Transformation�based implementation and optimization of programs exploiting the basic Andorra model.

The characteristics of CC and CLP systems are in principle very dierent However a recent trend towards convergence in the implementation techniques for these systems can be observed While CLP and Prolog systems have been incorporating capabilities to deal with userdened suspension and coroutining CC compilers have been trying to coalesce negrained tasks into coarsergrained sequential threads This convergence of techniques opens up the possibility of having a general purpose kernel language and abstract machine to serve as a compilation target for a variety of userlevel languages We propose a transformation technique directed towards such an objective In particular we report on techniques to support the Andorra computational model essentially emulating the AndorraI system via program transformation into a sequential language with delay primitives The system is automatic comprising an optional program analyzer and a basic transformer to the kernel language It turns out that a simple parallel CLP or Prolog system with dynamic scheduling is sucient as a kernel language for this purpose The preliminary results are quite encouraging performance of the resulting system is comparable to the current AndorraI implementation.

Energy-based Fair Queuing: Energy-centric Processor Scheduling Algorithm for Battery-limited Mobile Systems

Los dispositivos móviles modernos disponen cada vez de más funcionalidad debido al rápido avance de las tecnologías de las comunicaciones y computaciones móviles. Sin embargo, la capacidad de la batería no ha experimentado un aumento equivalente. Por ello, la experiencia de usuario en los sistemas móviles modernos se ve muy afectada por la vida de la batería, que es un factor inestable de difícil de control. Para abordar este problema, investigaciones anteriores han propuesto un esquema de gestion del consumo (PM) centrada en la energía y que proporciona una garantía sobre la vida operativa de la batería mediante la gestión de la energía como un recurso de primera clase en el sistema. Como el planificador juega un papel fundamental en la administración del consumo de energía y en la garantía del rendimiento de las aplicaciones, esta tesis explora la optimización de la experiencia de usuario para sistemas móviles con energía limitada desde la perspectiva de un planificador que tiene en cuenta el consumo de energía en un contexto en el que ésta es un recurso de primera clase. En esta tesis se analiza en primer lugar los factores que contribuyen de forma general a la experiencia de usuario en un sistema móvil. Después se determinan los requisitos esenciales que afectan a la experiencia de usuario en la planificación centrada en el consumo de energía, que son el reparto proporcional de la potencia, el cumplimiento de las restricciones temporales, y cuando sea necesario, el compromiso entre la cuota de potencia y las restricciones temporales. Para cumplir con los requisitos, el algoritmo clásico de fair queueing y su modelo de referencia se extienden desde los dominios de las comunicaciones y ancho de banda de CPU hacia el dominio de la energía, y en base a ésto, se propone el algoritmo energy-based fair queueing (EFQ) para proporcionar una planificación basada en la energía. El algoritmo EFQ está diseñado para compartir la potencia consumida entre las tareas mediante su planificación en función de la energía consumida y de la cuota reservada. La cuota de consumo de cada tarea con restricciones temporales está protegida frente a diversos cambios que puedan ocurrir en el sistema. Además, para dar mejor soporte a las tareas en tiempo real y multimedia, se propone un mecanismo para combinar con el algoritmo EFQ para dar preferencia en la planificación durante breves intervalos de tiempo a las tareas más urgentes con restricciones temporales.Las propiedades del algoritmo EFQ se evaluan a través del modelado de alto nivel y la simulación. Los resultados de las simulaciones indican que los requisitos esenciales de la planificación centrada en la energía pueden lograrse. El algoritmo EFQ se implementa más tarde en el kernel de Linux. Para evaluar las propiedades del planificador EFQ basado en Linux, se desarrolló un banco de pruebas experimental basado en una sitema empotrado, un programa de banco de pruebas multihilo, y un conjunto de pruebas de código abierto. A través de experimentos específicamente diseñados, esta tesis verifica primero las propiedades de EFQ en la gestión de la cuota de consumo de potencia y la planificación en tiempo real y, a continuación, explora los beneficios potenciales de emplear la planificación EFQ en la optimización de la experiencia de usuario para sistemas móviles con