A proposal for the diagnosis of uncertain dynamic systems based on interval models

The performance of a model-based diagnosis system could be affected by several uncertainty sources, such as,model errors,uncertainty in measurements, and disturbances. This uncertainty can be handled by mean of interval models.The aim of this thesis is to propose a methodology for fault detection, isolation and identification based on interval models. The methodology includes some algorithms to obtain in an automatic way the symbolic expression of the residual generators enhancing the structural isolability of the faults, in order to design the fault detection tests. These algorithms are based on the structural model of the system. The stages of fault detection, isolation, and identification are stated as constraint satisfaction problems in continuous domains and solved by means of interval based consistency techniques. The qualitative fault isolation is enhanced by a reasoning in which the signs of the symptoms are derived from analytical redundancy relations or bond graph models of the system. An initial and empirical analysis regarding the differences between interval-based and statistical-based techniques is presented in this thesis. The performance and efficiency of the contributions are illustrated through several application examples, covering different levels of complexity.

Design and evaluation of intelligent classifier based intrusion detection system

We have discovered a novel approach of intrusion detection system using an intelligent data classifier based on a self organizing map (SOM). We have surveyed all other unsupervised intrusion detection methods, different alternative SOM based techniques and KDD winner IDS methods. This paper provides a robust designed and implemented intelligent data classifier technique based on a single large size (30x30) self organizing map (SOM) having the capability to detect all types of attacks given in the DARPA Archive 1999 the lowest false positive rate being 0.04 % and higher detection rate being 99.73% tested using full KDD data sets and 89.54% comparable detection rate and 0.18% lowest false positive rate tested using corrected data sets.

ANALYSIS & STUDY OF AI TECHNIQUES FORAUTOMATIC CONDITION MONITORING OFRAILWAY TRACK INFRASTRUCTURE : Artificial Intelligence Techniques

Since the last decade the problem of surface inspection has been receiving great attention from the scientific community, the quality control and the maintenance of products are key points in several industrial applications.The railway associations spent much money to check the railway infrastructure. The railway infrastructure is a particular field in which the periodical surface inspection can help the operator to prevent critical situations. The maintenance and monitoring of this infrastructure is an important aspect for railway association.That is why the surface inspection of railway also makes importance to the railroad authority to investigate track components, identify problems and finding out the way that how to solve these problems. In railway industry, usually the problems find in railway sleepers, overhead, fastener, rail head, switching and crossing and in ballast section as well. In this thesis work, I have reviewed some research papers based on AI techniques together with NDT techniques which are able to collect data from the test object without making any damage. The research works which I have reviewed and demonstrated that by adopting the AI based system, it is almost possible to solve all the problems and this system is very much reliable and efficient for diagnose problems of this transportation domain. I have reviewed solutions provided by different companies based on AI techniques, their products and reviewed some white papers provided by some of those companies. AI based techniques likemachine vision, stereo vision, laser based techniques and neural network are used in most cases to solve the problems which are performed by the railway engineers.The problems in railway handled by the AI based techniques performed by NDT approach which is a very broad, interdisciplinary field that plays a critical role in assuring that structural components and systems perform their function in a reliable and cost effective fashion. The NDT approach ensures the uniformity, quality and serviceability of materials without causing any damage of that materials is being tested. This testing methods use some way to test product like, Visual and Optical testing, Radiography, Magnetic particle testing, Ultrasonic testing, Penetrate testing, electro mechanic testing and acoustic emission testing etc. The inspection procedure has done periodically because of better maintenance. This inspection procedure done by the railway engineers manually with the aid of AI based techniques.The main idea of thesis work is to demonstrate how the problems can be reduced of thistransportation area based on the works done by different researchers and companies. And I have also provided some ideas and comments according to those works and trying to provide some proposal to use better inspection method where it is needed.The scope of this thesis work is automatic interpretation of data from NDT, with the goal of detecting flaws accurately and efficiently. AI techniques such as neural networks, machine vision, knowledge-based systems and fuzzy logic were applied to a wide spectrum of problems in this area. Another scope is to provide an insight into possible research methods concerning railway sleeper, fastener, ballast and overhead inspection by automatic interpretation of data.In this thesis work, I have discussed about problems which are arise in railway sleepers,fastener, and overhead and ballasted track. For this reason I have reviewed some research papers related with these areas and demonstrated how their systems works and the results of those systems. After all the demonstrations were taking place of the advantages of using AI techniques in contrast with those manual systems exist previously.This work aims to summarize the findings of a large number of research papers deploying artificial intelligence (AI) techniques for the automatic interpretation of data from nondestructive testing (NDT). Problems in rail transport domain are mainly discussed in this work. The overall work of this paper goes to the inspection of railway sleepers, fastener, ballast and overhead.

