954 resultados para dynamic factor models


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Cycloidal drives are compact, high-ratio gear transmission systems used in a wide range of mechanical applications from conveyor drives to articulated robots. This research hypothesises that these drives can be successfully applied in dynamic loading situations and thereby focuses on the understanding of differences between static and dynamic loading conditions where load varies with time. New methods of studying the behaviour of these drives under static and dynamic loading circumstances were developed, leading to novel understanding and knowledge. A new model was developed to facilitate research and development on Cycloidal drives with potential benefits for manufacturing, robotics and mechanical-process-industries worldwide.

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Internationally there is a growing interest in the mental wellbeing of young people. However, it is unclear whether mental wellbeing is best conceptualized as a general wellbeing factor or a multidimensional construct. This paper investigated whether mental wellbeing, measured by the Mental Health Continuum-Short Form (MHC-SF), is best represented by: (1) a single-factor general model; (2) a three-factor multidimensional model or (3) a combination of both (bifactor model). 2,220 young Australians aged between 16 and 25 years completed an online survey including the MHC-SF and a range of other wellbeing and mental ill-health measures. Exploratory factor analysis supported a bifactor solution, comprised of a general wellbeing factor, and specific group factors of psychological, social and emotional wellbeing. Confirmatory factor analysis indicated that the bifactor model had a better fit than competing single and three-factor models. The MHC-SF total score was more strongly associated with other wellbeing and mental ill-health measures than the social, emotional or psychological subscale scores. Findings indicate that the mental wellbeing of young people is best conceptualized as an overarching latent construct (general wellbeing) to which emotional, social and psychological domains contribute. The MHC-SF total score is a valid and reliable measure of this general wellbeing factor.

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This paper illustrates the application of a new technique, based on Support Vector Clustering (SVC) for the direct identification of coherent synchronous generators in a large interconnected Multi-Machine Power Systems. The clustering is based on coherency measures, obtained from the time domain responses of the generators following system disturbances. The proposed clustering algorithm could be integrated into a wide-area measurement system that enables fast identification of coherent clusters of generators for the construction of dynamic equivalent models. An application of the proposed method is demonstrated on a practical 15 generators 72-bus system, an equivalent of Indian Southern grid in an attempt to show the effectiveness of this clustering approach. The effects of short circuit fault locations on coherency are also investigated.

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Sugganahalli, a rural vernacular community in a warm-humid region in South India, is under transition towards adopting modern construction practices. Vernacular local building elements like rubble walls and mud roofs are given way to burnt brick walls and reinforced cement concrete (RCC)/tin roofs. Over 60% of Indian population is rural, and implications of such transitions on thermal comfort and energy in buildings are crucial to understand. Vernacular architecture evolves adopting local resources in response to the local climate adopting passive solar designs. This paper investigates the effectiveness of passive solar elements on the indoor thermal comfort by adopting modern climate-responsive design strategies. Dynamic simulation models validated by measured data have also been adopted to determine the impact of the transition from vernacular to modern material-configurations. Age-old traditional design considerations were found to concur with modern understanding into bio-climatic response and climate-responsiveness. Modern transitions were found to increase the average indoor temperatures in excess of 7 degrees C. Such transformations tend to shift the indoor conditions to a psychrometric zone that is likely to require active air-conditioning. Also, the surveyed thermal sensation votes were found to lie outside the extended thermal comfort boundary for hot developing countries provided by Givoni in the bio-climatic chart.

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Representatives of several Internet service providers (ISPs) have expressed their wish to see a substantial change in the pricing policies of the Internet. In particular, they would like to see content providers (CPs) pay for use of the network, given the large amount of resources they use. This would be in clear violation of the ``network neutrality'' principle that had characterized the development of the wireline Internet. Our first goal in this article is to propose and study possible ways of implementing such payments and of regulating their amount. We introduce a model that includes the users' behavior, the utilities of the ISP and of the CPs, and, the monetary flow that involves the content users, the ISP and CP, and, in pUrticular, the CP's revenues from advertisements. We consider various game models and study the resulting equilibria; they are all combinations of a noncooperative game (in which the ISPs and CPs determine how much they will charge the users) with a ``cooperative'' one on how the CP and the ISP share the payments. We include in our model a possible asymmetric weighting parameter (that varies between zero to one). We also study equilibria that arise when one of the CPs colludes with the TSP. We also study two dynamic game models as well as the convergence of prices to the equilibrium values.

