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Barnabé-Lortie, Vincent. "Active Learning for One-class Classification". Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/33001.
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Active learning is a common solution for reducing labeling costs and maximizing the impact of human labeling efforts in binary and multi-class classification settings. However, when we are faced with extreme levels of class imbalance, a situation in which it is not safe to assume that we have a representative sample of the minority class, it has been shown effective to replace the binary classifiers with a one-class classifiers. In such a setting, traditional active learning methods, and many previously proposed in the literature for one-class classifiers, prove to be inappropriate, as they rely on assumptions about the data that no longer stand.
In this thesis, we propose a novel approach to active learning designed for one-class classification. The proposed method does not rely on many of the inappropriate assumptions of its predecessors and leads to more robust classification performance. The gist of this method consists of labeling, in priority, the instances considered to fit the learned class the least by previous iterations of a one-class classification model.
Throughout the thesis, we provide evidence for the merits of our method, then deepen our understanding of these merits by exploring the properties of the method that allow it to outperform the alternatives.
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Fortunato, Francesca <1990>. "High-dimensional and one-class classification". Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amsdottorato.unibo.it/8412/1/ThesisMain.pdf.
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When dealing with high-dimensional data and, in particular, when the number of attributes p is large comparatively to the sample size n, several classification methods cannot be applied. Fisher's linear discriminant rule or the quadratic discriminant one are unfeasible, as the inverse of the involved covariance matrices cannot be computed. A recent approach to overcome this problem is based on Random Projections (RPs), which have emerged as a powerful method for dimensionality reduction. In 2017, Cannings and Samworth introduced the RP method in the ensemble context to extend to the high-dimensional domain classification methods originally designed for low-dimensional data. Although the RP ensemble classifier allows improving classification accuracy, it may still include redundant information. Moreover, differently from other ensemble classifiers (e.g. Random Forest), it does not provide any insight on the actual classification importance of the input features. To account for these aspects, in the first part of this thesis, we investigate two new directions of the RP ensemble classifier. Firstly, combining the original idea of using the Multiplicative Binomial distribution as the reference model to describe and predict the ensemble accuracy and an important result on such distribution, we introduce a stepwise strategy for post-pruning (called Ensemble Selection Algorithm). Secondly, we propose a criterion (called Variable Importance in Projection) that uses the feature coefficients in the best discriminant projections to measure the variable importance in classification. In the second part, we faced the new challenges posed by the high-dimensional data in a recently emerging classification context: one-class classification. This is a special classification task, where only one class is fully known (the target class), while the information on the others is completely missing. In particular, we address this task by using Gini's transvariation probability as a measure of typicality, aimed at identifying the best boundary around the target class.
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Haskey, Stephen. "A modified One-Class-One-Network ANN architecture for dynamic phoneme adaptation". Thesis, Loughborough University, 1998. https://dspace.lboro.ac.uk/2134/12099.
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As computers begin to pervade aspects of our everyday lives, so the problem of communication from man-to-machine becomes increasingly evident. In recent years, there has been a concerted interest in speech recognition offering a user to communicate freely with a machine. However, this deceptively simple means for exchanging information is in fact extremely complex. A single utterance can contain a wealth of varied information concerning the speaker's gender, age, dialect and mood. Numerous subtle differences such as intonation, rhythm and stress further add to the complexity, increasing the variability between inter- and intra-speaker utterances. These differences pose an enormous problem, especially for a multi-user system since it is impractical to train for every variation of every utterance from every speaker. Consequently adaptation is of great importance, allowing a system with limited knowledge to dynamically adapt towards a new speakers characteristics. A new modified artificial neural network (ANN) was proposed incorporating One-Class-OneNetwork (OCON) subnet architectures connected via a common front-end adaptation layer. Using vowel phonemes from the TIMIT speech database, the adaptation was concentrated on neurons within the front-end layer, resulting in only information common to all classes, primarily speaker characteristics, being adapted. In addition, this prevented new utterances from interfering with phoneme unique information in the corresponding OCON subnets. Hence a more efficient adaptation procedure was created which, after adaptation towards a single class, also aided in the recognition of the remaining classes within the network. Compared with a conventional multi-layer perceptron network, results for inter- and intraspeaker adaptation showed an equally marked improvement for the recognition of adapted phonemes during both full neuron and front-layer neuron adaptation within the new modified architecture. When testing the effects of adaptation on the remaining unadapted vowel phonemes, the modified architecture (allowing only the neurons in the front-end layer to adapt) yielded better results than the modified architecture allowing full neuron adaptation. These results highlighted the storing of speaker information, common to all classes, in the front-end layer allowing efficient inter- and intra-speaker dynamic adaptation.
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Tran, Quang Duc. "One-class classification : an approach to handle class imbalance in multimodal biometric authentication". Thesis, City, University of London, 2014. http://openaccess.city.ac.uk/19662/.
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Biometric verification is the process of authenticating a person‟s identity using his/her physiological and behavioural characteristics. It is well-known that multimodal biometric systems can further improve the authentication accuracy by combining information from multiple biometric traits at various levels, namely sensor, feature, match score and decision levels. Fusion at match score level is generally preferred due to the trade-off between information availability and fusion complexity. However, combining match scores poses a number of challenges, when treated as a two-class classification problem due to the highly imbalanced class distributions. Most conventional classifiers assume equally balanced classes. They do not work well when samples of one class vastly outnumber the samples of the other class. These challenges become even more significant, when the fusion is based on user-specific processing due to the limited availability of the genuine samples per user. This thesis aims at exploring the paradigm of one-class classification to advance the classification performance of imbalanced biometric data sets. The contributions of the research can be enumerated as follows. Firstly, a thorough investigation of the various one-class classifiers, including Gaussian Mixture Model, k-Nearest Neighbour, K-means clustering and Support Vector Data Description, has been provided. These classifiers are applied in learning the user-specific and user-independent descriptions for the biometric decision inference. It is demonstrated that the one-class classifiers are particularly useful in handling the imbalanced learning problem in multimodal biometric authentication. User-specific approach is a better alternative with respect to user-independent counterpart because it is able to overcome the so-called within-class sub-concepts problem, which arises very often in multimodal biometric systems due to the existence of user variation. Secondly, a novel adapted score fusion scheme that consists of one-class classifiers and is trained using both the genuine user and impostor samples has been proposed. This method also replaces user-independent by user-specific description to learn the characteristics of the impostor class, and thus, reducing the degree of imbalanced proportion of data for different classes. Extensive experiments are conducted on the BioSecure DS2 and XM2VTS databases to illustrate the potential of the proposed adapted score fusion scheme, which provides a relative improvement in terms of Equal Error Rate of 32% and 20% as compared to the standard sum of scores and likelihood ratio based score fusion, respectively. Thirdly, a hybrid boosting algorithm, called r-ABOC has been developed, which is capable of exploiting the natural capabilities of both the well-known Real AdaBoost and one-class classification to further improve the system performance without causing overfitting. However, unlike the conventional Real AdaBoost, the individual classifiers in the proposed schema are trained on the same data set, but with different parameter choices. This does not only generate a high diversity, which is vital to the success of r-ABOC, but also reduces the number of user-specified parameters. A comprehensive empirical study using the BioSecure DS2 and XM2VTS databases demonstrates that r-ABOC may achieve a performance gain in terms of Half Total Error Rate of up to 28% with respect to other state-of-the-art biometric score fusion techniques. Finally, a Robust Imputation based on Group Method of Data Handling (RIBG) has been proposed to handle the missing data problem in the BioSecure DS2 database. RIBG is able to provide accurate predictions of incomplete score vectors. It is observed to achieve a better performance with respect to the state-of-the-art imputation techniques, including mean, median and k-NN imputations. An important feature of RIBG is that it does not require any parameter fine-tuning, and hence, is amendable to immediate applications.
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Harper, John-Paul. "The class number one problem in function fields". Thesis, Stellenbosch : Stellenbosch University, 2003. http://hdl.handle.net/10019.1/53619.
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Thesis (MComm)--Stellenbosch University, 2003.ENGLISH ABSTRACT: In this dissertation I investigate the class number one problem in function fields. More precisely I give a survey of the current state of research into extensions of a rational function field over a finite field with principal ring of integers. I focus particularly on the quadratic case and throughout draw analogies and motivations from the classical number field situation. It was the "Prince of Mathematicians" C.F. Gauss who first undertook an in depth study of quadratic extensions of the rational numbers and the corresponding rings of integers. More recently however work has been done in the situation of function fields in which the arithmetic is very similar. I begin with an introduction into the arithmetic in function fields over a finite field and prove the analogies of many of the classical results. I then proceed to demonstrate how the algebra and arithmetic in function fields can be interpreted geometrically in terms of curves and introduce the associated geometric language. After presenting some conjectures, I proceed to give a survey of known results in the situation of quadratic function fields. I present also a few results of my own in this section. Lastly I state some recent results regarding arbitrary extensions of a rational function field with principal ring of integers and give some heuristic results regarding class groups in function fields.
