Simultaneous Geometry Optimization and Material Selection for Truss Structures

In this work, we explore simultaneous design and material selection by posing it as an optimization problem. The underlying principles for our approach are Ashby's material selection procedure and structural optimization. For the simplicity and ease of initial implementation of the general procedure, truss structures under static load are considered in this work in view of maximum stiffness, minimum weight/cost and safety against failure. Along the lines of Ashby's material indices, a new design index is derived for trusses. This helps in choosing the most suitable material for any design of a truss. Using this, both the design space and material database are searched simultaneously using optimization algorithms. The important feature of our approach is that the formulated optimization problem is continuous even though the material selection is an inherently discrete problem.

Simultaneous feature selection and clustering using particle swarm optimization

Data clustering groups data so that data which are similar to each other are in the same group and data which are dissimilar to each other are in different groups. Since generally clustering is a subjective activity, it is possible to get different clusterings of the same data depending on the need. This paper attempts to find the best clustering of the data by first carrying out feature selection and using only the selected features, for clustering. A PSO (Particle Swarm Optimization)has been used for clustering but feature selection has also been carried out simultaneously. The performance of the above proposed algorithm is evaluated on some benchmark data sets. The experimental results shows the proposed methodology outperforms the previous approaches such as basic PSO and Kmeans for the clustering problem.

Simultaneous feature selection and feature weighting using Hybrid Tabu Search/K-nearest neighbor classifier

Feature selection and feature weighting are useful techniques for improving the classification accuracy of K-nearest-neighbor (K-NN) rule. The term feature selection refers to algorithms that select the best subset of the input feature set. In feature weighting, each feature is multiplied by a weight value proportional to the ability of the feature to distinguish pattern classes. In this paper, a novel hybrid approach is proposed for simultaneous feature selection and feature weighting of K-NN rule based on Tabu Search (TS) heuristic. The proposed TS heuristic in combination with K-NN classifier is compared with several classifiers on various available data sets. The results have indicated a significant improvement in the performance in classification accuracy. The proposed TS heuristic is also compared with various feature selection algorithms. Experiments performed revealed that the proposed hybrid TS heuristic is superior to both simple TS and sequential search algorithms. We also present results for the classification of prostate cancer using multispectral images, an important problem in biomedicine.

Simultaneous spectrometric determination of Cu2+, Mn2+ and Zn2+ in polivitaminic/polimineral drug using SPA and GA algorithms for variable selection

Este artigo apresenta uma aplicação do método para determinação espectrofotométrica simultânea dos íons divalentes de cobre, manganês e zinco à análise de medicamento polivitamínico/polimineral. O método usa 4-(2-piridilazo) resorcinol (PAR), calibração multivariada e técnicas de seleção de variáveis e foi otimizado o empregando-se o algoritmo das projeções sucessivas (APS) e o algoritmo genético (AG), para escolha dos comprimentos de onda mais informativos para a análise. Com essas técnicas, foi possível construir modelos de calibração por regressão linear múltipla (RLM-APS e RLM-AG). Os resultados obtidos foram comparados com modelos de regressão em componentes principais (PCR) e nos mínimos quadrados parciais (PLS). Demonstra-se a partir do erro médio quadrático de previsão (RMSEP) que os modelos apresentam desempenhos semelhantes ao prever as concentrações dos três analitos no medicamento. Todavia os modelos RLM são mais simples pois requerem um número muito menor de comprimentos de onda e são mais fáceis de interpretar que os baseados em variáveis latentes.

Data visualization with simultaneous feature selection

Data visualization algorithms and feature selection techniques are both widely used in bioinformatics but as distinct analytical approaches. Until now there has been no method of measuring feature saliency while training a data visualization model. We derive a generative topographic mapping (GTM) based data visualization approach which estimates feature saliency simultaneously with the training of the visualization model. The approach not only provides a better projection by modeling irrelevant features with a separate noise model but also gives feature saliency values which help the user to assess the significance of each feature. We compare the quality of projection obtained using the new approach with the projections from traditional GTM and self-organizing maps (SOM) algorithms. The results obtained on a synthetic and a real-life chemoinformatics dataset demonstrate that the proposed approach successfully identifies feature significance and provides coherent (compact) projections. © 2006 IEEE.

