Parameter estimation

A new kinetic model for the fluid catalytic cracking (FCC) riser is developed. An elementary reaction scheme, for the FCC, based on cracking of a large number of lumps in the form of narrow boiling pseudocomponents is proposed. The kinetic parameters are estimated using a semi-empirical approach based on normal probability distribution. The correlation proposed for the kinetic parameters' estimation contains four parameters that depend on the feed characteristics, catalyst activity, and coke forming tendency of the feed. This approach eliminates the need of determining a large number of rate constants required for conventional lumped models. The model seems to be more versatile than existing models and opens up a new dimension for making generic models suitable for the analysis and control studies of FCC units. The model also incorporates catalyst deactivation and two-phase flow in the riser reactor. Predictions of the model compare well with the yield pattern of industrial scale plant data reported in literature. ᭧

Individual tree mortality models were developed for the six major forest species of Austria: Norway spruce (Picea abies), white ®r (Abies alba), European larch (Larix decidua), Scots pine (Pinus sylvestris), European beech (Fagus silvatica), and oak (Quercus spp.); a joint model for the remaining broadleaf species was also developed. Data came from 5-year remeasurements of the permanent plot network of the Austrian National Forest Inventory. Parameters of the logistic equation were estimated using maximum likelihood methods. For all species, we found the hyperbolic transformation of diameter (D À1) to be highly signi®cant in predicting the high mortality rates for small diameter trees and decreasing mortality rates for larger diameters. For spruce, a quadratic transformation in D was needed to accurately model the increase in mortality observed for large, low-vigor trees with diameter >70 cm, which resulted in a U-shaped distribution. Crown ratio was also consistently signi®cant, except for oak. We likewise found basal-area-in-larger-trees (BAL) to be a highly signi®cant predictor of mortality rate for all species except ®r and oak. Predicted mortality rate increases as the basal area in larger trees increases and as crown ratio decreases. The resulting logistic mortality model had the same general form for all species, with the signs of all parameters conforming to expectations. In general, chi-square statistics indicate that the most important variable is D À1 , the second most important is crown ratio, and the third most important predictor is BAL. The relative importance of crown ratio appears to be greater for shade tolerant species (®r, beech, spruce) than for shade intolerant species (larch, Scots pine, oak). Examination of graphs of observed vs. predicted mortality rates reveals that the species-speci®c mortality models are all well behaved, and match the observed mortality rates quite well. The D À1 transformation is¯exible, as can be seen by comparing the rather different mortality rates of larch and Scots pine. Predicted and observed mortality rates with respect to crown ratio are quite close to the observed mortality rates for all but the smallest crown ratios (CR<20%), a class with very few observations. Finally, the logistic mortality models passed a validation test on independent data not used in parameter estimation. The key ingredient for obtaining a good mortality model is a data set that is both large and representative of the population under study, and the Austrian National Forest Inventory data satisfy both requirements.

We study the problem of multiclass classification within the framework of error correcting output codes (ECOC) using margin-based binary classifiers. Specifically, we address two important open problems in this context: decoding and model selection. The decoding problem concerns how to map the outputs of the classifiers into class codewords. In this paper we introduce a new decoding function that combines the margins through an estimate of their class conditional probabilities. Concerning model selection, we present new theoretical results bounding the leave-one-out (LOO) error of ECOC of kernel machines, which can be used to tune kernel hyperparameters. We report experiments using support vector machines as the base binary classifiers, showing the advantage of the proposed decoding function over other functions of the margin commonly used in practice.

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Recent advances in statistical software have led to the rapid diffusion of new methods for modelling longitudinal data. Multilevel (also known as hierarchical or random effects) models for binary outcomes have generally been based on a logistic-normal specification, by analogy with earlier work for normally distributed data. The appropriate application and interpretation of these models remains somewhat unclear, especially when compared with the computationally more straightforward semiparametric or 'marginal' modelling (GEE) approaches. In this paper we pose two interrelated questions. First, what limits should be placed on the interpretation of the coefficients and inferences derived from random-effect models involving binary outcomes? Second, what diagnostic checks are appropriate for evaluating whether such random-effect models provide adequate fits to the data? We address these questions by means of an extended case study using data on adolescent smoking from a large cohort study. Bayesian estimation methods are used to fit a discrete-mixture alternative to the standard logistic-normal model, and posterior predictive checking is used to assess model fit. Surprising parallels in the parameter estimates from the logistic-normal and mixture models are described and used to question the interpretability of the socalled 'subject-specific' regression coefficients from the standard multilevel approach. Posterior predictive checks suggest a serious lack of fit of both multilevel models. The results do not provide final answers to the two questions posed, but we expect that lessons learned from the case study will provide general guidance for further investigation of these important issues.

