Estimating Sensor Motion from Wide-Field Optical Flow on a Log-Dipolar Sensor

Log-polar image architectures, motivated by the structure of the human visual field, have long been investigated in computer vision for use in estimating motion parameters from an optical flow vector field. Practical problems with this approach have been: (i) dependence on assumed alignment of the visual and motion axes; (ii) sensitivity to occlusion form moving and stationary objects in the central visual field, where much of the numerical sensitivity is concentrated; and (iii) inaccuracy of the log-polar architecture (which is an approximation to the central 20°) for wide-field biological vision. In the present paper, we show that an algorithm based on generalization of the log-polar architecture; termed the log-dipolar sensor, provides a large improvement in performance relative to the usual log-polar sampling. Specifically, our algorithm: (i) is tolerant of large misalignmnet of the optical and motion axes; (ii) is insensitive to significant occlusion by objects of unknown motion; and (iii) represents a more correct analogy to the wide-field structure of human vision. Using the Helmholtz-Hodge decomposition to estimate the optical flow vector field on a log-dipolar sensor, we demonstrate these advantages, using synthetic optical flow maps as well as natural image sequences.

Strategies for Energy-Efficient Resource Management of Hybrid Programming Models

Many scientific applications are programmed using hybrid programming models that use both message passing and shared memory, due to the increasing prevalence of large-scale systems with multicore, multisocket nodes. Previous work has shown that energy efficiency can be improved using software-controlled execution schemes that consider both the programming model and the power-aware execution capabilities of the system. However, such approaches have focused on identifying optimal resource utilization for one programming model, either shared memory or message passing, in isolation. The potential solution space, thus the challenge, increases substantially when optimizing hybrid models since the possible resource configurations increase exponentially. Nonetheless, with the accelerating adoption of hybrid programming models, we increasingly need improved energy efficiency in hybrid parallel applications on large-scale systems. In this work, we present new software-controlled execution schemes that consider the effects of dynamic concurrency throttling (DCT) and dynamic voltage and frequency scaling (DVFS) in the context of hybrid programming models. Specifically, we present predictive models and novel algorithms based on statistical analysis that anticipate application power and time requirements under different concurrency and frequency configurations. We apply our models and methods to the NPB MZ benchmarks and selected applications from the ASC Sequoia codes. Overall, we achieve substantial energy savings (8.74 percent on average and up to 13.8 percent) with some performance gain (up to 7.5 percent) or negligible performance loss.

Adaptive uncertain information fusion to enhance plan selection in BDI agent systems

Correctly modelling and reasoning with uncertain information from heterogeneous sources in large-scale systems is critical when the reliability is unknown and we still want to derive adequate conclusions. To this end, context-dependent merging strategies have been proposed in the literature. In this paper we investigate how one such context-dependent merging strategy (originally defined for possibility theory), called largely partially maximal consistent subsets (LPMCS), can be adapted to Dempster-Shafer (DS) theory. We identify those measures for the degree of uncertainty and internal conflict that are available in DS theory and show how they can be used for guiding LPMCS merging. A simplified real-world power distribution scenario illustrates our framework. We also briefly discuss how our approach can be incorporated into a multi-agent programming language, thus leading to better plan selection and decision making.

Plataforma remota de gestão de um parque de GateBoxes

A monitorização de redes é um aspeto de elevada importância, principalmente em redes de média ou grande dimensão. A necessidade de utilização de uma ferramenta para realização dessa gestão facilita o trabalho e proporciona de uma forma mais rápida e eficaz a identificação de problemas na rede e nos seus sistemas. Neste sentido, o presente trabalho tem como objetivo o desenvolvimento de uma solução para a monitorização de GateBoxes, um dos produtos desenvolvidos e comercializados pela empresa NextToYou. A necessidade de monitorização das GateBoxes, por parte da NextToYou, é essencial para que possa detetar falhas no seu funcionamento ou realizar notificações aquando da deteção de problemas para uma rápida resolução. Neste contexto a empresa decidiu implementar uma ferramenta para a referida monitorização e propôs, no âmbito da tese, o desenvolvimento de uma aplicação que satisfizesse esses propósitos. Disponibilizou então, para o desenvolvimento uma plataforma, a WebForge, e definiu alguns requisitos funcionais dessa ferramenta, tais como, a monitorização remota de informação, gestão de alarmes, geração de avisos e notificações. Para a elaboração deste trabalho foram realizados estudos teóricos sobre o tema da gestão e monitorização remotas, realizando-se posteriormente o desenvolvimento de uma aplicação para a monitorização de GateBoxes. Após a implementação efetuou-se a validação do trabalho realizado através da execução de testes e demonstrações, de forma a poder validar e verificar o desempenho do sistema.