AFRIKAANSE OPSOMMING: In hierdie tesis ondersoek ek die klasgetal een probleem in funksieliggame. Meer spesifiek ondersoek ek die huidige staat van navorsing aangaande uitbreidings van 'n rasionale funksieliggaam oor 'n eindige liggaam sodat die ring van heelgetalle 'n hoofidealgebied is. Ek kyk in besonder na die kwadratiese geval, en deurgaans verwys ek na die analoog in die klassieke getalleliggaam situasie. Dit was die beroemde wiskundige C.F. Gauss wat eerste kwadratiese uitbreidings van die rasionale getalle en die ooreenstemende ring van heelgetalle in diepte ondersoek het. Onlangs het wiskundiges hierdie probleme ook ondersoek in die situasie van funksieliggame oor 'n eindige liggaam waar die algebraïese struktuur baie soortgelyk is. Ek begin met 'n inleiding tot die rekenkunde in funksieliggame oor 'n eindige liggaam en bewys die analogie van baie van die klassieke resultate. Dan verduidelik ek hoe die algebra in funksieliggame geometries beskou kan word in terme van kurwes en gee 'n kort inleiding tot die geometriese taal. Nadat ek 'n paar vermoedes bespreek, gee ek 'n oorsig van wat alreeds vir quadratiese funksieliggame bewys is. In hierdie afdeling word 'n paar resultate van my eie ook bewys. Dan vermeld ek 'n paar resultate aangaande algemene uitbreidings van 'n rasionale funksieliggaam oor 'n eindige liggaam waar die van ring heelgetalle 'n hoofidealgebied is. Laastens verwys ek na 'n paar heurisitiese resultate aangaande klasgroepe in funksieliggame.
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Nguyen, Dat. "MULTIPLE-INSTANCE AND ONE-CLASS RULE-BASED ALGORITHMS". VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/3059.
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In this work we developed rule-based algorithms for multiple-instance learning and one-class learning problems, namely, the mi-DS and OneClass-DS algorithms. Multiple-Instance Learning (MIL) is a variation of classical supervised learning where there is a need to classify bags (collection) of instances instead of single instances. The bag is labeled positive if at least one of its instances is positive, otherwise it is negative. One-class learning problem is also known as outlier or novelty detection problem. One-class classifiers are trained on data describing only one class and are used in situations where data from other classes are not available, and also for highly unbalanced data sets. Extensive comparisons and statistical testing of the two algorithms show that they generate models that perform on par with other state-of-the-art algorithms.
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Kilicer, Pinar. "The CM class number one problem for curves". Thesis, Bordeaux, 2016. http://www.theses.fr/2016BORD0046/document.
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Soit E une courbe elliptique sur C ayant multiplication complexe (CM) par l’ordre maximal OK d’un corps quadratique imaginaire K. Le premier théorème principal de la multiplication complexe affirme que le corps K(j(E)), obtenu en adjoignant à K le j-invariant de E, est égal au corps de classes de Hilbert de K, confer Cox [11, Theorem 11.1]. Notons que lorsque E est définie sur Q, le corps de classes de Hilbert K(j(E)) est égal à K et le groupe des classes ClK est trivial. Se pose alors le problème de déterminer les corps quadratiques totalement imaginaires K pour lesquels la courbe elliptique à multiplication complexe par OK correspondante est définie sur Q. De façon équivalente, il s’agit de trouver tous les corps quadratiques imaginaires dont le groupe des classes est trivial. Ce problème est connu sous le nom de problème du nombre de classes 1 de Gauss et a été résolu par Heegner en 1952 [16], Baker en 1967 [2] et Stark en 1967 [41]; les corps quadratiques imaginaires dont le groupe des classes est trivial sont les corps Q(racine carrée−d), où d e {3, 4, 7, 8, 11, 19, 43, 67, 163}. Dans les années ’50, Shimura et Taniyama [39] ont généralisé le premier théorème principal de la multiplication complexe aux variétés abéliennes. On dit qu’une variété abélienne A de dimension g a multiplication complexe si son anneau d’endomorphismes contient un ordre d’un corps CM de degré 2g. Soit K un corps CM de degré 2g et d’ordre maximal OK et soit un type CM de K. Soit A une variété abélienne complexe simplement polarisée de dimension g ayant multiplication complexe par OK. Le premier théorème principal de la multiplication complexe dans ce cadre affirme que le corps de classes H du corps du modules M de la variété abélienne simplement polarisée A est une extension non ramifiée du corps reflex Kr de K. De plus, le corps des classes H correspond au groupe d’idéaux I0(.r) (voir page 17) qui ne dépend que de (K,.), confer Théorème 1.5.6. Notons que le premier théorème de la multiplication complexe implique que si la variété abélienne polarisée A est définie sur Kr, le groupe des classes CM IKr/I0(.r) est trivial. Comme dans le cas des courbes elliptiques, on peut alors chercher à déterminer les couples CM (K,.) pour lesquels les variétés abéliennes correspondantes sont définies sur Kr. De fa¸con équivalente, il s’agit de déterminer les couples CM (K,.) dont le groupe des classes CM, IKr/I0(.r), est trivial. Dans cette thèse, on résout ce problème dans le cas des corps CM quartiques imaginaires (voir Chapitre 2) ainsi que dans celui des corps CM sextiques contenant un corps quadratique imaginaire (voir Chapitre 3). Enfin, on peut se demander quels sont les corps CM pour lesquels la variété abélienne simple à multiplication complexe admet Q comme corps de module. Murabayashi et Umegaki [31] ont déterminé les corps quartiques CM correspondant aux surfaces abéliennes simples à multiplication complexe de corps du module Q. Dans le chapitre 4, on détermine les corps CM sextiques correspondant aux variétés abéliennes simples à multiplication complexe de dimension 3 de corps du module QLet E be an elliptic curve over C with complex multiplication (CM) by the maximal order OK of an imaginary quadratic field K. The first main theorem of complex multiplication for elliptic curves then states that the field extension K(j(E)), obtained by adjoining the j-invariant of E to K, is equal to the Hilbert class field of K, see Theorem 11.1 in Cox [11]. Note that if E is defined over Q, then the Hilbert class field K(j(E)) is equal to K, which implies that the class group ClK is trivial. We can ask for which imaginary quadratic fields K the corresponding elliptic curve with CM by OK is defined over Q. This is equivalent to asking to find all imaginary quadratic fields with trivial class group ClK. This problem is known as Gauss’ class number one problem, which was solved by Heegner in 1952 [16], Baker in 1967 [2], and Stark in 1967 [41]. The imaginary quadratic fields with trivial class group are the fields Q(V−d) with d E {3, 4, 7, 8, 11, 19, 43, 67, 163}. In the 1950’s, Shimura and Taniyama [39] generalized the first main theorem of CM for elliptic curves to abelian varieties. We say that an abelian variety A of dimension g has CM if the endomorphism ring of A contains an order of a CM field of degree 2g. Let K be a CM field of degree 2g with maximal order OK, and let K be a CM type of K. Let A be a polarized simple abelian variety over C of dimension g that has CM by OK. Then the first main theorem of CM says that the field of moduli M of the polarized simple abelian variety A gives an unramified class field H over the reflex field Kr of K. Moreover, the class field H corresponds to the ideal group I0(?r) (see page 17), which only depends on (K,?), see Theorem 1.5.6. Note that the first main theorem of CM implies that if the polarized abelian variety A is defined over Kr, then the CM class group IKr/I0(?r) is trivial. As in the elliptic curve case, we can ask for which CM pairs (K,?) the corresponding CM abelian varieties are defined over Kr. Equivalently, we can ask for which CM pairs (K,?) the CM class group IKr/I0(?r) is trivial. In this thesis we give an answer to this problem for quartic CM fields (see Chapter 2), and for sextic CM fields containing an imaginary quadratic field (see Chapter 3). Furthermore, we can ask for which CM fields the corresponding simple CM abelian varieties have field of moduli Q. Murabayashi and Umegak [31] determined the quartic CM fields that correspond to a simple CM abelian surface with field of moduli Q. In Chapter 4, we determine the sextic CM fields that correspond to a simple CM abelian threefold with field of moduli Q
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Kaya, Hamza. "Using Social Graphs In One-class Collaborative Filtering Problem". Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12611131/index.pdf.
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One-class collaborative filtering is a special type of collaborative filtering methods that aims to deal with datasets that lack counter-examples. In this work, we introduced social networks as a new data source to the one-class collaborative filtering (OCCF) methods and sought ways to benefit from them when dealing with OCCF problems. We divided our research into two parts. In the first part, we proposed different weighting schemes based on social graphs for some well known OCCF
algorithms. One of the weighting schemes we proposed outperformed our baselines for some of the datasets we used. In the second part, we focused on the dataset differences in order to find out why our algorithm performed better on some of the datasets. We compared social graphs with the graphs of users and their neighbors generated by the k-NN algorithm. Our research showed that social graphs generated from a specialized domain better improves the recommendation performance than the social graphs generated from a more generic domain.
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Silva, Eduardo Germano da. "A one-class NIDS for SDN-based SCADA systems". reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2007. http://hdl.handle.net/10183/164632.
