Distributed computing for the factory-floor: a real-time approach using WorldFIP networks

Field communication systems (fieldbuses) are widely used as the communication support for distributed computer-controlled systems (DCCS) within all sort of process control and manufacturing applications. There are several advantages in the use of fieldbuses as a replacement for the traditional point-to-point links between sensors/actuators and computer-based control systems, within which the most relevant is the decentralisation and distribution of the processing power over the field. A widely used fieldbus is the WorldFIP, which is normalised as European standard EN 50170. Using WorldFIP to support DCCS, an important issue is “how to guarantee the timing requirements of the real-time traffic?” WorldFIP has very interesting mechanisms to schedule data transfers, since it explicitly distinguishes periodic and aperiodic traffic. In this paper, we describe how WorldFIP handles these two types of traffic, and more importantly, we provide a comprehensive analysis on how to guarantee the timing requirements of the real-time traffic.

Analysis of temperature time-series: embedding dynamics into the MDS method

Global warming and the associated climate changes are being the subject of intensive research due to their major impact on social, economic and health aspects of the human life. Surface temperature time-series characterise Earth as a slow dynamics spatiotemporal system, evidencing long memory behaviour, typical of fractional order systems. Such phenomena are difficult to model and analyse, demanding for alternative approaches. This paper studies the complex correlations between global temperature time-series using the Multidimensional scaling (MDS) approach. MDS provides a graphical representation of the pattern of climatic similarities between regions around the globe. The similarities are quantified through two mathematical indices that correlate the monthly average temperatures observed in meteorological stations, over a given period of time. Furthermore, time dynamics is analysed by performing the MDS analysis over slices sampling the time series. MDS generates maps describing the stations’ locus in the perspective that, if they are perceived to be similar to each other, then they are placed on the map forming clusters. We show that MDS provides an intuitive and useful visual representation of the complex relationships that are present among temperature time-series, which are not perceived on traditional geographic maps. Moreover, MDS avoids sensitivity to the irregular distribution density of the meteorological stations.

A pseudo-medium-wide 8-competitive interface for two-level compositional real-time scheduling of constrained- deadline sporadic tasks on a uniprocessor

Compositional real-time scheduling clearly requires that ”normal” real-time scheduling challenges are addressed but challenges intrinsic to compositionality must be addressed as well, in particular: (i) how should interfaces be described? and (ii) how should numerical values be assigned to parameters constituting the interfaces? The real-time systems community has traditionally used narrow interfaces for describing a component (for example, a utilization/bandwidthlike metric and the distribution of this bandwidth in time). In this paper, we introduce the concept of competitive ratio of an interface and show that typical narrow interfaces cause poor performance for scheduling constrained-deadline sporadic tasks (competitive ratio is infinite). Therefore, we explore more expressive interfaces; in particular a class called medium-wide interfaces. For this class, we propose an interface type and show how the parameters of the interface should be selected. We also prove that this interface is 8-competitive.

A framework for the response time analysis of fixed-priority tasks with stochastic inter-arrival times

Real-time scheduling usually considers worst-case values for the parameters of task (or message stream) sets, in order to provide safe schedulability tests for hard real-time systems. However, worst-case conditions introduce a level of pessimism that is often inadequate for a certain class of (soft) real-time systems. In this paper we provide an approach for computing the stochastic response time of tasks where tasks have inter-arrival times described by discrete probabilistic distribution functions, instead of minimum inter-arrival (MIT) values.

Fractional derivatives: probability interpretation and frequency response of rational approximations

The theory of fractional calculus (FC) is a useful mathematical tool in many applied sciences. Nevertheless, only in the last decades researchers were motivated for the adoption of the FC concepts. There are several reasons for this state of affairs, namely the co-existence of different definitions and interpretations, and the necessity of approximation methods for the real time calculation of fractional derivatives (FDs). In a first part, this paper introduces a probabilistic interpretation of the fractional derivative based on the Grünwald-Letnikov definition. In a second part, the calculation of fractional derivatives through Padé fraction approximations is analyzed. It is observed that the probabilistic interpretation and the frequency response of fraction approximations of FDs reveal a clear correlation between both concepts.

Time domain design of fractional differintegrators using least-squares

In this paper we propose the use of the least-squares based methods for obtaining digital rational approximations (IIR filters) to fractional-order integrators and differentiators of type sα, α∈R. Adoption of the Padé, Prony and Shanks techniques is suggested. These techniques are usually applied in the signal modeling of deterministic signals. These methods yield suboptimal solutions to the problem which only requires finding the solution of a set of linear equations. The results reveal that the least-squares approach gives similar or superior approximations in comparison with other widely used methods. Their effectiveness is illustrated, both in the time and frequency domains, as well in the fractional differintegration of some standard time domain functions.

