Sistema especialista fuzzy para dimensionamento de bombeio mecânico

Despite the emergence of other forms of artificial lift, sucker rod pumping systems remains hegemonic because of its flexibility of operation and lower investment cost compared to other lifting techniques developed. A successful rod pumping sizing necessarily passes through the supply of estimated flow and the controlled wear of pumping equipment used in the mounted configuration. However, the mediation of these elements is particularly challenging, especially for most designers dealing with this work, which still lack the experience needed to get good projects pumping in time. Even with the existence of various computer applications on the market in order to facilitate this task, they must face a grueling process of trial and error until you get the most appropriate combination of equipment for installation in the well. This thesis proposes the creation of an expert system in the design of sucker rod pumping systems. Its mission is to guide a petroleum engineer in the task of selecting a range of equipment appropriate to the context provided by the characteristics of the oil that will be raised to the surface. Features such as the level of gas separation, presence of corrosive elements, possibility of production of sand and waxing are taken into account in selecting the pumping unit, sucker-rod strings and subsurface pump and their operation mode. It is able to approximate the inferente process in the way of human reasoning, which leads to results closer to those obtained by a specialist. For this, their production rules were based on the theory of fuzzy sets, able to model vague concepts typically present in human reasoning. The calculations of operating parameters of the pumping system are made by the API RP 11L method. Based on information input, the system is able to return to the user a set of pumping configurations that meet a given design flow, but without subjecting the selected equipment to an effort beyond that which can bear

Detecção e diagnóstico de falhas não-supervisionados baseados em estimativa de densidade recursiva e classificador fuzzy auto-evolutivo

In this work, we propose a two-stage algorithm for real-time fault detection and identification of industrial plants. Our proposal is based on the analysis of selected features using recursive density estimation and a new evolving classifier algorithm. More specifically, the proposed approach for the detection stage is based on the concept of density in the data space, which is not the same as probability density function, but is a very useful measure for abnormality/outliers detection. This density can be expressed by a Cauchy function and can be calculated recursively, which makes it memory and computational power efficient and, therefore, suitable for on-line applications. The identification/diagnosis stage is based on a self-developing (evolving) fuzzy rule-based classifier system proposed in this work, called AutoClass. An important property of AutoClass is that it can start learning from scratch". Not only do the fuzzy rules not need to be prespecified, but neither do the number of classes for AutoClass (the number may grow, with new class labels being added by the on-line learning process), in a fully unsupervised manner. In the event that an initial rule base exists, AutoClass can evolve/develop it further based on the newly arrived faulty state data. In order to validate our proposal, we present experimental results from a level control didactic process, where control and error signals are used as features for the fault detection and identification systems, but the approach is generic and the number of features can be significant due to the computationally lean methodology, since covariance or more complex calculations, as well as storage of old data, are not required. The obtained results are significantly better than the traditional approaches used for comparison

Reducing the gap between mobile and intelligent software agents

This paper presents an approach to integrate an artificial intelligence (AI) technique, concretely rule-based processing, into mobile agents. In particular, it focuses on the aspects of designing and implementing an appropriate inference engine of small size to reduce migration costs. The main goal is combine two lines of agent research, First, the engineering oriented approach on mobile agent architectures, and, second, the AI related approach on inference engines driven by rules expressed in a restricted subset of first-order predicate logic (FOPL). In addition to size reduction, the main functions of this type of engine were isolated, generalized and implemented as dynamic components, making possible not only their migration with the agent, but also their dynamic migration and loading on demand. A set of classes for representing and exchanging knowledge between rule-based systems was also proposed.

A program for terpenoid skeleton prediction based on botanical information

This paper describes a new program developed to the SISTEMAT expert system, the SISOCBOT program. This program employs the botanical data analysis and predicts, at the end of analysis, the probable skeleton of a compound based on the input of family or genus names. The SISOCBOT program was tested with 78 samples involving 302 substances, pertaining to 38 carbon skeletons, and showed a high hit index on skeleton prediction, thus emphasizing the potential importance of these data for structural determination of natural products. © 2002 Elsevier Science Ltd. All rights reserved.

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.

