Développement et validation d'un instrument non-invasifde caractérisation du comportement musculaire respiratoire

Réalisé en cotutelle avec l'Université Joseph Fourier École Doctorale Ingénierie pour la Santé,la Cognition et l'Environnement (France)

MACHINE VISION FOR AUTOMATICVISUAL INSPECTION OF WOODENRAILWAY SLEEPERS USING UNSUPERVISED NEURAL NETWORKS

The motivation for this thesis work is the need for improving reliability of equipment and quality of service to railway passengers as well as a requirement for cost-effective and efficient condition maintenance management for rail transportation. This thesis work develops a fusion of various machine vision analysis methods to achieve high performance in automation of wooden rail track inspection.The condition monitoring in rail transport is done manually by a human operator where people rely on inference systems and assumptions to develop conclusions. The use of conditional monitoring allows maintenance to be scheduled, or other actions to be taken to avoid the consequences of failure, before the failure occurs. Manual or automated condition monitoring of materials in fields of public transportation like railway, aerial navigation, traffic safety, etc, where safety is of prior importance needs non-destructive testing (NDT).In general, wooden railway sleeper inspection is done manually by a human operator, by moving along the rail sleeper and gathering information by visual and sound analysis for examining the presence of cracks. Human inspectors working on lines visually inspect wooden rails to judge the quality of rail sleeper. In this project work the machine vision system is developed based on the manual visual analysis system, which uses digital cameras and image processing software to perform similar manual inspections. As the manual inspection requires much effort and is expected to be error prone sometimes and also appears difficult to discriminate even for a human operator by the frequent changes in inspected material. The machine vision system developed classifies the condition of material by examining individual pixels of images, processing them and attempting to develop conclusions with the assistance of knowledge bases and features.A pattern recognition approach is developed based on the methodological knowledge from manual procedure. The pattern recognition approach for this thesis work was developed and achieved by a non destructive testing method to identify the flaws in manually done condition monitoring of sleepers.In this method, a test vehicle is designed to capture sleeper images similar to visual inspection by human operator and the raw data for pattern recognition approach is provided from the captured images of the wooden sleepers. The data from the NDT method were further processed and appropriate features were extracted.The collection of data by the NDT method is to achieve high accuracy in reliable classification results. A key idea is to use the non supervised classifier based on the features extracted from the method to discriminate the condition of wooden sleepers in to either good or bad. Self organising map is used as classifier for the wooden sleeper classification.In order to achieve greater integration, the data collected by the machine vision system was made to interface with one another by a strategy called fusion. Data fusion was looked in at two different levels namely sensor-level fusion, feature- level fusion. As the goal was to reduce the accuracy of the human error on the rail sleeper classification as good or bad the results obtained by the feature-level fusion compared to that of the results of actual classification were satisfactory.

Optimization of query results in peer-to-peer database supported by semantics

The development of new technologies that use peer-to-peer networks grows every day, with the object to supply the need of sharing information, resources and services of databases around the world. Among them are the peer-to-peer databases that take advantage of peer-to-peer networks to manage distributed knowledge bases, allowing the sharing of information semantically related but syntactically heterogeneous. However, it is a challenge to ensure the efficient search for information without compromising the autonomy of each node and network flexibility, given the structural characteristics of these networks. On the other hand, some studies propose the use of ontology semantics by assigning standardized categorization of information. The main original contribution of this work is the approach of this problem with a proposal for optimization of queries supported by the Ant Colony algorithm and classification though ontologies. The results show that this strategy enables the semantic support to the searches in peer-to-peer databases, aiming to expand the results without compromising network performance. © 2011 IEEE.

O processo de construção do conhecimento histórico para alunos em educação de jovens e adultos: um olhar para as práticas de leitura e escrita

Pós-graduação em Educação - IBRC

Memória organizacional, memória corporativa e memória institucional: discussões conceituais e terminológicas

The proposal of present paper is to present a conceptual and terminological discussion relative to the terms: organizational memory, corporate memory and institutional memory that is a subject still little studied and explored in the academic area. The study is theoretical focus and the research kind is bibliographical. As research sources were utilized: books, digital libraries of theses and dissertations in the scope of the country and CAPES Journals Portal. This study is in course and is part of doctorate research "Organizational memory and the knowledge bases constitution". As the partial results, observes that the question about "memory" is studied by different areas of knowledge: Psychology, Neurosciences, History and others; the concepts about organizational memory and corporate memory are studied by Administration and Information Systems areas; the concept of institutional memory is more studied by Business Communication area focusing the company history. Beyond that, observes the use of another term, social memory, utilized by Information Science and History areas.

