No description available.

This work deals with fouling in the recovery boiler at Montes del Plata, Uruguay. Multivariate data analysis has been used to analyze the large amount of data that was available in order to investigate how different parameters affect the fouling problems. Principal Component Analysis (PCA) and Partial Least Square Projection (PLS) have in this work been used. PCA has been used to compare average values between time periods with high and low fouling problems while PLS has been used to study the correlation structures between the variables and consequently give an indication of which parameters that might be changed to improve the availability of the boiler. The results show that this recovery boiler tends to have problems with fouling that might depend on the distribution of air, the black liquor pressure or the dry solid content of the black liquor. The results also show that multivariate data analysis is a powerful tool for analyzing these types of fouling problems. / Detta arbete handlar om inkruster i sodapannan pa Montes del Plata, Uruguay. Multivariat dataanalys har anvands for att analysera den stora datamangd som fanns tillganglig for att undersoka hur olika parametrar paverkar inkrusterproblemen. Principal·· Component Analysis (PCA) och Partial Least Square Projection (PLS) har i detta jobb anvants. PCA har anvants for att jamfora medelvarden mellan tidsperioder med hoga och laga inkrusterproblem medan PLS har anvants for att studera korrelationen mellan variablema och darmed ge en indikation pa vilka parametrar som kan tankas att andras for att forbattra tillgangligheten pa sodapannan. Resultaten visar att sodapannan tenderar att ha problem med inkruster som kan hero pa fdrdelningen av luft, pa svartlutens tryck eller pa torrhalten i svartluten. Resultaten visar ocksa att multivariat dataanalys ar ett anvandbart verktyg for att analysera dessa typer av inkrusterproblem.

Over the last years, due to the development of quieter engines and drivetrains, the importance of addressing the vehicle wind noise problem has significantly increased.In this thesis work, several existing Transfer Path Analysis methods have been applied on an experimental database acquired during a wind tunnel test on a passenger car with the objective of analyzing the distribution of the wind noise sources and their contribution to the target microphones located inside the vehicle. A major challenge for the Transfer Path Analysis (TPA) consists of the high complexity of the aerodynamic sources exciting the structure. Moreover, the existence of multiple incoherent source phenomena, and the presence of distributed coherent source regions of different correlation scales make the analysis very complex.The thesis work provides a solid and comprehensive analysis of the results obtained by different methods. The outcomes can be potentially useful for optimizing the vehicle NVH performance in future practical cases. / Under de senaste åren har vikten av att arbeta med vägfordons problem med aerodynamisk ljudgenerering ökat avsevärt på grund av utvecklingen av tystare motorer och drivlinor. I det här projektet har flera existerande metoder för Transfer Path Analysis (TPA) tillämpats på en databas med experimentella data som samlats in vid vindtunneltest på en personbil, med målet att analysera fördelningen av källorna orsakade av vindbruset och deras påverkan på ljudnivån vid de uppsatta målmikrofonerna inuti fordonet. En stor utmaning för TPA är den höga komplexiteten hos de aerodynamiska källorna som exciterar strukturen. Vidare gör förekomsten av flera okorrelerade källor, och närvaron av distribuerade koherenta källregioner med olika korrelationsskalor, analysen mycket komplex. Arbetet presenterar en solid och omfattande analys av resultat som erhållits med olika metoder. Resultaten är potentiellt användbara för att optimera fordonets NVH-prestanda i praktiktiken i framtiden.

We analyse the Swedish central bank, the Riksbank’s, interest setting policy in a Taylor rule framework. In particular, we examine whether or not the Riksbank has reacted to fluctuations in asset prices during the period 1995:Q1 to 2016:Q2. This is done by estimating a forward-looking Taylor rule with interest rate smoothing, augmented with stock prices, house prices and the real exchange rate, using IV GMM. In general, we find that the Riksbank’s interest setting policy is well described by a forward-looking Taylor rule with interest rate smoothing and that the use of factors as instruments, derived from a PCA, serves to alleviate the weak-identification problem that tend to plague GMM. Moreover, apart from finding evidence that the Riksbank exhibit a substantial degree of policy rate inertia and has acted so as to stabilize inflation and the real economy, we also find evidence that the Riksbank has been reacting to fluctuations in stock prices, house prices, and the real exchange rate.