Simultaneous localization and planning on multiple map hypotheses

This paper presents a novel method to rank map hypotheses by the quality of localization they afford. The highest ranked hypothesis at any moment becomes the active representation that is used to guide the robot to its goal location. A single static representation is insufficient for navigation in dynamic environments where paths can be blocked periodically, a common scenario which poses significant challenges for typical planners. In our approach we simultaneously rank multiple map hypotheses by the influence that localization in each of them has on locally accurate odometry. This is done online for the current locally accurate window by formulating a factor graph of odometry relaxed by localization constraints. Comparison of the resulting perturbed odometry of each hypothesis with the original odometry yields a score that can be used to rank map hypotheses by their utility. We deploy the proposed approach on a real robot navigating a structurally noisy office environment. The configuration of the environment is physically altered outside the robots sensory horizon during navigation tasks to demonstrate the proposed approach of hypothesis selection.

Assessment of phytostabilization of Pb/Zn mine soils with organic amendments and a native metallicolous ecotype of Festuca Rubra l.: implications for amendment selection and recovery of soil health

La contaminación del suelo es una de las principales amenazas para los ecosistemas y la salud humana. Actualmente, desde un punto de vista tanto económico como ambiental, la fitoestabilización es la mejor tecnología para remediar suelos contaminados con elevadas concentraciones de metales como son los suelos mineros. La fitoestabilización asistida consiste en el empleo de plantas y enmiendas orgánicas y/o inorgánicas con el fin de reducir la movilidad y la biodisponibilidad de los contaminantes y recuperar la salud de suelo. En este trabajo se han realizado ensayos en microcosmos y en campo centrándonos en la salud del suelo minero contaminado con Pb y Zn durante un proceso de fitoestabilización empleando enmiendas orgánicas (purines vacunos, gallinaza, estiércol de oveja y lodos de papelera mezclados con gallinaza) y/o la especie metalífera Festuca rubra con el objetivo de (i) estudiar las interacciones suelo-enmienda responsables de los cambios inducidos por el proceso de quimioestabilización en las propiedades físicoquímicas y biológicas del suelo, (ii) evaluar la efectividad del proceso de fitoestabilización sobre suelos vegetados y de la revegetación sobre suelos desnudos (iii) valorar la idoneidad de distintos indicadores químicos y biológicos (parámetros microbianos y de la vegetación) para monitorizar la efectividad de la fitoestabilización asistida en términos de reducción de la biodisponibilidad de metales en el suelo, mejora de la vegetación y de la recuperación de la salud del suelo. La aplicación de enmiendas al suelo minero supone una entrada de materia orgánica y nutrientes que conduce a una disminución de la biodisponibilidad de metales, facilitando la colonización de las plantas y el crecimiento de la vegetación nativa, además de estimular la actividad microbiana del suelo. El pH del suelo es un factor crítico que condiciona la movilidad de los metales y la toxicidad del suelo. Las poblaciones microbianas de las enmiendas no modificaron la diversidad funcional de las comunidades microbianas nativas de la mina. Los purines vacunos y los lodos de papelera mezclados con gallinaza son los tratamientos más efectivos en el proceso de fitoestabilización asistida bajo condiciones de campo. La gallinaza fue el tratamiento que más estimuló el crecimiento de la vegetación nativa y la colonización en los suelos desnudos. El bioensayo de elongación radical de lechuga es un test sensible, sencillo y barato para evaluar la biodisponibilidad de metal y la ecotoxicidad del suelo. Los tocoferoles son biomarcadores de exposición a metales con potencial para su implementación en bioensayos de toxicidad. Este trabajo permite concluir que la población metalífera de F. rubra, combinada con enmiendas orgánicas, es una excelente candidata para los proyectos de fitoestabilización asistida. Además, la monitorización simultánea de los parámetros fisicoquímicos y microbiológicos del suelo y de su ecotoxicidad permite una evaluación adecuada de la salud del suelo, así como la selección de enmiendas apropiadas para el desarrollo de un proceso fitoestabilizador.

Simultaneous spectrophotometric determination of zinc, cadmium and mercury with hybrid linear analysis

A new method for simultaneous spectrophotometric determination of Zn, Cd and Hg using 2-(5-Br-2-pyridylazo)-5-diethylaminophenol as the color developing reagent was proposed. The absorption spectra of these three complexes have similar features with severe overlap in visible spectral range. For resolving these spectra, hybrid linear analysis was used, and the pure spectrum of each component was obtained from the calibration mixtures by least squares method. The effects of reaction condition, selection of wavelengths, determination of pure spectrum and additivity of absorbances etc. on the determination were discussed. The proposed method offers the advantages of simple, rapid, and accuracy. It has been successfully applied to the simultaneous determination of Zn, Cd and Hg in synthetic sample. A comparison was also made with the partial least squares method.

