Prematurity and low birth weight baby at birth: from research to intervention

Este artigo apresenta uma revisão bibliográfica dos estudos que, no âmbito da psicologia, se dedicaram ao parto prematuro e ao baixo peso do bebé à nascença. Debruça-se especialmente sobre as causas e consequências, a curto e a longo prazo, apresentadas na investigação actual, contemplando os pais e o bebé. Discute ainda a intervenção, no que se refere às estratégias preventivas e remediativas úteis nesta situação.This article is a review of various studies that, within the range of psychology, have researched premature labor and low birth weight at birth. It focuses mostly on the causes and consequences, both short and long term, that are presented within the line of research nowadays, considering both parents and baby. It also discusses intervention, concerning preventive and remeditative strategies that are suitable for this situation

Neural Networks Tool Condition Monitoring in Single-point Dressing Operations

Abstract Cognitive modeling of tool wear progress is employed to obtain a dependable trend of tool wear curves for optimal utilization of tool life and productivity improvement, while preserving the surface integrity of the ground parts. This paper describes a method to characterize the dresser wear condition utilizing vibration signals by applying a cognitive paradigm, such as Artificial Neural Networks (ANNs). Dressing tests with a single-point dresser were performed in a surface grinding machine and tool wear measurements taken along the experiments. The results show that ANN processing offers an effective method for the monitoring of grinding wheel wear based on vibration signal analysis

Prediction of Dressing in Grinding Operation via Neural Networks

Abstract In order to obtain a modelling and prediction of tool wear in grinding operations, a Cognitive System has been employed to observe the dressing need and its trend. This paper aims to find a methodology to characterize the condition of the wheel during grinding operations and, by the use of cognitive paradigms, to understand the need of dressing. The Acoustic Emission signal from the grinding operation has been employed to characterize the wheel condition and, by the feature extraction of such signal, a cognitive system, based on Artificial Neural Networks, has been implemented

Tool Condition Monitoring of Single-point Dressing Operation by Digital Signal Processing of AE and AI

Abstract This work aims at determining the right moment to stop single-point dressing the grinding wheel in order to optimize the grinding process as a whole. Acoustic emission signals and signal processing tools are used as primary approach. An acoustic emission (AE) sensor was connected to a signal processing module. The AE sensor was attached to the dresser holder, which was specifically built to perform dressing tests. In this work there were three types of test where the edit parameters of each dressing test are: the passes number, the dressing speed, the width of action of the dresser, the dressing time and the sharpness. Artificial Neural Networks (ANNs) technique is employed to classify and predict the best moment for stopping the dressing operation. During the ANNs use, the results from Supervised Neural Networks and Unsupervised Neural Networks are compared