Made available in DSpace on 2016-08-29T15:33:14Z (GMT). No. of bitstreams: 1 tese_4163_.pdf: 479699 bytes, checksum: 04f01a137084c0859b4494de6db8b3ac (MD5) Previous issue date: 2011-02-23 / Classificadores do tipo máquina de vetores de suporte (SVM) são atualmente considerados uma das técnicas mais poderosas para se resolver problemas de classificação com duas classes. Para aumentar o desempenho alcançado por classificadores SVM individuais, uma abordagem bem estabelecida é usar uma combinação de SVMs, a qual corresponde a um conjunto de classificadores SVMs que são, simultaneamente, individualmente precisos e coletivamente divergentes em suas decisões. Este trabalho propõe uma abordagem para se criar combinações de SVMs, baseada em um processo de três estágios. Inicialmente, são usadas execuções complementares de uma busca baseada em algoritmos genéticos (GEFS), com o objetivo de investigar globalmente o espaço de características para definir um conjunto de subconjuntos de características. Em seguida, para cada um desses subconjuntos de características definidos, uma SVM que usa parâmetros otimizados é construída. Por fim, é empregada uma busca local com o objetivo de selecionar um subconjunto otimizado dessas SVMs, e assim formar a combinação de SVMs que é finalmente produzida. Os experimentos foram realizados num contexto de detecção de defeitos em máquinas industriais. Foram usados 2000 exemplos de sinais de vibração de moto bombas instaladas em plataformas de petróleo. Os experimentos realizados mostram que o método proposto para se criar combinação de SVMs apresentou um desempenho superior em comparação a outras abordagens de classificação bem estabelecidas.

Made available in DSpace on 2014-06-11T19:35:40Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-08-11Bitstream added on 2014-06-13T19:05:49Z : No. of bitstreams: 1 almeida_jlcn_dr_guara.pdf: 2749824 bytes, checksum: 914ada61e3a445e7cda7ed446f711961 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / As máquinas síncronas de ímãs permanentes têm a vantagem de não consumir energia para a criação do campo magnético no rotor. O propósito específico desta Tese é o projeto, a construção e a avaliação de uma máquina com grande número de polos, de enrolamentos concentrados, com ímãs permanentes inseridos no interior do rotor, com duas formas geométricas diferentes: um com ímãs alinhados e a outra com ímãs inclinados axialmente. As justificativas para este estudo foram a possibilidade de eliminação de multiplicadores mecânicos de velocidade e sistemas auxiliares de excitação em centrais geradoras de energia. O projeto usou um motor de indução trifásico com rotor gaiola de esquilo de 250W de 12 polos. Partindo dos próprios dados construtivos do estator, calculou-se a densidade de fluxo magnético, especificando os ímãs permanentes de neodímio. No desenvolvimento foi construído um novo rotor modular e simples. Posteriormente, a máquina foi avaliada em laboratório com o objetivo de verificar a quantidade e a qualidade da energia com a máquina atuando como gerador. Por último, os resultados encontrados na avaliação da máquina funcionando como gerador com ímãs alinhados revelaram distorção harmônica total (DHT) 16%, capacidade de geração 475W a 221V – 60Hz e como aspecto desfavorável o alto torque de travamento. Com ímãs inclinados, DHT de 7 a 8%, capacidade de geração de 236W a 195V – 60Hz e baixo torque de travamento. Este trabalho mostra que os ímãs inclinados modularmente são uma opção de máquinas elétricas com satisfatório desempenho da máquina como gerador de energia com razoável qualidade no contexto de geração distribuída de pequena potência. Além das condições técnicas têm-se o baixo custo e a manutenção reduzida do gerador com ímãs inclinados em relação aos geradores síncronos convencionais / Synchronous machines of permanent magnets present the advantage of no power consumption for the creation of the magnetic field in the rotor. The specific purpose of this thesis is the project, its construction and the evaluation of a multi-pole of concentrated coils with permanent magnets inserted in the rotor by two different geometrical forms: aligned and skewed magnets. The vindications for this study were the possibility of eliminating both the mechanical speed reducers and the excitation energy systems in electrical powerplants. The project used a three-phase induction motor (squirrel cage rotor type) of 250W and 12 poles. Starting from the original stator constructive data, we proceeded the calculi of the magnetic flux density to determine the remanent flux of the neodymium permanent magnets. In the development of the work, a new simple and modular rotor was constructed. Subsequently, the machine was evaluated in a laboratory with the purpose of checking the quantity and quality of energy produced operating as a generator. The results obtained in the evaluation of the motor operating as a generator equipped with aligned magnets were 16% of total harmonic distortion (THD), with a generating capacity of 475W at 221V – 60 Hz, though with an adverse aspect of needing a high inertial torque (cogging torque). The machine working with skewed magnets produced 7 and 8% of total harmonic distortion (THD), with a generating capacity of 236W at 195V – 60 Hz and a low cogging torque. This work demonstrated that the modular skewed magnet is an option of electrical machines presenting satisfactory performance as a generator with a reasonable quality in the context of small distributed power generation. Besides the technical conditions, it should also be considered the low cost and reduced maintenance of skewed magnets in relation to the conventional synchronous generators

