面向结构洞的复杂网络关键节点排序

复杂网络中的结构洞节点对于信息传播具有重要作用, 现有关键节点排序方法多数没有兼顾结构洞节点和其他类型的关键节点进行排序. 本文根据结构洞理论与关键节点排序相关研究选取了网络约束系数、介数中心性、等级度、效率、网络规模、PageRank值以及聚类系数7个度量指标, 将基于ListNet的排序学习方法引入到复杂网络的关键节点排序问题中, 融合7个度量指标, 构建了一个能够综合评价面向结构洞节点的关键节点排序方法. 采用模拟网络和实际复杂网络进行了大量实验, 人工标准试验结果表明本文排序方法能够综合考虑结构洞节点和核心节点, 关键节点排序与人工排序结果具有较高的一致性. SIR传播模型评估实验结果表明由本文选择TOP-K节点发起的传播能够在较短的传播时间内达到最大的传播范围.     

Breaking chaotic shift key communication via adaptive key identification

This paper proposes an adaptive parameter identification method for breaking chaotic shift key communication from the transmitted signal in public channel. The sensitive dependence property of chaos on parameter mismatch is used for chaos adaptive synchronization and parameter identification. An index function about the synchronization error is defined and conjugate gradient method is used to minimize the index function and to search the transmitter's parameter (key). By using proposed method, secure key is recovered from transmitted signal generated by low dimensional chaos and hyper chaos switching communication. Multi-parameters can also be identified from the transmitted signal with noise.     

Extensible router for a quantum key distribution network

Building a quantum key distribution network is crucial for practical quantum cryptography. We present a scheme to build a star topology quantum key distribution network based on wavelength division multiplexing which, with current technology, can connect at least a hundred users. With the scheme, a 4-user demonstration network was built up and key exchanges were performed.     

云计算网络中边界节点识别方法改进研究

目前,云计算网络为人们的生产和生活提供了各种应用和服务,网络边界节点的识别问题一直较难解决。传统的网络中边界节点类型复杂,边界部署成本高,较多感知模型和静态场景难以实现。为此,提出一种改进的云计算网络中边界节点识别方法,通过制定边界部署规则确定边界节点部署数量及要求,对边界节点感知漏洞进行修补,保证边界节点对网络区域内的全覆盖识别,最后设计出云计算网络识别模型,实现了云计算网络中边界节点正确识别。仿真实验表明,提出的边界节点识别方法在稳定性、识别率和识别数量上都比传统方法有优越性,具有应用价值。     

利用节点效率评估复杂网络功能鲁棒性

为了克服现有复杂网络鲁棒性研究模型只考虑节点失效的局部影响性和网络拓扑鲁棒性的缺陷, 提出了一种利用节点效率来评估复杂网络功能鲁棒性的方法. 该方法综合考虑节点失效的全局影响性, 利用网络中节点的效率来定义各节点的负载、极限负载和失效模型, 通过打击后网络中最终失效节点的比例来衡量网络的功能鲁棒性, 并给出了其评估优化算法. 实验分析表明该方法对考虑节点负载的复杂网络功能鲁棒性的评定可行有效, 对于大型复杂网络可以获得理想的计算能力.     

The queueing model for quantum key distribution network

This paper develops a QKD (quantum key distribution)-based queueing model to investigate the data delay on QKD link and network, especially that based on trusted relays. It shows the mean packet delay performance of the QKD system. Furthermore, it proposes a key buffering policy which could effectively improve the delay performance in practice. The results will be helpful for quality of service in practical QKD systems.     

Analysis of the Chinese air route network as a complex network

The air route network, which supports all the flight activities of the civil aviation, is the most fundamental infrastructure of air traffic management system. In this paper, we study the Chinese air route network (CARN) within the framework of complex networks. We find that CARN is a geographical network possessing exponential degree distribution, low clustering coefficient, large shortest path length and exponential spatial distance distribution that is obviously different from that of the Chinese airport network (CAN). Besides, via investigating the flight data from 2002 to 2010, we demonstrate that the topology structure of CARN is homogeneous, howbeit the distribution of flight flow on CARN is rather heterogeneous. In addition, the traffic on CARN keeps growing in an exponential form and the increasing speed of west China is remarkably larger than that of east China. Our work will be helpful to better understand Chinese air traffic systems.     