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Sistemas elétricos possuem grande influência no desenvolvimento econômico mundial. Dada a importância da energia elétrica para nossa sociedade, os sistemas elétricos frequentemente são alvos de intrusões pela rede causadas pelas mais diversas motivações. Para minimizar ou até mesmo mitigar os efeitos de intrusões pela rede, estão sendo propostos mecanismos que aumentam o nível de segurança dos sistemas elétricos, como novos protocolos de comunicação e normas de padronização. Além disso, os sistemas elétricos estão passando por um intenso processo de modernização, tornando-os altamente dependentes de sistemas de rede responsáveis por monitorar e gerenciar componentes elétricos. Estes, então denominados Smart Grids, compreendem subsistemas de geração, transmissão, e distribuição elétrica, que são monitorados e gerenciados por sistemas de controle e aquisição de dados (SCADA). Nesta dissertação de mestrado, investigamos e discutimos a aplicabilidade e os benefícios da adoção de Redes Definidas por Software (SDN) para auxiliar o desenvolvimento da próxima geração de sistemas SCADA. Propomos também um sistema de detecção de intrusões (IDS) que utiliza técnicas específicas de classificação de tráfego e se beneficia de características das redes SCADA e do paradigma SDN/OpenFlow. Nossa proposta utiliza SDN para coletar periodicamente estatísticas de rede dos equipamentos SCADA, que são posteriormente processados por algoritmos de classificação baseados em exemplares de uma única classe (OCC). Dado que informações sobre ataques direcionados à sistemas SCADA são escassos e pouco divulgados publicamente por seus mantenedores, a principal vantagem ao utilizar algoritmos OCC é de que estes não dependem de assinaturas de ataques para detectar possíveis tráfegos maliciosos. Como prova de conceito, desenvolvemos um protótipo de nossa proposta. Por fim, em nossa avaliação experimental, observamos a performance e a acurácia de nosso protótipo utilizando dois tipos de algoritmos OCC, e considerando eventos anômalos na rede SCADA, como um ataque de negação de serviço (DoS), e a falha de diversos dispositivos de campo.Power grids have great influence on the development of the world economy. Given the importance of the electrical energy to our society, power grids are often target of network intrusion motivated by several causes. To minimize or even to mitigate the aftereffects of network intrusions, more secure protocols and standardization norms to enhance the security of power grids have been proposed. In addition, power grids are undergoing an intense process of modernization, and becoming highly dependent on networked systems used to monitor and manage power components. These so-called Smart Grids comprise energy generation, transmission, and distribution subsystems, which are monitored and managed by Supervisory Control and Data Acquisition (SCADA) systems. In this Masters dissertation, we investigate and discuss the applicability and benefits of using Software-Defined Networking (SDN) to assist in the deployment of next generation SCADA systems. We also propose an Intrusion Detection System (IDS) that relies on specific techniques of traffic classification and takes advantage of the characteristics of SCADA networks and of the adoption of SDN/OpenFlow. Our proposal relies on SDN to periodically gather statistics from network devices, which are then processed by One- Class Classification (OCC) algorithms. Given that attack traces in SCADA networks are scarce and not publicly disclosed by utility companies, the main advantage of using OCC algorithms is that they do not depend on known attack signatures to detect possible malicious traffic. As a proof-of-concept, we developed a prototype of our proposal. Finally, in our experimental evaluation, we observed the performance and accuracy of our prototype using two OCC-based Machine Learning (ML) algorithms, and considering anomalous events in the SCADA network, such as a Denial-of-Service (DoS), and the failure of several SCADA field devices.
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Pentukar, Sai Kiran. "OCLEP+: One-Class Intrusion Detection Using Length of Patterns". Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1496147438710588.
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Yoon, Seok Ho. "Explicit class field theory : one dimensional and higher dimensional". Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/50367/.
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This thesis investigates class field theory for one dimensional fields and higher dimensional fields. For one dimensional fields we cover the cases of local fields and global fields of positive characteristic. For higher dimensional fields we study the case of higher local fields of positive characteristic. The main content of the thesis is divided into two parts. The first part solves several problems directly related to Neukirch's axiomatic class field theory method. We first prove the famous Hilbert 90 Theorem in the case of tamely ramified extensions of local fields in an explicit way. This approach can be of use in understanding the role of the ring structure as opposed to the role of multiplication only in local class field theory. Next, we prove that for every local field, its `class field theory' is unique. Lastly, we establish the Neukirch axiom for global fields of positive characteristic, which leads to a new approach to class field theory for such fields, an approach that has not appeared in the previous literature. There are two main successful directions in higher local class field theory, one by Kato and another by Fesenko. While Kato used a technical cohomological method, Fesenko generalised the Neukirch method and gave the first proof of the existence theorem. In the second part of the thesis we deal with the third method in class field theory that works in positive characteristic only, the Kawada-Satake method. We generalise the classical Kawada-Satake method to higher local fields of positive characteristic. We correct substantial mistakes in a paper of Parshin on such class field theory. We develop the first complete presentation of the theory based on the generalised Kawada-Satake method using advanced properties of topological Milnor K-groups. These advanced properties include Fesenko's theorem about relations of topological and algebraic properties of Milnor K-groups.
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Long, James Ph D. Massachusetts Institute of Technology. "Automated structural damage detection using one class machine learning". Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90062.
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Thesis: S.M., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2014.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 101-103).
Measuring and analysing the vibration of structures using sensors can help identify and detect damage, potentially prolonging the life of structures and preventing disasters. Wireless sensor systems promise to make this technology more affordable and more widely applicable. Data driven structural health monitoring methodologies take raw signals obtained from sensor networks, and process them to obtain damage sensitive features. New measurements are then compared with baselines to detect damage. Because damage-sensitive features also exhibit variation due to environmental and operational changes, these comparisons are not always straightforward and sophisticated statistical analysis is necessary in order to detect abnormal changes in the damage sensitive features. In this thesis, an automated methodology which uses the one-class support vector machine (OCSVM) for damage detection and localisation is proposed. The OCSVM is a nonparametric machine learning method which can accurately classify new data points based only on data from the baseline condition of the structure. This methodology combines feature extraction, by means of autoregressive modeling, and wavelet analysis, with statistical pattern recognition using the OCSVM. The potential for embedding this damage detection methodology at the sensor level is also discussed. Efficacy is demonstrated using real experimental data from a steel frame laboratory structure, for various damage locations and scenarios.
by James Long.
S.M.
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Wang, Tian. "Abnormal detection in video streams via one-class learning methods". Thesis, Troyes, 2014. http://www.theses.fr/2014TROY0018/document.
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La vidéosurveillance représente l’un des domaines de recherche privilégiés en vision par ordinateur. Le défi scientifique dans ce domaine comprend la mise en œuvre de systèmes automatiques pour obtenir des informations détaillées sur le comportement des individus et des groupes. En particulier, la détection de mouvements anormaux de groupes d’individus nécessite une analyse fine des frames du flux vidéo. Dans le cadre de cette thèse, la détection de mouvements anormaux est basée sur la conception d’un descripteur d’image efficace ainsi que des méthodes de classification non linéaires. Nous proposons trois caractéristiques pour construire le descripteur de mouvement : (i) le flux optique global, (ii) les histogrammes de l’orientation du flux optique (HOFO) et (iii) le descripteur de covariance (COV) fusionnant le flux optique et d’autres caractéristiques spatiales de l’image. Sur la base de ces descripteurs, des algorithmes de machine learning (machines à vecteurs de support (SVM)) mono-classe sont utilisés pour détecter des événements anormaux. Deux stratégies en ligne de SVM mono-classe sont proposées : la première est basée sur le SVDD (online SVDD) et la deuxième est basée sur une version « moindres carrés » des algorithmes SVM (online LS-OC-SVM)One of the major research areas in computer vision is visual surveillance. The scientific challenge in this area includes the implementation of automatic systems for obtaining detailed information about the behavior of individuals and groups. Particularly, detection of abnormal individual movements requires sophisticated image analysis. This thesis focuses on the problem of the abnormal events detection, including feature descriptor design characterizing the movement information and one-class kernel-based classification methods. In this thesis, three different image features have been proposed: (i) global optical flow features, (ii) histograms of optical flow orientations (HOFO) descriptor and (iii) covariance matrix (COV) descriptor. Based on these proposed descriptors, one-class support vector machines (SVM) are proposed in order to detect abnormal events. Two online strategies of one-class SVM are proposed: The first strategy is based on support vector description (online SVDD) and the second strategy is based on online least squares one-class support vector machines (online LS-OC-SVM)
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Moulton, Richard Hugh. "Clustering to Improve One-Class Classifier Performance in Data Streams". Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38030.
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The classification task requires learning a decision boundary between classes by making use of training examples from each. A potential challenge for this task is the class imbalance problem, which occurs when there are many training instances available for a single class, the majority class, and few training instances for the other, the minority class [58]. In this case, it is no longer clear how to separate the majority class from something for which we have little to no knowledge. More worrying, often the minority class is the class of interest, e.g. for detecting abnormal conditions from streaming sensor data.
The one-class classification (OCC) paradigm addresses this scenario by casting the task as learning a decision boundary around the majority class with no need for minority class instances [110]. OCC has been thoroughly investigated, e.g. [20, 60, 90, 110], and many one-class classifiers have been proposed. One approach for improving one-class classifier performance on static data sets is learning in the context of concepts: the majority class is broken down into its constituent sub-concepts and a classifier is induced over each [100].
Modern machine learning research, however, is concerned with data streams: where potentially infinite amounts of data arrive quickly and need to be processed as they arrive. In these cases it is not possible to store all of the instances in memory, nor is it practical to wait until “the end of the data stream” before learning. An example is network intrusion detection: detecting an attack on the computer network should occur as soon as practicable. Many one-class classifiers for data streams have been described in the literature, e.g. [33, 108], and it is worth investigating whether the approach of learning in the context of concepts can be successfully applied to the OCC task for data streams as well.
