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Results for “Uncertainty modelling”

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At least 127 records · Page 7Linked to original sources

PubMedDate not supplied

Cybernetic model predictive control of a continuous bioreactor with cell recycle.

The control of poly-beta-hydroxybutyrate (PHB) productivity in a continuous bioreactor with cell recycle is studied by simulation. A cybernetic model of PHB synthesis in Alcaligenes eutrophus is developed. Model parameters are identified using experimental data, and simulation results are presented. The model is interfaced to a multirate model predictive control (MPC) algorithm. PHB productivity and concentration are controlled by manipulating dilution rate and recycle ratio. Unmeasured time varying disturbances are imposed to study regulatory control performance, including unreachable setpoints. With proper controller tuning, the nonlinear MPC algorithm can track productivity and concentration setpoints despite a change in the sign of PHB productivity gain with respect to dilution rate. It is shown that the nonlinear MPC algorithm is able to track the maximum achievable productivity for unreachable setpoints under significant process/model mismatch. The impact of model uncertainty upon controller performance is explored. The multirate MPC algorithm is tested using three controllers employing models that vary in complexity of regulation. It is shown that controller performance deteriorates as a function of decreasing biological complexity.

Algorithms↗

PubMed2004

Modelling the size separated particulate matter (SSPM10) from vehicular exhaust at traffic intersections in Mumbai.

The study was carried out to predict the size separated particulate matter below 10 microm size (SSPM10) from vehicular exhausts at traffic intersections using modified general finite line source model (GFLSM). Two air quality control regions (AQCRs) were selected in Mumbai City for this study. One was industrial area (AQCR1) containing the busy intersection, i.e. Marol link road, with the heavy inflow of two-three wheelers. And, the other was commercial busy district area (AQCR2) containing the busy intersection, i.e. Dadar circle, with a heavy traffic flow especially cars. The model was applied at both the traffic intersections. The data were collected for modelling study for three winter months in 1995 using cascade impactor of nine size ranges. The prediction results revealed that modified GFLSM underpredicted the SSPM10 concentrations for all the size ranges. However, showed considerable correlation between observed and predicted values for the size range below 4.7 microm at both the intersections. The relative high concentrations observed in the coarser range of 10-4.7 microm are attributed to the resuspension of the roadside particulate matter. Hence, the amount of underprediction was more for this range, which was due to the characteristics of model that does not take into account the factor for resuspension of roadside particulate matter caused by traffic movements. The model was also applied to predict the total particulate matter for downwind distances from the road intersection. The statistical evaluation of model was done, which indicated that the model's performance was good for the finer range of particles (below 4.7 microm) with r-square values of 0.49 and 0.57 found at both the intersections in AQCR1 and AQCR2, respectively. However, it is not unusual that the model uncertainty is likely to exist due to data input errors and stochastic fluctuations irrespective of the models accurateness. The statistical distribution model was therefore identified using Kolmogorov-Smirnov test. At both the intersections, SSPM10 concentration data were found lognormally distributed.

Air Pollutants↗

PubMed2004

Optimizing control of simulated moving beds--linear isotherm.

A new optimization based adaptive control strategy for simulated moving beds (SMBs) is proposed. A linearized reduced order model, which accounts for the periodic nature of the SMB process, is used for online optimization and control. The manipulated variables are the four inlet flow rates, the outputs are the raffinate and extract concentrations. Concentration measurements at the raffinate and extract outlets are used as the feedback information. The state estimate from the periodic Kalman filter is used for the prediction of the outlet concentrations over a chosen horizon. Predicted outlet concentrations are the basis for the calculation of the optimal input adjustments, which maximize the productivity and minimize the desorbent consumption subject to constraints on product purities. The realization of this concept is discussed and the implementation on a virtual eight column SMB platform is assessed, in the case of binary linear systems. For a whole series of typical plant disturbances it is shown that the proposed approach is effective in minimizing off-spec products and in achieving optimal SMB operation, also in the case where there are significant model uncertainties.

Models, Theoretical↗

PubMedDate not supplied

The representation of uncertainty in medical expert systems.

