theoretical impact of ignoring measurement error on spatial data analysis in the form of the asymptotic biases in regression coefficients and variance components when measurement error is ignored. Plausible implementations, from frequentist perspectives, of maximum likelihood estimation in spatial covariate measurement error models are also elusive. In this paper, we propose a new class of linear mixed models for spatial data in the presence of covariate measurement errors. We show that the naive estimators of the regression coefficients are attenuated while the naive estimators of the variance components are inflated, if measurement error is ignored. We further develop a structural modeling approach to obtaining the maximum likelihood estimator by accounting for the measurement error. We study the large sample properties of the proposed maximum likelihood estimator, and propose an EM algorithm to draw inference. All the asymptotic properties are shown under the increasing-domain asymptotic framework. We illustrate the method by analyzing the Scottish lip cancer data, and evaluate its performance through a simulation study, all of which elucidate the importance of adjusting for covariate measurement errors.
Learning (from) the errors of a systems biology model.
Engelhardt, Benjamin; Frőhlich, Holger; Kschischo, Maik
2016-02-11
Mathematical modelling is a labour intensive process involving several iterations of testing on real data and manual model modifications. In biology, the domain knowledge guiding model development is in many cases itself incomplete and uncertain. A major problem in this context is that biological systems are open. Missed or unknown external influences as well as erroneous interactions in the model could thus lead to severely misleading results. Here we introduce the dynamic elastic-net, a data driven mathematical method which automatically detects such model errors in ordinary differential equation (ODE) models. We demonstrate for real and simulated data, how the dynamic elastic-net approach can be used to automatically (i) reconstruct the error signal, (ii) identify the target variables of model error, and (iii) reconstruct the true system state even for incomplete or preliminary models. Our work provides a systematic computational method facilitating modelling of open biological systems under uncertain knowledge.
Sensitivity analysis of geometric errors in additive manufacturing medical models.
Pinto, Jose Miguel; Arrieta, Cristobal; Andia, Marcelo E; Uribe, Sergio; Ramos-Grez, Jorge; Vargas, Alex; Irarrazaval, Pablo; Tejos, Cristian
2015-03-01
Additive manufacturing (AM) models are used in medical applications for surgical planning, prosthesis design and teaching. For these applications, the accuracy of the AM models is essential. Unfortunately, this accuracy is compromised due to errors introduced by each of the building steps: image acquisition, segmentation, triangulation, printing and infiltration. However, the contribution of each step to the final error remains unclear. We performed a sensitivity analysis comparing errors obtained from a reference with those obtained modifying parameters of each building step. Our analysis considered global indexes to evaluate the overall error, and local indexes to show how this error is distributed along the surface of the AM models. Our results show that the standard building process tends to overestimate the AM models, i.e. models are larger than the original structures. They also show that the triangulation resolution and the segmentation threshold are critical factors, and that the errors are concentrated at regions with high curvatures. Errors could be reduced choosing better triangulation and printing resolutions, but there is an important need for modifying some of the standard building processes, particularly the segmentation algorithms. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Takuya eHonda
2013-10-01
Full Text Available While performing an action, the timing of when the sensory feedback is given can be used to establish the causal link between the action and its consequence. It has been shown that delaying the visual feedback while carrying an object makes people feel the mass of the object to be greater, suggesting that the feedback timing can also impact the perceived quality of an external object. In this study, we investigated the origin of the feedback timing information that influences the mass perception of the external object.Participants made a straight reaching movement while holding a manipulandum. The movement of the manipulandum was presented as a cursor movement on a monitor. In Experiment 1, various delays were imposed between the actual trajectory and the cursor movement. The participants’ perceived mass of the manipulandum significantly increased as the delay increased to 400 ms, but this gain did not reach significance when the delay was 800 ms. This suggests the existence of a temporal tuning mechanism for incorporating the visual feedback into the perception of mass. In Experiment 2, we examined whether the increased mass perception during the visual delay was due to the prediction error of the visual consequence of an action or to the actual delay of the feedback itself. After the participants adapted to the feedback delay, the perceived mass of the object became lighter than before, indicating that updating the temporal prediction model for the visual consequence diminishes the overestimation of the object’s mass. We propose that the misattribution of the visual delay into mass perception is induced by the sensorimotor prediction error, possibly when the amount of delay (error is within the range that can reasonably include the consequence of an action.
Consiglio, Maria C.; Hoadley, Sherwood T.; Allen, B. Danette
2009-01-01
Wind prediction errors are known to affect the performance of automated air traffic management tools that rely on aircraft trajectory predictions. In particular, automated separation assurance tools, planned as part of the NextGen concept of operations, must be designed to account and compensate for the impact of wind prediction errors and other system uncertainties. In this paper we describe a high fidelity batch simulation study designed to estimate the separation distance required to compensate for the effects of wind-prediction errors throughout increasing traffic density on an airborne separation assistance system. These experimental runs are part of the Safety Performance of Airborne Separation experiment suite that examines the safety implications of prediction errors and system uncertainties on airborne separation assurance systems. In this experiment, wind-prediction errors were varied between zero and forty knots while traffic density was increased several times current traffic levels. In order to accurately measure the full unmitigated impact of wind-prediction errors, no uncertainty buffers were added to the separation minima. The goal of the study was to measure the impact of wind-prediction errors in order to estimate the additional separation buffers necessary to preserve separation and to provide a baseline for future analyses. Buffer estimations from this study will be used and verified in upcoming safety evaluation experiments under similar simulation conditions. Results suggest that the strategic airborne separation functions exercised in this experiment can sustain wind prediction errors up to 40kts at current day air traffic density with no additional separation distance buffer and at eight times the current day with no more than a 60% increase in separation distance buffer.
A Model of Self-Monitoring Blood Glucose Measurement Error.
Vettoretti, Martina; Facchinetti, Andrea; Sparacino, Giovanni; Cobelli, Claudio
2017-07-01
A reliable model of the probability density function (PDF) of self-monitoring of blood glucose (SMBG) measurement error would be important for several applications in diabetes, like testing in silico insulin therapies. In the literature, the PDF of SMBG error is usually described by a Gaussian function, whose symmetry and simplicity are unable to properly describe the variability of experimental data. Here, we propose a new methodology to derive more realistic models of SMBG error PDF. The blood glucose range is divided into zones where error (absolute or relative) presents a constant standard deviation (SD). In each zone, a suitable PDF model is fitted by maximum-likelihood to experimental data. Model validation is performed by goodness-of-fit tests. The method is tested on two databases collected by the One Touch Ultra 2 (OTU2; Lifescan Inc, Milpitas, CA) and the Bayer Contour Next USB (BCN; Bayer HealthCare LLC, Diabetes Care, Whippany, NJ). In both cases, skew-normal and exponential models are used to describe the distribution of errors and outliers, respectively. Two zones were identified: zone 1 with constant SD absolute error; zone 2 with constant SD relative error. Goodness-of-fit tests confirmed that identified PDF models are valid and superior to Gaussian models used so far in the literature. The proposed methodology allows to derive realistic models of SMBG error PDF. These models can be used in several investigations of present interest in the scientific community, for example, to perform in silico clinical trials to compare SMBG-based with nonadjunctive CGM-based insulin treatments.
Error and Uncertainty Analysis for Ecological Modeling and Simulation
National Research Council Canada - National Science Library
Gertner, George
1998-01-01
The main objectives of this project are a) to develop a general methodology for conducting sensitivity and uncertainty analysis and building error budgets in simulation modeling over space and time; and b...
Efficiency in Linear Model with AR (1) and Correlated Error ...
African Journals Online (AJOL)
Nekky Umera
Assumptions in the classical normal linear regression model include that of lack of autocorrelation of the error terms ... which the classical linear regression model is based will usually be violated. These violations, seen in widespread .... we conclude in section 5. The Model. We assume a simple linear regression model:.
Curiosity and reward: Valence predicts choice and information prediction errors enhance learning.
Marvin, Caroline B; Shohamy, Daphna
2016-03-01
Curiosity drives many of our daily pursuits and interactions; yet, we know surprisingly little about how it works. Here, we harness an idea implied in many conceptualizations of curiosity: that information has value in and of itself. Reframing curiosity as the motivation to obtain reward-where the reward is information-allows one to leverage major advances in theoretical and computational mechanisms of reward-motivated learning. We provide new evidence supporting 2 predictions that emerge from this framework. First, we find an asymmetric effect of positive versus negative information, with positive information enhancing both curiosity and long-term memory for information. Second, we find that it is not the absolute value of information that drives learning but, rather, the gap between the reward expected and reward received, an "information prediction error." These results support the idea that information functions as a reward, much like money or food, guiding choices and driving learning in systematic ways. (c) 2016 APA, all rights reserved).
Dopamine reward prediction-error signalling: a two-component response
Schultz, Wolfram
2017-01-01
Environmental stimuli and objects, including rewards, are often processed sequentially in the brain. Recent work suggests that the phasic dopamine reward prediction-error response follows a similar sequential pattern. An initial brief, unselective and highly sensitive increase in activity unspecifically detects a wide range of environmental stimuli, then quickly evolves into the main response component, which reflects subjective reward value and utility. This temporal evolution allows the dopamine reward prediction-error signal to optimally combine speed and accuracy. PMID:26865020
Estimation of Mechanical Signals in Induction Motors using the Recursive Prediction Error Method
DEFF Research Database (Denmark)
Børsting, H.; Knudsen, Morten; Rasmussen, Henrik
1993-01-01
Sensor feedback of mechanical quantities for control applications in induction motors is troublesome and relative expensive. In this paper a recursive prediction error (RPE) method has successfully been used to estimate the angular rotor speed ........Sensor feedback of mechanical quantities for control applications in induction motors is troublesome and relative expensive. In this paper a recursive prediction error (RPE) method has successfully been used to estimate the angular rotor speed .....
Somarathna, P D S N; Minasny, Budiman; Malone, Brendan P; Stockmann, Uta; McBratney, Alex B
2018-03-08
Spatial modelling of environmental data commonly only considers spatial variability as the single source of uncertainty. In reality however, the measurement errors should also be accounted for. In recent years, infrared spectroscopy has been shown to offer low cost, yet invaluable information needed for digital soil mapping at meaningful spatial scales for land management. However, spectrally inferred soil carbon data are known to be less accurate compared to laboratory analysed measurements. This study establishes a methodology to filter out the measurement error variability by incorporating the measurement error variance in the spatial covariance structure of the model. The study was carried out in the Lower Hunter Valley, New South Wales, Australia where a combination of laboratory measured, and vis-NIR and MIR inferred topsoil and subsoil soil carbon data are available. We investigated the applicability of residual maximum likelihood (REML) and Markov Chain Monte Carlo (MCMC) simulation methods to generate parameters of the Matérn covariance function directly from the data in the presence of measurement error. The results revealed that the measurement error can be effectively filtered-out through the proposed technique. When the measurement error was filtered from the data, the prediction variance almost halved, which ultimately yielded a greater certainty in spatial predictions of soil carbon. Further, the MCMC technique was successfully used to define the posterior distribution of measurement error. This is an important outcome, as the MCMC technique can be used to estimate the measurement error if it is not explicitly quantified. Although this study dealt with soil carbon data, this method is amenable for filtering the measurement error of any kind of continuous spatial environmental data. Copyright © 2018 Elsevier B.V. All rights reserved.
Predictive Surface Complexation Modeling
Energy Technology Data Exchange (ETDEWEB)
Sverjensky, Dimitri A. [Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Earth and Planetary Sciences
2016-11-29
Surface complexation plays an important role in the equilibria and kinetics of processes controlling the compositions of soilwaters and groundwaters, the fate of contaminants in groundwaters, and the subsurface storage of CO_{2} and nuclear waste. Over the last several decades, many dozens of individual experimental studies have addressed aspects of surface complexation that have contributed to an increased understanding of its role in natural systems. However, there has been no previous attempt to develop a model of surface complexation that can be used to link all the experimental studies in order to place them on a predictive basis. Overall, my research has successfully integrated the results of the work of many experimentalists published over several decades. For the first time in studies of the geochemistry of the mineral-water interface, a practical predictive capability for modeling has become available. The predictive correlations developed in my research now enable extrapolations of experimental studies to provide estimates of surface chemistry for systems not yet studied experimentally and for natural and anthropogenically perturbed systems.
Prediction-error in the context of real social relationships modulates reward system activity
Directory of Open Access Journals (Sweden)
Joshua ePoore
2012-08-01
Full Text Available The human reward system is sensitive to both social (e.g., validation and non-social rewards (e.g., money and is likely integral for relationship development and reputation building. However, data is sparse on the question of whether implicit social reward processing meaningfully contributes to explicit social representations such as trust and attachment security in pre-existing relationships. This event-related fMRI experiment examined reward system prediction-error activity in response to a potent social reward—social validation—and this activity’s relation to both attachment security and trust in the context of real romantic relationships. During the experiment, participants’ expectations for their romantic partners’ positive regard of them were confirmed (validated or violated, in either positive or negative directions. Primary analyses were conducted using predefined regions of interest, the locations of which were taken from previously published research. Results indicate that activity for mid-brain and striatal reward system regions of interest was modulated by social reward expectation violation in ways consistent with prior research on reward prediction-error. Additionally, activity in the striatum during viewing of disconfirmatory information was associated with both increases in post-scan reports of attachment anxiety and decreases in post-scan trust, a finding that follows directly from representational models of attachment and trust.
Assessment of errors and uncertainty patterns in GIA modeling
DEFF Research Database (Denmark)
Barletta, Valentina Roberta; Spada, G.
2012-01-01
During the last decade many efforts have been devoted to the assessment of global sea level rise and to the determination of the mass balance of continental ice sheets. In this context, the important role of glacial-isostatic adjustment (GIA) has been clearly recognized. Yet, in many cases only one...... "preferred" GIA model has been used, without any consideration of the possible errors involved. Lacking a rigorous assessment of systematic errors in GIA modeling, the reliabil-ity of the results is uncertain. GIA sensitivity and uncertainties associated with the viscosity mod-els have been explored...... in the literature. However, at least two major sources of errors remain. The first is associated with the ice models, spatial distribution of ice and history of melting (this is especially the case of Antarctica), the second with the numerical implementation of model fea-tures relevant to sea level modeling...
Bayesian modeling of measurement error in predictor variables
Fox, Gerardus J.A.; Glas, Cornelis A.W.
2003-01-01
It is shown that measurement error in predictor variables can be modeled using item response theory (IRT). The predictor variables, that may be defined at any level of an hierarchical regression model, are treated as latent variables. The normal ogive model is used to describe the relation between
The Human Bathtub: Safety and Risk Predictions Including the Dynamic Probability of Operator Errors
International Nuclear Information System (INIS)
Duffey, Romney B.; Saull, John W.
2006-01-01
Reactor safety and risk are dominated by the potential and major contribution for human error in the design, operation, control, management, regulation and maintenance of the plant, and hence to all accidents. Given the possibility of accidents and errors, now we need to determine the outcome (error) probability, or the chance of failure. Conventionally, reliability engineering is associated with the failure rate of components, or systems, or mechanisms, not of human beings in and interacting with a technological system. The probability of failure requires a prior knowledge of the total number of outcomes, which for any predictive purposes we do not know or have. Analysis of failure rates due to human error and the rate of learning allow a new determination of the dynamic human error rate in technological systems, consistent with and derived from the available world data. The basis for the analysis is the 'learning hypothesis' that humans learn from experience, and consequently the accumulated experience defines the failure rate. A new 'best' equation has been derived for the human error, outcome or failure rate, which allows for calculation and prediction of the probability of human error. We also provide comparisons to the empirical Weibull parameter fitting used in and by conventional reliability engineering and probabilistic safety analysis methods. These new analyses show that arbitrary Weibull fitting parameters and typical empirical hazard function techniques cannot be used to predict the dynamics of human errors and outcomes in the presence of learning. Comparisons of these new insights show agreement with human error data from the world's commercial airlines, the two shuttle failures, and from nuclear plant operator actions and transient control behavior observed in transients in both plants and simulators. The results demonstrate that the human error probability (HEP) is dynamic, and that it may be predicted using the learning hypothesis and the minimum
Period, epoch, and prediction errors of ephemerides from continuous sets of timing measurements
Deeg, H. J.
2015-06-01
Space missions such as Kepler and CoRoT have led to large numbers of eclipse or transit measurements in nearly continuous time series. This paper shows how to obtain the period error in such measurements from a basic linear least-squares fit, and how to correctly derive the timing error in the prediction of future transit or eclipse events. Assuming strict periodicity, a formula for the period error of these time series is derived, σP = σT (12 / (N3-N))1 / 2, where σP is the period error, σT the timing error of a single measurement, and N the number of measurements. Compared to the iterative method for period error estimation by Mighell & Plavchan (2013), this much simpler formula leads to smaller period errors, whose correctness has been verified through simulations. For the prediction of times of future periodic events, usual linear ephemeris were epoch errors are quoted for the first time measurement, are prone to an overestimation of the error of that prediction. This may be avoided by a correction for the duration of the time series. An alternative is the derivation of ephemerides whose reference epoch and epoch error are given for the centre of the time series. For long continuous or near-continuous time series whose acquisition is completed, such central epochs should be the preferred way for the quotation of linear ephemerides. While this work was motivated from the analysis of eclipse timing measures in space-based light curves, it should be applicable to any other problem with an uninterrupted sequence of discrete timings for which the determination of a zero point, of a constant period and of the associated errors is needed.
Modeling of Bit Error Rate in Cascaded 2R Regenerators
DEFF Research Database (Denmark)
Öhman, Filip; Mørk, Jesper
2006-01-01
This paper presents a simple and efficient model for estimating the bit error rate in a cascade of optical 2R-regenerators. The model includes the influences of of amplifier noise, finite extinction ratio and nonlinear reshaping. The interplay between the different signal impairments and the rege......This paper presents a simple and efficient model for estimating the bit error rate in a cascade of optical 2R-regenerators. The model includes the influences of of amplifier noise, finite extinction ratio and nonlinear reshaping. The interplay between the different signal impairments...
Energy Technology Data Exchange (ETDEWEB)
Morley, Steven Karl [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2016-07-01
This report reviews existing literature describing forecast accuracy metrics, concentrating on those based on relative errors and percentage errors. We then review how the most common of these metrics, the mean absolute percentage error (MAPE), has been applied in recent radiation belt modeling literature. Finally, we describe metrics based on the ratios of predicted to observed values (the accuracy ratio) that address the drawbacks inherent in using MAPE. Specifically, we define and recommend the median log accuracy ratio as a measure of bias and the median symmetric accuracy as a measure of accuracy.
Wang, Hongcui; Kawahara, Tatsuya
CALL (Computer Assisted Language Learning) systems using ASR (Automatic Speech Recognition) for second language learning have received increasing interest recently. However, it still remains a challenge to achieve high speech recognition performance, including accurate detection of erroneous utterances by non-native speakers. Conventionally, possible error patterns, based on linguistic knowledge, are added to the lexicon and language model, or the ASR grammar network. However, this approach easily falls in the trade-off of coverage of errors and the increase of perplexity. To solve the problem, we propose a method based on a decision tree to learn effective prediction of errors made by non-native speakers. An experimental evaluation with a number of foreign students learning Japanese shows that the proposed method can effectively generate an ASR grammar network, given a target sentence, to achieve both better coverage of errors and smaller perplexity, resulting in significant improvement in ASR accuracy.
Gariel, C; Cogniat, B; Desgranges, F-P; Chassard, D; Bouvet, L
2018-03-01
Medication errors are not uncommon in hospitalized patients. Paediatric patients may have increased risk for medication errors related to complexity of weight-based dosing calculations or problems with drug preparation and dilution. This study aimed to determine the incidence of medication errors in paediatric anaesthesia in a university paediatric hospital, and to identify their characteristics and potential predictive factors. This prospective incident monitoring study was conducted between November 2015 and January 2016 in an exclusively paediatric surgical centre. Children medication error (2.6%). Drugs most commonly involved in medication errors were opioids and antibiotics. Incorrect dose was the most frequently reported type of error (n=27, 67.5%), with dilution error involved in 7/27 (26%) cases of incorrect dose. Duration of procedure >120 min was the only factor independently associated with medication error [adjusted odds ratio: 4 (95% confidence interval: 2-8); P=0.0001]. Medication errors are not uncommon in paediatric anaesthesia. Identification of the mechanisms related to medication errors might allow preventive measures that can be assessed in further studies. Copyright © 2017 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.
A Temperature Sensor Clustering Method for Thermal Error Modeling of Heavy Milling Machine Tools
Directory of Open Access Journals (Sweden)
Fengchun Li
2017-01-01
Full Text Available A clustering method is an effective way to select the proper temperature sensor location for thermal error modeling of machine tools. In this paper, a new temperature sensor clustering method is proposed. By analyzing the characteristics of the temperature of the sensors in a heavy floor-type milling machine tool, an indicator involving both the Euclidean distance and the correlation coefficient was proposed to reflect the differences between temperature sensors, and the indicator was expressed by a distance matrix to be used for hierarchical clustering. Then, the weight coefficient in the distance matrix and the number of the clusters (groups were optimized by a genetic algorithm (GA, and the fitness function of the GA was also rebuilt by establishing the thermal error model at one rotation speed, then deriving its accuracy at two different rotation speeds with a temperature disturbance. Thus, the parameters for clustering, as well as the final selection of the temperature sensors, were derived. Finally, the method proposed in this paper was verified on a machine tool. According to the selected temperature sensors, a thermal error model of the machine tool was established and used to predict the thermal error. The results indicate that the selected temperature sensors can accurately predict thermal error at different rotation speeds, and the proposed temperature sensor clustering method for sensor selection is expected to be used for the thermal error modeling for other machine tools.
Effect Of Oceanic Lithosphere Age Errors On Model Discrimination
DeLaughter, J. E.
2016-12-01
The thermal structure of the oceanic lithosphere is the subject of a long-standing controversy. Because the thermal structure varies with age, it governs properties such as heat flow, density, and bathymetry with important implications for plate tectonics. Though bathymetry, geoid, and heat flow for young (appears to be shallower than expected for older lithosphere indicating a plate model is a better fit. It is therefore useful to jointly fit bathymetry, geoid, and heat flow data to an inverse model to determine lithospheric structure details. Though inverse models usually include the effect of errors in bathymetry, heat flow, and geoid, they rarely examine the effects of errors in age. This may have the effect of introducing subtle biases into inverse models of the oceanic lithosphere. Because the inverse problem for thermal structure is both ill-posed and ill-conditioned, these overlooked errors may have a greater effect than expected. The problem is further complicated by the non-uniform distribution of age and errors in age estimates; for example, only 30% of the oceanic lithosphere is older than 80 MY and less than 3% is older than 150 MY. To determine the potential strength of such biases, I have used the age and error maps of Mueller et al (2008) to forward model the bathymetry for half space and GDH1 plate models. For ages less than 20 MY, both models give similar results. The errors induced by uncertainty in age are relatively large and suggest that when possible young lithosphere should be excluded when examining the lithospheric thermal model. As expected, GDH1 bathymetry converges asymptotically on the theoretical result for error-free data for older data. The resulting uncertainty is nearly as large as that introduced by errors in the other parameters; in the absence of other errors, the models can only be distinguished for ages greater than 80 MY. These results suggest that the problem should be approached with the minimum possible number of
Candidate Prediction Models and Methods
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik
2005-01-01
This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...
Bayesian analysis of data and model error in rainfall-runoff hydrological models
Kavetski, D.; Franks, S. W.; Kuczera, G.
2004-12-01
A major unresolved issue in the identification and use of conceptual hydrologic models is realistic description of uncertainty in the data and model structure. In particular, hydrologic parameters often cannot be measured directly and must be inferred (calibrated) from observed forcing/response data (typically, rainfall and runoff). However, rainfall varies significantly in space and time, yet is often estimated from sparse gauge networks. Recent work showed that current calibration methods (e.g., standard least squares, multi-objective calibration, generalized likelihood uncertainty estimation) ignore forcing uncertainty and assume that the rainfall is known exactly. Consequently, they can yield strongly biased and misleading parameter estimates. This deficiency confounds attempts to reliably test model hypotheses, to generalize results across catchments (the regionalization problem) and to quantify predictive uncertainty when the hydrologic model is extrapolated. This paper continues the development of a Bayesian total error analysis (BATEA) methodology for the calibration and identification of hydrologic models, which explicitly incorporates the uncertainty in both the forcing and response data, and allows systematic model comparison based on residual model errors and formal Bayesian hypothesis testing (e.g., using Bayes factors). BATEA is based on explicit stochastic models for both forcing and response uncertainty, whereas current techniques focus solely on response errors. Hence, unlike existing methods, the BATEA parameter equations directly reflect the modeler's confidence in all the data. We compare several approaches to approximating the parameter distributions: a) full Markov Chain Monte Carlo methods and b) simplified approaches based on linear approximations. Studies using synthetic and real data from the US and Australia show that BATEA systematically reduces the parameter bias, leads to more meaningful model fits and allows model comparison taking
Starns, Jeffrey J; Dubé, Chad; Frelinger, Matthew E
2018-05-01
In this report, we evaluate single-item and forced-choice recognition memory for the same items and use the resulting accuracy and reaction time data to test the predictions of discrete-state and continuous models. For the single-item trials, participants saw a word and indicated whether or not it was studied on a previous list. The forced-choice trials had one studied and one non-studied word that both appeared in the earlier single-item trials and both received the same response. Thus, forced-choice trials always had one word with a previous correct response and one with a previous error. Participants were asked to select the studied word regardless of whether they previously called both words "studied" or "not studied." The diffusion model predicts that forced-choice accuracy should be lower when the word with a previous error had a fast versus a slow single-item RT, because fast errors are associated with more compelling misleading memory retrieval. The two-high-threshold (2HT) model does not share this prediction because all errors are guesses, so error RT is not related to memory strength. A low-threshold version of the discrete state approach predicts an effect similar to the diffusion model, because errors are a mixture of responses based on misleading retrieval and guesses, and the guesses should tend to be slower. Results showed that faster single-trial errors were associated with lower forced-choice accuracy, as predicted by the diffusion and low-threshold models. Copyright © 2018 Elsevier Inc. All rights reserved.
GMM estimation in panel data models with measurement error
Wansbeek, T.J.
Griliches and Hausman (J. Econom. 32 (1986) 93) have introduced GMM estimation in panel data models with measurement error. We present a simple, systematic approach to derive moment conditions for such models under a variety of assumptions. (C) 2001 Elsevier Science S.A. All rights reserved.
Cooper, Jeffrey C; Dunne, Simon; Furey, Teresa; O'Doherty, John P
2012-01-01
The dorsal striatum plays a key role in the learning and expression of instrumental reward associations that are acquired through direct experience. However, not all learning about instrumental actions require direct experience. Instead, humans and other animals are also capable of acquiring instrumental actions by observing the experiences of others. In this study, we investigated the extent to which human dorsal striatum is involved in observational as well as experiential instrumental reward learning. Human participants were scanned with fMRI while they observed a confederate over a live video performing an instrumental conditioning task to obtain liquid juice rewards. Participants also performed a similar instrumental task for their own rewards. Using a computational model-based analysis, we found reward prediction errors in the dorsal striatum not only during the experiential learning condition but also during observational learning. These results suggest a key role for the dorsal striatum in learning instrumental associations, even when those associations are acquired purely by observing others.
A practical guideline for human error assessment: A causal model
Ayele, Y. Z.; Barabadi, A.
2017-12-01
To meet the availability target and reduce system downtime, effective maintenance have a great importance. However, maintenance performance is greatly affected in complex ways by human factors. Hence, to have an effective maintenance operation, these factors needs to be assessed and quantified. To avoid the inadequacies of traditional human error assessment (HEA) approaches, the application of Bayesian Networks (BN) is gaining popularity. The main purpose of this paper is to propose a HEA framework based on the BN for maintenance operation. The proposed framework aids for assessing the effects of human performance influencing factors on the likelihood of human error during maintenance activities. Further, the paper investigates how operational issues must be considered in system failure-rate analysis, maintenance planning, and prediction of human error in pre- and post-maintenance operations. The goal is to assess how performance monitoring and evaluation of human factors can effect better operation and maintenance.
Guo, Rong; Böhmer, Wendelin; Hebart, Martin; Chien, Samson; Sommer, Tobias; Obermayer, Klaus; Gläscher, Jan
2016-12-14
Goal-directed and instrumental learning are both important controllers of human behavior. Learning about which stimulus event occurs in the environment and the reward associated with them allows humans to seek out the most valuable stimulus and move through the environment in a goal-directed manner. Stimulus-response associations are characteristic of instrumental learning, whereas response-outcome associations are the hallmark of goal-directed learning. Here we provide behavioral, computational, and neuroimaging results from a novel task in which stimulus-response and response-outcome associations are learned simultaneously but dominate behavior at different stages of the experiment. We found that prediction error representations in the ventral striatum depend on which type of learning dominates. Furthermore, the amygdala tracks the time-dependent weighting of stimulus-response versus response-outcome learning. Our findings suggest that the goal-directed and instrumental controllers dynamically engage the ventral striatum in representing prediction errors whenever one of them is dominating choice behavior. Converging evidence in human neuroimaging studies has shown that the reward prediction errors are correlated with activity in the ventral striatum. Our results demonstrate that this region is simultaneously correlated with a stimulus prediction error. Furthermore, the learning system that is currently dominating behavioral choice dynamically engages the ventral striatum for computing its prediction errors. This demonstrates that the prediction error representations are highly dynamic and influenced by various experimental context. This finding points to a general role of the ventral striatum in detecting expectancy violations and encoding error signals regardless of the specific nature of the reinforcer itself. Copyright © 2016 the authors 0270-6474/16/3612650-11$15.00/0.
Ihssen, Niklas; Mussweiler, Thomas; Linden, David E J
2016-08-01
Reward properties of stimuli can undergo sudden changes, and the detection of these 'reversals' is often made difficult by the probabilistic nature of rewards/punishments. Here we tested whether and how humans use social information (someone else's choices) to overcome uncertainty during reversal learning. We show a substantial social influence during reversal learning, which was modulated by the type of observed behavior. Participants frequently followed observed conservative choices (no switches after punishment) made by the (fictitious) other player but ignored impulsive choices (switches), even though the experiment was set up so that both types of response behavior would be similarly beneficial/detrimental (Study 1). Computational modeling showed that participants integrated the observed choices as a 'social prediction error' instead of ignoring or blindly following the other player. Modeling also confirmed higher learning rates for 'conservative' versus 'impulsive' social prediction errors. Importantly, this 'conservative bias' was boosted by interpersonal similarity, which in conjunction with the lack of effects observed in a non-social control experiment (Study 2) confirmed its social nature. A third study suggested that relative weighting of observed impulsive responses increased with increased volatility (frequency of reversals). Finally, simulations showed that in the present paradigm integrating social and reward information was not necessarily more adaptive to maximize earnings than learning from reward alone. Moreover, integrating social information increased accuracy only when conservative and impulsive choices were weighted similarly during learning. These findings suggest that to guide decisions in choice contexts that involve reward reversals humans utilize social cues conforming with their preconceptions more strongly than cues conflicting with them, especially when the other is similar. Copyright © 2016 The Authors. Published by Elsevier B
Linear regression crash prediction models : issues and proposed solutions.
2010-05-01
The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...
Three-Phase Text Error Correction Model for Korean SMS Messages
Byun, Jeunghyun; Park, So-Young; Lee, Seung-Wook; Rim, Hae-Chang
In this paper, we propose a three-phase text error correction model consisting of a word spacing error correction phase, a syllable-based spelling error correction phase, and a word-based spelling error correction phase. In order to reduce the text error correction complexity, the proposed model corrects text errors step by step. With the aim of correcting word spacing errors, spelling errors, and mixed errors in SMS messages, the proposed model tries to separately manage the word spacing error correction phase and the spelling error correction phase. For the purpose of utilizing both the syllable-based approach covering various errors and the word-based approach correcting some specific errors accurately, the proposed model subdivides the spelling error correction phase into the syllable-based phase and the word-based phase. Experimental results show that the proposed model can improve the performance by solving the text error correction problem based on the divide-and-conquer strategy.
