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This paper describes a tool created to simulate the effects of evaporation ducts on wireless communication networks which are close to the sea surface. The tool is an extension of the ns-3 network simulator and implements two propagation loss models: Two Ray, which models reflection effects; and Three Ray, which models the effects of both reflection and refraction. As the Three Ray model has the evaporation duct height as one of its parameters, a model to estimate this height based on weather conditions (meteorological measurements) was also implemented. The tool allows us to show how signal is weakened with the distance from the transmitter to the receiver, under different evaporation duct heights. It has been of increading interest to model these effects due to the importance of wireless networks located over the sea surface for supporting different types of applications, such as surveillance, security and ecological monitoring. Extending ns-3 is of particular importance, as it is one of the most largely used simulators for designing and evaluating communication networks.

This article presents a solution of the inverse kinematics problem of 7-degrees-of-freedom serial redundant manipulators. A 7-degrees-of-freedom (7-DoF) redundant manipulator can avoid obstacles and thus improve operational performance. However, its inverse kinematics is difficult to solve since it has one more DoF than that necessary for reaching the whole workspace, which causes infinite solutions. In this article, Gröbner bases theory is proposed to solve the inverse kinematics. First, the Denavit–Hartenberg model for the manipulator is established. Second, different joint configurations are obtained using Gröbner bases theory. All solutions are confirmed with the aid of algebraic computing software, confirming that this method is accurate and easy to be implemented.

This paper presents an application of a stabilizing model predictive control (IHMPC) strategy with the underlying guarantee of feasibility to an oil production well system with Electric Submersible Pump (ESP) installation. The proposed controller is compared with a conventional finite-horizon MPC which provides some unfeasible solutions in the presence of an unmeasured disturbance, due to the typical conflict among the ESP-lifted oil well system constraints. The results show IHMPC as a viable choice to improve ESP-lifted well production since it can incorporate the desired requirements and provides stabilizing control actions.

This article presents an analysis of computational efficiency to solve the inverse kinematics problem of anthropomorphic robots. Two approaches are investigated: the first approach uses Paul’s method applied to the matrix obtained by the Denavit–Hartenberg algorithm and the second approach uses Gröbner bases theory. With each approach, the problem of inverse kinematics for an anthropomorphic robot will be solved. When comparing each method, this article will demonstrate that the method using Gröbner bases theory is more computationally efficient.

Purpose– The purpose of this paper is to compare four life data models, namely the exponential and the Weibull models, and their corresponding generalized versions,q-exponential andq-Weibull models, by means of one practical application.Design/methodology/approach– Application of the models to a practical example (a welding station), with estimation of parameters by the use of the least squares method, and the Akaike Information Criterion (AIC).Findings– The data of the example considered in this paper is divided into three regimes, decreasing, constant and increasing failure rate, and theq-Weibull model describes the bathtub curve displayed by the data with a single set of parameters.Practical implications– The simplicity and flexibility of theq-Weibull model may be very useful for practitioners of reliability analysis, and its benefits surpasses the inconvenience of the additional parameter, as AIC shows.Originality/value– Theq-Weibull model is compared in detail with other three models, through the analysis of one example that clearly exhibits a bathtub curve, and it is shown that it can describe the whole time range with a single set of parameters.

This article analyzes 10 electrical engineering undergraduate courses in Brazil. The analysis was performed using several institutional indices, namely, the National Student Proficiency Exam (ENADE), Preliminary Concept of the Degree (CPC), General Index of the Degree (IGC), Institutional Concept (CI) and Human Development Index (HDI). A regional analysis confirmed that the undergraduate electrical engineering courses in the north-northeast region of Brazil have a different structure from that of institutions in the south-southeast of the country. The courses at universities in the south-southeast region have the best institutional indicators, however, in general they have the lowest workloads. The total workloads range from 4974 hours (the largest) to 3600 hours (the lowest). Three electrical engineering courses have workloads that exceed 4000 hours (the Federal University of Santa Catarina [UFSC], the Federal University of Bahia [UFBA] and the Federal University of Para [UFPA]). On the other hand, of the top 4 courses with the highest scores in ENADE: UFSC, (the Federal University of Minas Gerais [UFMG], the Federal University of Rio Grande do Sul [UFRGS] and the University of Brasilia [UNB]), only one of them, UFSC, has a workload that exceeds 4000 hours.