energía limitada. Los resultados experimentales sobre la gestión de la cuota de energía muestran que EFQ es más eficaz que el planificador de Linux-CFS en la gestión de energía, logrando un reparto proporcional de la energía del sistema independientemente de en qué dispositivo se consume la energía. Los resultados experimentales en la planificación en tiempo real demuestran que EFQ puede lograr de forma eficaz, flexible y robusta el cumplimiento de las restricciones temporales aunque se dé el caso de aumento del el número de tareas o del error en la estimación de energía. Por último, un análisis comparativo de los resultados experimentales sobre la optimización de la experiencia del usuario demuestra que, primero, EFQ es más eficaz y flexible que los algoritmos tradicionales de planificación del procesador, como el que se encuentra por defecto en el planificador de Linux y, segundo, que proporciona la posibilidad de optimizar y preservar la experiencia de usuario para los sistemas móviles con energía limitada. Abstract Modern mobiledevices have been becoming increasingly powerful in functionality and entertainment as the next-generation mobile computing and communication technologies are rapidly advanced. However, the battery capacity has not experienced anequivalent increase. The user experience of modern mobile systems is therefore greatly affected by the battery lifetime,which is an unstable factor that is hard to control. To address this problem, previous works proposed energy-centric power management (PM) schemes to provide strong guarantee on the battery lifetime by globally managing energy as the first-class resource in the system. As the processor scheduler plays a pivotal role in power management and application performance guarantee, this thesis explores the user experience optimization of energy-limited mobile systemsfrom the perspective of energy-centric processor scheduling in an energy-centric context. This thesis first analyzes the general contributing factors of the mobile system user experience.Then itdetermines the essential requirements on the energy-centric processor scheduling for user experience optimization, which are proportional power sharing, time-constraint compliance, and when necessary, a tradeoff between the power share and the time-constraint compliance. To meet the requirements, the classical fair queuing algorithm and its reference model are extended from the network and CPU bandwidth sharing domain to the energy sharing domain, and based on that, the energy-based fair queuing (EFQ) algorithm is proposed for performing energy-centric processor scheduling. The EFQ algorithm is designed to provide proportional power shares to tasks by scheduling the tasks based on their energy consumption and weights. The power share of each time-sensitive task is protected upon the change of the scheduling environment to guarantee a stable performance, and any instantaneous power share that is overly allocated to one time-sensitive task can be fairly re-allocated to the other tasks. In addition, to better support real-time and multimedia scheduling, certain real-time friendly mechanism is combined into the EFQ algorithm to give time-limited scheduling preference to the time-sensitive tasks. Through high-level modelling and simulation, the properties of the EFQ algorithm are evaluated. The simulation results indicate that the essential requirements of energy-centric processor scheduling can be achieved. The EFQ algorithm is later implemented in the Linux kernel. To assess the properties of the Linux-based EFQ scheduler, an experimental test-bench based on an embedded platform, a multithreading test-bench program, and an open-source benchmark suite is developed. Through specifically-designed experiments, this thesis first verifies the properties of EFQ in power share management and real-time scheduling, and then, explores the potential benefits of employing EFQ scheduling in the user experience optimization for energy-limited mobile systems. Experimental results on power share management show that EFQ is more effective than the Linux-CFS scheduler in managing power shares and it can achieve a proportional sharing of the system power regardless of on which device the energy is spent. Experimental results on real-time scheduling demonstrate that EFQ can achieve effective, flexible and robust time-constraint compliance upon the increase of energy estimation error and task number. Finally, a comparative analysis of the experimental results on user experience optimization demonstrates that EFQ is more effective and flexible than traditional processor scheduling algorithms, such as those of the default Linux scheduler, in optimizing and preserving the user experience of energy-limited mobile systems.