This correspondence describes a method for estimating the parameters of an autoregressive (AR) process from a finite number of noisy measurements. The method uses a modified set of Yule-Walker (YW) equations that lead to a quadratic eigenvalue problem that, when solved, gives estimates of the AR parameters and the measurement noise variance.

Recent works have analyzed the potential performance of MIMO systems using dual-polarized antennas at both ends of the wireless link. These works assume Rayleigh and Ricean fading MIMO channel models. Here, we analyze the capacity of such systems in microcellular environments using a physical model of the MIMO channel. The use of a physical model permits us to analyze the impact of environmental parameters, like antennas location and orientation, on the system performance. As example, we present ergodic and outage capacity estimations in a specific urban environment from the predictions of a site-specific ray-tracing propagation tool.

Generative models of pattern individuality attempt to represent the distribution of observed quantitative features, e.g., by learning parameters from a database, and then use such distributions to determine the probability of two random patterns being the same. Considering fingerprint patterns, Gaussian distributions have been previously used for minutiae location and von-Mises distributions for minutiae orientation so as to determine the probability of random correspondence (PRC) between two fingerprints. Motivated by the fact that ridges have not been modeled in generative models, and using representative ridge points in fingerprint matching, ridge information is incorporated into the generative model by using a third distribution for ridge types. The joint probability of minutiae location, minutiae orientation and ridge type is modeled as a mixture distribution. The proposed model offers a more accurate fingerprint representation from which more reliable PRCs can be computed. Based on parameters estimated from fingerprint databases, PRCs using ridge types are seen to be much smaller than PRCs computed with only minutiae.

We present a robust null space method for linear equality constrained state space estimation. Exploiting a degeneracy in the estimator statistics, an orthogonal factorization is used to decompose the problem into stochastic and deterministic components, which are then solved separately. The resulting dimension reduction algorithm has enhanced numerical stability, solves the constrained problem completely, and can reduce computational load by reducing the problem size. The new method addresses deficiencies in commonly used pseudo-observation or projection methods, which either do not solve the constrained problem completely or have unstable numerical implementations, due in part to the degeneracy in the estimator statistics. We present a numerical example demonstrating the effectiveness of the new method compared to other current methods.

This paper presents the design, sensing principles and in vitro evaluation of a novel instrumented sock intended for prediction and prevention of acute decompensated heart failure. The sock contains a drift-free ankle size sensor and a leg tissue elasticity sensor. Both sensors are inexpensive and developed using innovative new sensing ideas. Preliminary tests with the sensor prototypes show promising results: The ankle size sensor is capable of measuring 1 mm changes in ankle diameter and the tissue elasticity sensor can detect 0.15 MPa differences in elasticity. A low-profile instrumented sock prototype with these two sensors has been successfully fabricated and will be evaluated in the future in an IRB-approved human study.

his paper is concerned with a computational solution for normal optical flow estimation using space-variant image sampling. The article describes one solution for the problem based on log-Tpol ar images, including the algorithm implementation on digital signal processing hardware. The log-polar structure achieves high data compressing rates by maintaining a high resolution area-the fovea-and a space-variant resolution-the retinal periphery. This sampling scheme enables the implementation of selective processing, characteristic of attentive vision. The solution was developed to be used with an active vision system to pursue moving objects, assuming that the cameras could also move. Some properties of the log-polar structure are also described and the main results of the implementation are presented.

In this study, an alternative method has been proposed for the parameter estimation in non-linear regression. This method is the genetic algorithms technique which is widely used in recent years. Unlike other parameter estimation methods, genetic algorithms do not require supplementary information and thus are functional for practical purposes. Genetic algorithms are shown to achieve effective solutions compared to other methods in the S-type growth models. The results have established that genetic algorithms with no need for auxiliary information can be used for parameter estimation in non-linear regression.

