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A challenge for the clinical management of Parkinson's disease (PD) is the large within- and between-patient variability in symptom profiles as well as the emergence of motor complications which represent a significant source of disability in patients. This thesis deals with the development and evaluation of methods and systems for supporting the management of PD by using repeated measures, consisting of subjective assessments of symptoms and objective assessments of motor function through fine motor tests (spirography and tapping), collected by means of a telemetry touch screen device. One aim of the thesis was to develop methods for objective quantification and analysis of the severity of motor impairments being represented in spiral drawings and tapping results. This was accomplished by first quantifying the digitized movement data with time series analysis and then using them in data-driven modelling for automating the process of assessment of symptom severity. The objective measures were then analysed with respect to subjective assessments of motor conditions. Another aim was to develop a method for providing comparable information content as clinical rating scales by combining subjective and objective measures into composite scores, using time series analysis and data-driven methods. The scores represent six symptom dimensions and an overall test score for reflecting the global health condition of the patient. In addition, the thesis presents the development of a web-based system for providing a visual representation of symptoms over time allowing clinicians to remotely monitor the symptom profiles of their patients. The quality of the methods was assessed by reporting different metrics of validity, reliability and sensitivity to treatment interventions and natural PD progression over time. Results from two studies demonstrated that the methods developed for the fine motor tests had good metrics indicating that they are appropriate to quantitatively and objectively assess the severity of motor impairments of PD patients. The fine motor tests captured different symptoms; spiral drawing impairment and tapping accuracy related to dyskinesias (involuntary movements) whereas tapping speed related to bradykinesia (slowness of movements). A longitudinal data analysis indicated that the six symptom dimensions and the overall test score contained important elements of information of the clinical scales and can be used to measure effects of PD treatment interventions and disease progression. A usability evaluation of the web-based system showed that the information presented in the system was comparable to qualitative clinical observations and the system was recognized as a tool that will assist in the management of patients.

Ένας σημαντικός παράγοντας για την αποτελεσματική συνεργασία μεταξύ των μελών ομάδας εργασίας, που είναι πρωταρχικής σημασίας για την επιτυχημένη υλοποίηση των τεχνικών έργων, είναι η αποδοτική επικοινωνία τους. Η επικοινωνία αυτή, όμως, δυσχεραίνεται λόγω του κατακερματισμού του κατασκευαστικού τομέα, του τεράστιου όγκου και της ποικιλομορφίας των πληροφοριών της διαδικασίας κατασκευής, των γεωγραφικών αποστάσεων μεταξύ του κεντρικού γραφείου της κατασκευαστικής εταιρίας και των εργοταξίων, καθώς και της μοναδικότητας και της πολυπλοκότητας των τεχνικών έργων. Οι προηγούμενες ερευνητικές προσπάθειες και οι εμπορικές εφαρμογές που σχετίζονται με τη διαχείριση πληροφοριών στη βιομηχανία των κατασκευών έχουν κυρίως επικεντρωθεί στην ηλεκτρονική διαχείριση εγγράφων (αλλά όχι ουσιαστικά στη διαχείριση πληροφοριών) και έχουν θέσει το εννοιολογικό πλαίσιο των διαδικτυακών βάσεων δεδομένων για τον κατασκευαστικό κλάδο. Περαιτέρω, μερικές ερευνητικές προσπάθειες έχουν οδηγήσει σε πιλοτική ανάπτυξη συστήματος διαχείρισης πληροφοριών, αλλά αυτές έχουν εστιάσει εν γένει σε μεμονωμένα τμήματα της κατασκευαστικής διαδικασίας και στις πιο βασικές πληροφορίες αυτών. Στα πλαίσια της εργασίας σχεδιάστηκε και αναπτύχθηκε ένα διαδικτυακό σύστημα διαχείρισης και διακίνησης πληροφοριών, ως προσπάθεια βελτίωσης της επικοινωνίας των συμμετεχόντων στην κατασκευαστική διαδικασία. Το σύστημα αποτελεί δεδομενο-κεντρική προσέγγιση, σε αντίθεση με τις εγγραφο-κεντρικές προσεγγίσεις των υπαρχόντων συστημάτων, και έχει δώσει έμφαση στον αναλυτικό σχεδιασμό της βάσης δεδομένων. Η ανάπτυξη του συστήματος βασίστηκε σε διερεύνηση που έγινε με χρήση ερωτηματολογίου και έδειξε ότι η ελληνική κατασκευαστική βιομηχανία δεν χρησιμοποιεί γενικά προηγμένα συστήματα διαχείρισης πληροφοριών κυρίως λόγω της περιορισμένης