Sparse kernel density estimator using orthogonal regression based on D-optimality experimental design

A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates.

Sparse multioutput radial basis function network construction using combined locally regularised orthogonal least square and D-optimality experimental design

A construction algorithm for multioutput radial basis function (RBF) network modelling is introduced by combining a locally regularised orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximised model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious RBF network model with excellent generalisation performance. The D-optimality design criterion enhances the model efficiency and robustness. A further advantage of the combined approach is that the user only needs to specify a weighting for the D-optimality cost in the combined RBF model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Sparse kernel regression modeling using combined locally regularized orthogonal least squares and D-optimality experimental design

The note proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Experimental design and model construction algorithms for radial basis function networks

New construction algorithms for radial basis function (RBF) network modelling are introduced based on the A-optimality and D-optimality experimental design criteria respectively. We utilize new cost functions, based on experimental design criteria, for model selection that simultaneously optimizes model approximation, parameter variance (A-optimality) or model robustness (D-optimality). The proposed approaches are based on the forward orthogonal least-squares (OLS) algorithm, such that the new A-optimality- and D-optimality-based cost functions are constructed on the basis of an orthogonalization process that gains computational advantages and hence maintains the inherent computational efficiency associated with the conventional forward OLS approach. The proposed approach enhances the very popular forward OLS-algorithm-based RBF model construction method since the resultant RBF models are constructed in a manner that the system dynamics approximation capability, model adequacy and robustness are optimized simultaneously. The numerical examples provided show significant improvement based on the D-optimality design criterion, demonstrating that there is significant room for improvement in modelling via the popular RBF neural network.

Regression based D-optimality experimental design for sparse kernel density estimation

This paper derives an efficient algorithm for constructing sparse kernel density (SKD) estimates. The algorithm first selects a very small subset of significant kernels using an orthogonal forward regression (OFR) procedure based on the D-optimality experimental design criterion. The weights of the resulting sparse kernel model are then calculated using a modified multiplicative nonnegative quadratic programming algorithm. Unlike most of the SKD estimators, the proposed D-optimality regression approach is an unsupervised construction algorithm and it does not require an empirical desired response for the kernel selection task. The strength of the D-optimality OFR is owing to the fact that the algorithm automatically selects a small subset of the most significant kernels related to the largest eigenvalues of the kernel design matrix, which counts for the most energy of the kernel training data, and this also guarantees the most accurate kernel weight estimate. The proposed method is also computationally attractive, in comparison with many existing SKD construction algorithms. Extensive numerical investigation demonstrates the ability of this regression-based approach to efficiently construct a very sparse kernel density estimate with excellent test accuracy, and our results show that the proposed method compares favourably with other existing sparse methods, in terms of test accuracy, model sparsity and complexity, for constructing kernel density estimates.

Nonlinear model structure detection using optimum experimental design and orthogonal least squares

A very efficient learning algorithm for model subset selection is introduced based on a new composite cost function that simultaneously optimizes the model approximation ability and model adequacy. The derived model parameters are estimated via forward orthogonal least squares, but the subset selection cost function includes an A-optimality design criterion to minimize the variance of the parameter estimates that ensures the adequacy and parsimony of the final model. An illustrative example is included to demonstrate the effectiveness of the new approach.

High-resolution global climate simulations with the ECMWF model in Project Athena: experimental design, model climate, and seasonal forecast skill

The sensitivity to the horizontal resolution of the climate, anthropogenic climate change, and seasonal predictive skill of the ECMWF model has been studied as part of Project Athena—an international collaboration formed to test the hypothesis that substantial progress in simulating and predicting climate can be achieved if mesoscale and subsynoptic atmospheric phenomena are more realistically represented in climate models. In this study the experiments carried out with the ECMWF model (atmosphere only) are described in detail. Here, the focus is on the tropics and the Northern Hemisphere extratropics during boreal winter. The resolutions considered in Project Athena for the ECMWF model are T159 (126 km), T511 (39 km), T1279 (16 km), and T2047 (10 km). It was found that increasing horizontal resolution improves the tropical precipitation, the tropical atmospheric circulation, the frequency of occurrence of Euro-Atlantic blocking, and the representation of extratropical cyclones in large parts of the Northern Hemisphere extratropics. All of these improvements come from the increase in resolution from T159 to T511 with relatively small changes for further resolution increases to T1279 and T2047, although it should be noted that results from this very highest resolution are from a previously untested model version. Problems in simulating the Madden–Julian oscillation remain unchanged for all resolutions tested. There is some evidence that increasing horizontal resolution to T1279 leads to moderate increases in seasonal forecast skill during boreal winter in the tropics and Northern Hemisphere extratropics. Sensitivity experiments are discussed, which helps to foster a better understanding of some of the resolution dependence found for the ECMWF model in Project Athena