Topic-based language model has attracted much attention as the propounding of semantic retrieval in recent years. Especially for the ASR text with errors, the topic representation is more reasonable than the exact term representation. Among these models, Latent Dirichlet Allocation(LDA) has been noted for its ability to discover the latent topic structure, and is broadly applied in many text-related tasks. But up to now its application in information retrieval(IR) is still limited to be a supplement to the standard document models, and furthermore, it has been pointed out that directly employing the basic LDA model will hurt retrieval performance. In this paper, we propose a lexicon-guided two-level LDA retrieval framework. It uses the HowNet to guide the first-level LDA model's parameter estimation, and further construct the second-level LDA models based on the first-level's inference results. We use TRECID 2005 ASR collection to evaluate it, and compare it with the vector space model(VSM) and latent semantic Indexing(LSI). Our experiments show the proposed method is very competitive.

This paper deals with iterative maximum-likelihood synchronization of a scalar parameter. An efficient implementation of the Newton-Raphson (NR) maximum-search method is proposed. Considering the latter implementation, the NR approach is shown to be an attractive alternative to synchronization methods based on the expectation-maximization (EM) algorithm. Simulation results for the case of phase-offset synchronization show that NR method usually increases the speed of convergence of the synchronization algorithm.

Objectives: Time-kill studies are commonly used in investigations of new antimicrobial agents. However, they typically provide descriptive information on pharmacodynamics. We developed a mathe- matical model to capture the relationship between microbial burden and antimicrobial agent concentrations. Methods: Time-kill studies were performed with 108 cfu/mL of Pseudomonas aeruginosa at baseline. Meropenem at 0, 0.25, 1, 4, 16 and 64 3

Fat is the prime energy source for birds during prolonged exercise, but protein is also catabolized. Estimates of the amount of catabolizable fat and protein (termed fat and protein fuel) are therefore important for studying energetics of birds. As fat and protein fuel can only be measured by sacrificing individuals or by use of technically complex methods, scoring systems were invented to estimate fat and protein fuel of birds in the field. The visible subcutaneous fat deposits and the thickness of the flight muscles are each scored on an ordinal scale but these scales do not correspond linearly to fat and protein fuel within species, which is needed for analyses such as flight range estimates. We developed an ANOVA-type model to estimate fat and protein fuel from fat scores (FS) and muscle scores (MS) along with total mass and a size measurement. Using data from 11 337 individuals of eight passerine species (Common Nightingale Luscinia megarhynchos, Eurasian Reed Warbler Acrocephalus scirpaceus, Melodious Warbler Hippolais polyglotta, Willow Warbler Phylloscopus trochilus, Orphean Warbler Sylvia hortensis, Garden Warbler Sylvia borin, Common Whitethroat Sylvia communis, Subalpine Warbler Sylvia cantillans) mist-netted in Mauritania, West Africa, we tested for independence of FS and MS and for variation in the relationship between scores and associated mass in response to physiological state. FS, MS and third primary length (size) explained variation in body mass of all eight species analysed (R 2 : 0.56-0.77). The parameter estimates of the model showed that fat and protein fuel increased monotonically with increasing fat and muscle scores. In two species we found small differences in the estimates between physiological states (seasons). We evaluated our model by comparing the predicted body mass of birds with both FS and MS equal to 0 with the mean body mass of individuals mist-netted with both scores equal to zero. The values were very close. The amount of fat extracted from dead Garden and Willow Warblers was within the range of predicted fat fuel derived from the model. We conclude that our model is a useful non-invasive method to estimate simultaneously mean fat and protein fuel of small passerines and we provide recommendations on its use.

This paper proposes a generation mechanism for cyclostationary and self-similar processes. The proposed model extracts the information from the immediate coarser scale and adds the innovations to it to obtain the finer scale representation of the stochastic process. Basic block of the proposed model is the subband coder. By cascading the blocks of subband coder and passing white noise as one of input in addition to coarser information at each stage, cyclostationary process is generated. For generation of selfsimilar processes, white noise input for each stage is given in particular fashion. The mapping from finer scale to the immediate coarser scale is obtained using proposed blurring model. We have also given scheme for estimation of parameters of the proposed generation mechanism. The parameters are estimated for a given statistics case as well as the given data case. The proposed model can be used in variety of applications such as speech and image processing, biomedical signal processing, seismic data processing etc. Index Terms-Modeling, statistically matched, stochastic processes, self-similar and cyclostationary process, parameter estimation, filter banks.

