Addressing the facilities layout design problem through constraint logic programming

One of the most difficult problems that face researchers experimenting with complex systems in real world applications is the Facility Layout Design Problem. It relies with the design and location of production lines, machinery and equipment, inventory storage and shipping facilities. In this work it is intended to address this problem through the use of Constraint Logic Programming (CLP) technology. The use of Genetic Algorithms (GA) as optimisation technique in CLP environment is also an issue addressed. The approach aims the implementation of genetic algorithm operators following the CLP paradigm.

Particle swarm design of digital circuits

Swarm Intelligence (SI) is the property of a system whereby the collective behaviors of (unsophisticated) agents interacting locally with their environment cause coherent functional global patterns to emerge. Particle swarm optimization (PSO) is a form of SI, and a population-based search algorithm that is initialized with a population of random solutions, called particles. These particles are flying through hyperspace and have two essential reasoning capabilities: their memory of their own best position and knowledge of the swarm's best position. In a PSO scheme each particle flies through the search space with a velocity that is adjusted dynamically according with its historical behavior. Therefore, the particles have a tendency to fly towards the best search area along the search process. This work proposes a PSO based algorithm for logic circuit synthesis. The results show the statistical characteristics of this algorithm with respect to number of generations required to achieve the solutions. It is also presented a comparison with other two Evolutionary Algorithms, namely Genetic and Memetic Algorithms.

Logic-based schedulability analysis for compositional hard real-time embedded systems

Over the past decades several approaches for schedulability analysis have been proposed for both uni-processor and multi-processor real-time systems. Although different techniques are employed, very little has been put forward in using formal specifications, with the consequent possibility for mis-interpretations or ambiguities in the problem statement. Using a logic based approach to schedulability analysis in the design of hard real-time systems eases the synthesis of correct-by-construction procedures for both static and dynamic verification processes. In this paper we propose a novel approach to schedulability analysis based on a timed temporal logic with time durations. Our approach subsumes classical methods for uni-processor scheduling analysis over compositional resource models by providing the developer with counter-examples, and by ruling out schedules that cause unsafe violations on the system. We also provide an example showing the effectiveness of our proposal.

A Fractional Calculus Perspective in the Evolutionary Design of Combinational Circuits

This paper analyses the performance of a genetic algorithm (GA) in the synthesis of digital circuits using two novel approaches. The first concept consists in improving the static fitness function by including a discontinuity evaluation. The measure of variability in the error of the Boolean table has similarities with the function continuity issue in classical calculus. The second concept extends the static fitness by introducing a fractional-order dynamical evaluation.

Natural Gas location design and operation

A major determinant of the level of effective natural gas supply is the ease to feed customers, minimizing system total costs. The aim of this work is the study of the right number of Gas Supply Units – GSUs - and their optimal location in a gas network. This paper suggests a GSU location heuristic, based on Lagrangean relaxation techniques. The heuristic is tested on the Iberian natural gas network, a system modelized with 65 demand nodes, linked by physical and virtual pipelines. Lagrangean heuristic results along with the allocation of loads to gas sources are presented, using a 2015 forecast gas demand scenario.

TINSEL – a web-based intelligent tutoring system on electrical installation design

This paper presents a project consisting on the development of an Intelligent Tutoring System, for training and support concerning the development of electrical installation projects to be used by electrical engineers, technicians and students. One of the major goals of this project is to devise a teaching model based on Intelligent Tutoring techniques, considering not only academic knowledge but also other types of more empirical knowledge, able to achieve successfully the training of electrical installation design.

An intelligent tutoring approach to support students and technicians in electrical installation design

Designing electric installation projects, demands not only academic knowledge, but also other types of knowledge not easily acquired through traditional instructional methodologies. A lot of additional empirical knowledge is missing and so the academic instruction must be completed with different kinds of knowledge, such as real-life practical examples and simulations. On the other hand, the practical knowledge detained by the most experienced designers is not formalized in such a way that is easily transmitted. In order to overcome these difficulties present in the engineers formation, we are developing an Intelligent Tutoring System (ITS), for training and support concerning the development of electrical installation projects to be used by electrical engineers, technicians and students.

