The fast clearing of faults is essential for preventing equipment damage and preserving the stability of the power transmission systems with smaller operating margins. This thesis examined the application of fault generated transients for fast detection and isolation of faults in a transmission system. The basis of the transient based protection scheme developed and implemented in this thesis is the fault current directions identified by a set of relays located at different nodes of the system. The direction of the fault currents relative to a relay location is determined by comparing the signs of the wavelet coefficients of the currents measured in all branches connected to the node. The faulted segment can be identified by combining the fault directions identified at different locations in the system. In order to facilitate this, each relay is linked with the relays located at the adjacent nodes through a telecommunication network. In order to prevent possible malfunctioning of relays due to transients originating from non-fault related events, a transient recognition system to supervise the relays is proposed. The applicability of different classification methods to develop a reliable transient recognition system was examined. A Hidden Markov Model classifier that utilizes the energies associated with the wavelet coefficients of the measured currents as input features was selected as the most suitable solution. Performance of the protection scheme was evaluated using a high voltage transmission system simulated in PSCAD/EMTDC simulation software. The custom models required to simulate the complete protection scheme were implemented in PSCAD/EMTDC. The effects of various factors such as fault impedance, signal noise, fault inception angle and current transformer saturation were investigated. The performance of the protection scheme was also tested with the field recorded signals. Hardware prototypes of the fault direction identification scheme and the transient classification system were implemented and tested under different practical scenarios using input signals generated with a real-time waveform playback instrument. The test results presented in this thesis successfully demonstrate the potential of using transient signals embedded in currents for detection, localization and recognition of faults in transmission networks in a fast and reliable manner.

Current physiotherapy practice relies on visual observation of the patient for diagnosis and assessment. The assessment process can potentially be automated to improve accuracy and reliability. This thesis proposes a method to recover patient joint angles and automatically extract movement profiles utilizing small and lightweight body-worn sensors. Joint angles are estimated from sensor measurements via the extended Kalman filter (EKF). Constant-acceleration kinematics is employed as the state evolution model. The forward kinematics of the body is utilized as the measurement model. The state and measurement models are used to estimate the position, velocity and acceleration of each joint, updated based on the sensor inputs from inertial measurement units (IMUs). Additional joint limit constraints are imposed to reduce drift, and an automated approach is developed for estimating and adapting the process noise during on-line estimation. Once joint angles are determined, the exercise data is segmented to identify each of the repetitions. This process of identifying when a particular repetition begins and ends allows the physiotherapist to obtain useful metrics such as the number of repetitions performed, or the time required to complete each repetition. A feature-guided hidden Markov model (HMM) based algorithm is developed for performing the segmentation. In a sequence of unlabelled data, motion segment candidates are found by scanning the data for velocity-based features, such as velocity peaks and zero crossings, which match the pre-determined motion templates. These segment potentials are passed into the HMM for template matching. This two-tier approach combines the speed of a velocity feature based approach, which only requires the data to be differentiated, with the accuracy of the more computationally-heavy HMM, allowing for fast and accurate segmentation. The proposed algorithms were verified experimentally on a dataset consisting of 20 healthy subjects performing rehabilitation exercises. The movement data was collected by IMUs strapped onto the hip, thigh and calf. The joint angle estimation system achieves an overall average RMS error of 4.27 cm, when compared against motion capture data. The segmentation algorithm reports 78% accuracy when the template training data comes from the same participant, and 74% for a generic template.