This thesis identifies that the idea of breaking the majority class into subconcepts to simplify the OCC problem has been demonstrated for static data sets, [100], but has not been applied in data streams. The primary contribution to the literature made by this thesis is the identification of how the majority class’s sub-concept structure can be used to improve the classification performance of streaming one-class classifiers while mitigating the challenges posed by the data stream environment. Three frameworks are developed, each using this knowledge to a different degree. These are applied with a selection of streaming one-class classifiers to both synthetic and benchmark data streams with performance compared to that of the one-class classifier learning independently. These results are analyzed and it is shown that scenarios exist where knowledge of sub-concepts can be used to improve one-class classifier performance.
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Nader, Patric. "One-class classification for cyber intrusion detection in industrial systems". Thesis, Troyes, 2015. http://www.theses.fr/2015TROY0021/document.
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La sécurité des infrastructures critiques a suscité l'attention des chercheurs récemment avec l'augmentation du risque des cyber-attaques et des menaces terroristes contre ces systèmes. La majorité des infrastructures est contrôlée par des systèmes SCADA (Supervisory Control And Data Acquisition) permettant le contrôle à distance des processus industriels, comme les réseaux électriques, le transport de gaz, la distribution d'eau potable, les centrales nucléaires, etc. Les systèmes traditionnels de détection d'intrusions sont incapables de détecter les nouvelles attaques ne figurant pas dans leurs bases de données. L'objectif de cette thèse est d'apporter une aide supplémentaire à ces systèmes pour assurer une meilleure protection contre les cyber-attaques.La complexité et la diversité des attaques rendent leur modélisation difficile. Pour surmonter cet obstacle, nous utilisons des méthodes d'apprentissage statistique mono-classes. Ces méthodes élaborent une fonction de décision à partir de données d'apprentissage, pour classer les nouveaux échantillons en données aberrantes ou données normales. La fonction de décision définie l’enveloppe d’une région de l’espace de données contenant la majeure partie des données d’apprentissage. Cette thèse propose des méthodes de classification mono-classe, des formulations parcimonieuses de ces méthodes, et une méthode en ligne pour la détection temps réel. Les performances de ces méthodes sont montrées sur des données benchmark de différents types d’infrastructures critiquesThe security of critical infrastructures has been an interesting topic recently with the increasing risk of cyber-attacks and terrorist threats against these systems. The majority of these infrastructures is controlled via SCADA (Supervisory Control And Data Acquisition) systems, which allow remote monitoring of industrial processes such as electrical power grids, gas pipelines, water distribution systems, wastewater collection systems, nuclear power plants, etc. Traditional intrusion detection systems (IDS) cannot detect new types of attacks not listed in their databases, so they cannot ensure maximum protection for these infrastructures.The objective of this thesis is to provide additional help to IDS to ensure better protection for industrial systems against cyber-attacks and intrusions. The complexity of studied systems and the diversity of attacks make modeling these attacks very difficult. To overcome this difficulty, we use machine learning, especially one-class classification. Based on training samples, these methods develop decision rules to classify new samples as outliers or normal ones. This dissertation proposes specific one-class classification approaches, sparse formulations of these approaches, and an online approach to improve the real-time detection. The relevance of these approaches is illustrated on benchmark data from three different types of critical infrastructures
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Kehl, Justin. "N-SLOPE: A One-Class Classification Ensemble For Nuclear Forensics". DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1871.
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One-class classification is a specialized form of classification from the field of machine learning. Traditional classification attempts to assign unknowns to known classes, but cannot handle novel unknowns that do not belong to any of the known classes. One-class classification seeks to identify these outliers, while still correctly assigning unknowns to classes appropriately. One-class classification is applied here to the field of nuclear forensics, which is the study and analysis of nuclear material for the purpose of nuclear incident investigations. Nuclear forensics data poses an interesting challenge because false positive identification can prove costly and data is often small, high-dimensional, and sparse, which is problematic for most machine learning approaches. A web application is built using the R programming language and the shiny framework that incorporates N-SLOPE: a machine learning ensemble. N-SLOPE combines five existing one-class classifiers with a novel one-class classifier introduced here and uses ensemble learning techniques to combine output. N-SLOPE is validated on three distinct data sets: Iris, Obsidian, and Galaxy Serpent 3, which is an enhanced version of a recent international nuclear forensics exercise. N-SLOPE achieves high classification accuracy on each data set of 100%, 83.33%, and 83.33%, respectively, while minimizing false positive detection rate to 0% across the board and correctly detecting every single novel unknown from each data set. N-SLOPE is shown to be a useful and powerful tool to aid in nuclear forensic investigations.
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Sharma, Shiven. "Learning the Sub-Conceptual Layer: A Framework for One-Class Classification". Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34648.
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In the realm of machine learning research and application, binary classification algorithms, i.e. algorithms that attempt to induce discriminant functions between two categories of data, reign supreme. Their fundamental property is the reliance on the availability of data from all known categories in order to induce functions that can offer acceptable levels of accuracy. Unfortunately, data from so-called ``real-world'' domains sometimes do not satisfy this property. In order to tackle this, researchers focus on methods such as sampling and cost-sensitive classification to make the data more conducive for binary classifiers.
However, as this thesis shall argue, there are scenarios in which even such explicit methods to rectify distributions fail. In such cases, one-class classification algorithms become a practical alternative. Unfortunately, if the domain is inherently complex, the advantage that they offer over binary classifiers becomes diminished. The work in this thesis addresses this issue, and builds a framework that allows for one-class algorithms to build efficient classifiers.
In particular, this thesis introduces the notion of learning along the lines sub-concepts in the domain; the complexity in domains arises due to the presence of sub-concepts, and by learning over them explicitly rather than on the entire domain as a whole, we can produce powerful one-class classification systems. The level of knowledge regarding these sub-concepts will naturally vary by domain, and thus we develop three distinct frameworks that take the amount of domain knowledge available into account. We demonstrate these frameworks over three real-world domains.
The first domain we consider is that of biometric authentication via a users swipe on a smartphone. We identify sub-concepts based on a users motion, and given that modern smartphones employ sensors that can identify motion, during learning as well as application, sub-concepts can be identified explicitly, and novel instances can be processed by the appropriate one-class classifier. The second domain is that of invasive isotope detection via gamma-ray spectra. The sub-concepts are based on environmental factors; however, the hardware employed cannot detect such concepts, and quantifying the precise source that creates these sub-concepts is difficult to ascertain. To remedy this, we introduce a novel framework in which we employ a sub-concept detector by means of a multi-class classifier, which pre-processes novel instances in order to send them to the correct one-class classifier. The third domain is that of compliance verification of the Comprehensive Test Ban Treaty (CTBT) through Xenon isotope measurements. This domain presents the worst case where sub-concepts are not known. To this end, we employ a generic version of our framework in which we simply cluster the domain and build classifiers over each cluster. In all cases, we demonstrate that learning in the context of domain concepts greatly improves the performance of one-class classifiers.
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Salmazzo, Natália. "Classificação One-Class para predição de adaptação de espécies em ambientes desconhecidos". reponame:Repositório Institucional da UFABC, 2016.
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Orientadora: Profa. Dra. Debora Maria Rossi de MedeirosDissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Ciência da Computação, 2016.
O crescente aumento da exploração do meio ambiente e da biodiversidade faz com que seja necessário preservar os recursos naturais para evitar escassez e reduzir os impactos ambientais. Utilizando dados de distribuição geográfica de espécies combinados com características ambientais e, é possível gerar modelos de distribuição geográfica de espécies. Esses modelos podem ser aplicados na solução de diversos problemas relacionados à manutenção da biodiversidade e preservação das espécies, por exemplo, como auxiliar na dentição de politicas publica e cenários para o uso sustentável do meio ambiente, estudar o potencial de crescimento e proliferação de espécies invasoras, e avaliar os impactos das mudanças climáticas na biodiversidade. Este trabalho propõe um método para a geração de modelos de distribuição de espécies através da aplicação de conceitos de aprendizado de maquina adaptados para a resolução de problemas de uma classe. Os modelos gerados possibilitam a localização de áreas com características similares ao habitat natural das espécies e, dessa forma, contribuem para a sua preservação. Para avaliar a sua acácia, o método proposto foi aplicado em uma base de dados real e algumas bases de Benchmark, e comparado com uma versão do algoritmo Suporta Vector Machies, para dados com uma única classe. O SVM é um dos algoritmos mais aplicados na modelagem de distribuição de espécies e esta disponível em algumas das soluções mais utilizados pelos pesquisadores da área, como o Openmodeller 1 e o Biodiversityr2, avaliação para outras situações, como bases de dados que incluam dados de ausência de espécies bases de dados com um maior numeram de exemplos, os resultados são promissores e indicam que o aprofundamento da pesquisa nessa área pode ter impacto relevante para a modelagem de distribuição de espécies, portanto oferece uma base solida para avaliação. Os resultados mostraram que o método proposto é vi Avel e competitivo. Em muitos casos, como para dados possíveis de serem separados linearmente, o novo método apresentou resultados superiores aos do SVM. Embora ainda seja necessário estender a sua avaliação para outras situações, como bases de dados que incluam dados de ausência de espécies e bases de dados com um maior numeram de exemplos, os resultados são promissores e indicam que o aprofundamento da pesquisa nessa área pode ter impacto relevante para a modelagem de distribuição de espécies.