'The application of Bayesian Optimization and Classifier Systems in Nurse Scheduling'

Abstract. Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person's assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems.

Bayesian Optimisation Algorithm for Nurse Scheduling, Scalable Optimization via Probabilistic Modeling

Our research has shown that schedules can be built mimicking a human scheduler by using a set of rules that involve domain knowledge. This chapter presents a Bayesian Optimization Algorithm (BOA) for the nurse scheduling problem that chooses such suitable scheduling rules from a set for each nurse’s assignment. Based on the idea of using probabilistic models, the BOA builds a Bayesian network for the set of promising solutions and samples these networks to generate new candidate solutions. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed algorithm may be suitable for other scheduling problems.

'The application of Bayesian Optimization and Classifier Systems in Nurse Scheduling'

Abstract. Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person's assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems.

A Bayesian optimization algorithm for the nurse scheduling problem

A Bayesian optimization algorithm for the nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse’s assignment. Unlike our previous work that used GAs to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. eventually, we will be able to identify and mix building blocks directly. The Bayesian optimization algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems.

Feasibility study and flow scheduling optimization of a multi-product pipeline system

The first goal of this study is to analyse a real-world multiproduct onshore pipeline system in order to verify its hydraulic configuration and operational feasibility by constructing a simulation model step by step from its elementary building blocks that permits to copy the operation of the real system as precisely as possible. The second goal is to develop this simulation model into a user-friendly tool that one could use to find an “optimal” or “best” product batch schedule for a one year time period. Such a batch schedule could change dynamically as perturbations occur during operation that influence the behaviour of the entire system. The result of the simulation, the ‘best’ batch schedule is the one that minimizes the operational costs in the system. The costs involved in the simulation are inventory costs, interface costs, pumping costs, and penalty costs assigned to any unforeseen situations. The key factor to determine the performance of the simulation model is the way time is represented. In our model an event based discrete time representation is selected as most appropriate for our purposes. This means that the time horizon is divided into intervals of unequal lengths based on events that change the state of the system. These events are the arrival/departure of the tanker ships, the openings and closures of loading/unloading valves of storage tanks at both terminals, and the arrivals/departures of trains/trucks at the Delivery Terminal. In the feasibility study we analyse the system’s operational performance with different Head Terminal storage capacity configurations. For these alternative configurations we evaluated the effect of different tanker ship delay magnitudes on the number of critical events and product interfaces generated, on the duration of pipeline stoppages, the satisfaction of the product demand and on the operative costs. Based on the results and the bottlenecks identified, we propose modifications in the original setup.

Spectral Region Optimization for Raman-Based Optical Biopsy of Inflammatory Lesions

Objective: The biochemical alterations between inflammatory fibrous hyperplasia (IFH) and normal tissues of buccal mucosa were probed by using the FT-Raman spectroscopy technique. The aim was to find the minimal set of Raman bands that would furnish the best discrimination. Background: Raman-based optical biopsy is a widely recognized potential technique for noninvasive real-time diagnosis. However, few studies had been devoted to the discrimination of very common subtle or early pathologic states as inflammatory processes that are always present on, for example, cancer lesion borders. Methods: Seventy spectra of IFH from 14 patients were compared with 30 spectra of normal tissues from six patients. The statistical analysis was performed with principal components analysis and soft independent modeling class analogy cross-validated, leave-one-out methods. Results: Bands close to 574, 1,100, 1,250 to 1,350, and 1,500 cm(-1) (mainly amino acids and collagen bands) showed the main intragroup variations that are due to the acanthosis process in the IFH epithelium. The 1,200 (C-C aromatic/DNA), 1,350 (CH(2) bending/collagen 1), and 1,730 cm(-1) (collagen III) regions presented the main intergroup variations. This finding was interpreted as originating in an extracellular matrix-degeneration process occurring in the inflammatory tissues. The statistical analysis results indicated that the best discrimination capability (sensitivity of 95% and specificity of 100%) was found by using the 530-580 cm(-1) spectral region. Conclusions: The existence of this narrow spectral window enabling normal and inflammatory diagnosis also had useful implications for an in vivo dispersive Raman setup for clinical applications.

A novel multiobjective optimization algorithm based on bacterial chemotaxis

In this article a novel algorithm based on the chemotaxis process of Echerichia coil is developed to solve multiobjective optimization problems. The algorithm uses fast nondominated sorting procedure, communication between the colony members and a simple chemotactical strategy to change the bacterial positions in order to explore the search space to find several optimal solutions. The proposed algorithm is validated using 11 benchmark problems and implementing three different performance measures to compare its performance with the NSGA-II genetic algorithm and with the particle swarm-based algorithm NSPSO. (C) 2009 Elsevier Ltd. All rights reserved.

Optimization of modal filters based on arrays of piezoelectric sensors

Modal filters may be obtained by a properly designed weighted sum of the output signals of an array of sensors distributed on the host structure. Although several research groups have been interested in techniques for designing and implementing modal filters based on a given array of sensors, the effect of the array topology on the effectiveness of the modal filter has received much less attention. In particular, it is known that some parameters, such as size, shape and location of a sensor, are very important in determining the observability of a vibration mode. Hence, this paper presents a methodology for the topological optimization of an array of sensors in order to maximize the effectiveness of a set of selected modal filters. This is done using a genetic algorithm optimization technique for the selection of 12 piezoceramic sensors from an array of 36 piezoceramic sensors regularly distributed on an aluminum plate, which maximize the filtering performance, over a given frequency range, of a set of modal filters, each one aiming to isolate one of the first vibration modes. The vectors of the weighting coefficients for each modal filter are evaluated using QR decomposition of the complex frequency response function matrix. Results show that the array topology is not very important for lower frequencies but it greatly affects the filter effectiveness for higher frequencies. Therefore, it is possible to improve the effectiveness and frequency range of a set of modal filters by optimizing the topology of an array of sensors. Indeed, using 12 properly located piezoceramic sensors bonded on an aluminum plate it is shown that the frequency range of a set of modal filters may be enlarged by 25-50%.