Hybrid fuzzy Monte Carlo and logic programming model for distribution network reconfiguration in the presence of outages

This paper presents a methodology for distribution networks reconfiguration in outage presence in order to choose the reconfiguration that presents the lower power losses. The methodology is based on statistical failure and repair data of the distribution power system components and uses fuzzy-probabilistic modelling for system component outage parameters. Fuzzy membership functions of system component outage parameters are obtained by statistical records. A hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. Once obtained the system states by Monte Carlo simulation, a logical programming algorithm is applied to get all possible reconfigurations for every system state. In order to evaluate the line flows and bus voltages and to identify if there is any overloading, and/or voltage violation a distribution power flow has been applied to select the feasible reconfiguration with lower power losses. To illustrate the application of the proposed methodology to a practical case, the paper includes a case study that considers a real distribution network.

Contexts-management strategy in considering the security in urban computing based on urban design

Urban Computing (UrC) provides users with the situation-proper information by considering context of users, devices, and social and physical environment in urban life. With social network services, UrC makes it possible for people with common interests to organize a virtual-society through exchange of context information among them. In these cases, people and personal devices are vulnerable to fake and misleading context information which is transferred from unauthorized and unauthenticated servers by attackers. So called smart devices which run automatically on some context events are more vulnerable if they are not prepared for attacks. In this paper, we illustrate some UrC service scenarios, and show important context information, possible threats, protection method, and secure context management for people.

Logic programming and fuzzy Monte Carlo for distribution network reconfiguration

This paper present a methodology to choose the distribution networks reconfiguration that presents the lower power losses. The proposed methodology is based on statistical failure and repair data of the distribution power system components and uses fuzzy-probabilistic modeling for system component outage parameters. The proposed hybrid method using fuzzy sets and Monte Carlo simulation based on the fuzzyprobabilistic models allows catching both randomness and fuzziness of component outage parameters. A logic programming algorithm is applied, once obtained the system states by Monte Carlo Simulation, to get all possible reconfigurations for each system state. To evaluate the line flows and bus voltages and to identify if there is any overloading, and/or voltage violation an AC load flow has been applied to select the feasible reconfiguration with lower power losses. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 115 buses distribution network.

Accounting logic and new professional in healthcare: a case study

The central place hospitals occupy in health systems transforms them into prime target of healthcare reforms. This study aims to identify current trends in organizational structure change in public hospitals and explore the role of accounting in attempts to develop controls over professionals within public hospitals. The analytical framework we proposed crosses the concept of “new professionalism” (Evetts, 2010), with the concept of “accounting logic” for controlling professionals (Broadbent and Laughlin, 1995). Looking for a more holistic overview, we developed a qualitative and exploratory study. The data were collected trough semi-structured interviews with doctors of a clinical hospital unit. Content analysis suggests that, although we cannot say that there is a complete and generalized integration of accounting information in the clinical decisions, important improvement has been made in that area. Despite the extensive literature developed on this topic, there is any empirical studies of authors are aware that allow us to realize how real doctors in reals day-to-day work integrated these trends of change in theirs clinical decisions.

Controlo difuso de um AGV

Neste trabalho pretende-se introduzir os conceitos associados à lógica difusa no controlo de sistemas, neste caso na área da robótica autónoma, onde é feito um enquadramento da utilização de controladores difusos na mesma. Foi desenvolvido de raiz um AGV (Autonomous Guided Vehicle) de modo a se implementar o controlador difuso, e testar o desempenho do mesmo. Uma vez que se pretende de futuro realizar melhorias e/ou evoluções optou-se por um sistema modular em que cada módulo é responsável por uma determinada tarefa. Neste trabalho existem três módulos que são responsáveis pelo controlo de velocidade, pela aquisição dos dados dos sensores e, por último, pelo controlador difuso do sistema. Após a implementação do controlador difuso, procedeu-se a testes para validar o sistema onde foram recolhidos e registados os dados provenientes dos sensores durante o funcionamento normal do robô. Este dados permitiram uma melhor análise do desempenho do robô. Verifica-se que a lógica difusa permite obter uma maior suavidade na transição de decisões, e que com o aumento do número de regras é possível tornar o sistema ainda mais suave. Deste modo, verifica-se que a lógica difusa é uma ferramenta útil e funcional para o controlo de aplicações. Como desvantagem surge a quantidade de dados associados à implementação, tais como, os universos de discurso, as funções de pertença e as regras. Ao se aumentar o número de regras de controlo do sistema existe também um aumento das funções de pertença consideradas para cada variável linguística; este facto leva a um aumento da memória necessária e da complexidade na implementação pela quantidade de dados que têm de ser tratados. A maior dificuldade no projecto de um controlador difuso encontra-se na definição das variáveis linguísticas através dos seus universos de discurso e das suas funções de pertença, pois a definição destes pode não ser a mais adequada ao contexto de controlo e torna-se necessário efectuar testes e, consequentemente, modificações à definição das funções de pertença para melhorar o desempenho do sistema. Todos os aspectos referidos são endereçados no desenvolvimento do AGV e os respectivos resultados são apresentados e analisados.

