Multidimensional polynomial powers of sigmoid (PPS) Wavelet neural networks

Wavelet functions have been used as the activation function in feedforward neural networks. An abundance of R&D has been produced on wavelet neural network area. Some successful algorithms and applications in wavelet neural network have been developed and reported in the literature. However, most of the aforementioned reports impose many restrictions in the classical backpropagation algorithm, such as low dimensionality, tensor product of wavelets, parameters initialization, and, in general, the output is one dimensional, etc. In order to remove some of these restrictions, a family of polynomial wavelets generated from powers of sigmoid functions is presented. We described how a multidimensional wavelet neural networks based on these functions can be constructed, trained and applied in pattern recognition tasks. As an example of application for the method proposed, it is studied the exclusive-or (XOR) problem.

In-process grinding monitoring by acoustic emission

This work aims to investigate the efficiency of digital signal processing tools of acoustic emission signals in order to detect thermal damages in grinding process. To accomplish such a goal, an experimental work was carried out for 15 runs in a surface grinding machine operating with an aluminum oxide grinding wheel and ABNT 1045. The acoustic emission signals were acquired from a fixed sensor placed on the workpiece holder. A high sampling rate data acquisition system at 2.5 MHz was used to collect the raw acoustic emission instead of root mean square value usually employed. Many statistics have shown effective to detect burn, such as the root mean square (RMS), correlation of the AE, constant false alarm (CFAR), ratio of power (ROP) and mean-value deviance (MVD). However, the CFAR, ROP, Kurtosis and correlation of the AE have been presented more sensitive than the RMS.

Automatic detection of thermal damage in grinding process by artificial neural network

Esse trabalho tem por objetivo o desenvolvimento de um sistema inteligente para detecção da queima no processo de retificação tangencial plana através da utilização de uma rede neural perceptron multi camadas, treinada para generalizar o processo e, conseqüentemente, obter o limiar de queima. em geral, a ocorrência da queima no processo de retificação pode ser detectada pelos parâmetros DPO e FKS. Porém esses parâmetros não são eficientes nas condições de usinagem usadas nesse trabalho. Os sinais de emissão acústica e potência elétrica do motor de acionamento do rebolo são variáveis de entrada e a variável de saída é a ocorrência da queima. No trabalho experimental, foram empregados um tipo de aço (ABNT 1045 temperado) e um tipo de rebolo denominado TARGA, modelo ART 3TG80.3 NVHB.

Time-domain analysis of piezoelectric impedance-based structural health monitoring using multilevel wavelet decomposition

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A multisampling time-domain CMOS imager with synchronous readout circuit

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

Digital radiography using digital detector arrays fulfills critical applications for offshore pipelines

Digital radiography in the inspection of welded pipes to be installed under deep water offshore gas and oil pipelines, like a presalt in Brazil, in the paper has been investigated. The aim is to use digital radiography for nondestructive testing of welds as it is already in use in the medical, aerospace, security, automotive, and petrochemical sectors. Among the current options, the DDA (Digital Detector Array) is considered as one of the best solutions to replace industrial films, as well as to increase the sensitivity to reduce the inspection cycle time. This paper shows the results of this new technique, comparing it to radiography with industrial films systems. In this paper, 20 test specimens of longitudinal welded pipe joints, specially prepared with artificial defects like cracks, lack of fusion, lack of penetration, and porosities and slag inclusions with varying dimensions and in 06 different base metal wall thicknesses, were tested and a comparison of the techniques was made. These experiments verified the purposed rules for parameter definitions and selections to control the required digital radiographic image quality as described in the draft international standard ISO/DIS 10893-7. This draft is first standard establishing the parameters for digital radiography on weld seam of welded steel pipes for pressure purposes to be used on gas and oil pipelines.

A New Approach for Real Time Fault Diagnosis in Induction Motors Based on Vibration Measurement

Condition monitoring is used to increase machinery availability and machinery performance, reducing consequential damage, increasing machine life, reducing spare parts inventories, and reducing breakdown maintenance. An efficient real time vibration measurement and analysis instruments is capable of providing warning and predicting faults at early stages. In this paper, a new methodology for the implementation of vibration measurement and analysis instruments in real time based on circuit architecture mapped from a MATLAB/Simulink model is presented. In this study, signal processing applications such as FIR filters and fast Fourier transform are treated as systems, which are implemented in hardware using a system generator toolbox, which translates a Simulink model in a hardware description language - HDL for FPGA implementations.

Ultrasonic densitometer using a multiple reflection technique

This work presents recent improvements in a density measurement cell with a double-element transducer that can eliminate diffraction effects. A new mechanical design combined with the use of more appropriate materials has resulted in better parallelism between interfaces, more robust assembly, and chemical resistance. A novel method of signal processing, named energy method, is introduced to obtain the reflection coefficient, reducing sensitivity to noise and improving accuracy. The measurement cell operation is verified both theoretically, using an acoustic wave propagation model, and experimentally, using homogeneous liquids with different densities. The accuracy in the density measurement is 0.2% when compared with the measurements made with a pycnometer.