BACKGROUND: The aim of this study was to provide a model-based analysis of the pharmacokinetics of remifentanil in infants and children undergoing cardiac surgery with cardiopulmonary bypass (CPB). METHODS: We studied nine patients aged 0.5 to 4 years who received a continuous remifentanil infusion via a computer-controlled infusion pump during cardiac surgery with mildly hypothermic CPB were studied. Arterial blood

Nonlinear regression is a useful statistical tool, relating observed data and a nonlinear function of unknown parameters. When the parameter-dependent nonlinear function is computationally intensive, a straightforward regression analysis by maximum likelihood is not feasible. The method pre-sented in this paper proposes to construct a faster running surrogate for such a computationally intensive nonlinear function, and to use it in a related non-linear statistical model that accounts for the uncertainty associated with this surrogate. A pivotal quantity in the Earth’s climate system is the climate sen-sitivity: the change in global temperature due to doubling of atmospheric CO2 concentrations. This, along with other climate parameters, are estimated by applying the statistical method developed in this paper, where the computa-tionally intensive nonlinear function is the MIT 2D climate model. 1. Introduction. A fundamental question in understanding the Earth’s cli-mate system is quan...

Whenever we have a set of discrete measures of a phenomenon and try to find an analytic function which models such phenomenon, we are solving a problem about finding some parameters that minimizes a computable error function. In this way, parameter estimation may be studied as an optimization problem, in which the fitness function we are trying to minimize is the error one. This work try to do that using a genetic algorithm to obtain three parameters of a function. Particularly, we use data about one village population over time to see the goodness of our algorithm.

Facial skin detection is an important step in facial surgical planning like as many other applications. There are many problems in facial skin detection. One of them is that the image features can be severely corrupted due to illumination, noise, and occlusion, where, shadows can cause numerous strong edges. Hence, in this papHence, in this pappper, we present an automatic Expectation-Maximization (EM) algorithm for facial skin color segmentation that uses knowledge of chromatic space and varying illumination conditions to correct and segment frontal and lateral facial color images, simultaneously. The proposed EM algorithm leads to a method that allows for more robust and accurate segmentation of facial images. The initialization of the model parameters is very important in convergence of algorithm. For this purpose, we use a method for robust parameter estimation of Gaussian mixture components. Also, we use an additional class, which includes all pixels not modeled explicitly by Gaussian with small variance, by a uniform probability density, and amending the EM algorithm appropriately, in order to obtain significantly better results. Experimental results on facial color images show that accurate estimates of the Gaussian mixture parameters are computed. Also, other results on images presenting a wide range of variations in lighting conditions, demonstrate the efficiency of the proposed color skin segmentation algorithm compared to conventional EM algorithm.

The purpose of this paper is twofold. First, we compare two representations of a fish stock: a complex cohort (age-class) model and a simple aggregate (surplus growth) model. A key question is whether the aggregate model is an appropriate simplification of the more complex model. The comparison is made with respect to the optimal fishing strategies that follow from each representation. With proper economic mechanisms in place, the difference between the two harvesting policies is surprisingly small. Second, we see the comparison as an example of an advanced sensitivity analysis, where sensitivity in terms of policy is considered rather than in terms of behavior. Assuming the ultimate aim of modeling is better policies, our choice seems a proper one. However, we also recognize that in many cases, less costly tests of behavioral sensitivity may prove more practical.

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A novel method, based on genetic algorithms, has been developed and applied to the solution of di erential equations. The new approach is based on the use of real numbers to form the candidate solutions which are improved iteratively by a suitable breeding process. The algorithm was tested in the calculation of the bound states of a double well potential and in the nonlinear density functional calculation. Comparison of the results with those obtained using the direct relaxation method shows excellent agreement.

Outliers in time series have the potential to affect parameter estimates and forecasts when using exponential smoothing. The aim of this study is to show the way in which important types of outliers can be incorporated into linear innovations state space models for exponential smoothing methods. The types of outliers include an additive outlier, a level shift, and a transitory change. The general innovations state space model and a special case which encompasses the common linear exponential smoothing methods are examined. A method for identifying outliers using innovations state space models is proposed. This method is investigated using both simulations and applications to real time series. The impact of an outlier's location on the forecasts and the estimation of parameters is examined. The forecasts from outlier and basic non-outlier models are compared. An automatic method is found to result in improved forecasts for both the simulated and real data.