The main aim of this work was the machining by hard turning of surfaces with optical surface quality. A numerical target had been set as a surface roughness Ra = 10nm. It has been shown that achieving roughness of that magnitude by hard turning is possible. Individual work pieces exhibited the desired surface properties for short lengths at a time, but it proved to be very difficult to achieve these surfaces consistently and over longer cuts. The factors influencing the surface roughness were identified as tool defects and machine vibration in addition to the standard cutting parameters and choice of cutting tool. A model of surface generation in hard turning has been developed and good correlation between simulated and experimentally determined surface roughnesses was achieved. By introducing a material partition equation which determines the proportional contribution of material removal mechanisms in the undeformed chip a comprehensive method for assessing the contributing factors in material removal was developed. While it has been shown that surfaces in hard turning are almost exclusively generated by chip removal and plastic deformation the developed model is versatile enough to include elastic deformation of the work piece. With the help of the model of surface generation in hard turning it has been possible to attribute magnitudes of the influencing factors with respect to the cutting parameters such as feed rate and tool corner radius, and the main disturbances - tool defects and machine vibration. From this conclusions were drawn on the requirements for machine tools and cutting tools, which will need to be realised to make ultra-precision hard turning of surfaces of optical quality a feasible manufacturing process.

Many text extraction and classification systems have been developed for English and other international languages; most of the languages are based on Roman letters. However, Arabic language is one of the difficult languages which have special rules and morphology. Not many systems have been developed for Arabic text categorization. Arabic language is one of the Semitic languages with morphology that is more complicated than English. Due to its complex morphology, there is a need for pre-processing routines to extract the roots of the words then classify them according to the group of acts or meaning. In this thesis, a system has been developed and tested for text classification. The system is based on two stages, the first is to extract the roots from text and the second is to classify the text according to predefined categories. The linguistic root extraction stage is composed of two main phases. The first phase is to handle removal of affixes including prefixes, suffixes and infixes. Prefixes and suffixes are removed depending on the length of the word, while checking its morphological pattern after each deduction to remove infixes. In the second phase, the root extraction algorithm is formulated to handle weak, defined, eliminated-long-vowel and two-letter geminated words, as there is a substantial great amount of irregular Arabic words in texts. Once the roots are extracted, they are checked against a predefined list of 3800 triliteral and 900 quad literal roots. Series of experiments has been conducted to improve and test the performance of the proposed algorithm. The obtained results revealed that the developed algorithm has better accuracy than the existing stemming algorithm. The second stage is the document classification stage. In this stage two non-parametric classifiers are tested, namely Artificial Neural Networks (ANN) and Support Vector Machine (SVM). The system is trained on 6 categories: culture, economy, international, local, religion and sports. The system is trained on 80% of the available data. From each category, the 10 top frequent terms are selected as features. Testing the classification algorithms has been done on the remaining 20% of the documents. The results of ANN and SVM are compared to the standard method used for text classification, the terms frequency-based method. Results show that ANN and SVM have better accuracy (80-90%) compared to the standard method (60-70%). The proposed method proves the ability to categorize the Arabic text documents into the appropriate categories with a high precision rate.