Melanoma risk prediction models
Directory of Open Access Journals (Sweden)
Nikolić Jelena
2014-01-01
Full Text Available Background/Aim. The lack of effective therapy for advanced stages of melanoma emphasizes the importance of preventive measures and screenings of population at risk. Identifying individuals at high risk should allow targeted screenings and follow-up involving those who would benefit most. The aim of this study was to identify most significant factors for melanoma prediction in our population and to create prognostic models for identification and differentiation of individuals at risk. Methods. This case-control study included 697 participants (341 patients and 356 controls that underwent extensive interview and skin examination in order to check risk factors for melanoma. Pairwise univariate statistical comparison was used for the coarse selection of the most significant risk factors. These factors were fed into logistic regression (LR and alternating decision trees (ADT prognostic models that were assessed for their usefulness in identification of patients at risk to develop melanoma. Validation of the LR model was done by Hosmer and Lemeshow test, whereas the ADT was validated by 10-fold cross-validation. The achieved sensitivity, specificity, accuracy and AUC for both models were calculated. The melanoma risk score (MRS based on the outcome of the LR model was presented. Results. The LR model showed that the following risk factors were associated with melanoma: sunbeds (OR = 4.018; 95% CI 1.724- 9.366 for those that sometimes used sunbeds, solar damage of the skin (OR = 8.274; 95% CI 2.661-25.730 for those with severe solar damage, hair color (OR = 3.222; 95% CI 1.984-5.231 for light brown/blond hair, the number of common naevi (over 100 naevi had OR = 3.57; 95% CI 1.427-8.931, the number of dysplastic naevi (from 1 to 10 dysplastic naevi OR was 2.672; 95% CI 1.572-4.540; for more than 10 naevi OR was 6.487; 95%; CI 1.993-21.119, Fitzpatricks phototype and the presence of congenital naevi. Red hair, phototype I and large congenital naevi were
Validation of the measurement model concept for error structure identification
International Nuclear Information System (INIS)
Shukla, Pavan K.; Orazem, Mark E.; Crisalle, Oscar D.
2004-01-01
The development of different forms of measurement models for impedance has allowed examination of key assumptions on which the use of such models to assess error structure are based. The stochastic error structures obtained using the transfer-function and Voigt measurement models were identical, even when non-stationary phenomena caused some of the data to be inconsistent with the Kramers-Kronig relations. The suitability of the measurement model for assessment of consistency with the Kramers-Kronig relations, however, was found to be more sensitive to the confidence interval for the parameter estimates than to the number of parameters in the model. A tighter confidence interval was obtained for Voigt measurement model, which made the Voigt measurement model a more sensitive tool for identification of inconsistencies with the Kramers-Kronig relations
A New Open-Loop Fiber Optic Gyro Error Compensation Method Based on Angular Velocity Error Modeling
Zhang, Yanshun; Guo, Yajing; Li, Chunyu; Wang, Yixin; Wang, Zhanqing
2015-01-01
With the open-loop fiber optic gyro (OFOG) model, output voltage and angular velocity can effectively compensate OFOG errors. However, the model cannot reflect the characteristics of OFOG errors well when it comes to pretty large dynamic angular velocities. This paper puts forward a modeling scheme with OFOG output voltage and temperature as the input variables and angular velocity error as the output variable. Firstly, the angular ve...
Using APEX to Model Anticipated Human Error: Analysis of a GPS Navigational Aid
VanSelst, Mark; Freed, Michael; Shefto, Michael (Technical Monitor)
1997-01-01
The interface development process can be dramatically improved by predicting design facilitated human error at an early stage in the design process. The approach we advocate is to SIMULATE the behavior of a human agent carrying out tasks with a well-specified user interface, ANALYZE the simulation for instances of human error, and then REFINE the interface or protocol to minimize predicted error. This approach, incorporated into the APEX modeling architecture, differs from past approaches to human simulation in Its emphasis on error rather than e.g. learning rate or speed of response. The APEX model consists of two major components: (1) a powerful action selection component capable of simulating behavior in complex, multiple-task environments; and (2) a resource architecture which constrains cognitive, perceptual, and motor capabilities to within empirically demonstrated limits. The model mimics human errors arising from interactions between limited human resources and elements of the computer interface whose design falls to anticipate those limits. We analyze the design of a hand-held Global Positioning System (GPS) device used for radical and navigational decisions in small yacht recalls. The analysis demonstrates how human system modeling can be an effective design aid, helping to accelerate the process of refining a product (or procedure).
Mitigating Errors in External Respiratory Surrogate-Based Models of Tumor Position
International Nuclear Information System (INIS)
Malinowski, Kathleen T.; McAvoy, Thomas J.; George, Rohini; Dieterich, Sonja; D'Souza, Warren D.
2012-01-01
Purpose: To investigate the effect of tumor site, measurement precision, tumor–surrogate correlation, training data selection, model design, and interpatient and interfraction variations on the accuracy of external marker-based models of tumor position. Methods and Materials: Cyberknife Synchrony system log files comprising synchronously acquired positions of external markers and the tumor from 167 treatment fractions were analyzed. The accuracy of Synchrony, ordinary-least-squares regression, and partial-least-squares regression models for predicting the tumor position from the external markers was evaluated. The quantity and timing of the data used to build the predictive model were varied. The effects of tumor–surrogate correlation and the precision in both the tumor and the external surrogate position measurements were explored by adding noise to the data. Results: The tumor position prediction errors increased during the duration of a fraction. Increasing the training data quantities did not always lead to more accurate models. Adding uncorrelated noise to the external marker-based inputs degraded the tumor–surrogate correlation models by 16% for partial-least-squares and 57% for ordinary-least-squares. External marker and tumor position measurement errors led to tumor position prediction changes 0.3–3.6 times the magnitude of the measurement errors, varying widely with model algorithm. The tumor position prediction errors were significantly associated with the patient index but not with the fraction index or tumor site. Partial-least-squares was as accurate as Synchrony and more accurate than ordinary-least-squares. Conclusions: The accuracy of surrogate-based inferential models of tumor position was affected by all the investigated factors, except for the tumor site and fraction index.
Predicting Human Error in Air Traffic Control Decision Support Tools and Free Flight Concepts
Mogford, Richard; Kopardekar, Parimal
2001-01-01
The document is a set of briefing slides summarizing the work the Advanced Air Transportation Technologies (AATT) Project is doing on predicting air traffic controller and airline pilot human error when using new decision support software tools and when involved in testing new air traffic control concepts. Previous work in this area is reviewed as well as research being done jointly with the FAA. Plans for error prediction work in the AATT Project are discussed. The audience is human factors researchers and aviation psychologists from government and industry.
Testing and Inference in Nonlinear Cointegrating Vector Error Correction Models
DEFF Research Database (Denmark)
Kristensen, Dennis; Rahbek, Anders
In this paper, we consider a general class of vector error correction models which allow for asymmetric and non-linear error correction. We provide asymptotic results for (quasi-)maximum likelihood (QML) based estimators and tests. General hypothesis testing is considered, where testing for linea...... symmetric non-linear error correction are considered. A simulation study shows that the finite sample properties of the bootstrapped tests are satisfactory with good size and power properties for reasonable sample sizes....... for linearity is of particular interest as parameters of non-linear components vanish under the null. To solve the latter type of testing, we use the so-called sup tests, which here requires development of new (uniform) weak convergence results. These results are potentially useful in general for analysis......In this paper, we consider a general class of vector error correction models which allow for asymmetric and non-linear error correction. We provide asymptotic results for (quasi-)maximum likelihood (QML) based estimators and tests. General hypothesis testing is considered, where testing...
Predictive error detection in pianists: A combined ERP and motion capture study
Directory of Open Access Journals (Sweden)
Clemens eMaidhof
2013-09-01
Full Text Available Performing a piece of music involves the interplay of several cognitive and motor processes and requires extensive training to achieve a high skill level. However, even professional musicians commit errors occasionally. Previous event-related potential (ERP studies have investigated the neurophysiological correlates of pitch errors during piano performance, and reported pre-error negativity already occurring approximately 70-100 ms before the error had been committed and audible. It was assumed that this pre-error negativity reflects predictive control processes that compare predicted consequences with actual consequences of one’s own actions. However, in previous investigations, correct and incorrect pitch events were confounded by their different tempi. In addition, no data about the underlying movements were available. In the present study, we exploratively recorded the ERPs and 3D movement data of pianists’ fingers simultaneously while they performed fingering exercises from memory. Results showed a pre-error negativity for incorrect keystrokes when both correct and incorrect keystrokes were performed with comparable tempi. Interestingly, even correct notes immediately preceding erroneous keystrokes elicited a very similar negativity. In addition, we explored the possibility of computing ERPs time-locked to a kinematic landmark in the finger motion trajectories defined by when a finger makes initial contact with the key surface, that is, at the onset of tactile feedback. Results suggest that incorrect notes elicited a small difference after the onset of tactile feedback, whereas correct notes preceding incorrect ones elicited negativity before the onset of tactile feedback. The results tentatively suggest that tactile feedback plays an important role in error-monitoring during piano performance, because the comparison between predicted and actual sensory (tactile feedback may provide the information necessary for the detection of an
Incorporating Measurement Error from Modeled Air Pollution Exposures into Epidemiological Analyses.
Samoli, Evangelia; Butland, Barbara K
2017-12-01
Outdoor air pollution exposures used in epidemiological studies are commonly predicted from spatiotemporal models incorporating limited measurements, temporal factors, geographic information system variables, and/or satellite data. Measurement error in these exposure estimates leads to imprecise estimation of health effects and their standard errors. We reviewed methods for measurement error correction that have been applied in epidemiological studies that use model-derived air pollution data. We identified seven cohort studies and one panel study that have employed measurement error correction methods. These methods included regression calibration, risk set regression calibration, regression calibration with instrumental variables, the simulation extrapolation approach (SIMEX), and methods under the non-parametric or parameter bootstrap. Corrections resulted in small increases in the absolute magnitude of the health effect estimate and its standard error under most scenarios. Limited application of measurement error correction methods in air pollution studies may be attributed to the absence of exposure validation data and the methodological complexity of the proposed methods. Future epidemiological studies should consider in their design phase the requirements for the measurement error correction method to be later applied, while methodological advances are needed under the multi-pollutants setting.
Consistent estimation of linear panel data models with measurement error
Meijer, Erik; Spierdijk, Laura; Wansbeek, Thomas
2017-01-01
Measurement error causes a bias towards zero when estimating a panel data linear regression model. The panel data context offers various opportunities to derive instrumental variables allowing for consistent estimation. We consider three sources of moment conditions: (i) restrictions on the
Identification of linear error-models with projected dynamical systems
Czech Academy of Sciences Publication Activity Database
Krejčí, Pavel; Kuhnen, K.
2004-01-01
Roč. 10, č. 1 (2004), s. 59-91 ISSN 1387-3954 Keywords : identification * error models * projected dynamical systems Subject RIV: BA - General Mathematics Impact factor: 0.292, year: 2004 http://www.informaworld.com/smpp/content~db=all~content=a713682517
Testing for spatial error dependence in probit models
Amaral, P. V.; Anselin, L.; Arribas-Bel, D.
2013-01-01
In this note, we compare three test statistics that have been suggested to assess the presence of spatial error autocorrelation in probit models. We highlight the differences between the tests proposed by Pinkse and Slade (J Econom 85(1):125-254, 1998), Pinkse (Asymptotics of the Moran test and a
Bayesian network models for error detection in radiotherapy plans
Kalet, Alan M.; Gennari, John H.; Ford, Eric C.; Phillips, Mark H.
2015-04-01
The purpose of this study is to design and develop a probabilistic network for detecting errors in radiotherapy plans for use at the time of initial plan verification. Our group has initiated a multi-pronged approach to reduce these errors. We report on our development of Bayesian models of radiotherapy plans. Bayesian networks consist of joint probability distributions that define the probability of one event, given some set of other known information. Using the networks, we find the probability of obtaining certain radiotherapy parameters, given a set of initial clinical information. A low probability in a propagated network then corresponds to potential errors to be flagged for investigation. To build our networks we first interviewed medical physicists and other domain experts to identify the relevant radiotherapy concepts and their associated interdependencies and to construct a network topology. Next, to populate the network’s conditional probability tables, we used the Hugin Expert software to learn parameter distributions from a subset of de-identified data derived from a radiation oncology based clinical information database system. These data represent 4990 unique prescription cases over a 5 year period. Under test case scenarios with approximately 1.5% introduced error rates, network performance produced areas under the ROC curve of 0.88, 0.98, and 0.89 for the lung, brain and female breast cancer error detection networks, respectively. Comparison of the brain network to human experts performance (AUC of 0.90 ± 0.01) shows the Bayes network model performs better than domain experts under the same test conditions. Our results demonstrate the feasibility and effectiveness of comprehensive probabilistic models as part of decision support systems for improved detection of errors in initial radiotherapy plan verification procedures.
Factors predictive of intravenous fluid administration errors in Australian surgical care wards
Han, P; Coombes, I; Green, B
2005-01-01
Background: Intravenous (IV) fluid administration is an integral component of clinical care. Errors in administration can cause detrimental patient outcomes and increase healthcare costs, although little is known about medication administration errors associated with continuous IV infusions. Objectives: (1) To ascertain the prevalence of medication administration errors for continuous IV infusions and identify the variables that caused them. (2) To quantify the probability of errors by fitting a logistic regression model to the data. Methods: A prospective study was conducted on three surgical wards at a teaching hospital in Australia. All study participants received continuous infusions of IV fluids. Parenteral nutrition and non-electrolyte containing intermittent drug infusions (such as antibiotics) were excluded. Medication administration errors and contributing variables were documented using a direct observational approach. Results: Six hundred and eighty seven observations were made, with 124 (18.0%) having at least one medication administration error. The most common error observed was wrong administration rate. The median deviation from the prescribed rate was –47 ml/h (interquartile range –75 to +33.8 ml/h). Errors were more likely to occur if an IV infusion control device was not used and as the duration of the infusion increased. Conclusions: Administration errors involving continuous IV infusions occur frequently. They could be reduced by more common use of IV infusion control devices and regular checking of administration rates. PMID:15933314
International Nuclear Information System (INIS)
Reer, B.; Mertens, J.
1996-05-01
Actions and errors by the operating personnel, which are of significance for the safety of a technical system, are classified according to various criteria. Each type of action thus identified is roughly discussed with respect to its quantifiability by state-of-the-art human reliability analysis (HRA) within a probabilistic safety assessment (PSA). Thereby, the principal limit of quantifying human actions are discussed with special emphasis on data quality and cognitive error modelling. In this connection, the basic procedure for a HRA is briefly described under realistic conditions. With respect to the quantitative part of a HRA - the determination of error probabilities - an evaluating description of the standard method THERP (Technique of Human Error Rate Prediction) is given using eight evaluation criteria. Furthermore, six new developments (EdF'sPHRA, HCR, HCR/ORE, SLIM, HEART, INTENT) are briefly described and roughly evaluated. The report concludes with a catalogue of requirements for HRA methods. (orig.) [de
International Nuclear Information System (INIS)
Javaux, Denis
2002-01-01
This paper describes a method for predicting the errors that may appear when human operators or users interact with systems behaving as finite state systems. The method is a generalization of a method used for predicting errors when interacting with autopilot modes on modern, highly computerized airliners [Proc 17th Digital Avionics Sys Conf (DASC) (1998); Proc 10th Int Symp Aviat Psychol (1999)]. A cognitive model based on spreading activation networks is used for predicting the user's model of the system and its impact on the production of errors. The model strongly posits the importance of implicit learning in user-system interaction and its possible detrimental influence on users' knowledge of the system. An experiment conducted with Airbus Industrie and a major European airline on pilots' knowledge of autopilot behavior on the A340-200/300 confirms the model predictions, and in particular the impact of the frequencies with which specific state transitions and contexts are experienced
Nguyen, Sy Quy
In this study, a prediction model based on finite element analysis is developed to predict cutting errors during the machining process of thin plates using a flexible setup configuration. The model is based on analysis of the material deformation of thin plates under the action of axial cutting forces using a specifically designed test bed to reproduce commonly used flexible setup in industry. The cutting process is simplified as a static analysis of the material deformation under the effect of the applied cutting forces. In the analysis, different positions of the cutting tool during the machining process are studied to determine the workpiece's geometrical profiles during milling. Several analyses are carried out for different positions of the cutting tool. The cutting force is also modeled to predict the cutting force for specific cutting conditions. This cutting force model is utilized as input to the finite element analysis of the material deformation of the workpiece during the machining process. The experimental system is also designed to conduct tests with different cutting conditions on the three-axis Huron K2X10 CNC milling machine to verify the predicted results obtained from the analysis model. The geometrical errors of the machined plates after machining are determined by using the Mitutoyo Bright Strato Coordinate Measurement Machine (CMM) to measure their geometrical profiles before and after machining processes. Finally, the mirror technique is utilized to compensate cutting deviations based on the predicted results of the workpiece's displacements. Adding the value from the prediction model to the designed cutting depth creates the updated tool path. The results show good agreement in the prediction of the thin plate deformation during the machining as compared to the experimental tests.
Confidence scores for prediction models
DEFF Research Database (Denmark)
Gerds, Thomas Alexander; van de Wiel, MA
2011-01-01
In medical statistics, many alternative strategies are available for building a prediction model based on training data. Prediction models are routinely compared by means of their prediction performance in independent validation data. If only one data set is available for training and validation......, then rival strategies can still be compared based on repeated bootstraps of the same data. Often, however, the overall performance of rival strategies is similar and it is thus difficult to decide for one model. Here, we investigate the variability of the prediction models that results when the same...... to distinguish rival prediction models with similar prediction performances. Furthermore, on the subject level a confidence score may provide useful supplementary information for new patients who want to base a medical decision on predicted risk. The ideas are illustrated and discussed using data from cancer...
Hierarchical prediction errors in midbrain and basal forebrain during sensory learning.
Iglesias, Sandra; Mathys, Christoph; Brodersen, Kay H; Kasper, Lars; Piccirelli, Marco; den Ouden, Hanneke E M; Stephan, Klaas E
2013-10-16
In Bayesian brain theories, hierarchically related prediction errors (PEs) play a central role for predicting sensory inputs and inferring their underlying causes, e.g., the probabilistic structure of the environment and its volatility. Notably, PEs at different hierarchical levels may be encoded by different neuromodulatory transmitters. Here, we tested this possibility in computational fMRI studies of audio-visual learning. Using a hierarchical Bayesian model, we found that low-level PEs about visual stimulus outcome were reflected by widespread activity in visual and supramodal areas but also in the midbrain. In contrast, high-level PEs about stimulus probabilities were encoded by the basal forebrain. These findings were replicated in two groups of healthy volunteers. While our fMRI measures do not reveal the exact neuron types activated in midbrain and basal forebrain, they suggest a dichotomy between neuromodulatory systems, linking dopamine to low-level PEs about stimulus outcome and acetylcholine to more abstract PEs about stimulus probabilities. Copyright © 2013 Elsevier Inc. All rights reserved.
A New Open-Loop Fiber Optic Gyro Error Compensation Method Based on Angular Velocity Error Modeling
Directory of Open Access Journals (Sweden)
Yanshun Zhang
2015-02-01
Full Text Available With the open-loop fiber optic gyro (OFOG model, output voltage and angular velocity can effectively compensate OFOG errors. However, the model cannot reflect the characteristics of OFOG errors well when it comes to pretty large dynamic angular velocities. This paper puts forward a modeling scheme with OFOG output voltage and temperature as the input variables and angular velocity error as the output variable. Firstly, the angular velocity error is extracted from OFOG output signals, and then the output voltage , temperature and angular velocity error are used as the learning samples to train a Radial-Basis-Function (RBF neural network model. Then the nonlinear mapping model over T, and is established and thus can be calculated automatically to compensate OFOG errors according to and . The results of the experiments show that the established model can be used to compensate the nonlinear OFOG errors. The maximum, the minimum and the mean square error of OFOG angular velocity are decreased by , and relative to their initial values, respectively. Compared with the direct modeling of gyro angular velocity, which we researched before, the experimental results of the compensating method proposed in this paper are further reduced by , and , respectively, so the performance of this method is better than that of the direct modeling for gyro angular velocity.
A new open-loop fiber optic gyro error compensation method based on angular velocity error modeling.
Zhang, Yanshun; Guo, Yajing; Li, Chunyu; Wang, Yixin; Wang, Zhanqing
2015-02-27
With the open-loop fiber optic gyro (OFOG) model, output voltage and angular velocity can effectively compensate OFOG errors. However, the model cannot reflect the characteristics of OFOG errors well when it comes to pretty large dynamic angular velocities. This paper puts forward a modeling scheme with OFOG output voltage u and temperature T as the input variables and angular velocity error Δω as the output variable. Firstly, the angular velocity error Δω is extracted from OFOG output signals, and then the output voltage u, temperature T and angular velocity error Δω are used as the learning samples to train a Radial-Basis-Function (RBF) neural network model. Then the nonlinear mapping model over T, u and Δω is established and thus Δω can be calculated automatically to compensate OFOG errors according to T and u. The results of the experiments show that the established model can be used to compensate the nonlinear OFOG errors. The maximum, the minimum and the mean square error of OFOG angular velocity are decreased by 97.0%, 97.1% and 96.5% relative to their initial values, respectively. Compared with the direct modeling of gyro angular velocity, which we researched before, the experimental results of the compensating method proposed in this paper are further reduced by 1.6%, 1.4% and 1.42%, respectively, so the performance of this method is better than that of the direct modeling for gyro angular velocity.
Modeling number of claims and prediction of total claim amount
Acar, Aslıhan Şentürk; Karabey, Uǧur
2017-07-01
In this study we focus on annual number of claims of a private health insurance data set which belongs to a local insurance company in Turkey. In addition to Poisson model and negative binomial model, zero-inflated Poisson model and zero-inflated negative binomial model are used to model the number of claims in order to take into account excess zeros. To investigate the impact of different distributional assumptions for the number of claims on the prediction of total claim amount, predictive performances of candidate models are compared by using root mean square error (RMSE) and mean absolute error (MAE) criteria.
Schroeder, Scott R; Salomon, Meghan M; Galanter, William L; Schiff, Gordon D; Vaida, Allen J; Gaunt, Michael J; Bryson, Michelle L; Rash, Christine; Falck, Suzanne; Lambert, Bruce L
2017-01-01
Background Drug name confusion is a common type of medication error and a persistent threat to patient safety. In the USA, roughly one per thousand prescriptions results in the wrong drug being filled, and most of these errors involve drug names that look or sound alike. Prior to approval, drug names undergo a variety of tests to assess their potential for confusability, but none of these preapproval tests has been shown to predict real-world error rates. Objectives We conducted a study to assess the association between error rates in laboratory-based tests of drug name memory and perception and real-world drug name confusion error rates. Methods Eighty participants, comprising doctors, nurses, pharmacists, technicians and lay people, completed a battery of laboratory tests assessing visual perception, auditory perception and short-term memory of look-alike and sound-alike drug name pairs (eg, hydroxyzine/hydralazine). Results Laboratory test error rates (and other metrics) significantly predicted real-world error rates obtained from a large, outpatient pharmacy chain, with the best-fitting model accounting for 37% of the variance in real-world error rates. Cross-validation analyses confirmed these results, showing that the laboratory tests also predicted errors from a second pharmacy chain, with 45% of the variance being explained by the laboratory test data. Conclusions Across two distinct pharmacy chains, there is a strong and significant association between drug name confusion error rates observed in the real world and those observed in laboratory-based tests of memory and perception. Regulators and drug companies seeking a validated preapproval method for identifying confusing drug names ought to consider using these simple tests. By using a standard battery of memory and perception tests, it should be possible to reduce the number of confusing look-alike and sound-alike drug name pairs that reach the market, which will help protect patients from potentially
An Emprical Point Error Model for Tls Derived Point Clouds
Ozendi, Mustafa; Akca, Devrim; Topan, Hüseyin
2016-06-01
The random error pattern of point clouds has significant effect on the quality of final 3D model. The magnitude and distribution of random errors should be modelled numerically. This work aims at developing such an anisotropic point error model, specifically for the terrestrial laser scanner (TLS) acquired 3D point clouds. A priori precisions of basic TLS observations, which are the range, horizontal angle and vertical angle, are determined by predefined and practical measurement configurations, performed at real-world test environments. A priori precision of horizontal (𝜎𝜃) and vertical (𝜎𝛼) angles are constant for each point of a data set, and can directly be determined through the repetitive scanning of the same environment. In our practical tests, precisions of the horizontal and vertical angles were found as 𝜎𝜃=±36.6𝑐𝑐 and 𝜎𝛼=±17.8𝑐𝑐, respectively. On the other hand, a priori precision of the range observation (𝜎𝜌) is assumed to be a function of range, incidence angle of the incoming laser ray, and reflectivity of object surface. Hence, it is a variable, and computed for each point individually by employing an empirically developed formula varying as 𝜎𝜌=±2-12 𝑚𝑚 for a FARO Focus X330 laser scanner. This procedure was followed by the computation of error ellipsoids of each point using the law of variance-covariance propagation. The direction and size of the error ellipsoids were computed by the principal components transformation. The usability and feasibility of the model was investigated in real world scenarios. These investigations validated the suitability and practicality of the proposed method.
Error Modelling and Experimental Validation for a Planar 3-PPR Parallel Manipulator
DEFF Research Database (Denmark)
Wu, Guanglei; Bai, Shaoping; Kepler, Jørgen Asbøl
2011-01-01
In this paper, the positioning error of a 3-PPR planar parallel manipulator is studied with an error model and experimental validation. First, the displacement and workspace are analyzed. An error model considering both configuration errors and joint clearance errors is established. Using this mo...
Standard error of prediction for multiway PLS 1 : background and a simulation study
Faber, N.M.; Bro, R.
2002-01-01
While a multitude of expressions has been proposed for calculating sample-specific standard errors of prediction when using partial least squares (PLS) regression for the calibration of first-order data, potential generalisations to multiway data are lacking to date. We have examined the adequacy of
Error-rate prediction for programmable circuits: methodology, tools and studied cases
Velazco, Raoul
2013-05-01
This work presents an approach to predict the error rates due to Single Event Upsets (SEU) occurring in programmable circuits as a consequence of the impact or energetic particles present in the environment the circuits operate. For a chosen application, the error-rate is predicted by combining the results obtained from radiation ground testing and the results of fault injection campaigns performed off-beam during which huge numbers of SEUs are injected during the execution of the studied application. The goal of this strategy is to obtain accurate results about different applications' error rates, without using particle accelerator facilities, thus significantly reducing the cost of the sensitivity evaluation. As a case study, this methodology was applied a complex processor, the Power PC 7448 executing a program issued from a real space application and a crypto-processor application implemented in an SRAM-based FPGA and accepted to be embedded in the payload of a scientific satellite of NASA. The accuracy of predicted error rates was confirmed by comparing, for the same circuit and application, predictions with measures issued from radiation ground testing performed at the cyclotron Cyclone cyclotron of HIF (Heavy Ion Facility) of Louvain-la-Neuve (Belgium).
Prediction and error growth in the daily forecast of precipitation from ...
Indian Academy of Sciences (India)
Home; Journals; Journal of Earth System Science; Volume 125; Issue 1. Prediction and error growth in the daily forecast of precipitation from the NCEP CFSv2 over the subdivisions of Indian subcontinent. Dhruva Kumar Pandey Shailendra Rai A K Sahai S Abhilash N K Shahi. Volume 125 Issue 1 February 2016 pp 29-45 ...
A Canonical Ensemble Correlation Prediction Model for Seasonal Precipitation Anomaly
Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Guilong
2001-01-01
This report describes an optimal ensemble forecasting model for seasonal precipitation and its error estimation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. This new CCA model includes the following features: (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States precipitation field. The predictor is the sea surface temperature.
Testing and Inference in Nonlinear Cointegrating Vector Error Correction Models
DEFF Research Database (Denmark)
Kristensen, Dennis; Rahbek, Anders
2013-01-01
We analyze estimators and tests for a general class of vector error correction models that allows for asymmetric and nonlinear error correction. For a given number of cointegration relationships, general hypothesis testing is considered, where testing for linearity is of particular interest. Unde...... versions that are simple to compute. A simulation study shows that the finite-sample properties of the bootstrapped tests are satisfactory with good size and power properties for reasonable sample sizes....... the null of linearity, parameters of nonlinear components vanish, leading to a nonstandard testing problem. We apply so-called sup-tests to resolve this issue, which requires development of new(uniform) functional central limit theory and results for convergence of stochastic integrals. We provide a full......We analyze estimators and tests for a general class of vector error correction models that allows for asymmetric and nonlinear error correction. For a given number of cointegration relationships, general hypothesis testing is considered, where testing for linearity is of particular interest. Under...
Pathiraja, S.; Anghileri, D.; Burlando, P.; Sharma, A.; Marshall, L.; Moradkhani, H.
2018-03-01
The global prevalence of rapid and extensive land use change necessitates hydrologic modelling methodologies capable of handling non-stationarity. This is particularly true in the context of Hydrologic Forecasting using Data Assimilation. Data Assimilation has been shown to dramatically improve forecast skill in hydrologic and meteorological applications, although such improvements are conditional on using bias-free observations and model simulations. A hydrologic model calibrated to a particular set of land cover conditions has the potential to produce biased simulations when the catchment is disturbed. This paper sheds new light on the impacts of bias or systematic errors in hydrologic data assimilation, in the context of forecasting in catchments with changing land surface conditions and a model calibrated to pre-change conditions. We posit that in such cases, the impact of systematic model errors on assimilation or forecast quality is dependent on the inherent prediction uncertainty that persists even in pre-change conditions. Through experiments on a range of catchments, we develop a conceptual relationship between total prediction uncertainty and the impacts of land cover changes on the hydrologic regime to demonstrate how forecast quality is affected when using state estimation Data Assimilation with no modifications to account for land cover changes. This work shows that systematic model errors as a result of changing or changed catchment conditions do not always necessitate adjustments to the modelling or assimilation methodology, for instance through re-calibration of the hydrologic model, time varying model parameters or revised offline/online bias estimation.
MODELING OF MANUFACTURING ERRORS FOR PIN-GEAR ELEMENTS OF PLANETARY GEARBOX
Directory of Open Access Journals (Sweden)
Ivan M. Egorov
2014-11-01
Full Text Available Theoretical background for calculation of k-h-v type cycloid reducers was developed relatively long ago. However, recently the matters of cycloid reducer design again attracted heightened attention. The reason for that is that such devices are used in many complex engineering systems, particularly, in mechatronic and robotics systems. The development of advanced technological capabilities for manufacturing of such reducers today gives the possibility for implementation of essential features of such devices: high efficiency, high gear ratio, kinematic accuracy and smooth motion. The presence of an adequate mathematical model gives the possibility for adjusting kinematic accuracy of the reducer by rational selection of manufacturing tolerances for its parts. This makes it possible to automate the design process for cycloid reducers with account of various factors including technological ones. A mathematical model and mathematical technique have been developed giving the possibility for modeling the kinematic error of the reducer with account of multiple factors, including manufacturing errors. The errors are considered in the way convenient for prediction of kinematic accuracy early at the manufacturing stage according to the results of reducer parts measurement on coordinate measuring machines. During the modeling, the wheel manufacturing errors are determined by the eccentricity and radius deviation of the pin tooth centers circle, and the deviation between the pin tooth axes positions and the centers circle. The satellite manufacturing errors are determined by the satellite eccentricity deviation and the satellite rim eccentricity. Due to the collinearity, the pin tooth and pin tooth hole diameter errors and the satellite tooth profile errors for a designated contact point are integrated into one deviation. Software implementation of the model makes it possible to estimate the pointed errors influence on satellite rotation angle error and
An empirical study of the complexity and randomness of prediction error sequences
Ratsaby, Joel
2011-07-01
We investigate a population of binary mistake sequences that result from learning with parametric models of different order. We obtain estimates of their error, algorithmic complexity and divergence from a purely random Bernoulli sequence. We study the relationship of these variables to the learner's information density parameter which is defined as the ratio between the lengths of the compressed to uncompressed files that contain the learner's decision rule. The results indicate that good learners have a low information density ρ while bad learners have a high ρ. Bad learners generate mistake sequences that are atypically complex or diverge stochastically from a purely random Bernoulli sequence. Good learners generate typically complex sequences with low divergence from Bernoulli sequences and they include mistake sequences generated by the Bayes optimal predictor. Based on the static algorithmic interference model of [18] the learner here acts as a static structure which "scatters" the bits of an input sequence (to be predicted) in proportion to its information density ρ thereby deforming its randomness characteristics.
