The Ceratitis capitata is a very important agricultural pest, whose reproduction behaviour is controled by chemical signals. Males initiate mating by creating leks, where they release sexual pheromones to attract females. The main goal of this diploma thesis was to determine the influence of host plants on the composition of male sex-pheromones C. capitata and to compare emanations of wild males with those originating from laboratory population. We studied the chemical composition of volatiles, released by calling males C. capitata from laboratory and two wild populations, using two-dimensional gas chromatography with time-of-flight mass spectrometric detection (GC×GC-TOFMS), gas chromatography with electroantennographic and flame ionization detection (GC-EAD-FID). All data were statistically analyzed by multivariate data analyses. Significant differences were observed in the quantitative and qualitative composition of the chemical emanations among males from the three populations. The GC-EAD-FID analyses revealed fourteen antenally active compounds with a possible behavioral function. Isomenthone, geraniol, bornyl acetate, geranyl acetone and ethyl octanoate were newly identified antenally active compounds of C. capitata male sex pheromone. Statistical analyses indicated that males and females of...

<p>In this thesis the oxidation of monoterpenes by O3 and NO2 and factors that influenced the oxidation were studied. In the environment both ozone (O3) and nitrogen dioxide (NO2) are present as oxidising gases, which causes sampling artefacts when using Tenax TA as an adsorbent to sample organic compounds in the air. A scrubber was developed to remove O3 and NO2 prior to the sampling tube, and artefacts during sampling were minimised when using the scrubber. The main organic compounds sampled in this thesis were two monoterpenes, alfa-pinene and delta-3-carene, due to their presence in both indoor and outdoor air. The recovery of the monoterpenes through the scrubber varied between 75-97% at relative humidities of 15-75%.</p><p>The reactions of alfa-pinene and delta-3-carene with O 3, NO2 and nitric oxide (NO) at different relative humidities (RHs) and reaction times were studied in a dark reaction chamber. The experiments were planned and performed according to an experimental design were the factors influencing the reaction (O3, NO2, NO, RH and reaction times) were varied between high and low levels. In the experiments up to 13% of the monoterpenes reacted when O3, NO2, and reaction time were at high levels, and NO, and RH were at low levels. In the evaluation eight and seven factors (including both single and interaction factors) were found to influence the amount of alfa-pinene and delta-3-carene reacted, respectively. The three most influencing factors for both of the monoterpenes were the O 3 level, the reaction time, and the RH. Increased O3 level and reaction time increased the amount of monoterpene reacted, and increased RH decreased the amount reacted.</p><p>A theoretical model of the reactions occurring in the reaction chamber was created. The amount of monoterpene reacted at different initial settings of O3, NO2, and NO were calculated, as well as the influence of different reaction pathways, and the concentrations of O3 and NO2, and NO at specific reaction times. The results of the theoretical model were that the reactivity of the gas mixture towards alfa-pinene and delta-3-carene was underestimated. But, the calculated concentrations of O3, NO2, and NO in the theoretical model were found to correspond to a high degree with experimental results performed under similar conditions. The possible associations between organic compounds in indoor air, building variables and the presence of sick building syndrome were studied using principal component analysis. The most complex model was able to separate 71% of the “sick” buildings from the “healthy” buildings. The most important variables that separated the “sick” buildings from the “healthy” buildings were a more frequent occurrence or a higher concentration of compounds with shorter retention times in the “sick” buildings.</p><p>The outcome of this thesis could be summarised as follows;</p><p>-</p><p>-</p><p>-</p><p>-</p>

