Power grid-oriented cascading failure vulnerability identifying method based on wireless sensors

In our paper, we study the vulnerability in cascading failures of the real-world network (power grid) under intentional attacks. Here, we use three indexes (B, K, k-shell) to measure the importance of nodes; that is, we define three attacks, respectively. Under these attacks, we measure the process of cascade effect in network by the number of avalanche nodes, the time steps, and the speed of the cascade propagation. Also, we define the node’s bearing capacity as a tolerant parameter to study the robustness of the network under three attacks. Taking the power grid as an example, we have obtained a good regularity of the collapse of the network when the node’s affordability is low. In terms of time and speed, under the betweenness-based attacks, the network collapses faster, but for the number of avalanche nodes, under the degree-based attack, the number of the failed nodes is highest. When the nodes’ bearing capacity becomes large, the regularity of the network’s performances is not obvious. The findings can be applied to identify the vulnerable nodes in real networks such as wireless sensor networks and improve their robustness against different attacks

Spatial imaging of Zn and other elements in Huanglongbing-affected grapefruit by synchrotron-based micro X-ray fluorescence investigation

Huanglongbing (HLB) is a highly destructive, fast-spreading disease of citrus, causing substantial economic losses to the citrus industry worldwide. Nutrient levels and their cellular distribution patterns in stems and leaves of grapefruit were analysed after graft-inoculation with lemon scions containing 'Candidatus Liberibacter asiaticus' (Las), the heat-tolerant Asian type of the HLB bacterium. After 12 months, affected plants showed typical HLB symptoms and significantly reduced Zn concentrations in leaves. Micro-XRF imaging of Zn and other nutrients showed that preferential localization of Zn to phloem tissues was observed in the stems and leaves collected from healthy grapefruit plants, but was absent from HLB-affected samples. Quantitative analysis by using standard references revealed that Zn concentration in the phloem of veins in healthy leaves was more than 10 times higher than that in HLB-affected leaves. No significant variation was observed in the distribution patterns of other elements such as Ca in stems and leaves of grapefruit plants with or without graft-inoculation of infected lemon scions. These results suggest that reduced phloem transport of Zn is an important factor contributing to HLB-induced Zn deficiency in grapefruit. Our report provides the first in situ, cellular level visualization of elemental variations within the tissues of HLB-affected citrus. © 2014 © The Author 2014. Published by Oxford University Press on behalf of the Society for Experimental Biology

Numerical Study on Indoor Wideband Channel Characteristics with Different Internal Wall

Effects of material and configuration of the internal wall on the performance of wideband channel are investigated by using the Finite Difference Time-Domain (FDTD) method. The indoor wideband channel characteristics, such as the path-loss, Root-Mean-Square (RMS) delay spread and number of the multipath components (MPCs), are presented. The simulated results demonstrate that the path-loss and MPCs are affected by the permittivity, dielectric loss tangent and thickness of the internal wall, while the RMS delay spread is almost not relevant with the dielectric permittivity. Furthermore, the comparison of simulated result with the measured one in a simple scenario has validated the simulation study

Exploring data-driven building energy-efficient design of envelopes based on their quantified impacts

Building performance design plays a key role in reducing the energy consumption of buildings. However, the widely used simulation-based design is facing several challenges, such as the labor-intensive modeling process and the performance gaps between design stage estimations and operational energy use. For these reasons, artificial intelligent methods are expected by designers to improve the efficiency and reliability of building energy-efficient design. To date, there has not been a practical data-driven design method of envelopes. This study aimed at exploring data-driven building energy-efficient design of envelopes based on their quantified impacts. A feature selection method and a game-theoretic method were applied to quantify the impacts of envelopes on space heating and cooling energy, which were performed on two building datasets, one of which is from the U.S. and the other from China. Random forest classifiers were developed to conduct the study. Based on discovered energy patterns and quantified impacts of envelopes on energy consumption, a rectified linear design method of envelopes was proposed with the idea of improving the performance of high-impact envelopes. Besides, a validation study was conducted on two office buildings in the hot-summer cold-winter region. To design the envelopes of a building, the data-driven analysis was driven by its similar buildings other than the whole dataset. Moreover, a detailed energy simulation was conducted to evaluate the energy performance of different design solutions. The results showed that compared with baseline design solutions, new strategies could save 1.05%–21.2% energy for space heating and cooling for these two case buildings. The proposed method is a general building envelope design approach and allows designers to easily find an energy-efficient configuration of envelopes. This study demonstrated the feasibility and effectiveness of the data-driven energy-efficient design of building envelopes

Nonparallel support vector machines for pattern classification

We propose a novel nonparallel classifier, called nonparallel support vector machine (NPSVM), for binary classification. Our NPSVM that is fully different from the existing nonparallel classifiers, such as the generalized eigenvalue proximal support vector machine (GEPSVM) and the twin support vector machine (TWSVM), has several incomparable advantages: 1) two primal problems are constructed implementing the structural risk minimization principle; 2) the dual problems of these two primal problems have the same advantages as that of the standard SVMs, so that the kernel trick can be applied directly, while existing TWSVMs have to construct another two primal problems for nonlinear cases based on the approximate kernel-generated surfaces, furthermore, their nonlinear problems cannot degenerate to the linear case even the linear kernel is used; 3) the dual problems have the same elegant formulation with that of standard SVMs and can certainly be solved efficiently by sequential minimization optimization algorithm, while existing GEPSVM or TWSVMs are not suitable for large scale problems; 4) it has the inherent sparseness as standard SVMs; 5) existing TWSVMs are only the special cases of the NPSVM when the parameters of which are appropriately chosen. Experimental results on lots of datasets show the effectiveness of our method in both sparseness and classification accuracy, and therefore, confirm the above conclusion further. In some sense, our NPSVM is a new starting point of nonparallel classifiers