Detection and classification of voltage disturbances using a Fuzzy-ARTMAP-wavelet network

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

Low voltage four-quadrant current multiplier: an improved topology for n-well CMOS process

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Structural damage detection by fuzzy clustering

The development of strategies for structural health monitoring (SHM) has become increasingly important because of the necessity of preventing undesirable damage. This paper describes an approach to this problem using vibration data. It involves a three-stage process: reduction of the time-series data using principle component analysis (PCA), the development of a data-based model using an auto-regressive moving average (ARMA) model using data from an undamaged structure, and the classification of whether or not the structure is damaged using a fuzzy clustering approach. The approach is applied to data from a benchmark structure from Los Alamos National Laboratory, USA. Two fuzzy clustering algorithms are compared: fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms. It is shown that while both fuzzy clustering algorithms are effective, the GK algorithm marginally outperforms the FCM algorithm. (C) 2008 Elsevier Ltd. All rights reserved.

Scintigraphic validation of AC Biosusceptometry to study the gastric motor activity and the intragastric distribution of food in humans

Abnormal intragastric distribution of food (IDF) and a phasic contractility in the proximal stomach have been related to dyspeptic symptoms. Thus, the behaviour of the stomach and the proximal region, in particular, continues to attract attention and demand for reliable and comfortable techniques. The aims of this study were to employ AC Biosus-ceptometry (ACB) and scintigraphy to evaluate IDF and gastric motor activity in humans. Fifteen healthy volunteers ingested 60 mL of yogurt containing 2 mCi of Tc-99m and 4 g of ferrite. Each volunteer had gastric motility and IDF evaluated twice on separate days; on one occasion by ACB and another by scintigraphy. Digital signal processing was performed in MatLab (Mathworks Inc., Natick, MA, USA). Results were expressed as mean +/- SD. Similar results of distal accumulation time (P < 0.001) were obtained for scintigraphy (6.93 +/- 3.25 min) and for ACB (7.04 +/- 3.65 min). Fast Fourier Transform revealed two dominant frequencies (P > 0.9). Besides the well-know frequency of 3 cpm, our results showed identical frequencies in proximal stomach recordings (P < 0.001) for scintigraphic (1.01 +/- 0.01 cpm) and ACB (0.98 +/- 0.06 cpm). In summary, our data showed that scintigraphy and ACB are promising techniques to evaluate several aspects of gastric motility. Moreover, ACB is non-invasive, radiation-free and deserves the same importance as conventional methods for this kind of analysis.

Sistema de aquisição de dados para estudos de transiente de pressão e detecção de vazamentos em oleodutos

In February 2011, the National Agency of Petroleum, Natural Gas and Biofuels (ANP) has published a new Technical Rules for Handling Land Pipeline Petroleum and Natural Gas Derivatives (RTDT). Among other things, the RTDT made compulsory the use of monitoring systems and leak detection in all onshore pipelines in the country. This document provides a study on the method for detection of transient pressure. The study was conducted on a industrial duct 16" diameter and 9.8 km long. The pipeline is fully pressurized and carries a multiphase mixture of crude oil, water and natural gas. For the study, was built an infrastructure for data acquisition and validation of detection algorithms. The system was designed with SCADA architecture. Piezoresistive sensors were installed at the ends of the duct and Digital Signal Processors (DSPs) were used for sampling, storage and processing of data. The study was based on simulations of leaks through valves and search for patterns that characterize the occurrence of such phenomena

Algoritmos e Arquiteturas VLSI para Detectores MIMO com Decisão Suave

The use of Multiple Input Multiple Output (MIMO) systems has permitted the recent evolution of wireless communication standards. The Spatial Multiplexing MIMO technique, in particular, provides a linear gain at the transmission capacity with the minimum between the numbers of transmit and receive antennas. To obtain a near capacity performance in SM-MIMO systems a soft decision Maximum A Posteriori Probability MIMO detector is necessary. However, such detector is too complex for practical solutions. Hence, the goal of a MIMO detector algorithm aimed for implementation is to get a good approximation of the ideal detector while keeping an acceptable complexity. Moreover, the algorithm needs to be mapped to a VLSI architecture with small area and high data rate. Since Spatial Multiplexing is a recent technique, it is argued that there is still much room for development of related algorithms and architectures. Therefore, this thesis focused on the study of sub optimum algorithms and VLSI architectures for broadband MIMO detector with soft decision. As a result, novel algorithms have been developed starting from proposals of optimizations for already established algorithms. Based on these results, new MIMO detector architectures with configurable modulation and competitive area, performance and data rate parameters are here proposed. The developed algorithms have been extensively simulated and the architectures were synthesized so that the results can serve as a reference for other works in the area

Feature selection through gravitational search algorithm

In this paper we deal with the problem of feature selection by introducing a new approach based on Gravitational Search Algorithm (GSA). The proposed algorithm combines the optimization behavior of GSA together with the speed of Optimum-Path Forest (OPF) classifier in order to provide a fast and accurate framework for feature selection. Experiments on datasets obtained from a wide range of applications, such as vowel recognition, image classification and fraud detection in power distribution systems are conducted in order to asses the robustness of the proposed technique against Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and a Particle Swarm Optimization (PSO)-based algorithm for feature selection.