Simultaneous Learning of Non-Linear Manifold and Dynamical Models for High-Dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. The model structure setup and parameter learning are done using a variational Bayesian approach, which enables automatic Bayesian model structure selection, hence solving the problem of over-fitting. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.

Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing

Statistical tests in vector autoregressive (VAR) models are typically based on large-sample approximations, involving the use of asymptotic distributions or bootstrap techniques. After documenting that such methods can be very misleading even with fairly large samples, especially when the number of lags or the number of equations is not small, we propose a general simulation-based technique that allows one to control completely the level of tests in parametric VAR models. In particular, we show that maximized Monte Carlo tests [Dufour (2002)] can provide provably exact tests for such models, whether they are stationary or integrated. Applications to order selection and causality testing are considered as special cases. The technique developed is applied to quarterly and monthly VAR models of the U.S. economy, comprising income, money, interest rates and prices, over the period 1965-1996.

Microhabitat selection and daily movements of two rodents (Necromys lasiurus and Oryzomys scotti) in Brazilian Cerrado, as revealed by a spool-and-line device

Studies on patterns of habitat use by mammals are necessary for understanding the mechanisms involved in their distribution and abundance. In this study, we used the spool-and-line method to investigate habitat utilization by two sigmodontine rodents from Brazilian Cerrado, Necromys lasiurus and Oryzomys scotti. We conducted the study in a Cerrado area in central Brazil (15 degrees 56'S and e 47 degrees 56'W) where the animals were caught in an area of 7.68 ha of Cerrado sensu stricto. Captured individuals were marked, equipped with a spool-and-Line device, and released at the same capture point. The next day we followed the thread to record their daily movements and find their nests. To investigate microhabitat selection we compared habitat characteristics along traits of each studied species with general habitat characteristics of the study area. Although the mean 24-h distance was greater for N. lasiurus (mean +/- SE: 41.9 +/- 42.2 m, N=3) than for O. scotti (28.7 +/- 14.2 m, N=6) this difference was not significant (Mann-Whitney test, U=26, P>0.6). We detected significant differences among observed microhabitats variables of both species and available microhabitat characteristics as determined by discriminant analysis (Wilks's lambda F=3.001; df=14, 116; P<0.001). Both species were associated to microhabitat characteristics whose values differed markedly from the overall available habitat. Along the first canonical discriminant function of the DFA both them were associated with greater grass height than the mean height available and along the second axis N. lasiurus selected areas with higher fruit availability and more shelters than those selected by 0. scotti. For stronger inferences regarding differential patterns of habitat utilization by Cerrado rodents we suggest the simultaneous use of both spool-and-line and standard trapping methods. (c) 2005 Deutsche Geseltschaft fur Saugetierkunde. Published by Elsevier GmbH. ALL rights reserved.

Silver as internal standard for simultaneous determination of Cd and Pb in whole blood by electrothermal atomic absorption spectrometry

The use of internal standardization for simultaneous atomic absorption spectrometry (SIMAAS) was investigated for Cd and Pb determination in whole blood. The comparison of thermochemical and physicochemical parameters allowed the selection of Ag, Bi, and Tl as internal standard candidates. Correlation graphs, plotted from the normalized absorbance signals (n = 20) of internal standard (axis y) versus analyte ( axis x), precision and accuracy were used to select Ag as the most appropriate internal standard. Blood samples were diluted (1 + 9) with 0.11% (m/v) Triton X-100 + 1.1% (v/v) HNO3 + 0.28% (m/v) NH4H2PO4 + 10 mug L-1 Ag+. Pyrolysis and atomization temperatures for the optimized heating program were 550 and 1700 degreesC, respectively. Characteristic masses based on integrated absorbance were 1.68 +/- 0.01 pg for Cd and 30.3 +/- 0.1 pg for Pb. The detection limits (DL) were 0.095 +/- 0.001 mug L-1 and 0.86 +/- 0.01 mug L-1 for Cd and Pb, respectively. The mean RSD for all determinations was the same for Cd (13 +/- 9%) with or without Ag as internal standard ( IS). on the other hand, the use of Ag as IS improved the RSD for Pb from 3.6 +/- 4.0% to 2.2 +/- 2.0%. An effective contribution of the internal standard Ag was verified in the recoveries of spiked samples (0.5 mug L-1 Cd2+ and 5.0 mug L-1 Pb2+). The mean recoveries were 81 +/- 8% and 91 +/- 4% for Cd, and 80 +/- 11% and 93 +/- 6% for Pb without and with IS correction, respectively. This is the first application of IS for a simultaneous determination by SIMAAS.