Health systems efficiency after the crisis in the OECD

This work evaluates the efficiency position of the health system of each OECD country. It identifies whether, or not, health systems changed in terms of quality and performance after the financial crisis. The health systems performance was calculated by fixed-effects estimator and by stochastic frontier analysis. The results suggest that many of those countries that the crisis affected the most are more efficient than the OECD average. In addition, some of those countries even managed to reach the top decile in the efficiency ranking. Finally, we analyze the stochastic frontier efficiency scores together with other health indicators to evaluate the health systems’ overall adjustments derived from the crisis.

Les dimensions négligées de l'évaluation de la performance : les valeurs et la qualité de vie au travail

L’objet de cette thèse est l’élaboration d’un modèle logique de mesure du maintien des valeurs, ainsi que son opérationnalisation afin d’entreprendre l’évaluation de la performance des systèmes de santé. Le maintien des valeurs est l’une des quatre fonctions de la théorie de l’action sociale de T.Parsons permettant d’analyser les systèmes d’action. Les autres fonctions sont l’adaptation, la production et l’atteinte des buts. Cette théorie est la base du modèle EGIPSS (évaluation globale et intégrée de la performance des systèmes de santé), dans lequel cette thèse s’insère. La fonction étudiée correspond, dans l’oeuvre de T.Parsons, au sous-système culturel. Elle renvoie à l’intangible, soit à l’univers symbolique par lequel l’action prend son sens et les fonctions du système s’articulent. Le modèle logique de mesure du maintien des valeurs est structuré autour de deux concepts principaux, les valeurs individuelles et organisationnelles et la qualité de vie au travail. À travers les valeurs individuelles et organisationnelles, nous mesurons la hiérarchie et l’intensité des valeurs, ainsi que le niveau de concordance interindividuelle et le degré de congruence entre les valeurs individuelles et organisationnelles. La qualité de vie au travail est composée de plusieurs concepts permettant d’analyser et d’évaluer l’environnement de travail, le climat organisationnel, la satisfaction au travail, les réactions comportementales et l’état de santé des employés. La mesure de ces différents aspects a donné lieu à la conception de trois questionnaires et de trente indicateurs. Ma thèse présente, donc, chacun des concepts sélectionnés et leurs articulations, ainsi que les outils de mesure qui ont été construits afin d’évaluer la dimension du maintien des valeurs. Enfin, nous exposons un exemple d’opérationnalisation de ce modèle de mesure appliqué à deux hôpitaux dans la province du Mato Grosso du Sud au Brésil. Cette thèse se conclut par une réflexion sur l’utilisation de l’évaluation comme outil de gestion soutenant l’amélioration de la performance et l’imputabilité. Ce projet comportait un double enjeu. Tout d’abord, la conceptualisation de la dimension du maintien des valeurs à partir d’une littérature abondante, mais manquant d’intégration théorique, puis la création d’outils de mesure permettant de saisir autant les aspects objectifs que subjectifs des valeurs et de la qualité de vie au travail. En effet, on trouve dans la littérature de nombreuses disciplines et de multiples courants théoriques tels que la psychologie industrielle et organisationnelle, la sociologie, les sciences infirmières, les théories sur le comportement organisationnel, la théorie des organisations, qui ont conçu des modèles pour analyser et comprendre les perceptions, les attitudes et les comportements humains dans les organisations. Ainsi, l’intérêt scientifique de ce projet découle de la création d’un modèle dynamique et intégrateur offrant une synthèse des différents champs théoriques abordant la question de l’interaction entre les perceptions individuelles et collectives au travail, les conditions objectives de travail et leurs influences sur les attitudes et les comportements au travail. D’autre part, ce projet revêt un intérêt opérationnel puisqu’il vise à fournir aux décideurs du système de santé des connaissances et données concernant un aspect de la performance fortement négligé par la plupart des modèles internationaux d’évaluation de la performance.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Information discovery in distributed systems - A novel concept based on modelling of information elements