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This work is licensed under a Creative Commons Attribution 3.0 License.

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Humans are particularly adept at modifying their behavior in accordance with changing environmental demands. Through various mechanisms of cognitive control, individuals are able to tailor actions to fit complex short- and long-term goals. The research described in this thesis uses functional magnetic resonance imaging to characterize the neural correlates of cognitive control at two levels of complexity: response inhibition and self-control in intertemporal choice. First, we examined changes in neural response associated with increased experience and skill in response inhibition; successful response inhibition was associated with decreased neural response over time in the right ventrolateral prefrontal cortex, a region widely implicated in cognitive control, providing evidence for increased neural efficiency with learned automaticity. We also examined a more abstract form of cognitive control using intertemporal choice. In two experiments, we identified putative neural substrates for individual differences in temporal discounting, or the tendency to prefer immediate to delayed rewards. Using dynamic causal models, we characterized the neural circuit between ventromedial prefrontal cortex, an area involved in valuation, and dorsolateral prefrontal cortex, a region implicated in self-control in intertemporal and dietary choice, and found that connectivity from dorsolateral prefrontal cortex to ventromedial prefrontal cortex increases at the time of choice, particularly when delayed rewards are chosen. Moreover, estimates of the strength of connectivity predicted out-of-sample individual rates of temporal discounting, suggesting a neurocomputational mechanism for variation in the ability to delay gratification. Next, we interrogated the hypothesis that individual differences in temporal discounting are in part explained by the ability to imagine future reward outcomes. Using a novel paradigm, we imaged neural response during the imagining of primary rewards, and identified negative correlations between activity in regions associated the processing of both real and imagined rewards (lateral orbitofrontal cortex and ventromedial prefrontal cortex, respectively) and the individual temporal discounting parameters estimated in the previous experiment. These data suggest that individuals who are better able to represent reward outcomes neurally are less susceptible to temporal discounting. Together, these findings provide further insight into role of the prefrontal cortex in implementing cognitive control, and propose neurobiological substrates for individual variation.

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Este trabalho tem por objetivo avaliar o comportamento dinâmico de pisos mistos (aço-concreto) sob a ação de cargas provenientes das atividades humanas rítmicas, especificamente a prática de ginástica aeróbica, sob o ponto de vista do conforto humano. Tal avaliação torna-se necessária por crescentes problemas estruturais associados às vibrações excessivas, decorrentes da concepção de sistemas estruturais com baixos níveis de amortecimento e com frequências naturais cada vez mais baixas e bastante próximas das faixas de frequência das excitações associadas às atividades humanas rítmicas. O modelo estrutural investigado baseiase em um piso misto (aço-concreto) submetido a aulas de ginástica aeróbica. A modelagem numérica do piso misto investigado foi realizada com base no emprego do programa ANSYS e foram utilizadas técnicas de discretização por meio do método dos elementos finitos (MEF). As cargas aplicadas sobre o piso, oriundas das atividades aeróbicas, são simuladas através de dois modelos de carregamentos dinâmicos distintos. Uma extensa análise paramétrica foi desenvolvida sobre o modelo estrutural investigado e a resposta dinâmica do sistema foi obtida, em termos dos deslocamentos e das acelerações, e comparada com os limites recomendados por normas e critérios de projeto. A resposta dinâmica do piso estudado viola os critérios de projeto relativos ao conforto humano e indica níveis de vibrações excessivas nos casos de carregamento dinâmicos analisados nesta dissertação.

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O atual nível das mudanças uso do solo causa impactos nas mudanças ambientais globais. Os processos de mudanças do uso e cobertura do solo são processos complexos e não acontecem ao acaso sobre uma região. Geralmente estas mudanças são determinadas localmente, regionalmente ou globalmente por fatores geográficos, ambientais, sociais, econômicos e políticos interagindo em diversas escalas temporais e espaciais. Parte desta complexidade é capturada por modelos de simulação de mudanças do uso e cobertura do solo. Uma etapa do processo de simulação do modelo CLUE-S é a quantificação da influência local dos impulsores de mudança sobre a probabilidade de ocorrência de uma classe de uso do solo. Esta influência local é obtida ajustando um modelo de regressão logística. Um modelo de regressão espacial é proposto como alternativa para selecionar os impulsores de mudanças. Este modelo incorpora a informação da vizinhança espacial existente nos dados que não é considerada na regressão logística. Baseado em um cenário de tendência linear para a demanda agregada do uso do solo, simulações da mudança do uso do solo para a microbacia do Coxim, Mato Grosso do Sul, foram geradas, comparadas e analisadas usando o modelo CLUE-S sob os enfoques da regressão logística e espacial para o período de 2001 a 2011. Ambos os enfoques apresentaram simulações com muito boa concordância, medidas de acurácia global e Kappa altos, com o uso do solo para o ano de referência de 2004. A diferença entre os enfoques foi observada na distribuição espacial da simulação do uso do solo para o ano 2011, sendo o enfoque da regressão espacial que teve a simulação com menor discrepância com a demanda do uso do solo para esse ano.

