解析地市级电力电网调度用数据网的组网

近年来,国家电力生产数据调度运营水平不断提升,其中的数据业务不断开展,对数据网技术应用提出了更高的要求,因此加强电力调度数据网建设具有明显的必要性与紧迫性。文章着重就地级市电力调度数据网的拓扑结构及其技术体制进行了分析,还对其将来的发展趋势作了进一步探讨。

SPEECH EMOTION RECOGNITION USING MODIFIED QUADRATIC DISCRIMINATION FUNCTION

Quadratic Discrimination Function （QDF） is commonly used in speech emotion recognition, which proceeds on the premise that the input data is normal distribution. In this paper, we propose a transformation to normalize the emotional features, emotion recognition. Features based on prosody then derivate a Modified QDF （MQDF） to speech and voice quality are extracted and Principal Component Analysis Neural Network （PCANN） is used to reduce dimension of the feature vectors. The results show that voice quality features are effective supplement for recognition, and the method in this paper could improve the recognition ratio effectively.

Quantitative analysis of globularization and modeling of TC17 alloy with basketweave microstructure

The globularization behavior and mechanism of TC17 alloy with basketweave microstructure were investigated, and the models of dynamic and static globularization kinetics were established. The quantitative and metallographic results show that the globularization of α phase is sensitive to the parameters of deformation and heat treatment. By EBSD analysis, the formation and evolution mechanisms of intra-α boundaries are related to discontinuous dynamic recrystallization and continuous dynamic recrystallization, which can form α grains with high and low misorientations between neighbour grains after the heat treatment, respectively. Based on the globularization behavior and mechanism, two modified JMAK models are developed to predict the dynamic and static globularization kinetics, and the mean absolute relative errors(MARE) of 10.67% and 13.80% indicate the accuracy of the dynamic and static globularization kinetics models. The results of this work can provide guidance for controlling microstructure of titanium alloy.

Effects of Different Extract of Pseudostellaria Heterophylla on Immunological Function in Mice based on Meta-analysis and Network Meta-analysis

To evaluate the effects of different extract of Pseudostellaria heterophylla on immunological function in mice based on Meta-analysis and Network meta-analysis,the article retrieved domestic and foreign databases according to the"PICO"retrieval strategy,and used Stata and ADDIS software for Meta analysis.A total of 6 reports,10 randomised controlled trials(RCTs)were eventually involved.Meta analysis results showed that:compared with the control group,the experimental group of polysaccharide,saponin and crude extract were better than that of the control(P<0.05),which significantly improved the immunological function in mice.Network meta-analysis results showed that the saponin had the best effect on the increase of phagocytic index and the differences were statistically significant[MD=1.50,95%CI(0.71,2.28),P<0.05];The polysaccharide and saponin had better effect on the increase of spleen index than the control and crude extract,and the differences were statistically significant[MD=0.77,95%CI(0.27,1.31),P<0.05],[MD=0.71,95%CI(0.01,1.50),P<0.05];the polysaccharide had the best effect on the increase of thymus index and the differences were statistically significant[MD=0.70,95%CI(0.11,1.34),P<0.05].The rank probability showed that the saponin of Pseudostellaria heterophylla had the maximum probability to increase phagocytic index of mice;the probability for the components of Pseudostellaria heterophylla increasing spleen index of mice was in the order of crude extract>polysaccharide>saponin;the probability for the components of Pseudostellaria heterophylla increasing thymus index of mice was in the order of saponin>polysaccharide>crude extract.Based on the available evidence,the extract of Pseudostellaria heterophylla could improve the immunity of mice,and the clinical effect of polysaccharide and saponin was the best,which provided a more valuable scientific reference for evidencing that the polysaccharide and saponin of Pseudostellaria heterophylla was the effective components for improving immunological function,and also was conducive to the proper further development of Pseudostellaria heterophylla resources.

Extracting invariable fault features of rotating machines with multi-ICA networks

This paper proposes novel multi-layer neural networks based on Independent Component Analysis for feature extraction of fault modes. By the use of ICA, invariable features embedded in multi-channel vibration measurements under different operating conditions (rotating speed and/or load) can be captured together.Thus, stable MLP classifiers insensitive to the variation of operation conditions are constructed. The successful results achieved by selected experiments indicate great potential of ICA in health condition monitoring of rotating machines.

Hybrid scheduling model and analysis of performance for switched industrial Ethernet

The fact that outburst traffic in industrial Ethemet was focused on that would bring self-similar phenomenon leading to the delay increase of the cyclical data, and a hybrid priority queue schedule model was proposed in which the outburst data was given the highest priority. Some properties of the self-similar outburst data were proved by network calculus, and its service curve scheduled by the switch was gained. And then the performance of the scheduling algorithm was obtained. The simulation results are close to those calculated by using network calculus model. Some results are of actual significance to the construction of switched industrial Ethernet.