This thesis attempts to investigate the problems associated with such schemes and suggests a software architecture, which is aimed towards achieving a meaningful discovery. Usage of information elements as a modelling base for efficient information discovery in distributed systems is demonstrated with the aid of a novel conceptual entity called infotron. The investigations are focused on distributed systems and their associated problems. The study was directed towards identifying suitable software architecture and incorporating the same in an environment where information growth is phenomenal and a proper mechanism for carrying out information discovery becomes feasible. An empirical study undertaken with the aid of an election database of constituencies distributed geographically, provided the insights required. This is manifested in the Election Counting and Reporting Software (ECRS) System. ECRS system is a software system, which is essentially distributed in nature designed to prepare reports to district administrators about the election counting process and to generate other miscellaneous statutory reports.

Integrated management of urban wastewater systems: a model-based approach

The main objective pursued in this thesis targets the development and systematization of a methodology that allows addressing management problems in the dynamic operation of Urban Wastewater Systems. The proposed methodology will suggest operational strategies that can improve the overall performance of the system under certain problematic situations through a model-based approach. The proposed methodology has three main steps: The first step includes the characterization and modeling of the case-study, the definition of scenarios, the evaluation criteria and the operational settings that can be manipulated to improve the system’s performance. In the second step, Monte Carlo simulations are launched to evaluate how the system performs for a wide range of operational settings combinations, and a global sensitivity analysis is conducted to rank the most influential operational settings. Finally, the third step consists on a screening methodology applying a multi-criteria analysis to select the best combinations of operational settings.