The development of the rule-based expert system has provided important new techniques for the representation of knowledge. However, continued use of this representational scheme has highlighted some of its deficiencies. In particular, many within scientific and non-scientific fields attempting to use the rule-base design to describe natural phenomena often find it difficult to represent the complexities of the world as 'absolute' rules. For this reason, many investigators acknowledge the need to add an uncertainty mechanism to the rule-base construct. Such a facility would allow the quantification of accuracy or strength of association within individual rules Although agreement exists on the need for an uncertainty representation facility, the debate concerning the most appropriate methodology is far from resolved. The purpose of this paper is to provide a review and commentary on the current state of debate over the five most popular candidate uncertainty models: symbolic representation, MYCIN certainty factors, Bayesian, Dempster-Shafer and fuzzy set logic. The advantages and disadvantages of each uncertainty calculi will be presented and assessed with respect to their applicability to the medical expert systems domain.

Bayes Theorem↗

PubMedDate not supplied

Estimating CE ratios under second-order uncertainty: the mean ratio versus the ratio of means.

Two methods have been presented for estimating cost-effectiveness ratios under conditions of second-order (model) uncertainty: one method estimates a mean ratio of cost to effect (the "mean ratio" approach), and the other estimates a ratio of mean cost to mean effect (the "ratio of means" approach). However, the question of which estimate is theoretically correct has not been formally addressed. The authors show that the "ratio of means" approach follows directly from the theoretical foundations of cost-effectiveness analysis, has attractive internal consistency properties, and is consistent with a simple vector algebra approach to the problem. In contrast, the "mean ratio" approach has not been shown to follow from first principles, is internally inconsistent, and can prescribe economically inefficient choices. It is concluded that the "ratio of means" procedure should be preferred unless persuasive arguments are presented to the contrary.

Computer Simulation↗

PubMed2004

Comparison of some modeling and control issues for a flexible two link manipulator.

The nonlinear dynamics of a flexible two link manipulator presents a challenging modeling and control problem with its lumped actuator dynamics coupled with distributed link dynamics. Issues related to modeling uncertainty and control robustness as well as a good understanding of the experimental issues are essential for advanced modeling and control for such systems. This paper reports some of the advanced modeling, control, and experimental issues for a flexible two link manipulator, including some novel approaches developed by the authors.

Algorithms↗

PubMed2003

Emission and fate assessment of methyl tertiary butyl ether in the Boston area airshed using a simple multimedia box model: comparison with urban air measurements.

Expected urban air concentrations of the gasoline additive methyl tertiary butyl ether (MTBE) were calculated using volatile emissions estimates and screening transport models, and these predictions were compared with Boston, MA, area urban air measurements. The total volatile flux of MTBE into the Boston primary metropolitan statistical area (PMSA) airshed was calculated based on estimated automobile nontailpipe emissions and the Universal Quasi-Chemical Functional-Group Activity Coefficient computed abundance of MTBE in gasoline vapor. The fate of MTBE in the Boston PMSA was assessed using both the European Union System for the Evaluation of Substances, which is a steady-state multimedia box model, and a simple airshed box model. Both models were parameterized based on the meteorological conditions observed during air sampling in the Boston area. Measured average urban air concentrations of 0.1 and 1 microg/m3 MTBE during February and September of 2000, respectively, were comparable to corresponding model predictions of 0.3 and 1 microg/m3 and could be essentially explained from estimated temperature-dependent volatile emissions rates, observed average wind speed (the airshed flushing rate), and reaction with ambient tropospheric hydroxyl radical (*OH), within model uncertainty. These findings support the proposition that one can estimate gasoline component source fluxes and use simple multimedia models to screen the potential impact of future proposed gasoline additives on urban airsheds.

Air Pollutants↗

PubMed2003

Use of Monte Carlo simulation to investigate uncertainty in exposure modeling.

OBJECTIVES: This study used Monte Carlo (MC) simulation to examine the influence of uncertainty on an exposure model and to determine whether a difference exists between two worker groups in a ceramic fiber manufacturing plant. METHODS: Data on work practices and conditions were gathered in interviews with long-serving employees. With the use of previously developed deterministic modeling techniques and likely distributions for model parameters, MC simulations generated exposure profiles for the two job titles. RESULTS: The exposure profiles overlapped considerably, although the average estimated exposure for one job was approximately double that of the other. However, when the correlation between the model parameters in the two jobs was considered, it was concluded that there was a significant difference in the two estimates. CONCLUSIONS: Models are increasingly being used to estimate exposure. Different work situations inevitably result in different exposure estimates. However, it is difficult to determine whether such differences in estimated exposure between worker groups are simply the result of uncertainty with respect to the model parameters or whether they reflect real differences between occupational groups. This study demonstrates the value of MC simulation in helping define the uncertainty in deterministic model estimates.

