Design and Material Selection of High-Speed Rotating Electrical Machines

The increasing emphasis on energy efficiency is starting to yield results in the reduction in greenhouse gas emissions; however, the effort is still far from sufficient. Therefore, new technical solutions that will enhance the efficiency of power generation systems are required to maintain the sustainable growth rate, without spoiling the environment. A reduction in greenhouse gas emissions is only possible with new low-carbon technologies, which enable high efficiencies. The role of the rotating electrical machine development is significant in the reduction of global emissions. A high proportion of the produced and consumed electrical energy is related to electrical machines. One of the technical solutions that enables high system efficiency on both the energy production and consumption sides is high-speed electrical machines. This type of electrical machines has a high system overall efficiency, a small footprint, and a high power density compared with conventional machines. Therefore, high-speed electrical machines are favoured by the manufacturers producing, for example, microturbines, compressors, gas compression applications, and air blowers. High-speed machine technology is challenging from the design point of view, and a lot of research is in progress both in academia and industry regarding the solution development. The solid technical basis is of importance in order to make an impact in the industry considering the climate change. This work describes the multidisciplinary design principles and material development in high-speed electrical machines. First, high-speed permanent magnet synchronous machines with six slots, two poles, and tooth-coil windings are discussed in this doctoral dissertation. These machines have unique features, which help in solving rotordynamic problems and reducing the manufacturing costs. Second, the materials for the high-speed machines are discussed in this work. The materials are among the key limiting factors in electrical machines, and to overcome this limit, an in-depth analysis of the material properties and behavior is required. Moreover, high-speed machines are sometimes operating in a harsh environment because they need to be as close as possible to the rotating tool and fully exploit their advantages. This sets extra requirements for the materials applied.

Aspergillus flavus infections in Galleria mellonella : a pathogen-host model system for the study of emerging diseases

To study emerging diseases, I employed a model pathogen-host system involving infections of insect larvae with the opportunistic fungus Aspergillus flavus, providing insight into three mechanisms ofpathogen evolution namely de novo mutation, genome decay, and virulence factoracquisition In Chapter 2 as a foundational experiment, A. flavus was serially propagated through insects to study the evolution of an opportunistic pathogen during repeated exposure to a single host. While A. flavus displayed de novo phenotypic alterations, namely decreased saprobic capacity, analysis of genotypic variation in Chapter 3 signified a host-imposed bottleneck on the pathogen population, emphasizing the host's role in shaping pathogen population structure. Described in Chapter 4, the serial passage scheme enabled the isolation of an A. flavus cysteine/methionine auxotroph with characteristics reminiscent of an obligate insect pathogen, suggesting that lost biosynthetic capacity may restrict host range based on nutrient availability and provide selection pressure for further evolution. As outlined in Chapter 6, cysteine/methionine auxotrophy had the pleiotrophic effect of increasing virulence factor production, affording the slow-growing auxotroph with a modified pathogenic strategy such that virulence was not reduced. Moreover in Chapter 7, transformation with a virulence factor from a facultative insect pathogen failed to increase virulence, demonstrating the necessity of an appropriate genetic background for virulence factor acquisition to instigate pathogen evolution.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Src family kinases are required for limb trajectory selection by spinal motor axons

Signal relay by guidance receptors at the axonal growth cone is a process essential for the assembly of a functional nervous system. We investigated the in vivo function of Src family kinases (SFKs) as growth cone guidance signaling intermediates in the context of spinal lateral motor column (LMC) motor axon projection toward the ventral or dorsal limb mesenchyme. Using in situ mRNA detection we determined that Src and Fyn are expressed in LMC motor neurons of chick and mouse embryos at the time of limb trajectory selection. Inhibition of SFK activity by C-terminal Src kinase (Csk) overexpression in chickLMCaxons using in ovo electroporation resulted inLMC axons selecting the inappropriate dorsoventral trajectory within the limb mesenchyme, with medial LMC axon projecting into the dorsal and ventral limb nerve with apparently random incidence. We also detected LMC axon trajectory choice errors in Src mutant mice demonstrating a nonredundant role for Src in motor axon guidance in agreement with gain and loss of Src function in chickLMCneurons which led to the redirection ofLMCaxons. Finally, Csk-mediated SFK inhibition attenuated the retargeting ofLMCaxons caused by EphA or EphB over-expression, implying the participation of SFKs in Eph-mediated LMC motor axon guidance. In summary, our findings demonstrate that SFKs are essential for motor axon guidance and suggest that they play an important role in relaying ephrin:Eph signals that mediate the selection of motor axon trajectory in the limb.