While numerous researchers have proposed dierent models to forecast trial sales for new products, there is little systematic understanding about which of these models works best, and under what circumstances these ®ndings change. In this paper, we provide a comprehensive investigation of eight leading published models and three dierent parameter estimation methods. Across 19 dierent datasets encompassing a variety of consumer packaged goods, we observe several systematic patterns that link dierences in model speci®cation and estimation to forecasting accuracy. Major ®ndings include the following observations: (1) when dealing with consumer packaged goods, simple models that allow for relatively limited¯exibility (e.g. no S-shaped curves) in the calibration period provide signi®cantly better forecasts than more complex speci®cations; (2) models that explicitly accommodate heterogeneity in purchasing rates across consumers tend to oer better forecasts than those that do not; and (3) maximum likelihood estimation appears to oer more accurate and stable forecasts than nonlinear least squares. We elaborate on these and other ®ndings, and oer suggested directions for future research in this area. # 1998 John Wiley & Sons, Ltd. KEY WORDS new product forecasting; new product trial; test market

In recent years, much attention has been focused upon predictive control of nonlinear systems. The implementation of such a control strategy for real processes has greatly improved their performance. This paper deals with a model-based predictive control (MBPC) strategy using a generalised Hammerstein model and its application to the temperature control of a semibatch reactor. Both unconstrained and constrained adaptive control problems are considered. A simple identification method based on the weighted recursive least squares method (WRLS) is used to estimate the model parameters on-line. An indirect adaptive nonlinear controller is designed by combining the predictive controller with an indirect parameter estimation algorithm. This adaptive scheme has been applied for the control of a semi-batch chemical reactor. Experimental results show that the performance of the generalised Hammerstein MBPC (NLMBPC) was significantly better than that of a linear model predictive controller (LMBPC).

ABSTRACT
This work aims at developing a generic and anisotropic point error model, which is capable of computing magnitude and
direction of a priori random errors, described in the form of error ellipsoids for each individual point of the cloud. The
direct TLS observations are the range (ρ), vertical (α) and horizontal (θ) angles, each of which is in fact associated with a
priori precision value. A practical methodology was designed and performed in real-world test environments to
determine these precision values. The methodology has two experimental parts. The first part is a static and repetitive
measurement configuration for the determination of a priori precisions of the vertical (𝜎𝛼) and horizontal (𝜎𝜃) angles. The
second part is the measurement of a test stand which contains four plates in white, light grey, dark grey and black colors,
for the determination of a priori precisions of the range observations (𝜎𝜌). The test stand measurement is performed in a
recursive manner so that sensor-to-object distance, incidence angle and surface reflectivity are parameterized. The
experiment was conducted with three TLSs, namely Faro Focus 3D X330, Riegl VZ400 and Z+F 5010x in the same
location and atmospheric conditions. 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. Validation of the proposed error model was performed in real world scenarios,
which revealed feasibility of the model.
Keywords: TLS (terrestrial laser scanner), range, incidence angle, reflectance, error ellipsoid, anisotropic, error model,
variance-covariance propagation

This paper proposes a strategy for the global sensitivity analysis in the flowsheet simulation on solid processes which allows to examine and quantify the influences of given parameters on specific target criteria. The strategy is basically divided into two parts: The first is the effect analysis which provides the range of possible values of a target variable. This permits an estimation of the accuracy of a simulation with uncertain parameters. As the second part the influence analysis builds a hierarchy of the different uncertain parameters according to their impacts on a specific target criterion. This latter information can be used to decrease the efforts for parameter estimation in a given process simulation by focussing the efforts on the most influential parameters. The strategy for global sensitivity analysis suggested here is applied to a soil washing process where an oil-contaminated soil is to be cleaned such that both the amount of the contaminant concentrate to be disposed and the residual oil contamination of the cleaned soil are to be minimized.