αποτελεσματικότητας των υπαρχόντων συστημάτων. Το προτεινόμενο σύστημα αποτελείται από μία δομημένη σχεσιακή βάση δεδομένων και μία δυναμική, οδηγούμενη από δεδομένα, διαδικτυακή εφαρμογή που επιτρέπει στους χρήστες του συστήματος να αλληλεπιδρούν μακρόθεν με τη βάση δεδομένων. Η βάση δεδομένων σχεδιάστηκε μετά από διερεύνηση και καταγραφή τόσο των χρηστών του συστήματος όσο και των απαιτούμενων πληροφοριών. Ο τελικός σχεδιασμός της βάσης δεδομένων, ο οποίος προέκυψε μετά από βρόχους μοντελοποίησης οντοτήτων και κανονικοποίησης, περιλαμβάνει 32 τύπους οντότητας με 42 σχέσεις μεταξύ τους και 172 ιδιότητες συνολικά. Ο σχεδιασμός της βάσης δεδομένων ολοκληρώθηκε με την ανάπτυξη 70 ερωτημάτων, που αποτελούν είτε συνδυασμό ανάκτησης δεδομένων που πληρούν συγκεκριμένα κριτήρια είτε προϊόντα επεξεργασίας των δεδομένων. Οι χρήστες του συστήματος μπορούν να αλληλεπιδρούν με τη βάση δεδομένων μέσω της διαδικτυακής εφαρμογής και να εκτελούν συγκεκριμένες λειτουργίες, όπως εισαγωγή, αναζήτηση, θέαση, τροποποίηση/ενημέρωση και διαγραφή δεδομένων. Η πρόσβαση στον ιστότοπο του συστήματος ελέγχεται με ονόματα και κωδικούς πρόσβασης. Η υλοποίηση της βάσης δεδομένων και της διαδικτυακής εφαρμογής έγινε με τα λογισμικά MS-Access και Dreamweaver MX αντίστοιχα. Ως τεχνολογία εξυπηρετητή χρησιμοποιήθηκαν οι Active Server Pages, ως γλώσσα προγραμματισμού η VBScript και ως εξυπηρετητής δικτύου ο Internet Information Server. Η λειτουργικότητα και η αποτελεσματικότητα του συστήματος ελέγχθηκαν με την πιλοτική εφαρμογή. Το σύστημα παρουσιάζει ικανοποιητική αποτελεσματικότητα ως προς τη δυναμική ενημέρωση των πληροφοριών, την επεξεργασία των δεδομένων και την παροχή αξιόπιστων αποτελεσμάτων. Με την αποκτηθείσα εμπειρία εκτιμάται ότι τα διαδικτυακά συστήματα διαχείρισης πληροφοριών με σχεδιασμό λεπτομερούς βάσης δεδομένων μπορούν να βελτιώσουν σημαντικά την επικοινωνία των συμμετεχόντων στην κατασκευή τεχνικών έργων με αποτέλεσμα την αύξηση της παραγωγικότητας της κατασκευαστικής ομάδας, την οικονομία χρόνου και πόρων, και τη βελτίωση της ποιότητας κατασκευής. / Communicating effectively among task groups is one of the most important factors for the success of a project. This communication, however, is hindered by the extended fragmentation of the construction industry, the large volumes and wide dissimilarity of the information involved in the construction process, and the uniqueness and complexity of construction projects. The distance between the construction company headquarters and construction sites augments the communication problem. Previous research efforts and commercial applications concerning information management for the construction industry have mainly focused on electronic document management and have set the conceptual framework of web databases for the construction industry. Further, some of the research efforts have included pilot implementation of management systems, but rather on parts of the construction process and including only the most basic information. A web-based system that facilitates construction information management and communication is designed and implemented. Unlike common document-based systems, the present work focuses on demonstrating the potential of data-centric web databases in enhancing the communication process during project execution. The system development has taken into consideration results from a questionnaire-based research in the Greek construction industry, which has indicated the absence of advanced information management systems, mainly because of the limited efficiency of existing systems. The proposed system consists of a relational database and a dynamic, data-driven web application that allows the end users to remotely interact with the database. The database design, which has been preceded by an end users analysis and an information analysis, has involved loops of entity modelling and normalisation. The database contains 32 tables with 42 relationships among them, a total of 172 fields, and 70 queries that constitute either retrieval of data that satisfy preset criteria or products of data processing. End users access the database through the internet and can perform certain transactions, such as insert, search, view, update, and delete data. The access to the system is controlled with usernames and passwords. The system has been implemented using MS-Access as the database management system and Macromedia Dreamweaver MX for developing the data-driven web application. Active Server Pages have been selected as server technology, VBScript as the programming language, and Internet Information Server as the web server. The effectiveness and efficiency of the system has been tested with a pilot application. Results from the pilot application demonstrate the technical feasibility of the system and its enhanced capability, compared to ordinary practices, to provide concise and reliable information, quick/remote access, and prompt updating potential. As a result, communication among project participants can improve, misunderstandings can be reduced or eliminated, and more time can be spent on decision-making. Ultimately, this will lead to increased productivity, time and cost savings, as well as to higher construction quality.

