Search

Maintenance is the process of preserving the good condition of a system to ensure its reliability and availability to perform specific operations. The way maintenance is nowadays performed in industry is changing thanks to the increasing availability of data and condition assessment methods. Soft sensors have been widely used over last years to monitor industrial processes and to predict process variables that are difficult to measured. The main objective of this study is to monitor and evaluate the condition of the compressor in a particular industrial gas turbine by developing a soft sensor following an autoencoder architecture. The data used to monitor and analyze its condition were captured by several sensors located along the compressor for around five years. The condition assessment of an industrial gas turbine compressor reveals significant changes over time, as well as a drift in its performance. These results lead to a qualitative indicator of the compressor behavior in long-term performance.

There has been an increasing relevance of the cultural sector in the economic and social development of different countries. However, this sector continues without much input from multi-criteria decision-making (MDCM) techniques and sustainability analysis, which are widely used in other sectors. This paper proposes an MCDM model to assess the sustainability of a musical institution&rsquo, s program. To define the parameters of the proposed model, qualitative interviews with relevant representatives of Catalan cultural institutions and highly recognized professionals in the sector were performed. The content of the 2015&ndash, 2016 season of the &lsquo, Palau de la M&uacute, sica Catalana&rsquo, a relevant Catalan musical institution located in Barcelona, was used as a case study to empirically test the method. The method allows the calculation of a season value index (SVI), which serves to make more sustainable decisions on musical season programs according to the established criteria. The sensitivity analysis carried out for different scenarios shows the robustness of the method. The research suggests that more complex decision settings, such as MCDM methods that are widely used in other sectors, can be easily applied to the sustainable management of any type of cultural institution. To the best of the authors&rsquo, knowledge, this method was never applied to a cultural institution and with real data.

Artificial Intelligence (AI) is among most important fields of knowledge and applications in a large variety of domains. Recently however, it has become a trending research topic propelled by Cloud computing, social networks and alike. Terms like machine learning, "Big Data" and artificial neural networks very frequently appear not only in scientific media but even in the mass media. In this project, we aim to design, implement and evaluate an AI technique, namely, deep learning, which has become very popular for face recognition. The problem is formulated from an engineering perspective: to design a small size system based on Raspberry Pi and an attached camera to it to detect and recognise human faces in real time. It should be mentioned that while for humans face recognition is a trivial task, we do it every day and with a full accuracy, for a computer, this is complex task. Recent applications from many industries show a large potential of intelligent systems that need to recognise faces with high accuracy. The thesis is essentially structured into two main parts. In the first part we formulate the problem, analyse potential solutions and propose a solution for its resolution. In the second part of the project we develop the proposed solution into an implementation of an intelligent system for a computationally limited and physical portable device (Raspberry Pi). The solution is empirically evaluated in terms of accuracy and performance using real data sets. The relevance of using such a small size intelligent system relies in the fact that this application can be installed in other devices, such as drones, easily, at low cost and without compromising the performance and speed of the said intelligent system.

In this master thesis, the problem of identification and control of a foam process in a waste treatment plant will be address. Using real data, an experimental model relating the anti-foam product injected using a pump and the measure of foam level measured used computer vision will be developed using identification techniques. Once the model have been obtained, a control law that aims at minimizing the foam level will be developed. The control system will be tested and implemented in a real waste treatment plant.

In order to create better decisions for business analytics, organizations increasingly use external structured, semi-structured, and unstructured data in addition to the (mostly structured) internal data. Current Extract-Transform-Load (ETL) tools are not suitable for this “open world scenario” because they do not consider semantic issues in the integration processing. Current ETL tools neither support processing semantic data nor create a semantic Data Warehouse (DW), a repository of semantically integrated data. This paper describes our programmable Semantic ETL (SETL) framework. SETL builds on Semantic Web (SW) standards and tools and supports developers by offering a number of powerful modules, classes, and methods for (dimensional and semantic) DW constructs and tasks. Thus it supports semantic data sources in addition to traditional data sources, semantic integration, and creating or publishing a semantic (multidimensional) DW in terms of a knowledge base. A comprehensive experimental evaluation comparing SETL to a solution made with traditional tools (requiring much more hand-coding) on a concrete use case, shows that SETL provides better programmer productivity, knowledge base quality, and performance.