Design de cenários de aprendizagem

A cada instante surgem novas soluções de aprendizagem, resultado da evolução tecnológica constante com que nos deparamos. Estas inovações potenciam uma transmissão do conhecimento entre o educador e o educando cada vez mais simplificada, rápida e eficiente. Alguns destes avanços têm em vista a centralização no aluno, através da delegação de tarefas e da disponibilização de conteúdos, investindo na autonomia e na auto-aprendizagem, de modo a que cada aluno crie o seu próprio método de estudo, e evolua gradualmente, com o acompanhamento de um professor ou sistema autónomo de aprendizagem. Com esta investigação, é pretendido fazer um estudo dos métodos de aprendizagem ao longo do tempo até à actualidade, enumerando algumas das ferramentas utilizadas no processo de aprendizagem, indicando os vários benefícios, bem como contrapartidas do uso das mesmas. Será também analisado um caso de estudo baseado numa destas ferramentas, descrevendo o seu funcionamento e modo de interacção entre as várias entidades participantes, apresentando os resultados obtidos. O caso de estudo consistirá na criação de um cenário específico de aprendizagem, na área da saúde, analisando-o em diferentes contextos, e evidenciando as características e benefícios de cada ambiente analisado, no processo aprendizagem. Será então demonstrado como é possível optimizar os processos de aprendizagem, utilizando ferramentas de informatização e automatização desses mesmos processos, de forma tornar o processo de ensino mais célere e eficaz, num ambiente controlável, e com as funcionalidades que a tecnologia actual permite.

Optimization of Cu(II) biosorption onto ascophyllum nodosum by factorial design methodology

A Box–Behnken factorial design coupled with surface response methodology was used to evaluate the effects of temperature, pH and initial concentration in the Cu(II) sorption process onto the marine macroalgae Ascophyllum nodosum. The effect of the operating variables on metal uptake capacitywas studied in a batch system and a mathematical model showing the influence of each variable and their interactions was obtained. Study ranges were 10–40ºC for temperature, 3.0–5.0 for pH and 50–150mgL−1 for initial Cu(II) concentration. Within these ranges, the biosorption capacity is slightly dependent on temperature but markedly increases with pH and initial concentration of Cu(II). The uptake capacities predicted by the model are in good agreement with the experimental values. Maximum biosorption capacity of Cu(II) by A. nodosum is 70mgg−1 and corresponds to the following values of those variables: temperature = 40ºC, pH= 5.0 and initial Cu(II) concentration = 150mgL−1.

Multi-criteria design optimization study of solvent extraction in mixer–settler units

Solvent extraction is considered as a multi-criteria optimization problem, since several chemical species with similar extraction kinetic properties are frequently present in the aqueous phase and the selective extraction is not practicable. This optimization, applied to mixer–settler units, considers the best parameters and operating conditions, as well as the best structure or process flow-sheet. Global process optimization is performed for a specific flow-sheet and a comparison of Pareto curves for different flow-sheets is made. The positive weight sum approach linked to the sequential quadratic programming method is used to obtain the Pareto set. In all investigated structures, recovery increases with hold-up, residence time and agitation speed, while the purity has an opposite behaviour. For the same treatment capacity, counter-current arrangements are shown to promote recovery without significant impairment in purity. Recycling the aqueous phase is shown to be irrelevant, but organic recycling with as many stages as economically feasible clearly improves the design criteria and reduces the most efficient organic flow-rate.