Robust fault diagnosis in power distribution systems based on fuzzy ARTMAP neural network-aided evidence theory

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

Development of intelligent systems for well drilling and petroleum production

Domains where knowledge representation is too complex to be described analytically and in a deterministic way is very common in the petroleum industry, particularly in the field of exploration and production. In these domains, applications of artificial intelligence techniques are very suitable, especially in cases where the preservation of corporate and technical knowledge is important. The Laboratory for Research on Artificial Intelligence Applied to Petroleum Engineering (LIAP) at Unicamp, has, during the last 10 years, dedicated research efforts to build intelligent systems in well drilling and petroleum production fields. In the following sections, recent advances in intelligent systems, under development in the research laboratory, are described. (C) 2001 Published by Elsevier B.V. B.V.

Decision-Making Tool for Knowledge-Based Projects in Offshore Production Systems

The development of an offshore field demands knowledge of many experts to choose the different components of an offshore production system. All the specialized parts of this knowledge are intrinsically related. The aim of this paper is to use Fuzzy Sets and knowledge-based systems to describe and formalize the phases of development of an offshore production system project, in order to share and to manage the required knowledge for carrying out a project, while at the same time proposing alternatives for the oil field configuration.

Urban Traffic Control and Monitoring - An Approach for the Brazilian Intelligent Cities Project

This paper describes an urban traffic control system which aims at contributing to a more efficient traffic management system in the cities of Brazil. It uses fuzzy sets, case-based reasoning, and genetic algorithms to handle dynamic and unpredictable traffic scenarios, as well as uncertain, incomplete, and inconsistent information. The system is composed by one supervisor and several controller agents, which cooperate with each other to improve the system's results through Artificial Intelligence Techniques.

Use of a semantically grained database system for distribution and control within design environments

This paper presents a technique to share the data stored in an object-oriented database aimed at designing environments. This technique shares data between two related databases, called the Original and Product databases, and is composed of three processes: data separation, evolution and integration. Whenever a block of data needs to be shared, it is spread into both databases, resulting in a block on the original database, and another into the Product database, with special links between them controlled by the Object Manager. These blocks do not need to be maintained identical during the evolution phase of the sharing process. Six types of links were defined, and by choosing one, the designer control the evolution and reintegration of the block in both databases. This process uses the composite object concept as the unit of control. The presented concepts can be applied to any data model with support to composite objects.

ARISupport - Interaction support for augmented reality systems

The communication between user and software is a basic stage in any Interaction System project. In interactive systems, this communication is established by the means of a graphical interface, whose objective is to supply a visual representation of the main entities and functions present in the Virtual Environment. New ways of interacting in computational systems have been minimizing the gap in the relationship between man and computer, and therefore enhancing its usability. The objective of this paper, therefore, is to present a proposal for a non-conventional user interface library called ARISupport, which supplies ARToolKit applications developers with an opportunity to create simple GUI interfaces, and provides some of the functionality used in Augmented Reality systems. © Springer-Verlag Berlin Heidelberg 2005.

Real-time crowd density estimation using images

This paper presents a technique for real-time crowd density estimation based on textures of crowd images. In this technique, the current image from a sequence of input images is classified into a crowd density class. Then, the classification is corrected by a low-pass filter based on the crowd density classification of the last n images of the input sequence. The technique obtained 73.89% of correct classification in a real-time application on a sequence of 9892 crowd images. Distributed processing was used in order to obtain real-time performance. © Springer-Verlag Berlin Heidelberg 2005.

An approach based on neural networks for identification of fault sections in radial distribution systems

The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to the successful detection process of faults, aiming that it is carried out in a reliable and safe way. The compilations of the results obtained from practical experiments accomplished in a pilot radial distribution feeder have demonstrated that the developed techniques provide accurate results, identifying and classifying efficiently the several occurrences of faults observed in the feeder.

Improving accuracy and speed of optimum-path forest classifier using combination of disjoint training subsets

The Optimum-Path Forest (OPF) classifier is a recent and promising method for pattern recognition, with a fast training algorithm and good accuracy results. Therefore, the investigation of a combining method for this kind of classifier can be important for many applications. In this paper we report a fast method to combine OPF-based classifiers trained with disjoint training subsets. Given a fixed number of subsets, the algorithm chooses random samples, without replacement, from the original training set. Each subset accuracy is improved by a learning procedure. The final decision is given by majority vote. Experiments with simulated and real data sets showed that the proposed combining method is more efficient and effective than naive approach provided some conditions. It was also showed that OPF training step runs faster for a series of small subsets than for the whole training set. The combining scheme was also designed to support parallel or distributed processing, speeding up the procedure even more. © 2011 Springer-Verlag.

A malware detection system inspired on the human immune system

Malicious programs (malware) can cause severe damage on computer systems and data. The mechanism that the human immune system uses to detect and protect from organisms that threaten the human body is efficient and can be adapted to detect malware attacks. In this paper we propose a system to perform malware distributed collection, analysis and detection, this last inspired by the human immune system. After collecting malware samples from Internet, they are dynamically analyzed so as to provide execution traces at the operating system level and network flows that are used to create a behavioral model and to generate a detection signature. Those signatures serve as input to a malware detector, acting as the antibodies in the antigen detection process. This allows us to understand the malware attack and aids in the infection removal procedures. © 2012 Springer-Verlag.

Estimação do diâmetro de furos em processo de furação utilizando sistemas de inteligência artificial: uma análise comparativa entre diferentes técnicas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Sistema de supervisão e controle de irrigação utilizando técnicas de inteligência artificial

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

Estatística espacial e redes neurais aplicadas no estudo de epidemias de huanglongbing e mancha preta na cultura dos citros

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Artificial neural networks applied to bandsawing process control

In the search for productivity increase, industry has invested on the development of intelligent, flexible and self-adjusting method, capable of controlling processes through the assistance of autonomous systems, independently whether they are hardware or software. Notwithstanding, simulating conventional computational techniques is rather challenging, regarding the complexity and non-linearity of the production systems. Compared to traditional models, the approach with Artificial Neural Networks (ANN) performs well as noise suppression and treatment of non-linear data. Therefore, the challenges in the wood industry justify the use of ANN as a tool for process improvement and, consequently, add value to the final product. Furthermore, Artificial Intelligence techniques such as Neuro-Fuzzy Networks (NFNs) have proven effective, since NFNs combine the ability to learn from previous examples and generalize the acquired information from the ANNs with the capacity of Fuzzy Logic to transform linguistic variables in rules.

An Optimum-Path Forest framework for intrusion detection in computer networks

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