Web Portal提高IMS多网元操作效率

随着江苏电信苏州大区中兴域IMS网络的正式上线,由于网络架构及维护方式的改变,维护压力也越来越大,特别是日常用户数据制作和查询涉及面广,数据人工干预及查询量大,另外考虑IMS用户数据制作和传统网元特点的差异性,基于IMS SPN网元开发集成了一个Web Portal操作维护界面,简化并调高用户数据制作和查询效率,同时也屏蔽了维护人员维护水平的差异性,此Web Portal维护操作工具已在江苏省中兴域及其他本地网推广试用。

中国电信“双提升”专项行动助力VoLTE试商用及Vo5G的展望

中国电信VoLTE商用在即,如何针对VoLTE的性能为实现'双提升'专项行动、建设VoLTE精品网络,提供清晰可行的依据、思路和优化策略,同时聚焦VoLTE的差异化优势,给VoLTE用户提供'打得通'、'接得快'、'听得清'、'不掉话'的感知体验积累经验。本文概述了中国电信VoLTE的网络架构、呼叫流程,从注册、未接通、时延、掉话、MOS质差、切换等6大问题进行分析定位;测试验证了开启八大VoLTE语音感知优化策略的前后效果对比,改善了语音MOS质量以及呼叫时延。对比了VoLTE和Vo5G的主要关键技术、能力和关键性能指标,展望了Vo5G将在移动性、时延性、接入速率等方面的显著提升。汇总了VoLTE的优化经验,加速了电信VoLTE业务试商用。

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.

Mapping methods for output-based objective speech quality assessment using data mining

Objective speech quality is difficult to be measured without the input reference speech.Mapping methods using data mining are investigated and designed to improve the output-based speech quality assessment algorithm.The degraded speech is firstly separated into three classes(unvoiced,voiced and silence),and then the consistency measurement between the degraded speech signal and the pre-trained reference model for each class is calculated and mapped to an objective speech quality score using data mining.Fuzzy Gaussian mixture model(GMM)is used to generate the artificial reference model trained on perceptual linear predictive(PLP)features.The mean opinion score(MOS)mapping methods including multivariate non-linear regression(MNLR),fuzzy neural network(FNN)and support vector regression(SVR)are designed and compared with the standard ITU-T P.563 method.Experimental results show that the assessment methods with data mining perform better than ITU-T P.563.Moreover,FNN and SVR are more efficient than MNLR,and FNN performs best with 14.50% increase in the correlation coefficient and 32.76% decrease in the root-mean-square MOS error.

Application and comparison of RNN, RBFNN and MNLR approaches on prediction of flotation column performance

Evaluation of grade and recovery plays an important role in process control and plant profitability in mineral processing operations, especially flotation. The accurate measurement or estimation of these two parameters, based on the secondary variables, is a critical issue. Data-driven modeling techniques, which entail comprehensive data analysis and implementation of machine learning methods for system forecast, provide an attractive alternative. In this paper, two types of artificial neural networks(ANNs),namely radial basis function neural network(RBFNN) and layer recurrent neural network(RNN), and also a multivariate nonlinear regression(MNLR) model were employed to predict metallurgical performance of the flotation column. The training capacity and the accuracy of these three above mentioned types of models were compared. In order to acquire data for the simulation, a case study was conducted at Sarcheshmeh copper complex pilot plant. Based on the root mean squared error and correlation coefficient values, at training and testing stages, the RNN forecasted the metallurgical performance of the flotation column better than RBF and MNLR models. The RNN could predict Cu grade and recovery with correlation coefficients of 0.92 and 0.9, respectively in testing process.

3-D direct current resistivity forward modeling by adaptive multigrid finite element method

Based on the fact that 3-D model discretization by artificial could not always be successfully implemented especially for large-scaled problems when high accuracy and efficiency were required, a new adaptive multigrid finite element method was proposed. In this algorithm, a-posteriori error estimator was employed to generate adaptively refined mesh on a given initial mesh. On these iterative meshes, V-cycle based multigrid method was adopted to fast solve each linear equation with each initial iterative term interpolated from last mesh. With this error estimator, the unknowns were nearly optimally distributed on the final mesh which guaranteed the accuracy. The numerical results show that the multigrid solver is faster and more stable compared with ICCG solver. Meanwhile, the numerical results obtained from the final model discretization approximate the analytical solutions with maximal relative errors less than 1%, which remarkably validates this algorithm.