Controle preditivo baseado em modelos (MPC) é uma técnica de controle amplamente utilizada na indústria de processos químicos. Por outro lado, o método de identificação em subespaços (SID) tem se mostrado uma alternativa eficiente para os métodos clássicos de identificação de sistemas. Pela combinação dos conceitos de MPC e SID, surgiu, no final da década de 90, uma nova técnica de controle, denominada controle preditivo com enfoque em subespaços (SPC). Essa técnica também é conhecida como controle preditivo orientado a dados. Ela substitui por um único passo as três etapas do projeto de um MPC: a identificação do modelo, o cálculo do observador de estados e a construção das matrizes de predição. Este trabalho tem como principal objetivo revisar estudos feitos na área de SPC, aplicar esse método em sistemas típicos da indústria química e propor novos algoritmos. São desenvolvidos três algoritmos de excitação interna para o método SPC, que permitem gerar dados persistentemente excitantes enquanto um controle mínimo do processo é garantido. Esses algoritmos possibilitam aplicar identificação em malha fechada, na qual o modelo do controlador SPC é reidentificado utilizando dados previamente excitados. Os controladores SPC e SPC com excitação interna são testados e comparados ao MPC por meio de simulações em dois processos distintos. O primeiro consiste em uma coluna debutanizadora de uma unidade de destilação, para a qual são disponibilizados dois modelos lineares referentes a pontos de operação diferentes. O segundo é um reator de polimerização de estireno com dinâmica não linear, cujo modelo fenomenológico é conhecido. Os resultados dos testes indicam que o SPC é mais suscetível a ruídos de medição. Entretanto, verifica-se que esse controlador corrige perturbações nos set-points das variáveis controladas mais rapidamente que o MPC. Simulações realizadas para o SPC com excitação interna mostram que os algoritmos propostos neste trabalho excitam o sistema satisfatoriamente, de modo que modelos mais precisos são obtidos na reidentificação com os dados excitados. / Model Predictive Control (MPC) technology is widely used in chemical process industries. Subspace identification (SID) on the other hand has proven to be an efficient alternative for classical system identification methods. Based on the results from MPC and SID, it was developed in the late 90s a new control approach, called Subspace Predictive Control (SPC). This approach is also known as data-driven predictive control. In this new method, one single operation replaces the three steps in a MPC controller design: system identification, the state observer design and the predictor matrices construction. The aim of this work is to review studies in the field of SPC, to apply this technology to typical systems of chemical industry and to propose new algorithms. It is developed three internal excitation algorithms for the SPC method, which allow the system to be persistently excited while a minimal control of the process is still guaranteed. These algorithms enable the application of closedloop identification, where the SPC controller model is re-identified using the previously excited data. The SPC controller and the SPC controller with internal excitation are tested through simulation for two different processes. The first one is a debutanizer column of a distillation unit for which two linear models corresponding to two different operating points are available. The second one is a non-linear system consisting of a styrene polymerization reactor. A phenomenological model is provided for this system. Tests results indicate that SPC is more susceptible to measurement noises. However, it is noticed that SPC controller corrects perturbations on set-points faster than MPC. Simulations for the SPC with internal excitation show that the proposed algorithms sufficiently excite the system, in the sense that more precise models are obtained from the re-identification with excited data.