Thermal Error Modeling of a Machine Tool Using Data Mining Scheme
Wang, Kun-Chieh; Tseng, Pai-Chang
In this paper the knowledge discovery technique is used to build an effective and transparent mathematic thermal error model for machine tools. Our proposed thermal error modeling methodology (called KRL) integrates the schemes of K-means theory (KM), rough-set theory (RS), and linear regression model (LR). First, to explore the machine tool's thermal behavior, an integrated system is designed to simultaneously measure the temperature ascents at selected characteristic points and the thermal deformations at spindle nose under suitable real machining conditions. Second, the obtained data are classified by the KM method, further reduced by the RS scheme, and a linear thermal error model is established by the LR technique. To evaluate the performance of our proposed model, an adaptive neural fuzzy inference system (ANFIS) thermal error model is introduced for comparison. Finally, a verification experiment is carried out and results reveal that the proposed KRL model is effective in predicting thermal behavior in machine tools. Our proposed KRL model is transparent, easily understood by users, and can be easily programmed or modified for different machining conditions.
Lahat, Ayelet; Lamm, Connie; Chronis-Tuscano, Andrea; Pine, Daniel S; Henderson, Heather A; Fox, Nathan A
2014-04-01
Behavioral inhibition (BI) is an early childhood temperament characterized by fearful responses to novelty and avoidance of social interactions. During adolescence, a subset of children with stable childhood BI develop social anxiety disorder and concurrently exhibit increased error monitoring. The current study examines whether increased error monitoring in 7-year-old, behaviorally inhibited children prospectively predicts risk for symptoms of social phobia at age 9 years. A total of 291 children were characterized on BI at 24 and 36 months of age. Children were seen again at 7 years of age, when they performed a Flanker task, and event-related potential (ERP) indices of response monitoring were generated. At age 9, self- and maternal-report of social phobia symptoms were obtained. Children high in BI, compared to those low in BI, displayed increased error monitoring at age 7, as indexed by larger (i.e., more negative) error-related negativity (ERN) amplitudes. In addition, early BI was related to later childhood social phobia symptoms at age 9 among children with a large difference in amplitude between ERN and correct-response negativity (CRN) at age 7. Heightened error monitoring predicts risk for later social phobia symptoms in children with high BI. Research assessing response monitoring in children with BI may refine our understanding of the mechanisms underlying risk for later anxiety disorders and inform prevention efforts. Copyright © 2014 American Academy of Child and Adolescent Psychiatry. All rights reserved.
Modeling gene expression measurement error: a quasi-likelihood approach
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Strimmer Korbinian
2003-03-01
Full Text Available Abstract Background Using suitable error models for gene expression measurements is essential in the statistical analysis of microarray data. However, the true probabilistic model underlying gene expression intensity readings is generally not known. Instead, in currently used approaches some simple parametric model is assumed (usually a transformed normal distribution or the empirical distribution is estimated. However, both these strategies may not be optimal for gene expression data, as the non-parametric approach ignores known structural information whereas the fully parametric models run the risk of misspecification. A further related problem is the choice of a suitable scale for the model (e.g. observed vs. log-scale. Results Here a simple semi-parametric model for gene expression measurement error is presented. In this approach inference is based an approximate likelihood function (the extended quasi-likelihood. Only partial knowledge about the unknown true distribution is required to construct this function. In case of gene expression this information is available in the form of the postulated (e.g. quadratic variance structure of the data. As the quasi-likelihood behaves (almost like a proper likelihood, it allows for the estimation of calibration and variance parameters, and it is also straightforward to obtain corresponding approximate confidence intervals. Unlike most other frameworks, it also allows analysis on any preferred scale, i.e. both on the original linear scale as well as on a transformed scale. It can also be employed in regression approaches to model systematic (e.g. array or dye effects. Conclusions The quasi-likelihood framework provides a simple and versatile approach to analyze gene expression data that does not make any strong distributional assumptions about the underlying error model. For several simulated as well as real data sets it provides a better fit to the data than competing models. In an example it also
Sang, Huiyan
2011-12-01
This paper investigates the cross-correlations across multiple climate model errors. We build a Bayesian hierarchical model that accounts for the spatial dependence of individual models as well as cross-covariances across different climate models. Our method allows for a nonseparable and nonstationary cross-covariance structure. We also present a covariance approximation approach to facilitate the computation in the modeling and analysis of very large multivariate spatial data sets. The covariance approximation consists of two parts: a reduced-rank part to capture the large-scale spatial dependence, and a sparse covariance matrix to correct the small-scale dependence error induced by the reduced rank approximation. We pay special attention to the case that the second part of the approximation has a block-diagonal structure. Simulation results of model fitting and prediction show substantial improvement of the proposed approximation over the predictive process approximation and the independent blocks analysis. We then apply our computational approach to the joint statistical modeling of multiple climate model errors. © 2012 Institute of Mathematical Statistics.
Koenig, Stephan; Uengoer, Metin; Lachnit, Harald
2018-04-01
The attentional learning theory of Pearce and Hall () predicts more attention to uncertain cues that have caused a high prediction error in the past. We examined how the cue-elicited pupil dilation during associative learning was linked to such error-driven attentional processes. In three experiments, participants were trained to acquire associations between different cues and their appetitive (Experiment 1), motor (Experiment 2), or aversive (Experiment 3) outcomes. All experiments were designed to examine differences in the processing of continuously reinforced cues (consistently followed by the outcome) versus partially reinforced, uncertain cues (randomly followed by the outcome). We measured the pupil dilation elicited by the cues in anticipation of the outcome and analyzed how this conditioned pupil response changed over the course of learning. In all experiments, changes in pupil size complied with the same basic pattern: During early learning, consistently reinforced cues elicited greater pupil dilation than uncertain, randomly reinforced cues, but this effect gradually reversed to yield a greater pupil dilation for uncertain cues toward the end of learning. The pattern of data accords with the changes in prediction error and error-driven attention formalized by the Pearce-Hall theory. © 2017 The Authors. Psychophysiology published by Wiley Periodicals, Inc. on behalf of Society for Psychophysiological Research.
Some aspects of statistical modeling of human-error probability
International Nuclear Information System (INIS)
Prairie, R.R.
1982-01-01
Human reliability analyses (HRA) are often performed as part of risk assessment and reliability projects. Recent events in nuclear power have shown the potential importance of the human element. There are several on-going efforts in the US and elsewhere with the purpose of modeling human error such that the human contribution can be incorporated into an overall risk assessment associated with one or more aspects of nuclear power. An effort that is described here uses the HRA (event tree) to quantify and model the human contribution to risk. As an example, risk analyses are being prepared on several nuclear power plants as part of the Interim Reliability Assessment Program (IREP). In this process the risk analyst selects the elements of his fault tree that could be contributed to by human error. He then solicits the HF analyst to do a HRA on this element
Directory of Open Access Journals (Sweden)
Seyma Caliskan Cavdar
2015-08-01
Full Text Available In this study, we try to examine whether the forecast errors obtained by the ANN models affect the breakout of financial crises. Additionally, we try to investigate how much the asymmetric information and forecast errors are reflected on the output values. In our study, we used the exchange rate of USD/TRY (USD, the Borsa Istanbul 100 Index (BIST, and gold price (GP as our output variables of our Artificial Neural Network (ANN models. We observe that the predicted ANN model has a strong explanation capability for the 2001 and 2008 crises. Our calculations of some symmetry measures such as mean absolute percentage error (MAPE, symmetric mean absolute percentage error (sMAPE, and Shannon entropy (SE, clearly demonstrate the degree of asymmetric information and the deterioration of the financial system prior to, during, and after the financial crisis. We found that the asymmetric information prior to crisis is larger as compared to other periods. This situation can be interpreted as early warning signals before the potential crises. This evidence seems to favor an asymmetric information view of financial crises.
Per-beam, planar IMRT QA passing rates do not predict clinically relevant patient dose errors
Energy Technology Data Exchange (ETDEWEB)
Nelms, Benjamin E.; Zhen Heming; Tome, Wolfgang A. [Canis Lupus LLC and Department of Human Oncology, University of Wisconsin, Merrimac, Wisconsin 53561 (United States); Department of Medical Physics, University of Wisconsin, Madison, Wisconsin 53705 (United States); Departments of Human Oncology, Medical Physics, and Biomedical Engineering, University of Wisconsin, Madison, Wisconsin 53792 (United States)
2011-02-15
Purpose: The purpose of this work is to determine the statistical correlation between per-beam, planar IMRT QA passing rates and several clinically relevant, anatomy-based dose errors for per-patient IMRT QA. The intent is to assess the predictive power of a common conventional IMRT QA performance metric, the Gamma passing rate per beam. Methods: Ninety-six unique data sets were created by inducing four types of dose errors in 24 clinical head and neck IMRT plans, each planned with 6 MV Varian 120-leaf MLC linear accelerators using a commercial treatment planning system and step-and-shoot delivery. The error-free beams/plans were used as ''simulated measurements'' (for generating the IMRT QA dose planes and the anatomy dose metrics) to compare to the corresponding data calculated by the error-induced plans. The degree of the induced errors was tuned to mimic IMRT QA passing rates that are commonly achieved using conventional methods. Results: Analysis of clinical metrics (parotid mean doses, spinal cord max and D1cc, CTV D95, and larynx mean) vs IMRT QA Gamma analysis (3%/3 mm, 2/2, 1/1) showed that in all cases, there were only weak to moderate correlations (range of Pearson's r-values: -0.295 to 0.653). Moreover, the moderate correlations actually had positive Pearson's r-values (i.e., clinically relevant metric differences increased with increasing IMRT QA passing rate), indicating that some of the largest anatomy-based dose differences occurred in the cases of high IMRT QA passing rates, which may be called ''false negatives.'' The results also show numerous instances of false positives or cases where low IMRT QA passing rates do not imply large errors in anatomy dose metrics. In none of the cases was there correlation consistent with high predictive power of planar IMRT passing rates, i.e., in none of the cases did high IMRT QA Gamma passing rates predict low errors in anatomy dose metrics or vice versa
Approximate Minimization of the Regularized Expected Error over Kernel Models
Czech Academy of Sciences Publication Activity Database
Kůrková, Věra; Sanguineti, M.
2008-01-01
Roč. 33, č. 3 (2008), s. 747-756 ISSN 0364-765X R&D Projects: GA ČR GA201/05/0557; GA ČR GA201/08/1744 Institutional research plan: CEZ:AV0Z10300504 Keywords : suboptimal solutions * expected error * convex functionals * kernel methods * model complexity * rates of convergence Subject RIV: BA - General Mathematics Impact factor: 1.086, year: 2008
International Nuclear Information System (INIS)
Fruehwirth, R.
1993-01-01
We present an estimation procedure of the error components in a linear regression model with multiple independent stochastic error contributions. After solving the general problem we apply the results to the estimation of the actual trajectory in track fitting with multiple scattering. (orig.)
Topological quantum error correction in the Kitaev honeycomb model
Lee, Yi-Chan; Brell, Courtney G.; Flammia, Steven T.
2017-08-01
The Kitaev honeycomb model is an approximate topological quantum error correcting code in the same phase as the toric code, but requiring only a 2-body Hamiltonian. As a frustrated spin model, it is well outside the commuting models of topological quantum codes that are typically studied, but its exact solubility makes it more amenable to analysis of effects arising in this noncommutative setting than a generic topologically ordered Hamiltonian. Here we study quantum error correction in the honeycomb model using both analytic and numerical techniques. We first prove explicit exponential bounds on the approximate degeneracy, local indistinguishability, and correctability of the code space. These bounds are tighter than can be achieved using known general properties of topological phases. Our proofs are specialized to the honeycomb model, but some of the methods may nonetheless be of broader interest. Following this, we numerically study noise caused by thermalization processes in the perturbative regime close to the toric code renormalization group fixed point. The appearance of non-topological excitations in this setting has no significant effect on the error correction properties of the honeycomb model in the regimes we study. Although the behavior of this model is found to be qualitatively similar to that of the standard toric code in most regimes, we find numerical evidence of an interesting effect in the low-temperature, finite-size regime where a preferred lattice direction emerges and anyon diffusion is geometrically constrained. We expect this effect to yield an improvement in the scaling of the lifetime with system size as compared to the standard toric code.
Evaluation of CASP8 model quality predictions
Cozzetto, Domenico
2009-01-01
The model quality assessment problem consists in the a priori estimation of the overall and per-residue accuracy of protein structure predictions. Over the past years, a number of methods have been developed to address this issue and CASP established a prediction category to evaluate their performance in 2006. In 2008 the experiment was repeated and its results are reported here. Participants were invited to infer the correctness of the protein models submitted by the registered automatic servers. Estimates could apply to both whole models and individual amino acids. Groups involved in the tertiary structure prediction categories were also asked to assign local error estimates to each predicted residue in their own models and their results are also discussed here. The correlation between the predicted and observed correctness measures was the basis of the assessment of the results. We observe that consensus-based methods still perform significantly better than those accepting single models, similarly to what was concluded in the previous edition of the experiment. © 2009 WILEY-LISS, INC.
Predictive validation of an influenza spread model.
Directory of Open Access Journals (Sweden)
Ayaz Hyder
Full Text Available BACKGROUND: Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. METHODS AND FINDINGS: We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998-1999. Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type. Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. CONCLUSIONS: Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve
Predictive Validation of an Influenza Spread Model
Hyder, Ayaz; Buckeridge, David L.; Leung, Brian
2013-01-01
Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive
Accuracy of devices for self-monitoring of blood glucose: A stochastic error model.
Vettoretti, M; Facchinetti, A; Sparacino, G; Cobelli, C
2015-01-01
Self-monitoring of blood glucose (SMBG) devices are portable systems that allow measuring glucose concentration in a small drop of blood obtained via finger-prick. SMBG measurements are key in type 1 diabetes (T1D) management, e.g. for tuning insulin dosing. A reliable model of SMBG accuracy would be important in several applications, e.g. in in silico design and optimization of insulin therapy. In the literature, the most used model to describe SMBG error is the Gaussian distribution, which however is simplistic to properly account for the observed variability. Here, a methodology to derive a stochastic model of SMBG accuracy is presented. The method consists in dividing the glucose range into zones in which absolute/relative error presents constant standard deviation (SD) and, then, fitting by maximum-likelihood a skew-normal distribution model to absolute/relative error distribution in each zone. The method was tested on a database of SMBG measurements collected by the One Touch Ultra 2 (Lifescan Inc., Milpitas, CA). In particular, two zones were identified: zone 1 (BG≤75 mg/dl) with constant-SD absolute error and zone 2 (BG>75mg/dl) with constant-SD relative error. Mean and SD of the identified skew-normal distributions are, respectively, 2.03 and 6.51 in zone 1, 4.78% and 10.09% in zone 2. Visual predictive check validation showed that the derived two-zone model accurately reproduces SMBG measurement error distribution, performing significantly better than the single-zone Gaussian model used previously in the literature. This stochastic model allows a more realistic SMBG scenario for in silico design and optimization of T1D insulin therapy.
Lardeux, Sylvie; Paleressompoulle, Dany; Pernaud, Remy; Cador, Martine; Baunez, Christelle
2013-10-01
The search for treatment of cocaine addiction raises the challenge to find a way to diminish motivation for the drug without decreasing it for natural rewards. Subthalamic nucleus (STN) inactivation decreases motivation for cocaine while increasing motivation for food, suggesting that STN can dissociate different rewards. Here, we investigated how rat STN neurons respond to cues predicting cocaine or sucrose and to reward delivery while rats are performing a discriminative stimuli task. We show that different neuronal populations of STN neurons encode cocaine and sucrose. In addition, we show that STN activity at the cue onset predicts future error. When changing the reward predicted unexpectedly, STN neurons show capacities of adaptation, suggesting a role in reward-prediction error. Furthermore, some STN neurons show a response to executive error (i.e., "oops neurons") that is specific to the missed reward. These results position the STN as a nexus where natural rewards and drugs of abuse are coded differentially and can influence the performance. Therefore, STN can be viewed as a structure where action could be taken for the treatment of cocaine addiction.
Linear mixed models for replication data to efficiently allow for covariate measurement error.
Bartlett, Jonathan W; De Stavola, Bianca L; Frost, Chris
2009-11-10
It is well known that measurement error in the covariates of regression models generally causes bias in parameter estimates. Correction for such biases requires information concerning the measurement error, which is often in the form of internal validation or replication data. Regression calibration (RC) is a popular approach to correct for covariate measurement error, which involves predicting the true covariate using error-prone measurements. Likelihood methods have previously been proposed as an alternative approach to estimate the parameters in models affected by measurement error, but have been relatively infrequently employed in medical statistics and epidemiology, partly because of computational complexity and concerns regarding robustness to distributional assumptions. We show how a standard random-intercepts model can be used to obtain maximum likelihood (ML) estimates when the outcome model is linear or logistic regression under certain normality assumptions, when internal error-prone replicate measurements are available. Through simulations we show that for linear regression, ML gives more efficient estimates than RC, although the gain is typically small. Furthermore, we show that RC and ML estimates remain consistent even when the normality assumptions are violated. For logistic regression, our implementation of ML is consistent if the true covariate is conditionally normal given the outcome, in contrast to RC. In simulations, this ML estimator showed less bias in situations where RC gives non-negligible biases. Our proposal makes the ML approach to dealing with covariate measurement error more accessible to researchers, which we hope will improve its viability as a useful alternative to methods such as RC.
Bootstrap prediction and Bayesian prediction under misspecified models
Fushiki, Tadayoshi
2005-01-01
We consider a statistical prediction problem under misspecified models. In a sense, Bayesian prediction is an optimal prediction method when an assumed model is true. Bootstrap prediction is obtained by applying Breiman's `bagging' method to a plug-in prediction. Bootstrap prediction can be considered to be an approximation to the Bayesian prediction under the assumption that the model is true. However, in applications, there are frequently deviations from the assumed model. In this paper, bo...
Likelihood-Based Inference in Nonlinear Error-Correction Models
DEFF Research Database (Denmark)
Kristensen, Dennis; Rahbæk, Anders
We consider a class of vector nonlinear error correction models where the transfer function (or loadings) of the stationary relation- ships is nonlinear. This includes in particular the smooth transition models. A general representation theorem is given which establishes the dynamic properties...... and a linear trend in general. Gaussian likelihood-based estimators are considered for the long- run cointegration parameters, and the short-run parameters. Asymp- totic theory is provided for these and it is discussed to what extend asymptotic normality and mixed normaity can be found. A simulation study...
Prediction models in complex terrain
DEFF Research Database (Denmark)
Marti, I.; Nielsen, Torben Skov; Madsen, Henrik
2001-01-01
The objective of the work is to investigatethe performance of HIRLAM in complex terrain when used as input to energy production forecasting models, and to develop a statistical model to adapt HIRLAM prediction to the wind farm. The features of the terrain, specially the topography, influence...... the performance of HIRLAM in particular with respect to wind predictions. To estimate the performance of the model two spatial resolutions (0,5 Deg. and 0.2 Deg.) and different sets of HIRLAM variables were used to predict wind speed and energy production. The predictions of energy production for the wind farms...... are calculated using on-line measurements of power production as well as HIRLAM predictions as input thus taking advantage of the auto-correlation, which is present in the power production for shorter pediction horizons. Statistical models are used to discribe the relationship between observed energy production...
Comparison of Predictive Modeling Methods of Aircraft Landing Speed
Diallo, Ousmane H.
2012-01-01
Expected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports. Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations. There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors. This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway. As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE). Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed. The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport. However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods. Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach. For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation. Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.
Human error data collection and comparison with predictions by SPAR-H.
Liu, Peng; Li, Zhizhong
2014-09-01
There is a scarcity of empirical data on human error for human reliability analysis (HRA). This situation can increase the variability and impair the validity of HRA outcomes in risk analysis. In this work, a microworld study was used to investigate the effects of performance shaping factors (PSFs) and their interrelationships and combined effects on the human error probability (HEP). The PSFs involved were task complexity, time availability, experience, and time pressure. The empirical data obtained were compared with predictions by the Standardized Plant Analysis Risk-Human Reliability Method (SPAR-H) and data from other sources. The comparison included three aspects: (1) HEP, (2) relative effects of the PSFs, and (3) error types. Results showed that the HEP decreased with experience and time availability levels. The significant relationship between task complexity and the HEP depended on time availability and experience, and time availability affected the HEP through time pressure. The empirical HEPs were higher than the HEPs predicted by SPAR-H under different PSF combinations, showing the tendency of SPAR-H to produce relatively optimistic results in our study. The relative effects of two PSFs (i.e., experience/training and stress/stressors) in SPAR-H agreed to some extent with those in our study. Several error types agreed well with those from operational experience and a database for nuclear power plants (NPPs). © 2014 Society for Risk Analysis.
Prediction of rainfall intensity measurement errors using commercial microwave communication links
Directory of Open Access Journals (Sweden)
A. Zinevich
2010-10-01
Full Text Available Commercial microwave radio links forming cellular communication networks are known to be a valuable instrument for measuring near-surface rainfall. However, operational communication links are more uncertain relatively to the dedicated installations since their geometry and frequencies are optimized for high communication performance rather than observing rainfall. Quantification of the uncertainties for measurements that are non-optimal in the first place is essential to assure usability of the data.
In this work we address modeling of instrumental impairments, i.e. signal variability due to antenna wetting, baseline attenuation uncertainty and digital quantization, as well as environmental ones, i.e. variability of drop size distribution along a link affecting accuracy of path-averaged rainfall measurement and spatial variability of rainfall in the link's neighborhood affecting the accuracy of rainfall estimation out of the link path. Expressions for root mean squared error (RMSE for estimates of path-averaged and point rainfall have been derived. To verify the RMSE expressions quantitatively, path-averaged measurements from 21 operational communication links in 12 different locations have been compared to records of five nearby rain gauges over three rainstorm events.
The experiments show that the prediction accuracy is above 90% for temporal accumulation less than 30 min and lowers for longer accumulation intervals. Spatial variability in the vicinity of the link, baseline attenuation uncertainty and, possibly, suboptimality of wet antenna attenuation model are the major sources of link-gauge discrepancies. In addition, the dependence of the optimal coefficients of a conventional wet antenna attenuation model on spatial rainfall variability and, accordingly, link length has been shown.
The expressions for RMSE of the path-averaged rainfall estimates can be useful for integration of measurements from multiple
Steger, Stefan; Brenning, Alexander; Bell, Rainer; Glade, Thomas
2016-12-01
There is unanimous agreement that a precise spatial representation of past landslide occurrences is a prerequisite to produce high quality statistical landslide susceptibility models. Even though perfectly accurate landslide inventories rarely exist, investigations of how landslide inventory-based errors propagate into subsequent statistical landslide susceptibility models are scarce. The main objective of this research was to systematically examine whether and how inventory-based positional inaccuracies of different magnitudes influence modelled relationships, validation results, variable importance and the visual appearance of landslide susceptibility maps. The study was conducted for a landslide-prone site located in the districts of Amstetten and Waidhofen an der Ybbs, eastern Austria, where an earth-slide point inventory was available. The methodological approach comprised an artificial introduction of inventory-based positional errors into the present landslide data set and an in-depth evaluation of subsequent modelling results. Positional errors were introduced by artificially changing the original landslide position by a mean distance of 5, 10, 20, 50 and 120 m. The resulting differently precise response variables were separately used to train logistic regression models. Odds ratios of predictor variables provided insights into modelled relationships. Cross-validation and spatial cross-validation enabled an assessment of predictive performances and permutation-based variable importance. All analyses were additionally carried out with synthetically generated data sets to further verify the findings under rather controlled conditions. The results revealed that an increasing positional inventory-based error was generally related to increasing distortions of modelling and validation results. However, the findings also highlighted that interdependencies between inventory-based spatial inaccuracies and statistical landslide susceptibility models are complex. The
Error apportionment for atmospheric chemistry-transport models – a new approach to model evaluation
Directory of Open Access Journals (Sweden)
E. Solazzo
2016-05-01
Full Text Available In this study, methods are proposed to diagnose the causes of errors in air quality (AQ modelling systems. We investigate the deviation between modelled and observed time series of surface ozone through a revised formulation for breaking down the mean square error (MSE into bias, variance and the minimum achievable MSE (mMSE. The bias measures the accuracy and implies the existence of systematic errors and poor representation of data complexity, the variance measures the precision and provides an estimate of the variability of the modelling results in relation to the observed data, and the mMSE reflects unsystematic errors and provides a measure of the associativity between the modelled and the observed fields through the correlation coefficient. Each of the error components is analysed independently and apportioned to resolved processes based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day and as a function of model complexity.The apportionment of the error is applied to the AQMEII (Air Quality Model Evaluation International Initiative group of models, which embrace the majority of regional AQ modelling systems currently used in Europe and North America.The proposed technique has proven to be a compact estimator of the operational metrics commonly used for model evaluation (bias, variance, and correlation coefficient, and has the further benefit of apportioning the error to the originating timescale, thus allowing for a clearer diagnosis of the processes that caused the error.
The balanced mind: the variability of task-unrelated thoughts predicts error-monitoring
DEFF Research Database (Denmark)
Allen, Micah Galen; Smallwood, Jonathan; Christensen, Joanna
2013-01-01
Self-generated thoughts unrelated to ongoing activities, also known as "mind-wandering," make up a substantial portion of our daily lives. Reports of such task-unrelated thoughts (TUTs) predict both poor performance on demanding cognitive tasks and blood-oxygen-level-dependent (BOLD) activity...... in the default mode network (DMN). However, recent findings suggest that TUTs and the DMN can also facilitate metacognitive abilities and related behaviors. To further understand these relationships, we examined the influence of subjective intensity, ruminative quality, and variability of mind......-wandering on response inhibition and monitoring, using the Error Awareness Task (EAT). We expected to replicate links between TUT and reduced inhibition, and explored whether variance in TUT would predict improved error monitoring, reflecting a capacity to balance between internal and external cognition. By analyzing...
MODEL PREDICTIVE CONTROL FUNDAMENTALS
African Journals Online (AJOL)
2012-07-02
Jul 2, 2012 ... Linear MPC. 1. Uses linear model: ˙x = Ax + Bu. 2. Quadratic cost function: F = xT Qx + uT Ru. 3. Linear constraints: Hx + Gu < 0. 4. Quadratic program. Nonlinear MPC. 1. Nonlinear model: ˙x = f(x, u). 2. Cost function can be nonquadratic: F = (x, u). 3. Nonlinear constraints: h(x, u) < 0. 4. Nonlinear program.
Global tropospheric ozone modeling: Quantifying errors due to grid resolution
Wild, Oliver; Prather, Michael J.
2006-06-01
Ozone production in global chemical models is dependent on model resolution because ozone chemistry is inherently nonlinear, the timescales for chemical production are short, and precursors are artificially distributed over the spatial scale of the model grid. In this study we examine the sensitivity of ozone, its precursors, and its production to resolution by running a global chemical transport model at four different resolutions between T21 (5.6° × 5.6°) and T106 (1.1° × 1.1°) and by quantifying the errors in regional and global budgets. The sensitivity to vertical mixing through the parameterization of boundary layer turbulence is also examined. We find less ozone production in the boundary layer at higher resolution, consistent with slower chemical production in polluted emission regions and greater export of precursors. Agreement with ozonesonde and aircraft measurements made during the NASA TRACE-P campaign over the western Pacific in spring 2001 is consistently better at higher resolution. We demonstrate that the numerical errors in transport processes on a given resolution converge geometrically for a tracer at successively higher resolutions. The convergence in ozone production on progressing from T21 to T42, T63, and T106 resolution is likewise monotonic but indicates that there are still large errors at 120 km scales, suggesting that T106 resolution is too coarse to resolve regional ozone production. Diagnosing the ozone production and precursor transport that follow a short pulse of emissions over east Asia in springtime allows us to quantify the impacts of resolution on both regional and global ozone. Production close to continental emission regions is overestimated by 27% at T21 resolution, by 13% at T42 resolution, and by 5% at T106 resolution. However, subsequent ozone production in the free troposphere is not greatly affected. We find that the export of short-lived precursors such as NOx by convection is overestimated at coarse resolution.
Battauz, Michela; Bellio, Ruggero
2011-01-01
This paper proposes a structural analysis for generalized linear models when some explanatory variables are measured with error and the measurement error variance is a function of the true variables. The focus is on latent variables investigated on the basis of questionnaires and estimated using item response theory models. Latent variable…
Duda, David P.; Minnis, Patrick
2009-01-01
Straightforward application of the Schmidt-Appleman contrail formation criteria to diagnose persistent contrail occurrence from numerical weather prediction data is hindered by significant bias errors in the upper tropospheric humidity. Logistic models of contrail occurrence have been proposed to overcome this problem, but basic questions remain about how random measurement error may affect their accuracy. A set of 5000 synthetic contrail observations is created to study the effects of random error in these probabilistic models. The simulated observations are based on distributions of temperature, humidity, and vertical velocity derived from Advanced Regional Prediction System (ARPS) weather analyses. The logistic models created from the simulated observations were evaluated using two common statistical measures of model accuracy, the percent correct (PC) and the Hanssen-Kuipers discriminant (HKD). To convert the probabilistic results of the logistic models into a dichotomous yes/no choice suitable for the statistical measures, two critical probability thresholds are considered. The HKD scores are higher when the climatological frequency of contrail occurrence is used as the critical threshold, while the PC scores are higher when the critical probability threshold is 0.5. For both thresholds, typical random errors in temperature, relative humidity, and vertical velocity are found to be small enough to allow for accurate logistic models of contrail occurrence. The accuracy of the models developed from synthetic data is over 85 percent for both the prediction of contrail occurrence and non-occurrence, although in practice, larger errors would be anticipated.
A method for the quantification of model form error associated with physical systems.
Energy Technology Data Exchange (ETDEWEB)
Wallen, Samuel P.; Brake, Matthew Robert
2014-03-01
In the process of model validation, models are often declared valid when the differences between model predictions and experimental data sets are satisfactorily small. However, little consideration is given to the effectiveness of a model using parameters that deviate slightly from those that were fitted to data, such as a higher load level. Furthermore, few means exist to compare and choose between two or more models that reproduce data equally well. These issues can be addressed by analyzing model form error, which is the error associated with the differences between the physical phenomena captured by models and that of the real system. This report presents a new quantitative method for model form error analysis and applies it to data taken from experiments on tape joint bending vibrations. Two models for the tape joint system are compared, and suggestions for future improvements to the method are given. As the available data set is too small to draw any statistical conclusions, the focus of this paper is the development of a methodology that can be applied to general problems.
Modelling bankruptcy prediction models in Slovak companies
Directory of Open Access Journals (Sweden)
Kovacova Maria
2017-01-01
Full Text Available An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression and early artificial intelligence models (e.g. artificial neural networks, there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models.
Artificial Neural Network Model for Predicting Compressive
Directory of Open Access Journals (Sweden)
Salim T. Yousif
2013-05-01
Full Text Available Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature. The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor affecting the output of the model. The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.
Tectonic predictions with mantle convection models
Coltice, Nicolas; Shephard, Grace E.