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2017, Director: Eloi Puertas i Prats, Currently, thanks to the Internet anyone has access to a large amount of data and for this reason it is essential to create new systems that help to understand that information in a little time. Recommender Systems are engines which allow to filter the information depending on people’s interests. There are different kinds of Recommenders and each of them has a different purpose. In this project, a case of use of a Collaborative filtering Recommender System is introduced employing every interaction users do while surfing the Stilavia web site as data input. In order to carry out this task, some scoring functions are required to generate a model. This model will be extrapolated throughout the whole dataset space thanks to a Machine Learning algorithm called Alternating Least Squares (ALS) that is available in a library of the Apache Spark framework. Lastly, the results of each scoring function will be tested and evaluated employing a statistic estimator.

The complexity of Business Intelligence activities has driven the proposal of several approaches for the effective modeling of Extract-Transform-Load (ETL) processes, based on the conceptual abstraction of their operations. Apart from fostering automation and maintainability, such modeling also provides the building blocks to identify and represent frequently recurring patterns. Despite some existing work on classifying ETL components and functionality archetypes, the issue of systematically mining such patterns and their connection to quality attributes such as performance has not yet been addressed. In this work, we propose a methodology for the identification of ETL structural patterns. We logically model the ETL workflows using labeled graphs and employ graph algorithms to identify candidate patterns and to recognize them on different workflows. We showcase our approach through a use case that is applied on implemented ETL processes from the TPC-DI specification and we present mined ETL patterns. Decomposing ETL processes to identified patterns, our approach provides a stepping stone for the automatic translation of ETL logical models to their conceptual representation and to generate fine-grained cost models at the granularity level of patterns.

Background: Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge in systems biology. Thanks to high-throughput technologies, a massive amount of gene-expression data has been accumulated in the public repositories. Modelling GRNs from multiple experiments (also called integrative analysis) has; therefore, naturally become a standard procedure in modern computational biology. Indeed, such analysis is usually more robust than the traditional approaches, which suffer from experimental biases and the low number of samples by analysing individual datasets. To date, there are mainly two strategies for the problem of interest: the first one (“data merging”) merges all datasets together and then infers a GRN whereas the other (“networks ensemble”) infers GRNs from every dataset separately and then aggregates them using some ensemble rules (such as ranksum or weightsum). Unfortunately, a thorough comparison of these two approaches is lacking. Results: In this work, we are going to present another meta-analysis approach for inferring GRNs from multiple studies. Our proposed meta-analysis approach, adapted to methods based on pairwise measures such as correlation or mutual information, consists of two steps: aggregating matrices of the pairwise measures from every dataset followed by extracting the network from the meta-matrix. Afterwards, we evaluate the performance of the two commonly used approaches mentioned above and our presented approach with a systematic set of experiments based on in silico benchmarks. Conclusions: We proposed a first systematic evaluation of different strategies for reverse engineering GRNs from multiple datasets. Experiment results strongly suggest that assembling matrices of pairwise dependencies is a better strategy for network inference than the two commonly used ones., SCOPUS: ar.j, info:eu-repo/semantics/published

This Bachelor Thesis aims to draw a first approach for building a highly versatile tool to simulate systems with adaptive modularity, that is, systems that are able to adapt their functional capabilities according to the type of mission or scanario they are involved into. The simulation framework follows an agent-based strategy, in which several agents with different roles (launcher, scenario, active agent) manage to accomplish missions. These missions consist of performing certain tasks of target observation in one or more spectral region (e.g, microwave, infrared, visible) with a certain set of temporal requirements and a score value. The goal of the system is to perform as many high-value observations as possible. If certain tasks require more requirements than a single agent can provide, it can build a coalition with other agents, which do carry enough resources for performing the task. These coalitions are temporal associations of agents that can be split or merged as considered by them, thus allowing the agents to adapt their modularity for better performing their mission. The main achievements attained in this thesis can be divided in two blocks: on the one hand, we have developed a support program using an open source Java-based platform that executes simulations upon a large set of possible scenarios (Turtlekit ), displays what agents are doing in real-time in a particular simulated case and stores their performance data for its posterior analysis; on the other hand, a widely known decentralized task allocation algorithm (Coupled Constraint Consensus Bundle-Based Algorithm, CCBBA) has been implemented and modiffied as necessary for handling speciffic constraints regarding adaptive modularity.