Ceramics↗

PubMed2006

Classification images with uncertainty.

Classification image and other similar noise-driven linear methods have found increasingly wider applications in revealing psychophysical receptive field structures or perceptual templates. These techniques are relatively easy to deploy, and the results are simple to interpret. However, being a linear technique, the utility of the classification-image method is believed to be limited. Uncertainty about the target stimuli on the part of an observer will result in a classification image that is the superposition of all possible templates for all the possible signals. In the context of a well-established uncertainty model, which pools the outputs of a large set of linear frontends with a max operator, we show analytically, in simulations, and with human experiments that the effect of intrinsic uncertainty can be limited or even eliminated by presenting a signal at a relatively high contrast in a classification-image experiment. We further argue that the subimages from different stimulus-response categories should not be combined, as is conventionally done. We show that when the signal contrast is high, the subimages from the error trials contain a clear high-contrast image that is negatively correlated with the perceptual template associated with the presented signal, relatively unaffected by uncertainty. The subimages also contain a "haze" that is of a much lower contrast and is positively correlated with the superposition of all the templates associated with the erroneous response. In the case of spatial uncertainty, we show that the spatial extent of the uncertainty can be estimated from the classification subimages. We link intrinsic uncertainty to invariance and suggest that this signal-clamped classification-image method will find general applications in uncovering the underlying representations of high-level neural and psychophysical mechanisms.

Adult↗

PubMed2001

A forward model-based validation of cardiovascular system identification.

We present a theoretical evaluation of a cardiovascular system identification method that we previously developed for the analysis of beat-to-beat fluctuations in noninvasively measured heart rate, arterial blood pressure, and instantaneous lung volume. The method provides a dynamical characterization of the important autonomic and mechanical mechanisms responsible for coupling the fluctuations (inverse modeling). To carry out the evaluation, we developed a computational model of the cardiovascular system capable of generating realistic beat-to-beat variability (forward modeling). We applied the method to data generated from the forward model and compared the resulting estimated dynamics with the actual dynamics of the forward model, which were either precisely known or easily determined. We found that the estimated dynamics corresponded to the actual dynamics and that this correspondence was robust to forward model uncertainty. We also demonstrated the sensitivity of the method in detecting small changes in parameters characterizing autonomic function in the forward model. These results provide confidence in the performance of the cardiovascular system identification method when applied to experimental data.

Autonomic Nervous System↗

PubMed2003

Sources of uncertainty in pesticide fate modelling.

There is worldwide interest in the application of probabilistic approaches to pesticide fate models to account for uncertainty in exposure assessments. The first steps in conducting a probabilistic analysis of any system are: (i) to identify where the uncertainties come from; and (ii) to pinpoint those uncertainties that are likely to affect most of the predictions made. This article aims at addressing those two points within the context of exposure assessment for pesticides through a review of the different sources of uncertainty in pesticide fate modelling. The extensive listing of sources of uncertainty clearly demonstrates that pesticide fate modelling is laced with uncertainty. More importantly, the review suggests that the probabilistic approaches, which are typically being deployed to account for uncertainty in the pesticide fate modelling, such as Monte Carlo modelling, ignore a number of key sources of uncertainty, which are likely to have a significant effect on the prediction of environmental concentrations for pesticides (e.g. model error, modeller subjectivity). Future research should concentrate on quantifying the impact these uncertainties have on exposure assessments and on developing procedures that enable their integration within probabilistic assessments.

Animals↗

PubMed2003

Mathematical models of renal fluid and electrolyte transport: acknowledging our uncertainty.

Mathematical models of renal tubular function, with detail at the cellular level, have been developed for most nephron segments, and these have generally been successful at capturing the overall bookkeeping of solute and water transport. Nevertheless, considerable uncertainty remains about important transport events along the nephron. The examples presented include the role of proximal tubule tight junctions in water transport and in regulation of Na(+) transport, the mechanism by which axial flow in proximal tubule modulates solute reabsorption, the effect of formate on proximal Cl(-) transport, the assessment of potassium transport along collecting duct segments inaccessible to micropuncture, the assignment of pathways for peritubular Cl(-) exit in outer medullary collecting duct, and the interaction of carbonic anhydrase-sensitive and -insensitive pathways for base exit from inner medullary collecting duct. Some of these uncertainties have had intense experimental interest well before they were cast as modeling problems. Indeed, many of the renal tubular models have been developed based on data acquired over two or three decades. Nevertheless, some uncertainties have been delineated as the result of model exploration and represent communications from the modelers back to the experimental community that certain issues should not be considered closed. With respect to model refinement, incorporating more biophysical detail about individual transporters will certainly enhance model reliability, but ultimate confidence in tubular models will still be contingent on experimental development of critical information at the tubular level.

