气垫带式输送机图解选型法

基于气垫带式输送机设计理论,提出"当量功率"、"当量风压"、"当量风量"、"空车功率"、"空车风量"、"空车风压"等概念,得到了一种气垫带式输送机图解选型方法,利用该方法可以直观、快捷地选择满足工程要求的气垫带式输送机型号、驱动功率、风量及风压等相关参数。

直接空冷机组电动给水泵选型建议

针对大容量直接空冷机组按照规定选用电动给水泵所伴生的问题,提出了按TRL工况选用电动给水泵的方法。此法节电效果巨大,就3台300MW直接空冷机组的电厂而言年节电约107kW h。

600MW直接空冷机组电动调速给水泵选型建议

针对600MW直接空冷机组按规定所选的电动调速给水泵存在的液力耦合器功耗大、电动机容量大等问题，提出以TRL工况汽轮机进汽量为基数确定给水泵设计参数的选型法。600MW直接空冷机组运行一年至少节电346×10^4kWh。

基于MATLAB GUI对燃气调压阀数值计算方法

调压阀的选型通常有流量表选型法和数值计算法。流量表选型法只针对具体规格和型号的调压阀。为解决不同压力和任意流量的调压阀选型,通过MATLAB GUI编制程序的方法对调压阀的类型和大小进行初选,可以快速选择不同种类和结构的调压阀,并明确阀门开度和可调比,为最终选择合适的调压阀提供数值依据。通过数值计算法和流量表选型法的数据对比,可以进一步验证数值计算法的合理性。

立式钻床立柱结构动静态分析与结构改进

以有限元理论为基础,建立立式钻床立柱结构的参数化模型,进行静态有限元分析,分析立柱结构承载及变形特点。在此基础上,采用选型法对立柱进行结构改进,并对改进后的模型做动态性能分析。结果表明:优化后,立柱静态性能提高,质量减少12.8%;立柱固有频率值与原固有频率值相近,满足结构动态性能的要求。

Method and standard for selecting elite nut trees of Korean pine

The fruit-bearing quantities of nut Korean pines (Pinus Koraiensis) of natural stands in Changbai Mountain, Xiaoxing'an Mountain, and Wanda Mountain and of artificial forest in Hegang area of Heilongjiang Province were investigated and measured by seed collection of singletree during 1988–1998. In order to evaluate the elite nut tree of fructification, the characteristics of fructification of Korena pine, including, the fruit-bearing quantity, output of seed, quantity of cone, cone size, seed size, the ratio of null seed by solid seed, seed percentage of cone, rate of the cones infested with pest, and fruit-bearing index, etc., were analyzed with the variance analysis, multiple comparison and stepwise regression to obtain the contribution ratio of each fruit-bearing factor to fruit-bearing quantity. The multiple correlation factors and the partial correlation factors for fruit-bearing quantities of Korean pine were determined for different geographical areas, and the cone length, thousand-grain-weight, and the seed percentage of cone were considered as important indices for selection of elite trees. The method of modified weighted coefficients was adopted to select the elite nut trees of Korean pine. Standards for selecting elite nut trees from the natural stands and artificial forest of Korean pine were established. This study could provde selection method and standard of elite nut trees for setting up seed orchard of Korean Pine.

Gaussian mixture model clustering with completed likelihood minimum message length criterion

An improved Gaussian mixture model （GMM）- based clustering method is proposed for the difficult case where the true distribution of data is against the assumed GMM. First, an improved model selection criterion, the completed likelihood minimum message length criterion, is derived. It can measure both the goodness-of-fit of the candidate GMM to the data and the goodness-of-partition of the data. Secondly, by utilizing the proposed criterion as the clustering objective function, an improved expectation- maximization （EM） algorithm is developed, which can avoid poor local optimal solutions compared to the standard EM algorithm for estimating the model parameters. The experimental results demonstrate that the proposed method can rectify the over-fitting tendency of representative GMM-based clustering approaches and can robustly provide more accurate clustering results.