Modelling, control and supervision for a class of hybrid systems

The aim of this thesis is to narrow the gap between two different control techniques: the continuous control and the discrete event control techniques DES. This gap can be reduced by the study of Hybrid systems, and by interpreting as Hybrid systems the majority of large-scale systems. In particular, when looking deeply into a process, it is often possible to identify interaction between discrete and continuous signals. Hybrid systems are systems that have both continuous, and discrete signals. Continuous signals are generally supposed continuous and differentiable in time, since discrete signals are neither continuous nor differentiable in time due to their abrupt changes in time. Continuous signals often represent the measure of natural physical magnitudes such as temperature, pressure etc. The discrete signals are normally artificial signals, operated by human artefacts as current, voltage, light etc. Typical processes modelled as Hybrid systems are production systems, chemical process, or continuos production when time and continuous measures interacts with the transport, and stock inventory system. Complex systems as manufacturing lines are hybrid in a global sense. They can be decomposed into several subsystems, and their links. Another motivation for the study of Hybrid systems is the tools developed by other research domains. These tools benefit from the use of temporal logic for the analysis of several properties of Hybrid systems model, and use it to design systems and controllers, which satisfies physical or imposed restrictions. This thesis is focused in particular types of systems with discrete and continuous signals in interaction. That can be modelled hard non-linealities, such as hysteresis, jumps in the state, limit cycles, etc. and their possible non-deterministic future behaviour expressed by an interpretable model description. The Hybrid systems treated in this work are systems with several discrete states, always less than thirty states (it can arrive to NP hard problem), and continuous dynamics evolving with expression: with Ki ¡ Rn constant vectors or matrices for X components vector. In several states the continuous evolution can be several of them Ki = 0. In this formulation, the mathematics can express Time invariant linear system. By the use of this expression for a local part, the combination of several local linear models is possible to represent non-linear systems. And with the interaction with discrete events of the system the model can compose non-linear Hybrid systems. Especially multistage processes with high continuous dynamics are well represented by the proposed methodology. Sate vectors with more than two components, as third order models or higher is well approximated by the proposed approximation. Flexible belt transmission, chemical reactions with initial start-up and mobile robots with important friction are several physical systems, which profits from the benefits of proposed methodology (accuracy). The motivation of this thesis is to obtain a solution that can control and drive the Hybrid systems from the origin or starting point to the goal. How to obtain this solution, and which is the best solution in terms of one cost function subject to the physical restrictions and control actions is analysed. Hybrid systems that have several possible states, different ways to drive the system to the goal and different continuous control signals are problems that motivate this research. The requirements of the system on which we work is: a model that can represent the behaviour of the non-linear systems, and that possibilities the prediction of possible future behaviour for the model, in order to apply an supervisor which decides the optimal and secure action to drive the system toward the goal. Specific problems can be determined by the use of this kind of hybrid models are: - The unity of order. - Control the system along a reachable path. - Control the system in a safe path. - Optimise the cost function. - Modularity of control The proposed model solves the specified problems in the switching models problem, the initial condition calculus and the unity of the order models. Continuous and discrete phenomena are represented in Linear hybrid models, defined with defined eighth-tuple parameters to model different types of hybrid phenomena. Applying a transformation over the state vector : for LTI system we obtain from a two-dimensional SS a single parameter, alpha, which still maintains the dynamical information. Combining this parameter with the system output, a complete description of the system is obtained in a form of a graph in polar representation. Using Tagaki-Sugeno type III is a fuzzy model which include linear time invariant LTI models for each local model, the fuzzyfication of different LTI local model gives as a result a non-linear time invariant model. In our case the output and the alpha measure govern the membership function. Hybrid systems control is a huge task, the processes need to be guided from the Starting point to the desired End point, passing a through of different specific states and points in the trajectory. The system can be structured in different levels of abstraction and the control in three layers for the Hybrid systems from planning the process to produce the actions, these are the planning, the process and control layer. In this case the algorithms will be applied to robotics ¡V a domain where improvements are well accepted ¡V it is expected to find a simple repetitive processes for which the extra effort in complexity can be compensated by some cost reductions. It may be also interesting to implement some control optimisation to processes such as fuel injection, DC-DC converters etc. In order to apply the RW theory of discrete event systems on a Hybrid system, we must abstract the continuous signals and to project the events generated for these signals, to obtain new sets of observable and controllable events. Ramadge & Wonham¡¦s theory along with the TCT software give a Controllable Sublanguage of the legal language generated for a Discrete Event System (DES). Continuous abstraction transforms predicates over continuous variables into controllable or uncontrollable events, and modifies the set of uncontrollable, controllable observable and unobservable events. Continuous signals produce into the system virtual events, when this crosses the bound limits. If this event is deterministic, they can be projected. It is necessary to determine the controllability of this event, in order to assign this to the corresponding set, , controllable, uncontrollable, observable and unobservable set of events. Find optimal trajectories in order to minimise some cost function is the goal of the modelling procedure. Mathematical model for the system allows the user to apply mathematical techniques over this expression. These possibilities are, to minimise a specific cost function, to obtain optimal controllers and to approximate a specific trajectory. The combination of the Dynamic Programming with Bellman Principle of optimality, give us the procedure to solve the minimum time trajectory for Hybrid systems. The problem is greater when there exists interaction between adjacent states. In Hybrid systems the problem is to determine the partial set points to be applied at the local models. Optimal controller can be implemented in each local model in order to assure the minimisation of the local costs. The solution of this problem needs to give us the trajectory to follow the system. Trajectory marked by a set of set points to force the system to passing over them. Several ways are possible to drive the system from the Starting point Xi to the End point Xf. Different ways are interesting in: dynamic sense, minimum states, approximation at set points, etc. These ways need to be safe and viable and RchW. And only one of them must to be applied, normally the best, which minimises the proposed cost function. A Reachable Way, this means the controllable way and safe, will be evaluated in order to obtain which one minimises the cost function. Contribution of this work is a complete framework to work with the majority Hybrid systems, the procedures to model, control and supervise are defined and explained and its use is demonstrated. Also explained is the procedure to model the systems to be analysed for automatic verification. Great improvements were obtained by using this methodology in comparison to using other piecewise linear approximations. It is demonstrated in particular cases this methodology can provide best approximation. The most important contribution of this work, is the Alpha approximation for non-linear systems with high dynamics While this kind of process is not typical, but in this case the Alpha approximation is the best linear approximation to use, and give a compact representation.

Very large inverse problems in atmosphere and ocean modelling

For the very large nonlinear dynamical systems that arise in a wide range of physical, biological and environmental problems, the data needed to initialize a numerical forecasting model are seldom available. To generate accurate estimates of the expected states of the system, both current and future, the technique of ‘data assimilation’ is used to combine the numerical model predictions with observations of the system measured over time. Assimilation of data is an inverse problem that for very large-scale systems is generally ill-posed. In four-dimensional variational assimilation schemes, the dynamical model equations provide constraints that act to spread information into data sparse regions, enabling the state of the system to be reconstructed accurately. The mechanism for this is not well understood. Singular value decomposition techniques are applied here to the observability matrix of the system in order to analyse the critical features in this process. Simplified models are used to demonstrate how information is propagated from observed regions into unobserved areas. The impact of the size of the observational noise and the temporal position of the observations is examined. The best signal-to-noise ratio needed to extract the most information from the observations is estimated using Tikhonov regularization theory. Copyright © 2005 John Wiley & Sons, Ltd.