Aggressive motion can occur in clinical and elderly care settings with people suffering from dementia, mental disorders, or other conditions that affect memory. Since identifying the nature of the event can be difficult with people who have memory and communication issues, other methods to identify and record aggressive motion would be useful for care providers to reduce re-occurrences of this activity. A wearable technology approach for human activity recognition was explored in this thesis to detect aggressive movements. This approach aims to provide a means to identify the person that initiated aggressive motion and to categorize the aggressive action. The main objective of this thesis was to determine the effectiveness of smartwatch accelerometer and gyroscope sensor data for classifying aggressive and non-aggressive activities. 30 able-bodied participants donned two Microsoft Bands 2 smartwatches and performed an activity circuit of similar aggressive and non-aggressive movements. Statistical and physical features were extracted from the smartwatch sensors signals, and subsequently used by multiple classifiers to determine on a machine learning platform six performance metrics (accuracy, sensitivity, specificity, precision, F-score, Matthews correlation coefficient). This thesis demonstrated: 1) the best features for a binary classification; 2) the best and most practical machine learning classifier and feature selector model; 3) the evaluation metrics differences between unilateral smartwatch and bilateral smartwatches; 4) the most suitable machine learning algorithm for a multinomial classification.

Doctor of Philosophy / Department of Computing and Information Sciences / Doina Caragea / Successful advances in biochemical technologies have led to inexpensive, time-efficient production of massive volumes of data, DNA and protein sequences. As a result, numerous computational methods for genome annotation have emerged, including machine learning and statistical analysis approaches that practically and efficiently analyze and interpret data. Traditional machine learning approaches to genome annotation typically rely on large amounts of labeled data in order to build quality classifiers. The process of labeling data can be expensive and time consuming, as it requires domain knowledge and expert involvement. Semi-supervised learning approaches that can make use of unlabeled data, in addition to small amounts of labeled data, can help reduce the costs associated with labeling. In this context, we focus on semi-supervised learning approaches for biological sequence classification. Although an attractive concept, semi-supervised learning does not invariably work as intended. Since the assumptions made by learning algorithms cannot be easily verified without considerable domain knowledge or data exploration, semi-supervised learning is not always "safe" to use. Advantageous utilization of the unlabeled data is problem dependent, and more research is needed to identify algorithms that can be used to increase the effectiveness of semi-supervised learning, in general, and for bioinformatics problems, in particular. At a high level, we aim to identify semi-supervised algorithms and data representations that can be used to learn effective classifiers for genome annotation tasks such as cassette exon identification, splice site identification, and protein localization. In addition, one specific challenge that we address is the "data imbalance" problem, which is prevalent in many domains, including bioinformatics. The data imbalance phenomenon arises when one of the classes to be predicted is underrepresented in the data because instances belonging to that class are rare (noteworthy cases) or difficult to obtain. Ironically, minority classes are typically the most important to learn, because they may be associated with special cases, as in the case of splice site prediction. We propose two main techniques to deal with the data imbalance problem, namely a technique based on "dynamic balancing" (augmenting the originally labeled data only with positive instances during the semi-supervised iterations of the algorithms) and another technique based on ensemble approaches. The results show that with limited amounts of labeled data, semisupervised approaches can successfully leverage the unlabeled data, thereby surpassing their completely supervised counterparts. A type of semi-supervised learning, known as "transductive" learning aims to classify the unlabeled data without generalizing to new, previously not encountered instances. Theoretically, this aspect makes transductive learning particularly suitable for the task of genome annotation, in which an entirely sequenced genome is typically available, sometimes accompanied by limited annotation. We study and evaluate various transductive approaches (such as transductive support vector machines and graph based approaches) and sequence representations for the problems of cassette exon identification. The results obtained demonstrate the effectiveness of transductive algorithms in sequence annotation tasks.

