An intelligent system to detect drilling problems through drilled cuttings return analysis

Cuttings return analysis is an important tool to detect and prevent problems during the petroleum well drilling process. Several measurements and tools have been developed for drilling problems detection, including mud logging, PWD and downhole torque information. Cuttings flow meters were developed in the past to provide information regarding cuttings return at the shale shakers. Their use, however, significantly impact the operation including rig space issues, interferences in geological analysis besides, additional personel required. This article proposes a non intrusive system to analyze the cuttings concentration at the shale shakers, which can indicate problems during drilling process, such as landslide, the collapse of the well borehole walls. Cuttings images are acquired by a high definition camera installed above the shakers and sent to a computer coupled with a data analysis system which aims the quantification and closure of a cuttings material balance in the well surface system domain. No additional people at the rigsite are required to operate the system. Modern Artificial intelligence techniques are used for pattern recognition and data analysis. Techniques include the Optimum-Path Forest (OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC). Field test results conducted on offshore floating vessels are presented. Results show the robustness of the proposed system, which can be also integrated with other data to improve the efficiency of drilling problems detection. Copyright 2010, IADC/SPE Drilling Conference and Exhibition.

Robust and fast vowel recognition using optimum-path forest

The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamental importance in most of the speech processing systems, vary from automatic interpretation of spoken language to biometrics. State-of-the-art systems for AVR are based on traditional machine learning models such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), however, such classifiers can not deal with efficiency and effectiveness at the same time, existing a gap to be explored when real-time processing is required. In this work, we present an algorithm for AVR based on the Optimum-Path Forest (OPF), which is an emergent pattern recognition technique recently introduced in literature. Adopting a supervised training procedure and using speech tags from two public datasets, we observed that OPF has outperformed ANNs, SVMs, plus other classifiers, in terms of training time and accuracy. ©2010 IEEE.

ANN statistical image recognition method for computer vision in agricultural mobile robot navigation

The main application area in this project, is to deploy image processing and segmentation techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. Thereby, 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 image recognition. Hence, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave computational platforms, along with the application of customized Back-propagation Multilayer Perceptron (MLP) 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 segmented images in which reasonably accurate results were obtained. © 2010 IEEE.

Optimum-Path Forest pruning parameter estimation through Harmony Search

Pattern recognition in large amount of data has been paramount in the last decade, since that is not straightforward to design interactive and real time classification systems. Very recently, the Optimum-Path Forest classifier was proposed to overcome such limitations, together with its training set pruning algorithm, which requires a parameter that has been empirically set up to date. In this paper, we propose a Harmony Search-based algorithm that can find near optimal values for that. The experimental results have showed that our algorithm is able to find proper values for the OPF pruning algorithm parameter. © 2011 IEEE.

Fault location in underground systems through optimum-path forest

In this paper we propose an accurate method for fault location in underground distribution systems by means of an Optimum-Path Forest (OPF) classifier. We applied the Time Domains Reflectometry method for signal acquisition, which was further analyzed by OPF and several other well known pattern recognition techniques. The results indicated that OPF and Support Vector Machines outperformed Artificial Neural Networks classifier. However, OPF has been much more efficient than all classifiers for training, and the second one faster for classification. © 2011 IEEE.

Electrical consumers data clustering through optimum-path forest

Non-technical losses identification has been paramount in the last decade. Since we have datasets with hundreds of legal and illegal profiles, one may have a method to group data into subprofiles in order to minimize the search for consumers that cause great frauds. In this context, a electric power company may be interested in to go deeper a specific profile of illegal consumer. In this paper, we introduce the Optimum-Path Forest (OPF) clustering technique to this task, and we evaluate the behavior of a dataset provided by a brazilian electric power company with different values of an OPF parameter. © 2011 IEEE.