Metodologia para diagnosticar a qualidade de energia elétrica referente à distorção harmônica em sistema trifásico de baixa tensão utilizando lógica fuzzy

Este trabalho ressalta a importância de monitorar e diagnosticar a qualidade de energia elétrica sob a ótica das distorções harmônicas presente nas instalações elétricas em sistema trifásico de baixa tensão através de uma proposta metodológica para analisar e diagnosticar o nível dos distúrbios harmônico avaliando o indicador total de distorção harmônica (THD), apoiado por um sistema especialista baseado em um sistema de inferência Fuzzy.

Preventive diagnosis of dairy cow lameness

This research aimed to develop a Fuzzy inference based on expert system to help preventing lameness in dairy cattle. Hoof length, nutritional parameters and floor material properties (roughness) were used to build the Fuzzy inference system. The expert system architecture was defined using Unified Modelling Language (UML). Data were collected in a commercial dairy herd using two different subgroups (H-1 and H-2), in order to validate the Fuzzy inference functions. The numbers of True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN) responses were used to build the classifier system up, after an established gold standard comparison. A Lesion Incidence Possibility (LIP) developed function indicates the chances of a cow becoming lame. The obtained lameness percentage in H-1 and H-2 was 8.40% and 1.77%, respectively. The system estimated a Lesion Incidence Possibility (LIP) of 5.00% and 2.00% in H-1 and H-2, respectively. The system simulation presented 3.40% difference from real cattle lameness data for H-1, while for H-2, it was 0.23%; indicating the system efficiency in decision-making.

Ditregra - An auxiliary program for structural determination of diterpenes

This work describes the creation of heuristics rules based on 13C-NMR spectroscopy that characterize several skeletal types of diterpenes. Using a collection of 2745 spectra we built a database linked to the expert system SISTEMAT. Several programs were applied to the database in order to discover characteristic signals that identify with a good performance, a large diversity of skeletal types. The heuristic approach used was able to differentiate groups of skeletons based firstly on the number of primary, secondary, tertiary and quaternary carbons, and secondly the program searches, for each group, if there are ranges of chemical shifts that identifies specific skeletal type. The program was checked with 100 new structures recently published and was able to identify the correct skeleton in 65 of the studied cases. When the skeleton has several hundreds of compounds, for example, the labdanes, the program employs the concept of subskeletal, and does not classify in the same group labdanes with double bounds at different positions. The chemical shift ranges for each subskeletal types and the structures of all skeletal types are given. The consultation program can be obtained from the authors. © 1997 - IOS Press. All rights reserved.

Classification of the severity of diabetic neuropathy: a new approach taking uncertainties into account using fuzzy logic

OBJECTIVE: This study proposes a new approach that considers uncertainty in predicting and quantifying the presence and severity of diabetic peripheral neuropathy. METHODS: A rule-based fuzzy expert system was designed by four experts in diabetic neuropathy. The model variables were used to classify neuropathy in diabetic patients, defining it as mild, moderate, or severe. System performance was evaluated by means of the Kappa agreement measure, comparing the results of the model with those generated by the experts in an assessment of 50 patients. Accuracy was evaluated by an ROC curve analysis obtained based on 50 other cases; the results of those clinical assessments were considered to be the gold standard. RESULTS: According to the Kappa analysis, the model was in moderate agreement with expert opinions. The ROC analysis (evaluation of accuracy) determined an area under the curve equal to 0.91, demonstrating very good consistency in classifying patients with diabetic neuropathy. CONCLUSION: The model efficiently classified diabetic patients with different degrees of neuropathy severity. In addition, the model provides a way to quantify diabetic neuropathy severity and allows a more accurate patient condition assessment.

