Tool Condition Monitoring of Single-Point Dresser Using Acoustic Emission and Neural Networks Models

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Pruning methods to MLP neural networks considering proportional apparent error rate for classification problems with unbalanced data

This article deals with classification problems involving unequal probabilities in each class and discusses metrics to systems that use multilayer perceptrons neural networks (MLP) for the task of classifying new patterns. In addition we propose three new pruning methods that were compared to other seven existing methods in the literature for MLP networks. All pruning algorithms presented in this paper have been modified by the authors to do pruning of neurons, in order to produce fully connected MLP networks but being small in its intermediary layer. Experiments were carried out involving the E. coli unbalanced classification problem and ten pruning methods. The proposed methods had obtained good results, actually, better results than another pruning methods previously defined at the MLP neural network area. (C) 2014 Elsevier Ltd. All rights reserved.

Observation of the diphoton decay of the Higgs boson and measurement of its properties

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Reference evapotranspiration models using different time scales in the Jaboticabal region of Sao Paulo, Brazil

The aim of this paper is to compare 18 reference evapotranspiration models to the standard Penman-Monteith model in the Jaboticabal, Sao Paulo, region for the following time scales: daily, 5-day, 15-day and seasonal. A total of 5 years of daily meteorological data was used for the following analyses: accuracy (mean absolute percentage error, Mape), precision (R-2) and tendency (bias) (systematic error, SE). The results were also compared at the 95% probability level with Tukey's test. The Priestley-Taylor (1972) method was the most accurate for all time scales, the Tanner-Pelton (1960) method was the most accurate in the winter, and the Thornthwaite (1948) method was the most accurate of the methods that only used temperature data in the equations.

Evaluation of measurement errors of temperature and relative humidity from HOBO data logger under different conditions of exposure to solar radiation

This study aimed to assess measurements of temperature and relative humidity obtained with HOBO a data logger, under various conditions of exposure to solar radiation, comparing them with those obtained through the use of a temperature/relative humidity probe and a copper-constantan thermocouple psychrometer, which are considered the standards for obtaining such measurements. Data were collected over a 6-day period (from 25 March to 1 April, 2010), during which the equipment was monitored continuously and simultaneously. We employed the following combinations of equipment and conditions: a HOBO data logger in full sunlight; a HOBO data logger shielded within a white plastic cup with windows for air circulation; a HOBO data logger shielded within a gill-type shelter (multi-plate prototype plastic); a copper-constantan thermocouple psychrometer exposed to natural ventilation and protected from sunlight; and a temperature/relative humidity probe under a commercial, multi-plate radiation shield. Comparisons between the measurements obtained with the various devices were made on the basis of statistical indicators: linear regression, with coefficient of determination; index of agreement; maximum absolute error; and mean absolute error. The prototype multi-plate shelter (gill-type) used in order to protect the HOBO data logger was found to provide the best protection against the effects of solar radiation on measurements of temperature and relative humidity. The precision and accuracy of a device that measures temperature and relative humidity depend on an efficient shelter that minimizes the interference caused by solar radiation, thereby avoiding erroneous analysis of the data obtained.

Biomass burning fuel consumption rates: a field measurement database

Landscape fires show large variability in the amount of biomass or fuel consumed per unit area burned. Fuel consumption (FC) depends on the biomass available to burn and the fraction of the biomass that is actually combusted, and can be combined with estimates of area burned to assess emissions. While burned area can be detected from space and estimates are becoming more reliable due to improved algorithms and sensors, FC is usually modeled or taken selectively from the literature. We compiled the peerreviewed literature on FC for various biomes and fuel categories to understand FC and its variability better, and to provide a database that can be used to constrain biogeochemical models with fire modules. We compiled in total 77 studies covering 11 biomes including savanna (15 studies, average FC of 4.6 t DM (dry matter) ha 1 with a standard deviation of 2.2), tropical forest (n = 19, FC = 126 +/- 77), temperate forest (n = 12, FC = 58 +/- 72), boreal forest (n = 16, FC = 35 +/- 24), pasture (n = 4, FC = 28 +/- 9.3), shifting cultivation (n = 2, FC = 23, with a range of 4.0-43), crop residue (n = 4, FC = 6.5 +/- 9.0), chaparral (n = 3, FC = 27 +/- 19), tropical peatland (n = 4, FC = 314 +/- 196), boreal peatland (n = 2, FC = 42 [42-43]), and tundra (n = 1, FC = 40). Within biomes the regional variability in the number of measurements was sometimes large, with e. g. only three measurement locations in boreal Russia and 35 sites in North America. Substantial regional differences in FC were found within the defined biomes: for example, FC of temperate pine forests in the USA was 37% lower than Australian forests dominated by eucalypt trees. Besides showing the differences between biomes, FC estimates were also grouped into different fuel classes. Our results highlight the large variability in FC, not only between biomes but also within biomes and fuel classes. This implies that substantial uncertainties are associated with using biome-averaged values to represent FC for whole biomes. Comparing the compiled FC values with co-located Global Fire Emissions Database version 3 (GFED3) FC indicates that modeling studies that aim to represent variability in FC also within biomes, still require improvements as they have difficulty in representing the dynamics governing FC.

