Técnicas de inteligência artificial para a geração dinâmica de set points para uma coluna de destilação

Artificial Intelligence techniques are applied to improve performance of a simulated oil distillation system. The chosen system was a debutanizer column. At this process, the feed, which comes to the column, is segmented by heating. The lightest components become steams, by forming the LPG (Liquefied Petroleum Gas). The others components, C5+, continue liquid. In the composition of the LPG, ideally, we have only propane and butanes, but, in practice, there are contaminants, for example, pentanes. The objective of this work is to control pentane amount in LPG, by means of intelligent set points (SP s) determination for PID controllers that are present in original instrumentation (regulatory control) of the column. A fuzzy system will be responsible for adjusting the SP's, driven by the comparison between the molar fraction of the pentane present in the output of the plant (LPG) and the desired amount. However, the molar fraction of pentane is difficult to measure on-line, due to constraints such as: long intervals of measurement, high reliability and low cost. Therefore, an inference system was used, based on a multilayer neural network, to infer the pentane molar fraction through secondary variables of the column. Finally, the results shown that the proposed control system were able to control the value of pentane molar fraction under different operational situations

Especificação de um bloco funcional implementando rede neural para redes industriais foundations fieldbus

This work proposes the specification of a new function block according to Foundation Fieldbus standards. The new block implements an artificial neural network, which may be useful in process control applications. The specification includes the definition of a main algorithm, that implements a neural network, as well as the description of some accessory functions, which provide safety characteristics to the block operation. Besides, it also describes the block attributes emphasizing its parameters, which constitute the block interfaces. Some experimental results, obtained from an artificial neural network implementation using actual standard functional blocks on a laboratorial FF network, are also shown, in order to demonstrate the possibility and also the convenience of integrating a neural network to Fieldbus devices

Uma linguagem de descrição arquitetural baseada em uma arquitetura de referência para sistemas ubíquos

A computação ubíqua é um paradigma no qual dispositivos com capacidade de processamento e comunicação são embutidos nos elementos comuns de nossas vidas (casas, carros, máquinas fotográficas, telefones, escolas, museus, etc), provendo serviços com um alto grau de mobilidade e transparência. O desenvolvimento de sistemas ubíquos é uma tarefa complexa, uma vez que envolve várias áreas da computação, como Engenharia de Software, Inteligência Artificial e Sistemas Distribuídos. Essa tarefa torna-se ainda mais complexa pela ausência de uma arquitetura de referência para guiar o desenvolvimento de tais sistemas. Arquiteturas de referência têm sido usadas para fornecer uma base comum e dar diretrizes para a construção de arquiteturas de softwares para diferentes classes de sistemas. Por outro lado, as linguagens de descrição arquitetural (ADLs) fornecem uma sintaxe para representação estrutural dos elementos arquiteturais, suas restrições e interações, permitindo-se expressar modelo arquitetural de sistemas. Atualmente não há, na literatura, ADLs baseadas em arquiteturas de referência para o domínio de computação ubíqua. De forma a permitir a modelagem arquitetural de aplicações ubíquas, esse trabalho tem como objetivo principal especificar UbiACME, uma linguagem de descrição arquitetural para aplicações ubíquas, bem como disponibilizar a ferramenta UbiACME Studio, que permitirá arquitetos de software realizar modelagens usando UbiACME. Para esse fim, inicialmente realizamos uma revisão sistemática, de forma a investigar na literatura relacionada com sistemas ubíquos, os elementos comuns a esses sistemas que devem ser considerados no projeto de UbiACME. Além disso, com base na revisão sistemática, definimos uma arquitetura de referência para sistemas ubíquos, RA-Ubi, que é a base para a definição dos elementos necessários para a modelagem arquitetural e, portanto, fornece subsídios para a definição dos elementos de UbiACME. Por fim, de forma a validar a linguagem e a ferramenta, apresentamos um experimento controlado onde arquitetos modelam uma aplicação ubíqua usando UbiACME Studio e comparam com a modelagem da mesma aplicação em SySML.