Using an object detector over a whole image can require significant processing time. This is so since the majority of the images, in common scenarios, is composed of non-trivial amounts of background information, such as sky, ground and water. To alleviate this computational load, image search space reduction methods can make the detection procedure focus on more distinctive image regions. In this sense, we propose here the use of saliency information to organize regions based on their probability of containing objects. The proposed method was grounded on a multi-scale spectral residue (MSR) analysis for saliency detection. For better search space reduction, our method enables fine control of search scale, presents more robustness to variations on saliency intensity along an object length, and also a straightforward way to control the balance between search space reduction and false negatives, both being a consequence of region selection. MSR was capable of making object detection three to five times faster compared to the same detector without MSR. A thorough analysis was accomplished to demonstrate the effectiveness of the proposed method using a custom LabelMe dataset of person images, and also a Pascal VOC 2007 dataset, containing several distinct object classes.

Several studies have shown that the satisfactory operation of oil wells with sucker rod pumps is attributed to the techniques and methods able to control the performance of the well. The Polytechnic School of Federal University of Bahia, through the Artificial Lift Lab, has nowadays a reduced plant of a sucker rod pump with an artificial well of 32m height fully instrumented, with full access and visible. Among some studies already performed with this sucker rod pump, there is the use of a dynamic model to control the fluid level in annular well. The presence of uncertainties is related to the fluid characteristics in the well, associated with either the electrical or the mechanical assembly, may jeopardize the desired performance of the PID controller. These uncertainties usually do not contribute to good results and may put in risk the well's productivity. The aim of this paper is to show a model reference adaptive controller to deal with some uncertainties in the system model to control the level of the annular well. The results show that the adaptive controller is able to deal satisfactorily with changes, such as: in the pumped fluid composition (parts of water, oil and gas), which is a very common situation in real production fields.

This work introduces a new information-theoretic methodology for choosing variables and their time lags in a prediction setting, particularly when neural networks are used in non-linear modeling. The first contribution of this work is the Cross Entropy Function (XEF) proposed to select input variables and their lags in order to compose the input vector of black-box prediction models. The proposed XEF method is more appropriate than the usually applied Cross Correlation Function (XCF) when the relationship among the input and output signals comes from a non-linear dynamic system. The second contribution is a method that minimizes the Joint Conditional Entropy (JCE) between the input and output variables by means of a Genetic Algorithm (GA). The aim is to take into account the dependence among the input variables when selecting the most appropriate set of inputs for a prediction problem. In short, theses methods can be used to assist the selection of input training data that have the necessary information to predict the target data. The proposed methods are applied to a petroleum engineering problem; predicting oil production. Experimental results obtained with a real-world dataset are presented demonstrating the feasibility and effectiveness of the method.

This paper present the characterization of the driving unit of a rodless mechanical pumping device, named linear Mechanical Submerged Pumping of double effect (MSP) which is the subject of a recent patent application at the National Institute of Intellectual Property (BR 2013 10 7033422). This rodless pumping device consists of an alternative hydraulic piston adapted to operate partially or totally submerged in oil production wells. The device is also a viable alternative for pumping water from deep aquifers. The goal of this work is to characterize and identify the main components used in the MSP concept, using the operating parameters and dimensions of conventional artificial lift wells, available in the Laboratory of Artificial Elevation of the Federal University of Bahia (LEA/UFBA). Our results allow estimating the characteristics of the components required to meet the requeriments for production considering the operating parameters of MSP. This new method of artificial lift and the alternative method of pumping fluid of linear wells or with deviation, justify investment in the production of specific components as well as producing the prototype, testing and validating the apparatus using trading components.