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The use of L1 regularisation for sparse learning has generated immense research interest, with successful application in such diverse areas as signal acquisition, image coding, genomics and collaborative filtering. While existing work highlights the many advantages of L1 methods, in this paper we find that L1 regularisation often dramatically underperforms in terms of predictive performance when compared with other methods for inferring sparsity. We focus on unsupervised latent variable models, and develop L1 minimising factor models, Bayesian variants of "L1", and Bayesian models with a stronger L0-like sparsity induced through spike-and-slab distributions. These spike-and-slab Bayesian factor models encourage sparsity while accounting for uncertainty in a principled manner and avoiding unnecessary shrinkage of non-zero values. We demonstrate on a number of data sets that in practice spike-and-slab Bayesian methods outperform L1 minimisation, even on a computational budget. We thus highlight the need to re-assess the wide use of L1 methods in sparsity-reliant applications, particularly when we care about generalising to previously unseen data, and provide an alternative that, over many varying conditions, provides improved generalisation performance.

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This paper belong to national "973" technological project undertaken by Shengli Oilfield. Work area is composed of turbidite reservoir of S212 and delta reservoir of S283 of Sheng2 unit in Shengtuo Oilfield that has a 36 years water injection development history. Change of the macroscopic, microscopic and filterational parameters and its mechanism have been studied in the 4 water-cut stages i.e. the primary, moderate, high and supper-high stage by using multi-disciplinary theories and approaches, computer techniques and physical simulation comprehensively. Dynamic reservoir models to different water-cut stages have been established. The study of forming mechanism and distribution of residual oil revealed the main types and spatial distribution of residual oil in different water-cut stages and the distribution mode has also been built up. Macroscopic, microscopic and filterational parameters selecting principle, optimizing and selecting standard, matching standard and laws and related database of various dynamic parameters in different water-cut stages have been established, which laid good basis for revealing reservoir macroscopic, microscopic and filterational parameters' dynamic change and residual oil distribution. The study indicated that in general, the macroscopic, microscopic and filterational parameters will slowly increase and become better in both shallow turbidite and delta reservoirs with the increasing of water cut, but different reservoirs have their own characteristics and change laws. Parameters of I~2 unit, whose petrophysical properties are better, increase more quickly than 8~3, whose petrophysical properties are more unfavorable. The changes was relatively quickly in high water-cut stage, while relatively slowly from primary to moderate and from high to supper-high water-cut stage. This paper firstly put forward that reservoir macroscopic, microscopic and filterational parameters are controlled by dynamic geological function of reservoir fluid, which is considered the major reason of reservoir parameters' dynamic changes and residual oil formation and distribution during reservoir development. Physical simulation of filterational parameters verified that forming mechanism and distribution of residual oil in different water-cut stages are also controlled by dynamic geological function of reservoir fluid. The idea of fluid geological function during reservoir development developed the theory of development geology, and has important practical values. This paper firstly constructed dynamic geological and mathematical models and five modes of residual oil distribution in Shengtuo Oilfield, and achieved four-dimensional forecast of residual oil distribution in different watercut stages. Dynamic changes and mechanism of macroscopic, microscopic and fliterational parameters of reservoir and their change process have been revealed. Forecast of residual oil distribution has been achieved by computers. This paper established the related theories, approaches and techniques for residual oil study, characterization and in different water-cut stages, and realized dynamic forecast of residual oil. It gained remarkable economic benefit and social effect in guiding field development. These theories and techniques had important meaningfulness for residual oil prediction in the terrestrial faulted basins not only in Shengli Oilfield but also in the east of China. Furthermore, this study has developed the theory of development geology.