2018-04-01
Over the past 15 yr, numerical models of convection in Earth's mantle have made a leap forward: they can now produce self-consistent plate-like behaviour at the surface together with deep mantle circulation. These digital tools provide a new window into the intimate connections between plate tectonics and mantle dynamics, and can therefore be used for tectonic predictions, in principle. This contribution explores this assumption. First, initial conditions at 30, 20, 10 and 0 Ma are generated by driving a convective flow with imposed plate velocities at the surface. We then compute instantaneous mantle flows in response to the guessed temperature fields without imposing any boundary conditions. Plate boundaries self-consistently emerge at correct locations with respect to reconstructions, except for small plates close to subduction zones. As already observed for other types of instantaneous flow calculations, the structure of the top boundary layer and upper-mantle slab is the dominant character that leads to accurate predictions of surface velocities. Perturbations of the rheological parameters have little impact on the resulting surface velocities. We then compute fully dynamic model evolution from 30 and 10 to 0 Ma, without imposing plate boundaries or plate velocities. Contrary to instantaneous calculations, errors in kinematic predictions are substantial, although the plate layout and kinematics in several areas remain consistent with the expectations for the Earth. For these calculations, varying the rheological parameters makes a difference for plate boundary evolution. Also, identified errors in initial conditions contribute to first-order kinematic errors. This experiment shows that the tectonic predictions of dynamic models over 10 My are highly sensitive to uncertainties of rheological parameters and initial temperature field in comparison to instantaneous flow calculations. Indeed, the initial conditions and the rheological parameters can be good enough
Semiparametric modeling: Correcting low-dimensional model error in parametric models
International Nuclear Information System (INIS)
Berry, Tyrus; Harlim, John
2016-01-01
In this paper, a semiparametric modeling approach is introduced as a paradigm for addressing model error arising from unresolved physical phenomena. Our approach compensates for model error by learning an auxiliary dynamical model for the unknown parameters. Practically, the proposed approach consists of the following steps. Given a physics-based model and a noisy data set of historical observations, a Bayesian filtering algorithm is used to extract a time-series of the parameter values. Subsequently, the diffusion forecast algorithm is applied to the retrieved time-series in order to construct the auxiliary model for the time evolving parameters. The semiparametric forecasting algorithm consists of integrating the existing physics-based model with an ensemble of parameters sampled from the probability density function of the diffusion forecast. To specify initial conditions for the diffusion forecast, a Bayesian semiparametric filtering method that extends the Kalman-based filtering framework is introduced. In difficult test examples, which introduce chaotically and stochastically evolving hidden parameters into the Lorenz-96 model, we show that our approach can effectively compensate for model error, with forecasting skill comparable to that of the perfect model.
Melanoma Risk Prediction Models
Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Predictive models of moth development
Degree-day models link ambient temperature to insect life-stages, making such models valuable tools in integrated pest management. These models increase management efficacy by predicting pest phenology. In Wisconsin, the top insect pest of cranberry production is the cranberry fruitworm, Acrobasis v...
Predictive Models and Computational Embryology
EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...
PENDEKATAN ERROR CORRECTION MODEL SEBAGAI PENENTU HARGA SAHAM
Directory of Open Access Journals (Sweden)
David Kaluge
2017-03-01
Full Text Available This research was to find the effect of profitability, rate of interest, GDP, and foreign exchange rate on stockprices. Approach used was error correction model. Profitability was indicated by variables EPS, and ROIwhile the SBI (1 month was used for representing interest rate. This research found that all variablessimultaneously affected the stock prices significantly. Partially, EPS, PER, and Foreign Exchange rate significantlyaffected the prices both in short run and long run. Interestingly that SBI and GDP did not affect theprices at all. The variable of ROI had only long run impact on the prices.
Measurement error in epidemiologic studies of air pollution based on land-use regression models.
Basagaña, Xavier; Aguilera, Inmaculada; Rivera, Marcela; Agis, David; Foraster, Maria; Marrugat, Jaume; Elosua, Roberto; Künzli, Nino
2013-10-15
Land-use regression (LUR) models are increasingly used to estimate air pollution exposure in epidemiologic studies. These models use air pollution measurements taken at a small set of locations and modeling based on geographical covariates for which data are available at all study participant locations. The process of LUR model development commonly includes a variable selection procedure. When LUR model predictions are used as explanatory variables in a model for a health outcome, measurement error can lead to bias of the regression coefficients and to inflation of their variance. In previous studies dealing with spatial predictions of air pollution, bias was shown to be small while most of the effect of measurement error was on the variance. In this study, we show that in realistic cases where LUR models are applied to health data, bias in health-effect estimates can be substantial. This bias depends on the number of air pollution measurement sites, the number of available predictors for model selection, and the amount of explainable variability in the true exposure. These results should be taken into account when interpreting health effects from studies that used LUR models.
Modeling conically scanning lidar error in complex terrain with WAsP Engineering
Energy Technology Data Exchange (ETDEWEB)
Bingoel, F.; Mann, J.; Foussekis, D.
2008-11-15
Conically scanning lidars assume the flow to be homogeneous in order to deduce the horizontal wind speed. However, in mountainous or complex terrain this assumption is not valid implying an erroneous wind speed. The magnitude of this error is measured by collocating a meteorological mast and a lidar at two Greek sites, one hilly and one mountainous. The maximum error for the sites investigated is of the order of 10%. In order to predict the error for various wind directions the flows at both sites are simulated with the linearized flow model, WAsP Engineering 2.0. The measurement data are compared with the model predictions with good results for the hilly site, but with less success at the mountainous site. This is a deficiency of the flow model, but the methods presented in this paper can be used with any flow model. An abbreviated version of this report has been submitted to Meteorologische Zeitschrift. This work is partly financed through the UPWIND project (WP6, D3) funded by the European Commission. (au)
Directory of Open Access Journals (Sweden)
M. Owens
2004-01-01
Full Text Available Three existing models of Interplanetary Coronal Mass Ejection (ICME transit between the Sun and the Earth are compared to coronagraph and in situ observations: all three models are found to perform with a similar level of accuracy (i.e. an average error between observed and predicted 1AU transit times of approximately 11h. To improve long-term space weather prediction, factors influencing CME transit are investigated. Both the removal of the plane of sky projection (as suffered by coronagraph derived speeds of Earth directed CMEs and the use of observed values of solar wind speed, fail to significantly improve transit time prediction. However, a correlation is found to exist between the late/early arrival of an ICME and the width of the preceding sheath region, suggesting that the error is a geometrical effect that can only be removed by a more accurate determination of a CME trajectory and expansion. The correlation between magnetic field intensity and speed of ejecta at 1AU is also investigated. It is found to be weak in the body of the ICME, but strong in the sheath, if the upstream solar wind conditions are taken into account.
Key words. Solar physics, astronomy and astrophysics (flares and mass ejections – Interplanetary physics (interplanetary magnetic fields; sources of the solar wind
National Research Council Canada - National Science Library
Reader, Grahame
2000-01-01
Sources of 72-h track errors> 300 n mi by four dynamical model tropical cyclone predictions in the Southern Hemisphere during the 1997-98 and 1998-99 seasons are studied using conceptual models Carr and Elsberry have previously...
National Research Council Canada - National Science Library
Reader, Grahame
2000-01-01
Sources of 72-h track errors > 300 n mi by four dynamical model tropical cyclone predictions in the Southern Hemisphere during the 1997-98 and 1998-99 seasons are studied using conceptual models Carr and Elsberry have previously...
Modelling the basic error tendencies of human operators
International Nuclear Information System (INIS)
Reason, J.
1988-01-01
The paper outlines the primary structural features of human cognition: a limited, serial workspace interacting with a parallel distributed knowledge base. It is argued that the essential computational features of human cognition - to be captured by an adequate operator model - reside in the mechanisms by which stored knowledge structures are selected and brought into play. Two such computational 'primitives' are identified: similarity-matching and frequency-gambling. These two retrieval heuristics, it is argued, shape both the overall character of human performance (i.e. its heavy reliance on pattern-matching) and its basic error tendencies ('strong-but-wrong' responses, confirmation, similarity and frequency biases, and cognitive 'lock-up'). The various features of human cognition are integrated with a dynamic operator model capable of being represented in software form. This computer model, when run repeatedly with a variety of problem configurations, should produce a distribution of behaviours which, in total, simulate the general character of operator performance. (author)
Modelling the basic error tendencies of human operators
International Nuclear Information System (INIS)
Reason, James
1988-01-01
The paper outlines the primary structural features of human cognition: a limited, serial workspace interacting with a parallel distributed knowledge base. It is argued that the essential computational features of human cognition - to be captured by an adequate operator model - reside in the mechanisms by which stored knowledge structures are selected and brought into play. Two such computational 'primitives' are identified: similarity-matching and frequency-gambling. These two retrieval heuristics, it is argued, shape both the overall character of human performance (i.e. its heavy reliance on pattern-matching) and its basic error tendencies ('strong-but-wrong' responses, confirmation, similarity and frequency biases, and cognitive 'lock-up'). The various features of human cognition are integrated with a dynamic operator model capable of being represented in software form. This computer model, when run repeatedly with a variety of problem configurations, should produce a distribution of behaviours which, in toto, simulate the general character of operator performance. (author)
DEFF Research Database (Denmark)
Andersen, Anders Holst; Korsgaard, Inge Riis; Jensen, Just
2002-01-01
In this paper, we consider selection based on the best predictor of animal additive genetic values in Gaussian linear mixed models, threshold models, Poisson mixed models, and log normal frailty models for survival data (including models with time-dependent covariates with associated fixed...
Koepke, C.; Irving, J.; Roubinet, D.
2014-12-01
Geophysical methods have gained much interest in hydrology over the past two decades because of their ability to provide estimates of the spatial distribution of subsurface properties at a scale that is often relevant to key hydrological processes. Because of an increased desire to quantify uncertainty in hydrological predictions, many hydrogeophysical inverse problems have recently been posed within a Bayesian framework, such that estimates of hydrological properties and their corresponding uncertainties can be obtained. With the Bayesian approach, it is often necessary to make significant approximations to the associated hydrological and geophysical forward models such that stochastic sampling from the posterior distribution, for example using Markov-chain-Monte-Carlo (MCMC) methods, is computationally feasible. These approximations lead to model structural errors, which, so far, have not been properly treated in hydrogeophysical inverse problems. Here, we study the inverse problem of estimating unsaturated hydraulic properties, namely the van Genuchten-Mualem (VGM) parameters, in a layered subsurface from time-lapse, zero-offset-profile (ZOP) ground penetrating radar (GPR) data, collected over the course of an infiltration experiment. In particular, we investigate the effects of assumptions made for computational tractability of the stochastic inversion on model prediction errors as a function of depth and time. These assumptions are that (i) infiltration is purely vertical and can be modeled by the 1D Richards equation, and (ii) the petrophysical relationship between water content and relative dielectric permittivity is known. Results indicate that model errors for this problem are far from Gaussian and independently identically distributed, which has been the common assumption in previous efforts in this domain. In order to develop a more appropriate likelihood formulation, we use (i) a stochastic description of the model error that is obtained through
Predictions models with neural nets
Directory of Open Access Journals (Sweden)
Vladimír Konečný
2008-01-01
Full Text Available The contribution is oriented to basic problem trends solution of economic pointers, using neural networks. Problems include choice of the suitable model and consequently configuration of neural nets, choice computational function of neurons and the way prediction learning. The contribution contains two basic models that use structure of multilayer neural nets and way of determination their configuration. It is postulate a simple rule for teaching period of neural net, to get most credible prediction.Experiments are executed with really data evolution of exchange rate Kč/Euro. The main reason of choice this time series is their availability for sufficient long period. In carry out of experiments the both given basic kind of prediction models with most frequent use functions of neurons are verified. Achieve prediction results are presented as in numerical and so in graphical forms.
Maggioni, V.; Anagnostou, E. N.; Reichle, R. H.
2013-01-01
The contribution of rainfall forcing errors relative to model (structural and parameter) uncertainty in the prediction of soil moisture is investigated by integrating the NASA Catchment Land Surface Model (CLSM), forced with hydro-meteorological data, in the Oklahoma region. Rainfall-forcing uncertainty is introduced using a stochastic error model that generates ensemble rainfall fields from satellite rainfall products. The ensemble satellite rain fields are propagated through CLSM to produce soil moisture ensembles. Errors in CLSM are modeled with two different approaches: either by perturbing model parameters (representing model parameter uncertainty) or by adding randomly generated noise (representing model structure and parameter uncertainty) to the model prognostic variables. Our findings highlight that the method currently used in the NASA GEOS-5 Land Data Assimilation System to perturb CLSM variables poorly describes the uncertainty in the predicted soil moisture, even when combined with rainfall model perturbations. On the other hand, by adding model parameter perturbations to rainfall forcing perturbations, a better characterization of uncertainty in soil moisture simulations is observed. Specifically, an analysis of the rank histograms shows that the most consistent ensemble of soil moisture is obtained by combining rainfall and model parameter perturbations. When rainfall forcing and model prognostic perturbations are added, the rank histogram shows a U-shape at the domain average scale, which corresponds to a lack of variability in the forecast ensemble. The more accurate estimation of the soil moisture prediction uncertainty obtained by combining rainfall and parameter perturbations is encouraging for the application of this approach in ensemble data assimilation systems.
Link Prediction via Sparse Gaussian Graphical Model
Directory of Open Access Journals (Sweden)
Liangliang Zhang
2016-01-01
Full Text Available Link prediction is an important task in complex network analysis. Traditional link prediction methods are limited by network topology and lack of node property information, which makes predicting links challenging. In this study, we address link prediction using a sparse Gaussian graphical model and demonstrate its theoretical and practical effectiveness. In theory, link prediction is executed by estimating the inverse covariance matrix of samples to overcome information limits. The proposed method was evaluated with four small and four large real-world datasets. The experimental results show that the area under the curve (AUC value obtained by the proposed method improved by an average of 3% and 12.5% compared to 13 mainstream similarity methods, respectively. This method outperforms the baseline method, and the prediction accuracy is superior to mainstream methods when using only 80% of the training set. The method also provides significantly higher AUC values when using only 60% in Dolphin and Taro datasets. Furthermore, the error rate of the proposed method demonstrates superior performance with all datasets compared to mainstream methods.
James W. Hardin; Henrik Schmeidiche; Raymond J. Carroll
2003-01-01
This paper discusses and illustrates the method of regression calibration. This is a straightforward technique for fitting models with additive measurement error. We present this discussion in terms of generalized linear models (GLMs) following the notation defined in Hardin and Carroll (2003). Discussion will include specified measurement error, measurement error estimated by replicate error-prone proxies, and measurement error estimated by instrumental variables. The discussion focuses on s...
Prediction skill of rainstorm events over India in the TIGGE weather prediction models
Karuna Sagar, S.; Rajeevan, M.; Vijaya Bhaskara Rao, S.; Mitra, A. K.
2017-12-01
Extreme rainfall events pose a serious threat of leading to severe floods in many countries worldwide. Therefore, advance prediction of its occurrence and spatial distribution is very essential. In this paper, an analysis has been made to assess the skill of numerical weather prediction models in predicting rainstorms over India. Using gridded daily rainfall data set and objective criteria, 15 rainstorms were identified during the monsoon season (June to September). The analysis was made using three TIGGE (THe Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble) models. The models considered are the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centre for Environmental Prediction (NCEP) and the UK Met Office (UKMO). Verification of the TIGGE models for 43 observed rainstorm days from 15 rainstorm events has been made for the period 2007-2015. The comparison reveals that rainstorm events are predictable up to 5 days in advance, however with a bias in spatial distribution and intensity. The statistical parameters like mean error (ME) or Bias, root mean square error (RMSE) and correlation coefficient (CC) have been computed over the rainstorm region using the multi-model ensemble (MME) mean. The study reveals that the spread is large in ECMWF and UKMO followed by the NCEP model. Though the ensemble spread is quite small in NCEP, the ensemble member averages are not well predicted. The rank histograms suggest that the forecasts are under prediction. The modified Contiguous Rain Area (CRA) technique was used to verify the spatial as well as the quantitative skill of the TIGGE models. Overall, the contribution from the displacement and pattern errors to the total RMSE is found to be more in magnitude. The volume error increases from 24 hr forecast to 48 hr forecast in all the three models.
International Nuclear Information System (INIS)
Carl Stern; Martin Lee
1999-01-01
Phase I work studied the feasibility of developing software for automatic component calibration and error correction in beamline optics models. A prototype application was developed that corrects quadrupole field strength errors in beamline models
International Nuclear Information System (INIS)
Hanafusa, Hidemitsu; Iwaki, Toshio; Embrey, D.
2000-01-01
The objective of this study was to develop and effective methodology for predicting and preventing errors in nuclear power plant maintenance tasks. A method was established by which chief maintenance personnel can predict and reduce errors when reviewing the maintenance procedures and while referring to maintenance supporting systems and methods in other industries including aviation and chemical plant industries. The method involves the following seven steps: 1. Identification of maintenance tasks. 2. Specification of important tasks affecting safety. 3. Assessment of human errors occurring during important tasks. 4. Identification of Performance Degrading Factors. 5. Dividing important tasks into sub-tasks. 6. Extraction of errors using Predictive Human Error Analysis (PHEA). 7. Development of strategies for reducing errors and for recovering from errors. By way of a trial, this method was applied to the pump maintenance procedure in nuclear power plants. This method is believed to be capable of identifying the expected errors in important tasks and supporting the development of error reduction measures. By applying this method, the number of accidents resulting form human errors during maintenance can be reduced. Moreover, the maintenance support base using computers was developed. (author)
Brassington, Gary
2017-04-01
The mean absolute error (MAE) and root mean square error (RMSE) are two metrics that are often used interchangeably as measures of ocean forecast accuracy. Recent literature has debated which of these should be preferred though their conclusions have largely been based on empirical arguments. We note that in general, RM SE2 = M AE2 + V ARk [|ɛ|] PIC PIC such that RMSE includes both the MAE as well as additional information related to the variance (biased estimator) of the errors ɛ with sample size k. The greater sensitivity of RMSE to a small number of outliers is directly attributable to the variance of absolute error. Further statistical properties for both metrics are derived and compared based on the assumption that the errors are Gaussian. For an unbiased (or bias corrected) model both MAE and RMSE are shown to estimate the total error standard deviation to within a constant coefficient such that ° -- M AE ≈ 2/πRM SE PIC . Both metrics have comparable behaviour in response to model bias and asymptote to the model bias as the bias increases. MAE is shown to be an unbiased estimator while RMSE is a biased estimator. MAE also has a lower sample variance compared with RMSE indicating MAE is the most robust choice. For real-time applications where there is a likelihood of "bad" observations we recommend ° - ° ---° - π- -1- π- π- TESD = 2 M AE ± √k- 2 - 1 2M AE PIC as an unbiased estimator of the total error standard deviation with error estimates (one standard deviation) based on the sample variance and defined as a scaling of the MAE itself. A sample size (k) on the order of 90 and 9000 provides an error scaling of 10% and 1% respectively. Nonetheless if the model performance is being analysed using a large sample of delayed-mode quality controlled observations then RMSE might be preferred where the second moment sensitivity to large model errors is important. Alternatively for model intercomparisons the information might compactly represented by a
Detection of microcalcifications in mammograms using error of prediction and statistical measures
Acha, Begoña; Serrano, Carmen; Rangayyan, Rangaraj M.; Leo Desautels, J. E.
2009-01-01
A two-stage method for detecting microcalcifications in mammograms is presented. In the first stage, the determination of the candidates for microcalcifications is performed. For this purpose, a 2-D linear prediction error filter is applied, and for those pixels where the prediction error is larger than a threshold, a statistical measure is calculated to determine whether they are candidates for microcalcifications or not. In the second stage, a feature vector is derived for each candidate, and after a classification step using a support vector machine, the final detection is performed. The algorithm is tested with 40 mammographic images, from Screen Test: The Alberta Program for the Early Detection of Breast Cancer with 50-μm resolution, and the results are evaluated using a free-response receiver operating characteristics curve. Two different analyses are performed: an individual microcalcification detection analysis and a cluster analysis. In the analysis of individual microcalcifications, detection sensitivity values of 0.75 and 0.81 are obtained at 2.6 and 6.2 false positives per image, on the average, respectively. The best performance is characterized by a sensitivity of 0.89, a specificity of 0.99, and a positive predictive value of 0.79. In cluster analysis, a sensitivity value of 0.97 is obtained at 1.77 false positives per image, and a value of 0.90 is achieved at 0.94 false positive per image.
DEFF Research Database (Denmark)
Lowes, F.J.; Olsen, Nils
2004-01-01
, led to quite inaccurate variance estimates. We estimate correction factors which range from 1/4 to 20, with the largest increases being for the zonal, m = 0, and sectorial, m = n, terms. With no correction, the OSVM variances give a mean-square vector field error of prediction over the Earth's surface......Most modern spherical harmonic geomagnetic models based on satellite data include estimates of the variances of the spherical harmonic coefficients of the model; these estimates are based on the geometry of the data and the fitting functions, and on the magnitude of the residuals. However...
Wireless model predictive control: Application to water-level system
Directory of Open Access Journals (Sweden)
Ramdane Hedjar
2016-04-01
Full Text Available This article deals with wireless model predictive control of a water-level control system. The objective of the model predictive control algorithm is to constrain the control signal inside saturation limits and maintain the water level around the desired level. Linear modeling of any nonlinear plant leads to parameter uncertainties and non-modeled dynamics in the linearized mathematical model. These uncertainties induce a steady-state error in the output response of the water level. To eliminate this steady-state error and increase the robustness of the control algorithm, an integral action is included in the closed loop. To control the water-level system remotely, the communication between the controller and the process is performed using radio channel. To validate the proposed scheme, simulation and real-time implementation of the algorithm have been conducted, and the results show the effectiveness of wireless model predictive control with integral action.
Peak-counts blood flow model-errors and limitations
International Nuclear Information System (INIS)
Mullani, N.A.; Marani, S.K.; Ekas, R.D.; Gould, K.L.
1984-01-01
The peak-counts model has several advantages, but its use may be limited due to the condition that the venous egress may not be negligible at the time of peak-counts. Consequently, blood flow measurements by the peak-counts model will depend on the bolus size, bolus duration, and the minimum transit time of the bolus through the region of interest. The effect of bolus size on the measurement of extraction fraction and blood flow was evaluated by injecting 1 to 30ml of rubidium chloride in the femoral vein of a dog and measuring the myocardial activity with a beta probe over the heart. Regional blood flow measurements were not found to vary with bolus sizes up to 30ml. The effect of bolus duration was studied by injecting a 10cc bolus of tracer at different speeds in the femoral vein of a dog. All intravenous injections undergo a broadening of the bolus duration due to the transit time of the tracer through the lungs and the heart. This transit time was found to range from 4-6 second FWHM and dominates the duration of the bolus to the myocardium for up to 3 second injections. A computer simulation has been carried out in which the different parameters of delay time, extraction fraction, and bolus duration can be changed to assess the errors in the peak-counts model. The results of the simulations show that the error will be greatest for short transit time delays and for low extraction fractions
Assessment of blood glucose predictors: the prediction-error grid analysis.
Sivananthan, Sampath; Naumova, Valeriya; Man, Chiara Dalla; Facchinetti, Andrea; Renard, Eric; Cobelli, Claudio; Pereverzyev, Sergei V
2011-08-01
Prediction of the future blood glucose (BG) evolution from continuous glucose monitoring (CGM) data is a promising direction in diabetes therapy management, and several glucose predictors have recently been proposed. This raises the problem of their assessment. There were attempts to use for such assessment the continuous glucose-error grid analysis (CG-EGA), originally developed for CGM devices. However, in the CG-EGA the BG rate of change is estimated from past BG readings, whereas predictors provide BG estimation ahead of time. Therefore, the original CG-EGA should be modified to assess predictors. Here we propose a new version of the CG-EGA, the Prediction-Error Grid Analysis (PRED-EGA). The analysis is based both on simulated data and on data from clinical trials, performed in the European FP7-project "DIAdvisor." Simulated data are used to test the ability of the analyzed CG-EGA modifications to capture erroneous predictions in controlled situation. Real data are used to show the impact of the different CG-EGA versions in the evaluation of a predictor. Using the data of 10 virtual and 10 real subjects and analyzing two different predictors, we demonstrate that the straightforward application of the CG-EGA does not adequately classify the prediction performance. For example, we observed that up to 70% of 20 min ahead predictions in the hyperglycemia region that are classified by this application as erroneous are, in fact, accurate. Moreover, for predictions during hypoglycemia the assessments produced by the straightforward application of the CG-EGA are not only too pessimistic (in up to 60% of cases), but this version is not able to detect real erroneous predictions. In contrast, the proposed modification of the CG-EGA, where the rate of change is estimated on the predicted BG profile, is an adequate metric for the assessment of predictions. We propose a new CG-EGA, the PRED-EGA, for the assessment of glucose predictors. The presented analysis shows that
DEFF Research Database (Denmark)
Poulsen, Per Rugaard; Cho, Byungchul; Keall, Paul
2010-01-01
. The mathematical formalism of the method includes an individualized measure of the position estimation error in terms of an estimated 1D Gaussian distribution for the unresolved target position[2]. The present study investigates how well this 1D Gaussian predicts the actual distribution of position estimation...... errors. Over 5000 CBCT acquisitions were simulated from a 46-patient thoracic/abdominal and a 17-patient prostate tumor motion database. The 1D Gaussian predicted the actual root-mean-square and 95th percentile of the position estimation error with mean errors ≤0.04mm and maximum errors ≤0.48mm....... This finding indicates that individualized root-mean-square errors and 95% confidence intervals can be applied reliably to the estimated target trajectories....
Approximating prediction uncertainty for random forest regression models
John W. Coulston; Christine E. Blinn; Valerie A. Thomas; Randolph H. Wynne
2016-01-01
Machine learning approaches such as random forest haveÂ increased for the spatial modeling and mapping of continuousÂ variables. Random forest is a non-parametric ensembleÂ approach, and unlike traditional regression approaches thereÂ is no direct quantification of prediction error. UnderstandingÂ prediction uncertainty is important when using model-basedÂ continuous maps as...
Behmanesh, Iman; Moaveni, Babak
2016-07-01
This paper presents a Hierarchical Bayesian model updating framework to account for the effects of ambient temperature and excitation amplitude. The proposed approach is applied for model calibration, response prediction and damage identification of a footbridge under changing environmental/ambient conditions. The concrete Young's modulus of the footbridge deck is the considered updating structural parameter with its mean and variance modeled as functions of temperature and excitation amplitude. The identified modal parameters over 27 months of continuous monitoring of the footbridge are used to calibrate the updating parameters. One of the objectives of this study is to show that by increasing the levels of information in the updating process, the posterior variation of the updating structural parameter (concrete Young's modulus) is reduced. To this end, the calibration is performed at three information levels using (1) the identified modal parameters, (2) modal parameters and ambient temperatures, and (3) modal parameters, ambient temperatures, and excitation amplitudes. The calibrated model is then validated by comparing the model-predicted natural frequencies and those identified from measured data after deliberate change to the structural mass. It is shown that accounting for modeling error uncertainties is crucial for reliable response prediction, and accounting only the estimated variability of the updating structural parameter is not sufficient for accurate response predictions. Finally, the calibrated model is used for damage identification of the footbridge.
Colas, Jaron T; Pauli, Wolfgang M; Larsen, Tobias; Tyszka, J Michael; O'Doherty, John P
2017-10-01
Prediction-error signals consistent with formal models of "reinforcement learning" (RL) have repeatedly been found within dopaminergic nuclei of the midbrain and dopaminoceptive areas of the striatum. However, the precise form of the RL algorithms implemented in the human brain is not yet well determined. Here, we created a novel paradigm optimized to dissociate the subtypes of reward-prediction errors that function as the key computational signatures of two distinct classes of RL models-namely, "actor/critic" models and action-value-learning models (e.g., the Q-learning model). The state-value-prediction error (SVPE), which is independent of actions, is a hallmark of the actor/critic architecture, whereas the action-value-prediction error (AVPE) is the distinguishing feature of action-value-learning algorithms. To test for the presence of these prediction-error signals in the brain, we scanned human participants with a high-resolution functional magnetic-resonance imaging (fMRI) protocol optimized to enable measurement of neural activity in the dopaminergic midbrain as well as the striatal areas to which it projects. In keeping with the actor/critic model, the SVPE signal was detected in the substantia nigra. The SVPE was also clearly present in both the ventral striatum and the dorsal striatum. However, alongside these purely state-value-based computations we also found evidence for AVPE signals throughout the striatum. These high-resolution fMRI findings suggest that model-free aspects of reward learning in humans can be explained algorithmically with RL in terms of an actor/critic mechanism operating in parallel with a system for more direct action-value learning.
Directory of Open Access Journals (Sweden)
Da Liu
2013-01-01
Full Text Available A combined forecast with weights adaptively selected and errors calibrated by Hidden Markov model (HMM is proposed to model the day-ahead electricity price. Firstly several single models were built to forecast the electricity price separately. Then the validation errors from every individual model were transformed into two discrete sequences: an emission sequence and a state sequence to build the HMM, obtaining a transmission matrix and an emission matrix, representing the forecasting ability state of the individual models. The combining weights of the individual models were decided by the state transmission matrixes in HMM and the best predict sample ratio of each individual among all the models in the validation set. The individual forecasts were averaged to get the combining forecast with the weights obtained above. The residuals of combining forecast were calibrated by the possible error calculated by the emission matrix of HMM. A case study of day-ahead electricity market of Pennsylvania-New Jersey-Maryland (PJM, USA, suggests that the proposed method outperforms individual techniques of price forecasting, such as support vector machine (SVM, generalized regression neural networks (GRNN, day-ahead modeling, and self-organized map (SOM similar days modeling.
Sensitivity of ice flow in Greenland to errors in model forcing, using the Ice Sheet System Model
Schlegel, N.; Larour, E. Y.; Seroussi, H.; Morlighem, M.; Halkides, D. J.
2013-12-01
A clear understanding of how ice sheets respond to climate change requires an examination ice sheet model uncertainty. This includes the quantification of uncertainties associated with model forcing, as well as the clarification of exactly what error sources most influence modeled ice flow dynamics. The Ice Sheet System Model (ISSM) is a finite-element model capable of simulating transient ice flow on an anisotropic mesh that can be refined to higher resolutions. This model also considers longitudinal stresses in the areas of enhanced ice flow, offering a distinct advantage in terms of modeling fast-flowing outlet glaciers. With use of established uncertainty quantification capabilities within ISSM, we compare the sensitivity of ice flow within key basins of the Greenland Ice Sheet to errors in various forcing, including surface mass balance components and temperature. We investigate how these errors propagate through the model as uncertainties in estimates of Greenland ice discharge. This work was performed at the California Institute of Technology's Jet Propulsion Laboratory under a contract with the National Aeronautics and Space Administration's Modeling, Analysis and Prediction (MAP) Program.
GA-ARMA Model for Predicting IGS RTS Corrections
Directory of Open Access Journals (Sweden)
Mingyu Kim
2017-01-01
Full Text Available The global navigation satellite system (GNSS is widely used to estimate user positions. For precise positioning, users should correct for GNSS error components such as satellite orbit and clock errors as well as ionospheric delay. The international GNSS service (IGS real-time service (RTS can be used to correct orbit and clock errors in real-time. Since the IGS RTS provides real-time corrections via the Internet, intermittent data loss can occur due to software or hardware failures. We propose applying a genetic algorithm autoregressive moving average (GA-ARMA model to predict the IGS RTS corrections during data loss periods. The RTS orbit and clock corrections are predicted up to 900 s via the GA-ARMA model, and the prediction accuracies are compared with the results from a generic ARMA model. The orbit prediction performance of the GA-ARMA is nearly equivalent to that of ARMA, but GA-ARMA’s clock prediction performance is clearly better than that of ARMA, achieving a 32% error reduction. Predicted RTS corrections are applied to the broadcast ephemeris, and precise point positioning accuracies are compared. GA-ARMA shows a significant accuracy improvement over ARMA, particularly in terms of vertical positioning.
