Influência dos detalhes arquitetônicos nas características do vento em helipontos no topo de edifícios

06

Distribuições de Probabilidade das Flutuações de Velocidade e Concentração nos Processos de Dispersão de Poluentes

Testes na atmosfera e num túnel de camada limite foram realizadospara avaliar o comportamento probabilístico das flutuações de velocidadee concentração associadas a um processo de dispersão. Distribuiçõespróximas à Gaussiana foram obtidas na analise das velocidades.Para as flutuações de concentração, diferentes comportamentos podemser determinados, conforme a região do campo das concentrações analisada

Multiview classification and dimensionality reduction of scalp and intracranial EEG data through tensor factorisation

Electroencephalography (EEG) signals arise as a mixture of various neural processes that occur in different spatial, frequency and temporal locations. In classification paradigms, algorithms are developed that can distinguish between these processes. In this work, we apply tensor factorisation to a set of EEG data from a group of epileptic patients and factorise the data into three modes; space, time and frequency with each mode containing a number of components or signatures. We train separate classifiers on various feature sets corresponding to complementary combinations of those modes and components and test the classification accuracy of each set. The relative influence on the classification accuracy of the respective spatial, temporal or frequency signatures can then be analysed and useful interpretations can be made. Additionaly, we show that through tensor factorisation we can perform dimensionality reduction by evaluating the classification performance with regards to the number mode components and by rejecting components with insignificant contribution to the classification accuracy

Spike pattern recognition by supervised classification in low dimensional embedding space

© The Author(s) 2016. This article is published with open access at Springerlink.com under the terms of the Creative Commons Attribution License 4.0, (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.Epileptiform discharges in interictal electroencephalography (EEG) form the mainstay of epilepsy diagnosis and localization of seizure onset. Visual analysis is rater-dependent and time consuming, especially for long-term recordings, while computerized methods can provide efficiency in reviewing long EEG recordings. This paper presents a machine learning approach for automated detection of epileptiform discharges (spikes). The proposed method first detects spike patterns by calculating similarity to a coarse shape model of a spike waveform and then refines the results by identifying subtle differences between actual spikes and false detections. Pattern classification is performed using support vector machines in a low dimensional space on which the original waveforms are embedded by locality preserving projections. The automatic detection results are compared to experts’ manual annotations (101 spikes) on a whole-night sleep EEG recording. The high sensitivity (97 %) and the low false positive rate (0.1 min−1), calculated by intra-patient cross-validation, highlight the potential of the method for automated interictal EEG assessment.Peer reviewedFinal Published versio

PLASMA SCIENCE & TECHNOLOGY

In this paper, neutronic analysis in a laser fusion inertial confinement fusion fission energy (LIFE) engine fuelled plutonium and minor actinides using a MCNP codes was investigated. LIFE engine fuel zone contained 10 vol% TRISO particles and 90 vol% natural lithium coolant mixture. TRISO fuel compositions have Mod(1): reactor grade plutonium (RG-Pu), Mod(2): weapon grade plutonium (WG-Pu) and Mod(3): minor actinides (MAs). Tritium breeding ratios (TBR) were computed as 1.52, 1.62 and 1.46 for Mod(1), Mod(2)and Mod(3), respectively. The operation period was computed as similar to 21 years when the reference TBR > 1.05 for a self-sustained reactor for all investigated cases. Blanket energy multiplication values (M) were calculated as 4.18, 4.95 and 3.75 for Mod(1), Mod(2) and Mod(3), respectively. The burnup (BU) values were obtained as similar to 1230, similar to 1550 and similar to 1060 GWd tM(-1), respectively. As a result, the higher BU were provided with using TRISO particles for all cases in LIFE engine

Power flattening in the fuel bundle of a CANDU reactor

WOS: 000223668400002The strong non-uniformity of the fission power production density in the CANDU fuel bundle could have been mitigated to a great degree. A satisfactory power flattening has been achieved through an appropriately evaluated method by varying the composition of the LWR spent fuel/ThO2 Mixture in a CANDU fuel bundle in radial direction and keeping fuel rod dimensions unchanged. This will help also to greatly simplify fuel rod fabrication and allow a higher degree of quality assurance standardization. Three different bundle fuel charges are investigated: (1) the reference case, uniformly fueled with natural UO2, (2) a bundle uniformly fueled with LWR spent fuel, and (3) a bundle fueled with variable mixed fuel composition in radial direction leading to a flat power profile (100% LWR spent fuel in the central rod, 80% LWR + 20% ThO2 in the second row, 60% LWR + 40% ThO2 in the third row and finally 40% LWR + 60% ThO2 in the peripheral fourth row). Burn-up grades for these three different bundle types are calculated as similar to7700, similar to27,300, and 10,000 MW.D/MT until reaching a lowest bundle criticality limit of k(infinity) = 1.06. The corresponding plant operation periods are 170, 660, and 240 days, respectively. (C) 2004 Elsevier B.V. All rights reserved