Scheduling distributed energy resources in an isolated grid - an artificial neural network approach

Power Systems (PS), have been affected by substantial penetration of Distributed Generation (DG) and the operation in competitive environments. The future PS will have to deal with large-scale integration of DG and other distributed energy resources (DER), such as storage means, and provide to market agents the means to ensure a flexible and secure operation. Virtual power players (VPP) can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. This paper proposes an artificial neural network (ANN) based methodology to support VPP resource schedule. The trained network is able to achieve good schedule results requiring modest computational means. A real data test case is presented.

An architecture for ambient intelligent environments

This paper presents the proposal of an architecture for developing systems that interact with Ambient Intelligence (AmI) environments. This architecture has been proposed as a consequence of a methodology for the inclusion of Artificial Intelligence in AmI environments (ISyRAmI - Intelligent Systems Research for Ambient Intelligence). The ISyRAmI architecture considers several modules. The first is related with the acquisition of data, information and even knowledge. This data/information knowledge deals with our AmI environment and can be acquired in different ways (from raw sensors, from the web, from experts). The second module is related with the storage, conversion, and handling of the data/information knowledge. It is understood that incorrectness, incompleteness, and uncertainty are present in the data/information/knowledge. The third module is related with the intelligent operation on the data/information/knowledge of our AmI environment. Here we include knowledge discovery systems, expert systems, planning, multi-agent systems, simulation, optimization, etc. The last module is related with the actuation in the AmI environment, by means of automation, robots, intelligent agents and users.

Scalable Unified Transform Architecture for Advanced Video Coding Embedded Systems

A novel high throughput and scalable unified architecture for the computation of the transform operations in video codecs for advanced standards is presented in this paper. This structure can be used as a hardware accelerator in modern embedded systems to efficiently compute all the two-dimensional 4 x 4 and 2 x 2 transforms of the H.264/AVC standard. Moreover, its highly flexible design and hardware efficiency allows it to be easily scaled in terms of performance and hardware cost to meet the specific requirements of any given video coding application. Experimental results obtained using a Xilinx Virtex-5 FPGA demonstrated the superior performance and hardware efficiency levels provided by the proposed structure, which presents a throughput per unit of area relatively higher than other similar recently published designs targeting the H.264/AVC standard. Such results also showed that, when integrated in a multi-core embedded system, this architecture provides speedup factors of about 120x concerning pure software implementations of the transform algorithms, therefore allowing the computation, in real-time, of all the above mentioned transforms for Ultra High Definition Video (UHDV) sequences (4,320 x 7,680 @ 30 fps).

FMRI 3D registration based on Fourier space subsets using neural networks

In this work, we present a neural network (NN) based method designed for 3D rigid-body registration of FMRI time series, which relies on a limited number of Fourier coefficients of the images to be aligned. These coefficients, which are comprised in a small cubic neighborhood located at the first octant of a 3D Fourier space (including the DC component), are then fed into six NN during the learning stage. Each NN yields the estimates of a registration parameter. The proposed method was assessed for 3D rigid-body transformations, using DC neighborhoods of different sizes. The mean absolute registration errors are of approximately 0.030 mm in translations and 0.030 deg in rotations, for the typical motion amplitudes encountered in FMRI studies. The construction of the training set and the learning stage are fast requiring, respectively, 90 s and 1 to 12 s, depending on the number of input and hidden units of the NN. We believe that NN-based approaches to the problem of FMRI registration can be of great interest in the future. For instance, NN relying on limited K-space data (possibly in navigation echoes) can be a valid solution to the problem of prospective (in frame) FMRI registration.