FAIRNESS OF SHARING PROTECTION FOR MULTICAST IN WDM NETWORKS

In this paper,the sharing schemes of multicast in survivable Wavelength-Division Multi-plexed(WDM) networks are studied and the concept of Shared Risk Link Group(SRLG) is considered.While the network resources are shared by the backup paths,the sharing way is possible to make the backup paths selfish.This selfishness leads the redundant hops of the backup route and a large number of primary lightpaths to share one backup link.The sharing schemes,especially,the self-sharing and cross-sharing,are investigated to avoid the selfishness when computing the backup light-tree.In order to decrease the selfishness of the backup paths,it is important to make the sharing links fair to be used.There is a trade-off between the self-sharing and cross-sharing,which is adjusted through simulation to adapt the sharing degree of each sharing scheme and save the network resources.

Prediction of blast boulders in open pit mines via multiple regression and artificial neural networks

The most important objective of blasting in open pit mines is rock fragmentation.Prediction of produced boulders(oversized crushed rocks) is a key parameter in designing blast patterns.In this study,the amount of boulder produced in blasting operations of Golegohar iron ore open pit mine,Iran was predicted via multiple regression method and artificial neural networks.Results of 33 blasts in the mine were collected for modeling.Input variables were:joints spacing,density and uniaxial compressive strength of the intact rock,burden,spacing,stemming,bench height to burden ratio,and specific charge.The dependent variable was ratio of boulder volume to pattern volume.Both techniques were successful in predicting the ratio.In this study,the multiple regression method was superior with coefficient of determination and root mean squared error values of 0.89 and 0.19,respectively.

A TWO-LAYER RECURRENT NEURAL NETWORK BASED APPROACH FOR OVERLAY MULTICAST

Overlay multicast has become one of the most promising multicast solutions for IP network,and Neutral Network(NN) has been a good candidate for searching optimal solutions to the constrained shortest routing path in virtue of its powerful capacity for parallel computation. Though traditional Hopfield NN can tackle the optimization problem,it is incapable of dealing with large scale networks due to the large number of neurons. In this paper,a neural network for overlay multicast tree com-putation is presented to reliably implement routing algorithm in real time. The neural network is constructed as a two-layer recurrent architecture,which is comprised of Independent Variable Neurons(IDVN) and Dependent Variable Neurons(DVN) ,according to the independence of the decision variables associated with the edges in directed graph. Compared with the heuristic routing algorithms,it is characterized as shorter computational time,fewer neurons,and better precision.

Equality Testing for Soil Grid Unit Resolutions to Polygon Unit Scales with DNDC Modeling of Regional SOC Pools

Matching soil grid unit resolutions with polygon unit map scales is important to minimize the uncertainty of regional soil organic carbon(SOC) pool simulation due to their strong influences on the modeling.A series of soil grid units at varying cell sizes was derived from soil polygon units at six map scales,namely,1:50 000(C5),1:200 000(D2),1:500 000(P5),1:1 000 000(N1),1:4 000 000(N4) and 1:14 000 000(N14),in the Taihu Region of China.Both soil unit formats were used for regional SOC pool simulation with a De Nitrification-DeC omposition(DNDC) process-based model,which spans the time period from 1982 to 2000 at the six map scales.Four indices,namely,soil type number(STN),area(AREA),average SOC density(ASOCD) and total SOC stocks(SOCS) of surface paddy soils that were simulated by the DNDC,were distinguished from all these soil polygon and grid units.Subjecting to the four index values(IV) from the parent polygon units,the variations in an index value(VIV,%) from the grid units were used to assess its dataset accuracy and redundancy,which reflects the uncertainty in the simulation of SOC pools.Optimal soil grid unit resolutions were generated and suggested for the DNDC simulation of regional SOC pools,matching their respective soil polygon unit map scales.With these optimal raster resolutions,the soil grid units datasets can have the same accuracy as their parent polygon units datasets without any redundancy,when VIV < 1% was assumed to be a criterion for all four indices.A quadratic curve regression model,namely,y = – 0.80 × 10^(–6)x^2 + 0.0228 x + 0.0211(R^2 = 0.9994,P < 0.05),and a power function model R? = 10.394?^(0.2153)(R^2 = 0.9759,P < 0.05) were revealed,which describe the relationship between the optimal soil grid unit resolution(y,km) and soil polygon unit map scale(1:10 000x),the ratio(R?,%) of the optimal soil grid size to average polygon patch size(?,km^2) and the ?,with the highest R^2 among different mathematical regressions,respectively.This knowledge may facilitate the grid partitioning of regions during the investigation and simulation of SOC pool dynamics at a certain map scale,and be referenced to other landscape polygon patches' mesh partition.

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.