Measurement of pseudorapidity distributions of charged particles in proton-proton collisions at root s=8 TeV by the CMS and TOTEM experiments

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Comparison of linear measurements and analyses taken from plaster models and three-dimensional images

Digital models are an alternative for carrying out analyses and devising treatment plans in orthodontics. The objective of this study was to evaluate the accuracy and the reproducibility of measurements of tooth sizes, interdental distances and analyses of occlusion using plaster models and their digital images. Thirty pairs of plaster models were chosen at random, and the digital images of each plaster model were obtained using a laser scanner (3Shape R-700, 3Shape A/S). With the plaster models, the measurements were taken using a caliper (Mitutoyo Digimatic(®), Mitutoyo (UK) Ltd) and the MicroScribe (MS) 3DX (Immersion, San Jose, Calif). For the digital images, the measurement tools used were those from the O3d software (Widialabs, Brazil). The data obtained were compared statistically using the Dahlberg formula, analysis of variance and the Tukey test (p < 0.05). The majority of the measurements, obtained using the caliper and O3d were identical, and both were significantly different from those obtained using the MS. Intra-examiner agreement was lowest when using the MS. The results demonstrated that the accuracy and reproducibility of the tooth measurements and analyses from the plaster models using the caliper and from the digital models using O3d software were identical.

Spatial modelling and prediction on river networks: up model, down model or hybrid?

Preservation of rivers and water resources is crucial in most environmental policies and many efforts are made to assess water quality. Environmental monitoring of large river networks are based on measurement stations. Compared to the total length of river networks, their number is often limited and there is a need to extend environmental variables that are measured locally to the whole river network. The objective of this paper is to propose several relevant geostatistical models for river modeling. These models use river distance and are based on two contrasting assumptions about dependency along a river network. Inference using maximum likelihood, model selection criterion and prediction by kriging are then developed. We illustrate our approach on two variables that differ by their distributional and spatial characteristics: summer water temperature and nitrate concentration. The data come from 141 to 187 monitoring stations in a network on a large river located in the Northeast of France that is more than 5000 km long and includes Meuse and Moselle basins. We first evaluated different spatial models and then gave prediction maps and error variance maps for the whole stream network.

Evaluating Measurement Invariance with Censored Ordinal Data: A Monte Carlo Comparison of Alternative Model Estimators and Scales of Measurement

Evaluations of measurement invariance provide essential construct validity evidence. However, the quality of such evidence is partly dependent upon the validity of the resulting statistical conclusions. The presence of Type I or Type II errors can render measurement invariance conclusions meaningless. The purpose of this study was to determine the effects of categorization and censoring on the behavior of the chi-square/likelihood ratio test statistic and two alternative fit indices (CFI and RMSEA) under the context of evaluating measurement invariance. Monte Carlo simulation was used to examine Type I error and power rates for the (a) overall test statistic/fit indices, and (b) change in test statistic/fit indices. Data were generated according to a multiple-group single-factor CFA model across 40 conditions that varied by sample size, strength of item factor loadings, and categorization thresholds. Seven different combinations of model estimators (ML, Yuan-Bentler scaled ML, and WLSMV) and specified measurement scales (continuous, censored, and categorical) were used to analyze each of the simulation conditions. As hypothesized, non-normality increased Type I error rates for the continuous scale of measurement and did not affect error rates for the categorical scale of measurement. Maximum likelihood estimation combined with a categorical scale of measurement resulted in more correct statistical conclusions than the other analysis combinations. For the continuous and censored scales of measurement, the Yuan-Bentler scaled ML resulted in more correct conclusions than normal-theory ML. The censored measurement scale did not offer any advantages over the continuous measurement scale. Comparing across fit statistics and indices, the chi-square-based test statistics were preferred over the alternative fit indices, and ΔRMSEA was preferred over ΔCFI. Results from this study should be used to inform the modeling decisions of applied researchers. However, no single analysis combination can be recommended for all situations. Therefore, it is essential that researchers consider the context and purpose of their analyses.