Influência de diferentes sistemas de cultivo nos atributos físicos e no carbono orgânico do solo

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

Relationship between citrus productivity and indicators of uniformity in trickle irrigation system

In trickle irrigation systems, the design is based on the pre-established emission uniformity (EU) which is the combined result of the equipment characteristics and its hydraulic configuration. However, this desired value of the EU may not be confirmed by the final project (in field conditions) and neither by the yield uniformity. The hypotheses of this research were: a) the EU of a trickle irrigation system at field conditions is equal to the emission uniformity pre-established in the its design; b) EU has always the lowest value when compared with other indicators of uniformity; c) the discharge variation coefficient (VC) is not equal to production variation coefficient in the operational unit; d) the difference between the discharge variation coefficient and the productivity variation coefficient depends on the water depth applied. This study aimed to evaluate the relationship between EU used in the irrigation system design and the final yield uniformity. The uniformity indicators evaluated were: EU, distribution uniformity (UD) and the index proposed by Barragan & Wu (2005). They were compared estimating the performance of a trickle irrigation system applied in a citrus orchard with dimensions of 400m x 600m. The design of the irrigation system was optimized by a Linear Programming model. The tree rows were leveled in the larger direction and the spacing adopted in the orchard was 7m x 4m. The manifold line was always operating on a slope condition. The sensitivity analysis involved different slopes, 0, 3, 6, 9 and 12%, and different values of emission uniformity, 60, 70, 75, 80, 85, 90 and 94%. The citrus yield uniformity was evaluated by the variation coefficient. The emission uniformity (EU) after design differed from the EU pre-established, more sharply in the initial values lower than 90%. Comparing the uniformity indexes, the EU always generated lower values when compared with the UD and with the index proposed by Barragan. The emitter variation coefficient was always lower than the productivity variation coefficient. To obtain uniformity of production, it is necessary to consider the irrigation system uniformity and mainly the water depth to be applied.

Operational safety: Development of electronic system for dynamic balance evaluation of farm tractors

The present study aimed at the development and evaluation of a low cost electronic device in order to provide safety for farm tractor users. The major accident occurrence in agricultural surroundings is from farm tractor side bending. Therefore, this sensor was designed to detect and alert about it. The results were satisfying. © 2013 Taylor & Francis Group.

ECG arrhythmia classification based on optimum-path forest

An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the non-invasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e.; cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graph-based pattern recognition technique, the optimum-path forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, specificity, and sensitivity) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e.; support vector machine (SVM), Bayesian and multilayer artificial neural network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e.; there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis. © 2012 Elsevier Ltd. All rights reserved.

Influence of Agroforestry and Traditional Management of Maize (Zea mays L.) on Soil Fertility

The aim of the work was to evaluate soil nutrient concentration at 0-5, 5-10, and 10-20 cm in maize (Zea mays L.) grown in sequence with black oats (Avena strigosa Schreb.) under Leucaena diversifolia alley cropping agroforestry system (AFS) and traditional management system/sole crop (without trees-TS), following a randomized block design. The experiment was carried out at the Brazilian Association of Biodynamic Agriculture, in Botucatu, São Paulo, Brazil. The treatments were: control (C), chemical fertilizer (F), biomass of L. diversifolia alley cropping (B), and biomass of L. diversifolia alley cropping + chemical fertilizer (B+F). After 2 yr, it was observed that pH, organic matter, and nutrient content had a tendency to show higher values in the treatments biomass+fertilizer, biomass, and fertilizer application, in both systems. Higher values in pH, organic matter, phosphorus, potassium, calcium, magnesium, sum of bases, cation exchange capacity, percentage base saturation, boron, copper, and manganese tended to occur in the agroforestry system. © 2013 Copyright Taylor and Francis Group, LLC.

Context-dependence in seed removal by lekking and non-lekking frugivorous birds in Brazilian Atlantic forest

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Optimizing feature selection through binary charged system search

Feature selection aims to find the most important information from a given set of features. As this task can be seen as an optimization problem, the combinatorial growth of the possible solutions may be inviable for a exhaustive search. In this paper we propose a new nature-inspired feature selection technique based on the Charged System Search (CSS), which has never been applied to this context so far. The wrapper approach combines the power of exploration of CSS together with the speed of the Optimum-Path Forest classifier to find the set of features that maximizes the accuracy in a validating set. Experiments conducted in four public datasets have demonstrated the validity of the proposed approach can outperform some well-known swarm-based techniques. © 2013 Springer-Verlag.

Sistemas de manejo e qualidade física em latossolo vermelho

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

Meio ambiente e agricultura na microbacia hidrográfica do Córrego Palmitalzinho - Regente Feijó/São Paulo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)