Integer and Fractional Order Entropy Analysis of Earthquake Data-series

This paper studies the statistical distributions of worldwide earthquakes from year 1963 up to year 2012. A Cartesian grid, dividing Earth into geographic regions, is considered. Entropy and the Jensen–Shannon divergence are used to analyze and compare real-world data. Hierarchical clustering and multi-dimensional scaling techniques are adopted for data visualization. Entropy-based indices have the advantage of leading to a single parameter expressing the relationships between the seismic data. Classical and generalized (fractional) entropy and Jensen–Shannon divergence are tested. The generalized measures lead to a clear identification of patterns embedded in the data and contribute to better understand earthquake distributions.

Entropy Analysis of Industrial Accident Data Series

Complex industrial plants exhibit multiple interactions among smaller parts and with human operators. Failure in one part can propagate across subsystem boundaries causing a serious disaster. This paper analyzes the industrial accident data series in the perspective of dynamical systems. First, we process real world data and show that the statistics of the number of fatalities reveal features that are well described by power law (PL) distributions. For early years, the data reveal double PL behavior, while, for more recent time periods, a single PL fits better into the experimental data. Second, we analyze the entropy of the data series statistics over time. Third, we use the Kullback–Leibler divergence to compare the empirical data and multidimensional scaling (MDS) techniques for data analysis and visualization. Entropy-based analysis is adopted to assess complexity, having the advantage of yielding a single parameter to express relationships between the data. The classical and the generalized (fractional) entropy and Kullback–Leibler divergence are used. The generalized measures allow a clear identification of patterns embedded in the data.

A framework for using real data with distributed low cost sensors

Currently, due to the widespread use of computers and the internet, students are trading libraries for the World Wide Web and laboratories with simulation programs. In most courses, simulators are made available to students and can be used to proof theoretical results or to test a developing hardware/product. Although this is an interesting solution: low cost, easy and fast way to perform some courses work, it has indeed major disadvantages. As everything is currently being done with/in a computer, the students are loosing the “feel” of the real values of the magnitudes. For instance in engineering studies, and mainly in the first years, students need to learn electronics, algorithmic, mathematics and physics. All of these areas can use numerical analysis software, simulation software or spreadsheets and in the majority of the cases data used is either simulated or random numbers, but real data could be used instead. For example, if a course uses numerical analysis software and needs a dataset, the students can learn to manipulate arrays. Also, when using the spreadsheets to build graphics, instead of using a random table, students could use a real dataset based, for instance, in the room temperature and its variation across the day. In this work we present a framework which uses a simple interface allowing it to be used by different courses where the computers are the teaching/learning process in order to give a more realistic feeling to students by using real data. A framework is proposed based on a set of low cost sensors for different physical magnitudes, e.g. temperature, light, wind speed, which are connected to a central server, that the students have access with an Ethernet protocol or are connected directly to the student computer/laptop. These sensors use the communication ports available such as: serial ports, parallel ports, Ethernet or Universal Serial Bus (USB). Since a central server is used, the students are encouraged to use sensor values results in their different courses and consequently in different types of software such as: numerical analysis tools, spreadsheets or simply inside any programming language when a dataset is needed. In order to do this, small pieces of hardware were developed containing at least one sensor using different types of computer communication. As long as the sensors are attached in a server connected to the internet, these tools can also be shared between different schools. This allows sensors that aren't available in a determined school to be used by getting the values from other places that are sharing them. Another remark is that students in the more advanced years and (theoretically) more know how, can use the courses that have some affinities with electronic development to build new sensor pieces and expand the framework further. The final solution provided is very interesting, low cost, simple to develop, allowing flexibility of resources by using the same materials in several courses bringing real world data into the students computer works.

An automated maneuver control framework for a remotely operated vehicle

OCEANS, 2001. MTS/IEEE Conference and Exhibition (Volume:2 )

A control framework for a remotely operated vehicle

A control framework enabling the automated maneuvering of a Remotely Operate Vehicle (ROV) is presented. The control architecture is structured according to the principle of composition of vehicle motions from a minimal set of elemental maneuvers that are designed and verified independently. The principled approach is based on distributed hybrid systems techniques, and spans integrated design, simulation and implementation as the same model is used throughout. Hybrid systems control techniques are used to synthesize the elemental maneuvers and to design protocols, which coordinate the execution of elemental maneuvers within a complex maneuver. This work is part of the Inspection of Underwater Structures (IES) project whose main objective is the implementation of a ROV-based system for the inspection of underwater structures.

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.

On the analysis of data emerging in non-linear and complex systemscomparison of two X-Ray prony spectra

New arguments proving that successive (repeated) measurements have a memory and actually remember each other are presented. The recognition of this peculiarity can change essentially the existing paradigm associated with conventional observation in behavior of different complex systems and lead towards the application of an intermediate model (IM). This IM can provide a very accurate fit of the measured data in terms of the Prony's decomposition. This decomposition, in turn, contains a small set of the fitting parameters relatively to the number of initial data points and allows comparing the measured data in cases where the “best fit” model based on some specific physical principles is absent. As an example, we consider two X-ray diffractometers (defined in paper as A- (“cheap”) and B- (“expensive”) that are used after their proper calibration for the measuring of the same substance (corundum a-Al2O3). The amplitude-frequency response (AFR) obtained in the frame of the Prony's decomposition can be used for comparison of the spectra recorded from (A) and (B) - X-ray diffractometers (XRDs) for calibration and other practical purposes. We prove also that the Fourier decomposition can be adapted to “ideal” experiment without memory while the Prony's decomposition corresponds to real measurement and can be fitted in the frame of the IM in this case. New statistical parameters describing the properties of experimental equipment (irrespective to their internal “filling”) are found. The suggested approach is rather general and can be used for calibration and comparison of different complex dynamical systems in practical purposes.

