This thesis concerns the acquisition of newly developed production equipment in factories. Types of Manufacturing Technology Acquisition (MTA) can range from in-house development through to outright purchase from a supplier. MTA projects often fail because the new equipment fails to perform well enough or the expected financial benefits are not achieved. The purpose of this research has been to find out how to select the right manufacturing technology acquisition projects, and having chosen them, how to make them successful. Very little guidance on this subject was found in the literature. Therefore the approach taken was to adapt techniques widely used in the much more deeply researched field of New Product Development (NPD). The Success Factor method was applied by conducting interviews with managers in a number of factories to develop lists of factors thought to affect success or failure in their MTA projects. The Portfolio method from NPD was adapted to the case of managing a total MT'A budget, and developed through its use in three annual cycles of equipment acquisition in the researcher's Company. A formula for Expected Commercial Value in NPD was modified to become Expected Manufacturing Benefit, and tried out in practice. An important consideration in manufacturing equipment projects was discovered to be the replication of projects which had proved successful, and ways to incorporate this factor into the project selection procedure were developed. A detailed case study of a single project was carried out, which verified the validity of the findings at the level of a single project. This case study also revealed the considerable impact that corporate strategic events can have on technology acquisitions. This led to a case study on corporate strategy in the Company being carried out. Finally the findings were brought together to present a complete model for managing MTA.

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Made available in DSpace on 2014-06-11T19:30:19Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-01-24Bitstream added on 2014-06-13T21:00:49Z : No. of bitstreams: 1 martins_ml_me_bauru_prot.pdf: 4978378 bytes, checksum: ea637726b18b8011f5c90e28e4cbad26 (MD5) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / Para o aprimoramento de técnicas de diagnóstico e tratamentos de doenças o desenvolvimento de materiais magnéticos é de primordial importância. Neste trabalho as ferritas de manganês e zindo de fórmula geral Mn(1-x)ZNxFe204, onde x assume os valores entre 0,15 e 0,30, foram preparadas pelo método dos precursores poliméricos e caracterizadas por: análises térmicas (termogravimetria associada com análise térmica diferencial (TG/DTA), análise composicional semi-quantitativa por energia dispersiva de raios X (EDX), difratometria de raios X (DRX), refinamento estrutural (Método de Rietveld), adsorção de N2 a 77K (BET), microscopia eletrônica de varredura (MEV), microscopia óptica, espectroscopia de absorção atômica (testes de lixiviação) e medidas de susceptibilidade e permeabilidade magnética. As amostras de ferritas foram também revestidas com quitosana e analisadas com algumas dessas técnicas. Os materiais foram desenvolvidos com o objetivo de utilizá-los como rastreador em biosusceptometria de corrente alternada(BAC), um método de diagnóstico. O método de síntese empregado proporcionou materiais monofásicos em ferrita com baixa porosidade e área superficial. Os materiais apresentaram baixa lixiviação de metais em soluções que simulam os meios fisiológicos do trato gastrointestinal. O revestimento com quitosana foi eficiente suprimindo a dissolução de metais em meios com pHs 7,5 e 6,8. Em pH 1,2, o revestimento se mostrou menos eficiente, porém, ainda assim foi capaz de reduzir a dissolução das espécies que constituem a ferrita. Quanto ao comportamento magnético, as amostras apresentaram magnetizações satisfatórias em 10K e em temperatura ambiente. A amostra contendo zinco a x=0,15 apresentou a mais alta saturação magnética em temperatura ambiente e foi utilizada em um teste com a técnica de BAC apresentando resultados satisfatórios / The development of magnetic materials is of primary importance for the improvement of diagnostic techniques and treatments of diseases. In this work, the manganese and zinc ferrites with general formula of Mn(1-x)ZNxFe204 and 0,15< x <0,30, was prepared by polymeric precursor method and characterized by the physical techniques as: thermal analysis [thermogravimetry and differential thermal analysis (TG/DTA)], ssemi-quantitative analysis of the composition by energy dispersive X ray (EDX), X ray diffractometry (XRD), structural refinement (Rietveld Method), N2 adsorption analysis at 77k (BET), scanning electron microscopy (SEM), optical microscopy, atomic absorption spectroscopy (lixiviation test) and magnetic susceptibility measurements. Chitosan coated samples were also prepared which were analyzed by some of those technique. The materials were developed to be used as a tracer in the alternating current biosusceptometry (ACB), a diagnostic method. The employed synthesis method provided monophasic samples of ferrites with low porosity and surface area. The materials showed low metal solubility in the media that simulate gastrointestinal tract solutions. The chitosan coating was efficient to suppress the metal dissolution in solutions with pHs 7.5 and 6.8. In pH=1.2 medium, the coating was not efficient, but still reduced the resolution of ferrite components. The magnetic measurements showed that the samples have satisfactory magnetizations at 10K and room temperature. The sample with x=0.15 exhibited the highest magnetic saturation at room temperature and it was applied in an ACB technique which showed satisfactory results