Longitudinal studies, where data on study subjects are collected over time, is increasingly involving multivariate longitudinal responses. Frequently, the heterogeneity observed in a multivariate longitudinal response can be attributed to underlying unobserved disease states in addition to any between-subject differences. We propose modeling such disease states using a hidden Markov model (HMM) approach and expand upon previous work, which incorporated random effects into HMMs for the analysis of univariate longitudinal data, to the setting of a multivariate longitudinal response. Multivariate longitudinal data are modeled jointly using separate but correlated random effects between longitudinal responses of mixed data types in addition to a shared underlying hidden process. We use a computationally efficient Bayesian approach via Markov chain Monte Carlo (MCMC) to fit such models. We apply this methodology to bivariate longitudinal response data from a smoking cessation clinical trial. Under these models, we examine how to incorporate a treatment effect on the disease states, as well as develop methods to classify observations by disease state and to attempt to understand patient dropout. Simulation studies were performed to evaluate the properties of such models and their applications under a variety of realistic situations.

No description available.

A segmentação dos nomes nas suas partes constitutivas é uma etapa fundamental no processo de integração de bases de dados por meio das técnicas de vinculação de registros. Esta separação dos nomes pode ser realizada de diferentes maneiras. Este estudo teve como objetivo avaliar a utilização do Modelo Escondido de Markov (HMM) na segmentação nomes e endereços de pessoas e a eficiência desta segmentação no processo de vinculação de registros. Foram utilizadas as bases do Sistema de Informações sobre Mortalidade (SIM) e do Subsistema de Informação de Procedimentos de Alta Complexidade (APAC) do estado do Rio de Janeiro no período entre 1999 a 2004. Uma metodologia foi proposta para a segmentação de nome e endereço sendo composta por oito fases, utilizando rotinas implementadas em PL/SQL e a biblioteca JAHMM, implementação na linguagem Java de algoritmos de HMM. Uma amostra aleatória de 100 registros de cada base foi utilizada para verificar a correção do processo de segmentação por meio do modelo HMM.Para verificar o efeito da segmentação do nome por meio do HMM, três processos de vinculação foram aplicados sobre uma amostra das duas bases citadas acima, cada um deles utilizando diferentes estratégias de segmentação, a saber: 1) divisão dos nomes pela primeira parte, última parte e iniciais do nome do meio; 2) divisão do nome em cinco partes; (3) segmentação segundo o HMM. A aplicação do modelo HMM como mecanismo de segmentação obteve boa concordância quando comparado com o observador humano. As diferentes estratégias de segmentação geraram resultados bastante similares na vinculação de registros, tendo a estratégia 1 obtido um desempenho pouco melhor que as demais. Este estudo sugere que a segmentação de nomes brasileiros por meio do modelo escondido de Markov não é mais eficaz do que métodos tradicionais de segmentação. / The segmentation of names into its constituent parts is a fundamental step in the integration of databases by means of record linkage techniques. This segmentation can be accomplished in different ways. This study aimed to evaluate the use of Hidden Markov Models (HMM) in the segmentation names and addresses of people and the efficiency of the segmentation on the record linkage process. Databases of the Information System on Mortality (SIM in portuguese) and Information Subsystem for High Complexity Procedures (APAC in portuguese) of the state of Rio de Janeiro between 1999 and 2004 were used. A method composed of eight stages has been proposed for segmenting the names and addresses using routines implemented in PL/SQL and a library called JAHMM, a Java implementation of HMM algorithms. A random sample of 100 records in each database was used to verify the correctness of the segmentation process using the hidden Markov model. In order to verify the effect of segmenting the names through the HMM, three record linkage process were applied on a sample of the aforementioned databases, each of them using a different segmentation strategy, namely: 1) dividing the name into first name , last name, and middle initials; 2) division of the name into five parts; 3) segmentation by HMM. The HMM segmentation mechanism was in good agreement when compared to a human observer. The three linkage processes produced very similar results, with the first strategy performing a little better than the others. This study suggests that the segmentation of Brazilian names by means of HMM is not more efficient than the traditional segmentation methods.