Tecnologias multimédia no apoio ao ensino aprendizagem de suporte básico de vida

A sociedade moderna encontra-se numa evolução progressiva e vertiginosa nomeadamente no que respeita às novas tecnologias. Independentemente da área de conhecimento, é de senso comum que cada vez mais é necessária uma formação sólida, sendo fundamental a preparação e a consolidação das futuras gerações na utilização das novas tecnologias. As plataformas de e-learning são hoje em dia uma realidade mais que afirmada e com aplicação em todos os setores de atividade. Na área da saúde, e mais concretamente no que diz respeito ao tema de Suporte Básico de Vida (SBV), foi-nos possível verificar que a maioria das pessoas revela falta de conhecimentos sobre o tema e, não obstante existirem cursos sobre este tema, a disponibilidade para a sua frequência nem sempre é possível pela distância, falta de tempo e disponibilidade para a sua frequência, bem como pelos custos envolvidos. O presente estudo pretende contribuir para uma análise e investigação sobre o potencial de utilização das novas Tecnologias Multimédia, aplicando-as ao ensino concreto de primeiros socorros e especialmente de Suporte Básico de Vida, num recurso educativo digital com o formato de Objeto de Aprendizagem. Para o efeito, será construído um Objeto de Aprendizagem temático, focalizando o tema de SBV. O repositório MERLOT foi utilizado como meio de distribuição global, no sentido de aferir o potencial e facilidade de distribuição e catalogação com metadados. Foi ainda colocado on-line o “SBVOA” (Objeto de Aprendizagem desenvolvido no presente estudo), numa página criada apenas para o efeito. Esta dissertação apresenta o estudo desenvolvido para investigação do potencial de aprendizagem proporcionado pelo Objeto de Aprendizagem desenvolvido, validando se pode ser uma alternativa interativa de educação em SBV, contribuindo deste modo para a diminuição de eventuais perdas de vida.

Multidimensional scaling visualization of earthquake phenomena

Earthquakes are associated with negative events, such as large number of casualties, destruction of buildings and infrastructures, or emergence of tsunamis. In this paper, we apply the Multidimensional Scaling (MDS) analysis to earthquake data. MDS is a set of techniques that produce spatial or geometric representations of complex objects, such that, objects perceived to be similar/distinct in some sense are placed nearby/distant on the MDS maps. The interpretation of the charts is based on the resulting clusters since MDS produces a different locus for each similarity measure. In this study, over three million seismic occurrences, covering the period from January 1, 1904 up to March 14, 2012 are analyzed. The events, characterized by their magnitude and spatiotemporal distributions, are divided into groups, either according to the Flinn–Engdahl seismic regions of Earth or using a rectangular grid based in latitude and longitude coordinates. Space-time and Space-frequency correlation indices are proposed to quantify the similarities among events. MDS has the advantage of avoiding sensitivity to the non-uniform spatial distribution of seismic data, resulting from poorly instrumented areas, and is well suited for accessing dynamics of complex systems. MDS maps are proven as an intuitive and useful visual representation of the complex relationships that are present among seismic events, which may not be perceived on traditional geographic maps. Therefore, MDS constitutes a valid alternative to classic visualization tools, for understanding the global behavior of earthquakes.

Real Time Pricing Approaches to Deal With Unexpected Wind Power Variations

The use of renewables have been increased I several countries around the world, namely in Europe. The wind power is generally the larger renewable resource with very specific characteristics in what concerns its variability and the inherent impacts in the power systems and electricity markets operation. This paper focuses on the Portuguese context of renewables use, including wind power. The work here presented includes the use of a real time pricing methodology developed by the authors aiming the reduction of electricity consumption in the moments of unexpected low wind power. A more specific example of application of real time pricing is demonstrated for the minimization of the operation costs in a distribution network. When facing lower wind power generation than expected from day ahead forecast, demand response is used in order to minimize the impacts of such wind availability change. In this way, consumers actively participate in regulation up and spinning reserve ancillary services through demand response programs.

Distributed energy resources scheduling considering real-time resources forecast

Energy resource scheduling is becoming increasingly important, such as the use of more distributed generators and electric vehicles connected to the distribution network. This paper proposes a methodology to be used by Virtual Power Players (VPPs), regarding the energy resource scheduling in smart grids and considering day-ahead, hour-ahead and realtime time horizons. This method considers that energy resources are managed by a VPP which establishes contracts with their owners. The full AC power flow calculation included in the model takes into account network constraints. In this paper, distribution function errors are used to simulate variations between time horizons, and to measure the performance of the proposed methodology. A 33-bus distribution network with large number of distributed resources is used.