The increasing exploitation of the environment and biodiversity makes it necessary to preserve the natural resources to avoid scarcity and reduce environmental impacts. Using geographical species distribution data combined with environmental and ecological characteristics, geographical species distribution models can be generated. These models can be applied in solving various problems related to the maintenance of biodiversity and species conservation, such as an aid in the denition of public policies and scenarios for sustainable use of the environment, study the potential for growth and proliferation of invasive species, and assess the impacts of climate change on biodiversity. This work proposes a method for generating geographical species distribution models by applying Machine Learning concepts adapted to solving one-class problems. The generated models enable the identication of areas with similar characteristics to the natural habitat of the species and therefore contribute to its preservation. To evaluate its eectiveness, the proposed method was applied to a real database and some benchmark bases, and compared with a version of the Support Vector Machines algorithm, for one-class classication . The SVM is one of the most applied algorithms for species distribution modelling and is available in some of the solutions most used by researchers in this eld, such as openModeller 3 and BiodiversityR 4. Therefore, it provides a solid base for evaluation. The results showed that the proposed method is viable and competitive. In many cases, such as when the data can be linearly separable, the results obtained by applying the new method were better than those of SVM. Although additional research is necessary to evaluate the method in dierent situations, such as by using databases that include species absence data and databases with a large number of examples, the results are promising and indicate that further research in this area could have a relevant impact to the species distribution modelling technique.
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Albrecht, Simon [Verfasser]. "On Functions in the Speiser Class with One Tract / Simon Albrecht". Kiel : Universitätsbibliothek Kiel, 2015. http://d-nb.info/1076270468/34.
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Duncan, Jonathan D. "First return recovery of Baire class one functions on ultrametric spaces". [Bloomington, Ind.] : Indiana University, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3278219.
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Thesis (Ph.D.)--Indiana University, Dept. of Mathematics, 2007.Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 5995. Advisers: Slawomir Solecki; Lawrence S. Moss. Title from dissertation home page (viewed May 9, 2008).
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Mack, Benjamin Manuel [Verfasser]. "Applied One-Class Classification of Remote Sensing Data / Benjamin Manuel Mack". Berlin : Freie Universität Berlin, 2017. http://d-nb.info/1135957193/34.
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Alzogbi, Anas [Verfasser], i Georg [Akademischer Betreuer] Lausen. "Recommending scientific publications: addressing the one-class problem and concept drift". Freiburg : Universität, 2019. http://d-nb.info/1185391312/34.
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Cooper, Douglas. "Speech Detection using Gammatone Features and One-Class Support Vector Machine". Master's thesis, University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5923.
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A network gateway is a mechanism which provides protocol translation and/or validation of network traffic using the metadata contained in network packets. For media applications such as Voice-over-IP, the portion of the packets containing speech data cannot be verified and can provide a means of maliciously transporting code or sensitive data undetected. One solution to this problem is through Voice Activity Detection (VAD). Many VAD's rely on time-domain features and simple thresholds for efficient speech detection however this doesn't say much about the signal being passed. More sophisticated methods employ machine learning algorithms, but train on specific noises intended for a target environment. Validating speech under a variety of unknown conditions must be possible; as well as differentiating between speech and non- speech data embedded within the packets. A real-time speech detection method is proposed that relies only on a clean speech model for detection. Through the use of Gammatone filter bank processing, the Cepstrum and several frequency domain features are used to train a One-Class Support Vector Machine which provides a clean-speech model irrespective of environmental noise. A Wiener filter is used to provide improved operation for harsh noise environments. Greater than 90% detection accuracy is achieved for clean speech with approximately 70% accuracy for SNR as low as 5dB.M.S.E.E.
Masters
Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering; Accelerated BS to MS
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Fourie, Christoff. "A one-class object-based system for sparse geographic feature identification". Thesis, Stellenbosch : University of Stellenbosch, 2011. http://hdl.handle.net/10019.1/6666.
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Thesis (MSc (Geography and Environmental Studies))--University of Stellenbosch, 2011.ENGLISH ABSTRACT: The automation of information extraction from earth observation imagery has become a field of active research. This is mainly due to the high volumes of remotely sensed data that remain unused and the possible benefits that the extracted information can provide to a wide range of interest groups. In this work an earth observation image processing system is presented and profiled that attempts to streamline the information extraction process, without degradation of the quality of the extracted information, for geographic object anomaly detection. The proposed system, implemented as a software application, combines recent research in automating image segment generation and automatically finding statistical classifier parameters and attribute subsets using evolutionary inspired search algorithms. Exploratory research was conducted on the use of an edge metric as a fitness function to an evolutionary search heuristic to automate the generation of image segments for a region merging segmentation algorithm having six control parameters. The edge metric for such an application is compared with an area based metric. The use of attribute subset selection in conjunction with a free parameter tuner for a one class support vector machine (SVM) classifier, operating on high dimensional object based data, was also investigated. For common earth observation anomaly detection problems using typical segment attributes, such a combined free parameter tuning and attribute subset selection system provided superior statistically significant results compared to a free parameter tuning only process. In some extreme cases, due to the stochastic nature of the search algorithm employed, the free parameter only strategy provided slightly better results. The developed system was used in a case study to map a single class of interest on a 22.5 x 22.5km subset of a SPOT 5 image and is compared with a multiclass classification strategy. The developed system generated slightly better classification accuracies than the multiclass classifier and only required samples from the class of interest.
AFIKAANSE OPSOMMING: Die outomatisering van die verkryging van inligting vanaf aardwaarnemingsbeelde het in sy eie reg 'n navorsingsveld geword as gevolg van die groot volumes data wat nie benut word nie, asook na aanleiding van die moontlike bydrae wat inligting wat verkry word van hierdie beelde aan verskeie belangegroepe kan bied. In hierdie tesis word 'n aardwaarneming beeldverwerkingsstelsel bekend gestel en geëvalueer. Hierdie stelsel beoog om die verkryging van inligting van aardwaarnemingsbeelde te vergemaklik deur verbruikersinteraksie te minimaliseer, sonder om die kwaliteit van die resultate te beïnvloed. Die stelsel is ontwerp vir geografiese voorwerp anomalie opsporing en is as 'n sagteware program geïmplementeer. Die program kombineer onlangse navorsing in die gebruik van evolusionêre soek-algoritmes om outomaties goeie beeldsegmente te verkry en parameters te vind, sowel as om kenmerke vir 'n statistiese klassifikasie van beeld segmente te selekteer. Verkennende navorsing is gedoen op die benutting van 'n rand metriek as 'n passings funksie in 'n evolusionêre soek heuristiek om outomaties goeie parameters te vind vir 'n streeks kombinering beeld segmentasie algoritme met ses beheer parameters. Hierdie rand metriek word vergelyk met 'n area metriek vir so 'n toepassing. Die nut van atribuut substel seleksie in samewerking met 'n vrye parameter steller vir 'n een klas steun vektor masjien (SVM) klassifiseerder is ondersoek op hoë dimensionele objek georiënteerde data. Vir algemene aardwaarneming anomalie opsporings probleme met 'n tipiese segment kenmerk versameling, het so 'n stelsel beduidend beter resultate as 'n eksklusiewe vrye parameter stel stelsel gelewer in sommige uiterste gevalle. As gevolg van die stogastiese aard van die soek algoritme het die eksklusiewe vrye parameter stel strategie effens beter resultate gelewer. Die stelsel is getoets in 'n gevallestudie waar 'n enkele klas op 'n 22.5 x 22.5km substel van 'n SPOT 5 beeld geïdentifiseer word. Die voorgestelde stelsel, wat slegs monsters van die gekose klas gebruik het, het beter klassifikasie akkuraathede genereer as die multi klas klassifiseerder.
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Fouche, Jacques Roux. "Increasing class one fruit in 'Granny Smith' and 'Cripps' Pink' apple". Thesis, Stellenbosch : University of Stellenbosch, 2009. http://hdl.handle.net/10019.1/2766.
