Ambiente para avaliação de controladores fuzzy aplicados ao método de elevação artificial por bombeio centrífugo submerso

From their early days, Electrical Submergible Pumping (ESP) units have excelled in lifting much greater liquid rates than most of the other types of artificial lift and developed by good performance in wells with high BSW, in onshore and offshore environments. For all artificial lift system, the lifetime and frequency of interventions are of paramount importance, given the high costs of rigs and equipment, plus the losses coming from a halt in production. In search of a better life of the system comes the need to work with the same efficiency and security within the limits of their equipment, this implies the need for periodic adjustments, monitoring and control. How is increasing the prospect of minimizing direct human actions, these adjustments should be made increasingly via automation. The automated system not only provides a longer life, but also greater control over the production of the well. The controller is the brain of most automation systems, it is inserted the logic and strategies in the work process in order to get you to work efficiently. So great is the importance of controlling for any automation system is expected that, with better understanding of ESP system and the development of research, many controllers will be proposed for this method of artificial lift. Once a controller is proposed, it must be tested and validated before they take it as efficient and functional. The use of a producing well or a test well could favor the completion of testing, but with the serious risk that flaws in the design of the controller were to cause damage to oil well equipment, many of them expensive. Given this reality, the main objective of the present work is to present an environment for evaluation of fuzzy controllers for wells equipped with ESP system, using a computer simulator representing a virtual oil well, a software design fuzzy controllers and a PLC. The use of the proposed environment will enable a reduction in time required for testing and adjustments to the controller and evaluated a rapid diagnosis of their efficiency and effectiveness. The control algorithms are implemented in both high-level language, through the controller design software, such as specific language for programming PLCs, Ladder Diagram language.

Gravitational Waves: completeness and connectedness by geodesics

Only few months ago some physicists gave the official announcement that gravitational waves exist, but, from a geometrical point of view, they have always been ``real objects'' and their properties have been widely investigated. The aim of this talk is introducing generalized plane waves and discussing some of their properties such as geodesic connectedness and geodesic completeness.

SCALABLE APPROXIMATION OF KERNEL FUZZY C-MEANS

Virtually every sector of business and industry that uses computing, including financial analysis, search engines, and electronic commerce, incorporate Big Data analysis into their business model. Sophisticated clustering algorithms are popular for deducing the nature of data by assigning labels to unlabeled data. We address two main challenges in Big Data. First, by definition, the volume of Big Data is too large to be loaded into a computer’s memory (this volume changes based on the computer used or available, but there is always a data set that is too large for any computer). Second, in real-time applications, the velocity of new incoming data prevents historical data from being stored and future data from being accessed. Therefore, we propose our Streaming Kernel Fuzzy c-Means (stKFCM) algorithm, which reduces both computational complexity and space complexity significantly. The proposed stKFCM only requires O(n2) memory where n is the (predetermined) size of a data subset (or data chunk) at each time step, which makes this algorithm truly scalable (as n can be chosen based on the available memory). Furthermore, only 2n2 elements of the full N × N (where N >> n) kernel matrix need to be calculated at each time-step, thus reducing both the computation time in producing the kernel elements and also the complexity of the FCM algorithm. Empirical results show that stKFCM, even with relatively very small n, can provide clustering performance as accurately as kernel fuzzy c-means run on the entire data set while achieving a significant speedup.

REAL TIME FUZZY CONTROLLER FOR QUADROTOR STABILITY CONTROL

In this report, we develop an intelligent adaptive neuro-fuzzy controller by using adaptive neuro fuzzy inference system (ANFIS) techniques. We begin by starting with a standard proportional-derivative (PD) controller and use the PD controller data to train the ANFIS system to develop a fuzzy controller. We then propose and validate a method to implement this control strategy on commercial off-the-shelf (COTS) hardware. An analysis is made into the choice of filters for attitude estimation. These choices are limited by the complexity of the filter and the computing ability and memory constraints of the micro-controller. Simplified Kalman filters are found to be good at estimation of attitude given the above constraints. Using model based design techniques, the models are implemented on an embedded system. This enables the deployment of fuzzy controllers on enthusiast-grade controllers. We evaluate the feasibility of the proposed control strategy in a model-in-the-loop simulation. We then propose a rapid prototyping strategy, allowing us to deploy these control algorithms on a system consisting of a combination of an ARM-based microcontroller and two Arduino-based controllers. We then use a combination of the code generation capabilities within MATLAB/Simulink in combination with multiple open-source projects in order to deploy code to an ARM CortexM4 based controller board. We also evaluate this strategy on an ARM-A8 based board, and a much less powerful Arduino based flight controller. We conclude by proving the feasibility of fuzzy controllers on Commercial-off the shelf (COTS) hardware, we also point out the limitations in the current hardware and make suggestions for hardware that we think would be better suited for memory heavy controllers.