Real-time Energy Resource Scheduling considering a Real Portuguese Scenario

The development in power systems and the introduction of decentralized generation and Electric Vehicles (EVs), both connected to distribution networks, represents a major challenge in the planning and operation issues. This new paradigm requires a new energy resources management approach which considers not only the generation, but also the management of loads through demand response programs, energy storage units, EVs and other players in a liberalized electricity markets environment. This paper proposes a methodology to be used by Virtual Power Players (VPPs), concerning the energy resource scheduling in smart grids, considering day-ahead, hour-ahead and real-time scheduling. The case study considers a 33-bus distribution network with high penetration of distributed energy resources. The wind generation profile is based on a real Portuguese wind farm. Four scenarios are presented taking into account 0, 1, 2 and 5 periods (hours or minutes) ahead of the scheduling period in the hour-ahead and realtime scheduling.

Deteção e identificação de anomalias em redes de distribuição de energia elétrica

Na ocorrência de anomalias nas redes de distribuição de energia elétrica, muitas vezes devido ao reduzido número de informação disponível, a determinação da localização dos defeitos é uma tarefa árdua e morosa. Consequentemente, impõe-se o recurso por parte das companhias elétricas a sistemas que, contribuindo para a diminuição do tempo despendido na localização dos defeitos, assegurem a redução da duração e frequência das falhas de alimentação. Esta dissertação pretende estudar os diversos sistemas de deteção de defeitos existentes, com destaque para a utilização de Indicadores de Passagem de Defeito e analisar o contributo destes sistemas para a melhoria dos Índices de Qualidade de Serviço. Abordar as dificuldades que se colocam à implementação destes sistemas, nomeadamente, pelas características específicas das redes de distribuição. Pretende, ainda, desenvolver uma metodologia e a respetiva ferramenta, que permita a deteção de defeitos baseada na utilização de Indicadores de Passagem de Defeito comunicantes, numa saída da rede de distribuição de média tensão pertencente à EDP. Analisar técnica e economicamente os benefícios a obter com a implementação da metodologia desenvolvida. Esta dissertação pretende, não só atingir os objetivos acima referidos, mas também, através deles, elaborar uma ferramenta útil para as Companhias Elétricas, no sentido de adotarem sistemas de deteção de defeitos e com fim principal de uma possível redução dos tempos de indisponibilidade de alimentação, intimamente associados à persecução de melhores índices de Qualidade de Serviço por parte das mesmas.

Distribuições de eventos extremos e telecomunicações

As distribuições de Lei de Potência (PL Power Laws), tais como a lei de Pareto e a lei de Zipf são distribuições estatísticas cujos tamanhos dos eventos são inversamente proporcionais à sua frequência. Estas leis de potência são caracterizadas pelas suas longas caudas. Segundo Vilfredo Pareto (1896), engenheiro, cientista, sociólogo e economista italiano, autor da Lei de Pareto, 80% das consequências advêm de 20% das causas. Segundo o mesmo, grande parte da economia mundial segue uma determinada distribuição, onde 80% da riqueza mundial é detida por 20% da população ou 80% da poluição mundial é feita por 20% dos países. Estas percentagens podem oscilar nos intervalos [75-85] e [15-25]. A mesma percentagem poderá ser aplicada à gestão de tempo, onde apenas 20% do tempo dedicado a determinado assunto produzirá cerca de 80% dos resultados obtidos. A lei de Pareto, também designada de regra 80/20, tem aplicações nas várias ciências e no mundo físico, nomeadamente na biodiversidade. O número de ocorrências de fenómenos extremos, aliados ao impacto nas redes de telecomunicações nas situações de catástrofe, no apoio imediato às populações e numa fase posterior de reconstrução, têm preocupado cada vez mais as autoridades oficiais de protecção civil e as operadoras de telecomunicações. O objectivo é o de preparar e adaptarem as suas estruturas para proporcionar uma resposta eficaz a estes episódios. Neste trabalho estuda-se o comportamento de vários fenómenos extremos (eventos críticos) e aproximam-se os dados por uma distribuição de Pareto (Lei de Pareto) ou lei de potência. No final, especula-se sobre a influência dos eventos críticos na utilização das redes móveis. É fundamental que as redes móveis estejam preparadas para lidar com as repercussões de fenómenos deste tipo.

Dynamical Analysis and Visualization of Tornadoes Time Series

In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns.

Using data mining with time series data in short-term stocks prediction

Data Mining (DM) methods are being increasingly used in prediction with time series data, in addition to traditional statistical approaches. This paper presents a literature review of the use of DM with time series data, focusing on short- time stocks prediction. This is an area that has been attracting a great deal of attention from researchers in the field. The main contribution of this paper is to provide an outline of the use of DM with time series data, using mainly examples related with short-term stocks prediction. This is important to a better understanding of the field. Some of the main trends and open issues will also be introduced.