This work proposes a new method of diagnosing the Sucker Rod Pump Systems, by analyzing segments between the points of opening and closing valves identified in the Downhole Dynamometer Cards. An analysis of the shape of the pumping condition classes represented in the Downhole Dynamometer Cards, especially regarding the behavior of segments between the valve opening and closing points is presented. The feasibility of the classification of Downhole Dynamometer Cards by the curvature characteristics of segments is demonstrated. Then, methods of classification of 16 pumping condition classes are presented, 4 by simple statistics and 12 of them extracting curvature characteristics of the segments andusing statistics to implement Mamdani fuzzy inference systems. Tests are developed with classifiers created by the method, and the test results over a library of Dynamometer Cards from real wells, previously classified by human experts, demonstrate the feasibility of the approach and the precision of the classification even without a deeper study to choose the better characteristics and statistics. In addition the classifiers were developed using samples of Dynamometer Cards from the literature, showing that the method reduces the need of many samples of the great variety of the Dynamometer Cards Shapes from real wells. Different from other methods, these results indicate the severity degree of many pumping condition classes and demonstrate the concomitant pumping condition detection feature. The classifiers were even able to spot misleading classifications by the human experts thanks to deeper understanding of the behavior of the Dynamometer Cards segments in the case of one or more anomalies in the system. The features to quantify and identify concomitant classes of so many pumping conditionsdemonstrated in this approach are novel in the area. The paper shows the feasibility of the approach and the potential for greater identification precision.

This article proposes a hybrid neural architecture (i.e. neuro-fuzzy) capable to generate Takagi-Sugeno fuzzy rules and to adjust the membership functions and the output functions. The main idea is to apply this algorithm to function approach tasks, whose input-output relationships are the only available information. The proposed algorithm spares previous data analysis. Initially, the user only defines the number of fuzzy rules that the system should produce. This model has the convenience of not requesting the previous and empiric knowledge of the fuzzy rules structure. The proposed model is initiated with an architecture fully connected; in other words, implicit rules do not exist in the connections of the net. Instead of this, the connections between the entrance layer and the T-norms are linked by synaptic weights. These weights must be adjusted and, in a posterior stage, are partially eliminated. The training process occurs in four stages: in the first stage is adjusted the synapses and the function parameters. The second stage occurs when the quadratic error reaches acceptable values. Then, a part of the synapses introduced between the first and the second layer are eliminated. In this stage the proposed net is equaled to the ANFIS model, because the disabled synapses reveal the base rules generated automatically by the algorithm. In the third stage, the adjustment process continues from an identical way to the ANFIS. Finally, in the fourth stage, is eliminated the lower relevant rules. This process makes the base of rules more intelligible.

The design of specialized hardware for Network Intrusion Detection has been subject of intense research over the last decade due to its considerably higher performance compared to software implementations. In this context, one of the limiting factors is the finite amount of memory resources versus the increasing number of threat patterns to be analyzed. This paper proposes an architecture based on the Huffman algorithm for encoding, storage and decoding of these patterns in order to optimize such resources. We have made tests with simulation and synthesis in FPGA of rule subsets of the Snort software, and analysis indicate a saving of up to 73 percent of the embedded memory resources of the chip.

Road segmentation plays an important role in many computer vision applications, either for in-vehicle perception or traffic surveillance. In camera-equipped vehicles, road detection methods are being developed for advanced driver assistance, lane departure, and aerial incident detection, just to cite a few. In traffic surveillance, segmenting road information brings special benefits: to automatically wrap regions of traffic analysis (consequently, speeding up flow analysis in videos), to help with the detection of driving violations (to improve contextual information in videos of traffic), and so forth. Methods and techniques can be used interchangeably for both types of application. Particularly, we are interested in segmenting road regions from the remaining of an image, aiming to support traffic flow analysis tasks. In our proposed method, road segmentation relies on a superpixel detection based on a novel edge density estimation method; in each superpixel, priors are extracted from features of gray-amount, texture homogeneity, traffic motion and horizon line. A feature vector with all those priors feeds a support vector machine classifier, which ultimately takes the superpixel-wise decision of being a road or not. A dataset of challenging scenes was gathered from traffic video surveillance cameras, in our city, to demonstrate the effectiveness of the method.