Τις τελευταίες δεκαετίες, υπό το πρίσμα της βιώσιμης ανάπτυξης, παρατηρείται μια στροφή προς τις Ανανεώσιμες Πηγές Ενέργειας (ΑΠΕ) και ταυτόχρονη μείωση της χρήσης συμβατικών καυσίμων, ως διέξοδο στην αντιμετώπιση των περιβαλλοντικών προβλημάτων όπως της κλιματικής αλλαγής. Η αποτίμηση, σε νομισματικούς όρους, της διείσδυσης των ΑΠΕ στο ενεργειακό μίγμα, πραγματοποιείται μέσα από τεχνικές μη αγοραίας εκτίμησης. Ο κύριος σκοπός της παρούσας διδακτορικής διατριβής είναι η αποτύπωση σε νομισματικές μονάδες, της προθυμίας πληρωμής των νοικοκυριών για καταβολή επιπρόσθετου χρηματικού ποσού για την υλοποίηση επενδύσεων με σκοπό την παραγωγή ηλεκτρικής ενέργειας από ΑΠΕ. Ιδιαίτερα, είναι η εξέταση της συσχέτισης των κοινωνικοοικονομικών χαρακτηριστικών, της ενεργειακής συμπεριφοράς και της κοινωνικής αποδοχής της τοπικής κοινωνίας για έργα εκμετάλλευσης ΑΠΕ, με την επιθυμία οικονομικής συνεισφοράς για την παραγωγή ηλεκτρικής ενέργειας προερχόμενης από ΑΠΕ αλλά και με την πραγματική καταβολή χρηματικού ποσού στο δίμηνο λογαριασμό της ΔΕΗ, μέσα από τεχνικές μη αγοραίας εκτίμησης. Η ενεργειακή συμπεριφορά και η προθυμία πληρωμής, εξετάζεται με τη μέθοδο της υποθετικής ή εξαρτημένης αξιολόγησης που εφαρμόζεται στη παρούσα διατριβή εκτιμώντας σε ένα υποθετικό σενάριο, με χρήση ερωτηματολογίου, εκφρασμένες προτιμήσεις του κοινού, ποσοτικοποιώντας ουσιαστικά μη νομισματικές αξίες. Το δειγματοληπτικό πλαίσιο περιορίστηκε στο νομό Αιτωλοακαρνανίας. Η τελική επιλογή των νοικοκυριών έγινε με μίξη δειγματοληψίας ευκολίας και δειγματοληψίας χιονοστιβάδας. Από τον Ιανουάριο έως τον Απρίλιο 2012, διανεμήθηκαν ερωτηματολόγια σε 280 νοικοκυριά εκ των οποίων επεστράφησαν συμπληρωμένα 201. Από την ανάλυση προκύπτει ότι είναι περισσότερο ενημερωμένοι για την ηλιακή ενέργεια και έπονται η αιολική, η βιομάζα και η υδροηλεκτρική. Επίσης, εκφράζουν θετική άποψη στο ενδεχόμενο υλοποίησης έργων εκμετάλλευσης ΑΠΕ και πιστεύουν ότι μελλοντικά θα καταλαμβάνουν μεγάλο μερίδιο στο ενεργειακό μίγμα. Το κόστος κατανάλωσης ηλεκτρικής ενέργειας για τα νοικοκυριά, υπολογίζεται περίπου στα 301-400 ευρώ ανά δίμηνο λογαριασμό της ΔΕΗ. Οι λιγότεροι από τους μισούς ερωτηθέντες είναι διατεθειμένοι να δώσουν έως 10 ευρώ επιπρόσθετα, για χρήση ηλεκτρικής ενέργειας από ΑΠΕ ενώ, υψηλό ποσοστό ερωτηθέντων εμφανίζεται μη διατεθειμένο να πληρώσει επιπρόσθετα λόγω έλλειψης χρημάτων. Παράλληλα, μια μερίδα είναι διατεθειμένη να προχωρήσει σε μείωση των εξόδων της κυρίως στο τομέα της ψυχαγωγίας, ώστε να εξοικονομήσει χρήματα για να επωμιστεί το επιπρόσθετο κόστος. Εξήχθησαν κύριες συνιστώσες από ομοειδείς ομάδες μεταβλητών όπως η ενημέρωση του κοινού για τις επιμέρους μορφές ΑΠΕ, ο μελλοντικός ρόλος των διαφόρων μορφών στο ενεργειακό μίγμα, οι συνέπειες επενδύσεων με χρήση ΑΠΕ, τα ενδεχόμενα εμπόδια κατά την υλοποίηση, τα μέτρα επίλυσης τους. Από την ανάλυση συστάδων διαπιστώνεται ότι το κοινό ομαδοποιείται σε δύο συστάδες. Το δείγμα που ανήκει στη πρώτη συστάδα είναι μεγαλύτερης ηλικίας, όχι τόσο μορφωμένο και οικονομικά ασθενέστερο. Το δείγμα της δεύτερης συστάδας είναι νεαρής ηλικίας, πιο μορφωμένο και οικονομικά πιο ισχυρό. Μετά από πολλαπλή παλινδρόμηση η συμμετοχή σε συστάδα δεν αποτέλεσε στατιστικά σημαντική μεταβλητή. Το μοντέλο παλινδρόμησης της προθυμίας πληρωμής έδειξε ότι παράμετροι όπως της ηλικίας, του αριθμού των μελών της οικογένειας και της κύριας συνιστώσας που αφορά στην ενημέρωση του κοινού ως προς τις