Inconsistent-tolerant base revision through Argument Theory Change

Reasoning and change over inconsistent knowledge bases (KBs) is of utmost relevance in areas like medicine and law. Argumentation may bring the possibility to cope with both problems. Firstly, by constructing an argumentation framework (AF) from the inconsistent KB, we can decide whether to accept or reject a certain claim through the interplay among arguments and counterarguments. Secondly, by handling dynamics of arguments of the AF, we might deal with the dynamics of knowledge of the underlying inconsistent KB. Dynamics of arguments has recently attracted attention and although some approaches have been proposed, a full axiomatization within the theory of belief revision was still missing. A revision arises when we want the argumentation semantics to accept an argument. Argument Theory Change (ATC) encloses the revision operators that modify the AF by analyzing dialectical trees-arguments as nodes and attacks as edges-as the adopted argumentation semantics. In this article, we present a simple approach to ATC based on propositional KBs. This allows to manage change of inconsistent KBs by relying upon classical belief revision, although contrary to it, consistency restoration of the KB is avoided. Subsequently, a set of rationality postulates adapted to argumentation is given, and finally, the proposed model of change is related to the postulates through the corresponding representation theorem. Though we focus on propositional logic, the results can be easily extended to more expressive formalisms such as first-order logic and description logics, to handle evolution of ontologies.

Frame-driven Extraction of Linked Data and Ontologies from Text

Ontology design and population -core aspects of semantic technologies- re- cently have become fields of great interest due to the increasing need of domain-specific knowledge bases that can boost the use of Semantic Web. For building such knowledge resources, the state of the art tools for ontology design require a lot of human work. Producing meaningful schemas and populating them with domain-specific data is in fact a very difficult and time-consuming task. Even more if the task consists in modelling knowledge at a web scale. The primary aim of this work is to investigate a novel and flexible method- ology for automatically learning ontology from textual data, lightening the human workload required for conceptualizing domain-specific knowledge and populating an extracted schema with real data, speeding up the whole ontology production process. Here computational linguistics plays a fundamental role, from automati- cally identifying facts from natural language and extracting frame of relations among recognized entities, to producing linked data with which extending existing knowledge bases or creating new ones. In the state of the art, automatic ontology learning systems are mainly based on plain-pipelined linguistics classifiers performing tasks such as Named Entity recognition, Entity resolution, Taxonomy and Relation extraction [11]. These approaches present some weaknesses, specially in capturing struc- tures through which the meaning of complex concepts is expressed [24]. Humans, in fact, tend to organize knowledge in well-defined patterns, which include participant entities and meaningful relations linking entities with each other. In literature, these structures have been called Semantic Frames by Fill- 6 Introduction more [20], or more recently as Knowledge Patterns [23]. Some NLP studies has recently shown the possibility of performing more accurate deep parsing with the ability of logically understanding the structure of discourse [7]. In this work, some of these technologies have been investigated and em- ployed to produce accurate ontology schemas. The long-term goal is to collect large amounts of semantically structured information from the web of crowds, through an automated process, in order to identify and investigate the cognitive patterns used by human to organize their knowledge.

Ontologies and Methods for Interoperability of Engineering Analysis Models (EAMS) in an E-Design Environment

ABSTRACT ONTOLOGIES AND METHODS FOR INTEROPERABILITY OF ENGINEERING ANALYSIS MODELS (EAMS) IN AN E-DESIGN ENVIRONMENT SEPTEMBER 2007 NEELIMA KANURI, B.S., BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCES PILANI INDIA M.S., UNIVERSITY OF MASSACHUSETTS AMHERST Directed by: Professor Ian Grosse Interoperability is the ability of two or more systems to exchange and reuse information efficiently. This thesis presents new techniques for interoperating engineering tools using ontologies as the basis for representing, visualizing, reasoning about, and securely exchanging abstract engineering knowledge between software systems. The specific engineering domain that is the primary focus of this report is the modeling knowledge associated with the development of engineering analysis models (EAMs). This abstract modeling knowledge has been used to support integration of analysis and optimization tools in iSIGHT FD , a commercial engineering environment. ANSYS , a commercial FEA tool, has been wrapped as an analysis service available inside of iSIGHT-FD. Engineering analysis modeling (EAM) ontology has been developed and instantiated to form a knowledge base for representing analysis modeling knowledge. The instances of the knowledge base are the analysis models of real world applications. To illustrate how abstract modeling knowledge can be exploited for useful purposes, a cantilever I-Beam design optimization problem has been used as a test bed proof-of-concept application. Two distinct finite element models of the I-beam are available to analyze a given beam design- a beam-element finite element model with potentially lower accuracy but significantly reduced computational costs and a high fidelity, high cost, shell-element finite element model. The goal is to obtain an optimized I-beam design at minimum computational expense. An intelligent KB tool was developed and implemented in FiPER . This tool reasons about the modeling knowledge to intelligently shift between the beam and the shell element models during an optimization process to select the best analysis model for a given optimization design state. In addition to improved interoperability and design optimization, methods are developed and presented that demonstrate the ability to operate on ontological knowledge bases to perform important engineering tasks. One such method is the automatic technical report generation method which converts the modeling knowledge associated with an analysis model to a flat technical report. The second method is a secure knowledge sharing method which allocates permissions to portions of knowledge to control knowledge access and sharing. Both the methods acting together enable recipient specific fine grain controlled knowledge viewing and sharing in an engineering workflow integration environment, such as iSIGHT-FD. These methods together play a very efficient role in reducing the large scale inefficiencies existing in current product design and development cycles due to poor knowledge sharing and reuse between people and software engineering tools. This work is a significant advance in both understanding and application of integration of knowledge in a distributed engineering design framework.