This paper describes our novel retrieval model that is based on contexts of query terms in documents (i.e., document contexts). Our model is novel because it explicitly takes into account of the document contexts instead of implicitly using the document contexts to find query expansion terms. Our model is based on simulating a user making relevance decisions, and it is a hybrid of various existing effective models and techniques. It estimates the relevance decision preference of a document context as the log-odds and uses smoothing techniques as found in language models to solve the problem of zero probabilities. It combines these estimated preferences of document contexts using different types of aggregation operators that comply with different relevance decision principles (e.g., aggregate relevance principle). Our model is evaluated using retrospective experiments (i.e., with full relevance information), because such experiments can (a) reveal the potential of our model, (b) isolate the problems of the model from those of the parameter estimation, (c) provide information about the major factors affecting the retrieval effectiveness of the model, and (d) show that whether the model obeys the probability ranking principle. Our model is promising as its mean average precision is 60-80% in our experiments using different TREC ad hoc English collections and the NTCIR-5 ad hoc Chinese collection. Our experiments showed that (a) the operators that are consistent with aggregate relevance principle were effective in combining the estimated preferences, and (b) that estimating probabilities using the contexts in the relevant documents can produce better retrieval effectiveness than using the entire relevant documents.

We present an approach for separating two speech signals when only one single recording of their linear mixture is available. For this purpose, we derive a filter, which we call the soft mask filter, using minimum mean square error (MMSE) estimation of the log spectral vectors of sources given the mixture's log spectral vectors. The soft mask filter's parameters are estimated using the mean and variance of the underlying sources which are modeled using the Gaussian composite source modeling (CSM) approach. It is also shown that the binary mask filter which has been empirically and extensively used in single channel speech separation techniques is, in fact, a simplified form of the soft mask filter. The soft mask filtering technique is compared with the binary mask and Wiener filtering approaches when the input consists of male+male, female+female, and male+female mixtures. The experimental results in terms of signal-to-noise ratio (SNR) and segmental SNR show that soft mask filtering outperforms binary mask and Wiener filtering.

Regression analysis is intended to be used when the researcher seeks to test a given hypothesis against a data set. Unfortunately, in many applications it is either not possible to specify a hypothesis, typically because the research is in a very early stage, or it is not desirable to form a hypothesis, typically because the number of potential explanatory variables is very large. In these cases, researchers have resorted either to overt data mining techniques such as stepwise regression, or covert data mining techniques such as running variations on regression models prior to running the final model (also known as “data peeking”). While data mining side-steps the need to form a hypothesis, it is highly susceptible to generating spurious results. This paper draws on the known properties of OLS estimators in the presence of omitted and extraneous variable models to propose a procedure for data mining that attempts to distinguish between parameter estimates that are significant due to...

Scene reconstruction from video sequences has become a prominent computer vision research area in recent years, due to its large number of applications in fields such as security, robotics and virtual reality. Despite recent progress in this field, there are still a number of issues that manifest as incomplete, incorrect or computationally-expensive reconstructions. The engine behind achieving reconstruction is the matching of features between images, where common conditions such as occlusions, lighting changes and texture-less regions can all affect matching accuracy. Subsequent processes that rely on matching accuracy, such as camera parameter estimation, structure computation and non-linear parameter optimization, are also vulnerable to additional sources of error, such as degeneracies and mathematical instability. Detection and correction of errors, along with robustness in parameter solvers, are a must in order to achieve a very accurate final scene reconstruction. However, err...

We compared a physiological model of 82Rb kinetics in the myocardium with two reduced-order models to determine their usefulness in assessing physiological parameters from dynamic PET data. A three-compartment model of 82Rb in the myocardium was used to simulate kinetic PET ROI data. Simulations were generated for eight different blood-flow rates reflecting the physiological range of interest. Two reduced-order models commonly used with myocardial PET studies were fit to the simulated data, and parameters of the reduced-order models were compared with the physiological parameters. Then all three models were fit to the simulated data with noise added. Monte Carlo simulations were used to evaluate and compare the diagnostic utility of the reduced-order models. A description length criterion was used to assess goodness of fit for each model. Finally, fits to simulated data were compared with fits to actual dynamic PET data. Fits of the reduced-order models to the three-compartment mode...