Efeito da fortificação alimentar com ácido fólico na prevalência de defeitos do tubo neural

OBJETIVO:Analisar o efeito de alimentos fortificados com ácido fólico na prevalência de defeitos de fechamento do tubo neural entre nascidos vivos. MÉTODOS: Estudo longitudinal de nascidos vivos do município de Recife (PE) entre 2000 e 2006. Os dados pesquisados foram obtidos do Sistema Nacional de Informações de Nascidos Vivos. Os defeitos de fechamento do tubo neural foram definidos de acordo com o Código Internacional de Doenças-10ª Revisão: anencefalia, encefalocele e espinha bífida. Compararam-se as prevalências nos períodos anterior (2000-2004) e posterior (2005-2006) ao período mandatório à fortificação. Analisou-se a tendência temporal das prevalências trimestrais de defeitos do fechamento do tubo neural pelos testes de Mann-Kendall e Sen's Slope. RESULTADOS: Não se identificou tendência de redução na ocorrência do desfecho (Teste de Mann-Kendall; p= 0,270; Sen's Slope =-0,008) no período estudado. Não houve diferença estatisticamente significativa entre as prevalências de defeitos do fechamento do tubo neural nos períodos anterior e posterior à fortificação dos alimentos com acido fólico de acordo com as características maternas. CONCLUSÕES: Embora não tenha sido observada redução dos defeitos do fechamento do tubo neural após o período mandatório de fortificação de alimentos com ácido fólico, os resultados encontrados não permitem descartar o seu benefício na prevenção desta malformação. São necessários estudos avaliando maior período e considerando o nível de consumo dos produtos fortificados pelas mulheres em idade fértil.

High Performance Multi-Standard Architecture for DCT Computation in H.264/AVC High Profile and HEVC Codecs

A new high performance architecture for the computation of all the DCT operations adopted in the H.264/AVC and HEVC standards is proposed in this paper. Contrasting to other dedicated transform cores, the presented multi-standard transform architecture is supported on a completely configurable, scalable and unified structure, that is able to compute not only the forward and the inverse 8×8 and 4×4 integer DCTs and the 4×4 and 2×2 Hadamard transforms defined in the H.264/AVC standard, but also the 4×4, 8×8, 16×16 and 32×32 integer transforms adopted in HEVC. Experimental results obtained using a Xilinx Virtex-7 FPGA demonstrated the superior performance and hardware efficiency levels provided by the proposed structure, which outperforms its more prominent related designs by at least 1.8 times. When integrated in a multi-core embedded system, this architecture allows the computation, in real-time, of all the transforms mentioned above for resolutions as high as the 8k Ultra High Definition Television (UHDTV) (7680×4320 @ 30fps).

Neural-network approach to modeling liquid crystals in complex confinement

Finding the structure of a confined liquid crystal is a difficult task since both the density and order parameter profiles are nonuniform. Starting from a microscopic model and density-functional theory, one has to either (i) solve a nonlinear, integral Euler-Lagrange equation, or (ii) perform a direct multidimensional free energy minimization. The traditional implementations of both approaches are computationally expensive and plagued with convergence problems. Here, as an alternative, we introduce an unsupervised variant of the multilayer perceptron (MLP) artificial neural network for minimizing the free energy of a fluid of hard nonspherical particles confined between planar substrates of variable penetrability. We then test our algorithm by comparing its results for the structure (density-orientation profiles) and equilibrium free energy with those obtained by standard iterative solution of the Euler-Lagrange equations and with Monte Carlo simulation results. Very good agreement is found and the MLP method proves competitively fast, flexible, and refinable. Furthermore, it can be readily generalized to the richer experimental patterned-substrate geometries that are now experimentally realizable but very problematic to conventional theoretical treatments.

A Compact and Scalable RNS Architecture

Conferência: IEEE 24th International Conference on Application-Specific Systems, Architectures and Processors (ASAP)- Jun 05-07, 2013

Mixed integer non linear programming and artificial neural network based approach to ancillary services dispatch in competitive electricity markets

Ancillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted to the market and includes a market clearing mechanism based on deterministic optimization. An Artificial Neural Network is used for day-ahead prediction of Regulation Down, regulation-up, Spin Reserve and Non-Spin Reserve requirements. Two test cases based on California Independent System Operator data concerning dispatch of Regulation Down, Regulation Up, Spin Reserve and Non-Spin Reserve services are included in this paper to illustrate the application of the proposed method: (1) dispatch considering simple bids; (2) dispatch considering complex bids.

