Dynamical robustness analysis of weighted complex networks

Robustness of weighted complex networks is analyzed from nonlinear dynamical point of view and with focus on different roles of high-degree and low-degree nodes. We find that the phenomenon for the low-degree nodes being the key nodes in the heterogeneous networks only appears in weakly weighted networks and for weak coupling. For all other parameters, the heterogeneous networks are always highly vulnerable to the failure of high-degree nodes; this point is the same as in the structural robustness analysis. We also find that with random inactivation, heterogeneous networks are always more robust than the corresponding homogeneous networks with the same average degree except for one special parameter. Thus our findings give an integrated picture for the dynamical robustness analysis on complex networks.     

On the robustness of Spanish telecommunication networks

Complex networks are considered robust against random failures but sensitive to failures in target. Under this perspective we analyze the robustness of the Spanish telecommunication networks for digital transmission, the Synchronous Digital Hierarchy (SDH), considering failures in the equipments that control the flow of information. Our results suggest that the robustness depends not only on the connectivity of equipments removed but also on the capacity and types of links associated with that removal. We have also observed that the robustness of SDH networks depends on the particular initial design and evolution of each Spanish province.     

韵律特征在概率线性判别分析说话人确认中的应用

研究韵律特征在说话人确认中的应用。将整个韵律轨迹以固定段长和段移进行片段划分,并对其进行勒让德多项式拟合从而获取连续性的韵律特征,将特征映射到总变化因子空间,并用概率线性判别分析来补偿说话人和场景的差异。在美国国家标准技术研究院2010年说话人识别评测扩展核心测试集5的基础上加入噪声构造测试集,并分别对韵律特征和传统Mel频率倒谱系数进行测试。结果显示,随着信噪比的逐渐减小,Mel频率倒谱系数性能出现大幅度下降,而韵律特征性能相对比较稳定,两种特征融合后能使系统性能得到进一步提升,等错率和最小检测错误代价相对于Mel频率倒谱系数单系统最多能分别下降9%和11%。实验表明,韵律特征应用于说话人识别中具有较强的噪声鲁棒性,且与传统的Mel频率倒谱系数存在较强的互补性。     

The complexity and robustness of metro networks

Transportation systems, being real-life examples of networks, are particularly interesting to analyze from the viewpoint of the new and rapidly emerging field of network science. Two particular concepts seem to be particularly relevant: scale-free patterns and small-worlds. By looking at 33 metro systems in the world, this paper adapts network science methodologies to the transportation literature, and offers one application to the robustness of metros; here, metro refers to urban rail transit with exclusive right-of-way, whether it is underground, at grade or elevated. We find that most metros are indeed scale-free (with scaling factors ranging from 2.10 to 5.52) and small-worlds; they show atypical behaviors, however, with increasing size. In particular, the presence of transfer-hubs (stations hosting more than three lines) results in relatively large scaling factors. The analysis provides insights/recommendations for increasing the robustness of metro networks. Smaller networks should focus on creating transfer stations, thus generating cycles to offer alternative routes. For larger networks, few stations seem to detain a certain monopole on transferring, it is therefore important to create additional transfers, possibly at the periphery of city centers; the Tokyo system seems to remarkably incorporate these properties.     

Robustness analysis of static routing on networks

Robustness is one of the crucial properties that needs to be considered in the design of routing strategies on networks. We study the robustness of three typical routing strategies, which are the SP (shortest path), EP (efficient path), and OP (optimal path) strategies, by simulating several different kinds of attacks including random attacks, target attacks and cascading failures on scale-free networks. Results of the average path length, betweenness centrality, network capacity, etc., demonstrate that the EP strategy is more robust than the other two, and the OP strategy is more reliable than the SP strategy in general. However, on the power-grid network, the OP strategy is more resistant against cascading failures than the EP and SP strategies.     