L'utilizzabilità degli atti nella fase investigativa. La prova per le indagini preliminari

The doctorate’s theme of research - Abstract My doctorate’s theme of research is about the Investigation in the Italian criminal proceedings. The Italian Code of criminal procedure of 1988 is the fruit of a new ideology that marks a departure from Italy’s prior inquisitorial tradition. According to criminal procedure Code of 1988, an accusatorial system separates the investigation and trial stages and the judge’s decision is based only on evidence collected in oral form in his presence in a public trial containing adversarial dynamics. The Italian Code of 1988 created a separation between criminal investigations and trial. Investigations are conducted by Public Prosecutor: he conducts the investigation phase in order to deem whether to file a formal charge against the defendant or to dismiss the case and the investigative evidence collected should serve only for this purpose. According to so called “inutilizzabilità fisiologica”’s rule, the evidence collected during investigations by prosecutor is not usable during trial by the judge: the results of the investigative efforts displayed by the parties should be kept outside of court. If the proceedings go on to trial, the case shall be deemed with only evidence collected in front of the judge. To create the separation of the trial phase from the investigation stage, there is the double-dossier system. During the investigations, evidence are collected in an investigation dossier. The trial judge will never see the investigation dossier and the trial judge’s decision is based on a new dossier, the trial dossier, with the evidence collected during the trial. The issue of my research is about the investigation, the so called “inutilizzabilità fisiologica”’s rule and also the exclusionary rules that concern the investigative phase and the decisions pronounced during the investigations (for example, the decisions concerning pre-trial confinement). 2 In fact, the exclusionary rule system (so called “inutilizzabilità patologica”) provides that evidence cannot be used in Italian criminal proceedings if it was the result of illegal inquiry.

Software zur automatischen Tumordiagnose in der funktionalen MRT der weiblichen Brust

In den westlichen Industrieländern ist das Mammakarzinom der häufigste bösartige Tumor der Frau. Sein weltweiter Anteil an allen Krebserkrankungen der Frau beläuft sich auf etwa 21 %. Inzwischen ist jede neunte Frau bedroht, während ihres Lebens an Brustkrebs zu erkranken. Die alterstandardisierte Mortalitätrate liegt derzeit bei knapp 27 %.rnrnDas Mammakarzinom hat eine relative geringe Wachstumsrate. Die Existenz eines diagnostischen Verfahrens, mit dem alle Mammakarzinome unter 10 mm Durchmesser erkannt und entfernt werden, würden den Tod durch Brustkrebs praktisch beseitigen. Denn die 20-Jahres-Überlebungsrate bei Erkrankung durch initiale Karzinome der Größe 5 bis 10 mm liegt mit über 95 % sehr hoch.rnrnMit der Kontrastmittel gestützten Bildgebung durch die MRT steht eine relativ junge Untersuchungsmethode zur Verfügung, die sensitiv genug zur Erkennung von Karzinomen ab einer Größe von 3 mm Durchmesser ist. Die diagnostische Methodik ist jedoch komplex, fehleranfällig, erfordert eine lange Einarbeitungszeit und somit viel Erfahrung des Radiologen.rnrnEine Computer unterstützte Diagnosesoftware kann die Qualität einer solch komplexen Diagnose erhöhen oder zumindest den Prozess beschleunigen. Das Ziel dieser Arbeit ist die Entwicklung einer vollautomatischen Diagnose Software, die als Zweitmeinungssystem eingesetzt werden kann. Meines Wissens existiert eine solche komplette Software bis heute nicht.rnrnDie Software führt eine Kette von verschiedenen Bildverarbeitungsschritten aus, die dem Vorgehen des Radiologen nachgeahmt wurden. Als Ergebnis wird eine selbstständige Diagnose für jede gefundene Läsion erstellt: Zuerst eleminiert eine 3d Bildregistrierung Bewegungsartefakte als Vorverarbeitungsschritt, um die Bildqualität der nachfolgenden Verarbeitungsschritte zu verbessern. Jedes kontrastanreichernde Objekt wird durch eine regelbasierte Segmentierung mit adaptiven Schwellwerten detektiert. Durch die Berechnung kinetischer und morphologischer Merkmale werden die Eigenschaften der Kontrastmittelaufnahme, Form-, Rand- und Textureeigenschaften für jedes Objekt beschrieben. Abschließend werden basierend auf den erhobenen Featurevektor durch zwei trainierte neuronale Netze jedes Objekt in zusätzliche Funde oder in gut- oder bösartige Läsionen klassifiziert.rnrnDie Leistungsfähigkeit der Software wurde auf Bilddaten von 101 weiblichen Patientinnen getested, die 141 histologisch gesicherte Läsionen enthielten. Die Vorhersage der Gesundheit dieser Läsionen ergab eine Sensitivität von 88 % bei einer Spezifität von 72 %. Diese Werte sind den in der Literatur bekannten Vorhersagen von Expertenradiologen ähnlich. Die Vorhersagen enthielten durchschnittlich 2,5 zusätzliche bösartige Funde pro Patientin, die sich als falsch klassifizierte Artefakte herausstellten.rn