επιμέρους μορφές ενέργειας ήταν σημαντικές. Παρατηρείται ότι όσο αυξάνεται ο αριθμός των μελών της οικογένειας, η ηλικία καθώς και η ενημέρωσή σε συγκεκριμένες μορφές ΑΠΕ, τόσο μεγαλύτερη είναι η κατά μέσο όρο καταβολή χρηματικού ποσού στο δίμηνο λογαριασμό της ΔΕΗ και τόσο αυξάνεται και η προθυμία τους για πληρωμή επιπρόσθετου ποσού για παραγωγή ηλεκτρικής ενέργειας που βασίζεται σε ΑΠΕ. Επιπρόσθετα, όσο αφορά στο κατά μέσο όρο εισόδημα των νοικοκυριών έχει μικρή θετική επίδραση ως προς τη πληρωμή του λογαριασμού της ΔΕΗ και πολύ αρνητική ως προς την προθυμία επιπρόσθετου χρηματικού ποσού. Τέλος, η συμμετοχή σε περιβαλλοντικές δράσεις εμφανίζει μικρή θετική επίδραση για πληρωμή στο λογαριασμό της ΔΕΗ αλλά αρνητική στη προθυμία πληρωμής. Το κοινό υποστηρίζει πως η ύπαρξη επενδύσεων ΑΠΕ θα έχει σε γενικές γραμμές, θετικές συνέπειες στην ευρύτερη περιοχή και πως η τοπική κοινωνία θα έχει θετική στάση αν ξεπεραστούν τα εμπόδια και ληφθούν συγκεκριμένα μέτρα όπως οικονομικά κίνητρα. Τα αποτελέσματα των μοντέλων πολλαπλής παλινδρόμησης συγκρίθηκαν και με ανάλυση κανονικοποιημένης συσχέτισης. Για περαιτέρω διερεύνηση προτείνεται η εξέταση άλλων υποθετικών σεναρίων και άλλων μορφών πληρωμής. / In the last decades, under the spectrum of sustainable development, a turn to the Renewable Energy Sources (RES) is observed and parallel reduction of the use of conventional fuels, as a way out to the confrontation of environmental problems such as climate change. The evaluation, in economic terms, of the penetration of RES in the energy mix, is realized through techniques of non market valuation. The main purpose of the present research is the evaluation in economic units of households Willingness to Pay (WTP) to deposit an additional amount of money to make investments in order to produce electric power from RES. Especially, it is the examination of correlation of socioeconomics characteristics, energy behavior and social acceptance of the local community for projects using renewable energy, with the willingness of economic contribution for the electricity production deriving from RES but also with the real deposit amount of money to a bi-monthly electricity bill, through techniques of non market valuation. The energy behavior and the WTP are examined by Contingent Valuation Method (CVM) which is applied in the present, appreciating a hypothetical scenario, with the use of a questionnaire, expressed preferences of the public, giving quantity essentially to non market values. The target framework is limited in the Aitoloakarnania County. The final choice of the households was made by a mix of convenience sampling and snowball sampling. From January until April 2012, were distributed questionnaires to 280 households from which 201 were returned completed. From the analysis is inferred that it is more informative about the solar energy and wind, biomass and hydroelectric follow. It is also expressed positive view in case of making projects of exploitation RES and believed that in the future they will occupy a great share in the energy mix. The cost of consumption of electric power for households is estimated about €301-400 per a bi-monthly electricity bill. Fewer than half of the respondents are available to pay €10 more, for the electric power from RES, whereas a high percentage appears not to be available to pay more because of lack of money. At the same time, a group of consumers is available to have a reduction in the expenses in the sector of entertainment in order to save money for facing the additional cost. Principal components from identical groups have been extracted such as the awareness of the public about the partial forms of RES, the future role of the different forms in the energy mix, the consequences of investments by using RES, the following obstacles during the implementation