Absorption↗

PubMed2004

Likelihood-based inference for stochastic models of sexual network formation.

Sexually-transmitted diseases (STDs) constitute a major public health concern. Mathematical models for the transmission dynamics of STDs indicate that heterogeneity in sexual activity level allow them to persist even when the typical behavior of the population would not support endemicity. This insight focuses attention on the distribution of sexual activity level in a population. In this paper, we develop several stochastic process models for the formation of sexual partnership networks. Using likelihood-based model selection procedures, we assess the fit of the different models to three large distributions of sexual partner counts: (1) Rakai, Uganda, (2) Sweden, and (3) the USA. Five of the six single-sex networks were fit best by the negative binomial model. The American women's network was best fit by a power-law model, the Yule. For most networks, several competing models fit approximately equally well. These results suggest three conclusions: (1) no single unitary process clearly underlies the formation of these sexual networks, (2) behavioral heterogeneity plays an essential role in network structure, (3) substantial model uncertainty exists for sexual network degree distributions. Behavioral research focused on the mechanisms of partnership formation will play an essential role in specifying the best model for empirical degree distributions. We discuss the limitations of inferences from such data, and the utility of degree-based epidemiological models more generally.

Databases as Topic↗

PubMed2006

Using complexity for the estimation of Bayesian networks.

Statistical inference of graphical models has become an important tool in the reconstruction of biological networks of the type which model, for example, gene regulatory interactions. In particular, the construction of a score-based Bayesian posterior density over the space of models provides an intuitive and computationally feasible method of assessing model uncertainty and of assigning statistical confidence to structural features. One problem which frequently occurs with this approach is the tendency to overestimate the degree of model complexity. Spurious graphical features obtained in this way may affect the inference in unpredictable ways, even when using scoring techniques, such as the Bayesian Information Criterion (BIC), that are specifically designed to compensate for overfitting. In this article we propose a simple adjustment to a BIC-based scoring procedure. The method proceeds in two steps. In the first step we derive an independent estimate of the parametric complexity of the model. In the second we modify the BIC score so that the mean parametric complexity of the posterior density is equal to the estimated value. The method is applied to a set of test networks, and to a collection of genes from the yeast genome known to possess regulatory relationships. A Bayesian network model with binary responses is employed. In the examples considered, we find that the number of spurious graph edges inferred is reduced, while the effect on the identification of true edges is minimal.

Algorithms↗

PubMed2003

An assessment of the emissions inventory processing systems EMS-2001 and SMOKE in grid-based air quality models.

In the United States, emission processing models such as Emissions Modeling System-2001 (EMS-2001), Emissions Preprocessor System-Version 2.5 (EPS2.5), and the Sparse Matrix Operator Kernel Emissions (SMOKE) model are currently being used to generate gridded, hourly, speciated emission inputs for urban and regional-scale photochemical models from aggregated pollutant inventories. In this study, two models, EMS-2001 and SMOKE, were applied with their default internal data sets to process a common inventory database for a high ozone (O3) episode over the eastern United States using the Carbon Bond IV (CB4) chemical speciation mechanism. A comparison of the emissions processed by these systems shows differences in all three of the major processing steps performed by the two models (i.e., in temporal allocation, spatial allocation, and chemical speciation). Results from a simulation with a photochemical model using these two sets of emissions indicate differences on the order of +/- 20 ppb in the predicted 1-hr daily maximum O3 concentrations. It is therefore critical to develop and implement more common and synchronized temporal, spatial, and speciation cross-reference systems such that the processes within each emissions model converge toward reasonably similar results. This would also help to increase confidence in the validity of photochemical grid model results by reducing one aspect of modeling uncertainty.

Air Pollutants↗

PubMed2005

Intercomparison of the community multiscale air quality model and CALGRID using process analysis.