Retrieval and analysis of sea surface air temperature and relative humidity

Air temperature and relative humidity have been the main parameters of meteorology study. In the past data could be obtained from in-situ observations, but the observations are local and sparse, especially over ocean. Now we can get them from satellites, yet it is hard to estimate them from sat- ellites directly so far. This paper presents a new method to retrieve monthly averaged sea air temper- ature （SAT） and relative humidity （RH） near sea surface from satellite data with artificial neural networks （ANN）. Compared with the observations in Pacific and Atlantic, the root mean square （RMS） and the correlation between the estimated SAT and the observations are about 0.91 ~C and 0.99, respectively. The RMS and the correlation of RH are about 3.73% and 0.65, respectively. Compared with the multiple regression method, the ANN methodology is more powerful in building nonlinear relations in this research. Thus the global monthly average SAT and RH are retrieved from the fixed ANN network from July 1987 to May 2004. In general the annual average SAT shows the increasing trend in recent 18 years. The abnormality of SAT is decomposed with the empirical or- thogonal function （EOF）. The leading three EOFs could explain 84% of the total variation. EOF1 （76.1%） presents the seasonal change of the SAT abnormality. EOF2 （4.6%） is mainly related with ENSO. EOF3 （3.3%） shows some new interesting phenomena appearing in the three main currents in Pacific, Atlantic and Indian Ocean.

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.

Asymptotic expansions of finite element solutions to Robin problems in H^3 and their application in extrapolation cascadic multigrid method

For the Poisson equation with Robin boundary conditions,by using a few techniques such as orthogonal expansion(M-type),separation of the main part and the finite element projection,we prove for the first time that the asymptotic error expansions of bilinear finite element have the accuracy of O(h3)for u∈H3.Based on the obtained asymptotic error expansions for linear finite elements,extrapolation cascadic multigrid method(EXCMG)can be used to solve Robin problems effectively.Furthermore,by virtue of Richardson not only the accuracy of the approximation is improved,but also a posteriori error estimation is obtained.Finally,some numerical experiments that confirm the theoretical analysis are presented.

Nonconforming finite element methods on quadrilateral meshes

It is well known that it is comparatively difficult to design nonconforming finite elements on quadri- lateral meshes by using Gauss-Legendre points on each edge of triangulations. One reason lies in that these de- grees of freedom associated with these Gauss-Legendre points are not all linearly independent for usual expected polynomial spaces, which explains why only several lower order nonconforming quadrilateral finite elements can be found in literature. The present paper proposes two families of nonconforming finite elements of any odd order and one family of nonconforming finite elements of any even order on quadrilateral meshes. Degrees of freedom are given for these elements, which are proved to be well-defined for their corresponding shape function spaces in a unifying way. These elements generalize three lower order nonconforming finite elements on quadri- laterals to any order. In addition, these nonconforming finite element spaces are shown to be full spaces which is somehow not discussed for nonconforming finite elements in literature before.

h-P Finite Element Approximation for Full-Potential Electronic Structure Calculations

The(continuous) finite element approximations of different orders for the computation of the solution to electronic structures were proposed in some papers and the performance of these approaches is becoming appreciable and is now well understood.In this publication,the author proposes to extend this discretization for full-potential electronic structure calculations by combining the refinement of the finite element mesh,where the solution is most singular with the increase of the degree of the polynomial approximations in the regions where the solution is mostly regular.This combination of increase of approximation properties,done in an a priori or a posteriori manner,is well-known to generally produce an optimal exponential type convergence rate with respect to the number of degrees of freedom even when the solution is singular.The analysis performed here sustains this property in the case of Hartree-Fock and Kohn-Sham problems.

A partition-of-unity based three-node triangular element with continuous nodal stress using radial-polynomial basis functions

A partition-of-unity （PU） based ＂FE-Meshfree＂ three-node triangular element （Trig3-RPIM） was recently developed for linear elastic problems. This Trig3-RPIM element employs hybrid shape functions that combine the shape functions of three-node triangular element （Trig3） and radial-polynomial basis functions for the purpose of synergizing the merits of both finite element method and meshfree method. Although Trig3-RPIM element is capable of obtaining higher accuracy and convergence rate than the Trig3 element and four-node iso-parametric quadrilateral element without adding extra nodes or degrees of freedom （DOFs）, the nodal stress field through Trig3-RP1M element is not continuous and extra stress smooth operations are still needed in the post processing stage. To further improve the property of Trig3-RPIM element, a new PU-based triangular element with continuous nodal stress, called Trig3-RPIMcns, is developed. Numerical examples including several linear, free vibration and forced vibration test problems, have confirmed the correctness and feasibility of the proposed Trig3-RPIMcns element.