and the measures of solving them. Cluster analysis identified that the public is grouped in two clusters. The sample which belongs to the first cluster is of older age, not so educated and economically weaker. The sample of the second cluster is of a younger age, more educated and financially more powerful. It is noted that after a multiple regression analysis, the participation in cluster did not constitute statistically important variable. The model of regression of WTP showed that parameters such as age, family members and the principal component which concerns the public awareness as concerning the partial forms of energy were important. It is obvious that as the number of family members is increasing, the age as well as the awareness in specific forms of RES, so grater is the average deposit of the amount of money in the bi-monthly electricity bill and the WTP is increasing in order to pay the additional amount for the production of electric power which is based on RES. In additional, the average income of the households has a small positive influence in regard with the payment of the electricity bill and very negative as concerning the willingness of additional amount of money. The public supports that the existence of RES investments will have generally positive consequences in the broad area that the local society will have positive stance if some obstacles are overcome and take certain measures such as financial motives. Finally, the results of multiple regression models were compared with the canonical correlation analysis. For further research is proposed the examination of other hypothetical scenarios and of other forms of payment.

En raison de la demande croissante d’énergie dans le monde et des nombreux dommages causés par l’utilisation des énergies fossiles, la contribution des énergies renouvelables a augmenté de manière significative dans le mix énergétique global dans le but de progresser vers un développement plus durable. Dans ce contexte, ce travail vise à l’élaboration d’une méthodologie générale pour la conception de systèmes photovoltaïques, basée sur les principes d’écoconception, en tenant compte simultanément des considérations technico-économiques et environnementales. Afin d’évaluer la performance environnementale des systèmes PV, une technique d’évaluation environnementale basée sur l’Analyse du Cycle de Vie (ACV) a été utilisée. Le modèle environnemental a été couplé d’une manière satisfaisante avec le modèle de conception d’un système PV connecté au réseau pour obtenir un modèle global, apte à un traitement par optimisation. Le modèle de conception du système PV résultant a été développé en faisant intervenir l’estimation du rayonnement solaire reçu dans une zone géographique concernée, le calcul de la quantité annuelle d’énergie produite à partir du rayonnement solaire reçu, les caractéristiques des différents composants et l’évaluation des critères technico-économiques à travers le temps de retour énergétique et le temps de retour sur investissement. Le modèle a ensuite été intégré dans une boucle d’optimisation multi-objectif externe basée sur une variante de l’algorithme génétique NSGA-II. Un ensemble de solutions du Pareto a été généré représentant le compromis optimal entre les différents objectifs considérés dans l’analyse. Une méthode basée sur une Analyse en Composantes Principales (ACP) est appliquée pour détecter et enlever les objectifs redondants de l’analyse sans perturber les caractéristiques principales de l’espace des solutions. Enfin, un outil d’aide à la décision basé sur M- TOPSIS a été utilisé pour sélectionner l’option qui offre un meilleur compromis entre toutes les fonctions objectifs considérées et étudiées. Bien que les modules photovoltaïques à base de silicium cristallin (c-Si) ont une meilleure performance