This study was designed to examine the similarities and differences between two advanced photochemical air quality modeling systems: EPA Models-3/CMAQ and CALGRID/CALMET. Both modeling systems were applied to an ozone episode that occurred along the I-5 urban corridor in western Washington and Oregon during July 11-14, 1996. Both models employed the same modeling domain and used the same detailed gridded emission inventory. The CMAQ model was run using both the CB-IV and RADM2 chemical mechanisms, while CALGRID was used with the SAPRC-97 chemical mechanism. Outputfrom the Mesoscale Meteorological Model (MM5) employed with observational nudging was used in both models. The two modeling systems, representing three chemical mechanisms and two sets of meteorological inputs, were evaluated in terms of statistical performance measures for both 1- and 8-h average observed ozone concentrations. The results showed that the different versions of the systems were more similar than different, and all versions performed well in the Portland region and downwind of Seattle but performed poorly in the more rural region north of Seattle. Improving the meteorological input into the CALGRID/CALMET system with planetary boundary layer (PBL) parameters from the Models-3/CMAQ meteorology preprocessor (MCIP) improved the performance of the CALGRID/CALMET system. The 8-h ensemble case was often the best performer of all the cases indicating that the models perform better over longer analysis periods. The 1-h ensemble case, derived from all runs, was not necessarily an improvement over the five individual cases, but the standard deviation about the mean provided a measure of overall modeling uncertainty. Process analysis was applied to examine the contribution of the individual processes to the species conservation equation. The process analysis results indicated that the two modeling systems arrive at similar solutions by very different means. Transport rates are faster and exhibit greater fluctuations in the CMAQ cases than in the CALGRID cases, which lead to different placement of the urban ozone plumes. The CALGRID cases, which rely on the SAPRC97 chemical mechanism, exhibited a greater diurnal production/loss cycle of ozone concentrations per hour compared to either the RADM2 or CBIV chemical mechanisms in the CMAQ cases. These results demonstrate the need for specialized process field measurements to confirm whether we are modeling ozone with valid processes.

Aerosols↗

PubMed1997

Beyond screening: problems and prospects for risk characterization of endocrine disruptors.

Over time, the NAS's original concept of "risk characterization" has broadened to include not just explication of the risk inferences themselves, but a forthright discussion of their dependence on the many less-than-certain elements of the inferential process. Ideally, such characterization communicates the span of possible conclusions that emerge from alternative reasonable interpretations of the information at hand, together with considered judgment regarding the likelihood that each interpretation provides a good representation of the actual situation. Uncertainties regarding the magnitudes of parameters in the various equations used in quantitative risk assessment are increasingly characterized using statistical tools for analysis and propagation of variance through the calculations. Of potentially greater concern, however, is "model uncertainty"-the more fundamental uncertainty regarding the soundness of our conception of what biological phenomena are operating, how they interact, and how they can be represented in quantitative models. This is particularly true for assessment of potential endocrine disruptors, since much fundamental information is lacking and unanswered questions exist about the consequences and interpretation of empirically observable effects. An approach is to define a series of alternative models, each embodying a different conception, and using expert scientific judgment to weight the individual results.

Animals↗

PubMed2003

Palaeoclimatic insights into future climate challenges.

Palaeoclimatic data document a sensitive climate system subject to large and perhaps difficult-to-predict abrupt changes. These data suggest that neither the sensitivity nor the variability of the climate are fully captured in some climate-change projections, such as the Intergovernmental Panel on Climate Change (IPCC) Summary for Policymakers. Because larger, faster and less-expected climate changes can cause more problems for economies and ecosystems, the palaeoclimatic data suggest the hypothesis that the future may be more challenging than anticipated in ongoing policy making. Large changes have occurred repeatedly with little net forcing. Increasing carbon dioxide concentration appears to have globalized deglacial warming, with climate sensitivity near the upper end of values from general circulation models (GCMs) used to project human-enhanced greenhouse warming; data from the warm Cretaceous period suggest a similarly high climate sensitivity to CO(2). Abrupt climate changes of the most recent glacial-interglacial cycle occurred during warm as well as cold times, linked especially to changing North Atlantic freshwater fluxes. GCMs typically project greenhouse-gas-induced North Atlantic freshening and circulation changes with notable but not extreme consequences; however, such models often underestimate the magnitude, speed or extent of past changes. Targeted research to assess model uncertainties would help to test these hypotheses.

Arctic Regions↗