This work presents a procedure to solve nonlinear dynamic data reconciliation (NDDR) problems with simultaneous parameter estimation based on particle swarm optimization (PSO). The performance of the proposed procedure is compared to the performance of a standard Gauss-Newton (GN) scheme in a real industrial problem, as presented previously by Simultaneous data reconciliation and parameter estimation in bulk polypropylene polymerizations in real time. Macromolecular Symposia 243, 91-103; 2008. In-line monitoring of bulk polypropylene reactors based on data reconciliation procedures. Macromolecular Symposia 271, 26-37]. Both methods are used to solve the NDDR problem in an industrial bulk propylene polymerization process, using real data in real time for the simultaneous estimation of model parameters and process states. A phenomenological model of the real process, based on the detailed mass and energy balances and constituted by a set of algebraic-differential equations, was implemented and used for interpretation of the actual plant behavior in real time. The resulting nonlinear dynamic optimization problem was solved iteratively on a moving time window, in order to capture the current process behavior and allow for dynamic adaptation of model parameters. Obtained results indicate that the proposed PSO procedure can be implemented in real time, allowing for estimation of more reliable process states and model parameters and leading to much more robust and reproducible numerical performance.

In this paper, we develop a framework to optimally manage the time-phased deployment planning of a new technology, namely Advanced Metering Infrastructure, in the Utility industry. Advanced Metering Infrastructure enable two-way communication between residential and commercial customers and the Utility grid, so that energy consumers may respond to price signals during stressful conditions in the grid in a price-sensitive fashion, and reduce the overall stress in the grid. It also enables consumers to shift discretionary energy loads from stressful, peak periods to off-peak periods. We present an end-to-end solution framework for addressing the various analytical challenges that are involved in developing an optimal deployment plan, from a business case development perspective. Our solution framework uses a judicious combination of system dynamics modeling, econometric modeling and mathematical programming based optimization modeling. A system dynamics model is used to estimate the d...

A 2D video distrometer (2DVD) provides raindrop size distribution (DSD) at nominal drop diameters that correspond to the mean of the bin sizes. Selection of bin width may influence the shape of DSD. Therefore, we investigated the effect of binning on the DSD parameter estimates. First, we studied the effect of binning by examining their ability to recover known parameters of simulated DSD. Second, real DSD data collected in the equatorial region by 2DVD were analyzed. We compared the DSD parameters calculated from binned DSD with those calculated from a drop-by-drop data basis. Both simulated and real DSDs were binned at 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, and 0.50 mm. In general, the DSD parameters increased with increasing bin width. With very large number of raindrop which should be accompanied by heavy rain, the bias due to bin width selection is small. However, the bias is significant in the opposite case. The average fractional error between a mass-weighted mean diameter (D m ) calculated from DSD and that derived from drop-by-drop data was relatively small for all rainfall rates. A rather high error was observed in the median volume diameter (D 0 ) which may be due to moment method and interpolation error. Finally, using small bin widths (0.20-0.30 mm) may be the best choice because the DSD parameters of these bin widths were very close to those obtained from drop-by-drop data.

This paper discusses the application of space-time autoregressive integrated moving average (STARIMA) methodology for representing traffic flow patterns. Traffic flow data are in the form of spatial time series and are collected at specific locations at constant intervals of time. Important spatial characteristics of the space-time process are incorporated in the STARIMA model through the use of weighting matrices estimated on the basis of the distances among the various locations where data are collected. These matrices distinguish the space-time approach from the vector autoregressive moving average (VARMA) methodology and enable the model builders to control the number of the parameters that have to be estimated. The proposed models can be used for short-term forecasting of space-time stationary traffic-flow processes and for assessing the impact of traffic-flow changes on other parts of the network. The three-stage iterative space-time model building procedure is illustrated using 7.5 min. average traffic flow data for a set of 25 loop-detectors located at roads that direct to the centre of the city of Athens, Greece. Data for two months with different traffic-flow characteristics are modelled in order to determine the stability of the parameter estimation.