PIASA: A power and interference aware resource management strategy for heterogeneous workloads in cloud data centers

Cloud data centers have been progressively adopted in different scenarios, as reflected in the execution of heterogeneous applications with diverse workloads and diverse quality of service (QoS) requirements. Virtual machine (VM) technology eases resource management in physical servers and helps cloud providers achieve goals such as optimization of energy consumption. However, the performance of an application running inside a VM is not guaranteed due to the interference among co-hosted workloads sharing the same physical resources. Moreover, the different types of co-hosted applications with diverse QoS requirements as well as the dynamic behavior of the cloud makes efficient provisioning of resources even more difficult and a challenging problem in cloud data centers. In this paper, we address the problem of resource allocation within a data center that runs different types of application workloads, particularly CPU- and network-intensive applications. To address these challenges, we propose an interference- and power-aware management mechanism that combines a performance deviation estimator and a scheduling algorithm to guide the resource allocation in virtualized environments. We conduct simulations by injecting synthetic workloads whose characteristics follow the last version of the Google Cloud tracelogs. The results indicate that our performance-enforcing strategy is able to fulfill contracted SLAs of real-world environments while reducing energy costs by as much as 21%.

Quadros Elétricos Inteligentes-Smart Panels

Na sociedade atual, a preocupação com o ambiente, por um lado, e com o conforto e a segurança, por outro, faz com que a sustentabilidade energética se assuma como uma forma de intervenção adequada às exigências de qualidade de vida e à eficiência no âmbito da economia. Nesta conformidade, é incontornável a mais-valia do Smart Panel, um quadro elétrico inteligente criado com vista à consecução daqueles desideratos, o que motivou o tema do presente trabalho. Assim, pretende-se demonstrar as potencialidades do Smart Panel, um novo conceito de quadro elétrico que visa a otimização da sua funcionalidade na gestão dinâmica e pragmática das instalações elétricas, nomeadamente no que respeita ao controlo, monitorização e atuação sobre os dispositivos, quer in loco quer, sobretudo, à distância. Para a consecução deste objetivo, concorrem outros que o potenciam, designadamente a compreensão do funcionamento do quadro elétrico (QE) tradicional, a comparação deste com o Smart Panel e a demonstração das vantagens da utilização desta nova tecnologia. A grande finalidade do trabalho desenvolvido é, por um lado, colocar a formação académica ao serviço de um bom desempenho profissional futuro, por outro ir ao encontro da tendência tecnológica inerente às necessidades que o homem, hoje, tem de controlar. Deste modo, num primeiro momento, é feita uma abordagem geral ao quadro eléctrico tradicional a fim de ser compreendido o seu funcionamento, aplicações e potencialidades. Para tanto, a explanação inclui a apresentação de conceitos teóricos subjacentes à conceção, produção e montagem do QE. São explicitados os diversos componentes que o integram e funções que desempenham, bem como as interações que estabelecem entre si e os normativos a que devem obedecer, para conformidade. Houve a preocupação de incluir imagens coadjuvantes das explicações, descrições e procedimentos técnicos. No terceiro capítulo é abordada a tecnologia Smart Panel, introduzindo o conceito e objetivos que lhe subjazem. Explicita-se o modo de funcionamento deste sistema que agrupa proteção, supervisão, controlo, armazenamento e manutenção preventiva, e demonstra-se de que forma a capacidade de leitura de dados, de comunicação e de comando do quadro elétrico à distância se afigura uma revolução tecnológica facilitadora do cumprimento das necessidades de segurança, conforto e economia da vida moderna. Os capítulos quarto, quinto e sexto versam uma componente prática do trabalho. No capítulo quarto é explanado um suporte formativo e posterior demonstração do kit de ensaio, que servirá de apoio à apresentação da tecnologia Smart Panel aos clientes. Além deste suporte de formação, no quinto capítulo é elaborada uma lista de procedimentos de verificação a serem executados aos componentes de comunicação que integram o Smart Panel, para fornecimento ao quadrista. Por fim, no sexto capítulo incluem-se dois casos de estudo: o estudo A centra-se na aplicação da tecnologia Smart Panel ao projeto de um QE tradicional, que implica fazer o levantamento de toda a aparelhagem existente e, de seguida, proceder à transposição para a tecnologia Smart Panel por forma a cumprir os requisitos estabelecidos pelo cliente. O estudo de caso B consiste na elaboração de um projeto de um quadro eléctrico com a tecnologia Smart Panel em função de determinados requisitos e necessidades do cliente, por forma a garantir as funções desejadas.