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

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

Classification and characterization of places for mapping of environment using hierarchical neural network and omnivision

Mobile robots need autonomy to fulfill their tasks. Such autonomy is related whith their capacity to explorer and to recognize their navigation environments. In this context, the present work considers techniques for the classification and extraction of features from images, using artificial neural networks. This images are used in the mapping and localization system of LACE (Automation and Evolutive Computing Laboratory) mobile robot. In this direction, the robot uses a sensorial system composed by ultrasound sensors and a catadioptric vision system equipped with a camera and a conical mirror. The mapping system is composed of three modules; two of them will be presented in this paper: the classifier and the characterizer modules. Results of these modules simulations are presented in this paper.

Phase behavior of the orange essential oil/sodium bis(2-ethylhexyl)sulfosuccinate/water system

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

Marginal and Internal Discrepancies Related to Margin Design of Ceramic Crowns Fabricated by a CAD/CAM System

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

Artificial immune systems for classification of petroleum well drilling operations

This paper presents two approaches of Artificial Immune System for Pattern Recognition (CLONALG and Parallel AIRS2) to classify automatically the well drilling operation stages. The classification is carried out through the analysis of some mud-logging parameters. In order to validate the performance of AIS techniques, the results were compared with others classification methods: neural network, support vector machine and lazy learning.

An ontology based for drilling report classification

This paper presents an application of an ontology based system for automated text analysis using a sample of a drilling report to demonstrate how the methodology works. The methodology used here consists basically of organizing the knowledge related to the drilling process by elaborating the ontology of some typical problems. The whole process was carried out with the assistance of a drilling expert, and by also using software to collect the knowledge from the texts. Finally, a sample of drilling reports was used to test the system, evaluating its performance on automated text classification.

Leak detection in petroleum pipelines using a fuzzy system

A methodology for pipeline leakage detection using a combination of clustering and classification tools for fault detection is presented here. A fuzzy system is used to classify the running mode and identify the operational and process transients. The relationship between these transients and the mass balance deviation are discussed. This strategy allows for better identification of the leakage because the thresholds are adjusted by the fuzzy system as a function of the running mode and the classified transient level. The fuzzy system is initially off-line trained with a modified data set including simulated leakages. The methodology is applied to a small-scale LPG pipeline monitoring case where portability, robustness and reliability are amongst the most important criteria for the detection system. The results are very encouraging with relatively low levels of false alarms, obtaining increased leakage detection with low computational costs. (c) 2005 Elsevier B.V. All rights reserved.

A Validated Stability-Indicating LC Method for Orbifloxacin in the Presence of Degradation Products

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

A Fast Large Scale Iris Database Classification with Optimum-Path Forest Technique: A Case Study

Majority of biometric researchers focus on the accuracy of matching using biometrics databases, including iris databases, while the scalability and speed issues have been neglected. In the applications such as identification in airports and borders, it is critical for the identification system to have low-time response. In this paper, a graph-based framework for pattern recognition, called Optimum-Path Forest (OPF), is utilized as a classifier in a pre-developed iris recognition system. The aim of this paper is to verify the effectiveness of OPF in the field of iris recognition, and its performance for various scale iris databases. This paper investigates several classifiers, which are widely used in iris recognition papers, and the response time along with accuracy. The existing Gauss-Laguerre Wavelet based iris coding scheme, which shows perfect discrimination with rotary Hamming distance classifier, is used for iris coding. The performance of classifiers is compared using small, medium, and large scale databases. Such comparison shows that OPF has faster response for large scale database, thus performing better than more accurate but slower Bayesian classifier.

Comparison of three systems of classification in predicting the outcome of diabetic foot ulcers in a Brazilian population

Objective: The aim was to compare there ulcer classification systems as predictors of the outcome of diabetic foot ulcers; the Wagner, the University of Texas (UT) and the size (area, depth), sepsis, arteriopathy, denervation system (S(AD)SAD) systems in specialist clinic in Brazil.Methods: Ulcer area, depth, appearance, infection and associated ischaemia and neuropathy were recorded in a consecutive series of 94 subjects. A novel score, the S(AD)SAD score, was derived from the sum of individual items of the S(AD)SAD system, and was evaluated. Follow-up was for at least 6 months. The primary outcome measure was the incidence of healing.Results: Mean age was 57.6 years; 57 (60.6%) were made. Forty-eight ulcers (51.1%) healed without surgery; 11 (12.2%) subjects underwent minor amputation. Significant differences in terms of healing were observed for depth (P = 0.002), infection (P = 0.006) and denervation (P = 0.002) using the S(AD)SAD system, for UT grade (P = 0.002) and stage (P = 0.032) and for Wagner grades (P = 0.002). Ulcers with an S(AD)SAD score of <= 9 (total possible 15) were 7.6 times more likely to heal than scores >= 10 (P < 0.001).Conclusions: All three systems predicted ulcer outcome. The S(AD)SAD score of ulcer severity could represent a useful addition to routine clinical practice. The association between outcome and ulcer depth confirms earlier reports. The association with infection was stronger than that reported from the centres in Europe or North America. The very strong association with neuropathy has only previously been observed in Tanzania. Studies designed to compare the outcome in different countries should adopt systems of classification, which are valid for the populations studied.

Hybrid visual-driven decision support system in video monitor manufacturing

An intelligent system that emulates human decision behaviour based on visual data acquisition is proposed. The approach is useful in applications where images are used to supply information to specialists who will choose suitable actions. An artificial neural classifier aids a fuzzy decision support system to deal with uncertainty and imprecision present in available information. Advantages of both techniques are exploited complementarily. As an example, this method was applied in automatic focus checking and adjustment in video monitor manufacturing. Copyright © 2005 IFAC.

Identification of structural damage in flexible structures using system norm and neural networks

Nowadays there is great interest in damage identification using non destructive tests. Predictive maintenance is one of the most important techniques that are based on analysis of vibrations and it consists basically of monitoring the condition of structures or machines. A complete procedure should be able to detect the damage, to foresee the probable time of occurrence and to diagnosis the type of fault in order to plan the maintenance operation in a convenient form and occasion. In practical problems, it is frequent the necessity of getting the solution of non linear equations. These processes have been studied for a long time due to its great utility. Among the methods, there are different approaches, as for instance numerical methods (classic), intelligent methods (artificial neural networks), evolutions methods (genetic algorithms), and others. The characterization of damages, for better agreement, can be classified by levels. A new one uses seven levels of classification: detect the existence of the damage; detect and locate the damage; detect, locate and quantify the damages; predict the equipment's working life; auto-diagnoses; control for auto structural repair; and system of simultaneous control and monitoring. The neural networks are computational models or systems for information processing that, in a general way, can be thought as a device black box that accepts an input and produces an output. Artificial neural nets (ANN) are based on the biological neural nets and possess habilities for identification of functions and classification of standards. In this paper a methodology for structural damages location is presented. This procedure can be divided on two phases. The first one uses norms of systems to localize the damage positions. The second one uses ANN to quantify the severity of the damage. The paper concludes with a numerical application in a beam like structure with five cases of structural damages with different levels of severities. The results show the applicability of the presented methodology. A great advantage is the possibility of to apply this approach for identification of simultaneous damages.

Classification of petroleum well drilling operations using Support Vector Machine (SVM)

During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a Support Vector Machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert. © 2006 IEEE.

Beyond Bretton Woods: what international monetary and financial system for the twenty-first century?

Includes bibliography

Taxation and the architecture of the international economic system

Includes bibliography