Sentiment analysis has emerged as a field that has attracted a significant amount of attention since it has a wide variety of applications that could benefit from its results, such as news analytics, marketing, question answering, knowledge management and so on. This area, however, is still early in its development where urgent improvements are required on many issues, particularly on the performance of sentiment classification. In this thesis, three key challenging issues affecting sentiment classification are outlined and innovative ways of addressing these issues are presented. First, text pre-processing has been found crucial on the sentiment classification performance. Consequently, a combination of several existing preprocessing methods is proposed for the sentiment classification process. Second, text properties of financial news are utilised to build models to predict sentiment. Two different models are proposed, one that uses financial events to predict financial news sentiment, and the other uses a new interesting perspective that considers the opinion reader view, as opposed to the classic approach that examines the opinion holder view. A new method to capture the reader sentiment is suggested. Third, one characteristic of financial news is that it stretches over a number of domains, and it is very challenging to infer sentiment between different domains. Various approaches for cross-domain sentiment analysis have been proposed and critically evaluated.

La rupture des pentes et l’éclatement des roches, qui représentent deux types de risques naturels fréquents dans le monde, peuvent engendrer des conséquences économiques importantes et des pertes en vie humaine. Malgré que les phénomènes soient étudiés depuis de longues années, il reste encore des questions ouvertes et sans réponse et il est donc encore nécessaire poursuivre les recherches sur cette thématique. Le présent travail de thèse est consacré la modélisation numérique de la stabilité des grandes pentes et de l’éclatement des massifs rocheux en utilisant des méthodes basées sur l’intelligence artificielle en proposant des modifications et des améliorations de telles méthodes. En se basant sur des observations de déplacements de terrain, le glissement de terrain, qui est phénomène commun de la rupture de pentes, est étudié par le processus de Gauss afin de prédire son apparition temporelle. Ensuite, la question d’évaluation de la stabilité des pentes est abordée en utilisant la stratégie de machine à vecteurs de pertinence (RVM) avec des hyper-paramètres adaptatifs. Une approche itérative est proposée afin de déterminer les valeurs optimales des hyper-paramètres. Afin d’améliorer la prédiction, l’évaluation complète de la stabilité des pentes est réalisée en proposant un modèle basé sur la théorie de flou (CM) associé à un processus analytique d’hiérarchisation pondérée (WAHP). Ce modèle est utilisé à l’évaluation de la stabilité de la pente de rive gauche de la centrale hydroélectrique de Jinping 1, dans la région Sud-Ouest de Chine. Enfin, dans la dernière partie, la problématique de l’éclatement des massifs rocheux est abordée en utilisant des modèles basés sur la théorie du flou, en se basant sur une synthèse de 164 cas réels. Des comparaisons entre les résultats numériques et des données de terrain sont présentées pour de différents cas étudiés dans cette thèse. / Slope failures and rock burst, which are two typical types of geological hazards, create tremendous economic damages and cause massive losses to the health of domestic humans or animals every year throughout the world. The severe situation implies that they are still in need to be further studied despite the fact that they have been discussed for a long time. The present work is devoted to presenting the analysis of slope failures and rock burst using some computational intelligent models with modifications and improvements. Then landslide, a common type of slope failure, is analyzed for time occurrence prediction using the Gaussian Process by means of field-observed displacement series. After that, the problem of slope stability evaluation is discussed using the strategy of relevance vector machine (RVM) with adaptive hyper-parameter. An iteration approach is presented to find optimal hyper-parameter values in this chapter. Afterwards, the comprehensive evaluation of slope stability is carried out with the cloud model (CM) and weighted analytical hierarchy process (WAHP) closely related to the left abutment slope of Jinping 1 Hydropower Station, southwest of China. Finally, prediction of rock burst classification is engaged using the cloud models synthesized with the attribution weights on the basis of 164 rock burst cases. In each modeling of the associated problems, comparisons are given on the performance of each strategy as well as some evaluations.

This thesis deals with thermal optimization of electric machines. There are described the connections of finite element methods with optimizations algorithms. This connection is used for modifications of brushless motor construction. The main parts of this work are the complete thermal analysis of EC machine and modifications of cooling.

