基于贝叶斯网络近似推理的网络脆弱性评估方法

针对大规模计算机网络的脆弱性评估,提出了一种基于贝叶斯网络近似推理的评估方法,对网络各组件和影响网络安全的因素进行建模,采用模型检测工具生成攻击状态转移图,描述网络脆弱性的利用过程,通过采用随机采样的方法对网络的攻击状态转移图进行近似推理,经过对采样样本的统计分析得到网络脆弱性评估的量化结果,为提升网络的安全性能提供理论依据。

移动对象数据库模型、查询语言及实时交通流分析

提出一种移动对象数据库模型——Dynamic Transportation Network Based Moving Objects Database(简称DTNMOD),并给出了DTNMOD中基于移动对象时空轨迹的网络实时动态交通流分析方法.在DTNMOD中,交通网络被表示成动态的时空网络,可以描述交通状态、拓扑结构以及交通参数随时间的变化过程;网络受限的移动对象则用网络移动点表示.DTNMOD模型包含了完整的数据类型和查询操作的定义,因此可以在任何可扩充数据库(如PostgreSQL或SECONDO)中实现,从而得到完整的数据库模型和查询语言.为了对相关模型的性能进行比较与分析,基于PostgreSQL实现了一个原型系统并进行了一系列的实验.实验结果表明,DTNMOD提供了良好的区域查询及连接查询性能.

基于DSP的动态传感器网络实验平台的设计和开发

介绍了一种基于DSP2812的动态传感器网络实验平台的设计与开发.该实验平台的设计由配备各种低成本、低功耗的传感器和无线通信模块的可移动的传感器节点组成.在介绍动态传感器网络实验平台的各个组成部分之后,对系统进行了的基本实验测试,并给出了测试结果.*

Comparisons of static, quasi-static and dynamic 3D porous media scale network models for two-phase immiscible flow in porous media

A dynamic 3D pore-scale network model is formulated for investigating the effect of interfacial tension and oil-water viscosity during chemical flooding. The model takes into account both viscous and capillary forces in analyzing the impact of chemical properties on flow behavior or displacement configuration, while the static model with conventional invasion percolation algorithm incorporates the capillary pressure only. From comparisons of simulation results from these models. it indicates that the static pore scale network model can be used successfully when the capillary number is low. With the capillary increases due to the enhancement of water viscosity or decrease of interfacial tension, only the quasi-static and dynamic model can give insight into the displacement mechanisms.

SOC dynamic power management using artificial neural network

Dynamic Power Management (DPM) is a technique to reduce power consumption of electronic system by selectively shutting down idle components. In this article we try to introduce back propagation network and radial basis network into the research of the system-level power management policies. We proposed two PM policies-Back propagation Power Management (BPPM) and Radial Basis Function Power Management (RBFPM) which are based on Artificial Neural Networks (ANN). Our experiments show that the two power management policies greatly lowered the system-level power consumption and have higher performance than traditional Power Management(PM) techniques-BPPM is 1.09-competitive and RBFPM is 1.08-competitive vs. 1.79, 1.45, 1.18-competitive separately for traditional timeout PM, adaptive predictive PM and stochastic PM.

Dynamic power management approaches based on neural network

Dynamic Power Management (DPM) is a technique to reduce power consumption of electronic system. by selectively shutting down idle components. In this article we try to introduce back propagation network and radial basis network into the research of the system-level policies. We proposed two PAY policies-Back propagation Power Management (BPPM) and Radial Basis Function Power management (RBFPM) which are based on Artificial Neural Networks (ANN). Our experiments show that the two power management policies greatly lowered the system-level power consumption and have higher performance than traditional Power Management(PM) techniques-BPPM is 1.09-competitive and RBFPM is 1.08-competitive vs. 1.79,145,1.18-competitive separately for traditional timeout PM, adaptive predictive PM and stochastic PM.

SOC dynamic power management using artificial neural network

Dynamic Power Management (DPM) is a technique to reduce power consumption of electronic system by selectively shutting down idle components. In this article we try to introduce back propagation network and radial basis network into the research of the system-level power management policies. We proposed two PM policies-Back propagation Power Management (BPPM) and Radial Basis Function Power Management (RBFPM) which are based on Artificial Neural Networks (ANN). Our experiments show that the two power management policies greatly lowered the system-level power consumption and have higher performance than traditional Power Management(PM) techniques-BPPM is 1.09-competitive and RBFPM is 1.08-competitive vs. 1.79 . 1.45 . 1.18-competitive separately for traditional timeout PM . adaptive predictive PM and stochastic PM.