Técnicas neuronais de agrupamentos aplicadas à detecção de anomalias em mamogramas digitais

This work proposes the development of an intelligent system for analysis of digital mammograms, capable to detect and to classify masses and microcalcifications. The digital mammograms will be pre-processed through techniques of digital processing of images with the purpose of adapting the image to the detection system and automatic classification of the existent calcifications in the suckles. The model adopted for the detection and classification of the mammograms uses the neural network of Kohonen by the algorithm Self Organization Map - SOM. The algorithm of Vector quantization, Kmeans it is also used with the same purpose of the SOM. An analysis of the performance of the two algorithms in the automatic classification of digital mammograms is developed. The developed system will aid the radiologist in the diagnosis and accompaniment of the development of abnormalities

Aplicações de arranjos de antenas de microfita com patch supercondutor

This work has as main objective the study of arrays of microstrip antennas with superconductor rectangular patch. The phases and the radiation patterns are analyzed. A study of the main theories is presented that explain the microscopic and macroscopic phenomena of superconductivity. The BCS, London equations and the Two Fluid Model, are theories used in the applications of superconductors, at the microstrip antennas and antennas arrays. Phase Arrangements will be analyzed in linear and planar configurations. The arrangement factors of these configurations are obtained, and the phase criteria and the spacing between the elements, are examined in order to minimize losses in the superconductor, compared with normal conductors. The new rectangular patch antenna, consist of a superconducting material, with the critical temperature of 233 K, whose formula is Tl5Ba4Ca2Cu9Oy, is analyzed by the method of the Transverse nTransmission Line (TTL), developed by H. C. C. Fernandes, applied in the Fourier Transform Domain (FTD). The TTL is a full-wave method, which has committed to obtaining the electromagnetic fields in terms of the transverse components of the structure. The inclusion of superconducting patch is made using the complex resistive boundary condition, using the impedance of the superconductor in the Dyadic Green function, in the structure. Results are obtained from the resonance frequency depending on the parameters of the antenna using superconducting material, radiation patterns in E-Plane and H -Plane, the phased antennas array in linear and planar configurations, for different values of phase angles and different spacing between the elements

Separação cega de fontes lineares e não lineares usando algoritmo genético, redes neurais artificiais RBF e negentropia de Rényi como medida de independência

Conventional methods to solve the problem of blind source separation nonlinear, in general, using series of restrictions to obtain the solution, often leading to an imperfect separation of the original sources and high computational cost. In this paper, we propose an alternative measure of independence based on information theory and uses the tools of artificial intelligence to solve problems of blind source separation linear and nonlinear later. In the linear model applies genetic algorithms and Rényi of negentropy as a measure of independence to find a separation matrix from linear mixtures of signals using linear form of waves, audio and images. A comparison with two types of algorithms for Independent Component Analysis widespread in the literature. Subsequently, we use the same measure of independence, as the cost function in the genetic algorithm to recover source signals were mixed by nonlinear functions from an artificial neural network of radial base type. Genetic algorithms are powerful tools for global search, and therefore well suited for use in problems of blind source separation. Tests and analysis are through computer simulations

Aplicações de Dispositivos de Microondas utilizando Substrato EBG/PBG para Comunicações Móveis

The modern society depends on an efficient communications system able to of transmitting and receiving information with a higher speed and reliability every time. The need for ever more efficient devices raises optimization techniques of microstrip devices, such as techniques to increase bandwidth: thicker substrates and substrate structures with EBG (Electromagnetic Band Gap) and PBG (Photonic Band Gap). This work has how aims the study of the application of PBG materials on substrates of planar structures in microstrip, more precisely in directional quadrature couplers and in rat-race and impedance of transformers. A study of the planar structures in microstrip and substrates EBG is presented. The PBG substrates can be used to optimize the radiation through the air, thus reducing the occurrence of surface waves and the resulting diffraction edge responsible for degradation of radiation pattern. Through specific programs in FORTRAN Power Station obtained the frequencies and couplings for each structure. Are used the program PACMO - Computer Aided Design in Microwave. Results are obtained of the frequency and coupling devices, ranging the frequency band used (cellular communication and Wimax systems) and the permittivity of the substrate, comparing the results of conventional material and PBG materials in the s and p polarizations.