Digital Rights Management：Model,Technology and Application

with rapid achievement of current information technology and computing ability and applications,much more digital content such as films,cartoons,design drawings,office documents and software source codes are produced in daily work,however to protect the content being copying,shared or deliberately stolen by inside or outside,digital rights management(DRM) became more and more important for digital content protection.In this paper,we studied various DRM model,technology and application,and first proposed DRM Security Infrastructure(DSI),in which we defined encryption,hash,signature algorithm,watermarking algorithms,authentication,usage control,trusted counter,conditional trace,secure payment,and based on the DSI we then proposed a whole classification approach and architecture of all kinds of DRMs,in which we proposed 6 typical classes of copyrights and content protection DRMs architecture:(1) Software-oriented DRM,(2) e Book-oriented DRM,(3) Video-oriented DRM,(4) Image-Oriented DRM(5) Unstructured data oriented DRM,(6) Text-oriented DRM.Based on the above DSI,we then proposed a dynamic DRM model selection method for various DRM application,which can be adapted dynamically for different technology of different applications,which can provide awhole solution for variant DRM development in a rapid and customized mode.The proposed DRM method,technology and application in this paper provided a common,flexible and extendable solution for variant DRM scenes,and can support rapid and customized development.Moreover,we proposed an opinion that the future life will enter into a new era that the content usage and consumption will not again adopt DRM technology rather than with law,liberty and morality.

The Research on Social Networks Public Opinion Propagation Influence Models and Its Controllability

Public opinion propagation control is one of the hot topics in contemporary social network research. With the rapid dissemination of information over the Internet, the traditional isolation and vaccination strategies can no longer achieve satisfactory results. A positive guidance technology for public opinion diffusion is urgently needed. First, based on the analysis of influence network controllability and public opinion diffusion, a positive guidance technology is proposed and a new model that supports external control is established. Second, in combination with the influence network, a public opinion propagation influence network model is designed and a public opinion control point selection algorithm(POCDNSA) is proposed. Finally, An experiment verified that this algorithm can lead to users receiving the correct guidance quickly and accurately, reducing the impact of false public opinion information; the effect of CELF is no better than that of the POCDNSA algorithm. The main reason is that the former is completely based on the diffusion cascade information contained in the training data, but does not consider the specific situation of the network structure and the diffusion of public opinion information in the closed set. thus, the effectiveness and feasibility of the algorithm is proven. The findings of this article therefore provide useful insights for the implementation of public opinion control.

A new improved Alopex-based evolutionary algorithm and its application to parameter estimation

In this work, focusing on the demerit of AEA (Alopex-based evolutionary algorithm) algorithm, an improved AEA algorithm (AEA-C) which was fused AEA with clonal selection algorithm was proposed. Considering the irrationality of the method that generated candidate solutions at each iteration of AEA, clonal selection algorithm could be applied to improve the method. The performance of the proposed new algorithm was studied by using 22 benchmark functions and was compared with original AEA given the same conditions. The experimental results show that the AEA-C clearly outperforms the original AEA for almost all the 22 benchmark functions with 10, 30, 50 dimensions in success rates, solution quality and stability. Furthermore, AEA-C was applied to estimate 6 kinetics parameters of the fermentation dynamics models. The standard deviation of the objective function calculated by the AEA-C is 41.46 and is far less than that of other literatures' results, and the fitting curves obtained by AEA-C are more in line with the actual fermentation process curves.

Type Selection of Burning Equipment for Slagging-Coal Boilers

The type selection of burning equipment for boilers is affected significantly by the slagging characteristics of coal. Based on the engineering statistics method, the designed furnace parameters are obtained from the 600-MW and 1 000-MW boilers with tangential firing and wall firing. The power and arrangement of the burners are analyzed. Their impacts on slagging on heating surfaces and the carbon contents in the ash and cinders are also discussed. Thermal parameters of furnace are recommended for boilers of 600 MW and 1 000 MW firing slagging coal in the design. The static or rotary classifier should be the first choice for the pulverizing system.

Smartphone Malware Detection Model Based on Artificial Immune System

In order to solve the problem that me traditional signature-based detection technology cannot effectively detect unknown malware, we propose in this study a smartphone malware detection model （SP-MDM） based on artificial immune system, in which static malware analysis and dynamic malware analysis techniques are combined, and antigens are generated by encoding the characteristics extracted from the malware. Based on negative selection algorithm, the mature detectors are generated. By introducing clonal selection algorithm, the detectors with higher affinity are selected to undergo a proliferation and somatic hyper-mutation process, so that more excellent detector offspring can be generated. Experimental result shows that the detection model has a higher detection rate for unknown smartphone malware, and better detection performance can be achieved by increasing the clone generation.