基于自归一化神经网络的脉冲星候选体选择

脉冲星候选体选择是脉冲星搜寻任务中的重要步骤.为了提高脉冲星候选体选择的准确率,提出了一种基于自归一化神经网络的候选体选择方法.该方法采用自归一化神经网络、遗传算法、合成少数类过采样这三种技术提升对脉冲星候选体的筛选能力.利用自归一化神经网络的自归一化性质克服了深层神经网络训练中梯度消失和爆炸的问题,大大加快了训练速度.为了消除样本数据的冗余性,利用遗传算法对脉冲星候选体的样本特征进行选择,得到了最优特征子集.针对数据中真实脉冲星样本数极少带来的严重类不平衡性,采用合成少数类过采样技术生成脉冲星候选体样本,降低了类不平衡率.以分类精度为评价指标,在3个脉冲星候选体数据集上的实验结果表明,本文提出的方法能有效提升脉冲星候选体选择的性能.     

Photorefraction images analysis through neural networks

The importance of an early evaluation of infants’ visual system condition is long time recognized. Non-corrected visual disorders may lead to major vision and developmental non-reversible limitations in the future. Among the objective methods of refraction, photorefractive techniques are specifically designed for screening young children. Over the years a number of photorefraction systems with different grades of complexity and automation were developed. A critical problem that one needs to deal with in any approach to these systems is the interpretation and classification of the photorefraction images. In digital photorefraction conventional image processing operators and Fourier techniques were currently used. In this communication we will report on the use of Neural Networks for automated classification of digital photorefraction images.     

Digital pulseshape analysis by neural networks for the Heidelberg-Moscow-Double-Beta-Decay-Experiment

The Heidelberg-Moscow Experiment is presently the most sensitive experiment looking for neutrinoless double-beta decay. Recently the already very low background has been lowered by means of a Digital Pulseshape Analysis using a one parameter cut to distinguish between pointlike events and multiple scattered events. To use all the information contained in a recorded digital pulse, we developed a new technique for event recognition based on neural networks. Received: 15 October 1999     

新型低温气液相平衡实验系统调试与验证

介绍了一套新型低温气液(VLE)相平衡实验系统的结构和原理,进行了初步调试并验证其性能指标,该系统具有测试温区大(108.15 K相似文献   

KDP晶体相位匹配角理论预测模型及其验证分析

在高功率固体激光器的终端光学组件内, 大口径薄型KDP (KH₂PO₄)晶体的精密装配和校准是实现惯性约束核聚变的关键技术之一. 为了达到晶体在线安装高效高精度的要求, 需要测量高功率激光三次谐波转换效率达到最高时的晶体相位匹配角分布. 本文针对Ⅰ/Ⅱ类大口径薄型KDP晶体三次谐波转换的方式, 根据晶体的非线性光学属性获得了晶体不同位置相位匹配角之间的关系; 根据激光束在晶体内的传输路径分析得到了晶体面形、相位匹配角与激光三次谐波转换效率达到最高时 晶体最佳偏转角之间的相互关系. 在此基础上, 建立了Ⅰ/Ⅱ类KDP晶体相位匹配角的理论预测模型, 并利用实验进行了验证和分析. 实验结果表明, 晶体相位匹配角的预测值与实验值之差在10.0 μrad以内, 验证了Ⅰ/Ⅱ类KDP晶体相位匹配角理论预测模型的正确性, 为获得晶体全口径相位匹配角分布提供了简单、高效的预测方法.     

Non-monotonic increase of robustness with capacity tolerance in power grids

The robustness of different scale power grids is analyzed based on complex network theory in terms of electrical betweenness and weighted efficiency. The robustness of a power grid does not always increase monotonically with the capacity. This property is different from the results obtained in previous studies, which have indicated that the robustness increases monotonically with capacity. To understand the non-monotonic phenomenon, the cascading failure is divided into several sub-stages, and we analyze the number of overloaded nodes and the average remaining load in each sub-stage. The results indicate that the increasing capacity is barely able to reduce the number of overloaded nodes at the beginning of malfunction, which may lead to more nodes being removed subsequently, including certain nodes with many connections or large load. More loads remain in the power grid such that certain nodes cannot take the load. This eventually causes overloading of more nodes and a decline in the robustness of the power grid. The conclusion may be useful for power grid planners seeking to design grids with cost-effective capacity.     