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Thesis (MscAgric (Horticulture))--University of Stellenbosch, 2009.ENGLISH ABSTRACT: Experiments were conducted to increase the percentage class one ‘Granny Smith’ and ‘Cripps’ Pink’ apples. ‘Granny Smith’ is the most widely grown apple cultivar in South Africa, but its profitability is compromised by the high incidence of sunburn, red blush and poor green colour development. ‘Cripps’ Pink’ is a very lucrative cultivar and producers are striving to maximise the production of fruit that qualify for export. Fruit technologists and growers are debating whether it is best to maximise class one fruit in ‘Cripps’ Pink’ by increasing total yield or by increasing fruit quality. The relationship between ‘Granny Smith’ canopy position and external fruit quality was investigated. Light exposure, peel temperature, green colour development, sunburn and red blush development was followed for individual fruit from the outer, intermediate and inner canopy. Dark green fruit were exposed to moderate to high light levels (25-50% full sun) during the first half of fruit development, similar to fruit that eventually developed sunburn and red blush. The difference came in during the latter half of fruit development when dark green fruit became shaded (3% full sun). Pale green fruit contained less chlorophyll due to consistent low light levels (2% full sun). Fruit at partially shaded canopy positions had a lower occurrence of sunburn and red blush than outside fruit and better green colour development than fruit from the heavily shaded inner canopy. Based on these data, pruning strategies and mulching were evaluated to alter canopy vigour and the light environment in such a way that green colour development is promoted and the occurrence of sunburn and red blush is reduced. In an older, vigorous orchard with a dense canopy, pruning was done to increase light distribution for green colour development and to induce more growth on the side of the trees that are prone to sunburn and red blush. Pruning improved green colour development without affecting sunburn or red blush. In a younger, non-vigorous orchard, pruning and mulching were used to invigorate the canopy to increase shading of fruit and thereby decrease sunburn and red blush. However, these treatments were not effective. Further research should focus on the use of shade nets, accompanied by rigorous pruning, to reduce sunburn and red blush while not decreasing green colour. Five different crop loads were established in an exceptionally high yielding (averaging over 100 ton·ha1) ‘Cripps’ Pink’ orchard by first the thinning of clusters, then the removal of small fruit and, finally, the selective removal of fruit from the shaded inner canopy. Treatments had no effect on iv fruit quality in the first season. The most severe thinning treatment increased the percentage class one fruit in the second season by increasing the number of fruit with adequate red blush. However, seen cumulatively, the higher crop loads yielded more class one fruit per hectare than the lower crop loads, without affecting reproductive and vegetative development or fruit storability. Producers should strive for the highest crop loads allowed by the fruit size limitations in cultivars that are not prone to alternate bearing
AFRIKAANSE OPSOMMING: Eksperimente is uitgevoer om die persentasie uitvoerkwaliteit ‘Granny Smith’ en ‘Cripps Pink’ appels te verhoog. ‘Granny Smith’ maak die grootste deel uit van appel aanplantings in Suid Afrika, maar die winsgewendheid daarvan word beperk deur `n hoë voorkoms van sonbrand, rooi blos en swak groen kleurontwikkeling. ‘Cripps’ Pink’ is `n baie winsgewende kultivar en produsente streef daarna om die persentasie uitvoerkwaliteit vrugte te maksimaliseer. Vrugte tegnoloë en produsente debatteer oor die wenslikheid daarvan om uitvoerkwaliteit vrugte te maksimeer deur totale produksie te verhoog of deur vrugkwaliteit te verbeter. Die verband tussen ‘Granny Smith’ draposisie in die blaredak en eksterne vrugkwaliteit is ondersoek. Ligvlakke, skiltemperatuur, groen kleurontwikkeling, sonbrand en rooi blos ontwikkeling is deur die loop van die seisoen gevolg vir individuele vrugte aan die buitekant, binnekant en intermediêre posisies binne die blaredak. Daar is gevind dat die donkerste groen vrugte, nes vrugte wat uiteindelik sonbrand en rooi blos ontwikkel het, blootgestel was aan matige tot hoë ligvlakke (25-50% vol son) gedurende die eerste helfte van vrugontwikkeling. Donker groen vrugte is egter oorskadu (3% vol son) tydens die tweede helfte van vrugontwikkeling. Vanweë konstante lae beligting (2% vol son) het binne vrugte min chlorofiel geakkumuleer en daarom is hierdie vrugte lig van kleur. Vrugte in gedeeltelike skadu ontwikkel min sonbrand en rooi blos in vergelyking met buite vrugte en toon beter groen kleurontwikkeling as vrugte in diep skadu binne die boom. Gegrond op bogenoemde resultate is die gebruik van snoei strategieë en deklae om die groeikrag en die ligomgewing van die boom te modifiseer, ten einde groen kleur ontwikkeling te bevorder en sonbrand en rooi blos te verminder, geëvalueer. In ‘n ouer, groeikragtige boord met ‘n digte blaredak is snoei gebruik om ligverspreiding te verbeter vir groen ontwikkeling en om meer groei te stimuleer aan die buitekant van die boom wat meer geneig is tot sonbrand en rooiblos. Groen kleur is wel verbeter, maar sonbrand en rooi blos is nie geaffekteer nie. In `n jonger, minder groeikragtige boord is ‘n deklaag aangebring en eenjarige lote getop, sodoende groei te stimuleer om sonbrand en rooi blos te verminder deur oorskaduwing van vrugte. Hierdie behandelings was egter nie effektief nie. Toekomstige navorsing moet fokus op die gebruik van skadunette tesame met ‘n nougesette snoei strategie om sonbrand en rooi blos te verminder sonder om groen kleur te verswak. Vyf verskillende vrugladings is geskep in `n uitermatig produktiewe (gemiddeld meer as 100 ton·ha-1) ‘Cripps’ Pink’ boord deur eers vrugtrosse uit te dun, gevolg deur die verwydering van klein vrugte en, laastens, die selektiewe verwydering van vrugte in die diep skaduwee van die binneste blaredak. Vrugkwaliteit is nie in die eerste seisoen nie deur oeslading geaffekteer nie. Die strafste uitdunbehandeling het wel die persentasie uitvoerbare vrugte in die tweede seisoen verhoog deur die aantal vrugte met voldoende rooi blos te vermeerder. Kumulatief gesien, het die hoër oesladings egter meer klas een vrugte per hektaar opgelewer sonder om die reproduktiewe en vegetatiewe ontwikkeling of die stoorvermoë van vrugte te affekteer. Produsente moet strewe na die hoogste oesladings wat toegelaat word deur vruggrootte beperkings in kultivars wat nie geneig is tot alternerende drag nie
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Lyon, Andrew. "One or two sons : class, gender and fertility in North India". Thesis, University of Edinburgh, 1988. http://hdl.handle.net/1842/19065.
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Hwang, Zaijong. "A new class of integrable two-mass mixtures in one-dimension". Thesis, University of Massachusetts Boston, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10247448.
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Among systems with many hard-core point particles that only interact by elastic collisions in one-dimension, it has long been thought that only those with equal mass particles were completely integrable, where the final state of the system through time evolution could be easily predicted from its initial state due to the existence of a maximal number of conserved quantities. In this thesis, we introduce a new class of integrable three-particle systems that contain two unequal masses. These integrable triplets can affect the rate of thermalization in a much larger system composed of particles with two unequal masses, the effect of which is demonstrated with a numerical simulation.
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Lozada, Hidalgo Marcelo. "One-atom-thick crystals as a novel class of proton conducting materials". Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/oneatomthick-crystals-as-a-novel-class-of-proton-conducting-materials(398d313b-4e8f-4341-817d-1612be3374bb).html.
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Graphene, a one-atom-thick sheet of carbon atoms, is impermeable to all atoms and molecules; the same can be expected for other 2D crystals like hexagonal boron nitride (hBN). In this work we show that monolayers of graphene and hBN are highly permeable to thermal protons. As a reference, we show that monolayers of molybdenum disulphide as well as bilayers of graphene and tetralayers of hBN are not. Moreover, we show that water plays a crucial role in the transport mechanism. Because of the zero point energy of vibration in the oxygen-hydrogen bonds in water, protons face energy barriers smaller than previously predicted by theory. The effect, revealed by substituting hydrogen for deuterium, also shows that protons and deuterons transport at different rates across the membranes; establishing them as membranes with subatomic selectivity. Beyond the purely scientific implications, our results establish monolayers of graphene and hBN as a promising new class of proton conducting materials with potential applications in fuel cells, hydrogen purification and isotope enrichment technologies.
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Xue, Yongjian. "Dynamic Transfer Learning for One-class Classification : a Multi-task Learning Approach". Thesis, Troyes, 2018. http://www.theses.fr/2018TROY0006.
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Le but de cette thèse est de minimiser la perte de performance d'un système de détection lorsqu'il rencontre un changement de distribution de données à la suite d’un événement connu (maintenance, ajout de capteur etc.). L'idée est d'utiliser l'approche d'apprentissage par transfert pour exploiter l'information apprise avant l’événement pour adapter le détecteur au système modifié. Un modèle d'apprentissage multitâche est proposé pour résoudre ce problème. Il utilise un paramètre pour équilibrer la quantité d'informations apportées par l'ancien système par rapport au nouveau. Ce modèle est formalisé de manière à pouvoir être résolu par un SVM mono-classe classique avec une matrice de noyau spécifique. Pour sélectionner le paramètre de contrôle, une méthode qui calcule les solutions pour toutes les valeurs du paramètre introduit et un critère de sélection de sa valeur optimale sont proposés. Les expériences menées dans le cas de changement de distribution et d’ajout de capteurs montrent que ce modèle permet une transition en douceur de l'ancien système vers le nouveau. De plus, comme le modèle proposé peut être formulé comme un SVM mono-classe classique, des algorithmes d'apprentissage en ligne pour SVM mono-classe sont étudiés dans le but d'obtenir un taux de fausses alarmes stable au cours de la phase de transition. Ils peuvent être appliqués directement à l'apprentissage en ligne du modèle proposéThe aim of this thesis is to minimize the performance loss of a one-class detection system when it encounters a data distribution change. The idea is to use transfer learning approach to transfer learned information from related old task to the new one. According to the practical applications, we divide this transfer learning problem into two parts, one part is the transfer learning in homogenous space and the other part is in heterogeneous space. A multi-task learning model is proposed to solve the above problem; it uses one parameter to balance the amount of information brought by the old task versus the new task. This model is formalized so that it can be solved by classical one-class SVM except with a different kernel matrix. To select the control parameter, a kernel path solution method is proposed. It computes all the solutions along that introduced parameter and criteria are proposed to choose the corresponding optimal solution at given number of new samples. Experiments show that this model can give a smooth transition from the old detection system to the new one whenever it encounters a data distribution change. Moreover, as the proposed model can be solved by classical one-class SVM, online learning algorithms for one-class SVM are studied later in the purpose of getting a constant false alarm rate. It can be applied to the online learning of the proposed model directly
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Nelson, Rebecca S. "Developing Mathematical Knowledge Through Class Discussion: One Teacher's Struggles in Implementing Reform". Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/26082.