Neuro-oncologists must ultimately rely on their acquired knowledge and accumulated experience to undertake the sensitive task of brain tumour diagnosis. This task strongly depends on indirect, non-invasive measurements, which are the source of valuable data in the form of signals and images. Expert radiologists should benefit from their use as part of an at least partially automated computer-based medical decision support system. This paper focuses on Magnetic Resonance Spectroscopy signal analysis and illustrates a method that combines Gaussian Decomposition, dimensionality reduction by Moving Window with Variance Analysis and classification using adaptively regularized Artificial Neural Networks. The method yields encouraging results in the task of binary classification of human brain tumours, even for tumour types that have seldom been analyzed from this viewpoint.

The project goal is the study and development of specific techniques for obtaining user preferences from several interaction mechanisms with the objective of making recommendations in a conversational group recommender system. In this project, we capture and model user's preferences.

Nowadays, a huge amount of high resolution satellite images are freely available. Such images allow researchers in environmental sciences to study the different natural habitats and farming practices in a remote way. However, satellite images content strongly depends on the season of the acquisition. Due to the periodicity of natural and agricultural dynamics throughout seasons, sequential patterns arise as a new opportunity to model the behaviour of these environments. In this paper, we describe some preliminary results obtained with a new framework for studying spatiotemporal evolutions over natural and agricultural areas using k-partite graphs and sequential patterns extracted from segmented Landsat images.

Explicacions per a les prediccions dels sistemes de recomanació. Machine learning. [ANGLÈS] An explanation system helps user to identify those items from a variety of choices that best match their needs and preferences. Moreover, it has the ability to establish trust and confidence with the user providing some benefits. In this project developed in Sony Deutschland, I present the results of my work designing and developing an explanation engine for a real recommender system, in concrete for the PlayStation Store. [CASTELLÀ] Un sistema de explicaciones ayuda a los usuarios a identificar aquellos productos que mejor satisfacen sus necesidades entre una amplia variedad. Además tienen la habilidad de establecer una relación con el usuario y transmitirle un cierto grado de confianza con el sistema aportando una serie de beneficios. En este proyecto desarrollado a Sony Deutschland, expongo los resultados de mi trabajo diseñando y desarrollando un motor de explicaciones para sistemas el sistema de recomendación en un entorno de comercio electrónico real, concretamente para la PlayStation Store. [CATALÀ] Un sistema d'explicacions ajuda als usuaris a identificar aquest productes que millor satisfan les seves necessitats entre una amplia varietat. A més, tenen la habilitat de establir una relació amb l?usuari i transmetre-li un grau de confiança amb el sistema aportant una sèrie de beneficis. En aquest projecte realitzat a Sony Deutschland, exposo els resultats del meu treball dissenyant i desenvolupant un motor de explicacions per a sistemes de recomanació en un entorn de comerç electrònic real, concretament per a la PlayStation Store.

