Estimation of magnet separation for magnetic suspension applications

This thesis describes a form of non-contact measurement using two dimensional hall effect sensing to resolve the location of a moving magnet which is part of a ‘magnetic spring’ type suspension system. This work was inspired by the field of Space Robotics, which currently relies on solid link suspension techniques for rover stability. This thesis details the design, development and testing of a novel magnetic suspension system with a possible application in space and terrestrial based robotics, especially when the robot needs to traverse rough terrain. A number of algorithms were developed, to utilize experimental data from testing, that can approximate the separation between magnets in the suspension module through observation of the magnetic fields. Experimental hardware was also developed to demonstrate how two dimensional hall effect sensor arrays could provide accurate feedback, with respects to the magnetic suspension modules operation, so that future work can include the sensor array in a real-time control system to produce dynamic ride control for space robots. The research performed has proven that two dimensional hall effect sensing with respects to magnetic suspension is accurate, effective and suitable for future testing.

Rainfall estimation by rain gauge-radar combination: a concurrent multiplicative-additive approach

A procedure (concurrent multiplicative-additive objective analysis scheme [CMA-OAS]) is proposed for operational rainfall estimation using rain gauges and radar data. On the basis of a concurrent multiplicative-additive (CMA) decomposition of the spatially nonuniform radar bias, within-storm variability of rainfall and fractional coverage of rainfall are taken into account. Thus both spatially nonuniform radar bias, given that rainfall is detected, and bias in radar detection of rainfall are handled. The interpolation procedure of CMA-OAS is built on Barnes' objective analysis scheme (OAS), whose purpose is to estimate a filtered spatial field of the variable of interest through a successive correction of residuals resulting from a Gaussian kernel smoother applied on spatial samples. The CMA-OAS, first, poses an optimization problem at each gauge-radar support point to obtain both a local multiplicative-additive radar bias decomposition and a regionalization parameter. Second, local biases and regionalization parameters are integrated into an OAS to estimate the multisensor rainfall at the ground level. The procedure is suited to relatively sparse rain gauge networks. To show the procedure, six storms are analyzed at hourly steps over 10,663 km2. Results generally indicated an improved quality with respect to other methods evaluated: a standard mean-field bias adjustment, a spatially variable adjustment with multiplicative factors, and ordinary cokriging.

A systematic method of parameterisation estimation using data assimilation

In numerical weather prediction, parameterisations are used to simulate missing physics in the model. These can be due to a lack of scientific understanding or a lack of computing power available to address all the known physical processes. Parameterisations are sources of large uncertainty in a model as parameter values used in these parameterisations cannot be measured directly and hence are often not well known; and the parameterisations themselves are also approximations of the processes present in the true atmosphere. Whilst there are many efficient and effective methods for combined state/parameter estimation in data assimilation (DA), such as state augmentation, these are not effective at estimating the structure of parameterisations. A new method of parameterisation estimation is proposed that uses sequential DA methods to estimate errors in the numerical models at each space-time point for each model equation. These errors are then fitted to pre-determined functional forms of missing physics or parameterisations that are based upon prior information. We applied the method to a one-dimensional advection model with additive model error, and it is shown that the method can accurately estimate parameterisations, with consistent error estimates. Furthermore, it is shown how the method depends on the quality of the DA results. The results indicate that this new method is a powerful tool in systematic model improvement.

Utveckling av metod för att bedöma behovet av ogräsbekämpning i spår

A system for weed management on railway embankments that is both adapted to the environment and efficient in terms of resources requires knowledge and understanding about the growing conditions of vegetation so that methods to control its growth can be adapted accordingly. Automated records could complement present-day manual inspections and over time come to replace these. One challenge is to devise a method that will result in a reasonable breakdown of gathered information that can be managed rationally by affected parties and, at the same time, serve as a basis for decisions with sufficient precision. The project examined two automated methods that may be useful for the Swedish Transport Administration in the future: 1) A machine vision method, which makes use of camera sensors as a way of sensing the environment in the visible and near infrared spectrum; and 2) An N-Sensor method, which transmits light within an area that is reflected by the chlorophyll in the plants. The amount of chlorophyll provides a value that can be correlated with the biomass. The choice of technique depends on how the information is to be used. If the purpose is to form a general picture of the growth of vegetation on railway embankments as a way to plan for maintenance measures, then the N-Sensor technique may be the right choice. If the plan is to form a general picture as well as monitor and survey current and exact vegetation status on the surface over time as a way to fight specific vegetation with the correct means, then the machine vision method is the better of the two. Both techniques involve registering data using GPS positioning. In the future, it will be possible to store this information in databases that are directly accessible to stakeholders online during or in conjunction with measures to deal with the vegetation. The two techniques were compared with manual (visual) estimations as to the levels of vegetation growth. The observers (raters) visual estimation of weed coverage (%) differed statistically from person to person. In terms of estimating the frequency (number) of woody plants (trees and bushes) in the test areas, the observers were generally in agreement. The same person is often consistent in his or her estimation: it is the comparison with the estimations of others that can lead to misleading results. The system for using the information about vegetation growth requires development. The threshold for the amount of weeds that can be tolerated in different track types is an important component in such a system. The classification system must be capable of dealing with the demands placed on it so as to ensure the quality of the track and other pre-conditions such as traffic levels, conditions pertaining to track location, and the characteristics of the vegetation. The project recommends that the Swedish Transport Administration: Discusses how threshold values for the growth of vegetation on railway embankments can be determined Carries out registration of the growth of vegetation over longer and a larger number of railway sections using one or more of the methods studied in the project Introduces a system that effectively matches the information about vegetation to its position Includes information about the growth of vegetation in the records that are currently maintained of the track’s technical quality, and link the data material to other maintenance-related databases Establishes a number of representative surfaces in which weed inventories (by measuring) are regularly conducted, as a means of developing an overview of the long-term development that can serve as a basis for more precise prognoses in terms of vegetation growth Ensures that necessary opportunities for education are put in place