Regularized multivariate regression models with skew-t error distributions
Chen, Lianfu
2014-06-01
We consider regularization of the parameters in multivariate linear regression models with the errors having a multivariate skew-t distribution. An iterative penalized likelihood procedure is proposed for constructing sparse estimators of both the regression coefficient and inverse scale matrices simultaneously. The sparsity is introduced through penalizing the negative log-likelihood by adding L1-penalties on the entries of the two matrices. Taking advantage of the hierarchical representation of skew-t distributions, and using the expectation conditional maximization (ECM) algorithm, we reduce the problem to penalized normal likelihood and develop a procedure to minimize the ensuing objective function. Using a simulation study the performance of the method is assessed, and the methodology is illustrated using a real data set with a 24-dimensional response vector. © 2014 Elsevier B.V.
Error modelling of quantum Hall array resistance standards
Marzano, Martina; Oe, Takehiko; Ortolano, Massimo; Callegaro, Luca; Kaneko, Nobu-Hisa
2018-04-01
Quantum Hall array resistance standards (QHARSs) are integrated circuits composed of interconnected quantum Hall effect elements that allow the realization of virtually arbitrary resistance values. In recent years, techniques were presented to efficiently design QHARS networks. An open problem is that of the evaluation of the accuracy of a QHARS, which is affected by contact and wire resistances. In this work, we present a general and systematic procedure for the error modelling of QHARSs, which is based on modern circuit analysis techniques and Monte Carlo evaluation of the uncertainty. As a practical example, this method of analysis is applied to the characterization of a 1 MΩ QHARS developed by the National Metrology Institute of Japan. Software tools are provided to apply the procedure to other arrays.
Schroeder, Scott R; Salomon, Meghan M; Galanter, William L; Schiff, Gordon D; Vaida, Allen J; Gaunt, Michael J; Bryson, Michelle L; Rash, Christine; Falck, Suzanne; Lambert, Bruce L
2017-05-01
Drug name confusion is a common type of medication error and a persistent threat to patient safety. In the USA, roughly one per thousand prescriptions results in the wrong drug being filled, and most of these errors involve drug names that look or sound alike. Prior to approval, drug names undergo a variety of tests to assess their potential for confusability, but none of these preapproval tests has been shown to predict real-world error rates. We conducted a study to assess the association between error rates in laboratory-based tests of drug name memory and perception and real-world drug name confusion error rates. Eighty participants, comprising doctors, nurses, pharmacists, technicians and lay people, completed a battery of laboratory tests assessing visual perception, auditory perception and short-term memory of look-alike and sound-alike drug name pairs (eg, hydroxyzine/hydralazine). Laboratory test error rates (and other metrics) significantly predicted real-world error rates obtained from a large, outpatient pharmacy chain, with the best-fitting model accounting for 37% of the variance in real-world error rates. Cross-validation analyses confirmed these results, showing that the laboratory tests also predicted errors from a second pharmacy chain, with 45% of the variance being explained by the laboratory test data. Across two distinct pharmacy chains, there is a strong and significant association between drug name confusion error rates observed in the real world and those observed in laboratory-based tests of memory and perception. Regulators and drug companies seeking a validated preapproval method for identifying confusing drug names ought to consider using these simple tests. By using a standard battery of memory and perception tests, it should be possible to reduce the number of confusing look-alike and sound-alike drug name pairs that reach the market, which will help protect patients from potentially harmful medication errors. Published by the BMJ
Dong, Li-hu; Li, Feng-ri; Jia, Wei-wei; Liu, Fu-xiang; Wang, He-zhi
2011-10-01
Based on the biomass data of 516 sampling trees, and by using non-linear error-in-variable modeling approach, the compatible models for the total biomass and the biomass of six components including aboveground part, underground part, stem, crown, branch, and foliage of 15 major tree species (or groups) in Heilongjiang Province were established, and the best models for the total biomass and components biomass were selected. The compatible models based on total biomass were developed by adopting the method of joint control different level ratio function. The heteroscedasticity of the models for total biomass was eliminated with log transformation, and the weighted regression was applied to the models for each individual component. Among the compatible biomass models established for the 15 major species (or groups) , the model for total biomass had the highest prediction precision (90% or more), followed by the models for aboveground part and stem biomass, with a precision of 87.5% or more. The prediction precision of the biomass models for other components was relatively low, but it was still greater than 80% for most test tree species. The modeling efficiency (EF) values of the total, aboveground part, and stem biomass models for all the tree species (or groups) were over 0.9, and the EF values of the underground part, crown, branch, and foliage biomass models were over 0.8.
Wang, Q; Mu, M.; Dijkstra, H.A.
2013-01-01
The links between optimal precursor (OPR) and optimally growing initial error (OGIE) in the predictability studies of Kuroshio large meander (LM) are investigated using the Conditional Nonlinear Optimal Perturbation approach within a 1.5-layer shallow-water model. The OPR is a kind of initial
Locatelli, R.; Bousquet, P.; Chevallier, F.; Fortems-Cheney, A.; Szopa, S.; Saunois, M.; Agusti-Panareda, A.; Bergmann, D.; Bian, H.; Cameron-Smith, P.; Chipperfield, M.P.; Gloor, E.; Houweling, S.; Kawa, S.R.; Krol, M.C.; Patra, P.K.; Prinn, R.G.; Rigby, M.; Saito, R.; Wilson, C.
2013-01-01
A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH4 model inter-comparison exercise,
Measured and predicted root-mean-square errors in square and triangular antenna mesh facets
Fichter, W. B.
1989-01-01
Deflection shapes of square and equilateral triangular facets of two tricot-knit, gold plated molybdenum wire mesh antenna materials were measured and compared, on the basis of root mean square (rms) differences, with deflection shapes predicted by linear membrane theory, for several cases of biaxial mesh tension. The two mesh materials contained approximately 10 and 16 holes per linear inch, measured diagonally with respect to the course and wale directions. The deflection measurement system employed a non-contact eddy current proximity probe and an electromagnetic distance sensing probe in conjunction with a precision optical level. Despite experimental uncertainties, rms differences between measured and predicted deflection shapes suggest the following conclusions: that replacing flat antenna facets with facets conforming to parabolically curved structural members yields smaller rms surface error; that potential accuracy gains are greater for equilateral triangular facets than for square facets; and that linear membrane theory can be a useful tool in the design of tricot knit wire mesh antennas.
Coelho Borges, Renata; Holsbach Costa, Marcio
2015-08-01
This work presents a theoretical analysis of the prediction-error method-based adaptive feedback canceller in hearing aid applications. The studied scene takes into account the occlusion effect caused by the partial or complete closing of the ventilation opening. Such a situation may occur in high gain applications to avoid undesired whistling. Deterministic recursive equations and steady-state conditions were derived for the mean weight behaviour of the predictor and the adaptive filter. The expected theoretical predictions were compared to Monte Carlo simulations, showing very accurate agreement. The simulation results suggest the steady-state performance of this feedback canceller is not affected by the occlusion effect, however the occlusion is still perceived, being annoying to the user.
What roles do errors serve in motor skill learning? An examination of two theoretical predictions.
Sanli, Elizabeth A; Lee, Timothy D
2014-01-01
Easy-to-difficult and difficult-to-easy progressions of task difficulty during skill acquisition were examined in 2 experiments that assessed retention, dual-task, and transfer tests of learning. Findings of the first experiment suggest that an easy-to difficult progression did not consistently induce implicit learning processes and was not consistently beneficial to performance under a secondary-task load. The findings of experiment two did not support the predictions made based on schema theory and only partially supported predictions based on reinvestment theory. The authors interpret these findings to suggest that the timing of error in relation to the difficulty of the task (functional task difficulty) plays a role in the transfer of learning to novel versions of a task.
What do saliency models predict?
Koehler, Kathryn; Guo, Fei; Zhang, Sheng; Eckstein, Miguel P.
2014-01-01
Saliency models have been frequently used to predict eye movements made during image viewing without a specified task (free viewing). Use of a single image set to systematically compare free viewing to other tasks has never been performed. We investigated the effect of task differences on the ability of three models of saliency to predict the performance of humans viewing a novel database of 800 natural images. We introduced a novel task where 100 observers made explicit perceptual judgments about the most salient image region. Other groups of observers performed a free viewing task, saliency search task, or cued object search task. Behavior on the popular free viewing task was not best predicted by standard saliency models. Instead, the models most accurately predicted the explicit saliency selections and eye movements made while performing saliency judgments. Observers' fixations varied similarly across images for the saliency and free viewing tasks, suggesting that these two tasks are related. The variability of observers' eye movements was modulated by the task (lowest for the object search task and greatest for the free viewing and saliency search tasks) as well as the clutter content of the images. Eye movement variability in saliency search and free viewing might be also limited by inherent variation of what observers consider salient. Our results contribute to understanding the tasks and behavioral measures for which saliency models are best suited as predictors of human behavior, the relationship across various perceptual tasks, and the factors contributing to observer variability in fixational eye movements. PMID:24618107
He, Minxue; Hogue, Terri S.; Franz, Kristie J.; Margulis, Steven A.; Vrugt, Jasper A.
2011-07-01
The current study evaluates the impacts of various sources of uncertainty involved in hydrologic modeling on parameter behavior and regionalization utilizing different Bayesian likelihood functions and the Differential Evolution Adaptive Metropolis (DREAM) algorithm. The developed likelihood functions differ in their underlying assumptions and treatment of error sources. We apply the developed method to a snow accumulation and ablation model (National Weather Service SNOW17) and generate parameter ensembles to predict snow water equivalent (SWE). Observational data include precipitation and air temperature forcing along with SWE measurements from 24 sites with diverse hydroclimatic characteristics. A multiple linear regression model is used to construct regionalization relationships between model parameters and site characteristics. Results indicate that model structural uncertainty has the largest influence on SNOW17 parameter behavior. Precipitation uncertainty is the second largest source of uncertainty, showing greater impact at wetter sites. Measurement uncertainty in SWE tends to have little impact on the final model parameters and resulting SWE predictions. Considering all sources of uncertainty, parameters related to air temperature and snowfall fraction exhibit the strongest correlations to site characteristics. Parameters related to the length of the melting period also show high correlation to site characteristics. Finally, model structural uncertainty and precipitation uncertainty dramatically alter parameter regionalization relationships in comparison to cases where only uncertainty in model parameters or output measurements is considered. Our results demonstrate that accurate treatment of forcing, parameter, model structural, and calibration data errors is critical for deriving robust regionalization relationships.
Comparison of Predictive Models for the Early Diagnosis of Diabetes.
Jahani, Meysam; Mahdavi, Mahdi
2016-04-01
This study develops neural network models to improve the prediction of diabetes using clinical and lifestyle characteristics. Prediction models were developed using a combination of approaches and concepts. We used memetic algorithms to update weights and to improve prediction accuracy of models. In the first step, the optimum amount for neural network parameters such as momentum rate, transfer function, and error function were obtained through trial and error and based on the results of previous studies. In the second step, optimum parameters were applied to memetic algorithms in order to improve the accuracy of prediction. This preliminary analysis showed that the accuracy of neural networks is 88%. In the third step, the accuracy of neural network models was improved using a memetic algorithm and resulted model was compared with a logistic regression model using a confusion matrix and receiver operating characteristic curve (ROC). The memetic algorithm improved the accuracy from 88.0% to 93.2%. We also found that memetic algorithm had a higher accuracy than the model from the genetic algorithm and a regression model. Among models, the regression model has the least accuracy. For the memetic algorithm model the amount of sensitivity, specificity, positive predictive value, negative predictive value, and ROC are 96.2, 95.3, 93.8, 92.4, and 0.958 respectively. The results of this study provide a basis to design a Decision Support System for risk management and planning of care for individuals at risk of diabetes.
Volcanic ash modeling with the NMMB-MONARCH-ASH model: quantification of offline modeling errors
Marti, Alejandro; Folch, Arnau
2018-03-01
Volcanic ash modeling systems are used to simulate the atmospheric dispersion of volcanic ash and to generate forecasts that quantify the impacts from volcanic eruptions on infrastructures, air quality, aviation, and climate. The efficiency of response and mitigation actions is directly associated with the accuracy of the volcanic ash cloud detection and modeling systems. Operational forecasts build on offline coupled modeling systems in which meteorological variables are updated at the specified coupling intervals. Despite the concerns from other communities regarding the accuracy of this strategy, the quantification of the systematic errors and shortcomings associated with the offline modeling systems has received no attention. This paper employs the NMMB-MONARCH-ASH model to quantify these errors by employing different quantitative and categorical evaluation scores. The skills of the offline coupling strategy are compared against those from an online forecast considered to be the best estimate of the true outcome. Case studies are considered for a synthetic eruption with constant eruption source parameters and for two historical events, which suitably illustrate the severe aviation disruptive effects of European (2010 Eyjafjallajökull) and South American (2011 Cordón Caulle) volcanic eruptions. Evaluation scores indicate that systematic errors due to the offline modeling are of the same order of magnitude as those associated with the source term uncertainties. In particular, traditional offline forecasts employed in operational model setups can result in significant uncertainties, failing to reproduce, in the worst cases, up to 45-70 % of the ash cloud of an online forecast. These inconsistencies are anticipated to be even more relevant in scenarios in which the meteorological conditions change rapidly in time. The outcome of this paper encourages operational groups responsible for real-time advisories for aviation to consider employing computationally
Episodic Memory Encoding Interferes with Reward Learning and Decreases Striatal Prediction Errors
Braun, Erin Kendall; Daw, Nathaniel D.
2014-01-01
Learning is essential for adaptive decision making. The striatum and its dopaminergic inputs are known to support incremental reward-based learning, while the hippocampus is known to support encoding of single events (episodic memory). Although traditionally studied separately, in even simple experiences, these two types of learning are likely to co-occur and may interact. Here we sought to understand the nature of this interaction by examining how incremental reward learning is related to concurrent episodic memory encoding. During the experiment, human participants made choices between two options (colored squares), each associated with a drifting probability of reward, with the goal of earning as much money as possible. Incidental, trial-unique object pictures, unrelated to the choice, were overlaid on each option. The next day, participants were given a surprise memory test for these pictures. We found that better episodic memory was related to a decreased influence of recent reward experience on choice, both within and across participants. fMRI analyses further revealed that during learning the canonical striatal reward prediction error signal was significantly weaker when episodic memory was stronger. This decrease in reward prediction error signals in the striatum was associated with enhanced functional connectivity between the hippocampus and striatum at the time of choice. Our results suggest a mechanism by which memory encoding may compete for striatal processing and provide insight into how interactions between different forms of learning guide reward-based decision making. PMID:25378157
Episodic memory encoding interferes with reward learning and decreases striatal prediction errors.
Wimmer, G Elliott; Braun, Erin Kendall; Daw, Nathaniel D; Shohamy, Daphna
2014-11-05
Learning is essential for adaptive decision making. The striatum and its dopaminergic inputs are known to support incremental reward-based learning, while the hippocampus is known to support encoding of single events (episodic memory). Although traditionally studied separately, in even simple experiences, these two types of learning are likely to co-occur and may interact. Here we sought to understand the nature of this interaction by examining how incremental reward learning is related to concurrent episodic memory encoding. During the experiment, human participants made choices between two options (colored squares), each associated with a drifting probability of reward, with the goal of earning as much money as possible. Incidental, trial-unique object pictures, unrelated to the choice, were overlaid on each option. The next day, participants were given a surprise memory test for these pictures. We found that better episodic memory was related to a decreased influence of recent reward experience on choice, both within and across participants. fMRI analyses further revealed that during learning the canonical striatal reward prediction error signal was significantly weaker when episodic memory was stronger. This decrease in reward prediction error signals in the striatum was associated with enhanced functional connectivity between the hippocampus and striatum at the time of choice. Our results suggest a mechanism by which memory encoding may compete for striatal processing and provide insight into how interactions between different forms of learning guide reward-based decision making. Copyright © 2014 the authors 0270-6474/14/3414901-12$15.00/0.
Evaluation of wave runup predictions from numerical and parametric models
Stockdon, Hilary F.; Thompson, David M.; Plant, Nathaniel G.; Long, Joseph W.
2014-01-01
Wave runup during storms is a primary driver of coastal evolution, including shoreline and dune erosion and barrier island overwash. Runup and its components, setup and swash, can be predicted from a parameterized model that was developed by comparing runup observations to offshore wave height, wave period, and local beach slope. Because observations during extreme storms are often unavailable, a numerical model is used to simulate the storm-driven runup to compare to the parameterized model and then develop an approach to improve the accuracy of the parameterization. Numerically simulated and parameterized runup were compared to observations to evaluate model accuracies. The analysis demonstrated that setup was accurately predicted by both the parameterized model and numerical simulations. Infragravity swash heights were most accurately predicted by the parameterized model. The numerical model suffered from bias and gain errors that depended on whether a one-dimensional or two-dimensional spatial domain was used. Nonetheless, all of the predictions were significantly correlated to the observations, implying that the systematic errors can be corrected. The numerical simulations did not resolve the incident-band swash motions, as expected, and the parameterized model performed best at predicting incident-band swash heights. An assimilated prediction using a weighted average of the parameterized model and the numerical simulations resulted in a reduction in prediction error variance. Finally, the numerical simulations were extended to include storm conditions that have not been previously observed. These results indicated that the parameterized predictions of setup may need modification for extreme conditions; numerical simulations can be used to extend the validity of the parameterized predictions of infragravity swash; and numerical simulations systematically underpredict incident swash, which is relatively unimportant under extreme conditions.
Hebbian errors in learning: an analysis using the Oja model.
Rădulescu, Anca; Cox, Kingsley; Adams, Paul
2009-06-21
Recent work on long term potentiation in brain slices shows that Hebb's rule is not completely synapse-specific, probably due to intersynapse diffusion of calcium or other factors. We previously suggested that such errors in Hebbian learning might be analogous to mutations in evolution. We examine this proposal quantitatively, extending the classical Oja unsupervised model of learning by a single linear neuron to include Hebbian inspecificity. We introduce an error matrix E, which expresses possible crosstalk between updating at different connections. When there is no inspecificity, this gives the classical result of convergence to the first principal component of the input distribution (PC1). We show the modified algorithm converges to the leading eigenvector of the matrix EC, where C is the input covariance matrix. In the most biologically plausible case when there are no intrinsically privileged connections, E has diagonal elements Q and off-diagonal elements (1-Q)/(n-1), where Q, the quality, is expected to decrease with the number of inputs n and with a synaptic parameter b that reflects synapse density, calcium diffusion, etc. We study the dependence of the learning accuracy on b, n and the amount of input activity or correlation (analytically and computationally). We find that accuracy increases (learning becomes gradually less useful) with increases in b, particularly for intermediate (i.e., biologically realistic) correlation strength, although some useful learning always occurs up to the trivial limit Q=1/n. We discuss the relation of our results to Hebbian unsupervised learning in the brain. When the mechanism lacks specificity, the network fails to learn the expected, and typically most useful, result, especially when the input correlation is weak. Hebbian crosstalk would reflect the very high density of synapses along dendrites, and inevitably degrades learning.
Generalized multiplicative error models: Asymptotic inference and empirical analysis
Li, Qian
This dissertation consists of two parts. The first part focuses on extended Multiplicative Error Models (MEM) that include two extreme cases for nonnegative series. These extreme cases are common phenomena in high-frequency financial time series. The Location MEM(p,q) model incorporates a location parameter so that the series are required to have positive lower bounds. The estimator for the location parameter turns out to be the minimum of all the observations and is shown to be consistent. The second case captures the nontrivial fraction of zero outcomes feature in a series and combines a so-called Zero-Augmented general F distribution with linear MEM(p,q). Under certain strict stationary and moment conditions, we establish a consistency and asymptotic normality of the semiparametric estimation for these two new models. The second part of this dissertation examines the differences and similarities between trades in the home market and trades in the foreign market of cross-listed stocks. We exploit the multiplicative framework to model trading duration, volume per trade and price volatility for Canadian shares that are cross-listed in the New York Stock Exchange (NYSE) and the Toronto Stock Exchange (TSX). We explore the clustering effect, interaction between trading variables, and the time needed for price equilibrium after a perturbation for each market. The clustering effect is studied through the use of univariate MEM(1,1) on each variable, while the interactions among duration, volume and price volatility are captured by a multivariate system of MEM(p,q). After estimating these models by a standard QMLE procedure, we exploit the Impulse Response function to compute the calendar time for a perturbation in these variables to be absorbed into price variance, and use common statistical tests to identify the difference between the two markets in each aspect. These differences are of considerable interest to traders, stock exchanges and policy makers.
Directory of Open Access Journals (Sweden)
Guohui Li
2017-01-01
Full Text Available Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme-point symmetric mode decomposition (ESMD and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs and residuals. Secondly, the fuzzy c-means is used to cluster the decomposed components, and then the deep belief network (DBN is used to predict it. Finally, the reconstructed IMFs and residuals are the final prediction results. Six kinds of prediction models are compared, which are DBN prediction model, EMD-DBN prediction model, EEMD-DBN prediction model, CEEMD-DBN prediction model, ESMD-DBN prediction model, and the proposed model in this paper. The same sunspots time series are predicted with six kinds of prediction models. The experimental results show that the proposed model has better prediction accuracy and smaller error.
International Nuclear Information System (INIS)
Jung, W.D.; Kim, T.W.; Park, C.K.
1991-01-01
This paper presents an integrated approach to prediction of human error probabilities with a computer program, HREP (Human Reliability Evaluation Program). HREP is developed to provide simplicity in Human Reliability Analysis (HRA) and consistency in the obtained results. The basic assumption made in developing HREP is that human behaviors can be quantified in two separate steps. One is the diagnosis error evaluation step and the other the response error evaluation step. HREP integrates the Human Cognitive Reliability (HCR) model and the HRA Event Tree technique. The former corresponds to the Diagnosis model, and the latter the Response model. HREP consists of HREP-IN and HREP-MAIN. HREP-IN is used to generate input files. HREP-MAIN is used to evaluate selected human errors in a given input file. HREP-MAIN is divided into three subsections ; the diagnosis evaluation step, the subaction evaluation step and the modification step. The final modification step takes dependency and/or recovery factors into consideration. (author)
MIAO, Enming; LIU, Yi; XU, Jianguo; LIU, Hui
2017-05-01
Aiming at the deficiency of the robustness of thermal error compensation models of CNC machine tools, the mechanism of improving the models' robustness is studied by regarding the Leaderway-V450 machining center as the object. Through the analysis of actual spindle air cutting experimental data on Leaderway-V450 machine, it is found that the temperature-sensitive points used for modeling is volatility, and this volatility directly leads to large changes on the collinear degree among modeling independent variables. Thus, the forecasting accuracy of multivariate regression model is severely affected, and the forecasting robustness becomes poor too. To overcome this effect, a modeling method of establishing thermal error models by using single temperature variable under the jamming of temperature-sensitive points' volatility is put forward. According to the actual data of thermal error measured in different seasons, it is proved that the single temperature variable model can reduce the loss of forecasting accuracy resulted from the volatility of temperature-sensitive points, especially for the prediction of cross quarter data, the improvement of forecasting accuracy is about 5 μm or more. The purpose that improving the robustness of the thermal error models is realized, which can provide a reference for selecting the modeling independent variable in the application of thermal error compensation of CNC machine tools.
A two-dimensional matrix correction for off-axis portal dose prediction errors
International Nuclear Information System (INIS)
Bailey, Daniel W.; Kumaraswamy, Lalith; Bakhtiari, Mohammad; Podgorsak, Matthew B.
2013-01-01
Purpose: This study presents a follow-up to a modified calibration procedure for portal dosimetry published by Bailey et al. [“An effective correction algorithm for off-axis portal dosimetry errors,” Med. Phys. 36, 4089–4094 (2009)]. A commercial portal dose prediction system exhibits disagreement of up to 15% (calibrated units) between measured and predicted images as off-axis distance increases. The previous modified calibration procedure accounts for these off-axis effects in most regions of the detecting surface, but is limited by the simplistic assumption of radial symmetry. Methods: We find that a two-dimensional (2D) matrix correction, applied to each calibrated image, accounts for off-axis prediction errors in all regions of the detecting surface, including those still problematic after the radial correction is performed. The correction matrix is calculated by quantitative comparison of predicted and measured images that span the entire detecting surface. The correction matrix was verified for dose-linearity, and its effectiveness was verified on a number of test fields. The 2D correction was employed to retrospectively examine 22 off-axis, asymmetric electronic-compensation breast fields, five intensity-modulated brain fields (moderate-high modulation) manipulated for far off-axis delivery, and 29 intensity-modulated clinical fields of varying complexity in the central portion of the detecting surface. Results: Employing the matrix correction to the off-axis test fields and clinical fields, predicted vs measured portal dose agreement improves by up to 15%, producing up to 10% better agreement than the radial correction in some areas of the detecting surface. Gamma evaluation analyses (3 mm, 3% global, 10% dose threshold) of predicted vs measured portal dose images demonstrate pass rate improvement of up to 75% with the matrix correction, producing pass rates that are up to 30% higher than those resulting from the radial correction technique alone. As
Penn, C. A.; Clow, D. W.; Sexstone, G. A.
2017-12-01
Water supply forecasts are an important tool for water resource managers in areas where surface water is relied on for irrigating agricultural lands and for municipal water supplies. Forecast errors, which correspond to inaccurate predictions of total surface water volume, can lead to mis-allocated water and productivity loss, thus costing stakeholders millions of dollars. The objective of this investigation is to provide water resource managers with an improved understanding of factors contributing to forecast error, and to help increase the accuracy of future forecasts. In many watersheds of the western United States, snowmelt contributes 50-75% of annual surface water flow and controls both the timing and volume of peak flow. Water supply forecasts from the Natural Resources Conservation Service (NRCS), National Weather Service, and similar cooperators use precipitation and snowpack measurements to provide water resource managers with an estimate of seasonal runoff volume. The accuracy of these forecasts can be limited by available snowpack and meteorological data. In the headwaters of the Rio Grande, NRCS produces January through June monthly Water Supply Outlook Reports. This study evaluates the accuracy of these forecasts since 1990, and examines what factors may contribute to forecast error. The Rio Grande headwaters has experienced recent changes in land cover from bark beetle infestation and a large wildfire, which can affect hydrological processes within the watershed. To investigate trends and possible contributing factors in forecast error, a semi-distributed hydrological model was calibrated and run to simulate daily streamflow for the period 1990-2015. Annual and seasonal watershed and sub-watershed water balance properties were compared with seasonal water supply forecasts. Gridded meteorological datasets were used to assess changes in the timing and volume of spring precipitation events that may contribute to forecast error. Additionally, a
AN EFFICIENT PATIENT INFLOW PREDICTION MODEL FOR HOSPITAL RESOURCE MANAGEMENT
Directory of Open Access Journals (Sweden)
Kottalanka Srikanth
2017-07-01
Full Text Available There has been increasing demand in improving service provisioning in hospital resources management. Hospital industries work with strict budget constraint at the same time assures quality care. To achieve quality care with budget constraint an efficient prediction model is required. Recently there has been various time series based prediction model has been proposed to manage hospital resources such ambulance monitoring, emergency care and so on. These models are not efficient as they do not consider the nature of scenario such climate condition etc. To address this artificial intelligence is adopted. The issues with existing prediction are that the training suffers from local optima error. This induces overhead and affects the accuracy in prediction. To overcome the local minima error, this work presents a patient inflow prediction model by adopting resilient backpropagation neural network. Experiment are conducted to evaluate the performance of proposed model inter of RMSE and MAPE. The outcome shows the proposed model reduces RMSE and MAPE over existing back propagation based artificial neural network. The overall outcomes show the proposed prediction model improves the accuracy of prediction which aid in improving the quality of health care management.
Selecting Human Error Types for Cognitive Modelling and Simulation
Mioch, T.; Osterloh, J.P.; Javaux, D.
2010-01-01
This paper presents a method that has enabled us to make a selection of error types and error production mechanisms relevant to the HUMAN European project, and discusses the reasons underlying those choices. We claim that this method has the advantage that it is very exhaustive in determining the
Assessment of errors and uncertainty patterns in GIA modeling
DEFF Research Database (Denmark)
Barletta, Valentina Roberta; Spada, G.
, such as time-evolving shorelines and paleo-coastlines. In this study we quantify these uncertainties and their propagation in GIA response using a Monte Carlo approach to obtain spatio-temporal patterns of GIA errors. A direct application is the error estimates in ice mass balance in Antarctica and Greenland...
Learning from Errors: A Model of Individual Processes
Tulis, Maria; Steuer, Gabriele; Dresel, Markus
2016-01-01
Errors bear the potential to improve knowledge acquisition, provided that learners are able to deal with them in an adaptive and reflexive manner. However, learners experience a host of different--often impeding or maladaptive--emotional and motivational states in the face of academic errors. Research has made few attempts to develop a theory that…
International Nuclear Information System (INIS)
Olsen, A.R.; Cunningham, M.E.
1980-01-01
With the increasing sophistication and use of computer codes in the nuclear industry, there is a growing awareness of the need to identify and quantify the uncertainties of these codes. In any effort to model physical mechanisms, the results obtained from the model are subject to some degree of uncertainty. This uncertainty has two primary sources. First, there is uncertainty in the model's representation of reality. Second, there is an uncertainty in the input data required by the model. If individual models are combined into a predictive sequence, the uncertainties from an individual model will propagate through the sequence and add to the uncertainty of results later obtained. Nuclear fuel rod stored-energy models, characterized as a combination of numerous submodels, exemplify models so affected. Each submodel depends on output from previous calculations and may involve iterative interdependent submodel calculations for the solution. The iterative nature of the model and the cost of running the model severely limit the uncertainty analysis procedures. An approach for uncertainty analysis under these conditions was designed for the particular case of stored-energy models. It is assumed that the complicated model is correct, that a simplified model based on physical considerations can be designed to approximate the complicated model, and that linear error propagation techniques can be used on the simplified model
Locatelli, R.; Bousquet, P.; Chevallier, F.; Fortems-Cheney, A.; Szopa, S.; Saunois, M.; Agusti-Panareda, A.; Bergmann, D.; Bian, H.; Cameron-Smith, P.; Chipperfield, M. P.; Gloor, E.; Houweling, S.; Kawa, S. R.; Krol, M.; Patra, P. K.; Prinn, R. G.; Rigby, M.; Saito, R.; Wilson, C.
2013-10-01
A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH4 model inter-comparison exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the three-component PYVAR-LMDZ-SACS (PYthon VARiational-Laboratoire de Météorologie Dynamique model with Zooming capability-Simplified Atmospheric Chemistry System) inversion system to produce 10 different methane emission estimates at the global scale for the year 2005. The same methane sinks, emissions and initial conditions have been applied to produce the 10 synthetic observation datasets. The same inversion set-up (statistical errors, prior emissions, inverse procedure) is then applied to derive flux estimates by inverse modelling. Consequently, only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes. In our framework, we show that transport model errors lead to a discrepancy of 27 Tg yr-1 at the global scale, representing 5% of total methane emissions. At continental and annual scales, transport model errors are proportionally larger than at the global scale, with errors ranging from 36 Tg yr-1 in North America to 7 Tg yr-1 in Boreal Eurasia (from 23 to 48%, respectively). At the model grid-scale, the spread of inverse estimates can reach 150% of the prior flux. Therefore, transport model errors contribute significantly to overall uncertainties in emission estimates by inverse modelling, especially when small spatial scales are examined. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher horizontal resolution in transport models. The large differences found between methane flux estimates inferred in these different configurations highly question the consistency of
Directory of Open Access Journals (Sweden)
R. Locatelli
2013-10-01
Full Text Available A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH4 model inter-comparison exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the three-component PYVAR-LMDZ-SACS (PYthon VARiational-Laboratoire de Météorologie Dynamique model with Zooming capability-Simplified Atmospheric Chemistry System inversion system to produce 10 different methane emission estimates at the global scale for the year 2005. The same methane sinks, emissions and initial conditions have been applied to produce the 10 synthetic observation datasets. The same inversion set-up (statistical errors, prior emissions, inverse procedure is then applied to derive flux estimates by inverse modelling. Consequently, only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes. In our framework, we show that transport model errors lead to a discrepancy of 27 Tg yr−1 at the global scale, representing 5% of total methane emissions. At continental and annual scales, transport model errors are proportionally larger than at the global scale, with errors ranging from 36 Tg yr−1 in North America to 7 Tg yr−1 in Boreal Eurasia (from 23 to 48%, respectively. At the model grid-scale, the spread of inverse estimates can reach 150% of the prior flux. Therefore, transport model errors contribute significantly to overall uncertainties in emission estimates by inverse modelling, especially when small spatial scales are examined. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher horizontal resolution in transport models. The large differences found between methane flux estimates inferred in these different configurations highly
Predictability of large-scale atmospheric motions: Lyapunov exponents and error dynamics.