Barriers of evidence based policy making in iran's health system

La formation des sociétés fondées sur la connaissance, le progrès de la technologie de communications et un meilleur échange d'informations au niveau mondial permet une meilleure utilisation des connaissances produites lors des décisions prises dans le système de santé. Dans des pays en voie de développement, quelques études sont menées sur des obstacles qui empêchent la prise des décisions fondées sur des preuves (PDFDP) alors que des études similaires dans le monde développé sont vraiment rares. L'Iran est le pays qui a connu la plus forte croissance dans les publications scientifiques au cours de ces dernières années, mais la question qui se pose est la suivante : quels sont les obstacles qui empêchent l'utilisation de ces connaissances de même que celle des données mondiales? Cette étude embrasse trois articles consécutifs. Le but du premier article a été de trouver un modèle pour évaluer l'état de l'utilisation des connaissances dans ces circonstances en Iran à l’aide d'un examen vaste et systématique des sources suivie par une étude qualitative basée sur la méthode de la Grounded Theory. Ensuite au cours du deuxième et troisième article, les obstacles aux décisions fondées sur des preuves en Iran, sont étudiés en interrogeant les directeurs, les décideurs du secteur de la santé et les chercheurs qui travaillent à produire des preuves scientifiques pour la PDFDP en Iran. Après avoir examiné les modèles disponibles existants et la réalisation d'une étude qualitative, le premier article est sorti sous le titre de «Conception d'un modèle d'application des connaissances». Ce premier article sert de cadre pour les deux autres articles qui évaluent les obstacles à «pull» et «push» pour des PDFDP dans le pays. En Iran, en tant que pays en développement, les problèmes se situent dans toutes les étapes du processus de production, de partage et d’utilisation de la preuve dans la prise de décision du système de santé. Les obstacles qui existent à la prise de décision fondée sur des preuves sont divers et cela aux différents niveaux; les solutions multi-dimensionnelles sont nécessaires pour renforcer l'impact de preuves scientifiques sur les prises de décision. Ces solutions devraient entraîner des changements dans la culture et le milieu de la prise de décision afin de valoriser la prise de décisions fondées sur des preuves. Les critères de sélection des gestionnaires et leur nomination inappropriée ainsi que leurs remplaçants rapides et les différences de paiement dans les secteurs public et privé peuvent affaiblir la PDFDP de deux façons : d’une part en influant sur la motivation des décideurs et d'autre part en détruisant la continuité du programme. De même, tandis que la sélection et le remplacement des chercheurs n'est pas comme ceux des gestionnaires, il n'y a aucun critère pour encourager ces deux groupes à soutenir le processus décisionnel fondés sur des preuves dans le secteur de la santé et les changements ultérieurs. La sélection et la promotion des décideurs politiques devraient être basées sur leur performance en matière de la PDFDP et les efforts des universitaires doivent être comptés lors de leurs promotions personnelles et celles du rang de leur institution. Les attitudes et les capacités des décideurs et des chercheurs devraient être encouragés en leur donnant assez de pouvoir et d’habiliter dans les différentes étapes du cycle de décision. Cette étude a révélé que les gestionnaires n'ont pas suffisamment accès à la fois aux preuves nationales et internationales. Réduire l’écart qui sépare les chercheurs des décideurs est une étape cruciale qui doit être réalisée en favorisant la communication réciproque. Cette question est très importante étant donné que l'utilisation des connaissances ne peut être renforcée que par l'étroite collaboration entre les décideurs politiques et le secteur de la recherche. Dans ce but des programmes à long terme doivent être conçus ; la création des réseaux de chercheurs et de décideurs pour le choix du sujet de recherche, le classement des priorités, et le fait de renforcer la confiance réciproque entre les chercheurs et les décideurs politiques semblent être efficace.