vis-à-vis de la production d’énergie, les résultats ont montré que leur impact environnement est le plus élevé des filières technologiques de production de panneaux. Les technologies en « couches minces » présentent quant à elles le meilleur compromis dans tous les scénarios étudiés. Une attention particulière a été accordée aux processus de recyclage des modules PV, en dépit du peu d’informations disponibles pour toutes les technologies évaluées. La cause majeure de ce manque d’information est la durée de vie relativement élevée des modules photovoltaïques. Les données relatives aux procédés de recyclage pour les technologies basées sur CdTe et m-Si sont introduites dans la procédure d’optimisation par l’écoconception. En tenant compte de la production d’énergie et du temps de retour sur énergie comme critères d’optimisation, l’avantage de la gestion de fin de vie des modules PV a été confirmé. Une étude économique de la stratégie de recyclage doit être considérée et étudiée afin d’avoir une vision plus globale pour la prise de décision. / Because of the increasing demand for the provision of energy worldwide and the numerous damages caused by a major use of fossil sources, the contribution of renewable energies has been increasing significantly in the global energy mix with the aim at moving towards a more sustainable development. In this context, this work aims at the development of a general methodology for designing PV systems based on ecodesign principles and taking into account simultaneously both techno-economic and environmental considerations. In order to evaluate the environmental performance of PV systems, an environmental assessment technique was used based on Life Cycle Assessment (LCA). The environmental model was successfully coupled with the design stage model of a PV grid-connected system (PVGCS). The PVGCS design model was then developed involving the estimation of solar radiation received in a specific geographic location, the calculation of the annual energy generated from the solar radiation received, the characteristics of the different components and the evaluation of the techno-economic criteria through Energy PayBack Time (EPBT) and PayBack Time (PBT). The performance model was then embedded in an outer multi-objective genetic algorithm optimization loop based on a variant of NSGA-II. A set of Pareto solutions was generated representing the optimal trade-off between the objectives considered in the analysis. A multi-variable statistical method (i.e., Principal Componet Analysis, PCA) was then applied to detect and omit redundant objectives that could be left out of the analysis without disturbing the main features of the solution space. Finally, a decision-making tool based on M-TOPSIS was used to select the alternative that provided a better compromise among all the objective functions that have been investigated. The results showed that while the PV modules based on c-Si have a better performance in energy generation, the environmental aspect is what makes them fall to the last positions. TF PV modules present the best trade-off in all scenarios under consideration. A special attention was paid to recycling process of PV module even if there is not yet enough information currently available for all the technologies evaluated. The main cause of this lack of information is the lifetime of PV modules. The data relative to the recycling processes for m-Si and CdTe PV technologies were introduced in the optimization procedure for ecodesign. By considering energy production and EPBT as optimization criteria into a bi-objective optimization cases, the importance of the benefits of PV modules end-of-life management was confirmed. An economic study of the recycling strategy must be investigated in order to have a more comprehensive view for decision making.