[ANGLÈS] The space exploration endeavor started in 1957 with the launch and operation of the first manmade satellite, the URSS Sputnik 1. Since then, multiple space programs have been developed, pushing the limits of technology and science but foremost unveiling the mysteries of the universe. In all these cases, the need for flexible and reliable communication systems has been primordial, allowing the return of collected science data and, when necessary, ensuring the well-being and safety of astronauts. To that end, multiple space communication networks have been globally deployed, be it through geographically distributed ground assets or through space relay satellites. Until now most of these systems have relied upon mature technology standards that have been adapted to the specific needs of particular missions and customers. Nevertheless, current trends in the space programs suggest that a shift of paradigm is needed: an Internet-like space network would increase the capacity and reliability of an interplanetary network while dramatically reducing its overall costs. In this context, the System Architecting Paradigm can be a good starting point. Through its formal decomposition of the system, it can help determine the architecturally distinguishing decisions and identify potential areas of commonality and cost reduction. This thesis presents a general framework to evaluate space communication relay systems for the near Earth domain. It indicates the sources of complexity in the modeling process, and discusses the validity and appropriateness of past approaches to the problem. In particular, it proposes a discussion of current models vis-à-vis the System Architecting Paradigm and how they fit into tradespace exploration studies. Next, the thesis introduces a computational performance model for the analysis and fast simulation of space relay satellite systems. The tool takes advantage of a specifically built-in rule-based expert system for storing the constitutive elements of the architecture and perform logical interactions between them. Analogously, it uses numerical models to assess the network topology over a given timeframe, perform physical layer computations and calculate plausible schedules for the overall system. In particular, it presents a newly developed heuristic scheduler that guarantees prioritization of specific missions and services while ensuring manageable computational times. [CASTELLÀ] El inicio de la carrera espacial se inició en 1957 con el lanzamiento y operación del primer satélite artificial, el Sputnik 1 de la URSS. Desde entonces se han desarrollado múltiples programas espaciales que han llevado al límite tanto la tecnología como la ciencia y han permitido desvelar los misterios del universo. En todos estos casos, la necesidad de sistemas de comunicación flexibles y fiables ha sido primordial con el fin de asegurar el retorno de los datos científicos recopilados y, en ciertos casos, garantizar la seguridad de los astronautas. Como consecuencia, múltiples redes de comunicaciones espaciales han sido desplegadas, ya sea a través de antenas globalmente distribuidas a través de la superficie terrestre o mediante satélites repetidores. Hasta ahora la mayoría de estos sistemas se ha basado en estándares tecnológicos maduros y testeados, los cuales se han adaptado con el fin de satisfacer las necesidades específicas de cada misión y cliente. Sin embargo, las tendencias actuales en el diseño de los nuevos programas espaciales indica que un cambio de paradigma es necesario: una red espacial a imagen de Internet permitiría incrementar la capacidad y fiabilidad de las comunicaciones interplanetarias y, a la vez, reducir dramáticamente sus costes. En este contexto, el paradigma de arquitectura de sistemas puede ser un buen punto de partida. Mediante la descomposición formal del sistema, puede ayudar a determinar las decisiones que tienen un impacto cabal en el diseño de la arquitectura así como identificar las áreas con tecnologías similares y de menor coste. Esta tesis presenta un marco teórico general para evaluar sistemas de comunicaciones espaciales para misiones que orbitan la Tierra. Adicionalmente, la tesis indica los principales orígenes de complejidad durante el modelado del sistema y presenta una discusión sobre la validez de anteriores estrategias para analizar el problema. En concreto, propone una comparación de anteriores modelos respecto el paradigma de arquitectura de sistemas y su grado de adecuación para evaluar y comprar arquitecturas. A continuación, la tesis introduce un modelo computacional para simular y evaluar el rendimiento de sistemas de repetidores por satélite. La herramienta utiliza un rule-based expert system específicamente diseñado con el fin de almacenar los principales elementos constitutivos de la arquitectura y comprender las interacciones lógicas entre ellos. Análogamente, el modelo usa métodos numéricos con el fin de calcular la evolución temporal de la topología de la red en un determinado intervalo de tiempo, así como su capa física y un posible programa de contactos. En concreto, presenta un nuevo scheduler heurístico que garantiza la correcta ordenación de las misiones y servicios a la vez que asegura un tiempo computacional aceptable. [CATALÀ] L'inici de la cursa espacial va iniciar-se l'any 1957 amb el llançament i operació del primer satèl·lit artificial, l'Sputnik 1 de la URSS. Des d'aleshores s'han dut a terme múltiples programes espacials que han portat al límit tant la tecnologia com la ciència i han permès desvelar els misteris de l'univers. En tots aquests casos, la necessitat de sistemes de comunicació flexibles i fiables ha sigut primordial per tal d'assegurar el retorn de les dades científiques recopilades i, en certs casos, garantir el benestar i seguretat dels astronautes. Com a conseqüència, múltiples xarxes de comunicacions espacials han sigut desplegades, ja sigui a través d'antenes globalment distribuïdes a través de la superfície terrestre o mitjançant satèl·lits repetidors. Fins ara la majoria d'aquests sistemes s'han basat en estàndards tecnològics madurs i testats, els quals s'han adaptat per tal de satisfer les necessitats específiques de cada missió i client. Això no obstant, les tendències actuals en el disseny dels nous programes espacials indica que un canvi de paradigma és necessari: una xarxa espacial a imatge d'Internet permetria incrementar la capacitat i fiabilitat de les comunicacions interplanetàries i, alhora, reduir dramàticament els seu costs. En aquest context, el paradigma d'arquitectura de sistemes pot ser un bon punt de partida. Mitjançant la descomposició formal del sistema, pot ajudar a determinar les decisions que tenen un impacte cabdal en el disseny de l'arquitectura així com permetre identificar àrees amb tecnologies similars i de menor cost. Aquesta tesi presenta un marc teòric general per avaluar sistemes de comunicacions espacials per missions orbitant la Terra. Addicionalment, la tesi indica els principals orígens de complexitat durant el modelatge del sistema i presenta una discussió sobre la validesa d'anteriors estratègies per analitzar el problema. En concret, proposa una comparació d'anteriors models respecte el paradigma d'arquitectura de sistemes i el seu grau d'adequació per avaluar i comparar arquitectures. A continuació, la tesi introdueix un model computacional per simular i avaluar el rendiment de sistemes de repetidors per satèl·lit. L'eina empra un rule-based expert system específicament dissenyat per tal d'emmagatzemar els principals elements constitutius de l'arquitectura i comprendre les interaccions lògiques entre ells. Anàlogament, el model utilitza mètodes numèrics per tal de calcular l'evolució temporal de la topologia de la xarxa en un determinat interval de temps, així com calcular la seva capa física i un possible programa de contactes. En concret, presenta un nou scheduler heurístic que garanteix la correcte ordenació de les missions i serveis tot assegurant un temps de computació acceptable.

