Neural networks models for wear patterns recognition of single-point dresser

Grinding is a workpiece finishing process for advanced products and surfaces. However, the constant friction between workpiece and grinding wheel causes the latter to lose its sharpness, thereby impairing the result of the grinding process. When this occurs, the dressing process is essential to sharpen the worn grains of the grinding wheel. The dressing conditions strongly influence the performance of the grinding operation; hence, monitoring them throughout the process can increase its efficiency. The purpose of this study was to classify the wear condition of a single-point dresser using intelligent systems whose inputs were obtained by digitally processing acoustic emission signals. Two multilayer perceptron (MLP) neural networks were compared for their classification ability, one using the root mean square (RMS) statistics and another the ratio of power (ROP) statistics as input. In this study, it was found that the harmonic content of the acoustic emission signal is influenced by the condition of the dresser, and that the condition of the tool under study can be classified by using the aforementioned statistics to feed a neural network. © IFAC.

Application of MLP and Kohonen networks for recognition of wear patterns of single-point dressers

Grinding is a parts finishing process for advanced products and surfaces. However, continuous friction between the workpiece and the grinding wheel causes the latter to lose its sharpness, thus impairing the grinding results. This is when the dressing process is required, which consists of sharpening the worn grains of the grinding wheel. The dressing conditions strongly affect the performance of the grinding operation; hence, monitoring them throughout the process can increase its efficiency. The objective of this study was to estimate the wear of a single-point dresser using intelligent systems whose inputs were obtained by the digital processing of acoustic emission signals. Two intelligent systems, the multilayer perceptron and the Kohonen neural network, were compared in terms of their classifying ability. The harmonic content of the acoustic emission signal was found to be influenced by the condition of dresser, and when used to feed the neural networks it is possible to classify the condition of the tool under study.

CENSUS HISTOGRAMS: A SIMPLE FEATURE EXTRACTION and MATCHING APPROACH FOR FACE RECOGNITION

Most face recognition approaches require a prior training where a given distribution of faces is assumed to further predict the identity of test faces. Such an approach may experience difficulty in identifying faces belonging to distributions different from the one provided during the training. A face recognition technique that performs well regardless of training is, therefore, interesting to consider as a basis of more sophisticated methods. In this work, the Census Transform is applied to describe the faces. Based on a scanning window which extracts local histograms of Census Features, we present a method that directly matches face samples. With this simple technique, 97.2% of the faces in the FERET fa/fb test were correctly recognized. Despite being an easy test set, we have found no other approaches in literature regarding straight comparisons of faces with such a performance. Also, a window for further improvement is presented. Among other techniques, we demonstrate how the use of SVMs over the Census Histogram representation can increase the recognition performance.

Chlamydial infection in a high risk population: association with vaginal flora patterns

This study aimed to determine the frequency of Chlamydia trachomatis (CT) infection among high risk Brazilian women and evaluate its association with vaginal flora patterns.This was a cross-sectional study, performed in an outpatient clinic of Bauru State Hospital, So Paulo, Brazil. A total of 142 women were included from 2006 to 2008. Inclusion criteria was dyspareunia, pain during bimanual exam, presence of excessive cervical mucus, cervical ectopy or with three or more episodes of abnormal vaginal flora (AVF) in the previous year before enrollment. Endocervical CT testing was performed by PCR. Vaginal swabs were collected for microscopic assessment of the microbial flora pattern. Gram-stained smears were classified in normal, intermediate or bacterial vaginosis (BV), and recognition of Candida sp. morphotypes. Wet mount smears were used for detection of Trichomonas vaginalis and aerobic vaginitis (AV).Thirty-four of 142 women (23.9%) tested positive for CT. AVF was found in 50 (35.2%) cases. The most frequent type of AVF was BV (17.6%). CT was strongly associated with the presence of AV (n = 7, 4.9%, P = 0.018), but not BV (n = 25, 17.6%, P = 0.80) or intermediate flora (n = 18, 12.7%, P = 0.28).A high rate of chlamydial infection was found in this population. Chlamydia infection is associated with aerobic vaginitis.

Chemical communication, aggression, and conspecific recognition in the fish Nile tilapia

The chemical modulation of agonistic behavior and conspecific recognition were tested in juveniles of the fish Nile tilapia, Oreochromis niloticus (L.). After a 7-day isolation period, the fish were grouped (four individuals per aquarium) for 7 days. Then fish of alpha and beta ranks (previously matched for similar size) were paired in a neutral territory for analysis of their agonistic interaction. Pairs composed of alpha and beta fish were established with either fish from the same group (familiar) or from two different groups (unfamiliar). The pairs were tested in contiguous compartments, either with water exchange between the compartments or in the absence of water exchange. In each condition the fish were separated by a transparent glass partition. Twelve pairs were tested in each experimental condition. Fish behavior was videotaped and the following variables were analyzed: (a) frequency of and time spent in agonistic patterns, (b) latency to start fighting, and (c) duration of swimming. Water exchange between compartments decreased agonistic interactions. This effect, however, was more pronounced in pairs of fish coming from the same group (in this case, subordinate fish spent less time in confrontations than dominant ones). We conclude that chemical communication decreases aggression in this species by (1) inducing an alarm reaction and (2) increasing conspecific recognition (thus stabilizing the dominance hierarchy). (C) 1997 Elsevier B.V.

Aircraft interior failure pattern recognition utilizing text mining and neural networks

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Evolvable hardware applied to voice recognition

This paper presents some results of the application on Evolvable Hardware (EHW) in the area of voice recognition. Evolvable Hardware is able to change inner connections, using genetic learning techniques, adapting its own functionality to external condition changing. This technique became feasible by the improvement of the Programmable Logic Devices. Nowadays, it is possible to have, in a single device, the ability to change, on-line and in real-time, part of its own circuit. This work proposes a reconfigurable architecture of a system that is able to receive voice commands to execute special tasks as, to help handicapped persons in their daily home routines. The idea is to collect several voice samples, process them through algorithms based on Mel - Ceptrais theory to obtain their numerical coefficients for each sample, which, compose the universe of search used by genetic algorithm. The voice patterns considered, are limited to seven sustained Portuguese vowel phonemes (a, eh, e, i, oh, o, u).

Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition

Structural health monitoring (SHM) is related to the ability of monitoring the state and deciding the level of damage or deterioration within aerospace, civil and mechanical systems. In this sense, this paper deals with the application of a two-step auto-regressive and auto-regressive with exogenous inputs (AR-ARX) model for linear prediction of damage diagnosis in structural systems. This damage detection algorithm is based on the. monitoring of residual error as damage-sensitive indexes, obtained through vibration response measurements. In complex structures there are. many positions under observation and a large amount of data to be handed, making difficult the visualization of the signals. This paper also investigates data compression by using principal component analysis. In order to establish a threshold value, a fuzzy c-means clustering is taken to quantify the damage-sensitive index in an unsupervised learning mode. Tests are made in a benchmark problem, as proposed by IASC-ASCE with different damage patterns. The diagnosis that was obtained showed high correlation with the actual integrity state of the structure. Copyright © 2007 by ABCM.

Pattern recognition structured heuristics methods for image processing in mobile robot navigation

In this project, the main focus is to apply image processing techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained. ©2010 IEEE.

Species limits, phylogeographic and hybridization patterns in Neotropical leaf frogs (Phyllomedusinae)

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