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A first order shear deformable finite strip with support displacements is introduced. Both nonlinear geometric effects and initial deflections may be considered. Support displacements are introduced by the use of a set of basis functions for support degrees of freedom. Each basis function is obtained by the solution of a Timoshenko beam under a unit displacement of the respective support degree of freedom. They are combined with the standard beam functions. The new finite strip can be applied to the analysis of rectangular containers and continuous plates with elastically restrained supports. The elastic restraints are introduced with independent springs acting along supports and nodal lines. The finite strip is extended to the analysis of unsymmetrically laminated clamped composite plates by the definition of equivalent elasticity modulii to find the basis functions. The new finite strip is used in the analysis of rectangular containers. It is shown that compressive horizontal forces exist in the walls of flexible containers filled with a liquid. This can only be predicted by the simultaneous consideration of the movement of the wall corners and the geometric nonlinearities, as can be done with the present model. A 'mode transition finite strip' which has unequal numbers of modes in the nodal lines is introduced. It can be used to economize the finite strip analysis of plates with loads that need a large number of modes, but spread only across a few of the strips. Also a study of the determination of transverse shear stresses by the use of the equilibrium equations and the displacement solution is made, resulting in some important and interesting observations. / Applied Science, Faculty of / Civil Engineering, Department of / Graduate

This thesis uses the Immersed Boundary Method (IBM) to simulate the movement of a human heart. The IBM was developed by Charles Peskin in the 70’s to solve Fluid-Structure Interaction models (FSI). The heart is embedded inside a fluid (blood) which moves according to the Navier-Stokes equation. The Navier-Stokes equation is solved by the Spectral Method. Forces on the heart muscle can be divided into two kinds: Active Force and Passive Force. Passive includes the effect of curvature (Peskin’s model), spring model, and the torsional spring (or beam) model. Active force is modeled by the 3-element Hill model, which was used in the 30’s to model skeletal muscle. We performed simulations with different combinations of these four forces. Numerical simulations are performed using MATLAB. We downloaded Peskin’s code from the Internet and modified the Force.m file to include the above four forces. This thesis only considers heart muscle movement in the organ (macroscopic) scale.

State-of-the-art deep learning methods for outlier detection make the assumption that outliers will appear far away from inlier data in the latent space produced by distribution mapping deep networks. However, this assumption fails in practice,because the divergence penalty adopted for this purpose encourages mapping outliers into the same high-probability regions as inliers. To overcome this shortcoming,we introduce a novel deep learning outlier detection method, called Outlier Preserving Distribution Mapping Autoencoder (OP-DMA), which succeeds to map outliers to low probability regions in the latent space of an autoencoder. For this we leverage the insight that outliers are likely to have a higher reconstruction error than inliers. We thus achieve outlier-preserving distribution mapping through weighting the reconstruction error of individual points by the value of a multivariate Gaussian probability density function evaluated at those points. This weighting implies that outliers will result in an overall penalty if they are mapped to low-probability regions. We show that if the global minimum of our newly proposed loss function is achieved,then our OP-DMA maps inliers to regions with a Mahalanobis distance less than \delta, and outliers to regions past this \delta, \delta being the inverse ChiSquared CDF evaluated at 1−\alpha with \alpha the percentage of outliers in the dataset. We evaluated OP-DMA on 11 benchmark real-world datasets and compared its performance against 7 different state-of-the-art outlier detection methods, including ALOCC and MO-GAAL. Our experiments show that OP-DMA outperforms the state-of-the-art methods on 7 of the datasets, and performs second best on 3 of the remaining 4 datasets, while no other method won on more than 1 dataset.