Análise de um controlador baseado no Jacobiano estimado da planta através de uma rede neural

This work presents an analysis of the control law based on an indirect hybrid scheme using neural network, initially proposed for O. Adetona, S. Sathanathan and L. H. Keel. Implementations of this control law, for a level plant of second order, was resulted an oscillatory behavior, even if the neural identifier has converged. Such results had motivated the investigation of the applicability of that law. Starting from that, had been made stability mathematical analysis and several implementations, with simulated plants and with real plants, for analyze the problem. The analysis has been showed the law was designed being despised some components of dynamic of the plant to be controlled. Thus, for plants that these components have a significant influence in its dynamic, the law tends to fail

Classificação Automática de Modulação Digital com uso de Correntropia para Ambientes de Rádio Cognitivo

Modern wireless systems employ adaptive techniques to provide high throughput while observing desired coverage, Quality of Service (QoS) and capacity. An alternative to further enhance data rate is to apply cognitive radio concepts, where a system is able to exploit unused spectrum on existing licensed bands by sensing the spectrum and opportunistically access unused portions. Techniques like Automatic Modulation Classification (AMC) could help or be vital for such scenarios. Usually, AMC implementations rely on some form of signal pre-processing, which may introduce a high computational cost or make assumptions about the received signal which may not hold (e.g. Gaussianity of noise). This work proposes a new method to perform AMC which uses a similarity measure from the Information Theoretic Learning (ITL) framework, known as correntropy coefficient. It is capable of extracting similarity measurements over a pair of random processes using higher order statistics, yielding in better similarity estimations than by using e.g. correlation coefficient. Experiments carried out by means of computer simulation show that the technique proposed in this paper presents a high rate success in classification of digital modulation, even in the presence of additive white gaussian noise (AWGN)

Lógica BDI fuzzy

Intendding to understand how the human mind operates, some philosophers and psycologists began to study about rationality. Theories were built from those studies and nowadays that interest have been extended to many other areas such as computing engineering and computing science, but with a minimal distinction at its goal: to understand the mind operational proccess and apply it on agents modelling to become possible the implementation (of softwares or hardwares) with the agent-oriented paradigm where agents are able to deliberate their own plans of actions. In computing science, the sub-area of multiagents systems has progressed using several works concerning artificial intelligence, computational logic, distributed systems, games theory and even philosophy and psycology. This present work hopes to show how it can be get a logical formalisation extention of a rational agents architecture model called BDI (based in a philosophic Bratman s Theory) in which agents are capable to deliberate actions from its beliefs, desires and intentions. The formalisation of this model is called BDI logic and it is a modal logic (in general it is a branching time logic) with three access relations: B, D and I. And here, it will show two possible extentions that tranform BDI logic in a modal-fuzzy logic where the formulae and the access relations can be evaluated by values from the interval [0,1]

Avaliação de arquitetura de agentes emocionais : uma abordagem baseada em métrica de qualidade e na influência das emoções sobre usuários

There is a need for multi-agent system designers in determining the quality of systems in the earliest phases of the development process. The architectures of the agents are also part of the design of these systems, and therefore also need to have their quality evaluated. Motivated by the important role that emotions play in our daily lives, embodied agents researchers have aimed to create agents capable of producing affective and natural interaction with users that produces a beneficial or desirable result. For this, several studies proposing architectures of agents with emotions arose without the accompaniment of appropriate methods for the assessment of these architectures. The objective of this study is to propose a methodology for evaluating architectures emotional agents, which evaluates the quality attributes of the design of architectures, in addition to evaluation of human-computer interaction, the effects on the subjective experience of users of applications that implement it. The methodology is based on a model of well-defined metrics. In assessing the quality of architectural design, the attributes assessed are: extensibility, modularity and complexity. In assessing the effects on users' subjective experience, which involves the implementation of the architecture in an application and we suggest to be the domain of computer games, the metrics are: enjoyment, felt support, warm, caring, trust, cooperation, intelligence, interestingness, naturalness of emotional reactions, believabiliy, reducing of frustration and likeability, and the average time and average attempts. We experimented with this approach and evaluate five architectures emotional agents: BDIE, DETT, Camurra-Coglio, EBDI, Emotional-BDI. Two of the architectures, BDIE and EBDI, were implemented in a version of the game Minesweeper and evaluated for human-computer interaction. In the results, DETT stood out with the best architectural design. Users who have played the version of the game with emotional agents performed better than those who played without agents. In assessing the subjective experience of users, the differences between the architectures were insignificant