Made available in DSpace on 2016-08-17T14:52:54Z (GMT). No. of bitstreams: 1 Romulo Alves Dias.PDF: 2725851 bytes, checksum: eaa8311ad62c875b230e288dfc66efa3 (MD5) Previous issue date: 2003-11-21 / The obsolescence of the IDS's attack identification mechanisms critically compromises the security level of the networks. This research work presents a proposal of a automatic updating model of the network attack detection mechanism for intrusion detection systems based on a society of intelligent agents. The Security Central Agency, a component of the model, distributes a mini-society of attack detection agents, called SAARA, that uses a neural network trained with data captured from several network traffic sources. A computational implementation of the SAARA model, focusing data driven attack detection, is presented for the multiagent IDS NIDIA. / A obsolescência dos mecanismos de identificação de ataques dos SDI´s tem comprometido de forma crítica o nível de segurança das redes de computadores. Esta dissertação apresenta uma proposta de um modelo de atualização automática do mecanismo de detecção de ataques de rede para sistemas de detecção de intrusão baseados na noção de sociedade de agentes inteligentes. A Agência Central de Segurança integrante deste modelo distribui uma mini-sociedade de agentes de detecção de ataque, denominada de SAARA, que utiliza uma rede neural treinada a partir de dados coletados de diversas fontes de tráfego. Uma implementação computacional da SAARA, abordando detecção de ataques orientados a dados, é apresentada dentro do contexto do SDI multiagentes NIDIA.

For smaller business it is important to have good and concrete data to make decisions because there are no big margins to test and fail or go on intuition. The purpose of this study is to create an understanding of how data can be used as a decision-making tool in small business. This paper studies the data two smaller companies collect and how they use the collected data. The study has a qualitative method with interviews which have been used for collecting result. The informants were selected based on their previous knowledge and experience.The collection of data in smaller business is both from internal and external sources that complement each other. It is important to have an understanding and knowledge of the visualizations otherwise it can be misleading. Visualizations are used to find patterns, trends and other affecting factors in the data. Segmentation is used by smaller business to understand their target market, customers and which customers they should direct their attention to. Data-driven decision making uses different sources of information, visualization and segmentation. With data-driven decision making it is important to maintain the overall perspective of the business, while all parts of the business are involved in the process. / För mindre företag är det viktigt att ha bra och konkret data för att fatta beslut eftersom det inte finns så stora marginaler att testa sig fram eller gå på intuition. Syftet med studien är att skapa förståelse för hur data kan användas som ett beslutsfattande verktyg i mindre företag. Studien undersöker vilken data mindre företag samlar in samt hur de använder den data som samlas in. Studien är en kvalitativ där intervjuer använts för datainsamling. Dessa gjordes på två mindre företag där informanterna valdes ut baserat på deras tidigare kunskaper och erfarenheter.Insamlingen av mindre företagens data sker både från interna och externa källor som kompletterar varandra. Det viktigt att ha förståelse och kunskap om visualiseringarna för att de inte ska bli missvisande. Visualiseringar används för att lättare hitta mönster, trender och andra påverkande faktorer i data. Segmentering används hos mindre företag för att förstå företagets målgrupp, kunder och vilka kunder de ska rikta sin uppmärksamhet till. För datadriven beslutsfattning används olika informationskällor, visualiseringar och segmentering. Med datadriven beslutsfattning är det viktigt att behålla helhetsperspektiv över företaget samtidigt som alla delar av företaget är involverade i processen.