Quantum key distribution series network protocol with M-classical Bobs

Secure key distribution among classical parties is impossible both between two parties and in a network. In this paper, we present a quantum key distribution (QKD) protocol to distribute secure key bits among one quantum party and numerous classical parties who have no quantum capacity. We prove that our protocol is completely robust, i.e., any eavesdropping attack should be detected with nonzero probability. Our calculations show that our protocol may be secure against Eve’s symmetrically individual attack.     

An artificial neural network for proton identification in HERMES data

The HERMES time-of-flight (TOF) system is used for proton identification, but must be carefully calibrated for systematic biases in the equipment. This paper presents an artificial neural network (ANN) trained to recognize protons from Λ0 decay using only raw event data such as time delay, momentum, and trajectory. To avoid the systematic errors associated with Monte Carlo models, we collect a sample of raw experimental data from the year 2000. We presume that when for a positive hadron (assigned one proton mass) and a negative hadron (assigned one π- mass) the reconstructed invariant mass lies within the Λ0 resonance, the positive hadron is more likely to be a proton. Such events are assigned an output value of one during the training process; all others were assigned the output value zero.The trained ANN is capable of identifying protons in independent experimental data, with an efficiencyequivalent to the traditional TOF calibration. By modifying the threshold for proton identification, a researchercan trade off between selection efficiency and background rejection power. This simple and convenient methodis applicable to similar detection problems in other experiments.     

An artificial neural network for proton identification in HERMES data

The HERMES time-of-flight （TOF） system is used for proton identification, but must be carefully calibrated for systematic biases in the equipment. This paper presents an artificial neural network （ANN） trained to recognize protons from ∧^0 decay using only raw event data such as time delay, momentum, and trajectory. To avoid the systematic errors associated with Monte Carlo models, we collect a sample of raw experimental data from the year 2000. We presume that when for a positive hadron （assigned one proton mass） and a negative hadron （assigned one π^- mass） the reconstructed invariant mass lies within the ∧^0 resonance, the positive hadron is more likely to be a proton. Such events are assigned an output value of one during the training process; all others were assigned the output value zero.
The trained ANN is capable of identifying protons in independent experimental data, with an efficiency equivalent to the traditional TOF calibration. By modifying the threshold for proton identification, a researcher can trade off between selection efficiency and background rejection power. This simple and convenient method is applicable to similar detection problems in other experiments.     

Synchronization-based approach for parameter identification in delayed chaotic network

This paper introduces an adaptive procedure for the problem of synchronization and parameter identification for chaotic networks with time-varying delay by combining adaptive control and linear feedback. In particular, we consider that the equations $\dot {x}_i (t)$ (for $i =r+1, r+2,\ldots , n$) can be expressed by the former $\dot {x}_i (t)$ (for $i = 1, 2,\ldots , r$), which is not the same as the previous equation. This approach is also able to track changes in the operating parameters of chaotic networks rapidly and the speed of synchronization and parameter estimation can be adjusted. In addition, this method is quite robust against the effect of slight noise and the estimated value of a parameter fluctuates around the correct value.     

冬季大气遥相关和海温关键区的复杂网络性质

基于大气遥相关指数和海表温度关键区,以各关键区高度场平均值和海表温度关键区平均值之间的相关系数作为连边条件,建立体现大气遥相关和海表温度关键区相互作用的关联网络,进而给出了大气和海洋关键区相互作用的空间图像.同时基于网络的结构特征量,研究了该网络的基本性质,给出了关键区中的网络关键节点,并分析了关键节点对网络性质的影响.研究结果表明:大气遥相关和海温关键区关联网络具有一定的小世界性质,即网络具有小的平均路径长度和较大的集群系数;网络的平均路径长度在阈值为0.1—0.4时系统基本保持稳定,即网络的性质保持不变,进而证明了以信度为0.01下的相关系数0.19作为连边阈值的有效性.对于网络中的单个节点网络特征分析发现,东亚-太平洋遥相关1区和2区以及太平洋-北美遥相关1区和4区在网络中具有较大的节点度,即大气中这4个区域在大气遥相关和海表温度关键区中作用较强;北太平洋涛动区域1,东亚-太平洋遥相关区域3的节点度则相对最小,即这个区域在大气遥相关和海表温度关键区相互作用中的影响较小.通过依次去除网络中度大的节点进一步证明了网络具有一定的鲁棒性.该研究对于重点把握遥相关和海温的一些重要区域具有一定指示意义,也从另一角度提供了研究海气相互作用新方法.     