A dissertation submitted in ful llment of the requirements for the degree of Master of Science in the School of Computer Science and Applied Mathematics Faculty of Science August 15, 2017 / Facial expression recognition is one of the most common means of communication used for complementing spoken word. However, people have grown to master ways of ex- hibiting deceptive expressions. Hence, it is imperative to understand di erences in expressions mostly for security purposes among others. Traditional methods employ machine learning techniques in di erentiating real and fake expressions. However, this approach does not always work as human subjects can easily mimic real expressions with a bit of practice. This study presents an approach that evaluates the time related dis- tance that exists between eye actions and an exhibited expression. The approach gives insights on some of the most fundamental characteristics of expressions. The study fo- cuses on nding and understanding the temporal relationship that exists between eye blinks and smiles. It further looks at the relationship that exits between eye closure and pain expressions. The study incorporates active appearance models (AAM) for feature extraction and support vector machines (SVM) for classi cation. It tests extreme learn- ing machines (ELM) in both smile and pain studies, which in turn, attains excellent results than predominant algorithms like the SVM. The study shows that eye blinks are highly correlated with the beginning of a smile in posed smiles while eye blinks are highly correlated with the end of a smile in spontaneous smiles. A high correlation is observed between eye closure and pain in spontaneous pain expressions. Furthermore, this study brings about ideas that lead to potential applications such as lie detection systems, robust health care monitoring systems and enhanced animation design systems among others. / MT 2018

O propósito deste trabalho é a modelagem experimental e estudo do controle do processo de hidroconformação de tubos. Assim, o trabalho visa o projeto e a construção de um dispositivo servo-controlado de hidroconformação de tubos com um sistema de controle digital. O trabalho compreende o projeto e a construção de um dispositivo a ser acoplado em uma prensa hidráulica, a instrumentação dos equipamentos usados e a implantação do sistema de controle automático do processo através de um computador PC e de placas de interface A/D e D/A. Os aplicativos de controle foram desenvolvidos em linguagem de alto nível no sistema operacional Windows. No projeto do aplicativo, inicialmente foram realizadas rotinas para testes do sistema em malha aberta. As demais rotinas são aquelas associadas às funções matemáticas do modelo fenomenológico do sistema de hidroconformação, aquelas destinadas ao controlador de malha fechada. O tipo de estratégia de controle a ser utilizada foi definida no decorrer do projeto e foi baseada em um modelo de processo não linear, linearizado em torno de cada ponto de operação. Para fins de obtenção do modelo nominal para o controlador, os atuadores e sensores tiveram suas dinâmicas desprezadas face a dinâmica do processo e suas curvas de processo foram levantadas experimentalmente. / This works aims is the experimental modeling of a tube hydroforming (THF) “T" branch, and de THF process automatic control study. Thus, the design and the construction of a servo-controlled hydraulic device for THF, with a automatic digital control system, is embedded in our objectives. Design and construction of device to append on a hydraulic press, implantation of the measurement equipment and implementation of the control system algorithms through a PC with I/O interface boards is necessary. Control algorithms were developed in Hi-level language for windows operating system. The application design was based on experimental initial tests performed with no feed-backing controlling mode. Routes related to phenomenological mathematical model of the THF process were validate against the literature database, and were devoted to the feed-backing controller mode. Control strategy to employ in final application was defined during the process calibration, based on the non-linear characteristics of the “T" branch THF. To obtain the final load path model sensors and cylinders had their dynamics neglected because the THF dynamic is very much higher, and had their behavior curves experimentally raised.

This research focuses on the study of the behavior of a high speed vehicle and particularly an air-cushion vehicle (ACV) in varying bathymetry. An extensive data acquisition system is developed to gather data during the experiments. Four groups of experiments are conducted in a wave tank using a scale model surface effect ship to generate a database that is post processed to assess phenomena under various conditions. Group No1 experiments involved characterizing the wave motion in the tank in the absence of the vehicle as the waves transformed in response to variation in water depth. Based on these experimental datasets, the wave breaking type and position are predicted using a machine learning approach and, more specifically, a neural network of the multilayer perceptron type. Group No2 experiments are in support of a parametric study to evaluate the vehicle's performance under calm water conditions when the control inputs are varied. A system identific ation approach based on the experimental data is proposed to create a model that predicts the vehicles translational motion. In group No3 the experiments involve the vehicle travelling with a non-zero forward speed and encountering transforming head and following seas. Transient and non-linear phenomena and relations among parameters are observed Group No 4 experiments involve the vehicle maintaining a position in the "surf-zone" under manual control, encountering breaking waves that break on its bow skirt. Non-linear phenomena are discussed based on the experimental results. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2015. / FAU Electronic Theses and Dissertations Collection

Thesis. 1975. B.S.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / Bibliography: leaf 104. / by Donald N. North and James M. Guyer. / B.S.

Thesis. 1978. Sc.D.--Massachusetts Institute of Technology. Dept. of Mechanical Engineering. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Vita. / Bibliography: p. 85-89. / Sc.D.

Thesis. 1977. M.S.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Bibliography : p. 165-168. / M.S.

Thesis. 1977. Sc.D.--Massachusetts Institute of Technology. Dept. of Mechanical Engineering. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / Sc.D.