Applying Data mining using Statistical Techniques for Career Selection

For years, choosing the right career by monitoring the trends and scope for different career paths have been a requirement for all youngsters all over the world. In this paper we provide a scientific, data mining based method for job absorption rate prediction and predicting the waiting time needed for 100% placement, for different engineering courses in India. This will help the students in India in a great deal in deciding the right discipline for them for a bright future. Information about passed out students are obtained from the NTMIS ( National technical manpower information system ) NODAL center in Kochi, India residing in Cochin University of science and technology

Developing a Child Friendly Text-to-Speech System

This paper discusses the implementation details of a child friendly, good quality, English text-to-speech (TTS) system that is phoneme-based, concatenative, easy to set up and use with little memory. Direct waveform concatenation and linear prediction coding (LPC) are used. Most existing TTS systems are unit-selection based, which use standard speech databases available in neutral adult voices.Here reduced memory is achieved by the concatenation of phonemes and by replacing phonetic wave files with their LPC coefficients. Linguistic analysis was used to reduce the algorithmic complexity instead of signal processing techniques. Sufficient degree of customization and generalization catering to the needs of the child user had been included through the provision for vocabulary and voice selection to suit the requisites of the child. Prosody had also been incorporated. This inexpensive TTS systemwas implemented inMATLAB, with the synthesis presented by means of a graphical user interface (GUI), thus making it child friendly. This can be used not only as an interesting language learning aid for the normal child but it also serves as a speech aid to the vocally disabled child. The quality of the synthesized speech was evaluated using the mean opinion score (MOS).

Improved Biometric Authentication System Using Multimodal Cue Integration

Biometrics is an efficient technology with great possibilities in the area of security system development for official and commercial applications. The biometrics has recently become a significant part of any efficient person authentication solution. The advantage of using biometric traits is that they cannot be stolen, shared or even forgotten. The thesis addresses one of the emerging topics in Authentication System, viz., the implementation of Improved Biometric Authentication System using Multimodal Cue Integration, as the operator assisted identification turns out to be tedious, laborious and time consuming. In order to derive the best performance for the authentication system, an appropriate feature selection criteria has been evolved. It has been seen that the selection of too many features lead to the deterioration in the authentication performance and efficiency. In the work reported in this thesis, various judiciously chosen components of the biometric traits and their feature vectors are used for realizing the newly proposed Biometric Authentication System using Multimodal Cue Integration. The feature vectors so generated from the noisy biometric traits is compared with the feature vectors available in the knowledge base and the most matching pattern is identified for the purpose of user authentication. In an attempt to improve the success rate of the Feature Vector based authentication system, the proposed system has been augmented with the user dependent weighted fusion technique.

User Profile Management and Selection in Context-Aware Service Platforms for Networks Beyond 3G

In recent years, progress in the area of mobile telecommunications has changed our way of life, in the private as well as the business domain. Mobile and wireless networks have ever increasing bit rates, mobile network operators provide more and more services, and at the same time costs for the usage of mobile services and bit rates are decreasing. However, mobile services today still lack functions that seamlessly integrate into users’ everyday life. That is, service attributes such as context-awareness and personalisation are often either proprietary, limited or not available at all. In order to overcome this deficiency, telecommunications companies are heavily engaged in the research and development of service platforms for networks beyond 3G for the provisioning of innovative mobile services. These service platforms are to support such service attributes. Service platforms are to provide basic service-independent functions such as billing, identity management, context management, user profile management, etc. Instead of developing own solutions, developers of end-user services such as innovative messaging services or location-based services can utilise the platform-side functions for their own purposes. In doing so, the platform-side support for such functions takes away complexity, development time and development costs from service developers. Context-awareness and personalisation are two of the most important aspects of service platforms in telecommunications environments. The combination of context-awareness and personalisation features can also be described as situation-dependent personalisation of services. The support for this feature requires several processing steps. The focus of this doctoral thesis is on the processing step, in which the user’s current context is matched against situation-dependent user preferences to find the matching user preferences for the current user’s situation. However, to achieve this, a user profile management system and corresponding functionality is required. These parts are also covered by this thesis. Altogether, this thesis provides the following contributions: The first part of the contribution is mainly architecture-oriented. First and foremost, we provide a user profile management system that addresses the specific requirements of service platforms in telecommunications environments. In particular, the user profile management system has to deal with situation-specific user preferences and with user information for various services. In order to structure the user information, we also propose a user profile structure and the corresponding user profile ontology as part of an ontology infrastructure in a service platform. The second part of the contribution is the selection mechanism for finding matching situation-dependent user preferences for the personalisation of services. This functionality is provided as a sub-module of the user profile management system. Contrary to existing solutions, our selection mechanism is based on ontology reasoning. This mechanism is evaluated in terms of runtime performance and in terms of supported functionality compared to other approaches. The results of the evaluation show the benefits and the drawbacks of ontology modelling and ontology reasoning in practical applications.