Semantic Methods for Intelligent Distributed Design Environments

Continuous advancements in technology have led to increasingly comprehensive and distributed product development processes while in pursuit of improved products at reduced costs. Information associated with these products is ever changing, and structured frameworks have become integral to managing such fluid information. Ontologies and the Semantic Web have emerged as key alternatives for capturing product knowledge in both a human-readable and computable manner. The primary and conclusive focus of this research is to characterize relationships formed within methodically developed distributed design knowledge frameworks to ultimately provide a pervasive real-time awareness in distributed design processes. Utilizing formal logics in the form of the Semantic Web’s OWL and SWRL, causal relationships are expressed to guide and facilitate knowledge acquisition as well as identify contradictions between knowledge in a knowledge base. To improve the efficiency during both the development and operational phases of these “intelligent” frameworks, a semantic relatedness algorithm is designed specifically to identify and rank underlying relationships within product development processes. After reviewing several semantic relatedness measures, three techniques, including a novel meronomic technique, are combined to create AIERO, the Algorithm for Identifying Engineering Relationships in Ontologies. In determining its applicability and accuracy, AIERO was applied to three separate, independently developed ontologies. The results indicate AIERO is capable of consistently returning relatedness values one would intuitively expect. To assess the effectiveness of AIERO in exposing underlying causal relationships across product development platforms, a case study involving the development of an industry-inspired printed circuit board (PCB) is presented. After instantiating the PCB knowledge base and developing an initial set of rules, FIDOE, the Framework for Intelligent Distributed Ontologies in Engineering, was employed to identify additional causal relationships through extensional relatedness measurements. In a conclusive PCB redesign, the resulting “intelligent” framework demonstrates its ability to pass values between instances, identify inconsistencies amongst instantiated knowledge, and identify conflicting values within product development frameworks. The results highlight how the introduced semantic methods can enhance the current knowledge acquisition, knowledge management, and knowledge validation capabilities of traditional knowledge bases.

Towards an Emergent Semantic Web

In his in uential article about the evolution of the Web, Berners-Lee [1] envisions a Semantic Web in which humans and computers alike are capable of understanding and processing information. This vision is yet to materialize. The main obstacle for the Semantic Web vision is that in today's Web meaning is rooted most often not in formal semantics, but in natural language and, in the sense of semiology, emerges not before interpretation and processing. Yet, an automated form of interpretation and processing can be tackled by precisiating raw natural language. To do that, Web agents extract fuzzy grassroots ontologies through induction from existing Web content. Inductive fuzzy grassroots ontologies thus constitute organically evolved knowledge bases that resemble automated gradual thesauri, which allow precisiating natural language [2]. The Web agents' underlying dynamic, self-organizing, and best-effort induction, enable a sub-syntactical bottom up learning of semiotic associations. Thus, knowledge is induced from the users' natural use of language in mutual Web interactions, and stored in a gradual, thesauri-like lexical-world knowledge database as a top-level ontology, eventually allowing a form of computing with words [3]. Since when computing with words the objects of computation are words, phrases and propositions drawn from natural languages, it proves to be a practical notion to yield emergent semantics for the Semantic Web. In the end, an improved understanding by computers on the one hand should upgrade human- computer interaction on the Web, and, on the other hand allow an initial version of human- intelligence amplification through the Web.