Efficient group communication for large-scale parallel clustering

Global communication requirements and load imbalance of some parallel data mining algorithms are the major obstacles to exploit the computational power of large-scale systems. This work investigates how non-uniform data distributions can be exploited to remove the global communication requirement and to reduce the communication cost in iterative parallel data mining algorithms. In particular, the analysis focuses on one of the most influential and popular data mining methods, the k-means algorithm for cluster analysis. The straightforward parallel formulation of the k-means algorithm requires a global reduction operation at each iteration step, which hinders its scalability. This work studies a different parallel formulation of the algorithm where the requirement of global communication can be relaxed while still providing the exact solution of the centralised k-means algorithm. The proposed approach exploits a non-uniform data distribution which can be either found in real world distributed applications or can be induced by means of multi-dimensional binary search trees. The approach can also be extended to accommodate an approximation error which allows a further reduction of the communication costs.

Blending systems thinking approaches for organisational analysis: reviewing child protection in England

This paper concerns the innovative use of a blend of systems thinking ideas in the ‘Munro Review of Child Protection’, a high-profile examination of child protection activities in England, conducted for the Department for Education. We go ‘behind the scenes’ to describe the OR methodologies and processes employed. The circumstances that led to the Review are outlined. Three specific contributions that systems thinking made to the Review are then described. First, the systems-based analysis and visualisation of how a ‘compliance culture’ had grown up. Second the creation of a large, complex systems map of current operations and the effects of past policies on them. Third, how the map gave shape to the range of issues the Review addressed and acted as an organising framework for the systemically coherent set of recommendations made. The paper closes with an outline of the main implementation steps taken so far to create a child protection system with the critically reflective properties of a learning organisation, and methodological reflections on the benefits of systems thinking to support organisational analysis.

Revisiting associations between student performance outcomes and formative assessment opportunities: is there any impact on student learning?

This project developed as a result of some inconclusive data from an investigation of whether a relationship existed between the use of formative assessment opportunities and performance, as measured by final grade. We were expecting to show our colleagues and students that use of formative assessment resources had the potential to improve performance. This first study, done in semester 1 2002, indicated that there was no apparent relationship even though the students reported how useful they found the resources. This led us to ask if there was a transition effect
such that students were not yet working in an independent way and making full use of the resources, and/or whether in order to see an effect we needed to persuade non-users of the resources to become users before investigating if use can be correlated with improvement in performance. With the 2002-3 NextEd ASCILITE Research Grant we set out to repeat our project and to look at use and usefulness of resources in both first and second semester, to encourage non-users to become users and to investigate use with performance. Now our story has a different ending.

Challenges in health services and health systems research

The relationship between emerging trends in healthcare systems and the consequent research priorities will be explored.

Governments and policy makers in developed countries are increasingly focused on the management of chronic disease, reflecting demographic changes and shifts in the burden of disease. Systems of quality improvement and reward are increasingly based on performance in chronic disease management. There is some evidence that countries with well-developed systems of primary care, such as Australia, achieve better health outcomes at less cost. In the past 15 years, almost all developed countries have undergone some type of health care reform. There has been a major focus on reducing costs; often involving shifting services from secondary to primary care. While there are few international comparisons, most suggest a complex relationship between the strength of primary care within the overall health services system and good performance, particularly with regard to lower costs of care and particularly relevant measures of health.

Aims for 21st century health systems
What, then, are the issues which are shaping contemporary general practice in developed countries? There are several imperatives: Safety, effectiveness, patient-centredness, timeliness, efficiency and equity. A study by the Nuffield Trust (Dargie, 1999) projected the shape of healthcare for the first fifteen years of this century. The study identified six issues that need to be addressed in the process of formulating health systems policies:

• Peoples’ expectations and financial sustainability
• Demography and ageing
• Information and knowledge management
• Scientific advance and new technology
• Workforce education and training
• Systems performance and quality (efficiency, effectiveness, economy
and equity)

Each of these six issues requires innovative thinking and priority setting on the part of the health sector, such as the delivery of health services in new and creative ways. Furthermore, there is a clear need for a finely tuned research, development and evaluation strategies to match these goals.