Recentemente, com o rápido progresso da informática, a qual tem criado novos sistemas semi-automáticos ou mesmo completamente automáticos para avaliar os parâmetros metalográficos, tornou-se possível oferecer aos usuários da prática metalográfica, uma nova opção para determinar os parâmetros de interesse, cuja determinação em materiais metálicos é executada por vários métodos e utilizada extensivamente na prática metalográfica, pois estes parâmetros contribuem significativamente para a resistência mecânica dos aços ao carbono. Porém a escolha do melhor método a ser adotado é um assunto bastante discutido por vários autores. As nove amostras das chapas de aços ao carbono comum recozidas, com porcentagem de carbono variando de 0,05% a 0,56% apresentando estrutura ferrítica ou ferrítico-perlítica foram selecionadas, objetivando ao estudo de uma correlação entre os métodos tradicionais mais utilizados, como também o método que utiliza o sistema de análise de imagem digitalizada, que enfatiza a diferença dos níveis de cinza entre as fases presentes. Os atuais resultados dessa correlação, representam um dos primeiros trabalhos na área. Os resultados dos achados metalográficos,explorando na íntegra a particularidade de cada método, demonstram que o método de análise de imagem, relacionado aos métodos tradicionais, conduz a uma rápida e precisa obtenção dos parâmetros, com uma boa reprodutividade dos resultados. / The evaluation of the metallographic parameters in metallic materials has been carried out by many methods and they have been used extensively in the metallographic practice. However, the best method to be adopted has been a subject of many discussion. Nowadays, with the fast advance of computerisation, it has been possible to create semi-automatic or completely automatic systems for evaluation of these metallographic parameters, which are very important in determining the mechanical strength of carbon steels. In this work nine samples were removed from carbon steel plates with carbon content varying from 0,05 to 0,56 for evaluation of the correlation between the traditional methods, as well as with the method which use a digitised image analysis. This late method emphasise to the difference on the grey levels of the phases present. The results from this work are one of the first in this area, and they exploited the particularity of each method. Also, they demonstrated that the image analysis method, when compared with the traditional ones, gives a rapid and precise evaluation of the metallographic parameters, with a very good results reproducibility.

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This thesis report presents the work of analysing current challenges in Multidisciplinary Analysis systems. Exemplary the system of an aerospace supplier, GKN Aerospace Sweden AB, is examined and several suggestions for improve- ment are implemented. The Multidisciplinary Analysis system, with company internal name Engineering Workbench, employs a set-based approach in exploring the design-space for jet engine components. A number of design cases with varied geometrical and environmental parameters is generated using Design of Experiment sampling methods. Each design case is then subjected to a set of analyses. Using the analyses results, a surrogate model of the parts behaviour in relation to the input parameters is created. This enables the product developer to get a general view of the model’s behaviour and also to react to changes in product requirements. Design research methodology is applied to further develop the Engineering Workbench into a versatile design support system and expand the functionality to include producibility assessment. In its original state, the execution of a study requires explicit domain knowledge and programming skills in several disciplines. The execution of a study is often halted by minor process errors. Several methods to improve this status are suggested and tested. Among those are the introduction of an interface to improve the usability and expand the range of possible users. Further the integration of a four level system architecture supporting a modular structure. Producibility assessment is enabled by developing an expert system where geometrical and simulation results can be caught, analysed and evaluated to produce producibility metrics. Evaluation of the implemented solutions indicate a step in the right direction. Further development towards Multidisciplinary Optimisation, involving experts in information technologies as well as case- based reasoning techniques is suggested and discussed.

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Thesis (Ph. D.)--Georgia State University, 2007. / Title from file title page. Ying Zhu, committee chair; Xiaolin Hu, Geoffrey Hubona, Roy Johnson, Rajshekhar Sunderraman, committee members. Electronic text (216 p. : ill. (some col.)) : digital, PDF file. Description based on contents viewed Nov. 29, 2007. Includes bibliographical references (p. 209-216).

Thesis (Ph.D.)--University of Adelaide, School of Computer Science, 2005. / "December 2004" Includes bibliographical references (leaves 183-203). Also available electronically as part of the Australian Digital Theses Program.

Thesis (Ph.D.)--University of Adelaide, School of Computer Science, 2005. / Title from screen page; viewed 30 Aug. 2005. "December 2004" Includes bibliographical references (leaves 183-203). Also available in print format.

Thesis (Ph. D.)--Michigan State University. Dept. of Computer Science, 2006. / Title from PDF t.p. (viewed on Nov. 20, 2008) Includes bibliographical references (p. 408-425). Also issued in print.