The term "personality" nowadays occurs more often not only in psychological studies of humans but also in animal studies. Studying of personality help us to define the behavioural characteristics which can vary within the age, sexes, species or enviroments. Behavioral experiments are used to detect these behavioral patterns and they can divide the animals into the different groups. The subject of our research became three populations of house mouse (Mus musculus sensu lato) which we tested in a series of experiments involving free exploration, forced exploration, hole- board test, test of vertical activity and Elevated plus-maze. These experiments should reveal wheter the mice differ in their behaviour through the context of sex, comensalism or subspecies. We found (with in excepcion of one test) that intrapopulation variability differences are very small but interpopulation differences purely increase in the cas of comensalism and effects of subspecies. Keywords: Mus musculus, comensalism, open fieldtest, Elevated plus-maze, Principal Component Analysis (PCA)

Made available in DSpace on 2014-12-17T15:42:13Z (GMT). No. of bitstreams: 1 LeomirASL_DISSERT.pdf: 2074968 bytes, checksum: 7f7bd033e89dd46efc6c92957584d5b9 (MD5) Previous issue date: 2013-07-23 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / This paper investigates the potential of near infrared spectroscopy (NIR) for forensic analysis of human hair samples in order to differentiate smokers from nonsmokers, using chemometric modeling as an analytical tool. We obtained a total of 19 hair samples, 9 smokers and 10 nonsmokers varying gender, hair color, age and duration of smoking, all collected directly from the head of the same great Natal-RN. From the NIR spectra obtained without any pretreatment of the samples was performed an exploratory multivariate chemical data by applying spectral pretreatments followed by principal component analysis (PCA). After chemometric modeling of the data was achieved without any experimental data beyond the NIR spectra, differentiate smokers from nonsmokers, by demonstrating the significant influence of tabacco on the chemical composition of hair as well as the potential of the methodology in forensic identification / Nesse trabalho investiga-se a potencialidade da espectroscopia no infravermelho pr?ximo (NIR) para an?lise forense de cabelo humano, a fim de diferenciar amostras fumantes de n?o fumantes, utilizando-se a modelagem quimiom?trica como ferramenta anal?tica. Foram obtidos um total de 19 amostras de cabelo, sendo 9 de indiv?duos fumantes e 10 de n?o fumantes variando sexo, cor do cabelo, idade e tempo de consumo de tabaco, todos coletados diretamente da cabe?a dos mesmos na grande Natal-RN. A partir dos espectros NIR, obtidos sem nenhum tratamento pr?vio das amostras, foi realizado um estudo explorat?rio dos dados qu?micos multivariados aplicando-se pr?-tratamentos espectrais seguido da an?lise dos componentes principais (PCA). Ap?s modelagem quimiom?trica dos dados, conseguiu-se, sem qualquer outro dado experimental al?m dos espectros NIR, diferenciarem indiv?duos fumantes de n?o fumantes, demonstrando-se a influ?ncia significativa do tabaco na composi??o qu?mica do cabelo, bem como a potencialidade da metodologia na identifica??o forense

This thesis has evaluated the use of Independent Component Analysis (ICA) on Surface Electromyography (sEMG), focusing on the biosignal applications. This research has identified and addressed the following four issues related to the use of ICA for biosignals: • The iterative nature of ICA • The order and magnitude ambiguity problems of ICA • Estimation of number of sources based on dependency and independency nature of the signals • Source separation for non-quadratic ICA (undercomplete and overcomplete) This research first establishes the applicability of ICA for sEMG and also identifies the shortcomings related to order and magnitude ambiguity. It has then developed, a mitigation strategy for these issues by using a single unmixing matrix and neural network weight matrix corresponding to the specific user. The research reports experimental verification of the technique and also the investigation of the impact of inter-subject and inter-experimental variations. The results demonstrate that while using sEMG without separation gives only 60% accuracy, and sEMG separated using traditional ICA gives an accuracy of 65%, this approach gives an accuracy of 99% for the same experimental data. Besides the marked improvement in accuracy, the other advantages of such a system are that it is suitable for real time operations and is easy to train by a lay user. The second part of this thesis reports research conducted to evaluate the use of ICA for the separation of bioelectric signals when the number of active sources may not be known. The work proposes the use of value of the determinant of the Global matrix generated using sparse sub band ICA for identifying the number of active sources. The results indicate that the technique is successful in identifying the number of active muscles for complex hand gestures. The results support the applications such as human computer interface. This thesis has also developed a method of determining the number of independent sources in a given mixture and has also demonstrated that using this information, it is possible to separate the signals in an undercomplete situation and reduce the redundancy in the data using standard ICA methods. The experimental verification has demonstrated that the quality of separation using this method is better than other techniques such as Principal Component Analysis (PCA) and selective PCA. This has number of applications such as audio separation and sensor networks.