Component based ant routing protocols analysis over mobile ad hoc networks

To deeply exploit the mechanisms of ant colony optimization （ACO） applied to develop routing in mobile ad hoe networks （MANETS）,some existing representative ant colony routing protocols were analyzed and compared.The analysis results show that every routing protocol has its own characteristics and competitive environment.No routing protocol is better than others in all aspects.Therefore,based on no free lunch theory,ant routing protocols were decomposed into three key components：route discovery,route maintenance （including route refreshing and route failure handling） and data forwarding.Moreover,component based ant routing protocol （CBAR） was proposed.For purpose of analysis,it only maintained basic ant routing process,and it was simple and efficient with a low overhead.Subsequently,different mechanisms used in every component and their effect on performance were analyzed and tested by simulations.Finally,future research strategies and trends were also summarized.

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.

Risk Factors Analysis on Traumatic Brain Injury Prognosis

Objective To investigate the independent risk factors of traumatic brain injury （TBI） prognosis. Methods A retrospective analysis was performed in 885 hospitalized TBI patients from January 1, 2003 to January 1, 2010 in the First Affiliated Hospital of Medical College of Xi＇an Jiaotong LIniversity. Sin- gle-factor and logistic regression analysis were conducted to evaluate the association of different variables with TBI outcome. Results The single-factor analysis revealed outcome, including age （P=0.044 for the age group （P〈0.O01）, cerebrospinal fluid leakage （P〈0.001）, reflex （P〈0.001）, shock （P〈0.001）, associated （P〈0.001）, cerebral contusion （P〈0.001）, diffuse significant association between several variables and TB1 40-60, P〈0.00l for the age group ≥60）, complications Glasgow Coma Scale （GCS） （P〈0.001）, pupillary light extra-cranial lesions （P=0.01）, subdural hematoma axonal injury （P〈0.001）, and subarachnoid hemorrhage （P〈0.001）, suggesting the influence of those factors on the prognosis of TBI. Furthermore, logistic regression analysis identified age, GCS score, pupillary light reflex, subdural hematoma, and subarachnoid hemorrhage as independent risk factors of TB1 prognosis. Conclusion Age, GCS score, papillary light reflex, subdural hematoma, and subarachnoid hemorrhage may be risk factors influencing the prognosis of TBI. Paying attention to those factors might improve the outcome of TBI in clinical treatment.

Self-similar traffic analysis in optical burst assembly

This paper investigates the traffic properties before and after assembly at edge node of Ethernet over optical burst switching （OBS） network for the first time. Burst and inter-arrival time distributions are simulated under time-based and length-based assembly schemes. Self-similar traffic Hurst parameter is compared through R/S and V/T plot. Finally three self-similar traffic generating methods are given. Simulation resuhs demonstrate that, muhi-source traffic increases self-similar degree, however after assembly, time-based scheme can decrease self similar degree, and aggregated burst size is close to Gaussian distribution. Length-based method has no effects on the self-similarity of input traffic. RMD is fit for study of burst network with large self-similarity.

Personalized Metaheuristic Clustering Onto Web Documents

Optimal clustering for the web documents is known to complicated cornbinatorial Optimization problem and it is hard to develop a generally applicable oplimal algorithm. An accelerated simuIated arlneaIing aIgorithm is developed for automatic web document classification. The web document classification problem is addressed as the problem of best describing a match between a web query and a hypothesized web object. The normalized term frequency and inverse document frequency coefficient is used as a measure of the match. Test beds are generated on - line during the search by transforming model web sites. As a result, web sites can be clustered optimally in terms of keyword vectofs of corresponding web documents.

A weighted selection combining scheme for cooperative spectrum prediction in cognitive radio networks

A weighted selection combining （WSC） scheme is proposed to improve prediction accuracy for cooperative spectrum prediction in cognitive radio networks by exploiting spatial diversity. First, a genetic algorithm-based neural network （GANN） is designed to perform spectrum prediction in consideration of both the characteristics of the primary users （PU） and the effect of fading. Then, a fusion selection method based on the iterative self-organizing data analysis （ISODATA） algorithm is designed to select the best local predictors for combination. Additionally, a reliability-based weighted combination rule is proposed to make an accurate decision based on local prediction results considering the diversity of the predictors. Finally, a Gaussian approximation approach is employed to study the performance of the proposed WSC scheme, and the expressions of the global prediction precision and throughput enhancement are derived. Simulation results reveal that the proposed WSC scheme outperforms the other cooperative spectrum prediction schemes in terms of prediction accuracy, and can achieve significant throughput gain for cognitive radio networks.

NETWORK ANALYSIS OF TERRORIST ACTIVITIES

This paper uses an extensive network approach to "East Turkistan" activities by building both the one-mode and the bipartite networks for these activities.In the one-mode network,centrality analysis and spectrum analysis are used to describe the importance of each vertex.On this basis,two types of core vertices——The center of communities and the intermediary vertices among communities— are distinguished.The weighted extreme optimization(WEO) algorithm is also applied to detect communities in the one-mode network.In the "terrorist-terrorist organization" bipartite network,the authors adopt centrality analysis as well as clustering analysis based on the original bipartite network in order to calculate the importance of each vertex,and apply the edge clustering coefficient algorithm to detect the communities.The comparative and empirical analysis indicates that this research has been proved to be an effective way to identify the core members,key organizations,and communities in the network of "East Turkistan" terrorist activity.The results can provide a scientific basis for the analysis of "East Turkistan" terrorist activity,and thus provide decision support for the real work of "anti-terrorism".