IONIC-CONDUCTIVITY AND DYNAMIC MECHANICAL RELAXATION OF A 2-COMPONENT EPOXY NETWORK-LICLO4 ELECTROLYTE SYSTEM

The correlation between mechanical relaxation and ionic conductivity was investigated in a two-component epoxy network-LiClO4 electrolyte system. The network was composed of diglycidyl ether of polyethylene glycol (DGEPEG) and triglycidyl ether of glycerol (TGEG). The effects of salt concentration, molecular weight of PEG in DGEPEG and the proportion of DGEPEG (1000) in DGEPEG/TGEG ratio on the ionic conductivity and the mechanical relaxation of the system were studied. It was found that, among the three influential factors, the former reinforces the network chains, reduces the free volume fraction and thus increases the relaxation time of the segmental motion, which in turn lowers the ionic conductivity of the specimen. Conversely, the latter two increase the free volume and thus the chain flexibility, showing an opposite effect. From the iso-free-volume plot of the shift factor log at and reduced ionic conductivity, it is noted that the plot can be used to examine the temperature dependence of segmental mobility and seems to be useful to judge whether the incorporated salt has been dissociated completely. Besides, the ionic conductivity and relaxation time at constant reference temperature are linearly correlated with each other in all the three cases. This result gives an additional experimental confirmation of the coordinated motion model of the ionic hopping with the moving polymer chain segment, which is generally used to explain the ionic conduction in non-glassy amorphous polymer electrolytes.

An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network

P-glycoprotein (P-gp), an ATP-binding cassette (ABC) transporter, functions as a biological barrier by extruding cytotoxic agents out of cells, resulting in an obstacle in chemotherapeutic treatment of cancer. In order to aid in the development of potential P-gp inhibitors, we constructed a quantitative structure-activity relationship (QSAR) model of flavonoids as P-gp inhibitors based on Bayesian-regularized neural network (BRNN). A dataset of 57 flavonoids collected from a literature binding to the C-terminal nucleotide-binding domain of mouse P-gp was compiled. The predictive ability of the model was assessed using a test set that was independent of the training set, which showed a standard error of prediction of 0.146 +/- 0.006 (data scaled from 0 to 1). Meanwhile, two other mathematical tools, back-propagation neural network (BPNN) and partial least squares (PLS) were also attempted to build QSAR models. The BRNN provided slightly better results for the test set compared to BPNN, but the difference was not significant according to F-statistic at p = 0.05. The PLS failed to build a reliable model in the present study. Our study indicates that the BRNN-based in silico model has good potential in facilitating the prediction of P-gp flavonoid inhibitors and might be applied in further drug design.

Compact Reconfigurable Binary-Decision-Diagram Logic Circuit on a GaAs Nanowire Network

We describe a reconfigurable binary-decision-diagram logic circuit based on Shannon's expansion of Boolean logic function and its graphical representation on a semiconductor nanowire network. The circuit is reconfigured by using programmable switches that electrically connect and disconnect a small number of branches. This circuit has a compact structure with a small number of devices compared with the conventional look-up table architecture. A variable Boolean logic circuit was fabricated on an etched GaAs nanowire network having hexagonal topology with Schottky wrap gates and SiN-based programmable switches, and its correct logic operation together with dynamic reconfiguration was demonstrated.

Dynamic P-I and P-V Curves for Semiconductor Lasers and Modulators

In this paper, we propose the dynamic P-V curve for modulator and P-I curve for laser diode, and present a simple approach to deriving the curves from the small-signal frequency responses measured using a microwave network analyzer. The linear response range, modulation efficiency, optimal driving conditions at different frequency can, therefore, be determined. It is demonstrated that the large-signal performance of electro-absorption (EA) modulator and the directly modulated semiconductor lasers can be predicted from the dynamic curved surface. Experiments show a good agreement between the evaluated characteristics and the measured large-signal performance.

A shortest path algorithm for moving objects in spatial network databases

One of the most important kinds of queries in Spatial Network Databases (SNDB) to support location-based services (LBS) is the shortest path query. Given an object in a network, e.g. a location of a car on a road network, and a set of objects of interests, e.g. hotels,gas station, and car, the shortest path query returns the shortest path from the query object to interested objects. The studies of shortest path query have two kinds of ways, online processing and preprocessing. The studies of preprocessing suppose that the interest objects are static. This paper proposes a shortest path algorithm with a set of index structures to support the situation of moving objects. This algorithm can transform a dynamic problem to a static problem. In this paper we focus on road networks. However, our algorithms do not use any domain specific information, and therefore can be applied to any network. This algorithm’s complexity is O(klog2 i), and traditional Dijkstra’s complexity is O((i + k)2).

Microstructure and some dynamic performances of Ti0.17Zr0.08V0.34RE0.01Cr0.1Ni0.3 (RE = Ce, Dy) hydrogen storage electrode alloys

Microstructure and some dynamic performances of Ti0.17Zr0.08V0.34RE0.01Cr0.1Ni0.3 (RE=Ce, Dy) hydrogen storage electrode alloys have been investigated using XRD, FESEM-EDS, ICP-MS and EIS measurements. The alloy is composed of V-based solid solution phase with a dendritic shape and a continuous C14 Laves phase with a network shape surrounding the dendrite. Pressure-composition isotherm curves indicate that the alloy with Dy addition has a lower equilibrium hydrogen pressure and a wider plateau region. The alloy electrode with Dy addition has higher discharge capacity, while the alloy electrode with Ce addition has better activation and higher cycle stability. The alloy electrode with Ce addition has better electrochemical activity with higher exchange current density (127.5 mA g(-1)), lower charge transfer resistance (1.37 Omega) and lower apparent activation energy (30.5 kJ mol(-1)). The capacity degradation behavior for the alloy electrode is attributed to two main factors: one is the dissolutions of V and Zr element to KOH solution, and another is the larger charge transfer resistance which increases with increasing cycle number.