abstract: Machine learning technology has made a lot of incredible achievements in recent years. It has rivalled or exceeded human performance in many intellectual tasks including image recognition, face detection and the Go game. Many machine learning algorithms require huge amount of computation such as in multiplication of large matrices. As silicon technology has scaled to sub-14nm regime, simply scaling down the device cannot provide enough speed-up any more. New device technologies and system architectures are needed to improve the computing capacity. Designing specific hardware for machine learning is highly in demand. Efforts need to be made on a joint design and optimization of both hardware and algorithm. For machine learning acceleration, traditional SRAM and DRAM based system suffer from low capacity, high latency, and high standby power. Instead, emerging memories, such as Phase Change Random Access Memory (PRAM), Spin-Transfer Torque Magnetic Random Access Memory (STT-MRAM), and Resistive Random Access Memory (RRAM), are promising candidates providing low standby power, high data density, fast access and excellent scalability. This dissertation proposes a hierarchical memory modeling framework and models PRAM and STT-MRAM in four different levels of abstraction. With the proposed models, various simulations are conducted to investigate the performance, optimization, variability, reliability, and scalability. Emerging memory devices such as RRAM can work as a 2-D crosspoint array to speed up the multiplication and accumulation in machine learning algorithms. This dissertation proposes a new parallel programming scheme to achieve in-memory learning with RRAM crosspoint array. The programming circuitry is designed and simulated in TSMC 65nm technology showing 900X speedup for the dictionary learning task compared to the CPU performance. From the algorithm perspective, inspired by the high accuracy and low power of the brain, this dissertation proposes a bio-plausible feedforward inhibition spiking neural network with Spike-Rate-Dependent-Plasticity (SRDP) learning rule. It achieves more than 95% accuracy on the MNIST dataset, which is comparable to the sparse coding algorithm, but requires far fewer number of computations. The role of inhibition in this network is systematically studied and shown to improve the hardware efficiency in learning. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2017

Pronouns are used frequently in language, and perform a range of functions. Some pronouns are used to express coreference, and others are not. Languages and genres differ in how and when they use pronouns and this poses a problem for Statistical Machine Translation (SMT) systems (Le Nagard and Koehn, 2010; Hardmeier and Federico, 2010; Novák, 2011; Guillou, 2012; Weiner, 2014; Hardmeier, 2014). Attention to date has focussed on coreferential (anaphoric) pronouns with NP antecedents, which when translated from English into a language with grammatical gender, must agree with the translation of the head of the antecedent. Despite growing attention to this problem, little progress has been made, and little attention has been given to other pronouns. The central claim of this thesis is that pronouns performing different functions in text should be handled differently by SMT systems and when evaluating pronoun translation. This motivates the introduction of a new framework to categorise pronouns according to their function: Anaphoric/cataphoric reference, event reference, extra-textual reference, pleonastic, addressee reference, speaker reference, generic reference, or other function. Labelling pronouns according to their function also helps to resolve instances of functional ambiguity arising from the same pronoun in the source language having multiple functions, each with different translation requirements in the target language. The categorisation framework is used in corpus annotation, corpus analysis, SMT system development and evaluation. I have directed the annotation and conducted analyses of a parallel corpus of English-German texts called ParCor (Guillou et al., 2014), in which pronouns are manually annotated according to their function. This provides a first step toward understanding the problems that SMT systems face when translating pronouns. In the thesis, I show how analysis of manual translation can prove useful in identifying and understanding systematic differences in pronoun use between two languages and can help inform the design of SMT systems. In particular, the analysis revealed that the German translations in ParCor contain more anaphoric and pleonastic pronouns than their English originals, reflecting differences in pronoun use. This raises a particular problem for the evaluation of pronoun translation. Automatic evaluation methods that rely on reference translations to assess pronoun translation, will not be able to provide an adequate evaluation when the reference translation departs from the original source-language text. I also show how analysis of the output of state-of-the-art SMT systems can reveal how well current systems perform in translating different types of pronouns and indicate where future efforts would be best directed. The analysis revealed that biases in the training data, for example arising from the use of “it” and “es” as both anaphoric and pleonastic pronouns in both English and German, is a problem that SMT systems must overcome. SMT systems also need to disambiguate the function of those pronouns with ambiguous surface forms so that each pronoun may be translated in an appropriate way. To demonstrate the value of this work, I have developed an automated post-editing system in which automated tools are used to construct ParCor-style annotations over the source-language pronouns. The annotations are then used to resolve functional ambiguity for the pronoun “it” with separate rules applied to the output of a baseline SMT system for anaphoric vs. non-anaphoric instances. The system was submitted to the DiscoMT 2015 shared task on pronoun translation for English-French. As with all other participating systems, the automatic post-editing system failed to beat a simple phrase-based baseline. A detailed analysis, including an oracle experiment in which manual annotation replaces the automated tools, was conducted to discover the causes of poor system performance. The analysis revealed that the design of the rules and their strict application to the SMT output are the biggest factors in the failure of the system. The lack of automatic evaluation metrics for pronoun translation is a limiting factor in SMT system development. To alleviate this problem, Christian Hardmeier and I have developed a testing regimen called PROTEST comprising (1) a hand-selected set of pronoun tokens categorised according to the different problems that SMT systems face and (2) an automated evaluation script. Pronoun translations can then be automatically compared against a reference translation, with mismatches referred for manual evaluation. The automatic evaluation was applied to the output of systems submitted to the DiscoMT 2015 shared task on pronoun translation. This again highlighted the weakness of the post-editing system, which performs poorly due to its focus on producing gendered pronoun translations, and its inability to distinguish between pleonastic and event reference pronouns.