A Multi-Agent System for the Solution of Fuzzy Incidents in Telecommunications

This article presents a multi-agent expert system (SMAF) , that allows the input of incidents which occur in different elements of the telecommunications area. SMAF interacts with experts and general users, and each agent with all the agents? community, recording the incidents and their solutions in a knowledge base, without the analysis of their causes. The incidents are expressed using keywords taken from natural language (originally Spanish) and their main concepts are recorded with their severities as the users express them. Then, there is a search of the best solution for each incident, being helped by a human operator using a distancenotions between them.

Automatic Adaptation of Airport Surface Surveillance to Sensor Quality

This paper describes a novel method to enhance current airport surveillance systems used in Advanced Surveillance Monitoring Guidance and Control Systems (A-SMGCS). The proposed method allows for the automatic calibration of measurement models and enhanced detection of nonideal situations, increasing surveillance products integrity. It is based on the definition of a set of observables from the surveillance processing chain and a rule based expert system aimed to change the data processing methods

Efficient local unfolding with ancestor stacks for full prolog

The integration of powerful partial evaluation methods into practical compilers for logic programs is still far from reality. This is related both to 1) efficiency issues and to 2) the complications of dealing with practical programs. Regarding efnciency, the most successful unfolding rules used nowadays are based on structural orders applied over (covering) ancestors, i.e., a subsequence of the atoms selected during a derivation. Unfortunately, maintaining the structure of the ancestor relation during unfolding introduces significant overhead. We propose an efficient, practical local unfolding rule based on the notion of covering ancestors which can be used in combination with any structural order and allows a stack-based implementation without losing any opportunities for specialization. Regarding the second issue, we propose assertion-based techniques which allow our approach to deal with real programs that include (Prolog) built-ins and external predicates in a very extensible manner. Finally, we report on our implementation of these techniques in a practical partial evaluator, embedded in a state of the art compiler which uses global analysis extensively (the Ciao compiler and, specifically, its preprocessor CiaoPP). The performance analysis of the resulting system shows that our techniques, in addition to dealing with practical programs, are also significantly more efficient in time and somewhat more efficient in memory than traditional tree-based implementations.

Análisis estático de sistemas de membranas para la determinación de reglas activas