Recommender engines (REs) also known as recommender systems are software tools and techniques providing suggestions to a user. The suggestions provided are aimed at supporting their users in various decision making processes such as what items to buy, what music to listen, what profiles to browse, or what news to read. This thesis studies the feasibility of the integration of a recommender engine as a module in a Liferay portal, and shows the process of its design and implementation using the Apache Mahout library. As such our work tackles two major problems which are: (1) the implementation of the recommender engine using the Apache Mahout library, and (2) the integration of the recommender in Liferay portal. Prior to the design of the application and the decisions made at the design stage, are shown the analysis of the requirements for the recommender system in the context of the business. The decisions made at design time are explained and the risks involved are analyzed and mitigated. Finally conclusions regarding the integration of the recommender engine in Liferay portal are made. This study was performed as part of an internship at Everis (www.everis.com).

The main goal in this work is to learn user preferences in situations where the objects to be treated are formed only by textual information and we continuously have information of selections made by the user. This work has been divided in two major parts: the first one including the algorithms and techniques to rank a set of alternatives, and the second one including the techniques to maintain the profile up to date. Regarding the first part, the goal is to evaluate an object of type text, i.e. given the user preferences to assign the degree of potential interest on that object. This will allow us to evaluate the set of alternatives and to sort them according to the user preferences. Concerning the second part, the main goal is to design a method to update the user profile, given the user selection from a set of alternatives in the first part. This method will allow to adapt a user profile in an unsupervised and dynamic way. To achieve these objectives it is necessary to fulfil the tasks discussed in this document and named below in the document organization.

This paper reports on a novel learning activity that aims at introducing machine learning contents in an Audio and Video Technologies study plan. The proposed methodology follows a Problem Based Learning (PBL) [Doud99] approach inspired in the actual problems solved by the researchers in the field. It was observed that this scientific community has defined very clear and evaluable strategies to test their approaches. There exist several evaluation campaigns whose completion require an understanding of the main concepts involved in machine learning [Smeaton09]. Moreover, their practical nature and transparent evaluation makes them very convenient to be used in a course to drive the learning experience of the students. The main contribution of this activity is a mapping of the classic stages in the definition and solving of a machine learning problem into a set of active learning activities aimed at solving a practical problem.