Multipedia: Enriching DBpedia with Multimedia Information

Enriching knowledge bases with multimedia information makes it possible to complement textual descriptions with visual and audio information. Such complementary information can help users to understand the meaning of assertions, and in general improve the user experience with the knowledge base. In this paper we address the problem of how to enrich ontology instances with candidate images retrieved from existing Web search engines. DBpedia has evolved into a major hub in the Linked Data cloud, interconnecting millions of entities organized under a consistent ontology. Our approach taps into the Wikipedia corpus to gather context information for DBpedia instances and takes advantage of image tagging information when this is available to calculate semantic relatedness between instances and candidate images. We performed experiments with focus on the particularly challenging problem of highly ambiguous names. Both methods presented in this work outperformed the baseline. Our best method leveraged context words from Wikipedia, tags from Flickr and type information from DBpedia to achieve an average precision of 80%.

Modelo de diseño orientado a marcos para sistemas basados en el conocimiento

El objetivo de esta Tesis es crear un Modelo de Diseño Orientado a Marcos que, intermedio entre el Mundo Externo y el Modelo Interno del Mundo que supone el sistema ímplementado, disminuya la pérdida de conocimiento que se produce al formalizar la realidad en Bases de Conocimientos. El modelo disminuye la pérdida de conocimiento al formalizar Bases de Conocimiento, acercando el formalismo de Marcos al Mundo Externo, porque: 1. Crea una base teórica que uniformiza el concepto de Marco en el plano de la Formalización, estableciendo un conjunto de restricciones sintácticas y semánticas que impedirán, al Ingeniero del Conocimiento (IC) cuando formaliza, definir elementos no permitidos o el uso indebido de ellos. 2. Se incrementa la expresividad del formalismo al asociar a cada una de las propiedades de un marco clase un parámetro adicional que simboliza la representatividad de la propiedad en el concepto. Este parámetro, y las técnicas de inferencia que trabajan con él, permitirán al IC introducir en el Modelo Formalizado conocimiento que antes no introducía al construir la base de conocimientos y que, sin embargo, sí existía en la realidad. 3. Se propone una técnica de equiparación que trabaja con el conocimiento incierto presente en el dominio. Esta técnica de equiparación, utiliza la representatividad de las propiedades en los marcos clase y el grado de certeza de las propiedades de las entidades para calcular el valor de equiparación y, así, determinar en qué medida los marcos clase seleccionados son consistentes con la descripción de la situación actual dada por una entidad. 4. Proporciona nuevas técnicas de inferencia basadas en la transferencia de propiedades y modifica las ya existentes. Las transferencias de propiedades realizadas sobre relaciones "ad hoc" definidas por el IC al construir el sistema, es una nueva técnica de inferencia independiente y complementaria a la transferencia de propiedades llamada tradicionalmente Herencia (cesión de propiedades entre padres e hijos). A esta nueva técnica, se le ha llamado Donación, es decir, cesión de propiedades entre marcos sin parentesco. Como aportación práctica, se ha construido un entorno de construcción de Sistemas Basados en el Conocimiento formalizados en Marcos, donde se han introducido todos los nuevos conceptos del Modelo Teórico de la Tesis. Se trata de una cierta anidación. Es decir, son marcos que permiten formalizar cualquier SBC en marcos. El entorno permitirá al IC formalizar bases de conocimientos automáticamente y éste podrá validar el conocimiento del dominio en la fase de formalización en lugar de tener que esperar a que la BC esté implementada. Todo ello lleva a describir el Modelo de Diseño Orientado a Marcos como un puente que aproxima y comunica el Mundo Externo con el Modelo Interno asociado a la realidad e implementado en una computadora, disminuyendo así las diversas pérdidas de conocimiento que si bien no ocurren simultáneamente al construir Sistemas Basados en el Conocimiento, sí coexisten en él.---ABSTRACT---The goal of this thesis is to créate a Frame-Orlented Deslgn Model that, bridging the Outside World and the implemented system's Internal Model of the World, reduces the amount of knowledge lost when reality is formalized in Knowledge Bases (KB). The model diminishes the loss of knowledge when formalizing a KB and brings the Frame-formalized Model closer to the Outside World because: 1. It creates a theory that standardizes the concept of trame at the formalization level to establish a set of syntactic and semantic constraints that will prevent the Knowledge Engineer (KE) from defining forbidden elements or their undue use in the formalization process. 2. The formalism's expressiveness is increased by associating an additional parameter to each of the properties of a class frame to symbolize the representativeness of the concept property. This parameter and the related inference techniques will allow the KE to enter knowledge into the Formalized Model that actually existed but that was not used previously when building the KB. 3. The proposed technique involves matching and works with uncertain knowledge present in the domain. This matching technique takes the representativeness of the properties in the class frame and the degree of certainty of the properties of the entities to calcúlate the matching valué and thus determine to what extent the class frames selected are consistent with the description of the present situation given by an entity. 4. It offers new inference techniques based on property transfer and alters existing ones. Property transfer on ad hoc relations defined by the KE when building a system is a new inference technique independent of and complementary to property transfer traditionally termed Inheritance (transfer of properties between parents and children). This new technique has been callad Donation (transfer of properties between trames without relationships). 5. It improves control of the procedural knowledge defined in the trames by introducing OO concepta. A frame-formalized KBS building environment has been constructed, incorporating all the new concepts of the theoretical model set out in the thesis. There is some embedding, that is, they are trames that provide for any KBS to be formalizad in trames. The environment will enable the KE to formaliza KB automatically, and he will be able to valídate the domain knowledge in the formalization stage instead of havíng to wait until the KB has been implemented. This is a description of the Frame-oriented Design Model, a bridge that brings closer and communicates the Outside World with the Interna! Model associated to reality and implemented on a computar, thus reducing the different losses in knowledge that, though they do not occur simultaneosly when building a Knowledge-based System, coexist within it.