Vannitsem, Stéphane
2017-03-01
The deterministic equations describing the dynamics of the atmosphere (and of the climate system) are known to display the property of sensitivity to initial conditions. In the ergodic theory of chaos, this property is usually quantified by computing the Lyapunov exponents. In this review, these quantifiers computed in a hierarchy of atmospheric models (coupled or not to an ocean) are analyzed, together with their local counterparts known as the local or finite-time Lyapunov exponents. It is shown in particular that the variability of the local Lyapunov exponents (corresponding to the dominant Lyapunov exponent) decreases when the model resolution increases. The dynamics of (finite-amplitude) initial condition errors in these models is also reviewed, and in general found to display a complicated growth far from the asymptotic estimates provided by the Lyapunov exponents. The implications of these results for operational (high resolution) atmospheric and climate modelling are also discussed.
International Nuclear Information System (INIS)
Vedam, S.; Docef, A.; Fix, M.; Murphy, M.; Keall, P.
2005-01-01
The synchronization of dynamic multileaf collimator (DMLC) response with respiratory motion is critical to ensure the accuracy of DMLC-based four dimensional (4D) radiation delivery. In practice, however, a finite time delay (response time) between the acquisition of tumor position and multileaf collimator response necessitates predictive models of respiratory tumor motion to synchronize radiation delivery. Predicting a complex process such as respiratory motion introduces geometric errors, which have been reported in several publications. However, the dosimetric effect of such errors on 4D radiation delivery has not yet been investigated. Thus, our aim in this work was to quantify the dosimetric effects of geometric error due to prediction under several different conditions. Conformal and intensity modulated radiation therapy (IMRT) plans for a lung patient were generated for anterior-posterior/posterior-anterior (AP/PA) beam arrangements at 6 and 18 MV energies to provide planned dose distributions. Respiratory motion data was obtained from 60 diaphragm-motion fluoroscopy recordings from five patients. A linear adaptive filter was employed to predict the tumor position. The geometric error of prediction was defined as the absolute difference between predicted and actual positions at each diaphragm position. Distributions of geometric error of prediction were obtained for all of the respiratory motion data. Planned dose distributions were then convolved with distributions for the geometric error of prediction to obtain convolved dose distributions. The dosimetric effect of such geometric errors was determined as a function of several variables: response time (0-0.6 s), beam energy (6/18 MV), treatment delivery (3D/4D), treatment type (conformal/IMRT), beam direction (AP/PA), and breathing training type (free breathing/audio instruction/visual feedback). Dose difference and distance-to-agreement analysis was employed to quantify results. Based on our data, the
Predictability in models of the atmospheric circulation
Houtekamer, P.L.
1992-01-01
It will be clear from the above discussions that skill forecasts are still in their infancy. Operational skill predictions do not exist. One is still struggling to prove that skill predictions, at any range, have any quality at all. It is not clear what the statistics of the analysis error
Directory of Open Access Journals (Sweden)
Labudde Dirk
2009-06-01
Full Text Available Abstract Background A lot of high-throughput studies produce protein-protein interaction networks (PPINs with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs were previously used for predicting protein function and finding complexes in high-error PPINs. We retrieve second level neighbors in PPINs, and complement these with structural domain-domain interactions (SDDIs representing binding evidence on proteins, forming PPI-SDDI-PPI triangles. Results We find low overlap between PPINs, SDDIs and known complexes, all well below 10%. We evaluate the overlap of PPI-SDDI-PPI triangles with known complexes from Munich Information center for Protein Sequences (MIPS. PPI-SDDI-PPI triangles have ~20 times higher overlap with MIPS complexes than using second-level neighbors in PPINs without SDDIs. The biological interpretation for triangles is that a SDDI causes two proteins to be observed with common interaction partners in high-throughput experiments. The relatively few SDDIs overlapping with PPINs are part of highly connected SDDI components, and are more likely to be detected in experimental studies. We demonstrate the utility of PPI-SDDI-PPI triangles by reconstructing myosin-actin processes in the nucleus, cytoplasm, and cytoskeleton, which were not obvious in the original PPIN. Using other complementary datatypes in place of SDDIs to form triangles, such as PubMed co-occurrences or threading information, results in a similar ability to find protein complexes. Conclusion Given high-error PPINs with missing information, triangles of mixed datatypes are a promising direction for finding protein complexes. Integrating PPINs with SDDIs improves finding complexes. Structural SDDIs partially explain the high functional similarity of second-level neighbors in PPINs. We estimate that
Directory of Open Access Journals (Sweden)
Rebecca J. Brooker
2014-07-01
Full Text Available Temperamentally fearful children are at increased risk for the development of anxiety problems relative to less-fearful children. This risk is even greater when early environments include high levels of harsh parenting behaviors. However, the mechanisms by which harsh parenting may impact fearful children's risk for anxiety problems are largely unknown. Recent neuroscience work has suggested that punishment is associated with exaggerated error-related negativity (ERN, an event-related potential linked to performance monitoring, even after the threat of punishment is removed. In the current study, we examined the possibility that harsh parenting interacts with fearfulness, impacting anxiety risk via neural processes of performance monitoring. We found that greater fearfulness and harsher parenting at 2 years of age predicted greater fearfulness and greater ERN amplitudes at age 4. Supporting the role of cognitive processes in this association, greater fearfulness and harsher parenting also predicted less efficient neural processing during preschool. This study provides initial evidence that performance monitoring may be a candidate process by which early parenting interacts with fearfulness to predict risk for anxiety problems.
Brooker, Rebecca J; Buss, Kristin A
2014-07-01
Temperamentally fearful children are at increased risk for the development of anxiety problems relative to less-fearful children. This risk is even greater when early environments include high levels of harsh parenting behaviors. However, the mechanisms by which harsh parenting may impact fearful children's risk for anxiety problems are largely unknown. Recent neuroscience work has suggested that punishment is associated with exaggerated error-related negativity (ERN), an event-related potential linked to performance monitoring, even after the threat of punishment is removed. In the current study, we examined the possibility that harsh parenting interacts with fearfulness, impacting anxiety risk via neural processes of performance monitoring. We found that greater fearfulness and harsher parenting at 2 years of age predicted greater fearfulness and greater ERN amplitudes at age 4. Supporting the role of cognitive processes in this association, greater fearfulness and harsher parenting also predicted less efficient neural processing during preschool. This study provides initial evidence that performance monitoring may be a candidate process by which early parenting interacts with fearfulness to predict risk for anxiety problems. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.
Albert, Paul S
2018-02-21
Prediction of preterm birth as well as characterizing the etiological factors affecting both the recurrence and incidence of preterm birth (defined as gestational age at birth ≤ 37 wk) are important problems in obstetrics. The National Institute of Child Health and Human Development (NICHD) consecutive pregnancy study recently examined this question by collecting data on a cohort of women with at least 2 pregnancies over a fixed time interval. Unfortunately, measurement error due to the dating of conception may induce sizable error in computing gestational age at birth. This article proposes a flexible approach that accounts for measurement error in gestational age when making inference. The proposed approach is a hidden Markov model that accounts for measurement error in gestational age by exploiting the relationship between gestational age at birth and birth weight. We initially model the measurement error as being normally distributed, followed by a mixture of normals that has been proposed on the basis of biological considerations. We examine the asymptotic bias of the proposed approach when measurement error is ignored and also compare the efficiency of this approach to a simpler hidden Markov model formulation where only gestational age and not birth weight is incorporated. The proposed model is compared with alternative models for estimating important covariate effects on the risk of subsequent preterm birth using a unique set of data from the NICHD consecutive pregnancy study. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.
Sensitivity, Error and Uncertainty Quantification: Interfacing Models at Different Scales
International Nuclear Information System (INIS)
Krstic, Predrag S.
2014-01-01
Discussion on accuracy of AMO data to be used in the plasma modeling codes for astrophysics and nuclear fusion applications, including plasma-material interfaces (PMI), involves many orders of magnitude of energy, spatial and temporal scales. Thus, energies run from tens of K to hundreds of millions of K, temporal and spatial scales go from fs to years and from nm’s to m’s and more, respectively. The key challenge for the theory and simulation in this field is the consistent integration of all processes and scales, i.e. an “integrated AMO science” (IAMO). The principal goal of the IAMO science is to enable accurate studies of interactions of electrons, atoms, molecules, photons, in many-body environment, including complex collision physics of plasma-material interfaces, leading to the best decisions and predictions. However, the accuracy requirement for a particular data strongly depends on the sensitivity of the respective plasma modeling applications to these data, which stresses a need for immediate sensitivity analysis feedback of the plasma modeling and material design communities. Thus, the data provision to the plasma modeling community is a “two-way road” as long as the accuracy of the data is considered, requiring close interactions of the AMO and plasma modeling communities.
Students’ errors in solving combinatorics problems observed from the characteristics of RME modeling
Meika, I.; Suryadi, D.; Darhim
2018-01-01
This article was written based on the learning evaluation results of students’ errors in solving combinatorics problems observed from the characteristics of Realistic Mathematics Education (RME); that is modeling. Descriptive method was employed by involving 55 students from two international-based pilot state senior high schools in Banten. The findings of the study suggested that the students still committed errors in simplifying the problem as much 46%; errors in making mathematical model (horizontal mathematization) as much 60%; errors in finishing mathematical model (vertical mathematization) as much 65%; and errors in interpretation as well as validation as much 66%.
Application of grey theory in identification model of human error criticality
International Nuclear Information System (INIS)
Li Pengcheng; Zhang Li; Wang Yiqun
2009-01-01
The identification model for human error criticality is constructed on the basis of the principle of the Failure Mode and Effects Analysis. It consists of three decision-making factors, namely the scale of probability of occurrence of human error mode, the scale of probability of error-effect and the scale of error-consequence criticality. It is difficult to consider the weight of every factor, this paper employs the grey theory to identify the human error criticality, which provides a new viewpoint for the identification of the priority of error criticality and origin, which overcome the problem for the actual weight distribution. (authors)
Modeling of alpha-particle-induced soft error rate in DRAM
International Nuclear Information System (INIS)
Shin, H.
1999-01-01
Alpha-particle-induced soft error in 256M DRAM was numerically investigated. A unified model for alpha-particle-induced charge collection and a soft-error-rate simulator (SERS) was developed. The author investigated the soft error rate of 256M DRAM and identified the bit-bar mode as one of dominant modes for soft error. In addition, for the first time, it was found that trench-oxide depth has a significant influence on soft error rate, and it should be determined by the tradeoff between soft error rate and cell-to-cell isolation characteristics
Dreano, Denis
2017-04-05
Specification and tuning of errors from dynamical models are important issues in data assimilation. In this work, we propose an iterative expectation-maximisation (EM) algorithm to estimate the model error covariances using classical extended and ensemble versions of the Kalman smoother. We show that, for additive model errors, the estimate of the error covariance converges. We also investigate other forms of model error, such as parametric or multiplicative errors. We show that additive Gaussian model error is able to compensate for non additive sources of error in the algorithms we propose. We also demonstrate the limitations of the extended version of the algorithm and recommend the use of the more robust and flexible ensemble version. This article is a proof of concept of the methodology with the Lorenz-63 attractor. We developed an open-source Python library to enable future users to apply the algorithm to their own nonlinear dynamical models.
Line bisection error predicts the presence and severity of neglect dyslexia in paragraph reading.
Reinhart, Stefan; Wagner, Prisca; Schulz, Anna; Keller, Ingo; Kerkhoff, Georg
2013-01-01
Cancellation tasks and line bisection tasks are commonly used to diagnose spatial neglect after right hemisphere lesions. In such tasks, neglect patients often show leftsided omissions of targets in cancellation tests as well as a pathological rightward deviation in horizontal line bisection. However, double dissociations have also been reported and the relation between performance in both tasks is not clear. Another impairment frequently associated with the neglect syndrome are omissions or misread initial letters of single words, a phenomenon termed neglect dyslexia (ND). Omissions of whole words on the contralesional side of the page are generally considered as egocentric or space-based errors, whereas misreadings of the left part of a word in ND can be viewed as a type of stimulus-centered or word-based, perceptual error. As words, sentences and horizontal lines have a similar spatial layout in the sense that they all are horizontally aligned, long stimuli with a canonical left-right orientation (with a defined beginning on the left and an end on the right side), we hypothesized a significant association between the horizontal line bisection error (LBE) in neglect and the extent (number) of neglected or substituted letters within single words in ND (neglect dyslexia extension, NDE). To this purpose, we computed Center-of-Cancellation (CoC) scores in a cancellation task as well as Center-of-Reading (CoR) scores in an experimental paragraph reading test. We found that the CoR was a better indicator for egocentric word omissions than the CoC in a group of 17 patients with left visuospatial neglect. Furthermore, the LBE predicted the severity of ND, indicated by highly significant correlations between the LBE and the extent of the neglected letter string within single words (NDE; r=0.73, p<0.001) as well as between the LBE and the frequency of ND errors (r=0.61; p=0.009). In contrast, we found no significant correlation between the CoC and the severity of ND. These
Assessment of errors and uncertainty patterns in GIA modeling
DEFF Research Database (Denmark)
Barletta, Valentina Roberta; Spada, G.
2012-01-01
During the last decade many efforts have been devoted to the assessment of global sea level rise and to the determination of the mass balance of continental ice sheets. In this context, the important role of glacial-isostatic adjustment (GIA) has been clearly recognized. Yet, in many cases only one......, such as time-evolving shorelines and paleo coastlines. In this study we quantify these uncertainties and their propagation in GIA response using a Monte Carlo approach to obtain spatio-temporal patterns of GIA errors. A direct application is the error estimates in ice mass balance in Antarctica and Greenland...... due to GIA. GIA errors are also important in the far field of previously glaciated areas and in the time evolution of global indicators. In this regard we also account for other possible errors sources which can impact global indicators like the sea level history related to GIA....
Chilcott, J; Tappenden, P; Rawdin, A; Johnson, M; Kaltenthaler, E; Paisley, S; Papaioannou, D; Shippam, A
2010-05-01
Health policy decisions must be relevant, evidence-based and transparent. Decision-analytic modelling supports this process but its role is reliant on its credibility. Errors in mathematical decision models or simulation exercises are unavoidable but little attention has been paid to processes in model development. Numerous error avoidance/identification strategies could be adopted but it is difficult to evaluate the merits of strategies for improving the credibility of models without first developing an understanding of error types and causes. The study aims to describe the current comprehension of errors in the HTA modelling community and generate a taxonomy of model errors. Four primary objectives are to: (1) describe the current understanding of errors in HTA modelling; (2) understand current processes applied by the technology assessment community for avoiding errors in development, debugging and critically appraising models for errors; (3) use HTA modellers' perceptions of model errors with the wider non-HTA literature to develop a taxonomy of model errors; and (4) explore potential methods and procedures to reduce the occurrence of errors in models. It also describes the model development process as perceived by practitioners working within the HTA community. A methodological review was undertaken using an iterative search methodology. Exploratory searches informed the scope of interviews; later searches focused on issues arising from the interviews. Searches were undertaken in February 2008 and January 2009. In-depth qualitative interviews were performed with 12 HTA modellers from academic and commercial modelling sectors. All qualitative data were analysed using the Framework approach. Descriptive and explanatory accounts were used to interrogate the data within and across themes and subthemes: organisation, roles and communication; the model development process; definition of error; types of model error; strategies for avoiding errors; strategies for
Holladay, J T; Moran, J R; Kezirian, G M
2001-01-01
To demonstrate analytical methods for evaluating the results of keratorefractive surgical procedures and emphasize the importance of intraocular astigmatism. University of Texas Medical School, Houston, Texas, USA. A standard data set, provided by an editor of this journal, comprising the preoperative and postoperative keratometric and refractive measurements of 100 eyes that had keratorefractive surgery was evaluated by 2 methods, vector and spheroequivalent (SEQ) analysis. The individual and aggregate surgically induced refractive changes (SIRCs) and prediction errors were determined from the refractive and keratometric measurements using both methods and then compared. The refraction vertex distance, keratometric index of refraction, and corneal asphericity were used to make the results calculated from refractive data directly comparable to those derived from keratometric data. Doubled-angle and equivalency plots as well as frequency and cumulative histograms were used to display the data. Standard descriptive statistics were used to determine the mean and standard deviation of the aggregate induced astigmatism after converting the polar values (cylinder and axis) to Cartesian (x and y) values. The preoperative SEQ refractive errors were undercorrected by at least 0.25 diopter (D) in most cases (78%). Six percent were corrected within +/- 0.24 D, and 16% were overcorrected by at least 0.25 D SEQ. The mean SEQ was -6.68 D +/- 2.49 (SD) before and -0.61 +/- 0.82 D after surgery, reflecting a SIRC SEQ of -6.07 +/- 2.40 D. The defocus equivalent (DEQ) was 7.41 +/- 2.53 D before and 0.96 +/- 0.74 D after surgery; for a nominal 3.0 mm pupil, this corresponded to an estimated improvement in uncorrected visual acuity (UCVA) from worse than 20/200 to better than 20/25, respectively. The predictability of the treatment decreased as the attempted refractive correction increased. The average magnitude of the refractive astigmatism was 1.46 +/- 0.61 D before and 0.40 +/- 0
Bayesian modeling of measurement error in predictor variables using item response theory
Fox, Gerardus J.A.; Glas, Cornelis A.W.
2000-01-01
This paper focuses on handling measurement error in predictor variables using item response theory (IRT). Measurement error is of great important in assessment of theoretical constructs, such as intelligence or the school climate. Measurement error is modeled by treating the predictors as unobserved
Error sources in atomic force microscopy for dimensional measurements: Taxonomy and modeling
DEFF Research Database (Denmark)
Marinello, F.; Voltan, A.; Savio, E.
2010-01-01
: scanning system, tip-surface interaction, environment, and data processing. The discussed errors include scaling effects, squareness errors, hysteresis, creep, tip convolution, and thermal drift. A mathematical model of the measurement system is eventually described, as a reference basis for errors...
The effect of error models in the multiscale inversion of binary permeability fields
Ray, J.; Bloemenwaanders, B. V.; McKenna, S. A.; Marzouk, Y. M.
2010-12-01
We present results from a recently developed multiscale inversion technique for binary media, with emphasis on the effect of subgrid model errors on the inversion. Binary media are a useful fine-scale representation of heterogeneous porous media. Averaged properties of the binary field representations can be used to characterize flow through the porous medium at the macroscale. Both direct measurements of the averaged properties and upscaling are complicated and may not provide accurate results. However, it may be possible to infer upscaled properties of the binary medium from indirect measurements at the coarse scale. Multiscale inversion, performed with a subgrid model to connect disparate scales together, can also yield information on the fine-scale properties. We model the binary medium using truncated Gaussian fields, and develop a subgrid model for the upscaled permeability based on excursion sets of those fields. The subgrid model requires an estimate of the proportion of inclusions at the block scale as well as some geometrical parameters of the inclusions as inputs, and predicts the effective permeability. The inclusion proportion is assumed to be spatially varying, modeled using Gaussian processes and represented using a truncated Karhunen-Louve (KL) expansion. This expansion is used, along with the subgrid model, to pose as a Bayesian inverse problem for the KL weights and the geometrical parameters of the inclusions. The model error is represented in two different ways: (1) as a homoscedastic error and (2) as a heteroscedastic error, dependent on inclusion proportionality and geometry. The error models impact the form of the likelihood function in the expression for the posterior density of the objects of inference. The problem is solved using an adaptive Markov Chain Monte Carlo method, and joint posterior distributions are developed for the KL weights and inclusion geometry. Effective permeabilities and tracer breakthrough times at a few
Modeling the North American vertical datum of 1988 errors in the conterminous United States
Li, X.
2018-02-01
A large systematic difference (ranging from -20 cm to +130 cm) was found between NAVD 88 (North AmericanVertical Datum of 1988) and the pure gravimetric geoid models. This difference not only makes it very difficult to augment the local geoid model by directly using the vast NAVD 88 network with state-of-the-art technologies recently developed in geodesy, but also limits the ability of researchers to effectively demonstrate the geoid model improvements on the NAVD 88 network. Here, both conventional regression analyses based on various predefined basis functions such as polynomials, B-splines, and Legendre functions and the Latent Variable Analysis (LVA) such as the Factor Analysis (FA) are used to analyze the systematic difference. Besides giving a mathematical model, the regression results do not reveal a great deal about the physical reasons that caused the large differences in NAVD 88, which may be of interest to various researchers. Furthermore, there is still a significant amount of no-Gaussian signals left in the residuals of the conventional regression models. On the other side, the FA method not only provides a better not of the data, but also offers possible explanations of the error sources. Without requiring extra hypothesis tests on the model coefficients, the results from FA are more efficient in terms of capturing the systematic difference. Furthermore, without using a covariance model, a novel interpolating method based on the relationship between the loading matrix and the factor scores is developed for predictive purposes. The prediction error analysis shows that about 3-7 cm precision is expected in NAVD 88 after removing the systematic difference.
Modeling the North American vertical datum of 1988 errors in the conterminous United States
Directory of Open Access Journals (Sweden)
Li X.
2018-01-01
Full Text Available A large systematic difference (ranging from −20 cm to +130 cm was found between NAVD 88 (North AmericanVertical Datum of 1988 and the pure gravimetric geoid models. This difference not only makes it very difficult to augment the local geoid model by directly using the vast NAVD 88 network with state-of-the-art technologies recently developed in geodesy, but also limits the ability of researchers to effectively demonstrate the geoid model improvements on the NAVD 88 network. Here, both conventional regression analyses based on various predefined basis functions such as polynomials, B-splines, and Legendre functions and the Latent Variable Analysis (LVA such as the Factor Analysis (FA are used to analyze the systematic difference. Besides giving a mathematical model, the regression results do not reveal a great deal about the physical reasons that caused the large differences in NAVD 88, which may be of interest to various researchers. Furthermore, there is still a significant amount of no-Gaussian signals left in the residuals of the conventional regression models. On the other side, the FA method not only provides a better not of the data, but also offers possible explanations of the error sources. Without requiring extra hypothesis tests on the model coefficients, the results from FA are more efficient in terms of capturing the systematic difference. Furthermore, without using a covariance model, a novel interpolating method based on the relationship between the loading matrix and the factor scores is developed for predictive purposes. The prediction error analysis shows that about 3-7 cm precision is expected in NAVD 88 after removing the systematic difference.
What Determines Prediction Errors In Benefits Transfer Models?
Colombo, Sergio; Hanley, Nick
2007-01-01
The aim of BT techniques is to provide decision makers with a monetary valuation of environmental goods and service in a cost-effective and timely manner, since original valuation studies are both expensive and time-consuming. Demands for environmental valuation estimates are rising in the policy community in both Europe and the US. In the UK, widespread use of benefits transfer has already occurred within policy making and regulatory bodies, for instance in the setting of water quality targe...
Ruiz, María Herrojo; Strübing, Felix; Jabusch, Hans-Christian; Altenmüller, Eckart
2011-04-15
Skilled performance requires the ability to monitor ongoing behavior, detect errors in advance and modify the performance accordingly. The acquisition of fast predictive mechanisms might be possible due to the extensive training characterizing expertise performance. Recent EEG studies on piano performance reported a negative event-related potential (ERP) triggered in the ACC 70 ms before performance errors (pitch errors due to incorrect keypress). This ERP component, termed pre-error related negativity (pre-ERN), was assumed to reflect processes of error detection in advance. However, some questions remained to be addressed: (i) Does the electrophysiological marker prior to errors reflect an error signal itself or is it related instead to the implementation of control mechanisms? (ii) Does the posterior frontomedial cortex (pFMC, including ACC) interact with other brain regions to implement control adjustments following motor prediction of an upcoming error? (iii) Can we gain insight into the electrophysiological correlates of error prediction and control by assessing the local neuronal synchronization and phase interaction among neuronal populations? (iv) Finally, are error detection and control mechanisms defective in pianists with musician's dystonia (MD), a focal task-specific dystonia resulting from dysfunction of the basal ganglia-thalamic-frontal circuits? Consequently, we investigated the EEG oscillatory and phase synchronization correlates of error detection and control during piano performances in healthy pianists and in a group of pianists with MD. In healthy pianists, the main outcomes were increased pre-error theta and beta band oscillations over the pFMC and 13-15 Hz phase synchronization, between the pFMC and the right lateral prefrontal cortex, which predicted corrective mechanisms. In MD patients, the pattern of phase synchronization appeared in a different frequency band (6-8 Hz) and correlated with the severity of the disorder. The present
Model-observer similarity, error modeling and social learning in rhesus macaques.
Directory of Open Access Journals (Sweden)
Elisabetta Monfardini
Full Text Available Monkeys readily learn to discriminate between rewarded and unrewarded items or actions by observing their conspecifics. However, they do not systematically learn from humans. Understanding what makes human-to-monkey transmission of knowledge work or fail could help identify mediators and moderators of social learning that operate regardless of language or culture, and transcend inter-species differences. Do monkeys fail to learn when human models show a behavior too dissimilar from the animals' own, or when they show a faultless performance devoid of error? To address this question, six rhesus macaques trained to find which object within a pair concealed a food reward were successively tested with three models: a familiar conspecific, a 'stimulus-enhancing' human actively drawing the animal's attention to one object of the pair without actually performing the task, and a 'monkey-like' human performing the task in the same way as the monkey model did. Reward was manipulated to ensure that all models showed equal proportions of errors and successes. The 'monkey-like' human model improved the animals' subsequent object discrimination learning as much as a conspecific did, whereas the 'stimulus-enhancing' human model tended on the contrary to retard learning. Modeling errors rather than successes optimized learning from the monkey and 'monkey-like' models, while exacerbating the adverse effect of the 'stimulus-enhancing' model. These findings identify error modeling as a moderator of social learning in monkeys that amplifies the models' influence, whether beneficial or detrimental. By contrast, model-observer similarity in behavior emerged as a mediator of social learning, that is, a prerequisite for a model to work in the first place. The latter finding suggests that, as preverbal infants, macaques need to perceive the model as 'like-me' and that, once this condition is fulfilled, any agent can become an effective model.
Using SMAP to identify structural errors in hydrologic models
Crow, W. T.; Reichle, R. H.; Chen, F.; Xia, Y.; Liu, Q.
2017-12-01
Despite decades of effort, and the development of progressively more complex models, there continues to be underlying uncertainty regarding the representation of basic water and energy balance processes in land surface models. Soil moisture occupies a central conceptual position between atmosphere forcing of the land surface and resulting surface water fluxes. As such, direct observations of soil moisture are potentially of great value for identifying and correcting fundamental structural problems affecting these models. However, to date, this potential has not yet been realized using satellite-based retrieval products. Using soil moisture data sets produced by the NASA Soil Moisture Active/Passive mission, this presentation will explore the use of the remotely-sensed soil moisture data products as a constraint to reject certain types of surface runoff parameterizations within a land surface model. Results will demonstrate that the precision of the SMAP Level 4 Surface and Root-Zone soil moisture product allows for the robust sampling of correlation statistics describing the true strength of the relationship between pre-storm soil moisture and subsequent storm-scale runoff efficiency (i.e., total storm flow divided by total rainfall both in units of depth). For a set of 16 basins located in the South-Central United States, we will use these sampled correlations to demonstrate that so-called "infiltration-excess" runoff parameterizations under predict the importance of pre-storm soil moisture for determining storm-scale runoff efficiency. To conclude, we will discuss prospects for leveraging this insight to improve short-term hydrologic forecasting and additional avenues for SMAP soil moisture products to provide process-level insight for hydrologic modelers.
DEFF Research Database (Denmark)
Odgaard, Peter Fogh; Stoustrup, Jakob; Mataji, B.
2007-01-01
of the prediction error. These proposed dynamical uncertainty models result in an upper and lower bound on the predicted performance of the plant. The dynamical uncertainty models are used to estimate the uncertainty of the predicted performance of a coal-fired power plant. The proposed scheme, which uses dynamical...
Modelo de error en imágenes comprimidas con wavelets Error Model in Wavelet-compressed Images
Directory of Open Access Journals (Sweden)
Gloria Puetamán G.
2007-06-01
Full Text Available En este artículo se presenta la compresión de imágenes a través de la comparación entre el modelo Wavelet y el modelo Fourier, utilizando la minimización de la función de error. El problema que se estudia es específico, consiste en determinar una base {ei} que minimice la función de error entre la imagen original y la recuperada después de la compresión. Es de resaltar que existen muchas aplicaciones, por ejemplo, en medicina o astronomía, en donde no es aceptable ningún deterioro de la imagen porque toda la información contenida, incluso la que se estima como ruido, se considera imprescindible.In this paper we study image compression as a way to compare Wavelet and Fourier models, by minimizing the error function. The particular problem we consider is to determine basis {ei} minimizing the error function between the original image and the recovered one after compression. It is to be noted or remarked that there are many applications in such diverse ﬁelds as for example medicine and astronomy, where no image deteriorating is acceptable since even noise is considered essential.
Elashiry Ahmed A.; Youssef Mohamed A.; Abdel Hamid Mohamed A.
2015-01-01
There are three well-established detecting methods for cycle slip error, which are: Doppler measurement method, Phase-Code differencing method, and Phase-Phase Differencing Method. The first method depends on the comparison between observables and the fact that Doppler measurements are immune to cycle slip error. This method is considered as the most precise method for cycle slip detecting, because it succeed in detecting and predicting the smallest cycle slip size (1 cycle) in case the local...
Khoshgoftaar, Taghi M; Van Hulse, Jason; Napolitano, Amri
2010-05-01
Neural network algorithms such as multilayer perceptrons (MLPs) and radial basis function networks (RBFNets) have been used to construct learners which exhibit strong predictive performance. Two data related issues that can have a detrimental impact on supervised learning initiatives are class imbalance and labeling errors (or class noise). Imbalanced data can make it more difficult for the neural network learning algorithms to distinguish between examples of the various classes, and class noise can lead to the formulation of incorrect hypotheses. Both class imbalance and labeling errors are pervasive problems encountered in a wide variety of application domains. Many studies have been performed to investigate these problems in isolation, but few have focused on their combined effects. This study presents a comprehensive empirical investigation using neural network algorithms to learn from imbalanced data with labeling errors. In particular, the first component of our study investigates the impact of class noise and class imbalance on two common neural network learning algorithms, while the second component considers the ability of data sampling (which is commonly used to address the issue of class imbalance) to improve their performances. Our results, for which over two million models were trained and evaluated, show that conclusions drawn using the more commonly studied C4.5 classifier may not apply when using neural networks.
New Approaches for Channel Prediction Based on Sinusoidal Modeling
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Ekman Torbjörn
2007-01-01
Full Text Available Long-range channel prediction is considered to be one of the most important enabling technologies to future wireless communication systems. The prediction of Rayleigh fading channels is studied in the frame of sinusoidal modeling in this paper. A stochastic sinusoidal model to represent a Rayleigh fading channel is proposed. Three different predictors based on the statistical sinusoidal model are proposed. These methods outperform the standard linear predictor (LP in Monte Carlo simulations, but underperform with real measurement data, probably due to nonstationary model parameters. To mitigate these modeling errors, a joint moving average and sinusoidal (JMAS prediction model and the associated joint least-squares (LS predictor are proposed. It combines the sinusoidal model with an LP to handle unmodeled dynamics in the signal. The joint LS predictor outperforms all the other sinusoidal LMMSE predictors in suburban environments, but still performs slightly worse than the standard LP in urban environments.