Local estimation of the optimal trajectory for brain connectomics

Nowadays, many people rely on online services to plan a trip. However, they are usually faced with the problem of being supplied with lots of information. In consequence, they have to invest a great deal of time to decide what to visit, when, etc. This huge amount of possibilities available on the net makes it difficult for users to discern the more interesting offers from the rest. As a result, the more appealing offers can go unnoticed. In order to improve the tourist experience, recommender systems offer personalised information to users. In other words, the system selects the more suitable and adequate offers for users and offers activities appropriate to their profile. In this thesis, we present the EnoSigTur system, a smart recommender system for tourists interested in experiences related to the wine sector. It has been developed in the Parc Científic i Tecnològic de Turisme i Oci of Vila-seca with the collaboration of researchers in the Universitat Rovira i Virgili. Trough a web application, the system allows users to know wine production activities available in the region of Tarragona. Users just have to indicate their interests in general terms and the system will select the more convenient activities for them. EnoSigTur is capable of modifying the initial information of the users preferences by studying the interaction between the user and the system, and offering them more adjusted recommendations. This system also allows users to plan a trip by providing advanced planning services; for example, date, length of the trip, etc. A mobile phone application will permit users to monitor the planned trip while it is taking place. EnoSigTur is designed to supply a user-friendly and flexible service either to visitors with a superficial knowledge of the wine production area, or to experienced visitors who have already been in contact with these types of activities. Moreover, as we suggested before, it provides personalised recommendations according to users interests, gives clients the necessary tools to plan the trip and makes it possible for them to discover other activities in the region. In the following chapters we will deal with the main problems that tourism presents in terms of information search and decision-making processes. We will also present the recommender systems and the ontology, so that the reader will be able to grasp the gist of the project. Finally, we will give details of our recommender system.

This paper is the final document written to gather the impressions and conclusions which we have come to during the development of this master thesis. In this research project you will find the description of a new kind of artificial neural network, Heterogeneous Neural Network 2 (HNN2), which can be seen as a general abstraction of the Radial Basis Function network. The model of neuron used is an improved version of the one presented by Belanche [1] and the neural network is initialized using a clustering algorithm, Leader2, developed at [2]. We will explain the way we have followed to get this artificial neural network that works allways with understandable information, uses the concept of similarity and allows users to improve the algorithm results taking advantage of expert information. The basic Heterogeneous Neural Network (HNN) is also known as Similarity Neural Network (SNN), by the importance of the similarity measures inside this method. The basic idea is that a combination of similarity functions, comparing variables independently, is more capable of catching better the singularity of an heterogeneous data set than other methods which require previous data transformation. Each variable has its own characteristics, which is information that can be used by the expert that knows it to choose its most suitable similarity function, taking advantage of all the information he has. If this is done for each variable, we will be working probably with a similarity measure that understands better the data. Missing values are also a relevant characteristic of heterogeneous data, so we have to learn to deal with them. All these ideas are applied to HNN and Leader2, joint to several improvements performed to the neural network, like regularization or Alternate Optimization, in order to fit better the data but avoiding overfitting. This is why we have called it Heterogeneous Neural Network 2 (HNN2). This document is divided in several chapters. Initially, we will give an in-depth description of the problem which we want to solve. In the second chapter, State of the art, you will get a wide perspective of how was the field in which this project has been developed before we started. Then, there is a description of the used methodology, where you can find the main decisions and the development itself, followed by the explanation of the experimental settings done to test the HNN2. Their results are commented and evaluated in the next chapter, and next some conclusions are inferred. Finally, you will find the references used in the research and several annexes with additional relevant information. But in first term, before starting the description of the problem and in the way of making the reading easier, it is necessary to provide you some vocabulary to know exactly the meaning we have given to several key words. Next, in the same terms, you will find the most used symbols with their description.