Mixture modeling within the context of pharmacokinetic (PK)/pharmacodynamic (PD) mixed effects modeling is a useful tool to explore a population for the presence of two or more subpopulations, not explained by evaluated covariates. At present, statistical tests for the existence of mixed populations have not been developed. Therefore, a simulation study was undertaken to evaluate mixture modeling with NONMEM and explore the following questions. First, what is the probability of concluding that a mixed population exists when there truly is not a mixture (false positive significance level)? Second, what is the probability of concluding that a mixed population (two subpopulations) exists when there is truly a mixed population (power), and how well can the mixture be estimated, both in terms of the population parameters and the individual subject classification. Seizure count data were simulated using a Poisson distribution such that each subject's count could decrease from its baseline value, as a function of dose via an Emax model. The dosing design for the simulation was based on a trial with the investigational anti-epileptic drug pregabalin. Four hundred and forty seven subjects received pregabalin as add on therapy for partial seizures, each with a baseline seizure count and up to three subsequent seizure counts. For the mixtures, the two subpopulations were simulated to differ in their Emax values and relative proportions. One subpopulation always had its Emax set to unity (Emax hi), allowing the count to approach zero with increasing dose. The other subpopulation was allowed to vary in its Emax value (Emax lo=0.75, 0.5, 0.25, and 0) and in its relative proportion (pr) of the population (pr=0.05, 0.10, 0.25, and 0.50) giving a total of 4 ⋅ 4=16 different mixtures explored. Three hundred data sets were simulated for each scenario and estimations performed using NONMEM. Metrics used information about the parameter estimates, their standard errors (SE), the difference between minimum objective function (MOF) values for mixture and non-mixture models (MOF(δ)), the proportion of subjects classified correctly, and the estimated conditional probabilities of a subject being simulated as having Emax lo (Emax hi) given that they were estimated as having Emaxlo (Emax hi) and being estimated as having Emaxlo (Emax hi) given that they were simulated as having Emax lo (Emax hi). The false positive significance level was approximately 0.04 (using all 300 runs) or 0.078 (using only those runs with a successful covariance step), when there was no mixture. When simulating mixed data and for those characterizations with successful estimation and covariance steps, the median (range) percentage of 95% confidence intervals containing the true values for the parameters defining the mixture were 94% (89–96%), 89.5% (58–96%), and 95% (92–97%) for pr, Emax lo, and Emax hi, respectively. The median value of the estimated parameters pr, Emax lo (excluding the case when Emax lo was simulated to equal 0) and Emax hi within a scenario were within ±28% of the true values. The median proportion of subjects classified correctly ranged from 0.59 to 0.96. In conclusion, when no mixture was present the false positive probability was less than 0.078 and when mixtures were present they were characterized with varying degrees of success, depending on the nature of the mixture. When the difference between subpopulations was greater (as Emax lo approached zero or pr approached 0.5) the mixtures became easier to characterize.

Models of oven-dried cork weight at tree level were developed using dendrometric variables and rotation cycle of cork production (9 or 10 years) as predictors. The models were based on data obtained from permanent plots laid out in five cork regions, covering most of the cork production area in Portugal. Dendrometric variables included those related to tree size (as perimeter at breast height, stem height or crown dimensions), related to management decisions in the stripping process (as stripped length in stem and branches, stripping surface, stripping intensity or stripping coefficient) and those gathered from a cork sample (as cork thickness, cork density or cork moisture). Variables were grouped according to the complexity of measurement with the aim of developing models suitable for management, that include variables with cheaper measurement, and for research, including variables with higher measurement cost. Models were structured as mixed models with random effects decomposed in regional, plot and tree-level effects, allowing higher sensitivity and more realistic variance-covariance structures comparing with fixed effects models. The variance component at tree level reflects 80-90% of total variability and at plot level is only 1-3% The nine selected models, five for research and four for management purposes, are linear models that reflect the allometry in the relationship between cork weight and tree size. #

... about geometrical sizes of the windings and electromagnetic characteristics of the transformeriron core. ... IEEE TRANSACI&#x27;IONS ON INSTRUMENTATION AND MEASUREMENT, VOL. ... accurate unbiased evaluation of the time domain model parameters of two coupled inductors. ...

In this paper, our objective is to test the statistical hypothesis
: ( ) ( ) forall against : Ho F x Fox x H1 F(x) Fo (x) = ≠ for some x , where F o(x) is a known
distribution function. In this study, a goodness of fit test statistics for Gumbel distribution
based on Kullback-Leibler information is studied. The performance of the test under simple
random sampling is investigated using Monte Carlo simulation. The Gumbel parameters are
estimated by using several methods of estimation such as maximum likelihood, order
statistics, moments, and L-moments. Ten different distributions are considered under the
alternative hypothesis. For all the distributions considered, it is found that the test statistics
based on estimators found by moment and order statistic methods have the highest power,
except for weibull and Lognormal distributions.

We develop a canonical, adaptive cascade-structure IIR notch filter to detect and track multiple time-varying frequencies in additive white Gaussian noise. The algorithm uses allpass frequency transformation filters and a truncated gradient. Simulations indicate that our algorithm is computationally simple, converges rapidly, and has good frequency resolution