We present a system for data-driven therapy decision support based on techniques from the field of recommender systems. Two methods for therapy recommendation, namely, Collaborative Recommender and Demographic-based Recommender, are proposed. Both algorithms aim to predict the individual response to different therapy options using diverse patient data and recommend the therapy which is assumed to provide the best outcome for a specific patient and time, that is, consultation. The proposed methods are evaluated using a clinical database incorporating patients suffering from the autoimmune skin disease psoriasis. The Collaborative Recommender proves to generate both better outcome predictions and recommendation quality. However, due to sparsity in the data, this approach cannot provide recommendations for the entire database. In contrast, the Demographic-based Recommender performs worse on average but covers more consultations. Consequently, both methods profit from a combination into an overall recommender system.

The big data framework applied to smart meters offers an exception platform for data-driven forecasting and decision making to achieve sustainable energy efficiency. Buying-in consumer confidence through respecting occupants' energy consumption behavior and preferences towards improved participation in various energy programs is imperative but difficult to obtain. The key elements for understanding and predicting household energy consumption are activities occupants perform, appliances and the times that appliances are used, and inter-appliance dependencies. This information can be extracted from the context rich big data from smart meters, although this is challenging because: (1) it is not trivial to mine complex interdependencies between appliances from multiple concurrent data streams; (2) it is difficult to derive accurate relationships between interval based events, where multiple appliance usage persist; (3) continuous generation of the energy consumption data can trigger changes in appliance associations with time and appliances. To overcome these challenges, we propose an unsupervised progressive incremental data mining technique using frequent pattern mining (appliance-appliance associations) and cluster analysis (appliance-time associations) coupled with a Bayesian network based prediction model. The proposed technique addresses the need to analyze temporal energy consumption patterns at the appliance level, which directly reflect consumers' behaviors and provide a basis for generalizing household energy models. Extensive experiments were performed on the model with real-world datasets and strong associations were discovered. The accuracy of the proposed model for predicting multiple appliances usage outperformed support vector machine during every stage while attaining accuracy of 81.65\%, 85.90\%, 89.58\% for 25\%, 50\% and 75\% of the training dataset size respectively. Moreover, accuracy results of 81.89\%, 75.88\%, 79.23\%, 74.74\%, and 72.81\% were obtained for short-term (hours), and long-term (day, week, month, and season) energy consumption forecasts, respectively.

La construction de modèles de pronostic nécessite la compréhension du processus de dégradation des composants critiques surveillés afin d’estimer correctement leurs durées de fonctionnement avant défaillance. Un processus de d´dégradation peut être modélisé en utilisant des modèles de Connaissance issus des lois de la physique. Cependant, cette approche n´nécessite des compétences Pluridisciplinaires et des moyens expérimentaux importants pour la validation des modèles générés, ce qui n’est pas toujours facile à mettre en place en pratique. Une des alternatives consiste à apprendre le modèle de dégradation à partir de données issues de capteurs installés sur le système. On parle alors d’approche guidée par des données. Dans cette thèse, nous proposons une approche de pronostic guidée par des données. Elle vise à estimer à tout instant l’état de santé du composant physique et prédire sa durée de fonctionnement avant défaillance. Cette approche repose sur deux phases, une phase hors ligne et une phase en ligne. Dans la phase hors ligne, on cherche à sélectionner, parmi l’ensemble des signaux fournis par les capteurs, ceux qui contiennent le plus d’information sur la dégradation. Cela est réalisé en utilisant un algorithme de sélection non supervisé développé dans la thèse. Ensuite, les signaux sélectionnés sont utilisés pour construire différents indicateurs de santé représentant les différents historiques de données (un historique par composant). Dans la phase en ligne, l’approche développée permet d’estimer l’état de santé du composant test en faisant appel au filtre Bayésien discret. Elle permet également de calculer la durée de fonctionnement avant défaillance du composant en utilisant le classifieur k-plus proches voisins (k-NN) et le processus de Gauss pour la régression. La durée de fonctionnement avant défaillance est alors obtenue en comparant l’indicateur de santé courant aux indicateurs de santé appris hors ligne. L’approche développée à été vérifiée sur des données expérimentales issues de la plateforme PRO-NOSTIA sur les roulements ainsi que sur des données fournies par le Prognostic Center of Excellence de la NASA sur les batteries et les turboréacteurs. / Constructing prognostics models rely upon understanding the degradation process of the monitoredcritical components to correctly estimate the remaining useful life (RUL). Traditionally, a degradationprocess is represented in the form of physical or experts models. Such models require extensiveexperimentation and verification that are not always feasible in practice. Another approach that buildsup knowledge about the system degradation over time from component sensor data is known as datadriven. Data driven models require that sufficient historical data have been collected.In this work, a two phases data driven method for RUL prediction is presented. In the offline phase, theproposed method builds on finding variables that contain information about the degradation behaviorusing unsupervised variable selection method. Different health indicators (HI) are constructed fromthe selected variables, which represent the degradation as a function of time, and saved in the offlinedatabase as reference models. In the online phase, the method estimates the degradation state usingdiscrete Bayesian filter. The method finally finds the most similar offline health indicator, to the onlineone, using k-nearest neighbors (k-NN) classifier and Gaussian process regression (GPR) to use it asa RUL estimator. The method is verified using PRONOSTIA bearing as well as battery and turbofanengine degradation data acquired from NASA data repository. The results show the effectiveness ofthe method in predicting the RUL.

