基于元网络分析的重大基础设施建设项目风险评估框架与实证

重大基础设施项目具有战略性、集成性、复杂性等特征,项目容易受到多种风险因素的综合影响,导致项目目标的偏离。现有风险评估与风险决策的方法缺乏对于风险因素、风险事件之间关联的分析。为了实现重大基础设施建设项目综合系统的风险评估,本文采用元网络分析方法,构建项目目标、风险事件和风险因素的交互模型,揭示重大基础设施风险事件发生机制的黑箱过程。风险评估过程中,通过多个网络叠加运算分析每个风险因素对于各种风险事件以及项目各目标的影响情况,改进了以往仅对风险因素单一影响程度的风险评估方法。同时,本研究选择我国某河流水电站过坝运输项目方案比选的风险评估过程验证方法的适用性。

复杂网络理论在反恐战争中的应用

结合《Science》杂志"复杂系统与网络"特刊中的两篇论文,讨论了目前使用复杂网络方法在研究恐怖主义集团过程中数据收集方法的不足之处,分析了最新的数据收集手段和其发展方向。同时研究了复杂网络理论只能处理静态网络和易受噪声影响的局限性,分析了动态网络分析技术的优势,指出了复杂网络理论对于网络中心战的重要性。最后,简要评述了复杂网络在反恐战争中应用的现状、意义和缺陷。

开源软件开发者和源代码协调性的网络分析

开源软件开发本着自愿参加和开放服务的原则吸引着越来越多的软件开发者,但是开源社区合作协调的管理一直是个难题。本文对开源软件开发者社区与其中的源代码管理系统的协调性进行了元网络分析实证研究。操作项目代码的次数可作为衡量开源软件成败的一个重要指标,而该指标与开发者和源代码之间的相互依存关系有密切联系。本文用Sourceforge.net开源软件孵化平台的CVS源代码管理系统中的记录文件构建开发者和源代码间的依存网络,分析了该网络中的依存关系对软件成功的影响,并从中介性、等级性、边缘性、一致性和邻接性五个方面探讨了相互依存中的协调性问题。本文提出的方法和得到的结论可帮助开发者降低沟通成本,更有效地协调软件开发中开发者和源代码中的依存关系。

装配式建筑施工安全风险评估与控制的BN-MNA模型及应用

为了系统、有效地对装配式建筑施工安全风险进行评估与控制,文章建立了BN-MNA模型并开展实证研究。首先,构建包括风险源、工种、管理责任、时间和空间等关联要素的建筑施工安全风险系统;然后,基于系统框架,构建装配式建筑施工安全风险的元网络分析(MNA)模型;随后,通过贝叶斯网络(BN)引入概率以构建BN-MNA模型,进行风险源重要度分析和风险源节点的风险评估值定量计算;最后,针对风险性较大风险源节点,输出风险控制路径以明确相关责任主体,做到事前关联事件精准控制,同时,对已发生风险事件反向诊断,实现事后有效控制。研究表明:BN-MNA模型能够有效实现装配式建筑施工安全风险评估与控制一体化目标,通过关键风险概率推演为装配式建筑施工安全风险控制提供现实依据。

Prediction of pre-oxidation efficiency of refractory gold concentrate by ozone in ferric sulfate solution using artificial neural networks

An artificial neural network model was developed to predict the oxidation of refractory gold concentrate （RGC） by ozone and ferric ions. The concentration of ozone and ferric ions, pulp density, oxygen amount, leaching time and temperature were employed as inputs to the network; the output of the network was the percentage of the ferric extraction iron from RGC. The multilayered feed-forward networks were trained by 33 sets of input-output patterns using a back propagation algorithm; a three-layer network with 8 neurons in the hidden layer gave optimal results. The model gave good predictions of high correlation coefficient （R2=0.966）. The predictions by ANN are more accurate when compared with conventional multivariate regression analysis （MVRA）. In addition, calculation with ANN model indicates that temperature is the predominant parameter and ozone concentration is the lesser influential parameter in the pre-oxidation process of refractory gold ore. The ANN neural network model accurately estimates the ferric extraction during pretreatment process of RGC in gold smelter plants and can be used to optimize the process parameters.