O uso de dados biométricos da face para verificação automática de identidade é um dos maiores desafios em sistemas de controle de acesso seguro. O processo é extremamente complexo e influenciado por muitos fatores relacionados à forma, posição, iluminação, rotação, translação, disfarce e oclusão de características faciais. Hoje existem muitas técnicas para se reconhecer uma face. Esse trabalho apresenta uma investigação buscando identificar uma face no banco de dados ORL com diferentes grupos de treinamento. É proposto um algoritmo para o reconhecimento de faces baseado na técnica de subespaço LDA (PCA + LDA) utilizando uma rede neural SOM para representar cada classe (face) na etapa de classificação/identificação. Aplicando o método do subespaço LDA busca-se extrair as características mais importantes na identificação das faces previamente conhecidas e presentes no banco de dados, criando um espaço dimensional menor e discriminante com relação ao espaço original. As redes SOM são responsáveis pela memorização das características de cada classe. O algoritmo oferece maior desempenho (taxas de reconhecimento entre 97% e 98%) com relação às adversidades e fontes de erros que prejudicam os métodos de reconhecimento de faces tradicionais. / The use of biometric technique for automatic personal identification is one of the biggest challenges in the security field. The process is complex because it is influenced by many factors related to the form, position, illumination, rotation, translation, disguise and occlusion of face characteristics. Now a days, there are many face recognition techniques. This work presents a methodology for searching a face in the ORL database with some different training sets. The algorithm for face recognition was based on sub-space LDA (PCA + LDA) technique using a SOM neural net to represent each class (face) in the stage of classification/identification. By applying the sub-space LDA method, we extract the most important characteristics in the identification of previously known faces that belong to the database, creating a reduced and more discriminated dimensional space than the original space. The SOM nets are responsible for the memorization of each class characteristic. The algorithm offers great performance (recognition rates between 97% and 98%) considering the adversities and sources of errors inherent to the traditional methods of face recognition.

Wireless sensor networks form an emerging class of computing devices capable of observing the world with an unprecedented resolution, and promise to provide a revolutionary instrument for environmental monitoring. Such a network is composed of a collection of battery-operated wireless sensors, or sensor nodes, each of which is equipped with sensing, processing and wireless communication capabilities. Thanks to advances in microelectronics and wireless technologies, wireless sensors are small in size, and can be deployed at low cost over different kinds of environments in order to monitor both over space and time the variations of physical quantities such as temperature, humidity, light, or sound. <p><p>In environmental monitoring studies, many applications are expected to run unattended for months or years. Sensor nodes are however constrained by limited resources, particularly in terms of energy. Since communication is one order of magnitude more energy-consuming than processing, the design of data collection schemes that limit the amount of transmitted data is therefore recognized as a central issue for wireless sensor networks.<p><p>An efficient way to address this challenge is to approximate, by means of mathematical models, the evolution of the measurements taken by sensors over space and/or time. Indeed, whenever a mathematical model may be used in place of the true measurements, significant gains in communications may be obtained by only transmitting the parameters of the model instead of the set of real measurements. Since in most cases there is little or no a priori information about the variations taken by sensor measurements, the models must be identified in an automated manner. This calls for the use of machine learning techniques, which allow to model the variations of future measurements on the basis of past measurements.<p><p>This thesis brings two main contributions to the use of learning techniques in a sensor network. First, we propose an approach which combines time series prediction and model selection for reducing the amount of communication. The rationale of this approach, called adaptive model selection, is to let the sensors determine in an automated manner a prediction model that does not only fits their measurements, but that also reduces the amount of transmitted data. <p><p>The second main contribution is the design of a distributed approach for modeling sensed data, based on the principal component analysis (PCA). The proposed method allows to transform along a routing tree the measurements taken in such a way that (i) most of the variability in the measurements is retained, and (ii) the network load sustained by sensor nodes is reduced and more evenly distributed, which in turn extends the overall network lifetime. The