L'essor des services et des technologies de l'information et de la communication (TIC) est généralement considéré comme le marqueur d'une dématérialisation de l'économie – phénomène grandement attendu dans le cadre des discussions sur le développement durable. Nous nous plaçons dans le débat autour de cette hypothèse en questionnant la matérialité des TIC et des services à travers le cas des services informationnels – activités de génération d'information répondant à un besoin décisionnel. Nous examinons plus en détail les services d'optimisation basés sur les réseaux de capteurs – services offerts à l'aide d'informations collectées par des capteurs intégrés dans un réseau sans fil, permettant de surveiller un processus dans le but de l'optimiser, et par là-même de diminuer ses impacts. Nous proposons ainsi une méthode intégrée d'analyse environnementale et d'éco-conception des services d'optimisation basés sur les réseaux de capteurs. Celle-ci permet de déterminer la pertinence environnementale d'un service, c'est-à-dire sa capacité à générer moins d'impacts qu'il n'en évite, mais également de le concevoir avec cette pertinence en vue. Notre apport scientifique réside dans la proposition d'un modèle d'impact pour les services informationnels et d'un modèle de cycle de vie des réseaux de capteurs, ainsi que dans le développement d'un outil d'analyse basé sur la simulation réseau. L'application de la méthode proposée au cas d'un service d'optimisation de la collecte de déchets nous permet de conclure que, si des marges de manœuvres importantes sont identifiables, la pertinence environnementale de ces services ne doit pas être considérée comme acquise, tout comme le potentiel de dématérialisation des TIC et des services. / The increasing share of economic value created by information and communication technology (ICT) and by services is generally seen as a sign of dematerialization of the economy – a step towards sustainability. This thesis is a contribution to the ongoing debate about this hypothesis. It examines the impacts of ICT and services, particularly those of information services – activities which consist of generating information required in decision-making processes. It focuses specifically on optimization services that use wireless sensor networks (WSN): services aiming at optimizing a process (thus diminishing its environmental impact) and based on information generated by a WSN. We present an integrated method that helps to analyze the impacts and ecodesigning optimization services that use WSN. This method allows for determining the environmental relevance of a service, i.e. whether it generates a smaller impact than it is helps to avoid, and for designing the service with this relevance in mind. Our scientific contribution is to formalize an environmental impact model for information services and a life cycle model for WSN, so as to develop an assessment tool based on network simulation. An integral part of this method is the development of an environmental impact model for information services as well as a life cycle model for WSN, which constitute our main scientific contribution. We implemented these by means of an assessment tool based on network simulation. The application of the proposed method to a case of an optimization service for waste collection allowed us to conclude that there is significant room for ecodesign. However, the environmental relevance of those services should not been taken for granted, nor should the dematerialization potential of ICT and services.

The character table of a finite group G is constructed by computing the eigenvectors of matrix equations determined by the centre of the group algebra. The numerical character values are expressed in algebraic form. A variant using a certain sub-algebra of the centre of the group algebra is used to ease problems associated with determining the conjugacy classes of elements of G. The simple group of order 50,232,960 and its subgroups PSL(2,17) and PSL(2,19) are constructed using general techniques. A combination of hand and machine calculation gives the character tables of the known simple groups of order < 106 excepting Sp(4,4) and PSL(2,q). The characters of the non- Abelian 2-groups of order < 2 6 are computed. Miscellaneous computations involving the symmetric group Sn are given.