Fault diagnosis method of track circuit based on KPCA-SAE

At present,ZPW-2000 track circuit fault diagnosis is artificially analyzed and monitored.Its discrimination method not only is low efficient and takes a long period,but also requires highly experienced personnel to analyze the data.Therefore,we introduce kernel principal component analysis and stacked auto-encoder network(KPCA-SAD)into the fault diagnosis of ZPW-2000 track circuit.According to the working principle and fault characteristics of track circuit,a fault diagnosis model of KPCA-SAE network is established.The relevant parameters of key components recorded in the data collected by field staff are used as the fault feature parameters.The KPCA method is used to reduce the dimension and noise of fault document matrix to avoid information redundancy.The SAE network is trained by the processed fault data.The model parameters are optimized overall by using back propagation(BP)algorithm.The KPCA-SAE model is simulated in Matlab platform and is finally proved to be effective and feasible.Compared with the traditional method of artificially analyzing fault data and other intelligent algorithms,the KPCA-SAE based classifier has higher fault identification accuracy.

Fault diagnosis method of train control RBC system based on KPCA-SOM network

Radio block center(RBC)system is the core equipment of China train control system-3(CTCS-3).Now,the fault analysis of RBC system mainly depends on manual work,and the diagnostic results are inaccurate and inefficient.Therefore,the intelligent fault diagnosis method of RBC system based on one-hot model,kernel principal component analysis(KPCA)and self-organizing map(SOM)network was proposed.Firstly,the fault document matrix based on one-hot model was constructed by the fault feature lexicon selected manually and fault tracking record table.Secondly,the KPCA method was used to reduce the dimension and noise of the fault document matrix to avoid information redundancy.Finally,the processed data were input into the SOM network to train the KPCA-SOM fault classification model.Compared with back propagation(BP)neural network algorithm and SOM network algorithm,common fault patterns of train control RBC system can be effectively distinguished by KPCA-SOM intelligent diagnosis model,and the accuracy and processing efficiency are further improved.

Application of artificial neural networks and multivariate statistics to estimate UCS using textural characteristics

Before any rock engineering project,mechanical parameters of rocks such as uniaxial compressive strength and young modulus of intact rock get measured using laboratory or in-situ tests,but in some situations preparing the required specimens is impossible.By this time,several models have been established to evaluate UCS and E from rock substantial properties.Artificial neural networks are powerful tools which are employed to establish predictive models and results have shown the priority of this technique compared to classic statistical techniques.In this paper,ANN and multivariate statistical models considering rock textural characteristics have been established to estimate UCS of rock and to validate the responses of the established models,they were compared with laboratory results.For this purpose a data set for 44 samples of sandstone was prepared and for each sample some textural characteristics such as void,mineral content and grain size as well as UCS were determined.To select the best predictors as inputs of the UCS models,this data set was subjected to statistical analyses comprising basic descriptive statistics,bivariate correlation,curve fitting and principal component analyses.Results of such analyses have shown that void,ferroan calcitic cement,argillaceous cement and mica percentage have the most effect on USC.Two predictive models for UCS were developed using these variables by ANN and linear multivariate regression.Results have shown that by using simple textural characteristics such as mineral content,cement type and void,strength of studied sandstone can be estimated with acceptable accuracy.ANN and multivariate statistical UCS models,revealed responses with 0.87 and 0.76 regressions,respectively which proves higher potential of ANN model for predicting UCS compared to classic statistical models.