With the growing installation of surveillance video cameras in both private and public areas, it is an immediate requirement to develop intelligent video analysis system for the large-scale camera network. As a prerequisite step of person tracking and person retrieval in intelligent video analysis, person re-identification, which targets in matching person images across camera views is an important topic in computer vision community and has been received increasing attention in the recent years. In the supervised learning methods, the person re-identification task is formulated as a classification problem to extract matched person images/videos (positives) from unmatched person images/videos (negatives). Although the state-of-the-art supervised classification models could achieve encouraging re-identification performance, the assumption that label information is available for all the cameras, is impractical in large-scale camera network. That is because collecting the label information of every training subject from every camera in the large-scale network can be extremely time-consuming and expensive. While the unsupervised learning methods are flexible, their performance is typically weaker than the supervised ones. Though sufficient labels of the training subjects are not available from all the camera views, it is still reasonable to collect sufficient labels from a pair of camera views in the camera network or a few labeled data from each camera pair. Along this direction, we address two scenarios of person re-identification in large-scale camera network in this thesis, i.e. unsupervised domain adaptation and semi-supervised learning and proposed three methods to learn discriminative model using all available label information and domain knowledge in person re-identification. In the unsupervised domain adaptation scenario, we consider data with sufficient labels as the source domain, while data from the camera pair missing label information as the target domain. A novel domain adaptive approach is proposed to estimate the target label information and incorporate the labeled data from source domain with the estimated target label information for discriminative learning. Since the discriminative constraint of Support Vector Machines (SVM) can be relaxed into a necessary condition, which only relies on the mean of positive pairs (positive mean), a suboptimal classification model learning without target positive data can be those using target positive mean. A reliable positive mean estimation is given by using both the labeled data from the source domain and potential positive data selected from the unlabeled data in the target domain. An Adaptive Ranking Support Vector Machines (AdaRSVM) method is also proposed to improve the discriminability of the suboptimal mean based SVM model using source labeled data. Experimental results demonstrate the effectiveness of the proposed method. Different from the AdaRSVM method that using source labeled data, we can also improve the above mean based method by adapting it onto target unlabeled data. In more general situation, we improve a pre-learned classifier by adapting it onto target unlabeled data, where the pre-learned classifier can be domain adaptive or learned from only source labeled data. Since it is difficult to estimate positives from the imbalanced target unlabeled data, we propose to alternatively estimate positive neighbors which refer to data close to any true target positive. An optimization problem for positive neighbor estimation from unlabeled data is derived and solved by aligning the cross-person score distributions together with optimizing for multiple graphs based label propagation. To utilize the positive neighbors to learn discriminative classification model, a reliable multiple region metric learning method is proposed to learn a target adaptive metric using regularized affine hulls of positive neighbors as positive regions. Experimental results demonstrate the effectiveness of the proposed method. In the semi-supervised learning scenario, we propose a discriminative feature learning using all available information from the surveillance videos. To enrich the labeled data from target camera pair, image sequences (videos) of the tagged persons are collected from the surveillance videos by human tracking. To extract the discriminative and adaptable video feature representation, we propose to model the intra-view variations by a video variation dictionary and a video level adaptable feature by multiple sources domain adaptation and an adaptability-discriminability fusion. First, a novel video variation dictionary learning is proposed to model the large intra-view variations and solved as a constrained sparse dictionary learning problem. Second, a frame level adaptable feature is generated by multiple sources domain adaptation using the variation modeling. By mining the discriminative information of the frames from the reconstruction error of the variation dictionary, an adaptability-discriminability (AD) fusion is proposed to generate the video level adaptable feature. Experimental results demonstrate the effectiveness of the proposed method.

Cognitive Behavioural Analysis System of Psychotherapy (CBASP) has been developed to treat individuals suffering from chronic depression. There is a growing evidence base to suggest that CBASP is effective for these individuals. Given these findings, it is important to understand the process of change during CBASP and how it is affected by the components of the therapy. Purpose: A systematic review and meta analysis aimed to establish whether there is evidence for one of the theoretical foundations of CBASP; that a hostile-submissive interpersonal style is associated with major depressive disorder, and in particular with chronic forms of depression, as suggested by McCullough (2000). An empirical study then aimed to investigate whether the components of CBASP are associated with symptom change for chronically depressed individuals during therapy. It also sought to examine whether individuals experienced change differently in CBASP if it was delivered without using Disciplined Personal Involvement (DPI) by the therapist. The aim of this research was to investigate the process of change within the context of CBASP for individuals receiving the therapy, and to evaluate the usefulness of a multilevel modelling approach to analysing singe-case data. Methods: The literature was systematically searched for research reporting a relationship between depression and interpersonal hostility and/or submissiveness and a meta-analysis conducted to test the strength of this relationship. An empirical study presents analyses of two datasets. The first is a multilevel modelling analysis of data from a CBASP case series, seeking to determine what role the components of CBASP have in symptom change during therapy. A single-case, multiple baseline study then examined the process of symptom change during CBASP. This study included individuals experiencing chronic depression, who completed a series of baseline observations followed by up to 20 sessions of CBASP over a six-month period. Participants were assigned to either receive manualised CBASP, or a form of CBASP without the interpersonal focus. The latter study employed mixed models to evaluate change in individuals in CBASP, and sought to evaluate this novel approach to single-case analysis. Results. The meta analytic review provided preliminary support for McCullough’s (2000) hypothesis that chronically depressed individuals tend to present as more hostile and submissive than individuals with first-episode MDD. Findings from the empirical study suggest that acquisition learning in relation to the situational analysis exercise in CBASP is associated with symptom change but not learning in relation to the interpersonal discrimination exercise. Findings from the single-case analysis, however, provided limited evidence that CBASP without the interpersonal focus is associated with less change over the first few sessions of therapy than CBASP. Multilevel modeling analysis of single cases appeared to provide a useful approach to evaluating within-individual change in therapy, compared with traditional methods such as clinically significant change indices. Discussion: The findings of this thesis provide preliminary evidence for components of McCullough’s (2000) CBASP model. The review’s results pointed to a need for more methodologically sound studies to further investigate the role of interpersonal style in the aetiology and maintenance of chronic depression. Analyses in the empirical study appeared to support the use of Situational Analysis in bringing about symptom change in therapy, but findings were mixed in relation to the interpersonal components of CBASP. The use of a small-N design with multiple baselines allowed for a preliminary analysis of the role of DPI, but incomplete data limited this analysis to the first half of therapy.