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The purpose of this case study was to examine the experience of one secondary mathematics teacher during his efforts to facilitate mathematical discussions in a secondary algebra class. Class discussions and interviews were documented and analyzed to investigate the patterns of discussion, the teacher's role in facilitating discussion, and the struggles encountered by the teacher through his attempts to enact reform-oriented strategies. The investigation focused on the teacher's vision for and actual practice in utilizing reform-oriented strategies for discussion in his Algebra class. The teacher's vision of reform was largely based on NCTM Standards recommendations for reforming the teaching and learning of mathematics. Data was collected through various sources and was analyzed using qualitative methods in the spirit of naturalistic inquiry. Data was generated through classroom observations, teacher interviews, audiotapes of whole-class discussions, and personal log notes written by the researcher. Although the teacher expressed intentions to reform discussion in his classroom, he continued to experience struggles in realizing his intentions. Class discussions that were largely teacher-directed and followed traditional patterns of interaction. The teacher struggled with three main issues: classroom challenges, perceptions of student resistance, and teacher authority. The findings of this study suggest that mathematics teachers, even those with extensive experience and grounded knowledge in current reform initiatives, will need to further develop strategies for facilitating a Standards-like environment in their classrooms. Pre-service and in-service educators should also examine the development of programs that can better reflect actual practice and build on classroom teachers' prior experiences and practice.Ph. D.
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Klerx, Timo [Verfasser]. "Anomaly detection as a one-class problem in discrete event systems / Timo Klerx". Paderborn : Universitätsbibliothek, 2017. http://d-nb.info/1135258287/34.
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Pavy, Anne M. "SV-Means: A Fast One-Class Support Vector Machine-Based Level Set Estimator". Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1516047120200949.
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Edem, Ini Benedict. "Irreducible variational formulation of a class of one-point quadrature solid-shell finite elements". Thesis, University of Newcastle Upon Tyne, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.576650.
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In this thesis, a systematic formulation of a new class of physically stabilised one-point quadrature, low order 8-node solid-shell finite elements is undertaken .. The significance, aim and distinguishing contribution of the present research is to obtain a new class of accurate and locking-free, purely displacement variational solid-shell finite element formulation without recourse to mixed variational formu- lations pervasive in shell finite element literature. Standard displacement-based finite elements in shell analysis are known to exhibit an overly stiff behaviour in in-extensional and near-incompressible deformation known as locking, amplified by a decreasing shell mesh thickness domain. Trends in advanced structural shell simulations however require the inclusion of the shell thickness domain for realistic simulations, intensifying active research in the formulation of low order, so-called solid-shells elements specially formulated to avoid the locking phenomenon in three dimensional finite element shell analysis. To further achieve computational efficiency and coarse mesh accuracy in element computations, recent research effort has been geared towards development of the class of mixed variational and the enhanced assumed strain (EAS) approach in physically stabilised, re- duced integration framework. The alternative class of solid-shell elements intro- duced in this thesis is achieved through a systematic design of a displacement space function which enhances the standard hexahedral element isoparametric displacement field using a minimum number of hierarchical quadratic displace- ment fields. The chosen displacement fields expand the elements strain space con- siderably, significantly improving deformation accuracy and robust performance. In combination with the one-point quadrature and physical stabilisation concept, the assumed natural strain (ANS) and the B-bar method, a new class of effi- cient displacement-based solid-shell elements with kinematics described entirely by displacement degrees of freedom is obtained for three dimensional shell analy- sis. An exhaustive investigation within linear elasticity using a wide selection of numerical benchmark problems and coupled with robust comparison with state of the art shell, solid-shell and 3D solid finite elements demonstrates the suit- ability and validity of the new solid-shell element class. The broad applicability of the formulation to various geometric configurations of beams, plates and shell structures exhibiting coarse mesh accuracy, distortion tolerant characteristics and locking-free behaviour is also demonstrated.
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Englhardt, Adrian [Verfasser], i K. [Akademischer Betreuer] Böhm. "Cost-Quality Trade-Offs in One-Class Active Learning / Adrian Englhardt ; Betreuer: K. Böhm". Karlsruhe : KIT-Bibliothek, 2021. http://d-nb.info/1239180578/34.
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Milopoulou, Filia. "Preschool class: a one-way bridge to primary school : Examining preschool class teachers’ perspectives on preparatory practices for children’s transition to primary school". Thesis, Högskolan för lärande och kommunikation, Högskolan i Jönköping, Förskolepedagogisk-didaktisk forskning, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-40444.
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The preschool class was built in 1998 in an effort by the Swedish government to achieve a smooth transition for the children from preschool to compulsory school. This voluntary, one-year class was envisioned as a bridge between preschool and compulsory school, and the activities would be unlike those in preschool or school. Nevertheless, presently the role of the preschool class seems to be unclear to the preschool class teachers. This is all the more concerning given that in 2018 this class will become mandatory children. The aim of this qualitative study was to investigate how teachers understand preschool class and “preschool class pedagogy” as a distinct practice, that is “not preschool” and “not school”, and if and how in that case, mandatory attendance in preschool class will change their view. In this qualitative study, transition theories were applied to characterize teachers’ understanding of the preschool class meaning as a transition year, its preparatory role, and the teachers’ perspectives on this transition class. The sample consisted of four preschool class teachers who were either former or current preschool class teachers. The study data was based on semi-structured interviews, observations and document analysis. This study found that the preschool class teachers understand this class as a one-way bridge to primary school and emphasized the importance of the new policy of the mandatory attendance as a beneficial change for the children.
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Fong, Kau-wah Simon. "Playing the game a behavioural approach to homework submission management in a form one class /". Click to view the E-thesis via HKUTO, 1991. http://sunzi.lib.hku.hk/HKUTO/record/B38627280.
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Burr, Frederick Jerome. "Cephalometric evaluation of one-phase and two-phase treatment alternatives in matched class II subjects". View the abstract Download the full-text PDF version, 2007. http://etd.utmem.edu/ABSTRACTS/2007-019-Burr-index.html.
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Thesis (M.S. )--University of Tennessee Health Science Center, 2007.Title from title page screen (May 16, 2008). Research advisor: Edward F. Harris, PhD. Document formatted into pages (x, 189 p. : ill.). Vita. Abstract. Includes bibliographical references (p. 110-123).
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Fong, Kau-wah Simon, i 方九華. "Playing the game: a behavioural approach to homework submission management in a form one class". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1991. http://hub.hku.hk/bib/B38627280.
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Dalvi, Aditi. "Performance of One-class Support Vector Machine (SVM) in Detection of Anomalies in the Bridge Data". University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin150478019017791.
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Tu, Mengwei. "Middle class one-child migrants : between transnational aspirations in the UK and family responsibility in China". Thesis, University of Kent, 2016. https://kar.kent.ac.uk/57410/.
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The rapid economic development in China since 1978 and the one-child policy enacted in 1979 produced a large number of middle class families with only one child. Initially inspired by the Western means to affluence, these one-child migrants came to the UK (mostly) as international students and remained after their study to take up opportunities to gain work in the professions. As the only child they were faced with the dilemma of seeking greater mobility in the host country, and looking after their ageing parents in China. This research seeks to understand the factors behind their decision to migrate or to settle, and how the only-child migrants in the UK negotiate filial obligations in relation to their parents in China. The transnational one-child family provides a unique case for investigating how families function and change under extreme conditions; as well as how traditional family values and practices shift in the 21st Century within the context of rapid modernisation. The findings of the research are based on interviews with 27 one-child migrants, 6 migrants with siblings and 7 parents of the one-child participants. The migrant participants are from different regions of China and were dispersed in the UK. The majority of the sample has an urban middle class background. By including parents and children from the same family, which is rarely done in transnational family research, this study is able to analyse the separated household members from both sides. The detailed accounts of the migrants' lived experience reveal a multi-level decision-making process over the life course of these families, a predominately parent-to-child intergenerational material transfer, as well as a reconfigured reciprocity between one-child migrants and their parents. Families represent the most intimate form of a transnational social field. One-child transnational families in this study have shown intense circulation of material resources, emotion and care forged by the biological bond and family contract; these families played a crucial "bridging" role that connects the social spaces between China and the UK. As a result individual migrants are actively involved in relationships in multi-layered spaces, and simultaneously influenced by these relationships. International education and the globalised late-capital world gave rise to a demographic of Western-educated Chinese migrants who are relatively mobile, while some aspects of their lives are still geographically bound. By introducing the time factor into the family-migration nexus the thesis shows that migrants' establishment in the host country and their transnational ties are neither incompatible, nor are they static; their relationship evolves with the life cycle of the individuals, and the changing society in both the host country and China.
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Guttridge, Martin Gordon. "An improved method for one-dimensional isoelectric focussing of HLA class 1 molecules and its clinical application". Thesis, University of the West of England, Bristol, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.293274.