Diseño de nuevas técnicas evolutivas para videojuegos de lucha

El objetivo de esta tesis fin de máster es la construcción mediante técnicas evolutivas de bases de conocimiento con reglas difusas para desarrollar un sistema autónomo que sea capaz de jugar con destreza a un videojuego de lucha en 2D. El uso de la lógica difusa permite manejar imprecisión, la cual está implícita en las variables de entrada al sistema y favorece la comprensión a nivel humano del comportamiento general del controlador. Se ha diseñado, para obtener la base de conocimiento que permita al sistema tomar las decisiones adecuadas durante el combate, un nuevo operador para algoritmos evolutivos. Se ha observado que la programación genética guiada por gramáticas (PGGG) muestra un sesgo debido al cruce que se suele emplear para obtener nuevos individuos en el proceso evolutivo. Para solventar este problema, se propone el método de sedimentación, capaz de evitar la tendencia que tiene la PGGG a generar bases de conocimiento con pocas reglas, de forma independiente a la gramática. Este método se inspira en la sedimentación que se produce en el fondo de los lechos marinos y permite obtener un sustrato de reglas óptimas que forman la solución final una vez que converge el algoritmo.---ABSTRACT---The objective of this thesis is the construction by evolutionary techniques of fuzzy rule-base system to develop an autonomous controller capable of playing a 2D fighting game. The use of fuzzy logic handles imprecision, which is implicit in the input variables of the system and makes the behavior of the controller easier to understand by humans. A new operator for evolutionary algorithms is designed to obtain the knowledge base that allows the system to take appropriate decision during combat. It has been observed that the grammar guided genetic programming (GGGP) shows a bias due to the crossing that is often used for obtaining new individuals in the evolutionary process. To solve this problem, the sedimentation method, able to avoid the tendency of the PGGG to generate knowledge bases with few rules, independently of the grammar is proposed. This method is inspired by the sedimentation that occurs on the bottom of the seabed and creates an optimal rules substrate that ends on the final solution once the algorithm converges.

Using the Dempster-Shafer theory of evidence to resolve ABox inconsistencies

Automated ontology population using information extraction algorithms can produce inconsistent knowledge bases. Confidence values assigned by the extraction algorithms may serve as evidence in helping to repair inconsistencies. The Dempster-Shafer theory of evidence is a formalism, which allows appropriate interpretation of extractors’ confidence values. This chapter presents an algorithm for translating the subontologies containing conflicts into belief propagation networks and repairing conflicts based on the Dempster-Shafer plausibility.

AquaLog : an ontology-driven question answering system for organizational semantic intranets

The semantic web vision is one in which rich, ontology-based semantic markup will become widely available. The availability of semantic markup on the web opens the way to novel, sophisticated forms of question answering. AquaLog is a portable question-answering system which takes queries expressed in natural language and an ontology as input, and returns answers drawn from one or more knowledge bases (KBs). We say that AquaLog is portable because the configuration time required to customize the system for a particular ontology is negligible. AquaLog presents an elegant solution in which different strategies are combined together in a novel way. It makes use of the GATE NLP platform, string metric algorithms, WordNet and a novel ontology-based relation similarity service to make sense of user queries with respect to the target KB. Moreover it also includes a learning component, which ensures that the performance of the system improves over the time, in response to the particular community jargon used by end users.