Fuzzy distributed artificial intelligence systems

In this paper, we introduce a DAI approach called hereinafter Fuzzy Distributed Artificial Intelligence (FDAI). Through the use of fuzzy logic, we have been able to develop mechanisms that we feel may effectively improve current DAI systems, giving much more flexibility and providing the subsidies which a formal theory can bring. The appropriateness of the FDAI approach is explored in an important application, a fuzzy distributed traffic-light control system, where we have been able to aggregate and study several issues concerned with fuzzy and distributed artificial intelligence. We also present a number of current research directions necessary to develop the FDAI approach more fully.

APPLICATIONS OF ARTIFICIAL-INTELLIGENCE IN ORGANIC-CHEMISTRY .17. NEW PROGRAMS IN THE SISTEMAT PROJECT

This work describes the new improvements of the SISTEMAT project, one system for structural elucidation mainly in the field of Natural Products Chemistry. Some examples of the resolution of problems using C-13 Nuclear Magnetic Resonance and Mass Spectroscopy are given. Programs to discover new heuristic rules for structure generation are discussed. The data base contains about 10000 C-13 NMR spectra.

Petroleum well drilling monitoring through cutting image analysis and artificial intelligence techniques

Petroleum well drilling monitoring has become an important tool for detecting and preventing problems during the well drilling process. In this paper, we propose to assist the drilling process by analyzing the cutting images at the vibrating shake shaker, in which different concentrations of cuttings can indicate possible problems, such as the collapse of the well borehole walls. In such a way, we present here an innovative computer vision system composed by a real time cutting volume estimator addressed by support vector regression. As far we know, we are the first to propose the petroleum well drilling monitoring by cutting image analysis. We also applied a collection of supervised classifiers for cutting volume classification. (C) 2010 Elsevier Ltd. All rights reserved.

Combining artificial intelligence and human problem solving: Proposing a new architecture for ITS's

This paper shows a comparative study between the Artificial Intelligence Problem Solving and the Human Problem Solving. The study is based on the solution by many ways of problems proposed via multiple-choice questions. General techniques used by humans to solve this kind of problems are grouped in blocks and each block is divided in steps. A new architecture for ITS - Intelligent Tutoring System is proposed to support experts' knowledge representation and novices' activities. Problems are represented by a text and feasible answers with particular meaning and form, to be rigorously analyzed by the solver to find the right one. Paths through a conceptual space of states represent each right solution.

Using artificial intelligence for the control of medium voltage substations

This paper introduces a method for the supervision and control of devices in electric substations using fuzzy logic and artificial neural networks. An automatic knowledge acquisition process is included which allows the on-line processing of operator actions and the extraction of control rules to replace gradually the human operator. Some experimental results obtained by the application of the implemented software in a simulated environment with random signal generators are presented.

Automatic microstructural characterization and classification using artificial intelligence techniques on ultrasound signals

Secondary phases such as Laves and carbides are formed during the final solidification stages of nickel based superalloy coatings deposited during the gas tungsten arc welding cold wire process. However, when aged at high temperatures, other phases can precipitate in the microstructure, like the γ″ and δ phases. This work presents a new application and evaluation of artificial intelligent techniques to classify (the background echo and backscattered) ultrasound signals in order to characterize the microstructure of a Ni-based alloy thermally aged at 650 and 950 °C for 10, 100 and 200 h. The background echo and backscattered ultrasound signals were acquired using transducers with frequencies of 4 and 5 MHz. Thus with the use of features extraction techniques, i.e.; detrended fluctuation analysis and the Hurst method, the accuracy and speed in the classification of the secondary phases from ultrasound signals could be studied. The classifiers under study were the recent optimum-path forest (OPF) and the more traditional support vector machines and Bayesian. The experimental results revealed that the OPF classifier was the fastest and most reliable. In addition, the OPF classifier revealed to be a valid and adequate tool for microstructure characterization through ultrasound signals classification due to its speed, sensitivity, accuracy and reliability. © 2013 Elsevier B.V. All rights reserved.

Classification of masses in mammographic image using wavelet domain features and polynomial classifier

Breast cancer is the most common cancer among women. In CAD systems, several studies have investigated the use of wavelet transform as a multiresolution analysis tool for texture analysis and could be interpreted as inputs to a classifier. In classification, polynomial classifier has been used due to the advantages of providing only one model for optimal separation of classes and to consider this as the solution of the problem. In this paper, a system is proposed for texture analysis and classification of lesions in mammographic images. Multiresolution analysis features were extracted from the region of interest of a given image. These features were computed based on three different wavelet functions, Daubechies 8, Symlet 8 and bi-orthogonal 3.7. For classification, we used the polynomial classification algorithm to define the mammogram images as normal or abnormal. We also made a comparison with other artificial intelligence algorithms (Decision Tree, SVM, K-NN). A Receiver Operating Characteristics (ROC) curve is used to evaluate the performance of the proposed system. Our system is evaluated using 360 digitized mammograms from DDSM database and the result shows that the algorithm has an area under the ROC curve Az of 0.98 ± 0.03. The performance of the polynomial classifier has proved to be better in comparison to other classification algorithms. © 2013 Elsevier Ltd. All rights reserved.