On the hardware and software architecture of the robuter mobile platform: a hands-on approach

The Robuter is a robotic mobile platform that is located in the “Hands-On” Laboratory of the IPP-Hurray! Research Group, at the School of Engineering of the Polytechnic Institute of Porto. Recently, the Robuter was subject of an upgrading process addressing two essential areas: the Hardware Architecture and the Software Architecture. This upgrade in process was triggered due to technical problems on-board of the robot and also to the fact that the hardware/software architecture has become obsolete. This Technical Report overviews the most important aspects of the new Hardware and Software Architectures of the Robuter. This document also presents a first approach on the first steps towards the use of the Robuter platform, and provides some hints on future work that may be carried out using this mobile platform.

An architecture for reliable distributed computer-controlled systems

In Distributed Computer-Controlled Systems (DCCS), both real-time and reliability requirements are of major concern. Architectures for DCCS must be designed considering the integration of processing nodes and the underlying communication infrastructure. Such integration must be provided by appropriate software support services. In this paper, an architecture for DCCS is presented, its structure is outlined, and the services provided by the support software are presented. These are considered in order to guarantee the real-time and reliability requirements placed by current and future systems.

Multi-μ: an Ada 95 based architecture for fault tolerance support of real-time systems

This paper presents an architecture (Multi-μ) being implemented to study and develop software based fault tolerant mechanisms for Real-Time Systems, using the Ada language (Ada 95) and Commercial Off-The-Shelf (COTS) components. Several issues regarding fault tolerance are presented and mechanisms to achieve fault tolerance by software active replication in Ada 95 are discussed. The Multi-μ architecture, based on a specifically proposed Fault Tolerance Manager (FTManager), is then described. Finally, some considerations are made about the work being done and essential future developments.

Specification of software architecture reconfiguration

In the past years, Software Architecture has attracted increased attention by academia and industry as the unifying concept to structure the design of complex systems. One particular research area deals with the possibility of reconfiguring architectures to adapt the systems they describe to new requirements. Reconfiguration amounts to adding and removing components and connections, and may have to occur without stopping the execution of the system being reconfigured. This work contributes to the formal description of such a process. Taking as a premise that a single formalism hardly ever satisfies all requirements in every situation, we present three approaches, each one with its own assumptions about the systems it can be applied to and with different advantages and disadvantages. Each approach is based on work of other researchers and has the aesthetic concern of changing as little as possible the original formalism, keeping its spirit. The first approach shows how a given reconfiguration can be specified in the same manner as the system it is applied to and in a way to be efficiently executed. The second approach explores the Chemical Abstract Machine, a formalism for rewriting multisets of terms, to describe architectures, computations, and reconfigurations in a uniform way. The last approach uses a UNITY-like parallel programming design language to describe computations, represents architectures by diagrams in the sense of Category Theory, and specifies reconfigurations by graph transformation rules.

EMMON: a system architecture for large- scale, dense and real-time WSNs

In spite of the significant amount of scientific work in Wireless Sensor Networks (WSNs), there is a clear lack of effective, feasible and usable WSN system architectures that address both functional and non-functional requirements in an integrated fashion. This poster abstract outlines the EMMON system architecture for large-scale, dense, real-time embedded monitoring. EMMON relies on a hierarchical network architecture together with integrated middleware and command&control mechanisms. It has been designed to use standard commercially– available technologies, while maintaining as much flexibility as possible to meet specific applications’ requirements. The EMMON WSN architecture has been validated through extensive simulation and experimental evaluation, including through a 300+ node test-bed, the largest WSN test-bed in Europe to date

Poster: EMMON: a WSN system architecture and toolset for large-scale and dense real-time embedded monitoring

Wireless sensor networks (WSNs) have attracted growing interest in the last decade as an infrastructure to support a diversity of ubiquitous computing and cyber-physical systems. However, most research work has focused on protocols or on specific applications. As a result, there remains a clear lack of effective and usable WSN system architectures that address both functional and non-functional requirements in an integrated fashion. This poster outlines the EMMON system architecture for large-scale, dense, real-time embedded monitoring. It provides a hierarchical communication architecture together with integrated middleware and command and control software. It has been designed to maintain as much as flexibility as possible while meeting specific applications requirements. EMMON has been validated through extensive analytical, simulation and experimental evaluations, including through a 300+ nodes test-bed the largest single-site WSN test-bed in Europe.