Attentional Selection in Object Recognition

A key problem in object recognition is selection, namely, the problem of identifying regions in an image within which to start the recognition process, ideally by isolating regions that are likely to come from a single object. Such a selection mechanism has been found to be crucial in reducing the combinatorial search involved in the matching stage of object recognition. Even though selection is of help in recognition, it has largely remained unsolved because of the difficulty in isolating regions belonging to objects under complex imaging conditions involving occlusions, changing illumination, and object appearances. This thesis presents a novel approach to the selection problem by proposing a computational model of visual attentional selection as a paradigm for selection in recognition. In particular, it proposes two modes of attentional selection, namely, attracted and pay attention modes as being appropriate for data and model-driven selection in recognition. An implementation of this model has led to new ways of extracting color, texture and line group information in images, and their subsequent use in isolating areas of the scene likely to contain the model object. Among the specific results in this thesis are: a method of specifying color by perceptual color categories for fast color region segmentation and color-based localization of objects, and a result showing that the recognition of texture patterns on model objects is possible under changes in orientation and occlusions without detailed segmentation. The thesis also presents an evaluation of the proposed model by integrating with a 3D from 2D object recognition system and recording the improvement in performance. These results indicate that attentional selection can significantly overcome the computational bottleneck in object recognition, both due to a reduction in the number of features, and due to a reduction in the number of matches during recognition using the information derived during selection. Finally, these studies have revealed a surprising use of selection, namely, in the partial solution of the pose of a 3D object.

Model Selection in Summary Evaluation

A difficulty in the design of automated text summarization algorithms is in the objective evaluation. Viewing summarization as a tradeoff between length and information content, we introduce a technique based on a hierarchy of classifiers to rank, through model selection, different summarization methods. This summary evaluation technique allows for broader comparison of summarization methods than the traditional techniques of summary evaluation. We present an empirical study of two simple, albeit widely used, summarization methods that shows the different usages of this automated task-based evaluation system and confirms the results obtained with human-based evaluation methods over smaller corpora.

Feature Selection for Face Detection

We present a new method to select features for a face detection system using Support Vector Machines (SVMs). In the first step we reduce the dimensionality of the input space by projecting the data into a subset of eigenvectors. The dimension of the subset is determined by a classification criterion based on minimizing a bound on the expected error probability of an SVM. In the second step we select features from the SVM feature space by removing those that have low contributions to the decision function of the SVM.

Recommender System Based on Consumer Product Reviews

Consumer reviews, opinions and shared experiences in the use of a product is a powerful source of information about consumer preferences that can be used in recommender systems. Despite the importance and value of such information, there is no comprehensive mechanism that formalizes the opinions selection and retrieval process and the utilization of retrieved opinions due to the difficulty of extracting information from text data. In this paper, a new recommender system that is built on consumer product reviews is proposed. A prioritizing mechanism is developed for the system. The proposed approach is illustrated using the case study of a recommender system for digital cameras

Biased selection within the social health insurance market in Colombia

Las regulaciones como primaje comunitario, paquetes estandarizados y afiliación abierta, orientadas a reducir el impacto de las fallas en los mercados de seguros, tienen un efecto limitado puesto que abren espacio a la selección sesgada. A partir de 1993, el sistema de seguridad social en salud en Colombia fue reformado hacia un enfoque de mercado con la expectativa de mejorar el desempeño de los monopolios preexistentes exponiéndolos a la competencia de nuevos entrantes. La hipótesis que se maneja en el trabajo es que las fallas de mercado pueden llevar a selección sesgada favoreciendo a los nuevos entrantes. Se analizaron dos encuestas de hogares utilizando el estado de salud auto reportado y la presencia de enfermedad crónica como indicadores prospectivos del riesgo de los afiliados. Se encuentra que hay selección sesgada, llevando a selección adversa entre los aseguradores preexistentes, y a selección favorable entre los nuevos entrantes. Este patrón se observa en 1997 y se incrementa en el 2003. Aunque las entidades preexistentes son entidades públicas, y su tamaño disminuyó sustancialmente entre estos años, se analizan sus implicaciones fiscales en términos de financiación adicional por parte del gobierno.