997 resultados para distributed agency


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[ES]En las próximas décadas, el sistema de generación, transmisión y distribución de energía eléctrica afrontará los retos más importantes de su historia. La escasez de los recursos energéticos tradicionales, los efectos de los gases invernadero y el aumento imparable ,de la demanda llaman a transitar hacia un nuevo tipo de infraestructura capaz de explotar el potencial que ofrecen las nuevas fuentes de energía renovable, y de conceder autonomía y capacidad de decisión a los usuarios. Este nuevo modelo de red eléctrica es conocido como Smart Grid, y es habitualmente propuesto como una red distribuida, reactiva e inteligente.

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This paper describes a series of design games, specifically aimed at exploring shifts in human agency, how they are managed, and the impact this will have on the design of future context-aware applications. The games focussed on understanding information handling issues in dental practice with participants from the University of Queensland Dental School playing an active role in the activities. Participatory design activities reveal how technology solution impact on dental practices. By finding methods of representing technological possibilities in ways which can easily be understood we enhance the contribution that dentists can make to the design process.

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Software Engineering Society of Korean; Institute for Information Scientists and Engineers; IEEE Reliability Society; KAIST (Korea Advanced Institute of Science and Technology); Korea Information Promotion Agency; Samsung SDS

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The distributed outstar, a generalization of the outstar neural network for spatial pattern learning, is introduced. In the outstar, signals from a source node cause weights to learn and recall arbitrary patterns across a target field of nodes. The distributed outstar replaces the outstar source node with a source field of arbitrarily many nodes, whose activity pattern may be arbitrarily distributed or compressed. Learning proceeds according to a principle of atrophy due to disuse, whereby a path weight decreases in joint proportion to the transmitted path signal and the degree of disuse of the target node. During learning, the total signal to a target node converges toward that node's activity level. Weight changes at a node are apportioned according to the distributed pattern of converging signals. Three synaptic transmission functions, by a product rule, a capacity rule, and a threshold rule, are examined for this system. The three rules are computationally equivalent when source field activity is maximally compressed, or winner-take-all. When source field activity is distributed, catastrophic forgetting may occur. Only the threshold rule solves this problem. Analysis of spatial pattern learning by distributed codes thereby leads to the conjecture that the unit of long-term memory in such a system is an adaptive threshold, rather than the multiplicative path weight widely used in neural models.

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BP (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI-90-00530); Air Force Office of Scientific Research (90-0175, 90-0128); Army Research Office (DAAL-03-88-K0088)

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Reduced flexibility of low carbon generation could pose new challenges for future energy systems. Both demand response and distributed storage may have a role to play in supporting future system balancing. This paper reviews how these technically different, but functionally similar approaches compare and compete with one another. Household survey data is used to test the effectiveness of price signals to deliver demand responses for appliances with a high degree of agency. The underlying unit of storage for different demand response options is discussed, with particular focus on the ability to enhance demand side flexibility in the residential sector. We conclude that a broad range of options, with different modes of storage, may need to be considered, if residential demand flexibility is to be maximised.

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Current scientific applications have been producing large amounts of data. The processing, handling and analysis of such data require large-scale computing infrastructures such as clusters and grids. In this area, studies aim at improving the performance of data-intensive applications by optimizing data accesses. In order to achieve this goal, distributed storage systems have been considering techniques of data replication, migration, distribution, and access parallelism. However, the main drawback of those studies is that they do not take into account application behavior to perform data access optimization. This limitation motivated this paper which applies strategies to support the online prediction of application behavior in order to optimize data access operations on distributed systems, without requiring any information on past executions. In order to accomplish such a goal, this approach organizes application behaviors as time series and, then, analyzes and classifies those series according to their properties. By knowing properties, the approach selects modeling techniques to represent series and perform predictions, which are, later on, used to optimize data access operations. This new approach was implemented and evaluated using the OptorSim simulator, sponsored by the LHC-CERN project and widely employed by the scientific community. Experiments confirm this new approach reduces application execution time in about 50 percent, specially when handling large amounts of data.

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There is currently a strong interest in mirrorless lasing systems(1), in which the electromagnetic feedback is provided either by disorder (multiple scattering in the gain medium) or by order (multiple Bragg reflection). These mechanisms correspond, respectively, to random lasers(2) and photonic crystal lasers(3). The crossover regime between order and disorder, or correlated disorder, has also been investigated with some success(4-6). Here, we report one-dimensional photonic-crystal lasing (that is, distributed feedback lasing(7,8)) with a cold atom cloud that simultaneously provides both gain and feedback. The atoms are trapped in a one-dimensional lattice, producing a density modulation that creates a strong Bragg reflection with a small angle of incidence. Pumping the atoms with auxiliary beams induces four-wave mixing, which provides parametric gain. The combination of both ingredients generates a mirrorless parametric oscillation with a conical output emission, the apex angle of which is tunable with the lattice periodicity.