KClone: A Proposed Approach to Fast Precise Code Clone Detection

In all applications of clone detection it is important to have precise and efficient clone identification algorithms. This paper proposes and outlines a new algorithm, KClone for clone detection that incorporates a novel combination of lexical and local dependence analysis to achieve precision, while retaining speed. The paper also reports on the initial results of a case study using an implementation of KClone with which we have been experimenting. The results indi- cate the ability of KClone to find types-1,2, and 3 clones compared to token-based and PDG-based techniques. The paper also reports results of an initial empirical study of the performance of KClone compared to CCFinderX.

New Insights on Nontechnical Losses Characterization Through Evolutionary-Based Feature Selection

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

A New Impedance Measurement System for PZT-Based Structural Health Monitoring

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Beta: uma ferramenta para geração de testes de unidade a partir de especificações B

Formal methods and software testing are tools to obtain and control software quality. When used together, they provide mechanisms for software specification, verification and error detection. Even though formal methods allow software to be mathematically verified, they are not enough to assure that a system is free of faults, thus, software testing techniques are necessary to complement the process of verification and validation of a system. Model Based Testing techniques allow tests to be generated from other software artifacts such as specifications and abstract models. Using formal specifications as basis for test creation, we can generate better quality tests, because these specifications are usually precise and free of ambiguity. Fernanda Souza (2009) proposed a method to define test cases from B Method specifications. This method used information from the machine s invariant and the operation s precondition to define positive and negative test cases for an operation, using equivalent class partitioning and boundary value analysis based techniques. However, the method proposed in 2009 was not automated and had conceptual deficiencies like, for instance, it did not fit in a well defined coverage criteria classification. We started our work with a case study that applied the method in an example of B specification from the industry. Based in this case study we ve obtained subsidies to improve it. In our work we evolved the proposed method, rewriting it and adding characteristics to make it compatible with a test classification used by the community. We also improved the method to support specifications structured in different components, to use information from the operation s behavior on the test case generation process and to use new coverage criterias. Besides, we have implemented a tool to automate the method and we have submitted it to more complex case studies

A graph-based framework for thermal faceprint characterization

Thermal faceprint has been paramount in the last years. Since we can handle with face recognition using images acquired in the infrared spectrum, an unique individual's signature can be obtained through the blood vessels network of the face. In this work, we propose a novel framework for thermal faceprint extraction using a collection of graph-based techniques, which were never used to this task up to date. A robust method of thermal face segmentation is also presented. The experiments, which were conducted over the UND Collection C dataset, have showed promising results. © 2011 Springer-Verlag.

Improving Parkinson's disease identification through evolutionary-based feature selection

Parkinson's disease (PD) automatic identification has been actively pursued over several works in the literature. In this paper, we deal with this problem by applying evolutionary-based techniques in order to find the subset of features that maximize the accuracy of the Optimum-Path Forest (OPF) classifier. The reason for the choice of this classifier relies on its fast training phase, given that each possible solution to be optimized is guided by the OPF accuracy. We also show results that improved other ones recently obtained in the context of PD automatic identification. © 2011 IEEE.

Desenvolvimento de metodologia high-throughput para estudo populacional do mosquito Aedes aegypti e comparação de dados de genes mitocondriais

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

Aplicação de sensores virtuais na inferência da temperatura de banho no processo de fabricação de alumínio primário

As indústrias buscam a todo o momento reduzir seus gastos operacionais para aumentar seus lucros e sua competitividade. Uma boa gestão é o fator mais importante, porém uma boa gestão é feita com auxílio de ferramentas que permitam o acesso às informações relevantes para o processo, que tenham bastante influência na tomada de decisões estratégicas, com o menor custo possível. O uso de sensores virtuais tem sido aplicado cada vez mais nas indústrias. Por ser flexível, ele pode ser adaptado a qualquer tipo de medição, promovendo uma redução de custos operacionais sem comprometer, e em alguns casos até melhorar, a qualidade da informação gerada. Como estão totalmente baseados em software, não estão sujeitos a danos físicos como os sensores reais, além de permitirem uma melhor adaptação a ambientes hostis e de difícil acesso. A razão do sucesso destes tipos de sensores é a utilização de técnicas de inteligência computacional, as quais têm sido usadas na modelagem de vários processos não lineares altamente complexos. Atualmente, muitas indústrias já utilizam com sucesso os sensores virtuais, e este trabalho explora a sua utilização, em conjunto com as Redes Neurais Artificiais, em um processo químico em uma importante indústria de alumínio brasileira cujo controle é muito difícil pois é muito difícil extrair medidas da planta dada sua natureza corrosiva e cujas medições exigem certo custo operacional além de estarem sujeitas a ruídos. A aplicação dos sensores virtuais poderá reduzir os intervalos de medições bem como os custos operacionais. Ao longo deste trabalho será apresentada a metodologia de como projetar o sensor virtual utilizando o processo químico como estudo de caso, seguindo a literatura recomendada.