This correspondence considers the use of punctured quasi-arithmetic (QA) codes for the Slepian–Wolf problem. These entropy codes are defined by finite state machines for memoryless and first-order memory sources. Puncturing an entropy coded bit-stream leads to an ambiguity at the decoder side. The decoder makes use of a correlated version of the original message in order to remove this ambiguity. A complete distributed source coding (DSC) scheme based on QA encoding with side information at the decoder is presented, together with iterative structures based on QA codes. The proposed schemes are adapted to memoryless and first-order memory sources. Simulation results reveal that the proposed schemes are efficient in terms of decoding performance for short sequences compared to well-known DSC solutions using channel codes.

Nowadays, the gap between data communications and telecommunications disappears rapidly, which leads to augment of the complexity and size of supporting networks. This calls for the software developers to embrace a new way of developing management software. This is the impetus for the TM Forum’s New Generation Operations Systems and Software (NGOSS) initiative. It aims to deliver a framework for rapid and flexible integration of Operation and Business Support Systems in telecommunications and throughout the wider communications industry. The enhanced Telecom Operations Map (eTOM) is a key part of NGOSS. It plays an important role as a guidebook built on TM Forum Telecom Operations Map (TOM). The eTOM model describes the full scope of business processes required by a service provider and defines key elements and how they interact.

In very complex and unstructured domains, the Intelligent Decision Support Systems become very important tools for the expert, since allow to manage a quantity of information in a way that would be impossible to do manually. Inside this kind of systems, the classification tools are one of the most common, and, specifically, the clustering techniques. However, these techniques have problems when managing huge amount of variables and classes, because the interpretation of the generated classes becomes very complicate. For this reason, in this project we want to generate an automatically conceptual interpretation of the classes generated by a clustering technique to help in the labor of the expert with a clearer vision of what is representing each class in order to understand quickly and easy what are the properties and characteristics of these data.

This project belongs to one of three parts of a bigger project: the aim of the big one is to develop an automated routine which will do the mechanic analysis of a piece of an aircraft engine, such as the stress analysis, the displacement analysis, or the stiffness analysis, all of them by using FEA systems. This Master thesis project is a piece of study of a simplified turbofan jet engine, which will be called during the project “the whole engine model” (WEM). The project was done in collaboration with Volvo Aero which is a Swedish company that deals with aerospace business, specialized in structural jet commercial engines. The purpose of this labor is to try to make Volvo Aero’s product development activities more effective, by making the right things, and efficient by automating processes. In the general project, the aim of the model is to calculate some design parameters which need to be optimized, as they can be, for instance, the relation among the tip clearance, the hole radius and the fan radius: in order to get a maximum power by reducing the tip clearance surface, but with enough security to assure that there won’t be contact between the fan and the case. Other parameters such as the optimal length of the bearing will be discussed and calculated also. The specific goal within this Master’s thesis project is to develop a model, with its geometric features added with rules. Validerat; 20101217 (root)

The original publication is available at www.springerlink.com Detecting the location and identity of users is a first step in creating contextaware applications for technologically-endowed environments. We propose a system that makes use of motion detection, person tracking, face identification, feature-based identification, audio-based localization, and audio-based identification modules, fusing information with particle filters to obtain robust localization and identification. The data streams are processed with the help of the generic client-server middleware SmartFlow, resulting in a flexible architecture that runs across different platforms.

This paper surveys the most relevant research on combining Data Warehouse (DW) and Web data. It studies the XML technologies that are currently being used to integrate, store, query, and retrieve Web data and their application to DWs. The paper reviews different DW distributed architectures and the use of XML languages as an integration tool in these systems. It also introduces the problem of dealing with semistructured data in a DW. It studies Web data repositories, the design of multidimensional databases for XML data sources, and the XML extensions of OnLine Analytical Processing techniques. The paper addresses the application of information retrieval technology in a DW to exploit text-rich document collections. The authors hope that the paper will help to discover the main limitations and opportunities that offer the combination of the DW and the Web fields, as well as to identify open research lines

A new map (ΛE) between fuzzy subsets of a universe X endowed with a T-indistinguishability operator E is introduced. The main feature of ΛE is that it has the columns of E as fixed points, and thus it provides us with a new criterion to decide whether a generator is a column. Two well known maps (φE and ψE) are also reviewed, in order to compare them with ΛE. Interesting properties of the fixed points of φE and [Formula: see text] are studied. Among others, the fixed points of ΛE ( Fix (ΛE)) are proved to be the maximal fuzzy points of (X, E) and the fixed points of [Formula: see text] coincide with the Image of ΛE. An isometric embedding of X into Fix (ΛE) is established and studied.