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

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

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)

O conhecimento do senso comum e os limites da inteligência artificial

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Abordagens bio-inspiradas aplicadas ao estudo da cognição: um encontro entre biologia, psicologia e filosofia

Pós-graduação em Filosofia - FFC

GRIOT - sistema CAD inteligente para design de calçados

O presente trabalho é o estudo dos aspectos da metodologia projetual, face às novas tecnologias da informática, Inteligência Artificial (Sistemas Especialistas) e CAD, consideradas as reais possibilidades de automatização no processo de concepção em Design. Esse artigo propõe uma metodologia para a construção de sistema inteligente capaz de auxiliar o designer nas tarefas projetuais. A indústria de calçados foi utilizada como estudo de caso para a aplicação da metodologia, onde as reais possibilidades de automação são verificadas.

Fertility Rates Following Fixed-Time Artificial Insemination in Dairy Heifers in a Practical Progesterone-Based Protocol

Background: In bovines, more efficient management practices are important for maximizing profitability. In order to increase the pregnancy rates in artificial insemination (AI) programs, several hormonal protocols were developed to synchronize the follicular wave and the moment of ovulation in beef and dairy cattle. In dairy cattle, detection of estrus can be difficult due to a number of factors including the incidence of silent estrus. Hormonal treatments designed to control both luteal and follicular function has permitting efficient synchronizations of time of ovulation. Thus, the AI can be performed in a large number of animals on a fixed schedule without the need for detection of estrus. Using these management techniques, the fixed-time artificial insemination (TAI) can overcome the problem of accurate estrus detection and help in reducing the incidence of repeat breeding. In addition, with TAI in cattle operations, it is possible to facilitate management practices and commercialization, and to reduce the time and semen wasting with animals inseminated at incorrect times. The investigation of practical and efficient TAI protocols is important for reducing the labor and animal handling of TAI in dairy cattle, as well as for increasing the profitability of the cattle management system. This study was carried out in order to investigate the effectiveness of TAI in dairy heifers treated with a practical progesterone-based protocol.Materials, Methods & Results: This experiment was conducted at the university farm located in southwestern Brazil, during May 2009. Thirty-nine cycling crossbred dairy heifers were employed in this study. All animals received a single intramuscular injection of estradiol benzoate and intravaginal progesterone releasing device in a random stage of the estrous cycle (Day 0). on day 7 the animals were treated with PGF2a analogue and on day 9 the device was removed. Forty-eight hours after the device removal (day 11) a synthetic analogue of GnRH was administered and the animals were fixed-time artificially inseminated at the time of GnRH injection. The inseminations were performed using four different batches from the same Holstein bull. Among the heifers that were synchronized (87.2%), 30.8% ovulated until 24 h after TAI and 56.4% ovulated between 24 and 32 h after TAI. The conception rate was 61.5%. No effects of ovulation time in conception rates were detected. The conception rate from heifers that ovulated until 24 h after TAI was 58.3% and from heifers that ovulated between 24 and 32 h after TAI was 77.3%. The mean of ovulatory follicle in heifers that ovulated until 24 h was 14.3 mm and in heifers that ovulated between 24 and 32 h was 11.9 mm.Discussion: Taking together, the findings of the present study, along with those of others, emphasize the concept that development of practical methods for TAI offers significant advantages to dairy producers if conception rates are close or greater to those obtained after breeding at detected estrus. Thus, the results of the present study reinforce the possibility of making dairy cattle production more cost-effective using TAI. In conclusion, with the progesterone-based TAI protocol of the present experiment all synchronized animals ovulated up to 32 h after GnRH+TAI and no effects of ovulation time related to conception rate was detected. The exogenous control of luteal and follicular development facilitated the reproductive management and animal handling. Also, inseminating the heifers at the moment of GnRH injection in a progesterone-based TAI protocol is a practical strategy and provided satisfactory results regarding ovulation and conception rates in dairy heifers.

Modeling and analysis of artificial neural networks applied in operations research

Artificial neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear processing elements. Systems based on artificial neural networks have high computational rates due to the use of a massive number of these computational elements. Neural networks with feedback connections provide a computing model capable of solving a rich class of optimization problems. In this paper, a modified Hopfield network is developed for solving problems related to operations research. The internal parameters of the network are obtained using the valid-subspace technique. Simulated examples are presented as an illustration of the proposed approach. Copyright (C) 2000 IFAC.