A segmentação dos nomes nas suas partes constitutivas é uma etapa fundamental no processo de integração de bases de dados por meio das técnicas de vinculação de registros. Esta separação dos nomes pode ser realizada de diferentes maneiras. Este estudo teve como objetivo avaliar a utilização do Modelo Escondido de Markov (HMM) na segmentação nomes e endereços de pessoas e a eficiência desta segmentação no processo de vinculação de registros. Foram utilizadas as bases do Sistema de Informações sobre Mortalidade (SIM) e do Subsistema de Informação de Procedimentos de Alta Complexidade (APAC) do estado do Rio de Janeiro no período entre 1999 a 2004. Uma metodologia foi proposta para a segmentação de nome e endereço sendo composta por oito fases, utilizando rotinas implementadas em PL/SQL e a biblioteca JAHMM, implementação na linguagem Java de algoritmos de HMM. Uma amostra aleatória de 100 registros de cada base foi utilizada para verificar a correção do processo de segmentação por meio do modelo HMM.Para verificar o efeito da segmentação do nome por meio do HMM, três processos de vinculação foram aplicados sobre uma amostra das duas bases citadas acima, cada um deles utilizando diferentes estratégias de segmentação, a saber: 1) divisão dos nomes pela primeira parte, última parte e iniciais do nome do meio; 2) divisão do nome em cinco partes; (3) segmentação segundo o HMM. A aplicação do modelo HMM como mecanismo de segmentação obteve boa concordância quando comparado com o observador humano. As diferentes estratégias de segmentação geraram resultados bastante similares na vinculação de registros, tendo a estratégia 1 obtido um desempenho pouco melhor que as demais. Este estudo sugere que a segmentação de nomes brasileiros por meio do modelo escondido de Markov não é mais eficaz do que métodos tradicionais de segmentação. / The segmentation of names into its constituent parts is a fundamental step in the integration of databases by means of record linkage techniques. This segmentation can be accomplished in different ways. This study aimed to evaluate the use of Hidden Markov Models (HMM) in the segmentation names and addresses of people and the efficiency of the segmentation on the record linkage process. Databases of the Information System on Mortality (SIM in portuguese) and Information Subsystem for High Complexity Procedures (APAC in portuguese) of the state of Rio de Janeiro between 1999 and 2004 were used. A method composed of eight stages has been proposed for segmenting the names and addresses using routines implemented in PL/SQL and a library called JAHMM, a Java implementation of HMM algorithms. A random sample of 100 records in each database was used to verify the correctness of the segmentation process using the hidden Markov model. In order to verify the effect of segmenting the names through the HMM, three record linkage process were applied on a sample of the aforementioned databases, each of them using a different segmentation strategy, namely: 1) dividing the name into first name , last name, and middle initials; 2) division of the name into five parts; 3) segmentation by HMM. The HMM segmentation mechanism was in good agreement when compared to a human observer. The three linkage processes produced very similar results, with the first strategy performing a little better than the others. This study suggests that the segmentation of Brazilian names by means of HMM is not more efficient than the traditional segmentation methods.

Dans cette thèse, nous nous intéressons à une classe particulière de modèles stochastiques de croissance de plantes structure-fonction à laquelle appartient le modèle GreenLab. L’objectif est double. En premier lieu, il s’agit d’étudier les processus stochastiques sous-jacents à l’organogenèse. Un nouveau cadre de travail combinatoire reposant sur l’utilisation de grammaires formelles a été établi dans le but d’étudier la distribution des nombres d’organes ou plus généralement des motifs dans la structure des plantes. Ce travail a abouti `a la mise en place d’une méthode symbolique permettant le calcul de distributions associées `a l’occurrence de mots dans des textes générés aléatoirement par des L-systèmes stochastiques. La deuxième partie de la thèse se concentre sur l’estimation des paramètres liés au processus de création de biomasse par photosynthèse et de son allocation. Le modèle de plante est alors écrit sous la forme d’un modèle de Markov caché et des méthodes d’inférence bayésienne sont utilisées pour résoudre le problème. / This PhD focuses on a particular class of stochastic models of functional-structural plant growth to which the GreenLab model belongs. First, the stochastic processes underlying the organogenesis phenomenon were studied. A new combinatorial framework based on formal grammars was built to study the distributions of the number of organs or more generally patterns in plant structures. This work led to the creation of a symbolic method which allows the computation of the distributions associated to word occurrences in random texts generated by stochastic L-systems. The second part of the PhD tackles the estimation of the parameters of the functional submodel (linked to the creation of biomass by photosynthesis and its allocation). For this purpose, the plant model was described by a hidden Markov model and Bayesian inference methods were used to solve the problem.