Contexte. Les premières années de vie apparaissent particulièrement propices au développement de la sensibilisation allergénique. Objectifs. Cette thèse vise à : i) décrire les profils de sensibilisation allergénique chez le nourrisson et l’enfant, ii) étudier l’association entre ces profils et la morbidité allergique et iii) identifier les facteurs de risque de cette sensibilisation. Méthodes. Dans le cadre du suivi de la cohorte prospective de naissances en population générale Pollution and Asthma Risk: an Infant Study (PARIS), la sensibilisation allergénique a été évaluée chez 1 860 nourrissons à 18 mois et 1 007 enfants à 8/9 ans par dosage des IgE spécifiques dirigées contre 16 et 19 allergènes, respectivement. Les informations concernant la santé et le cadre de vie des enfants ont été recueillies par questionnaires standardisés répétés. Des profils de sensibilisation et des profils de morbidité ont été identifiés par classification non supervisée et mis en relation par régression logistique multinomiale. Enfin, les facteurs associés à la sensibilisation allergénique chez le nourrisson ont été étudiés par régression logistique multivariée. Résultats. Dès 18 mois, 13,8% des enfants étaient sensibilisés et 6,2%, multi-sensibilisés. À 8/9 ans, ces prévalences étaient de 34,5% et 19,8%, respectivement. Les profils de sensibilisation identifiés chez le nourrisson (3) et dans l’enfance (5) différaient au regard de la morbidité allergique. L’analyse étiologique a permis de préciser le rôle des expositions précoces aux allergènes et aux microorganismes sur la sensibilisation allergénique. Conclusion. Cette thèse contribue à une meilleure compréhension de l’histoire naturelle de la sensibilisation allergénique, et ce, dès les premières années de vie. Cette connaissance est essentielle à la prévention des maladies allergiques qui en découlent. / Background. The first years of life appear to be critical for the development of allergic sensitization. Objectives. This thesis aims: i) to describe allergic sensitization profiles in infants and children, ii) to assess the link between these sensitization profiles and allergic morbidity, and iii) to identify risk factors for allergic sensitization. Methods. This work concerns children involved in the Pollution and Asthma Risk: an Infant Study (PARIS) population-based prospective birth cohort. Allergic sensitization was assessed in 1,860 18-month-old infants and 1,007 8/9-year-old children by specific IgE measurements towards 16 and 19 allergens, respectively. Lifelong health and living condition data were collected by repeated standardized questionnaires. Sensitization profiles and morbidity profiles were identified using unsupervised classification, and related to each other by multinomial logistic regression. Finally, risk factors for early allergic sensitization were assessed by multivariate logistic regression. Results. As soon as 18 months of age, 13.8% of children were sensitized and 6.2%, multi-sensitized. When 8/9 years old, corresponding prevalence was 34.5% and 19.8%, respectively. Sensitization profiles identified in infancy (3) and in childhood (5) differed in terms of allergic morbidity. Risk factor analysis allowed to clarify the role of early exposure to allergens and microorganisms on allergic sensitization. Conclusion. This thesis improves the natural history of allergic sensitization understanding, as soon as the first years of life. This knowledge is essential for subsequent disease preventing.