Implementação de uma matriz de neurônios dinamicamente reconfigurável para descrição de topologias de redes neurais artificiais multilayer perceptrons

The Artificial Neural Networks (ANN), which is one of the branches of Artificial Intelligence (AI), are being employed as a solution to many complex problems existing in several areas. To solve these problems, it is essential that its implementation is done in hardware. Among the strategies to be adopted and met during the design phase and implementation of RNAs in hardware, connections between neurons are the ones that need more attention. Recently, are RNAs implemented both in application specific integrated circuits's (Application Specific Integrated Circuits - ASIC) and in integrated circuits configured by the user, like the Field Programmable Gate Array (FPGA), which have the ability to be partially rewritten, at runtime, forming thus a system Partially Reconfigurable (SPR), the use of which provides several advantages, such as flexibility in implementation and cost reduction. It has been noted a considerable increase in the use of FPGAs for implementing ANNs. Given the above, it is proposed to implement an array of reconfigurable neurons for topologies Description of artificial neural network multilayer perceptrons (MLPs) in FPGA, in order to encourage feedback and reuse of neural processors (perceptrons) used in the same area of the circuit. It is further proposed, a communication network capable of performing the reuse of artificial neurons. The architecture of the proposed system will configure various topologies MLPs networks through partial reconfiguration of the FPGA. To allow this flexibility RNAs settings, a set of digital components (datapath), and a controller were developed to execute instructions that define each topology for MLP neural network.

Implementação de uma matriz de neurônios dinamicamente reconfigurável para descrição de topologias de redes neurais artificiais multilayer perceptrons

The Artificial Neural Networks (ANN), which is one of the branches of Artificial Intelligence (AI), are being employed as a solution to many complex problems existing in several areas. To solve these problems, it is essential that its implementation is done in hardware. Among the strategies to be adopted and met during the design phase and implementation of RNAs in hardware, connections between neurons are the ones that need more attention. Recently, are RNAs implemented both in application specific integrated circuits's (Application Specific Integrated Circuits - ASIC) and in integrated circuits configured by the user, like the Field Programmable Gate Array (FPGA), which have the ability to be partially rewritten, at runtime, forming thus a system Partially Reconfigurable (SPR), the use of which provides several advantages, such as flexibility in implementation and cost reduction. It has been noted a considerable increase in the use of FPGAs for implementing ANNs. Given the above, it is proposed to implement an array of reconfigurable neurons for topologies Description of artificial neural network multilayer perceptrons (MLPs) in FPGA, in order to encourage feedback and reuse of neural processors (perceptrons) used in the same area of the circuit. It is further proposed, a communication network capable of performing the reuse of artificial neurons. The architecture of the proposed system will configure various topologies MLPs networks through partial reconfiguration of the FPGA. To allow this flexibility RNAs settings, a set of digital components (datapath), and a controller were developed to execute instructions that define each topology for MLP neural network.

Da atividade dialógica: aspectos lógicos

CRUZ, Ângela M. P.; Lycurgo, Tassos. Da atividade dialógica: aspectos lógicos. Revista Vivência, Natal, v. 26, p. 51-58, 2004.

Algoritmos de agrupamento aplicados a dados de expressão gênica de câncer: um estudo comparativo

The use of clustering methods for the discovery of cancer subtypes has drawn a great deal of attention in the scientific community. While bioinformaticians have proposed new clustering methods that take advantage of characteristics of the gene expression data, the medical community has a preference for using classic clustering methods. There have been no studies thus far performing a large-scale evaluation of different clustering methods in this context. This work presents the first large-scale analysis of seven different clustering methods and four proximity measures for the analysis of 35 cancer gene expression data sets. Results reveal that the finite mixture of Gaussians, followed closely by k-means, exhibited the best performance in terms of recovering the true structure of the data sets. These methods also exhibited, on average, the smallest difference between the actual number of classes in the data sets and the best number of clusters as indicated by our validation criteria. Furthermore, hierarchical methods, which have been widely used by the medical community, exhibited a poorer recovery performance than that of the other methods evaluated. Moreover, as a stable basis for the assessment and comparison of different clustering methods for cancer gene expression data, this study provides a common group of data sets (benchmark data sets) to be shared among researchers and used for comparisons with new methods