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Landslides are widely distributed along the main stream banks of the Three Gorges Reservoir area. Especially with the acceleration of the human economic activities in the recent 30 years, the occurrence of landslide hazards in the local area trends to be more serious. Because of the special geological, topographic and climatic conditions of the Three Gorges areas, many Paleo-landslides are found along the gentle slope terrain of the population relocation sites. Under the natural condition, the Paleo-landslides usually keep stable. The Paleo-landslides might revive while they are influenced under the strong rainfall, water storage and migration engineering disturbance. Therefore, the prediction and prevention of landslide hazards have become the important problem involving with the safety of migration engineering of the Three Gorges Reservoir area.The past research on the landslides of the Three Gorges area is mainly concentrated on the stability analysis of individual landslide, and importance was little attached to the knowledge on the geological environment background of the formation of regional landslides. So, the relationship between distribution and evolution of landslides and globe dynamic processes was very scarce in the past research. With further study, it becomes difficult to explain the reasons for the magnitude and frequency of major geological hazards in terms of single endogenic or exogenic processes. It is possible to resolve the causes of major landslides in the Three Gorges area through the systematic research of regional tectonics and river evolution history.In present paper, based on the view of coupling of earth's endogenic and exogenic processes, the author researches the temporal and spacial distribution and formation evolution of major landslides(Volume^lOOX 104m3) in the Three Gorges Reservoir area through integration of first-hand sources statistics, .geological evolution history, isotope dating and numerical simulation method etc. And considering the main formation factors of landslides (topography, geology and rainfall condition), the author discusses the occurrence probability and prediction model of rainfall induced landslides.The distribution and magnitude of Paleo-landslides in the Three Gorges area is mainly controlled by lithology, geological structure, bank slope shape and geostress field etc. The major Paleo-landslides are concentrated on the periods 2.7-15.0 X 104aB.R, which conrresponds to the warm and wettest Paleoclimate stages. In the same time, the Three Gorges area experiences with the quickest crust uplift phase since 15.0X 104aB.P. It is indicated that the dynamic factor of polyphase major Paleo-landslides is the coupling processes of neotectonic movement and Quaternary climate changes. Based on the numerical simulation results of the formation evolution of Baota landslide, the quick crust uplift makes the deep river incision and the geostress relief causes the rock body of banks flexible. Under the strong rainfall condition, the pore-water pressure resulted from rain penetration and high flood level can have the shear strength of weak structural plane decrease to a great degree. Therefore, the bank slope is easy to slide at the slope bottom where shear stress concentrates. Finally, it forms the composite draught-traction type landslide of dip stratified rocks.The susceptibility idea for the rainfall induced landslide is put forward in this paper and the degree of susceptibility is graded in terms of the topography and geological conditions of landslides. Base on the integration with geological environment factors and rainfall condition, the author gives a new probabilistic prediction model for rainfall induced landslides. As an example from Chongqing City of the Three Gorges area, selecting the 5 factors of topography, lithology combination, slope shape, rock structure and hydrogeology and 21 kinds of status as prediction variables, the susceptibility zonation is carried out by information methods. The prediction criterion of landslides is established by two factors: the maximum 24 hour rainfall and the antecedent effective precipitation of 15 days. The new prediction model is possible to actualize the real-time regional landslide prediction and improve accuracy of landslide forecast.

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In this study, the recognition of handwritten Chinese characters is studied by analyzing their static and dynamic factors. The main results are as follows: 1) The static factors of handwritten Chinese characters that have significant effects on recognition are similarity with other handwritten characters, changes of relative positions of strokes and shape changes of strokes; 2) The methods of writing handwritten Chinese characters used by different calligraphers are significantly consistent; 3)The subjects are sensitive to dynamic information contained in the handwritten Chinese characters; 4)The dynamic factors that effect recognition of handwritten Chinese characters are changes in the number of strokes and changes in the direction of strokes; 5)Although both static and dynamic factors of handwritten Chinese characters have significant effects on the recognition of handwritten Chinese characters, the effect of static factors plays a major role and the effect of dynamic factor plays of secondary role.

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BACKGROUND: Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. RESULTS: Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. CONCLUSIONS: Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data.

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We evaluate the conditional performance of U.K. equity unit trusts using the approach of Lynch and Wachter (2007, 2008) relative to three conditional linear factor models. We find significant time variation in the conditional performance of some trust portfolios and individual trusts using the lag term spread as the information variable. The conditional performance of the trusts is countercyclical and larger trusts have more countercyclical performance than smaller trusts within certain investment sectors. These patterns in conditional trust performance cannot be fully explained by the underlying securities that the trusts hold.