Assessment of errors and uncertainty patterns in GIA modeling
DEFF Research Database (Denmark)
Barletta, Valentina Roberta; Spada, G.
During the last decade many efforts have been devoted to the assessment of global sea level rise and to the determination of the mass balance of continental ice sheets. In this context, the important role of glacial-isostatic adjustment (GIA) has been clearly recognized. Yet, in many cases only one......, such as time-evolving shorelines and paleo-coastlines. In this study we quantify these uncertainties and their propagation in GIA response using a Monte Carlo approach to obtain spatio-temporal patterns of GIA errors. A direct application is the error estimates in ice mass balance in Antarctica and Greenland...
Quantifying Confidence in Model Predictions for Hypersonic Aircraft Structures
2015-03-01
Falsification Power of Posterior p-Value Approach for Various Sample Sizes (Light Blue = 10, Dark Blue = 20, Green = 50, Red = 100...aerothermal model predictions and Glass and Hunt data........... 36 Table 4.12. Correlations between model error parameters in simultaneous posterior samples ...M1 using Latin Hypercube sampling . For each of those samples , a Markov Chain Monte Carlo ( MCMC ) algorithm called slice sampling is employed using
Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese
Sumit Goyal; Gyanendra Kumar Goyal
2012-01-01
This paper presents the capability of Time–delay artificial neural network models for predicting shelf life of processed cheese. Datasets were divided into two subsets (30 for training and 6 for validation). Models with single and multi layers were developed and compared with each other. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash -
Sutcliffo Coefficient were used as performance evaluators, Time- delay model predicted the shelf life of...
Efficient thermal error prediction in a machine tool using finite element analysis
International Nuclear Information System (INIS)
Mian, Naeem S; Fletcher, Simon; Longstaff, Andrew P; Myers, Alan
2011-01-01
Thermally induced errors have a major significance on the positional accuracy of a machine tool. Heat generated during the machining process produces thermal gradients that flow through the machine structure causing linear and nonlinear thermal expansions and distortions of associated complex discrete structures, producing deformations that adversely affect structural stability. The heat passes through structural linkages and mechanical joints where interfacial parameters such as the roughness and form of the contacting surfaces affect the thermal resistance and thus the heat transfer coefficients. This paper presents a novel offline technique using finite element analysis (FEA) to simulate the effects of the major internal heat sources such as bearings, motors and belt drives of a small vertical milling machine (VMC) and the effects of ambient temperature pockets that build up during the machine operation. Simplified models of the machine have been created offline using FEA software and evaluated experimental results applied for offline thermal behaviour simulation of the full machine structure. The FEA simulated results are in close agreement with the experimental results ranging from 65% to 90% for a variety of testing regimes and revealed a maximum error range of 70 µm reduced to less than 10 µm
Space-time data fusion under error in computer model output: an application to modeling air quality.
Berrocal, Veronica J; Gelfand, Alan E; Holland, David M
2012-09-01
We provide methods that can be used to obtain more accurate environmental exposure assessment. In particular, we propose two modeling approaches to combine monitoring data at point level with numerical model output at grid cell level, yielding improved prediction of ambient exposure at point level. Extending our earlier downscaler model (Berrocal, V. J., Gelfand, A. E., and Holland, D. M. (2010b). A spatio-temporal downscaler for outputs from numerical models. Journal of Agricultural, Biological and Environmental Statistics 15, 176-197), these new models are intended to address two potential concerns with the model output. One recognizes that there may be useful information in the outputs for grid cells that are neighbors of the one in which the location lies. The second acknowledges potential spatial misalignment between a station and its putatively associated grid cell. The first model is a Gaussian Markov random field smoothed downscaler that relates monitoring station data and computer model output via the introduction of a latent Gaussian Markov random field linked to both sources of data. The second model is a smoothed downscaler with spatially varying random weights defined through a latent Gaussian process and an exponential kernel function, that yields, at each site, a new variable on which the monitoring station data is regressed with a spatial linear model. We applied both methods to daily ozone concentration data for the Eastern US during the summer months of June, July and August 2001, obtaining, respectively, a 5% and a 15% predictive gain in overall predictive mean square error over our earlier downscaler model (Berrocal et al., 2010b). Perhaps more importantly, the predictive gain is greater at hold-out sites that are far from monitoring sites. © 2011, The International Biometric Society.
Directory of Open Access Journals (Sweden)
Philip J Kellman
Full Text Available Latent fingerprint examination is a complex task that, despite advances in image processing, still fundamentally depends on the visual judgments of highly trained human examiners. Fingerprints collected from crime scenes typically contain less information than fingerprints collected under controlled conditions. Specifically, they are often noisy and distorted and may contain only a portion of the total fingerprint area. Expertise in fingerprint comparison, like other forms of perceptual expertise, such as face recognition or aircraft identification, depends on perceptual learning processes that lead to the discovery of features and relations that matter in comparing prints. Relatively little is known about the perceptual processes involved in making comparisons, and even less is known about what characteristics of fingerprint pairs make particular comparisons easy or difficult. We measured expert examiner performance and judgments of difficulty and confidence on a new fingerprint database. We developed a number of quantitative measures of image characteristics and used multiple regression techniques to discover objective predictors of error as well as perceived difficulty and confidence. A number of useful predictors emerged, and these included variables related to image quality metrics, such as intensity and contrast information, as well as measures of information quantity, such as the total fingerprint area. Also included were configural features that fingerprint experts have noted, such as the presence and clarity of global features and fingerprint ridges. Within the constraints of the overall low error rates of experts, a regression model incorporating the derived predictors demonstrated reasonable success in predicting objective difficulty for print pairs, as shown both in goodness of fit measures to the original data set and in a cross validation test. The results indicate the plausibility of using objective image metrics to predict expert
Model-Based Angular Scan Error Correction of an Electrothermally-Actuated MEMS Mirror.
Zhang, Hao; Xu, Dacheng; Zhang, Xiaoyang; Chen, Qiao; Xie, Huikai; Li, Suiqiong
2015-12-10
In this paper, the actuation behavior of a two-axis electrothermal MEMS (Microelectromechanical Systems) mirror typically used in miniature optical scanning probes and optical switches is investigated. The MEMS mirror consists of four thermal bimorph actuators symmetrically located at the four sides of a central mirror plate. Experiments show that an actuation characteristics difference of as much as 4.0% exists among the four actuators due to process variations, which leads to an average angular scan error of 0.03°. A mathematical model between the actuator input voltage and the mirror-plate position has been developed to predict the actuation behavior of the mirror. It is a four-input, four-output model that takes into account the thermal-mechanical coupling and the differences among the four actuators; the vertical positions of the ends of the four actuators are also monitored. Based on this model, an open-loop control method is established to achieve accurate angular scanning. This model-based open loop control has been experimentally verified and is useful for the accurate control of the mirror. With this control method, the precise actuation of the mirror solely depends on the model prediction and does not need the real-time mirror position monitoring and feedback, greatly simplifying the MEMS control system.
Error assessment of digital elevation models obtained by interpolation
Directory of Open Access Journals (Sweden)
Jean François Mas
2009-10-01
Full Text Available Son pocos los estudios enfocados en la evaluación de los errores inherentes a los modelos digitales de elevación (MDE. Por esta razón se evaluaron los errores de los MDE obtenidos por diferentes metodos de interpolación (ARC/INFO, IDRISI, ILWIS y NEW-MIEL y con diferentes resoluciones, con la finalidad de obtener una representación del relieve más precisa. Esta evaluación de los métodos de interpolación es crucial, si se tiene en cuenta que los MDE son la forma más efectiva de representación de la superficie terrestre para el análisis del terreno y que son ampliamente utilizados en ciencias ambientales. Los resultados obtenidos muestran que la resolución, el método de interpolación y los insumos (curvas de nivel solas o con datos de escurrimientos y puntos acotados influyen de manera importante en la magnitud de la cantidad de los errores generados en el MDE. En este estudio, que se llevó a cabo con base en curvas de nivel cada 50 m en una zona montañosa, la resolución más idónea fue de 30 m. El MDE con el menor error (Error Medio Cuadrático −EMC− de 7.3 m fue obtenido con ARC/INFO. Sin embargo, programas sin costo como NEWMIEL o ILWIS permitieron la obtención de resultados con un EMC de 10 m.
Towards New Empirical Versions of Financial and Accounting Models Corrected for Measurement Errors
Francois-Éric Racicot; Raymond Théoret; Alain Coen
2006-01-01
In this paper, we propose a new empirical version of the Fama and French Model based on the Hausman (1978) specification test and aimed at discarding measurement errors in the variables. The proposed empirical framework is general enough to be used for correcting other financial and accounting models of measurement errors. Removing measurement errors is important at many levels as information disclosure, corporate governance and protection of investors.
Directory of Open Access Journals (Sweden)
Rômulo Oliveira
2018-02-01
Full Text Available This study aims to assess the characteristics and uncertainty of Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM (IMERG Level 3 rainfall estimates and to improve those estimates using an error model over the central Amazon region. The S-band Amazon Protection National System (SIPAM radar is used as reference and the Precipitation Uncertainties for Satellite Hydrology (PUSH framework is adopted to characterize uncertainties associated with the satellite precipitation product. PUSH is calibrated and validated for the study region and takes into account factors like seasonality and surface type (i.e., land and river. Results demonstrated that the PUSH model is suitable for characterizing errors in the IMERG algorithm when compared with S-band SIPAM radar estimates. PUSH could efficiently predict the satellite rainfall error distribution in terms of spatial and intensity distribution. However, an underestimation (overestimation of light satellite rain rates was observed during the dry (wet period, mainly over rivers. Although the estimated error showed a lower standard deviation than the observed error, the correlation between satellite and radar rainfall was high and the systematic error was well captured along the Negro, Solimões, and Amazon rivers, especially during the wet season.
Mulej Bratec, Satja; Xie, Xiyao; Wang, Yijun; Schilbach, Leonhard; Zimmer, Claus; Wohlschläger, Afra M; Riedl, Valentin; Sorg, Christian
2017-02-15
Cognitive emotion regulation (CER) is a critical human ability to face aversive emotional stimuli in a flexible way, via recruitment of specific prefrontal brain circuits. Animal research reveals a central role of ventral striatum in emotional behavior, for both aversive conditioning, with striatum signaling aversive prediction errors (aPE), and for integrating competing influences of distinct striatal inputs from regions such as the prefrontal cortex (PFC), amygdala, hippocampus and ventral tegmental area (VTA). Translating these ventral striatal findings from animal research to human CER, we hypothesized that successful CER would affect the balance of competing influences of striatal afferents on striatal aPE signals, in a way favoring PFC as opposed to 'subcortical' (i.e., non-isocortical) striatal inputs. Using aversive Pavlovian conditioning with and without CER during fMRI, we found that during CER, superior regulators indeed reduced the modulatory impact of 'subcortical' striatal afferents (hippocampus, amygdala and VTA) on ventral striatal aPE signals, while keeping the PFC impact intact. In contrast, inferior regulators showed an opposite pattern. Our results demonstrate that ventral striatal aPE signals and associated competing modulatory inputs are critical mechanisms underlying successful cognitive regulation of aversive emotions in humans. Copyright © 2017 Elsevier Inc. All rights reserved.
Prediction errors to emotional expressions: the roles of the amygdala in social referencing.
Meffert, Harma; Brislin, Sarah J; White, Stuart F; Blair, James R
2015-04-01
Social referencing paradigms in humans and observational learning paradigms in animals suggest that emotional expressions are important for communicating valence. It has been proposed that these expressions initiate stimulus-reinforcement learning. Relatively little is known about the role of emotional expressions in reinforcement learning, particularly in the context of social referencing. In this study, we examined object valence learning in the context of a social referencing paradigm. Participants viewed objects and faces that turned toward the objects and displayed a fearful, happy or neutral reaction to them, while judging the gender of these faces. Notably, amygdala activation was larger when the expressions following an object were less expected. Moreover, when asked, participants were both more likely to want to approach, and showed stronger amygdala responses to, objects associated with happy relative to objects associated with fearful expressions. This suggests that the amygdala plays two roles in social referencing: (i) initiating learning regarding the valence of an object as a function of prediction errors to expressions displayed toward this object and (ii) orchestrating an emotional response to the object when value judgments are being made regarding this object. Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.
Allam, Ahmed M; Abbas, Hazem M
2010-12-01
Neural cryptography deals with the problem of "key exchange" between two neural networks using the mutual learning concept. The two networks exchange their outputs (in bits) and the key between the two communicating parties is eventually represented in the final learned weights, when the two networks are said to be synchronized. Security of neural synchronization is put at risk if an attacker is capable of synchronizing with any of the two parties during the training process. Therefore, diminishing the probability of such a threat improves the reliability of exchanging the output bits through a public channel. The synchronization with feedback algorithm is one of the existing algorithms that enhances the security of neural cryptography. This paper proposes three new algorithms to enhance the mutual learning process. They mainly depend on disrupting the attacker confidence in the exchanged outputs and input patterns during training. The first algorithm is called "Do not Trust My Partner" (DTMP), which relies on one party sending erroneous output bits, with the other party being capable of predicting and correcting this error. The second algorithm is called "Synchronization with Common Secret Feedback" (SCSFB), where inputs are kept partially secret and the attacker has to train its network on input patterns that are different from the training sets used by the communicating parties. The third algorithm is a hybrid technique combining the features of the DTMP and SCSFB. The proposed approaches are shown to outperform the synchronization with feedback algorithm in the time needed for the parties to synchronize.
Comparison of the prediction error in cataract surgery with Lenstar and conventional ultrasound
Directory of Open Access Journals (Sweden)
Hou-Cheng Liang
2013-12-01
Full Text Available AIM: To compare the prediction errors(PEin cataract surgery with Lenstar and conventional ultrasound. METHODS: The data of age-related cataract patients were retrospectively analyzed from March, 2013 to June, 2013 in our hospital. Preoperative measurements of ocular biological parameters and calculation of intraocular lens(IOLdegree using SRK/T's formula with ultrasound, keratometry and Lenstar were performed. Cataract extraction combined with IOL implantation in capsule was taken in every patient. Retinoscopy was taken postoperatively after 3 months. Comparison of the two inspection methods for measuring axial length, mean corneal curvature and postoperative refractive PE and absolute value of PE(APE. RESULTS: Preoperative axial length was 24.68±1.70mm and 24.42±1.65mm with Lenstar and ultrasound, respectively, and there was significant difference(t=-12.688, Pr=0.992, Pt=-1.241, P=0.217, but was the significant correlation(r=0.963, Pt=-5.494, Pt=6.379, PCONCLUSION: Accurate ocular biological parameters can be achieved with Lenstar, and postoperative PE is more precise with Lenstar compared with conventional ultrasound. Lenstar can be used for precise calculation of IOL degree in cataract operation.
Cardiac Concomitants of Feedback and Prediction Error Processing in Reinforcement Learning
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Lucas Kastner
2017-10-01
Full Text Available Successful learning hinges on the evaluation of positive and negative feedback. We assessed differential learning from reward and punishment in a monetary reinforcement learning paradigm, together with cardiac concomitants of positive and negative feedback processing. On the behavioral level, learning from reward resulted in more advantageous behavior than learning from punishment, suggesting a differential impact of reward and punishment on successful feedback-based learning. On the autonomic level, learning and feedback processing were closely mirrored by phasic cardiac responses on a trial-by-trial basis: (1 Negative feedback was accompanied by faster and prolonged heart rate deceleration compared to positive feedback. (2 Cardiac responses shifted from feedback presentation at the beginning of learning to stimulus presentation later on. (3 Most importantly, the strength of phasic cardiac responses to the presentation of feedback correlated with the strength of prediction error signals that alert the learner to the necessity for behavioral adaptation. Considering participants' weight status and gender revealed obesity-related deficits in learning to avoid negative consequences and less consistent behavioral adaptation in women compared to men. In sum, our results provide strong new evidence for the notion that during learning phasic cardiac responses reflect an internal value and feedback monitoring system that is sensitive to the violation of performance-based expectations. Moreover, inter-individual differences in weight status and gender may affect both behavioral and autonomic responses in reinforcement-based learning.
Modeling Dynamics of Wikipedia: An Empirical Analysis Using a Vector Error Correction Model
Directory of Open Access Journals (Sweden)
Liu Feng-Jun
2017-01-01
Full Text Available In this paper, we constructed a system dynamic model of Wikipedia based on the co-evolution theory, and investigated the interrelationships among topic popularity, group size, collaborative conflict, coordination mechanism, and information quality by using the vector error correction model (VECM. This study provides a useful framework for analyzing the dynamics of Wikipedia and presents a formal exposition of the VECM methodology in the information system research.
Hwang, Jinsang; Yun, Hongsik; Suh, Yongcheol; Cho, Jeongho; Lee, Dongha
2012-09-25
This study developed a smartphone application that provides wireless communication, NRTIP client, and RTK processing features, and which can simplify the Network RTK-GPS system while reducing the required cost. A determination method for an error model in Network RTK measurements was proposed, considering both random and autocorrelation errors, to accurately calculate the coordinates measured by the application using state estimation filters. The performance evaluation of the developed application showed that it could perform high-precision real-time positioning, within several centimeters of error range at a frequency of 20 Hz. A Kalman Filter was applied to the coordinates measured from the application, to evaluate the appropriateness of the determination method for an error model, as proposed in this study. The results were more accurate, compared with those of the existing error model, which only considered the random error.
Error assessment of digital elevation models obtained by interpolation
Jean François Mas; Azucena Pérez Vega
2009-01-01
Son pocos los estudios enfocados en la evaluación de los errores inherentes a los modelos digitales de elevación (MDE). Por esta razón se evaluaron los errores de los MDE obtenidos por diferentes metodos de interpolación (ARC/INFO, IDRISI, ILWIS y NEW-MIEL) y con diferentes resoluciones, con la finalidad de obtener una representación del relieve más precisa. Esta evaluación de los métodos de interpolación es crucial, si se tiene en cuenta que los MDE son la forma más efectiva de representació...
Directory of Open Access Journals (Sweden)
Shirali
2016-03-01
Full Text Available Background In complex socio-technical systems like aviation systems, human error is said to be the main cause of air transport incidents, accounting for about 75 percent of these incidents and events. air traffic management (ATM is considered a highly reliable industry; however, there is a persistent need to identify safety vulnerabilities and reduce them or their effects, as ATM is very human-centered and will remain so, at least in the mid-term (e.g., until 2025. Objectives The current study aimed to conduct a predictive analysis of controllers’ cognitive errors using the TRACEr technique in an airport control tower. Materials and Methods This paper was done as a qualitative case study to identify controllers’ errors in an airport control tower. First, the controllers’ tasks were described by means of interviews and observation, and then the most critical tasks, which were more likely to have more errors, were chosen to be examined. In the next step, the tasks were broken down into sub-tasks using the hierarchical analysis method and presented as HTA charts. Finally, for all the sub-tasks, different error modes and mechanisms of their occurrence were identified and the results were recorded on TRACEr worksheets. Results The analysis of TRACEr worksheets showed that of a total 315 detected errors, perception and memory errors are the most important errors in tower control controllers’ tasks, and perceptual and spatial confusion is the most important psychological factor related to their occurrence. Conclusions The results of this study led to the identification of many of the errors and conditions that affect the performance of controllers, providing the ability to define safety and ergonomic interventions to reduce the risk of human error. Therefore, the results of this study can be a basis for planning ATM to prioritize prevention programs and safety enhancement
Analysis of Error Propagation Within Hierarchical Air Combat Models
2016-06-01
of the factors (variables), the other variables were fixed at their baseline levels. The red dots with the standard deviation error bars represent...conducted an analysis to determine if the means and variances of MOEs of interest were statistically different by experimental design (Pav, 2015). To do...summarized data. In the summarized data set, we summarize each Design Point (DP) by its mean and standard deviation , over the stochastic replications. The
On the Influence of Weather Forecast Errors in Short-Term Load Forecasting Models
Fay, D.; Ringwood, John; Condon, M.
2004-01-01
Weather information is an important factor in load forecasting models. This weather information usually takes the form of actual weather readings. However, online operation of load forecasting models requires the use of weather forecasts, with associated weather forecast errors. A technique is proposed to model weather forecast errors to reflect current accuracy. A load forecasting model is then proposed which combines the forecasts of several load forecasting models. This approach allows the...
Analysis of errors in spectral reconstruction with a Laplace transform pair model
International Nuclear Information System (INIS)
Archer, B.R.; Bushong, S.C.
1985-01-01
The sensitivity of a Laplace transform pair model for spectral reconstruction to random errors in attenuation measurements of diagnostic x-ray units has been investigated. No spectral deformation or significant alteration resulted from the simulated attenuation errors. It is concluded that the range of spectral uncertainties to be expected from the application of this model is acceptable for most scientific applications. (author)
Potential Hydraulic Modelling Errors Associated with Rheological Data Extrapolation in Laminar Flow
International Nuclear Information System (INIS)
Shadday, Martin A. Jr.
1997-01-01
The potential errors associated with the modelling of flows of non-Newtonian slurries through pipes, due to inadequate rheological models and extrapolation outside of the ranges of data bases, are demonstrated. The behaviors of both dilatant and pseudoplastic fluids with yield stresses, and the errors associated with treating them as Bingham plastics, are investigated
A prediction method based on wavelet transform and multiple models fusion for chaotic time series
International Nuclear Information System (INIS)
Zhongda, Tian; Shujiang, Li; Yanhong, Wang; Yi, Sha
2017-01-01
In order to improve the prediction accuracy of chaotic time series, a prediction method based on wavelet transform and multiple models fusion is proposed. The chaotic time series is decomposed and reconstructed by wavelet transform, and approximate components and detail components are obtained. According to different characteristics of each component, least squares support vector machine (LSSVM) is used as predictive model for approximation components. At the same time, an improved free search algorithm is utilized for predictive model parameters optimization. Auto regressive integrated moving average model (ARIMA) is used as predictive model for detail components. The multiple prediction model predictive values are fusion by Gauss–Markov algorithm, the error variance of predicted results after fusion is less than the single model, the prediction accuracy is improved. The simulation results are compared through two typical chaotic time series include Lorenz time series and Mackey–Glass time series. The simulation results show that the prediction method in this paper has a better prediction.
Error budget analysis of SCIAMACHY limb ozone profile retrievals using the SCIATRAN model
Directory of Open Access Journals (Sweden)
N. Rahpoe
2013-10-01
Full Text Available A comprehensive error characterization of SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY limb ozone profiles has been established based upon SCIATRAN transfer model simulations. The study was carried out in order to evaluate the possible impact of parameter uncertainties, e.g. in albedo, stratospheric aerosol optical extinction, temperature, pressure, pointing, and ozone absorption cross section on the limb ozone retrieval. Together with the a posteriori covariance matrix available from the retrieval, total random and systematic errors are defined for SCIAMACHY ozone profiles. Main error sources are the pointing errors, errors in the knowledge of stratospheric aerosol parameters, and cloud interference. Systematic errors are of the order of 7%, while the random error amounts to 10–15% for most of the stratosphere. These numbers can be used for the interpretation of instrument intercomparison and validation of the SCIAMACHY V 2.5 limb ozone profiles in a rigorous manner.
Adjusting Satellite Rainfall Error in Mountainous Areas for Flood Modeling Applications
Zhang, X.; Anagnostou, E. N.; Astitha, M.; Vergara, H. J.; Gourley, J. J.; Hong, Y.
2014-12-01
This study aims to investigate the use of high-resolution Numerical Weather Prediction (NWP) for evaluating biases of satellite rainfall estimates of flood-inducing storms in mountainous areas and associated improvements in flood modeling. Satellite-retrieved precipitation has been considered as a feasible data source for global-scale flood modeling, given that satellite has the spatial coverage advantage over in situ (rain gauges and radar) observations particularly over mountainous areas. However, orographically induced heavy precipitation events tend to be underestimated and spatially smoothed by satellite products, which error propagates non-linearly in flood simulations.We apply a recently developed retrieval error and resolution effect correction method (Zhang et al. 2013*) on the NOAA Climate Prediction Center morphing technique (CMORPH) product based on NWP analysis (or forecasting in the case of real-time satellite products). The NWP rainfall is derived from the Weather Research and Forecasting Model (WRF) set up with high spatial resolution (1-2 km) and explicit treatment of precipitation microphysics.In this study we will show results on NWP-adjusted CMORPH rain rates based on tropical cyclones and a convective precipitation event measured during NASA's IPHEX experiment in the South Appalachian region. We will use hydrologic simulations over different basins in the region to evaluate propagation of bias correction in flood simulations. We show that the adjustment reduced the underestimation of high rain rates thus moderating the strong rainfall magnitude dependence of CMORPH rainfall bias, which results in significant improvement in flood peak simulations. Further study over Blue Nile Basin (western Ethiopia) will be investigated and included in the presentation. *Zhang, X. et al. 2013: Using NWP Simulations in Satellite Rainfall Estimation of Heavy Precipitation Events over Mountainous Areas. J. Hydrometeor, 14, 1844-1858.
Directory of Open Access Journals (Sweden)
Volodymyr Kharchenko
2017-03-01
Full Text Available Purpose: the aim of this study is to research applied models of air traffic controllers’ errors prevention in terminal control areas (TMA under uncertainty conditions. In this work the theoretical framework descripting safety events and errors of air traffic controllers connected with the operations in TMA is proposed. Methods: optimisation of terminal control area formal description based on the Threat and Error management model and the TMA network model of air traffic flows. Results: the human factors variables associated with safety events in work of air traffic controllers under uncertainty conditions were obtained. The Threat and Error management model application principles to air traffic controller operations and the TMA network model of air traffic flows were proposed. Discussion: Information processing context for preventing air traffic controller errors, examples of threats in work of air traffic controllers, which are relevant for TMA operations under uncertainty conditions.
Orthogonality of the Mean and Error Distribution in Generalized Linear Models.
Huang, Alan; Rathouz, Paul J
2017-01-01
We show that the mean-model parameter is always orthogonal to the error distribution in generalized linear models. Thus, the maximum likelihood estimator of the mean-model parameter will be asymptotically efficient regardless of whether the error distribution is known completely, known up to a finite vector of parameters, or left completely unspecified, in which case the likelihood is taken to be an appropriate semiparametric likelihood. Moreover, the maximum likelihood estimator of the mean-model parameter will be asymptotically independent of the maximum likelihood estimator of the error distribution. This generalizes some well-known results for the special cases of normal, gamma and multinomial regression models, and, perhaps more interestingly, suggests that asymptotically efficient estimation and inferences can always be obtained if the error distribution is nonparametrically estimated along with the mean. In contrast, estimation and inferences using misspecified error distributions or variance functions are generally not efficient.
Directory of Open Access Journals (Sweden)
Angel Lago-Rodriguez
2016-09-01
Full Text Available Prolonged exposure to movement perturbations leads to creation of motor memories which decay towards previous states when the perturbations are removed. However, it remains unclear whether this decay is due only to a spontaneous and passive recovery of the previous state. It has recently been reported that activation of reinforcement-based learning mechanisms delays the onset of the decay. This raises the question whether other motor learning mechanisms may also contribute to the retention and/or decay of the motor memory. Therefore, we aimed to test whether mechanisms of error-based motor adaptation are active during the decay of the motor memory. Forty-five right-handed participants performed point-to-point reaching movements under an external dynamic perturbation. We measured the expression of the motor memory through error-clamped (EC trials, in which lateral forces constrained movements to a straight line towards the target. We found greater and faster decay of the motor memory for participants who had access to full online visual feedback during these EC trials (Cursor group, when compared with participants who had no EC feedback regarding movement trajectory (Arc group. Importantly, we did not find between-group differences in adaptation to the external perturbation. In addition, we found greater decay of the motor memory when we artificially increased feedback errors through the manipulation of visual feedback (Augmented-Error group. Our results then support the notion of an active decay of the motor memory, suggesting that adaptive mechanisms are involved in correcting for the mismatch between predicted movement trajectories and actual sensory feedback, which leads to greater and faster decay of the motor memory.
On low-frequency errors of uniformly modulated filtered white-noise models for ground motions
Safak, Erdal; Boore, David M.
1988-01-01
Low-frequency errors of a commonly used non-stationary stochastic model (uniformly modulated filtered white-noise model) for earthquake ground motions are investigated. It is shown both analytically and by numerical simulation that uniformly modulated filter white-noise-type models systematically overestimate the spectral response for periods longer than the effective duration of the earthquake, because of the built-in low-frequency errors in the model. The errors, which are significant for low-magnitude short-duration earthquakes, can be eliminated by using the filtered shot-noise-type models (i. e. white noise, modulated by the envelope first, and then filtered).
Directory of Open Access Journals (Sweden)
Roque Calvo
2016-09-01
Full Text Available The development of an error compensation model for coordinate measuring machines (CMMs and its integration into feature measurement is presented. CMMs are widespread and dependable instruments in industry and laboratories for dimensional measurement. From the tip probe sensor to the machine display, there is a complex transformation of probed point coordinates through the geometrical feature model that makes the assessment of accuracy and uncertainty measurement results difficult. Therefore, error compensation is not standardized, conversely to other simpler instruments. Detailed coordinate error compensation models are generally based on CMM as a rigid-body and it requires a detailed mapping of the CMM’s behavior. In this paper a new model type of error compensation is proposed. It evaluates the error from the vectorial composition of length error by axis and its integration into the geometrical measurement model. The non-explained variability by the model is incorporated into the uncertainty budget. Model parameters are analyzed and linked to the geometrical errors and uncertainty of CMM response. Next, the outstanding measurement models of flatness, angle, and roundness are developed. The proposed models are useful for measurement improvement with easy integration into CMM signal processing, in particular in industrial environments where built-in solutions are sought. A battery of implementation tests are presented in Part II, where the experimental endorsement of the model is included.
Calvo, Roque; D’Amato, Roberto; Gómez, Emilio; Domingo, Rosario
2016-01-01
The development of an error compensation model for coordinate measuring machines (CMMs) and its integration into feature measurement is presented. CMMs are widespread and dependable instruments in industry and laboratories for dimensional measurement. From the tip probe sensor to the machine display, there is a complex transformation of probed point coordinates through the geometrical feature model that makes the assessment of accuracy and uncertainty measurement results difficult. Therefore, error compensation is not standardized, conversely to other simpler instruments. Detailed coordinate error compensation models are generally based on CMM as a rigid-body and it requires a detailed mapping of the CMM’s behavior. In this paper a new model type of error compensation is proposed. It evaluates the error from the vectorial composition of length error by axis and its integration into the geometrical measurement model. The non-explained variability by the model is incorporated into the uncertainty budget. Model parameters are analyzed and linked to the geometrical errors and uncertainty of CMM response. Next, the outstanding measurement models of flatness, angle, and roundness are developed. The proposed models are useful for measurement improvement with easy integration into CMM signal processing, in particular in industrial environments where built-in solutions are sought. A battery of implementation tests are presented in Part II, where the experimental endorsement of the model is included. PMID:27690052
Kaplan, D; Wenger, R N
1993-10-01
This article presents a didactic discussion on the role of asymptotically independent test statistics and separable hypotheses as they pertain to issues of specification error, power, and model modification in the covariance structure modeling framework. Specifically, it is shown that when restricting two parameter estimates on the basis of the multivariate Wald test, the condition of asymptotic independence is necessary but not sufficient for the univariate Wald test statistics to sum to the multivariate Wald test. Instead, what is required is mutual asymptotic independence (MAI) among the univariate tests. This result generalizes to sets of multivariate tests as well. When MA1 is lacking, hypotheses can exhibit transitive relationships. It is also shown that the pattern of zero and non-zero elements of the covariance matrix of the estimates are indicative of mutually asymptotically independent test statistics, separable and transitive hypotheses. The concepts of MAI, separability, and transitivity serve as an explanatory framework for how specification errors are propagated through systems of equations and how power analyses are differentially affected by specification errors of the same magnitude. A small population study supports the major findings of this article. The question of univariate versus multivariate sequential model modification is also addressed. We argue that multivariate sequential model modification strategies do not take into account the typical lack of MA1 thus inadvertently misleading substantive investigators. Instead, a prudent approach favors univariate sequential model modification.