Property of robustness to size and its realization on fractal dimension

Images in one class often have varied sizes due to different imaging system. Thus it will provide convenience to image classification if the indicator used in the classification is robust to the size of images. We regard the robustness to size of image as a property of image indicator. The property means that images from one class have small variance with the sizes, and is different from such traditional properties as the robustness to scale, rotation and illumination. Fractal dimension is an indicator which has the three traditional properties. We realize the property on fractal dimension in the statistical sense by modifying differential-box counting method. Tests on two classes of images demonstrate the effectiveness of the modifications. Tests on scaling process give a standard of FD’ robustness as 0.0611, and experiments on both the two class and four sets of images show the statistical validity of the standard and verify the realization. An indicator with this property can be a tool for the classification.     

Applying backpropagation neural networks to fringe analysis

Advances in image processing and optics technology, allied to the development of algorithmic techniques such as the fast Fourier transform and phase stepping, have allowed automatic fringe analysis to be successfully applied to many problems in visual inspection and noncontact surface measurement. However, when confronted with complicated or noisy images the algorithmic techniques tend to be less successful, implying an alternative approach may be necessary. Neural networks offer such an alternative. They have already been applied with some success to such conceptually similar pattern recognition problems, as the classification of fingerprints, the recognition of facial expressions and the identification of hand-written characters. Here, neural networks are applied to two simple fringe analysis problems. Firstly, to find the radius of a one-dimensional curved surface from its simulated fringe projection intensity distribution and, secondly, to identify four lensshaped objects of different radii of curvature from real fringe patterns obtained under different illumination conditions. In the first experiment, the backpropagation and radial basis function network paradigms are compared. In the second case, backpropagation is compared with the fuzzy-artmap paradigm. Performance criteria are the number of training data presentations, the accuracy of interpolation in the simulation experiment and the classification precision for the real data.     

Proactive robustness control of heterogeneously loaded networks

A proactive measure to increase the robustness of heterogeneously loaded networks against cascades of overload failures is proposed. It is based on load-dependent weights. Compared to simple hop weights, respective shortest flow paths turn a previously heterogeneous load distribution into a more homogeneous one for the nodes and links of the network. The use of these flow paths increases the networks robustness and at the same time reduces the investment costs into the networks capacity layout. These findings are of relevance for critical infrastructures like communication and transportation networks.     

Study on verification device of screen spatial location parameters of sky screen target

For the original verification method of screen spatial location parameters of sky screen target is complex and low efficiency, the new verification device without disassembly the sky screen target is designed in this paper. The stroboscopic light sources including the LED, collimating lens, frosted glass and small aperture are designed, and the datum plane is precisely established. By setting the threshold method of oscilloscope, the translation stage moving distance relative to the datum plane are recorded, when the amplitude of the output signal of the sky screen target just reaches the preset threshold value. According to the formula, the screen spatial location parameters of sky screen target are achieved. Experimental results show that the calibration accuracy of screen surface space position parameters is better than ±1′, which is consistent with the theoretical error analysis. This verification device makes up for original verification deficiencies of disassembly sky screen target, saves test time, increases the verification efficiency of sky screen target, and has a broad range market demand.     

Thouless-anderson-palmer equations for neural networks

Previous derivation of the Thouless-Anderson-Palmer (TAP) equations for the Hopfield model by the cavity method yielded results that were inconsistent with those of the perturbation theory as well as the results derived by the replica theory of the model. Here we present a derivation of the TAP equation for the Hopfield model by the cavity method and show that it agrees with the form derived by perturbation theory. We also use the cavity method to derive TAP equations for the pseudoinverse neural network model. These equations are consistent with the results of the replica theory of these models.     

A robust decentralized reputation management system for service selection

Reputation mechanism is a novel approach to automate QoS-aware service selection in service oriented computing. The reputation system collects ratings on QoS that consumers feedback and aggregates them to derive a reputation value, which can in turn assist other consumers in service selection in future. However, current approaches fail to combat the malicious ratings and hence the calculated reputation values can be biased severely or even manipulated. Moreover, the centralized management of rating data restricts its application to large open environment. In this paper, we present a robust decentralized reputation system which can resist various unfair ratings and manipulation behaviours. It can evolve and become more mature against malicious ratings with the system running continuously. At last, we experimentally verify the robustness of the proposed approach through a simulation study.