Experimental study of fatigue degree quantification for multi-feature fusion identification

A comprehensive quantification method of fatigue degree is proposed concerning subjective and objective quantifications.Using the fatigue degree test software,fatigue degree is objectively quantified by analyzing the reaction and operation abilities of drivers about traffic signals.By comparison experiment with that EEG signal based,multivariate statistical analysis and fusion identification based on BP neural network(BPNN) results show that the experimental procedure is simple and practical,and the proposed method can reveal the correlation between fatigue feature parameters and fatigue degree in theory,and also can achieve accurate and reliable quantification of fatigue degree,especially under the associated action of multiple fatigue feature parameters.

Estimation of reservoir porosity using probabilistic neural network and seismic attributes

Porosity is one of the most important properties of oil and gas reservoirs. The porosity data that come from well log are only available at well points. It is necessary to use other method to estimate reservoir porosity.Seismic data contain abundant lithological information. Because there are inherent correlations between reservoir property and seismic data,it is possible to estimate reservoir porosity by using seismic data and attributes.Probabilistic neural network is a powerful tool to extract mathematical relation between two data sets. It has been used to extract the mathematical relation between porosity and seismic attributes. Firstly,a seismic impedance volume is calculated by seismic inversion. Secondly,several appropriate seismic attributes are extracted by using multi-regression analysis. Then a probabilistic neural network model is trained to obtain a mathematical relation between porosity and seismic attributes. Finally,this trained probabilistic neural network model is implemented to calculate a porosity data volume. This methodology could be utilized to find advantageous areas at the early stage of exploration. It is also helpful for the establishment of a reservoir model at the stage of reservoir development.

Bistability Analysis of Excitatory-Inhibitory Neural Networks in Limited-Sustained-Activity Regime

Bistable behavior of neuronal complex networks is investigated in the limited-sustained-activity regime when the network is composed of excitatory and inhibitory neurons.The standard stability analysis is performed on the two metastable states separately.Both theoretical analysis and numerical simulations show consistently that the difference between time scales of excitatory and inhibitory populations can influence the dynamical behaviors of the neuronal networks dramatically,leading to the transition from bistable behaviors with memory effects to the collapse of bistable behaviors.These results may suggest one possible neuronal information processing by only tuning time scales.

A Methodology for Bridge Condition Evaluation

Due to the substantial role of bridges in transportation networks and in accordance with the limited funding for bridge management, remediation strategies have to be prioritised. A conservative bridge assessment will result in unnecessary actions, such as costly bridge strengthening or repairs. On the other hand, any bridge maintenance negligence and delayed actions may lead to heavy future costs or degraded assets. The accuracy of decisions developed by any manager or bridge engineer relies on the accuracy of the bridge condition assessment which emanates from visual inspection. Many bridge rating systems are based on a very subjective procedure and are associated with uncertainty and personal bias. The developing condition rating method described herein is an important step in adding more holism and objectivity to the current approaches. Structural importance and material vulnerability are the two main factors that should be considered in the evaluation of element structural index and the causal factor as the representative of age, environment, road class and inspection is implemented as a coefficient to the OSCI （overall structural condition index）. The AHP （analytical hierarchy process） has been applied to evaluate the priority vector of the causal parameters.

Intrinsic elastic conductors with internal buckled electron pathway for flexible electromagnetic interference shielding and tumor ablation

The elastic conductor is crucial in wearable electronics and soft robotics.The ideal intrinsic elastic bulk conductors show uniform three-dimensional conductive networks and stable resistance during large stretch.A challenge is that the variation of resistance is high under deformation due to disconnection of conductive pathway for bulk elastic conductors.Our strategy is to introduce buckled structure into the conductive network,by self-assembly of a carbon nanotube layer on the interconnecting micropore surface of a prestrained foam,followed by strain relaxation.Both unfolding of buckles and flattening of micropores contributed to the stability of the resistance under deformation(2.0%resistance variation under 70%strain).Microstructural analysis and finite element analysis illustrated different patterns of two-dimensional buckling structures could be obtained due to the imperfections in the conductive layer.Applications as all-directional interconnects,stretchable electromagnetic interference shielding and electrothermal tumor ablation were demonstrated.