Computational tools for comparing asymmetric GARCH models via Bayes factors

In this paper we use Markov chain Monte Carlo (MCMC) methods in order to estimate and compare GARCH models from a Bayesian perspective. We allow for possibly heavy tailed and asymmetric distributions in the error term. We use a general method proposed in the literature to introduce skewness into a continuous unimodal and symmetric distribution. For each model we compute an approximation to the marginal likelihood, based on the MCMC output. From these approximations we compute Bayes factors and posterior model probabilities. (C) 2012 IMACS. Published by Elsevier B.V. All rights reserved.

Error Autocorrelation and Linear Regression for Temperature-Based Evapotranspiration Estimates Improvement

Estimates of evapotranspiration on a local scale is important information for agricultural and hydrological practices. However, equations to estimate potential evapotranspiration based only on temperature data, which are simple to use, are usually less trustworthy than the Food and Agriculture Organization (FAO)Penman-Monteith standard method. The present work describes two correction procedures for potential evapotranspiration estimates by temperature, making the results more reliable. Initially, the standard FAO-Penman-Monteith method was evaluated with a complete climatologic data set for the period between 2002 and 2006. Then temperature-based estimates by Camargo and Jensen-Haise methods have been adjusted by error autocorrelation evaluated in biweekly and monthly periods. In a second adjustment, simple linear regression was applied. The adjusted equations have been validated with climatic data available for the Year 2001. Both proposed methodologies showed good agreement with the standard method indicating that the methodology can be used for local potential evapotranspiration estimates.

Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing

Background: In the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells separately. However, this approach usually fails to estimate accurate gene networks due to the limited length of time series data and measurement noise. Thus, approaches that identify changes on regulations by using time series data on both conditions in an efficient manner are demanded. Methods: We propose a new statistical approach that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to identify changes on regulations between the conditions. In the mathematical model of our approach, hidden binary variables are newly introduced to indicate the presence of regulations on each condition. The use of the hidden binary variables enables an efficient data usage; data on both conditions are used for commonly existing regulations, while for condition specific regulations corresponding data are only applied. Also, the similarity of networks on two conditions is automatically considered from the design of the potential function for the hidden binary variables. For the estimation of the hidden binary variables, we derive a new variational annealing method that searches the configuration of the binary variables maximizing the marginal likelihood. Results: For the performance evaluation, we use time series data from two topologically similar synthetic networks, and confirm that our proposed approach estimates commonly existing regulations as well as changes on regulations with higher coverage and precision than other existing approaches in almost all the experimental settings. For a real data application, our proposed approach is applied to time series data from normal Human lung cells and Human lung cells treated by stimulating EGF-receptors and dosing an anticancer drug termed Gefitinib. In the treated lung cells, a cancer cell condition is simulated by the stimulation of EGF-receptors, but the effect would be counteracted due to the selective inhibition of EGF-receptors by Gefitinib. However, gene expression profiles are actually different between the conditions, and the genes related to the identified changes are considered as possible off-targets of Gefitinib. Conclusions: From the synthetically generated time series data, our proposed approach can identify changes on regulations more accurately than existing methods. By applying the proposed approach to the time series data on normal and treated Human lung cells, candidates of off-target genes of Gefitinib are found. According to the published clinical information, one of the genes can be related to a factor of interstitial pneumonia, which is known as a side effect of Gefitinib.

Measurement of Direct Photons in Au plus Au Collisions at root s(NN)=200 GeV

We report the measurement of direct photons at midrapidity in Au + Au collisions at root s(NN) = 200 GeV. The direct photon signal was extracted for the transverse momentum range of 4 GeV/c < pT < 22 GeV/c, using a statistical method to subtract decay photons from the inclusive photon sample. The direct photon nuclear modification factor R-AA was calculated as a function of p(T) for different Au + Au collision centralities using the measured p + p direct photon spectrum and compared to theoretical predictions. R-AA was found to be consistent with unity for all centralities over the entire measured pT range. Theoretical models that account for modifications of initial direct photon production due to modified parton distribution functions in Au and the different isospin composition of the nuclei predict a modest change of R-AA from unity. They are consistent with the data. Models with compensating effects of the quark-gluon plasma on high-energy photons, such as suppression of jet-fragmentation photons and induced-photon bremsstrahlung from partons traversing the medium, are also consistent with this measurement.

First measurement of beta decay half-lives in neutron-rich Tl and Bi isotopes

Neutron-rich isotopes around lead, beyond N = 126, have been studied exploiting the fragmentation of an uranium primary beam at the FRS-RISING setup at GSI. For the first time beta-decay half-lives of Bi-219 and Tl-211,Tl-212,Tl-213 isotopes have been derived. The half-lives have been extracted using a numerical simulation developed for experiments in high-background conditions. Comparison with state of the art models used in r-process calculations is given, showing a systematic underestimation of the experimental values, at variance from close-lying nuclei. (c) 2012 Elsevier B.V. All rights reserved.