Prioritized Data Compression using Wavelets

The volume of data and the velocity with which it is being generated by com- putational experiments on high performance computing (HPC) systems is quickly outpacing our ability to effectively store this information in its full fidelity. There- fore, it is critically important to identify and study compression methodologies that retain as much information as possible, particularly in the most salient regions of the simulation space. In this paper, we cast this in terms of a general decision-theoretic problem and discuss a wavelet-based compression strategy for its solution. We pro- vide a heuristic argument as justification and illustrate our methodology on several examples. Finally, we will discuss how our proposed methodology may be utilized in an HPC environment on large-scale computational experiments

Deenah

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Monitoramento da condição de uma broca escalonada no processo de furação utilizando análise de sinais, aquisição de imagens e redes neurais

Orientador: Prof. Dr. Dalberto Dias da CostaDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia de Manufatura. Defesa : Curitiba, 10/12/2021Inclui referências: p. 83-93Resumo: Grande parte dos produtos fabricados por meio de processos de usinagem passam em algum momento por um processo de furação. Porém a usinagem de furos complexos pode ser considerada um gargalo, uma vez que o baixo avanço e as sucessivas trocas de ferramentas aumentam o tempo de produção. Por isso o uso de ferramentas combinadas, comumente chamadas de brocas escalonadas, é uma prática recorrente para diminuir essa desvantagem, principalmente na produção de grandes lotes. Porém sua geometria mais complexa, relativamente a uma broca não escalonada, tem influência na sua vida útil, normalmente a quebra de cavaco é deficiente e o desgaste ocorre de diferentes maneiras ao longo das arestas de corte, tornando o controle de vida uma tarefa não trivial. Além dessa preocupação, hoje as empresas metalmecânicas enfrentam a necessidade de adequar seus sistemas de produção para se tornarem aderentes ao ambiente da Indústria 4.0, o que exige o acompanhamento das atividades de manufatura em tempo real incluindo, é claro, o monitoramento da vida útil da ferramenta. Para cumprir esse desafio, uma etapa importante é a seleção e avaliação de sensores capazes de adquirir dados relevantes a serem empregados na avaliação direta e indireta da deterioração desse tipo de ferramenta complexa. Neste contexto o presente trabalho propõe a avaliação da eficácia da utilização de Redes Neurais Artificiais, através das técnicas de aprendizado de máquina e aprendizado profundo, para determinação do fim de vida de uma broca escalonada fabricada em metal duro, com diâmetros aproximados de 18 e 24 mmm fabricada em metal duro, utilizando três técnicas de coleta de sinais. A metodologia proposta consistiu em um sensor de corrente e um acelerômetro próximo ao fuso principal e uma câmera CMOS de alta resolução próxima ao magazine de ferramentas. A partir dos resultados alcançados até o momento, é possível argumentar que as imagens de alta qualidade das arestas de corte da broca puderam ser capturadas sem interferir nas operações de usinagem. Além disso, observou-se que os sinais brutos da aceleração RMS de vibração, nas faixas de rotação da ferramenta, apresentaram uma correlação de 82% em relação a vida útil da broca. Porém somente após a extração das características estatísticas de todos os sinais coletados foi possível o atingimento da máxima acurácia para os dados, 95%. A principal conclusão deste trabalho foi que os dados desses três métodos avaliados podem ser integrados para desenvolver um sistema autônomo, que pode decidir o momento correto para trocar ferramentas complexas como a broca investigada neste trabalho, chegando a uma acurácia global de 96%.Abstract: Most products manufactured through machining processes go through a drilling process at some point of the production chain. However, machining complex holes can be considered a bottleneck, since the low feed and successive tool changes increase production time. Therefore, the use of combined tools, commonly called step drills, is a recurrent practice to reduce this disadvantage, especially in large batch production. However, its more complex geometry, relative to a non-stepped drill, has an influence on its useful life, normally the chip breaking is deficient, and the wear occurs in different ways along the cutting edges, making the life control a non-trivial task. In addition to this concern, metal-mechanical companies today face the need to adapt their production systems to become adherent to the Industry 4.0 environment, which requires monitoring of manufacturing activities in real time, including, of course, monitoring the useful life of the tool. To meet this challenge, an important step is the selection and evaluation of sensors capable of acquiring relevant data to be used in the direct and indirect evaluation of the deterioration of this type of complex tool. In this context, the present work proposes the evaluation of the effectiveness of the use of Artificial Neural Networks, through machine learning and deep learning techniques, to determine the end of an carbide step drill, with 18 to 24 mm outside diameter, life using three signal acquisition techniques. The proposed methodology consisted of a current sensor and an accelerometer near the main spindle and a high-resolution CMOS camera near the tool magazine. From the results achieved so far, it is possible to argue that high quality images of the drill edges can be captured without interfering with the machining operations. Furthermore, it was observed that the raw signals of the vibration RMS acceleration, in the tool rotation ranges, presented a correlation of 82% in relation to the drill life. However, only after extracting the statistical characteristics of all collected signals was possible to reach the maximum accuracy for the data algorithm, 95%. The main conclusion of this work was that the data from these three evaluated methods can be integrated to develop an autonomous system, which can decide the right time to exchange complex tools like the drill investigated in this work, reaching an overall system accuracy of 96%

XMM-Newton Observation of a Distant X-ray Selected Cluster of Galaxies at z=1.26 with Possible Cluster Interaction

We report on the XMM-Newton (XMM) observation of RXJ1053.7+5735, one of the most distant (z = 1.26) X-ray selected clusters of galaxies, which also shows an unusual double-lobed X-ray morphology, indicative of possible cluster-cluster interaction. The cluster was discovered during our ROSAT deep pointings in the direction of the Lockman Hole. The XMM observations were performed with the European Photon Imaging Camera (EPIC) during the performance verification phase. Total effective exposure time was ~ 100 ksec. The best fit temperature based on a simultaneous fit of spectra from the all EPIC cameras is 4.9(+1.5/-0.9) keV. Metallicity is poorly constrained even using the joint fit of all spectra, with an upper limit on the iron abundance of 0.62 solar. Using the best fit model parameters, we derived a bolometric luminosity of L(bol) = 3.4x10^44 h_{50}^-2 erg /s. Despite the fact that it was observed at fairly large off-axis angle, the temperature errors are much smaller compared with those of typical measurements based on ASCA or Beppo-Sax observations of z > 0.6 clusters, demonstrating the power of the XMM for determining the X-ray temperature for high-z clusters. The measured temperature and luminosity show that one can easily reach the intrinsically X-ray faint and cool cluster regime comparable with those of z ~ 0.4 clusters observed by past satellites. The new cluster temperature and L(bol) we have measured for RXJ1053.7+5735 is consistent with a weak/no evolution of the L(bol) - Tx relation out to z ~ 1.3, which lends support to a low Omega universe, although more data-points of z > 1 clusters are required for a more definitive statement. The caution has to be also exercised in interpreting the result, because of the uncertainty associated with the dynamical status of this cluster.Comment: Accepted for pubblication in A&A. 7 figures (One color figure is changed to black and white.