framework can be seen as a truly distributed approach for the principal component analysis, and finds applications not only for approximated data collection tasks, but also for event detection or recognition tasks. <p><p>/<p><p>Les réseaux de capteurs sans fil forment une nouvelle famille de systèmes informatiques permettant d'observer le monde avec une résolution sans précédent. En particulier, ces systèmes promettent de révolutionner le domaine de l'étude environnementale. Un tel réseau est composé d'un ensemble de capteurs sans fil, ou unités sensorielles, capables de collecter, traiter, et transmettre de l'information. Grâce aux avancées dans les domaines de la microélectronique et des technologies sans fil, ces systèmes sont à la fois peu volumineux et peu coûteux. Ceci permet leurs deploiements dans différents types d'environnements, afin d'observer l'évolution dans le temps et l'espace de quantités physiques telles que la température, l'humidité, la lumière ou le son.<p><p>Dans le domaine de l'étude environnementale, les systèmes de prise de mesures doivent souvent fonctionner de manière autonome pendant plusieurs mois ou plusieurs années. Les capteurs sans fil ont cependant des ressources limitées, particulièrement en terme d'énergie. Les communications radios étant d'un ordre de grandeur plus coûteuses en énergie que l'utilisation du processeur, la conception de méthodes de collecte de données limitant la transmission de données est devenue l'un des principaux défis soulevés par cette technologie. <p><p>Ce défi peut être abordé de manière efficace par l'utilisation de modèles mathématiques modélisant l'évolution spatiotemporelle des mesures prises par les capteurs. En effet, si un tel modèle peut être utilisé à la place des mesures, d'importants gains en communications peuvent être obtenus en utilisant les paramètres du modèle comme substitut des mesures. Cependant, dans la majorité des cas, peu ou aucune information sur la nature des mesures prises par les capteurs ne sont disponibles, et donc aucun modèle ne peut être a priori défini. Dans ces cas, les techniques issues du domaine de l'apprentissage machine sont particulièrement appropriées. Ces techniques ont pour but de créer ces modèles de façon autonome, en anticipant les mesures à venir sur la base des mesures passées. <p><p>Dans cette thèse, deux contributions sont principalement apportées permettant l'applica-tion de techniques d'apprentissage machine dans le domaine des réseaux de capteurs sans fil. Premièrement, nous proposons une approche qui combine la prédiction de série temporelle avec la sélection de modèles afin de réduire la communication. La logique de cette approche, appelée sélection de modèle adaptive, est de permettre aux unités sensorielles de determiner de manière autonome un modèle de prédiction qui anticipe correctement leurs mesures, tout en réduisant l'utilisation de leur radio.<p><p>Deuxièmement, nous avons conçu une méthode permettant de modéliser de façon distribuée les mesures collectées, qui se base sur l'analyse en composantes principales (ACP). La méthode permet de transformer les mesures le long d'un arbre de routage, de façon à ce que (i) la majeure partie des variations dans les mesures des capteurs soient conservées, et (ii) la charge réseau soit réduite et mieux distribuée, ce qui permet d'augmenter également la durée de vie du réseau. L'approche proposée permet de véritablement distribuer l'ACP, et peut être utilisée pour des applications impliquant la collecte de données, mais également pour la détection ou la classification d'événements. <p> / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

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金融風暴喚起各界改革財務報表未能反映實際虧損的缺失，因此，新版國際財務報導準則第9號及第17號公報應運而生，未來正式接軌後，對於壽險業的財報將產生重大衝擊，更突顯其資產負債管理之重要性，故本研究係採用主成分分析建構極端利率情境，並考量折現率需反映現時狀況下，於資產面分別以攤銷後成本或公允價值衡量、負債面採公允價值評價，欲探討資產負債配置及攤銷後成本比重不同時，利率變動對於壽險公司股東權益波動度之影響，以供壽險業參考。 研究結果發現攤銷後成本比重能夠有效控制股東權益波動度。再者，壽險公司應審慎評估海外投資比例，並配合其壽險商品外幣保單之銷售策略加以布局，同時謹慎考量會計決策，適當選擇攤銷後成本權重，方能有效控制資產負債表之波動。 / The financial crisis has caused wide public concern since it is failed to reflect the actual losses in financial statements. As a result, International Accounting Standards Board (IASB) issued new International Financial Reporting Standards, IFRS 9 and IFRS 17. The surplus of life insurers may fluctuate sharply if assets and liabilities don’t match appropriately under these new IFRS Standards. We follow the international regulation standard by using principal component analysis to generate extreme interest rate shock scenarios. This study examines the volatility of surplus under extreme interest rate shock scenarios for different combinations of liabilities, fair-valued assets, and amortized cost assets. In particular, the assets are measured at amortized cost or fair value, and all liabilities were acquired at fair value approach. In the numerical analysis, we showed that it is one of the most effective methods to control the surplus volatility by adjusting the percentage of amortized cost assets. Furthermore, life insurer should adjust the percentage of foreign investments and insurance policies carefully in order to reduce the fluctuation in shareholders’ equity.