Esta tesis doctoral se enmarca dentro de la computación con membranas. Se trata de un tipo de computación bio-inspirado, concretamente basado en las células de los organismos vivos, en las que se producen múltiples reacciones de forma simultánea. A partir de la estructura y funcionamiento de las células se han definido diferentes modelos formales, denominados P sistemas. Estos modelos no tratan de modelar el comportamiento biológico de una célula, sino que abstraen sus principios básicos con objeto de encontrar nuevos paradigmas computacionales. Los P sistemas son modelos de computación no deterministas y masivamente paralelos. De ahí el interés que en los últimos años estos modelos han suscitado para la resolución de problemas complejos. En muchos casos, consiguen resolver de forma teórica problemas NP-completos en tiempo polinómico o lineal. Por otra parte, cabe destacar también la aplicación que la computación con membranas ha tenido en la investigación de otros muchos campos, sobre todo relacionados con la biología. Actualmente, una gran cantidad de estos modelos de computación han sido estudiados desde el punto de vista teórico. Sin embargo, el modo en que pueden ser implementados es un reto de investigación todavía abierto. Existen varias líneas en este sentido, basadas en arquitecturas distribuidas o en hardware dedicado, que pretenden acercarse en lo posible a su carácter no determinista y masivamente paralelo, dentro de un contexto de viabilidad y eficiencia. En esta tesis doctoral se propone la realización de un análisis estático del P sistema, como vía para optimizar la ejecución del mismo en estas plataformas. Se pretende que la información recogida en tiempo de análisis sirva para configurar adecuadamente la plataforma donde se vaya a ejecutar posteriormente el P sistema, obteniendo como consecuencia una mejora en el rendimiento. Concretamente, en esta tesis se han tomado como referencia los P sistemas de transiciones para llevar a cabo el estudio de dicho análisis estático. De manera un poco más específica, el análisis estático propuesto en esta tesis persigue que cada membrana sea capaz de determinar sus reglas activas de forma eficiente en cada paso de evolución, es decir, aquellas reglas que reúnen las condiciones adecuadas para poder ser aplicadas. En esta línea, se afronta el problema de los estados de utilidad de una membrana dada, que en tiempo de ejecución permitirán a la misma conocer en todo momento las membranas con las que puede comunicarse, cuestión que determina las reglas que pueden aplicarse en cada momento. Además, el análisis estático propuesto en esta tesis se basa en otra serie de características del P sistema como la estructura de membranas, antecedentes de las reglas, consecuentes de las reglas o prioridades. Una vez obtenida toda esta información en tiempo de análisis, se estructura en forma de árbol de decisión, con objeto de que en tiempo de ejecución la membrana obtenga las reglas activas de la forma más eficiente posible. Por otra parte, en esta tesis se lleva a cabo un recorrido por un número importante de arquitecturas hardware y software que diferentes autores han propuesto para implementar P sistemas. Fundamentalmente, arquitecturas distribuidas, hardware dedicado basado en tarjetas FPGA y plataformas basadas en microcontroladores PIC. El objetivo es proponer soluciones que permitan implantar en dichas arquitecturas los resultados obtenidos del análisis estático (estados de utilidad y árboles de decisión para reglas activas). En líneas generales, se obtienen conclusiones positivas, en el sentido de que dichas optimizaciones se integran adecuadamente en las arquitecturas sin penalizaciones significativas. Summary Membrane computing is the focus of this doctoral thesis. It can be considered a bio-inspired computing type. Specifically, it is based on living cells, in which many reactions take place simultaneously. From cell structure and operation, many different formal models have been defined, named P systems. These models do not try to model the biological behavior of the cell, but they abstract the basic principles of the cell in order to find out new computational paradigms. P systems are non-deterministic and massively parallel computational models. This is why, they have aroused interest when dealing with complex problems nowadays. In many cases, they manage to solve in theory NP problems in polynomial or lineal time. On the other hand, it is important to note that membrane computing has been successfully applied in many researching areas, specially related to biology. Nowadays, lots of these computing models have been sufficiently characterized from a theoretical point of view. However, the way in which they can be implemented is a research challenge, that it is still open nowadays. There are some lines in this way, based on distributed architectures or dedicated hardware. All of them are trying to approach to its non-deterministic and parallel character as much as possible, taking into account viability and efficiency. In this doctoral thesis it is proposed carrying out a static analysis of the P system in order to optimize its performance in a computing platform. The general idea is that after data are collected in analysis time, they are used for getting a suitable configuration of the computing platform in which P system is going to be performed. As a consequence, the system throughput will improve. Specifically, this thesis has made use of Transition P systems for carrying out the study in static analysis. In particular, the static analysis proposed in this doctoral thesis tries to achieve that every membrane can efficiently determine its active rules in every evolution step. These rules are the ones that can be applied depending on the system configuration at each computational step. In this line, we are going to tackle the problem of the usefulness states for a membrane. This state will allow this membrane to know the set of membranes with which communication is possible at any time. This is a very important issue in determining the set of rules that can be applied. Moreover, static analysis in this thesis is carried out taking into account other properties such as membrane structure, rule antecedents, rule consequents and priorities among rules. After collecting all data in analysis time, they are arranged in a decision tree structure, enabling membranes to obtain the set of active rules as efficiently as possible in run-time system. On the other hand, in this doctoral thesis is going to carry out an overview of hardware and software architectures, proposed by different authors in order to implement P systems, such as distributed architectures, dedicated hardware based on PFGA, and computing platforms based on PIC microcontrollers. The aim of this overview is to propose solutions for implementing the results of the static analysis, that is, usefulness states and decision trees for active rules. In general, conclusions are satisfactory, because these optimizations can be properly integrated in most of the architectures without significant penalties.