XSOFT: estudo de reconhecimento, exploração e prevenção de vulnerabilidades de software através de uma distribuição LINUX

This work aims to give greater visibility to the issue of software security, due to people talk a lot in security conferences, that much of both IT (Information Technology) staff and, more specifically, IS (Information Security) staff does not know this, and, thanks to the spread of the mobile computing and of the cloud computing, this lack of deeper knowledge on this subject is increasingly becoming worrisome. It aims too, make applications to be developed in a security manner, priorizing the security of the information processed. It attempts to demonstrate the secure coding techniques, the principles of software security, the means to identify software vulnerabilities, the cutting-edge software exploitation techniques and the mechanisms of mitigation. Nowadays, the security guys are in charge of the most of the security tests in applications, audits and pentests, and it is undeniable that the so-called security experts, most often come from computer network field, having few experience in software development and programming. Therefore, the development process does not consider the security issue, thanks to the lack of knowledge on the subject by the developer, and the security tests could be improved whether security experts had a greater know-how on application development. Given this problem, the goal here is to integrate information security with software development, spreading out the process of secure software development. To achieve this, a Linux distribution with proof of concept applicati... (Complete abstract click electronic access below)

Ridge-based fingerprint matching using hough transform

Despite the efficacy of minutia-based fingerprint matching techniques for good-quality images captured by optical sensors, minutia-based techniques do not often perform so well on poor-quality images or fingerprint images captured by small solid-state sensors. Solid-state fingerprint sensors are being increasingly deployed in a wide range of applications for user authentication purposes. Therefore, it is necessary to develop new fingerprint-matching techniques that utilize other features to deal with fingerprint images captured by solid-state sensors. This paper presents a new fingerprint matching technique based on fingerprint ridge features. This technique was assessed on the MSU-VERIDICOM database, which consists of fingerprint impressions obtained from 160 users (4 impressions per finger) using a solid-state sensor. The combination of ridge-based matching scores computed by the proposed ridge-based technique with minutia-based matching scores leads to a reduction of the false non-match rate by approximately 1.7% at a false match rate of 0.1%. © 2005 IEEE.

Using Source-Code Analysis to Help End-user Programmers Create Dependable Software

Not long ago, most software was written by professional programmers, who could be presumed to have an interest in software engineering methodologies and in tools and techniques for improving software dependability. Today, however, a great deal of software is written not by professionals but by end-users, who create applications such as multimedia simulations, dynamic web pages, and spreadsheets. Applications such as these are often used to guide important decisions or aid in important tasks, and it is important that they be sufficiently dependable, but evidence shows that they frequently are not. For example, studies have shown that a large percentage of the spreadsheets created by end-users contain faults. Despite such evidence, until recently, relatively little research had been done to help end-users create more dependable software. We have been working to address this problem by finding ways to provide at least some of the benefits of formal software engineering techniques to end-user programmers. In this talk, focusing on the spreadsheet application paradigm, I present several of our approaches, focusing on methodologies that utilize source-code-analysis techniques to help end-users build more dependable spreadsheets. Behind the scenes, our methodologies use static analyses such as dataflow analysis and slicing, together with dynamic analyses such as execution monitoring, to support user tasks such as validation and fault localization. I show how, to accommodate the user base of spreadsheet languages, an interface to these methodologies can be provided in a manner that does not require an understanding of the theory behind the analyses, yet supports the interactive, incremental process by which spreadsheets are created. Finally, I present empirical results gathered in the use of our methodologies that highlight several costs and benefits trade-offs, and many opportunities for future work.

Software Application That Evaluates the Diagnostic Accuracy of Nursing Students

PURPOSE: Apply the educational software Fuzzy Kitten with undergraduate Brazilian nursing students. METHODS: This software, based on fuzzy logic, generates performance scores that evaluate the ability to identify defining characteristics/risk factors present in clinical cases, relate them with nursing diagnoses, and determine the diagnoses freely or using a decision support model. FINDINGS: There were differences in student performance compared to the year of the course. The time to perform the activity did not present a significant relation to the performance. The students' scores in the diagnoses indicated by the model was superior (p = .01). CONCLUSIONS: The software was able to evaluate the diagnostic accuracy of students. IMPLICATIONS: The software enables an objective evaluation of diagnostic accuracy.