This paper presents experimental results showing that the dynamic load supported by the polished rod of oil well pumping unity can be inferred from statoric current of a three-phase induction motor (which is given by sensorless vector AC-drive) using a Hammerstein-Wiener model with wavelet networks in series with an autoregressive model with exogenous input (ARX). The results obtained in the validation stage show that, on the basis of the experimental set used in this work, the best estimated model is suitable to represent the dynamic load of the polished-rod of the pumping unit.

Accuracy in image object detection has been usually achieved at the expense of much computational load. Therefore a trade-off between detection performance and fast execution commonly represents the ultimate goal of an object detector in real life applications. In this present work, we propose a novel method toward that goal. The proposed method was grounded on a multi-scale spectral residual (MSR) analysis for saliency detection. Compared to a regular sliding window search over the images, in our experiments, MSR was able to reduce by 75% (in average) the number of windows to be evaluated by an object detector. The proposed method was thoroughly evaluated over a subset of Label Me dataset (person images), improving detection performance in most cases.

This present work has been developed in view of a research project that aims a mapping process between infrared images and thermo pair sensors readings in diesel motors by the use of artificial neural network. The consistence analysis of a set of examples used in the supervised training of artificial neural networks is presented. The proposed approach is based on the entropy analysis of both input and output data. The principal component analysis method is applied to avoid redundant information that can lead to an entropy level overestimation.

Each part of a mobile robot has particular aspects of its own, which must be integrated in order to successfully conclude a specific task. Among these parts, sensing enables to construct a representation of landmarks of the surroundings with the goal of supplying relevant information for the robot’s navigation. The present work describes the architecture of a perception system based on data fusion from a CMOS camera and distance sensors. The aim of the proposed architecture is the spatial location of objects on a soccer field. An SVM is used for both recognition and object location and the process of fusion is made by means of a fuzzy system, using a TSK model.

This paper presents an efficient implementation of a supervised learning method based on membership function training in the context of Mamdani fuzzy models. The main idea is to adjust the antecedent and consequent membership functions that are of asymmetric trapezoidal form by backpropagating the output error through the fuzzy net. The proposed implementation is analogous to the training scheme commonly used with Takagi-Sugeno fuzzy models but it requires additional procedures that are related to some specific characteristics of the Mamdani fuzzy structures. Some numerical results are provided as illustrations.

The production and operations in oil and gas fields are getting more complex, with more and more demanding restrictions for safety, energy efficiency, environment, which sets new technical challenges for automation and control. On the other hand, modeling and system identification tools and advanced control systems can be an important key to enhance stability and cost effectiveness and support the personnel (operators, engineers, etc.). This article will illustrate some results obtained with the application of modeling and advanced control systems for artificial lift using smart field technologies.

This paper presents the development of an Artificial Neural Network system for Dynamometer Card pattern recognition in oil well rod pump systems. It covers the establishment of pattern classes and a set of standards for training and validation, the study of descriptors which allow the design and the implementation of features extractor, training, analysis and finally the validation and performance test with a real data base.

This paper presents a new method for the downhole dynamometer card pattern creation. This method use artificial intelligence concepts and real card data picked up from the oil fields acquired data. The main idea is to allow field team to generate patterns based on their own experience supported by an intelligent algorithm designed to perform graphical analysis of the card shape. The main idea is to allow pattern recognition and its classification toward the solution of rod pumping problems in oil wells. Successful results were reached when the proposed method was applied in PETROBRAS/UN-BA real databases. Tests performed with real data allowed the development of a new set of patterns used to analyze well performance in Buracica and Balsamo oil fields. These tests showed good results when compared with the ones obtained from empirical patterns only. In addition, this approach allowed to immediately increasing the pattern number from two to a set of eight, improving the sucker-rod pumped wells analysis and management.