Preston, Jonathan L.; Hull, Margaret; Edwards, Mary Louise
2013-01-01
Purpose: To determine if speech error patterns in preschoolers with speech sound disorders (SSDs) predict articulation and phonological awareness (PA) outcomes almost 4 years later. Method: Twenty-five children with histories of preschool SSDs (and normal receptive language) were tested at an average age of 4;6 (years;months) and were followed up…
Tso, Chak-Hau Michael; Kuras, Oliver; Wilkinson, Paul B.; Uhlemann, Sebastian; Chambers, Jonathan E.; Meldrum, Philip I.; Graham, James; Sherlock, Emma F.; Binley, Andrew
2017-11-01
Measurement errors can play a pivotal role in geophysical inversion. Most inverse models require users to prescribe or assume a statistical model of data errors before inversion. Wrongly prescribed errors can lead to over- or under-fitting of data; however, the derivation of models of data errors is often neglected. With the heightening interest in uncertainty estimation within hydrogeophysics, better characterisation and treatment of measurement errors is needed to provide improved image appraisal. Here we focus on the role of measurement errors in electrical resistivity tomography (ERT). We have analysed two time-lapse ERT datasets: one contains 96 sets of direct and reciprocal data collected from a surface ERT line within a 24 h timeframe; the other is a two-year-long cross-borehole survey at a UK nuclear site with 246 sets of over 50,000 measurements. Our study includes the characterisation of the spatial and temporal behaviour of measurement errors using autocorrelation and correlation coefficient analysis. We find that, in addition to well-known proportionality effects, ERT measurements can also be sensitive to the combination of electrodes used, i.e. errors may not be uncorrelated as often assumed. Based on these findings, we develop a new error model that allows grouping based on electrode number in addition to fitting a linear model to transfer resistance. The new model explains the observed measurement errors better and shows superior inversion results and uncertainty estimates in synthetic examples. It is robust, because it groups errors together based on the electrodes used to make the measurements. The new model can be readily applied to the diagonal data weighting matrix widely used in common inversion methods, as well as to the data covariance matrix in a Bayesian inversion framework. We demonstrate its application using extensive ERT monitoring datasets from the two aforementioned sites.
Irving, J.; Koepke, C.; Elsheikh, A. H.
2017-12-01
Bayesian solutions to geophysical and hydrological inverse problems are dependent upon a forward process model linking subsurface parameters to measured data, which is typically assumed to be known perfectly in the inversion procedure. However, in order to make the stochastic solution of the inverse problem computationally tractable using, for example, Markov-chain-Monte-Carlo (MCMC) methods, fast approximations of the forward model are commonly employed. This introduces model error into the problem, which has the potential to significantly bias posterior statistics and hamper data integration efforts if not properly accounted for. Here, we present a new methodology for addressing the issue of model error in Bayesian solutions to hydrogeophysical inverse problems that is geared towards the common case where these errors cannot be effectively characterized globally through some parametric statistical distribution or locally based on interpolation between a small number of computed realizations. Rather than focusing on the construction of a global or local error model, we instead work towards identification of the model-error component of the residual through a projection-based approach. In this regard, pairs of approximate and detailed model runs are stored in a dictionary that grows at a specified rate during the MCMC inversion procedure. At each iteration, a local model-error basis is constructed for the current test set of model parameters using the K-nearest neighbour entries in the dictionary, which is then used to separate the model error from the other error sources before computing the likelihood of the proposed set of model parameters. We demonstrate the performance of our technique on the inversion of synthetic crosshole ground-penetrating radar traveltime data for three different subsurface parameterizations of varying complexity. The synthetic data are generated using the eikonal equation, whereas a straight-ray forward model is assumed in the inversion
Improved prediction error filters for adaptive feedback cancellation in hearing aids
DEFF Research Database (Denmark)
Ngo, Kim; van Waterschoot, Toon; Christensen, Mads Græsbøll
2013-01-01
Acoustic feedback is a well-known problem in hearing aids, caused by the undesired acoustic coupling between the hearing aid loudspeaker and microphone. Acoustic feedback produces annoying howling sounds and limits the maximum achievable hearing aid amplification. This paper is focused on adaptive...... feedback cancellation (AFC) where the goal is to adaptively model the acoustic feedback path and estimate the feedback signal, which is then subtracted from the microphone signal. The main problem in identifying the acoustic feedback path model is the correlation between the near-end signal...... a number of improved PEF designs that are inspired by harmonic sinusoidal modeling and pitch prediction of speech signals. The resulting PEM-based AFC algorithms are evaluated in terms of the maximum stable gain (MSG), filter misadjustment, and computational complexity. Simulation results for a hearing aid...
OOK power model based dynamic error testing for smart electricity meter
Wang, Xuewei; Chen, Jingxia; Yuan, Ruiming; Jia, Xiaolu; Zhu, Meng; Jiang, Zhenyu
2017-02-01
This paper formulates the dynamic error testing problem for a smart meter, with consideration and investigation of both the testing signal and the dynamic error testing method. To solve the dynamic error testing problems, the paper establishes an on-off-keying (OOK) testing dynamic current model and an OOK testing dynamic load energy (TDLE) model. Then two types of TDLE sequences and three modes of OOK testing dynamic power are proposed. In addition, a novel algorithm, which helps to solve the problem of dynamic electric energy measurement’s traceability, is derived for dynamic errors. Based on the above researches, OOK TDLE sequence generation equipment is developed and a dynamic error testing system is constructed. Using the testing system, five kinds of meters were tested in the three dynamic power modes. The test results show that the dynamic error is closely related to dynamic power mode and the measurement uncertainty is 0.38%.
2014-07-01
times, issues of interruptions and multitasking have become mainstream concerns. For example, Time magazine (Wallis, 2006) and the New York Times...Thompson, 2005) both reported stories about interruptions and multitasking and how they affect performance. The information technology research firm...talking to a friend, it is easy to collect data. Second, providing ordered information to another person is a general class of problems that include
Sun, Guodong; Peng, Fei; Mu, Mu
2017-12-01
Model parameter errors are an important cause of uncertainty in soil moisture simulation. In this study, a conditional nonlinear optimal perturbation related to parameter (CNOP-P) approach and a sophisticated land surface model (the Common Land Model, CoLM) are employed in four regions in China to explore extent of uncertainty in soil moisture simulations due to model parameter errors. The CNOP-P approach facilitates calculation of the upper bounds of uncertainty due to parameter errors and investigation of the nonlinear effects of parameter combination on uncertainties in simulation and prediction. The range of uncertainty for simulated soil moisture was found to be from 0.04 to 0.58 m3 m-3. Based on the CNOP-P approach, a new approach is applied to explore a relatively sensitive and important parameter combination for soil moisture simulations and predictions. It is found that the relatively sensitive parameter combination is region- and season-dependent. Furthermore, the results show that simulation of soil moisture could be improved if the errors in these important parameter combinations are reduced. In four study regions, the average extent of improvement (61.6%) in simulating soil moisture using the new approach based on the CNOP-P is larger than that (53.4%) using the one-at-a-time (OAT) approach. These results indicate that simulation and prediction of soil moisture is improved by considering the nonlinear effects of important physical parameter combinations. In addition, the new approach based on the CNOP-P is found to be an effective method to discern the nonlinear effects of important physical parameter combinations on numerical simulation and prediction.
Tops, Mattie; Koole, Sander L.; Wijers, Albertus A.
2013-01-01
The present research investigates the association between concern over mistakes (CoM), a facet of the personality style of perfectionism, and the error positivity (Pe), a response-locked event-related brain potential that relates to error-awareness. Sixteen healthy right-handed female participants
Robust estimation of errors-in-variables models using M-estimators
Guo, Cuiping; Peng, Junhuan
2017-07-01
The traditional Errors-in-variables (EIV) models are widely adopted in applied sciences. The EIV model estimators, however, can be highly biased by gross error. This paper focuses on robust estimation in EIV models. A new class of robust estimators, called robust weighted total least squared estimators (RWTLS), is introduced. Robust estimators of the parameters of the EIV models are derived from M-estimators and Lagrange multiplier method. A simulated example is carried out to demonstrate the performance of the presented RWTLS. The result shows that the RWTLS algorithm can indeed resist gross error to achieve a reliable solution.
Wagner, Sean
2014-01-01
The Cassini spacecraft has executed nearly 300 maneuvers since 1997, providing ample data for execution-error model updates. With maneuvers through 2017, opportunities remain to improve on the models and remove biases identified in maneuver executions. This manuscript focuses on how execution-error models can be used to judge maneuver performance, while providing a means for detecting performance degradation. Additionally, this paper describes Cassini's execution-error model updates in August 2012. An assessment of Cassini's maneuver performance through OTM-368 on January 5, 2014 is also presented.
Jackson, Kate; Correia, Carlos; Lardière, Olivier; Andersen, Dave; Bradley, Colin
2015-01-15
We use a theoretical framework to analytically assess temporal prediction error functions on von-Kármán turbulence when a zonal representation of wavefronts is assumed. The linear prediction models analyzed include auto-regressive of an order up to three, bilinear interpolation functions, and a minimum mean square error predictor. This is an extension of the authors' previously published work Correia et al. [J. Opt. Soc. Am. A31, 101 (2014)JOAOD61084-752910.1364/JOSAA.31.000101], in which the efficacy of various temporal prediction models was established. Here we examine the tolerance of these algorithms to specific forms of model errors, thus defining the expected change in behavior of the previous results under less ideal conditions. Results show that ±100% wind speed error and ±50 deg are tolerable before the best linear predictor delivers poorer performance than the no-prediction case.
Hughes, Charmayne M L; Baber, Chris; Bienkiewicz, Marta; Worthington, Andrew; Hazell, Alexa; Hermsdörfer, Joachim
2015-01-01
Approximately 33% of stroke patients have difficulty performing activities of daily living, often committing errors during the planning and execution of such activities. The objective of this study was to evaluate the ability of the human error identification (HEI) technique SHERPA (Systematic Human Error Reduction and Prediction Approach) to predict errors during the performance of daily activities in stroke patients with left and right hemisphere lesions. Using SHERPA we successfully predicted 36 of the 38 observed errors, with analysis indicating that the proportion of predicted and observed errors was similar for all sub-tasks and severity levels. HEI results were used to develop compensatory cognitive strategies that clinicians could employ to reduce or prevent errors from occurring. This study provides evidence for the reliability and validity of SHERPA in the design of cognitive rehabilitation strategies in stroke populations.
Chen, Yuzhen; Xie, Fugui; Liu, Xinjun; Zhou, Yanhua
2014-07-01
Parallel robots with SCARA(selective compliance assembly robot arm) motions are utilized widely in the field of high speed pick-and-place manipulation. Error modeling for these robots generally simplifies the parallelogram structures included by the robots as a link. As the established error model fails to reflect the error feature of the parallelogram structures, the effect of accuracy design and kinematic calibration based on the error model come to be undermined. An error modeling methodology is proposed to establish an error model of parallel robots with parallelogram structures. The error model can embody the geometric errors of all joints, including the joints of parallelogram structures. Thus it can contain more exhaustively the factors that reduce the accuracy of the robot. Based on the error model and some sensitivity indices defined in the sense of statistics, sensitivity analysis is carried out. Accordingly, some atlases are depicted to express each geometric error's influence on the moving platform's pose errors. From these atlases, the geometric errors that have greater impact on the accuracy of the moving platform are identified, and some sensitive areas where the pose errors of the moving platform are extremely sensitive to the geometric errors are also figured out. By taking into account the error factors which are generally neglected in all existing modeling methods, the proposed modeling method can thoroughly disclose the process of error transmission and enhance the efficacy of accuracy design and calibration.
Optimization of arterial age prediction models based in pulse wave
International Nuclear Information System (INIS)
Scandurra, A G; Meschino, G J; Passoni, L I; Dai Pra, A L; Introzzi, A R; Clara, F M
2007-01-01
We propose the detection of early arterial ageing through a prediction model of arterial age based in the coherence assumption between the pulse wave morphology and the patient's chronological age. Whereas we evaluate several methods, a Sugeno fuzzy inference system is selected. Models optimization is approached using hybrid methods: parameter adaptation with Artificial Neural Networks and Genetic Algorithms. Features selection was performed according with their projection on main factors of the Principal Components Analysis. The model performance was tested using the bootstrap error type .632E. The model presented an error smaller than 8.5%. This result encourages including this process as a diagnosis module into the device for pulse analysis that has been developed by the Bioengineering Laboratory staff
Optimization of arterial age prediction models based in pulse wave
Energy Technology Data Exchange (ETDEWEB)
Scandurra, A G [Bioengineering Laboratory, Electronic Department, Mar del Plata University (Argentina); Meschino, G J [Bioengineering Laboratory, Electronic Department, Mar del Plata University (Argentina); Passoni, L I [Bioengineering Laboratory, Electronic Department, Mar del Plata University (Argentina); Dai Pra, A L [Engineering Aplied Artificial Intelligence Group, Mathematics Department, Mar del Plata University (Argentina); Introzzi, A R [Bioengineering Laboratory, Electronic Department, Mar del Plata University (Argentina); Clara, F M [Bioengineering Laboratory, Electronic Department, Mar del Plata University (Argentina)
2007-11-15
We propose the detection of early arterial ageing through a prediction model of arterial age based in the coherence assumption between the pulse wave morphology and the patient's chronological age. Whereas we evaluate several methods, a Sugeno fuzzy inference system is selected. Models optimization is approached using hybrid methods: parameter adaptation with Artificial Neural Networks and Genetic Algorithms. Features selection was performed according with their projection on main factors of the Principal Components Analysis. The model performance was tested using the bootstrap error type .632E. The model presented an error smaller than 8.5%. This result encourages including this process as a diagnosis module into the device for pulse analysis that has been developed by the Bioengineering Laboratory staff.
Error propagation of partial least squares for parameters optimization in NIR modeling
Du, Chenzhao; Dai, Shengyun; Qiao, Yanjiang; Wu, Zhisheng
2018-03-01
A novel methodology is proposed to determine the error propagation of partial least-square (PLS) for parameters optimization in near-infrared (NIR) modeling. The parameters include spectral pretreatment, latent variables and variable selection. In this paper, an open source dataset (corn) and a complicated dataset (Gardenia) were used to establish PLS models under different modeling parameters. And error propagation of modeling parameters for water quantity in corn and geniposide quantity in Gardenia were presented by both type І and type II error. For example, when variable importance in the projection (VIP), interval partial least square (iPLS) and backward interval partial least square (BiPLS) variable selection algorithms were used for geniposide in Gardenia, compared with synergy interval partial least squares (SiPLS), the error weight varied from 5% to 65%, 55% and 15%. The results demonstrated how and what extent the different modeling parameters affect error propagation of PLS for parameters optimization in NIR modeling. The larger the error weight, the worse the model. Finally, our trials finished a powerful process in developing robust PLS models for corn and Gardenia under the optimal modeling parameters. Furthermore, it could provide a significant guidance for the selection of modeling parameters of other multivariate calibration models.
Dogn, Li-hu; Li, Feng-ri; Song, Yu-wen
2015-03-01
Based on the biomass data of 276 sampling trees of Pinus koraiensis, Abies nephrolepis, Picea koraiensis and Larix gmelinii, the mono-element and dual-element additive system of biomass equations for the four conifer species was developed. The model error structure (additive vs. multiplicative) of the allometric equation was evaluated using the likelihood analysis, while nonlinear seemly unrelated regression was used to estimate the parameters in the additive system of biomass equations. The results indicated that the assumption of multiplicative error structure was strongly supported for the biomass equations of total and tree components for the four conifer species. Thus, the additive system of log-transformed biomass equations was developed. The adjusted coefficient of determination (Ra 2) of the additive system of biomass equations for the four conifer species was 0.85-0.99, the mean relative error was between -7.7% and 5.5%, and the mean absolute relative error was less than 30.5%. Adding total tree height in the additive systems of biomass equations could significantly improve model fitting performance and predicting precision, and the biomass equations of total, aboveground and stem were better than biomass equations of root, branch, foliage and crown. The precision of each biomass equation in the additive system varied from 77.0% to 99.7% with a mean value of 92.3% that would be suitable for predicting the biomass of the four natural conifer species.
Local and omnibus goodness-of-fit tests in classical measurement error models
Ma, Yanyuan
2010-09-14
We consider functional measurement error models, i.e. models where covariates are measured with error and yet no distributional assumptions are made about the mismeasured variable. We propose and study a score-type local test and an orthogonal series-based, omnibus goodness-of-fit test in this context, where no likelihood function is available or calculated-i.e. all the tests are proposed in the semiparametric model framework. We demonstrate that our tests have optimality properties and computational advantages that are similar to those of the classical score tests in the parametric model framework. The test procedures are applicable to several semiparametric extensions of measurement error models, including when the measurement error distribution is estimated non-parametrically as well as for generalized partially linear models. The performance of the local score-type and omnibus goodness-of-fit tests is demonstrated through simulation studies and analysis of a nutrition data set.
Li, Qiuying; Pham, Hoang
2017-01-01
In this paper, we propose a software reliability model that considers not only error generation but also fault removal efficiency combined with testing coverage information based on a nonhomogeneous Poisson process (NHPP). During the past four decades, many software reliability growth models (SRGMs) based on NHPP have been proposed to estimate the software reliability measures, most of which have the same following agreements: 1) it is a common phenomenon that during the testing phase, the fault detection rate always changes; 2) as a result of imperfect debugging, fault removal has been related to a fault re-introduction rate. But there are few SRGMs in the literature that differentiate between fault detection and fault removal, i.e. they seldom consider the imperfect fault removal efficiency. But in practical software developing process, fault removal efficiency cannot always be perfect, i.e. the failures detected might not be removed completely and the original faults might still exist and new faults might be introduced meanwhile, which is referred to as imperfect debugging phenomenon. In this study, a model aiming to incorporate fault introduction rate, fault removal efficiency and testing coverage into software reliability evaluation is developed, using testing coverage to express the fault detection rate and using fault removal efficiency to consider the fault repair. We compare the performance of the proposed model with several existing NHPP SRGMs using three sets of real failure data based on five criteria. The results exhibit that the model can give a better fitting and predictive performance.
Iowa calibration of MEPDG performance prediction models.
2013-06-01
This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement : performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 : representative p...
Error modeling and tolerance design of a parallel manipulator with full-circle rotation
Directory of Open Access Journals (Sweden)
Yanbing Ni
2016-05-01
Full Text Available A method for improving the accuracy of a parallel manipulator with full-circle rotation is systematically investigated in this work via kinematic analysis, error modeling, sensitivity analysis, and tolerance allocation. First, a kinematic analysis of the mechanism is made using the space vector chain method. Using the results as a basis, an error model is formulated considering the main error sources. Position and orientation error-mapping models are established by mathematical transformation of the parallelogram structure characteristics. Second, a sensitivity analysis is performed on the geometric error sources. A global sensitivity evaluation index is proposed to evaluate the contribution of the geometric errors to the accuracy of the end-effector. The analysis results provide a theoretical basis for the allocation of tolerances to the parts of the mechanical design. Finally, based on the results of the sensitivity analysis, the design of the tolerances can be solved as a nonlinearly constrained optimization problem. A genetic algorithm is applied to carry out the allocation of the manufacturing tolerances of the parts. Accordingly, the tolerance ranges for nine kinds of geometrical error sources are obtained. The achievements made in this work can also be applied to other similar parallel mechanisms with full-circle rotation to improve error modeling and design accuracy.
SLC beam line error analysis using a model-based expert system
International Nuclear Information System (INIS)
Lee, M.; Kleban, S.
1988-02-01
Commissioning particle beam line is usually a very time-consuming and labor-intensive task for accelerator physicists. To aid in commissioning, we developed a model-based expert system that identifies error-free regions, as well as localizing beam line errors. This paper will give examples of the use of our system for the SLC commissioning. 8 refs., 5 figs
Itakura, Kota; Hatakeyama, Go; Akiyoshi, Masanori; Komoda, Norihisa
Recently, there are various proposals on tool for multi-agent simulation. However, in such simulation tools, analysts who do not have programming skill spend a lot of time to develop programs because notation of simulation models is not defined sufficiently and programming language is varied on tools. To solve this problem, a programming environment that defines the notation of simulation model has poposed. In this environment, analysts can design simulation with a graph representation and get the program code without writing programs. However, it is difficult to find errors that cause unintended behavior in simulation. Therefore, we propose a support method as a model debugger which helps users to find errors. The debugger generates candidates of errors, using a user's report of unintended behavior based on “typical report patterns”. Candidates of errors are extracted from “tree structure of error-inducing factors” that consists of source patterns of errors. In this paper, we executed experiments that compare time needed for examinees to find errors. Experimental results show the time to find errors by utilizing our model debugger is shortened.
Carroll, Raymond J.
2010-05-01
This paper considers identification and estimation of a general nonlinear Errors-in-Variables (EIV) model using two samples. Both samples consist of a dependent variable, some error-free covariates, and an error-prone covariate, for which the measurement error has unknown distribution and could be arbitrarily correlated with the latent true values; and neither sample contains an accurate measurement of the corresponding true variable. We assume that the regression model of interest - the conditional distribution of the dependent variable given the latent true covariate and the error-free covariates - is the same in both samples, but the distributions of the latent true covariates vary with observed error-free discrete covariates. We first show that the general latent nonlinear model is nonparametrically identified using the two samples when both could have nonclassical errors, without either instrumental variables or independence between the two samples. When the two samples are independent and the nonlinear regression model is parameterized, we propose sieve Quasi Maximum Likelihood Estimation (Q-MLE) for the parameter of interest, and establish its root-n consistency and asymptotic normality under possible misspecification, and its semiparametric efficiency under correct specification, with easily estimated standard errors. A Monte Carlo simulation and a data application are presented to show the power of the approach.
International Nuclear Information System (INIS)
Pollock, D.; Kim, K.; Gunst, R.; Schucany, W.
1993-05-01
Linear estimation of cold magnetic field quality based on warm multipole measurements is being considered as a quality control method for SSC production magnet acceptance. To investigate prediction uncertainties associated with such an approach, axial-scan (Z-scan) magnetic measurements from SSC Prototype Collider Dipole Magnets (CDM's) have been studied. This paper presents a preliminary evaluation of the explanatory ability of warm measurement multipole variation on the prediction of cold magnet multipoles. Two linear estimation methods are presented: least-squares regression, which uses the assumption of fixed independent variable (xi) observations, and the measurement error model, which includes measurement error in the xi's. The influence of warm multipole measurement errors on predicted cold magnet multipole averages is considered. MSD QA is studying warm/cold correlation to answer several magnet quality control questions. How well do warm measurements predict cold (2kA) multipoles? Does sampling error significantly influence estimates of the linear coefficients (slope, intercept and residual standard error)? Is estimation error for the predicted cold magnet average small compared to typical variation along the Z-Axis? What fraction of the multipole RMS tolerance is accounted for by individual magnet prediction uncertainty?
Differential measurement errors in zero-truncated regression models for count data.
Huang, Yih-Huei; Hwang, Wen-Han; Chen, Fei-Yin
2011-12-01
Measurement errors in covariates may result in biased estimates in regression analysis. Most methods to correct this bias assume nondifferential measurement errors-i.e., that measurement errors are independent of the response variable. However, in regression models for zero-truncated count data, the number of error-prone covariate measurements for a given observational unit can equal its response count, implying a situation of differential measurement errors. To address this challenge, we develop a modified conditional score approach to achieve consistent estimation. The proposed method represents a novel technique, with efficiency gains achieved by augmenting random errors, and performs well in a simulation study. The method is demonstrated in an ecology application. © 2011, The International Biometric Society.
Development and estimation of a semi-compensatory model with a flexible error structure
DEFF Research Database (Denmark)
Kaplan, Sigal; Shiftan, Yoram; Bekhor, Shlomo
2012-01-01
distributed error terms across alternatives at the choice stage. This study relaxes the assumption by introducing nested substitution patterns and alternatively random taste heterogeneity at the choice stage, thus equating the structural flexibility of semi-compensatory models to their compensatory...... counterparts. The proposed model is applied to off-campus rental apartment choice by students. Results show the feasibility and importance of introducing a flexible error structure into semi-compensatory models....
Model complexity control for hydrologic prediction
Schoups, G.; Van de Giesen, N.C.; Savenije, H.H.G.
2008-01-01
A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore
On the asymptotic ergodic capacity of FSO links with generalized pointing error model
Al-Quwaiee, Hessa
2015-09-11
Free-space optical (FSO) communication systems are negatively affected by two physical phenomenon, namely, scintillation due to atmospheric turbulence and pointing errors. To quantize the effect of these two factors on FSO system performance, we need an effective mathematical model for them. Scintillations are typically modeled by the log-normal and Gamma-Gamma distributions for weak and strong turbulence conditions, respectively. In this paper, we propose and study a generalized pointing error model based on the Beckmann distribution. We then derive the asymptotic ergodic capacity of FSO systems under the joint impact of turbulence and generalized pointing error impairments. © 2015 IEEE.
ANALYZING NUMERICAL ERRORS IN DOMAIN HEAT TRANSPORT MODELS USING THE CVBEM.
Hromadka, T.V.
1987-01-01
Besides providing an exact solution for steady-state heat conduction processes (Laplace-Poisson equations), the CVBEM (complex variable boundary element method) can be used for the numerical error analysis of domain model solutions. For problems where soil-water phase change latent heat effects dominate the thermal regime, heat transport can be approximately modeled as a time-stepped steady-state condition in the thawed and frozen regions, respectively. The CVBEM provides an exact solution of the two-dimensional steady-state heat transport problem, and also provides the error in matching the prescribed boundary conditions by the development of a modeling error distribution or an approximate boundary generation.
Error Modelling for Multi-Sensor Measurements in Infrastructure-Free Indoor Navigation
Directory of Open Access Journals (Sweden)
Laura Ruotsalainen
2018-02-01
Full Text Available The long-term objective of our research is to develop a method for infrastructure-free simultaneous localization and mapping (SLAM and context recognition for tactical situational awareness. Localization will be realized by propagating motion measurements obtained using a monocular camera, a foot-mounted Inertial Measurement Unit (IMU, sonar, and a barometer. Due to the size and weight requirements set by tactical applications, Micro-Electro-Mechanical (MEMS sensors will be used. However, MEMS sensors suffer from biases and drift errors that may substantially decrease the position accuracy. Therefore, sophisticated error modelling and implementation of integration algorithms are key for providing a viable result. Algorithms used for multi-sensor fusion have traditionally been different versions of Kalman filters. However, Kalman filters are based on the assumptions that the state propagation and measurement models are linear with additive Gaussian noise. Neither of the assumptions is correct for tactical applications, especially for dismounted soldiers, or rescue personnel. Therefore, error modelling and implementation of advanced fusion algorithms are essential for providing a viable result. Our approach is to use particle filtering (PF, which is a sophisticated option for integrating measurements emerging from pedestrian motion having non-Gaussian error characteristics. This paper discusses the statistical modelling of the measurement errors from inertial sensors and vision based heading and translation measurements to include the correct error probability density functions (pdf in the particle filter implementation. Then, model fitting is used to verify the pdfs of the measurement errors. Based on the deduced error models of the measurements, particle filtering method is developed to fuse all this information, where the weights of each particle are computed based on the specific models derived. The performance of the developed method is
Beauchamp, James W.; Horner, Andrew
2003-10-01
The correspondence of various error metrics to human discrimination data was investigated. Time-varying harmonic amplitude data were obtained from spectral analysis of eight musical instrument sounds (bassoon, clarinet, flute, horn, oboe, saxophone, trumpet, and violin). The data were altered using fixed random multipliers on the harmonic amplitudes, and the sounds were additively resynthesized with estimated average spectral errors ranging from 1% to 50%. Listeners attempted to discriminate the randomly altered sounds from reference sounds resynthesized from the original data. Then, various error metrics were used to calculate the spectral differences between the original and altered sounds, and the R2 correspondence between the error metrics and the discrimination data was measured. A relative-amplitude spectral error metric gave the best correspondence to average subject discrimination data, capturing over 90% of the variation relative to a Fourth-order regression curve, although other formulas gave similar results. Error metrics which used a small number of representative analysis frames gave results which compared favorably to using all frames of the analysis.
Directory of Open Access Journals (Sweden)
Yun Shi
2014-01-01
Full Text Available Modern observation technology has verified that measurement errors can be proportional to the true values of measurements such as GPS, VLBI baselines and LiDAR. Observational models of this type are called multiplicative error models. This paper is to extend the work of Xu and Shimada published in 2000 on multiplicative error models to analytical error analysis of quantities of practical interest and estimates of the variance of unit weight. We analytically derive the variance-covariance matrices of the three least squares (LS adjustments, the adjusted measurements and the corrections of measurements in multiplicative error models. For quality evaluation, we construct five estimators for the variance of unit weight in association of the three LS adjustment methods. Although LiDAR measurements are contaminated with multiplicative random errors, LiDAR-based digital elevation models (DEM have been constructed as if they were of additive random errors. We will simulate a model landslide, which is assumed to be surveyed with LiDAR, and investigate the effect of LiDAR-type multiplicative error measurements on DEM construction and its effect on the estimate of landslide mass volume from the constructed DEM.
Our calibrated model has poor predictive value: An example from the petroleum industry
International Nuclear Information System (INIS)
Carter, J.N.; Ballester, P.J.; Tavassoli, Z.; King, P.R.
2006-01-01
It is often assumed that once a model has been calibrated to measurements then it will have some level of predictive capability, although this may be limited. If the model does not have predictive capability then the assumption is that the model needs to be improved in some way. Using an example from the petroleum industry, we show that cases can exit where calibrated models have limited predictive capability. This occurs even when there is no modelling error present. It is also shown that the introduction of a small modelling error can make it impossible to obtain any models with useful predictive capability. We have been unable to find ways of identifying which calibrated models will have some predictive capacity and those which will not
Nonlinear Model Predictive Control with Constraint Satisfactions for a Quadcopter
Wang, Ye; Ramirez-Jaime, Andres; Xu, Feng; Puig, Vicenç
2017-01-01
This paper presents a nonlinear model predictive control (NMPC) strategy combined with constraint satisfactions for a quadcopter. The full dynamics of the quadcopter describing the attitude and position are nonlinear, which are quite sensitive to changes of inputs and disturbances. By means of constraint satisfactions, partial nonlinearities and modeling errors of the control-oriented model of full dynamics can be transformed into the inequality constraints. Subsequently, the quadcopter can be controlled by an NMPC controller with the updated constraints generated by constraint satisfactions. Finally, the simulation results applied to a quadcopter simulator are provided to show the effectiveness of the proposed strategy.
Regression Model to Predict Global Solar Irradiance in Malaysia
Directory of Open Access Journals (Sweden)
Hairuniza Ahmed Kutty
2015-01-01
Full Text Available A novel regression model is developed to estimate the monthly global solar irradiance in Malaysia. The model is developed based on different available meteorological parameters, including temperature, cloud cover, rain precipitate, relative humidity, wind speed, pressure, and gust speed, by implementing regression analysis. This paper reports on the details of the analysis of the effect of each prediction parameter to identify the parameters that are relevant to estimating global solar irradiance. In addition, the proposed model is compared in terms of the root mean square error (RMSE, mean bias error (MBE, and the coefficient of determination (R2 with other models available from literature studies. Seven models based on single parameters (PM1 to PM7 and five multiple-parameter models (PM7 to PM12 are proposed. The