A novel continuous time representation for the scheduling of pipeline systems with pumping yield rate constraints

Pipeline systems play a key role in the petroleum business. These operational systems provide connection between ports and/or oil fields and refineries (upstream), as well as between these and consumer markets (downstream). The purpose of this work is to propose a novel MINLP formulation based on a continuous time representation for the scheduling of multiproduct pipeline systems that must supply multiple consumer markets. Moreover, it also considers that the pipeline operates intermittently and that the pumping costs depend on the booster stations yield rates, which in turn may generate different flow rates. The proposed continuous time representation is compared with a previously developed discrete time representation [Rejowski, R., Jr., & Pinto, J. M. (2004). Efficient MILP formulations and valid cuts for multiproduct pipeline scheduling. Computers and Chemical Engineering, 28, 1511] in terms of solution quality and computational performance. The influence of the number of time intervals that represents the transfer operation is studied and several configurations for the booster stations are tested. Finally, the proposed formulation is applied to a larger case, in which several booster configurations with different numbers of stages are tested. (C) 2007 Elsevier Ltd. All rights reserved.

S/MIMO MC-CDMA Heuristic Multiuser Detectors Based on Single-Objective Optimization

This paper analyzes the complexity-performance trade-off of several heuristic near-optimum multiuser detection (MuD) approaches applied to the uplink of synchronous single/multiple-input multiple-output multicarrier code division multiple access (S/MIMO MC-CDMA) systems. Genetic algorithm (GA), short term tabu search (STTS) and reactive tabu search (RTS), simulated annealing (SA), particle swarm optimization (PSO), and 1-opt local search (1-LS) heuristic multiuser detection algorithms (Heur-MuDs) are analyzed in details, using a single-objective antenna-diversity-aided optimization approach. Monte- Carlo simulations show that, after convergence, the performances reached by all near-optimum Heur-MuDs are similar. However, the computational complexities may differ substantially, depending on the system operation conditions. Their complexities are carefully analyzed in order to obtain a general complexity-performance framework comparison and to show that unitary Hamming distance search MuD (uH-ds) approaches (1-LS, SA, RTS and STTS) reach the best convergence rates, and among them, the 1-LS-MuD provides the best trade-off between implementation complexity and bit error rate (BER) performance.

Scheduling of head-dependent cascaded hydro systems: Mixed-integer quadratic programming approach

This paper is on the problem of short-term hydro, scheduling, particularly concerning head-dependent cascaded hydro systems. We propose a novel mixed-integer quadratic programming approach, considering not only head-dependency, but also discontinuous operating regions and discharge ramping constraints. Thus, an enhanced short-term hydro scheduling is provided due to the more realistic modeling presented in this paper. Numerical results from two case studies, based on Portuguese cascaded hydro systems, illustrate the proficiency of the proposed approach.

Real-time energy resources scheduling considering intensive wind penetration

The use of distributed energy resources, based on natural intermittent power sources, like wind generation, in power systems imposes the development of new adequate operation management and control methodologies. A short-term Energy Resource Management (ERM) methodology performed in two phases is proposed in this paper. The first one addresses the day-ahead ERM scheduling and the second one deals with the five-minute ahead ERM scheduling. The ERM scheduling is a complex optimization problem due to the high quantity of variables and constraints. In this paper the main goal is to minimize the operation costs from the point of view of a virtual power player that manages the network and the existing resources. The optimization problem is solved by a deterministic mixedinteger non-linear programming approach. A case study considering a distribution network with 33 bus, 66 distributed generation, 32 loads with demand response contracts and 7 storage units and 1000 electric vehicles has been implemented in a simulator developed in the field of the presented work, in order to validate the proposed short-term ERM methodology considering the dynamic power system behavior.

ANN-Based LMP forecasting in a distribution network with large penetration of DG

In recent years, power systems have experienced many changes in their paradigm. The introduction of new players in the management of distributed generation leads to the decentralization of control and decision-making, so that each player is able to play in the market environment. In the new context, it will be very relevant that aggregator players allow midsize, small and micro players to act in a competitive environment. In order to achieve their objectives, virtual power players and single players are required to optimize their energy resource management process. To achieve this, it is essential to have financial resources capable of providing access to appropriate decision support tools. As small players have difficulties in having access to such tools, it is necessary that these players can benefit from alternative methodologies to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), and intended to support smaller players. In this case the present methodology uses a training set that is created using energy resource scheduling solutions obtained using a mixed-integer linear programming (MIP) approach as the reference optimization methodology. The trained network is used to obtain locational marginal prices in a distribution network. The main goal of the paper is to verify the accuracy of the ANN based approach. Moreover, the use of a single ANN is compared with the use of two or more ANN to forecast the locational marginal price.