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. 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. 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. 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. 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. / 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. 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. 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. 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. 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.

Face recognition is a popular research area where there are different approaches studied in the literature. In this thesis, a holistic Principal Component Analysis (PCA) based method, namely Eigenface method is studied in detail and three of the methods based on the Eigenface method are compared. These are the Bayesian PCA where Bayesian classifier is applied after dimension reduction with PCA, the Subspace Linear Discriminant Analysis (LDA) where LDA is applied after PCA and Eigenface where Nearest Mean Classifier applied after PCA. All the three methods are implemented on the Olivetti Research Laboratory (ORL) face database, the Face Recognition Technology (FERET) database and the CNN-TURK Speakers face database. The results are compared with respect to the effects of changes in illumination, pose and aging. Simulation results show that Subspace LDA and Bayesian PCA perform slightly well with respect to PCA under changes in pose / however, even Subspace LDA and Bayesian PCA do not perform well under changes in illumination and aging although they perform better than PCA.

In recent years, the celebration activities of various types of folk-custom festivals in Taiwan have already been getting more and more attention from civilians. Festivities throughout the whole island are traditionally accompanied by loud and brightly colored firework displays. Among these activities, the firework display during the Chinese Lantern Festival in Kaohsiung City is one of the largest festivals in Taiwan every year. Therefore, it is important to investigate the influences of firework displays on ambient air quality during the Chinese Lantern Festival in Kaohsiung City. Field measurement of ambient gaseous pollutants and particulate matter (PM) was conducted on February 9-12, 2009, the Chinese Lantern Festival, in Kaohsiung City. Moreover, three kinds of firework powders obtained from the same factory producing Kaohsiung Lantern Festival fireworks were burned in a combustion chamber to determine the physicochemical properties of firework aerosols. Metallic elements were analyzed with an inductively coupled plasma-atomic emission spectrometer (ICP-AES). Ionic species and carbonaceous contents in the PM samples were analyzed with an ion chromatography (IC) and an elemental analyzer (EA), respectively. Finally, the source identification and apportionment of PM were analyzed by principal component analysis (PCA), enrichment factor (EF), and receptor modeling (CMB). For inorganic gaseous pollutants, the concentration peaks of NO, NO2, O3, CO were observed during the firework periods, and the concentration peak of NO was approximately 8.8 times higher than those during the non-firework periods. This study further revealed that, even at nighttime, ambient O3 could be reduced dramatically during the firework periods, whenas NO2 concentration increased concurrently, due to titration effects resulting from the prompt reaction of NO with O3 to form NO2 and O2. For organic gaseous pollutants, the concentration peak of toluene during the firework periods was approximately 2.2-4.1 times higher than those during the non-firework periods. Several metallic elements of PM during the firework display periods were obviously higher than those during the non-firework periods. On February 10, the concentrations of Mg, K, Pb, and Sr in PM2.5 were 10 times higher than those during the non-firework periods. Besides, the Cl-/Na+ ratio was slightly smaller than 1 in Kaohsiung Harbor, but it was approximately 3 during the firework display periods since Cl- came form chlorine content in firework aerosols at this time. Moreover, OC/EC ratio increased up to 2.8. In addition to the analysis of gaseous pollutants and PM during the Chinese Lantern Festival in Kaohsiung City, this study burned firework powders in a self-designed combustion chamber to measure the physicochemical properties of firework aerosols. In the results, K, Mg, Cl-, OC were major contents (<10%) in the aerosols produced from the burning firework powders. Moreover, Cl-/Na+ and OC/EC ratio were 15.0~23.4 and 2.9~3.2, respectively. Consequently, Cl-/Na+ and OC/EC ratio can be used as two important indicators of firework displays. Results obtained from PCA and CMB receptor modeling showed that the major sources of aerosols during the firework display periods were firework displays, motor/diesel vehicle exhanst, soil dusts, and marine aerosols. Besides, the firework displays on February 10 contributed approximately 25.2% and 16.6% of PM10 at two sampling sites, respectively.