A Gradient-Type Method For Real-Time State Estimation Of Water Distribution Networks

Drinking water distribution networks risk exposure to malicious or accidental contamination. Several levels of responses are conceivable. One of them consists to install a sensor network to monitor the system on real time. Once a contamination has been detected, this is also important to take appropriate counter-measures. In the SMaRT-OnlineWDN project, this relies on modeling to predict both hydraulics and water quality. An online model use makes identification of the contaminant source and simulation of the contaminated area possible. The objective of this paper is to present SMaRT-OnlineWDN experience and research results for hydraulic state estimation with sampling frequency of few minutes. A least squares problem with bound constraints is formulated to adjust demand class coefficient to best fit the observed values at a given time. The criterion is a Huber function to limit the influence of outliers. A Tikhonov regularization is introduced for consideration of prior information on the parameter vector. Then the Levenberg-Marquardt algorithm is applied that use derivative information for limiting the number of iterations. Confidence intervals for the state prediction are also given. The results are presented and discussed on real networks in France and Germany.

The impact of structured teaching methods on the quality of education

This paper estimates the impact of the use of structured methods on the quality of education of the students in primary public school in Brazil. Structure methods encompass a range of pedagogical and managerial instruments applied to the education system. In recent years, several municipalities in the State of São Paulo have contracted out private educational providers to implement these structured methods in their schooling system. Their pedagogical proposal involves structuring curriculum contents, elaboration and use of teachers and students textbooks, and training and supervision of the teachers and instructors. Using a difference in differences estimation strategy, we find that the fourth and eighth grader students in the municipalities with structured methods performed better in Portuguese and Math than students in municipalities not exposed to the methods. We find no differences in approval rates. However, a robustness check is not able to discard the possibility that unobserved municipal characteristics may affect the results.

An evolving possibilistic fuzzy modeling approach for value-at-risk estimation

Market risk exposure plays a key role for nancial institutions risk management. A possible measure for this exposure is to evaluate losses likely to incurwhen the price of the portfolio's assets declines using Value-at-Risk (VaR) estimates, one of the most prominent measure of nancial downside market risk. This paper suggests an evolving possibilistic fuzzy modeling approach for VaR estimation. The approach is based on an extension of the possibilistic fuzzy c-means clustering and functional fuzzy rule-based modeling, which employs memberships and typicalities to update clusters and creates new clusters based on a statistical control distance-based criteria. ePFM also uses an utility measure to evaluate the quality of the current cluster structure. Computational experiments consider data of the main global equity market indexes of United States, London, Germany, Spain and Brazil from January 2000 to December 2012 for VaR estimation using ePFM, traditional VaR benchmarks such as Historical Simulation, GARCH, EWMA, and Extreme Value Theory and state of the art evolving approaches. The results show that ePFM is a potential candidate for VaR modeling, with better performance than alternative approaches.

Quality management and competitiveness: the diffusion of the ISO 9000 standards in Latin America and recommendations for government strategies

Includes bibliography

A Bayesian nonparametric model for Taguchi's on-line quality monitoring procedure for attributes

A Bayesian nonparametric model for Taguchi's on-line quality monitoring procedure for attributes is introduced. The proposed model may accommodate the original single shift setting to the more realistic situation of gradual quality deterioration and allows the incorporation of an expert's opinion on the production process. Based on the number of inspections to be carried out until a defective item is found, the Bayesian operation for the distribution function that represents the increasing sequence of defective fractions during a cycle considering a mixture of Dirichlet processes as prior distribution is performed. Bayes estimates for relevant quantities are also obtained. © 2012 Elsevier B.V.

The quality gap in Chile's education system

Includes bibliography

Efeito de pontas e volumes de aplicação sobre os depósitos da pulverização em plantas de feijoeiro, Bidens pilosa L. e Brachiaria plantaginea (Link) Hitchc

Pós-graduação em Agronomia (Agricultura) - FCA

Análise do suporte de reativos em um sistema elétrico de potência com link HVDC

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Infrastructure, transport and integration: the link with productive development and regional competitiveness

A vision of the role played by infrastructure, transport and related services in the development of competitiveness and productivity is fundamental for proposing public policies linked to productive development. In particular, the supply costs and the quality of public utility and transport services are extremely relevant to countries’ productivity, GDP growth and competitiveness, and also for the development and economic integration of Latin America.