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Brazeau, Lisamarie O. "Cephalometric analysis of posttreatment changes in class ii division 1 patients treated in either one or two phases". [Gainesville, Fla.] : University of Florida, 2004. http://purl.fcla.edu/fcla/etd/UFE0004880.
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Thesis (M.S.)--University of Florida, 2004.Typescript. Title from title page of source document. Document formatted into pages; contains 31 pages. Includes Vita. Includes bibliographical references.
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Nayfeh, Nader Ali. "Local and Global Stability and Dynamics of a Class of Nonlinear Time-Delayed One-Degree-of-Freedom Systems". Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/40403.
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We investigate the dynamics and stability of nonlinear time-delayed one-degree-of-freedom systems possessing quadratic and cubic nonlinearities and subjected to external and parametric disturbances. Due to the time-delay terms, the trivial solution of the unforced system undergoes Hopf bifurcations. We use the method of multiple scales to determine the normal forms of the Hopf bifurcations and hence determine whether they are locally supercritical or subcritical. Then, we use a combination of a path following scheme, the normal forms, and the method of harmonic balance to calculate and trace small- and large-amplitude limit cycles and use Floquet theory to ascertain their stability and hence generate global bifurcation diagrams. We validate these diagrams using numerical simulations. We apply the results to two important physical problems: machine-tool chatter in lathes and control of the sway of container cranes using time-delayed position feedback. We find that the Hopf bifurcations in machine tools are globally subcritical even when they are locally supercritical. We find multiple large-amplitude solutions coexisting with the linearly stable trivial solution. Consequently, there are three operating regions for machine tools: an unconditionally stable region, an unconditionally unstable region, and a conditionally stable region. In the latter region, the multiple responses lead to hysteresis. Then, we investigate the use of bifurcation control to transform the subcritical bifurcations into supercritical ones. We find that cubic-velocity feedback with appropriate gains can shrink or even eliminate the conditionally stable region. Then, we find that time-delayed acceleration feedback with an appropriate gain can completely eliminate the linear instability region. In contrast, we find that the Hopf bifurcations in controlled cranes are locally and globally supercritical. Finally, we investigate the effectiveness of time-delayed position feedback in rejecting external and parametric disturbances in ship-mounted cranes.Ph. D.
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Hsu, Teng-Cheng 1973. "Causes and impacts of Class One engineering changes : an exploratory study based on three defense aircraft acquisition programs". Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/47898.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics; and, (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Technology and Policy Program, 1999.Includes bibliographical references (leaves 169-173).
Past studies on engineering changes have focused on products other than defense aerospace products, and have concentrated primarily on the design-manufacturing interface within single companies. Thus, engineering changes in the context of US defense aerospace product development - where the user community, the acquisition community, and the contractors share the responsibility for developing a product - remain largely unexplored. This research focused on three defense aircraft acquisition program case studies, referred to hereafter as Programs A, B, and C. The primary goal of these studies was to develop a better understanding of the causes and impacts of Class I engineering changes in the US defense aerospace product development context. Class I engineering changes, simply referred to as engineering changes below, are those that fundamentally modify the form, fit, and/or function of a product such that the results before and after the engineering changes are different, and are visible to all communities involved with developing the product. In addition, this research sought to identify ways in which contractors and customers may help to reduce the number of undesirable engineering changes. For the three case-study programs, requirements definition issues, changes in user needs, the need to fix deficiencies, and technological changes were found to be the four dominant causes of engineering changes. It was also found that program characteristics determined the dominant causes in each of the programs. Engineering changes due to the four dominant causes across the three case-study programs were found to be most likely of high-impact. The scope of impact of engineering changes remained relatively constant with respect to time, and engineering changes rarely led to subsequent, unanticipated engineering changes. Thorough requirements definition facilitated by the use of integrated product teams (IPTs), prioritization on program schedule, and the use of mature technologies combined to allow Program C to make frequent engineering changes to accommodate evolving user needs and changes in technology without any program schedule delay. It was also found that had IPTs been used during the development phases of Programs A and B, the prime contractors and their suppliers might have been able to avoid some engineering changes.
by Teng-Cheng Hsu.
S.M.
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Makaluza, Nolitha. "Storytelling for early literacy development in isiXhosa: a case study of a grad one class in the Western Cape". University of the Western Cape, 2018. http://hdl.handle.net/11394/5853.
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Magister Educationis - MEd (Language Education)Early literacy development forms the basis of learning as it equips young learners with reading skills which help them to unlock the code of written texts across the curriculum. Literacy components include listening, oral, writing, spelling, sentence construction, reading and comprehension. Literacy in the learners’ home languages forms a strong foundation for learning. Story-telling is one of the approaches to literacy development. It may involve traditional folktales which can be used to enhance learners’ literacy skills through listening, speech and writing. Traditional folktales form part of traditional literature that is disseminated largely through oral communication and behavioural examples. They connect to the past, although they reflect the present socio-cultural and educational activities which encourage children’s critical, problem solving and decision-making skills. The study investigated how storytelling was used to teach literacy to Grade one learners. It also determined how traditional isiXhosa folktales were incorporated in literacy lessons and how teachers utilized them in developing learners’ literacy through listening, speaking, reading and writing. The Social constructivism theory has been used as a lens to understand how storytelling supports learner’s literacy development, and their socialisation in the society. The study was conducted in one primary school in the Western Cape where isiXhosa was used as a medium of instruction in the Foundation Phase. It focussed on Grade one to explore the use of storytelling for literacy development in IsiXhosa. It followed a qualitative research approach which involved classroom observations, semi-structured interviews and document analysis. The findings of the study reveal that teachers acknowledged the value of folktales as part of storytelling as a learner-centred approach that enhances early literacy development. However, there were constraints regarding the lack of appropriate literacy resources in IsiXhosa and the exclusion of traditional stories in the formal curriculum. The study concludes that storytelling is one of the powerful learner-centred approaches for literacy development and that parental support should be strengthened to build the relationship between the school and the community as a means of maintaining learners’ culture and identity.
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Vongprateep, Kwanjai Pearl. "Parents' Social and Cultural Capital| One Parent Group's Influence on Student Engagement in an Upper Middle Class High School". Thesis, University of Redlands, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3637653.
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Parent involvement has been shown to have positive effects on student achievement and engagement, yet the lack of necessary social and/or cultural capital prevents some parents from being involved. Applying a conceptual framework based on Bourdieu's concepts of social and cultural capital, this study examines the social and cultural capital that one parent advisory group possessed and the influence their involvement had on student engagement at an upper middle class high school. This qualitative study used a semi-structured interview protocol and narrative inquiry approach. Using NVivo software, 15 participants' interviews were coded and analyzed for emergent themes in the areas of social and cultural capital. Twelve themes emerged. Social capital themes included: Group memberships that provided a sense of empowerment and responsibility, social networks that highlighted the importance of the community and knowing the right people, and relationships that were purposeful, caring, supportive, and trusting. Cultural capital themes included: having privileged status, knowing how the system works, setting priorities for action and change, and having high expectations and values. Despite the fact that these parents had an extensive network of social and cultural capital, the findings from this study suggests caring relationships that instill a value in education, and that high expectations might be the most important form of capital parents need to be effectively involved. Implications and recommendations for practice and future research are discussed.
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Morris, Owen Christopher. "On a class of one-parameter operator semigroups with state space Rn x Zm generated by pseudo-differential operators". Thesis, Swansea University, 2013. https://cronfa.swan.ac.uk/Record/cronfa42779.
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The thesis shows that, under suitable conditions, a pseudo-differential operator, defined on some "nice" set of functions on Rn x Zm, with continuous negative definite symbol q(x,xi,o) extends to a generator of a Feller semigroup. Sections 1-5 are the preliminary sections, these sections discuss some harmonic analysis concerning locally compact Abelian groups. The essence of this thesis are Sections 6-13, which deals with obtaining the estimates required for the fulfilment of the conditions of the Hille-Yosida-Ray theorem.
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Oliveira, Ailton Andrade de. "One-class support vector machines na construção de bases normativas de medidas neuroanatômicas utilizando imagens estruturais de ressonância magnética". reponame:Repositório Institucional da UFABC, 2013.
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岑翠萍 i Tsui-ping Betty Shum. "Students' perceptions of instructional language practices in the English classroom: a case study of asecondary one class in Hong Kong". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B41263212.
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Chao-Sen, Wang, i 王朝森. "Applications of One-Class-One-Network". Thesis, 1994. http://ndltd.ncl.edu.tw/handle/67162928399997193923.
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碩士國立臺灣科技大學
工程技術研究所
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In this paper, we use the one-class-one-network (OCON) architecture of neural network to do the classification of human faces and time warped trajectories. By using the architecture of OCON, each subnetwork of OCON has fewer connection weights that make the network more easy to converge and can be trained independently and simultaneously. First, we use mean and standard deviation trajectories to represent human faces and use the OCON architecture of recurrent neural network to do the classification of human faces, i.e., we regard the problem of human face recognition as the one of trajectory classification. Secondly, we proposed the OCON architecture of time warping recurrent neural network (TWRNN) to deal with time warping problem that is difficult to be solved for the other schemes. From the simulation result, the network completely removes time warping and is able to handle difficult classification problem successfully. Lastly, we propose the OCON architecture of MLP (multi-layer perceptrons) which has interval inputs, weights and outputs different from general MLP and represent the human faces as a few interval From the simulation result, the network can classify different human faces very successfully for both inside and outside tests.