In this paper, a methodology to obtain a set of fuzzy rules for classification systems is presented. The system is represented in a layered fuzzy network, in which the links from input to hidden nodes represents the antecedents of the rules, and the consequents are represented by links from hidden to output nodes. Specific genetic algorithms are used in two phases to extract the rules. In the first phase an initial version of the rules is extracted, and in second one, the labels are refined. The procedure is illustrated by applying it to two real-world classification problems

Multiple-valued logics are useful for dealing with uncertainty and imprecision in Knowledge-Based Systems. Different problems can require different logics. Then we need mechanisms to translate the information exchanged between two problems with different logics. In this paper, we introduce the logical foundations of such logics and the communication mechanisms that preserve some deductive properties. We also describe a tool to assist users in the declaration of logics and their communication mechanisms

In this paper we propose a method to solve a linear programming problem involving fuzzy parameters whose possibility distributions are given by fuzzy numbers. To address the above problem we have used a preference relationship of fuzzy numbers that leads us to a solving method that produces the so-called $\alpha$-degree feasible solutions. It must be pointed out that the final solution of the problem depends critically on this degree of feasibility, which is in conflict with the optimal value of the objective function. Then DM faces a bi-objective problem that we will solve through a Compromise Programming approach, whose solution lets the Decision-Maker express his own preferences about feasibility versus optimality. Our proposed method will be illustrated by a numerical example

In this paper we try to answer to the following questions: What can be made by applying fuzzy logic? and What can not be made by applying fuzzy logic? The question will be analyzed from both theoretical and applied point of view. A (partial) answer will be given for three topics: a) as calculus procedure b) as reasoning mechanism and c) as engineering tool.

In this paper we describe the language Milord. The description is made in terms of computer language concepts and not in terms of the logical semantics underlying it. In this sense the paper complements others in which the focus of the description has been either the object level multi-valued language description, or the reflective component of the architecture, or even the several applications built using it. All the necessary elements to understand how a system programmed in Milord executes have room in this full description: types, facts, rules, modules, local logics, control rategies, ... Although the description is guided by the Milord language syntax, this is by no means a user's manual, which would deserve a much longer document, but a language summary description that places all the components of the language in their correct place.

It has been recognized that AI programs suffer from a lack of generality, the first gross symptom being that a small variation to the problem being solved usually causes a major revision of the theory describing it. The lack of generality seems an unavoidable consequence of the process of approximating the world while building theories about it. In this paper we propose an approach where generality is achieved by formulating, for each problem at hand, an appropriate local theory, a theory containing the needed information. The process of theory formulation and reformulation is formalized using contexts.

While the coupling of artificial of neural networks (ANN) and symbolic AI (SAI) is a strategy adopted in many hybrid systems, a real integration of the two methodologies has not been thoroughly investigated yet: so far, most hybrid systems have been viewed as just an engineering shortcut to solve complex problems in which one methodology alone seems too weak. In this paper, an approach to integrating ANN and SAI is presented. The basic idea explored here is that there is much more to hybrid models than a coupling of methodologies for the sake of avoiding engineering problems.

The paper is a review of our theoretical analysis of uncertainty processing in a broad class of truth-functional expert systems similar to MYCIN and PROSPECTOR, main attention being paid to parallel combination of rules. Algebraic and probabilistic aspects are stressed. The role of Dempster-Shafer theory is investigated.

The last ten years have seen the emrgence of some new ways of applying computers to organizations. This has been done by focusing attention on the suport of the key decisions that must be made in organizations. Decision Support Systems (DSS) is the term that is used to designate systems specifically designed to be used by mangers during their decision making. This paper addresses the problem of using microcomputer-basrd DSS in small firms and the problems that arise in the implementation of DSS'S in microcomputers.