Volatility transmission between stock and exchange-rate markets: A connectedness analysis

This paper empirically investigates volatility transmission among stock and foreign exchange markets in seven major world economies during the period July 1988 to January 2015. To this end, we first perform a static and dynamic analysis to measure the total volatility connectedness in the entire period (the system-wide approach) using a framework recently proposed by Diebold and Yilmaz (2014). Second, we make use of a dynamic analysis to evaluate the net directional connectedness for each market. To gain further insights, we examine the time-varying behaviour of net pair-wise directional connectedness during the financial turmoil periods experienced in the sample period Our results suggest that slightly more than half of the total variance of the forecast errors is explained by shocks across markets rather than by idiosyncratic shocks. Furthermore, we find that volatility connectedness varies over time, with a surge during periods of increasing economic and financial instability.

Analysis of Transmission System Adequacy Considering the Constrained Fuzzy Power Flow

Power flow calculations are one of the most important tools for power system planning and operation. The need to account for uncertainties when performing power flow studies led, among others methods, to the development of the fuzzy power flow (FPF). This kind of models is especially interesting when a scarcity of information exists, which is a common situation in liberalized power systems (where generation and commercialization of electricity are market activities). In this framework, the symmetric/constrained fuzzy power flow (SFPF/CFPF) was proposed in order to avoid some of the problems of the original FPF model. The SFPF/CFPF models are suitable to quantify the adequacy of transmission network to satisfy “reasonable demands for the transmission of electricity” as defined, for instance, in the European Directive 2009/72/EC. In this work it is illustrated how the SFPF/CFPF may be used to evaluate the impact on the adequacy of a transmission system originated by specific investments on new network elements

Constrained fuzzy power flow models with correlation between nodal injections

This paper extends the symmetric/constrained fuzzy powerflow models by including the potential correlations between nodal injections. Therefore, the extension of the model allows the specification of fuzzy generation and load values and of potential correlations between nodal injections. The enhanced version of the symmetric/constrained fuzzy powerflow model is applied to the 30-bus IEEE test system. The results prove the importance of the inclusion of data correlations in the analysis of transmission system adequacy.

Probabilistic Security Constrained Fuzzy Power Flow Models

In restructured power systems, generation and commercialization activities became market activities, while transmission and distribution activities continue as regulated monopolies. As a result, the adequacy of transmission network should be evaluated independent of generation system. After introducing the constrained fuzzy power flow (CFPF) as a suitable tool to quantify the adequacy of transmission network to satisfy 'reasonable demands for the transmission of electricity' (as stated, for instance, at European Directive 2009/72/EC), the aim is now showing how this approach can be used in conjunction with probabilistic criteria in security analysis. In classical security analysis models of power systems are considered the composite system (generation plus transmission). The state of system components is usually modeled with probabilities and loads (and generation) are modeled by crisp numbers, probability distributions or fuzzy numbers. In the case of CFPF the component’s failure of the transmission network have been investigated. In this framework, probabilistic methods are used for failures modeling of the transmission system components and possibility models are used to deal with 'reasonable demands'. The enhanced version of the CFPF model is applied to an illustrative case.

Applying fuzzy logic to comparative distribution modelling: a case study with two sympatric amphibians.

We modelled the distributions of two toads (Bufo bufo and Epidalea calamita) in the Iberian Peninsula using the favourability function, which makes predictions directly comparable for different species and allows fuzzy logic operations to relate different models. The fuzzy intersection between individual models, representing favourability for the presence of both species simultaneously, was compared with another favourability model built on the presences shared by both species. The fuzzy union between individual models, representing favourability for the presence of any of the two species, was compared with another favourabilitymodel based on the presences of either or both of them. The fuzzy intersections between favourability for each species and the complementary of favourability for the other (corresponding to the logical operation “A and not B”) were compared with models of exclusive presence of one species versus the exclusive presence of the other. The results of modelling combined species data were highly similar to those of fuzzy logic operations between individual models, proving fuzzy logic and the favourability function valuable for comparative distribution modelling. We highlight several advantages of fuzzy logic over other forms of combining distribution models, including the possibility to combine multiple species models for management and conservation planning.

Entrepreneurship rates the fuzzy-set approach

A fuzzy-set qualitative comparative analysis is applied to determine the necessary and sufficient conditions for higher entrepreneur rates. Based on Global Entrepreneurship Monitor data, it is shown that the most relevant conditions are Media Attention to Entrepreneurship, as well as Perceived Capabilities and Perceived Opportunities. The non-existence of Fear of Failure is also an important factor in determining higher entrepreneurship rates. When the sample is split, this condition is more important for most developed countries. This can be viewed as relevant information for policymakers to better define their policies to promote entrepreneurship, which is a key to more sustainable growth in countries.

What are the conditions for good innovation results? A fuzzy-set approach for European Union

Innovation is one of the main concerns of European Union countries since the beginning of the century. Despite failing to reach their targets, innovation remains a priority because innovation enables countries to achieve better economic performance. This study analyzes the relation between the level of innovation and the economic effects and applies a fuzzy-set qualitative comparative analysis to study the relation between six conditions and two different outcomes. The data comes from the Union Innovation Scoreboard. The study finds that research systems, linkages and entrepreneurship, and intellectual assets are necessary conditions for the outcomes of a high level of innovation and positive economic effects. The main sufficient condition for both outcomes is a good research system.