A slugfest of intuitions: contextualism and experimental design

This paper considers ways that experimental design can affect judgments about informally presented context shifting experiments. Reasons are given to think that judgments about informal context shifting experiments are affected by an exclusive reliance on binary truth value judgments and by experimenter bias. Exclusive reliance on binary truth value judgments may produce experimental artifacts by obscuring important differences of degree between the phenomena being investigated. Experimenter bias is an effect generated when, for example, experimenters disclose (even unconsciously) their own beliefs about the outcome of an experiment. Eliminating experimenter bias from context shifting experiments makes it far less obvious what the “intuitive” responses to those experiments are. After it is shown how those different kinds of bias can affect judgments about informal context shifting experiments, those experiments are revised to control for those forms of bias. The upshot of these investigations is that participants in the contextualist debate who employ informal experiments should pay just as much attention to the design of their experiments as those who employ more formal experimental techniques if they want to avoid obscuring the phenomena they aim to uncover

Estudo da secagem do feijão verde (Vigna unguiculata L. Walp) - análise experimental do processo combinado leito fixo/leito de jorro

Green bean is considered as one of most traditional Brazilian Northeast dishes. Green beans drying preliminary experiments show that combine processes, fixed-bed/spouted bed, resulted in dehydrated beans with uniform humidity and the recovery of the beans properties after their rehydration. From this assays was defined an initial humidity suited for the spouted bed process. A fixed-bed pre-drying process until a level of 40% humidity gave the best results. The spouted bed characteristic hydrodynamic curves were presented for different beans loads, where changes in the respective beans physical properties were evidenced during the fluidynamic assay, due simultaneous drying process. One 22 factorial experimental design was carried out with three repetition in the central point, considering as entry variables: drying air velocity and temperature. The response variables were the beans brakeage, water fraction evaporated during 20 and 50 minutes of drying and the humidity ratio. They are presented still the modeling of the drying of the green beans in fine layer in the drier of tray and the modeling of the shrinking of the beans of the drying processes fixed-bed and spouted bed

Electrodeposition of copper on titanium wires: Taguchi experimental design approach

Electrodeposition of thin copper layer was carried out on titanium wires in acidic sulphate bath. The influence of titanium surface preparation, cathodic current density, copper sulphate and sulphuric acid concentrations, electrical charge density and stirring of the solution on the adhesion of the electrodeposits was studied using the Taguchi statistical method. A L(16) orthogonal array with the six factors of control at two levels each and three interactions was employed. The analysis of variance of the mean adhesion response and signal-to-noise ratio showed the great influence of cathodic current density on adhesion. on the contrary, the other factors as well as the three investigated interactions revealed low or no significant effect. From this study optimized electrolysis conditions were defined. The copper electrocoating improved the electrical conductivity of the titanium wire. This shows that copper electrocoated titanium wires could be employed for both electrical purpose and mechanical reinforcement in superconducting magnets. (C) 2008 Elsevier B.V. All rights reserved.

Keratinase production by new strain of Bacillus Amyloliquefaciens: Application of statistical experimental design for optimization of keratinase production

Feathers are rich in amino acids and can be employed as a dietary protein supplement for animal feed. Microbial degradation is an alternative technology for improving the nutritional value of feathers. Other potential applications of keratinase include use in the leather industry, detergents and medicine as well as the pharmaceutical for the treatment of acne, psoriasis and calluses. A new keratinolytic enzyme production bacterium was isolated from a poultry processing plant. To improve keratinase yield, statistically based experimental designs were applied to optimize three significant variables: temperature, substrate concentration (feathers) and agitation speed. Response surface methodology demonstrated an increase in keratinolytic activity at temperature, agitation speed and substrate concentration of 26.6°C, 150 rpm and 2%, respectively. Liquid chromatography revealed the release of amino acids in the Bacillus amyloliquefaciens culture broth, thereby demonstrating the potential of feather meal in the animal feed industry. © Global Science Publications.

Produção de ciclodextrina glicosiltransferase por Bacillus sp subgrupo alcalophilus: otimização por planejamento experimental

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Determination of Opiates in Whole Blood and Vitreous Humor: A Study of the Matrix Effect and an Experimental Design to Optimize Conditions for the Enzymatic Hydrolysis of Glucuronides

Faculty of Medicine University of Sao Paulo

Study of Candidate Genes for production and carcass traits in Italian Heavy Pigs: identification of a new DNA Markers, Expression Studies and Association Analysis using Different Experimental Design

Heavy pig breeding in Italy is mainly oriented for the production of high quality processed products. Of particular importance is the dry cured ham production, which is strictly regulated and requires specific carcass characteristics correlated with green leg characteristics. Furthermore, as pigs are slaughtered at about 160 kg live weight, the Italian pig breeding sector faces severe problems of production efficiency that are related to all biological aspects linked to growth, feed conversion, fat deposition and so on. It is well known that production and carcass traits are in part genetically determined. Therefore, as a first step to understand genetic basis of traits that could have a direct or indirect impact on dry cured ham production, a candidate gene approach can be used to identify DNA markers associated with parameters of economic importance. In this thesis, we investigated three candidate genes for carcass and production traits (TRIB3, PCSK1, MUC4) in pig breeds used for dry cured ham production, using different experimental approaches in order to find molecular markers associated with these parameters.