基于神经网络的图像识别方法研究

图像识别涉及大量的信息运算,要求处理速度快、识别精度高,神经网络的实时性和容错性要符合图像识别的要求。利用改进的BP神经网络算法对旋转畸变图像进行了定位和识别,改进算法将附加动量项与自适应学习速率相结合,有效地抑制了网络陷入局部极小点,提高了网络的训练速度。结果表明,基于神经网络的图像识别方法是有效的、可行的。     

全局耦合网络的参量辨识与时空混沌同步

研究了参量未知的离散型时空混沌系统构成全局耦合网络的参量辨识与同步问题.首先将Milosavljevic所设计的控制律加以推广.利用推广后的控制律进行了网络节点时空混沌系统中未知参量的有效辨识,并完成了该网络的完全同步.进一步以物理中具有时空混沌行为的一维对流方程的离散形式作为实例进行了仿真分析. 关键词： 同步 参量辨识 全局耦合网络 时空混沌     

Identification of functionally key residues in maltose transporter with an elastic network model-based thermodynamic method

Periplasmic binding protein-dependent maltose transport system (MBP-MalFGK₂) of Escherichia coli, an important member of the Adenosine triphosphate-binding cassette transporter superfamily, is in charge of the transportation of maltoses across cellular membrane. Studies have shown that this transport processes are activated by the binding of maltose and are accompanied by large-scale cooperative movements between different domains which are mediated by a network of important residues related to signal transduction and allosteric regulation. In this paper, the functionally crucial residues and long-range allosteric pathway of the regulation of the system by substrate were identified by utilising a coarse-grained thermodynamic method proposed by our group. The residues whose perturbations markedly change the binding free energy between maltoses and MBP-MalFGK₂ were considered to be key residues. In result, the key residues in 62 clusters distributed in different subdomains were identified successfully, and the results from our calculation are highly consistent with experimental and theoretical observations. Furthermore, we explored the long-range cooperation within the transporter. These studies will help us better understand the physical mechanism of the effects of the maltose on MBP-MalFGK₂ by long-range allosteric modulation.     

Analysis of topological properties in a seismic network

In this study, we examine a seismic network by considering the cell resolution and the temporal causality. Seismic activity data from the Korean peninsula are mapped onto a growing network with links between consecutive events. After we especially show a seismic network with spatial heterogeneity and scales in an area, topological properties of this seismic activity network are measured and found. Our result for the robustness of these properties can be compared with other calculated findings.     

Empirical analysis of the worldwide maritime transportation network

In this paper we present an empirical study of the worldwide maritime transportation network (WMN) in which the nodes are ports and links are container liners connecting the ports. Using the different representations of network topology — the spaces L and P, we study the statistical properties of WMN including degree distribution, degree correlations, weight distribution, strength distribution, average shortest path length, line length distribution and centrality measures. We find that WMN is a small-world network with power law behavior. Important nodes are identified based on different centrality measures. Through analyzing weighted clustering coefficient and weighted average nearest neighbors degree, we reveal the hierarchy structure and rich-club phenomenon in the network.     

Structure identification of an uncertain network coupled with complex-variable chaotic systems via adaptive impulsive control

In this paper, structure identification of an uncertain network coupled with complex-variable chaotic systems is in- vestigated. Both the topological structure and the system parameters can be unknown and need to be identified. Based on impulsive stability theory and the Lyapunov function method, an impulsive control scheme combined with an adaptive strategy is adopted to design effective and universal network estimators. The restriction on the impulsive interval is relaxed by adopting an adaptive strategy. Further, the proposed method can monitor the online switching topology effectively. Several numerical simulations are provided to illustrate the effectiveness of the theoretical results.     

非对称信道传输效率的测量设备无关量子密钥分配研究

测量设备无关量子密钥分配方案可以移除所有的探测器侧信道漏洞,通过结合诱骗态方案可以生成无条件安全的密钥.本文研究了非对称信道传输效率下三强度诱骗态测量设备无关量子密钥分配系统的密钥生成率与信道传输损耗的关系,比较了对称信道传输效率和非对称信道传输效率下的距离比率对单边传输效率、单光子误码率和量子密钥生成率的影响,仿真结果表明随着信道不匹配度逐渐增加,可容忍信道传输损耗由对称信道情形下的62 dB分别降至38 dB(距离比率为0.5)和17 dB(距离比率为0.1),能够安全提取密钥的可容忍传输损耗下降较快,密钥生成率的安全传输距离也随之降低.实验中可以采取调节信号光强度的方式提高非对称传输效率下测量设备无关量子密钥分配系统的密钥生成率,为实用的量子密钥分配实验提供了重要的理论参数.