A fuzzy clustering approach to a demand response model

This paper proposes a novel demand response model using a fuzzy subtractive cluster approach. The model development provides support to domestic consumer decisions on controllable loads management, considering consumers’ consumption needs and the appropriate load shape or rescheduling in order to achieve possible economic benefits. The model based on fuzzy subtractive clustering method considers clusters of domestic consumption covering an adequate consumption range. Analysis of different scenarios is presented considering available electric power and electric energy prices. Simulation results are presented and conclusions of the proposed demand response model are discussed.

Instituições de Ensino Superior e movimentos migratórios em Portugal: uma aplicação do método fuzzy

As Instituições de Ensino Superior (IES) localizam-se ao longo de todo o território português, em cidades de dimensão distinta mas sempre âncoras dos territórios envolventes. Um dos efeitos mais imediatos, entre os “efeitos de procura”, relaciona-se com a dimensão populacional das cidades onde as IES estão instaladas. Logo que todos os agentes diretamente envolvidos com a IES chegam à (permanecem na) cidade – funcionários docentes e não docentes e estudantes – provocam efeitos vários, quer pela dimensão demográfica (quer em termos de volume quer de estrutura) quer pelos efeitos multiplicadores na atividade económica. O enquadramento teórico deste estudo prende-se com duas teorias fundamentais: os estudos acerca dos impactes das IES e a teoria das migrações jovens. Esta investigação visa estudar a existência de correlações entre as cidades que acolhem as IES, estas instituições e os movimentos migratórios ao longo do país. As questões de investigação são as seguintes: a dimensão das IES está relacionada com a dimensão da cidade onde está instalada e a capacidade de atração de ambas é proporcional? Podem as IES servir para inverter os fluxos migratórios que se verificam com destino às cidades onde existem IES? Os objectivos do trabalho são: - relacionar a dimensão das IES com as cidades de acolhimento, bem como os respectivos níveis de atração; - estudar os fluxos migratórios, destacando os jovens do conjunto do total da população que se desloca para as cidades / concelhos onde existem IES. Utilizar-se-ão dados relativos i) aos estabelecimentos da rede pública, universitária e politécnica; ii) às migrações internas em Portugal por grupos de idades e, iii) caracterização das cidades de acolhimento das IES. Os dados serão analisados com métodos de estatística descritiva, multivariada e com a metodologia fuzzy que visa conhecer as condições necessárias e/ou suficientes da atratividade das cidades relativamente aos fluxos migratórios jovens.

A fuzzy clustering approach to a demand response model

This paper proposes a novel demand response model using a fuzzy subtractive cluster approach. The model development provides support to domestic consumer decisions on controllable loads management, considering consumers’ consumption needs and the appropriate load shape or rescheduling in order to achieve possible economic benefits. The model based on fuzzy subtractive clustering method considers clusters of domestic consumption covering an adequate consumption range. Analysis of different scenarios is presented considering available electric power and electric energy prices. Simulation results are presented and conclusions of the proposed demand response model are discussed.

Data-stream driven Fuzzy-granular approaches for system maintenance

Intelligent systems are currently inherent to the society, supporting a synergistic human-machine collaboration. Beyond economical and climate factors, energy consumption is strongly affected by the performance of computing systems. The quality of software functioning may invalidate any improvement attempt. In addition, data-driven machine learning algorithms are the basis for human-centered applications, being their interpretability one of the most important features of computational systems. Software maintenance is a critical discipline to support automatic and life-long system operation. As most software registers its inner events by means of logs, log analysis is an approach to keep system operation. Logs are characterized as Big data assembled in large-flow streams, being unstructured, heterogeneous, imprecise, and uncertain. This thesis addresses fuzzy and neuro-granular methods to provide maintenance solutions applied to anomaly detection (AD) and log parsing (LP), dealing with data uncertainty, identifying ideal time periods for detailed software analyses. LP provides deeper semantics interpretation of the anomalous occurrences. The solutions evolve over time and are general-purpose, being highly applicable, scalable, and maintainable. Granular classification models, namely, Fuzzy set-Based evolving Model (FBeM), evolving Granular Neural Network (eGNN), and evolving Gaussian Fuzzy Classifier (eGFC), are compared considering the AD problem. The evolving Log Parsing (eLP) method is proposed to approach the automatic parsing applied to system logs. All the methods perform recursive mechanisms to create, update, merge, and delete information granules according with the data behavior. For the first time in the evolving intelligent systems literature, the proposed method, eLP, is able to process streams of words and sentences. Essentially, regarding to AD accuracy, FBeM achieved (85.64+-3.69)%; eGNN reached (96.17+-0.78)%; eGFC obtained (92.48+-1.21)%; and eLP reached (96.05+-1.04)%. Besides being competitive, eLP particularly generates a log grammar, and presents a higher level of model interpretability.