With the growing phenomenon of an aging population, an increasing numberof older people are living alone for domestic and social reasons. Based on thisfact, falling accidents become one of the most important factors in threateningthe lives of the elderly. Therefore, it is necessary to set up an application to de-tect the daily activities of the elderly. However, falling detection is difficult to recognize because the "falling" motion is an instantaneous motion and easy to confuse with others.In this thesis, three data mining methods were employed on wearable sensors' value; first which contains the continuous data set concerning eleven activities of daily living, and then an analysis of the different results was performed. Not only could the fall be detected, but other activities could also be classified. In detail, three methods including Back Propagation Neural Network, Support Vector Machine and Hidden Markov Model are applied separately to train the data set.What highlights the project is that a new  idea is put forward, the aim of which is to design a methodology of accurate classification in the time-series data set. The proposed approach, which includes obtaining of classifier parts and the application parts allows the generalization of classification. The preliminary results indicate that the new method achieves the high accuracy of classification,and significantly performs better than other data mining methods in this experiment.

Master of Science / Department of Computing and Information Sciences / David A. Gustafson / The use of hidden Markov models in autism recognition and analysis is investigated. More specifically, we would like to be able to determine a person's level of autism (AS, HFA, MFA, LFA) using hidden Markov models trained on observations taken from a subject's behavior in an experiment. A preliminary model is described that includes the three mental states self-absorbed, attentive, and join-attentive. Futhermore, observations are included that are more or less indicative of each of these states. Two experiments are described, the first on a single subject and the second on two subjects. Data was collected from one individual in the second experiment and observations were prepared for input to hidden Markov models and the resulting hidden Markov models were studied. Several questions subsequently arose and tests, written in Java using the JaHMM hidden Markov model tool- kit, were conducted to learn more about the hidden Markov models being used as autism recognizers and the training algorithms being used to train them. The tests are described along with the corresponding results and implications. Finally, suggestions are made for future work. It turns out that we aren't yet able to produce hidden Markov models that are indicative of a persons level of autism and the problems encountered are discussed and the suggested future work is intended to further investigate the use of hidden Markov models in autism recognition.

Network-based attacks against computer systems are a common and increasing problem. Attackers continue to increase the sophistication and complexity of their attacks with the goal of removing sensitive data or disrupting operations. Attack detection technology works very well for the detection of known attacks using a signature-based intrusion detection system. However, attackers can utilize attacks that are undetectable to those signature-based systems whether they are truly new attacks or modified versions of known attacks. Anomaly-based intrusion detection systems approach the problem of attack detection by detecting when traffic differs from a learned baseline. In the case of this research, the focus was on a relatively new area known as payload anomaly detection. In payload anomaly detection, the system focuses exclusively on the payload of packets and learns the normal contents of those payloads. When a payload's contents differ from the norm, an anomaly is detected and may be a potential attack. A risk with anomaly-based detection mechanisms is they suffer from high false positive rates which reduce their effectiveness. This research built upon previous research in payload anomaly detection by combining multiple techniques of detection in a layered approach. The layers of the system included a high-level navigation layer, a request payload analysis layer, and a request-response analysis layer. The system was tested using the test data provided by some earlier payload anomaly detection systems as well as new data sets. The results of the experiments showed that by combining these layers of detection into a single system, there were higher detection rates and lower false positive rates.