AlNiCo magnets are known for high-temperature stability and superior corrosion resistance and have been widely used for various applications. Reported magnetic energy density ((BH) max) for these magnets is around 10 MGOe. Theoretical calculations show that ((BH) max) of 20 MGOe is achievable which will be helpful in covering the gap between AlNiCo and Rare-Earth Elements (REE) based magnets. An extended family of AlNiCo alloys was studied in this dissertation that consists of eight elements, and hence it is important to determine composition-property relationship between each of the alloying elements and their influence on the bulk properties. In the present research, we proposed a novel approach to efficiently use a set of computational tools based on several concepts of artificial intelligence to address a complex problem of design and optimization of high temperature REE-free magnetic alloys. A multi-dimensional random number generation algorithm was used to generate the initial set of chemical concentrations. These alloys were then examined for phase equilibria and associated magnetic properties as a screening tool to form the initial set of alloy. These alloys were manufactured and tested for desired properties. These properties were fitted with a set of multi-dimensional response surfaces and the most accurate meta-models were chosen for prediction. These properties were simultaneously extremized by utilizing a set of multi-objective optimization algorithm. This provided a set of concentrations of each of the alloying elements for optimized properties. A few of the best predicted Pareto-optimal alloy compositions were then manufactured and tested to evaluate the predicted properties. These alloys were then added to the existing data set and used to improve the accuracy of meta-models. The multi-objective optimizer then used the new meta-models to find a new set of improved Pareto-optimized chemical concentrations. This design cycle was repeated twelve times in this work. Several of these Pareto-optimized alloys outperformed most of the candidate alloys on most of the objectives. Unsupervised learning methods such as Principal Component Analysis (PCA) and Heirarchical Cluster Analysis (HCA) were used to discover various patterns within the dataset. This proves the efficacy of the combined meta-modeling and experimental approach in design optimization of magnetic alloys.

Methodologies designed to support new product development are receiving increasing interest in recent literature. A significant percentage of new product failure is attributed to a mismatch between designed product features and consumer liking. A variety of methodologies have been proposed and tested for consumer liking or preference prediction, ranging from statistical methodologies e.g. multiple linear regression (MLR) to non-statistical approaches e.g. artificial neural networks (ANN), support vector machines (SVM), and belief rule-based (BRB) systems. BRB has been previously tested for consumer preference prediction and target setting in case studies from the beverages industry. Results have indicated a number of technical and conceptual advantages which BRB holds over the aforementioned alternative approaches. This thesis focuses on presenting further advantages and applications of the BRB methodology for consumer liking prediction. The features and advantages are selected in response to challenges raised by three addressed case studies. The first case study addresses a novel industry for BRB application: the fast moving consumer goods industry, the personal care sector. A series of challenges are tackled. Firstly, stepwise linear regression, principal component analysis and AutoEncoder are tested for predictors’ selection and data reduction. Secondly, an investigation is carried out to analyse the impact of employing complete distributions, instead of averages, for sensory attributes. Moreover, the effect of modelling instrumental measurement error is assessed. The second case study addresses a different product from the personal care sector. A bi-objective prescriptive approach for BRB model structure selection and validation is proposed and tested. Genetic Algorithms and Simulated Annealing are benchmarked against complete enumeration for searching the model structures. A novel criterion based on an adjusted Akaike Information Criterion is designed for identifying the optimal model structure from the Pareto frontier based on two objectives: model complexity and model fit. The third case study introduces yet another novel industry for BRB application: the pastry and confectionary specialties industry. A new prescriptive framework, for rule validation and random training set allocation, is designed and tested. In all case studies, the BRB methodology is compared with the most popular alternative approaches: MLR, ANN, and SVM. The results indicate that BRB outperforms these methodologies both conceptually and in terms of prediction accuracy.