Statistical and Geometrical Way of Model Selection for a Family of Subdivision Schemes

The objective of this article is to introduce a generalized algorithm to produce the m-point n-ary approximating subdivision schemes(for any integer m, n ≥ 2). The proposed algorithm has been derived from uniform B-spline blending functions. In particular, we study statistical and geometrical/traditional methods for the model selection and assessment for selecting a subdivision curve from the proposed family of schemes to model noisy and noisy free data. Moreover, we also discuss the deviation of subdivision curves generated by proposed family of schemes from convex polygonal curve. Furthermore, visual performances of the schemes have been presented to compare numerically the Gibbs oscillations with the existing family of schemes.

Adaptive quantile regression with precise risk bounds

An adaptive local smoothing method for nonpaxametric conditional quantile regression models is considered in this paper. Theoretical properties of the procedure are examined. The proposed method is fully adaptive in the sense that no prior information about the structure of the model is assumed. The fully adaptive feature not only allows varying bandwidths to accommodate jumps or instantaneous slope changes, but also al- lows the algorithm to be spatially adaptive. Under general conditions, precise risk bounds for homogeneous and heterogeneous cases of the underlying conditional quantile curves are established. An automatic selection algo- rithm for locally adaptive bandwidths is also given, which is applicable to higher dimensional cases. Simulation studies and data analysis confirm that the proposed methodology works well.

Acquisition of English Reflexive Verb Constructions:Roles of Syntactical Indeterminacy and Token Frequency

This study draws on usage-based approach to language learning and investigates the role of syntactical indeterminacy and token frequency in constructional acquisition, for which reflexive verb constructions were selected as the testing field. Syntactic structures of reflexive verbs are either by reflexive constructions or adjectival passive, which have a polysemous interrelationship within the verb. To examine whether syntactic indeterminacy and token frequency play a role in the acquisition of reflexive verb constructions, a test of reflexive verb constructions and a baseline test formed with transitive verbs were developed and administered to L2 learners of an intermediate proficiency level. The findings show： （1） L2 reflexive verb constructions were less acquired than transitive constructions, suggesting that syntactic indeterminacy had an impact upon sentence production; （2） no significant difference was found between the productions of reflexive constructions and adjectival passives, but descriptive statistics showed that learners were attracted to the adjectival passive for production; （3） production of both syntactic structures reflected token frequency trend from COCA, indicating the important role of frequency in complex constructional acquisition.

A SELF-SIMILAR LOCAL NEURO-FUZZY MODEL FOR SHORT-TERM DEMAND FORECASTING

This paper proposes a selfsimilar local neurofuzzy （SSLNF） model with mutual informati onbased input selection algorithm for the shortterm electricity demand forecasting. The proposed self similar model is composed of a number of local models, each being a local linear neurofuzzy （LLNF） model, and their associated validity functions and can be interpreted itself as an LLNF model. The proposed model is trained by a nested local liner model tree （NLOLIMOT） learning algorithm which partitions the input space into axisorthogonal subdomains and then fits an LLNF model and its associated validity function on each subdomain. Furthermore, the proposed approach allows different input spaces for rule premises （validity functions） and consequents （local models）. This appealing property is employed to assign the candidate input variables （i.e., previous load and temperature） which influence shortterm electricity demand in linear and nonlinear ways to local models and validity functions, respectively. Numerical results from shortterm load forecasting in the New England in 2002 demonstrated the accuracy of the SSLNF model for the STLF applications.

机房电力有源滤波器和静止无功补偿器应用实用技术（二）

本文介绍了数据中心机房供配电系统电力无功补偿和谐波治理方面节能降耗并提高其系统安全可靠性的先进的补偿技术和应用方面的实用技术，包括静止无功补偿器SVG和电力有源滤波器APF工作原理、计算选型要点和方法、机房供配电系统电力补偿方案和SVG、APF应用注意事项，通俗易懂、实用性强。

机房电力有源滤波器和静止无功补偿器应用实用技术（一）

本文介绍了数据中心机房供配电系统电力无功补偿和谐波治理方面节能降耗并提高其系统安全可靠性的先进的补偿技术和应用方面的实用技术，包括静止无功补偿器SVG和电力有源滤波器APF工作原理、计算选型要点和方法、机房供配电系统电力补偿方案和SVG、APF应用注意事项，通俗易懂、实用性强。

Variable Selection of Varying Dispersion Student-t Regression Models

The Student-t regression model is a useful extension of the normal model,which can be used for statistical modeling of data sets involving errors with heavy tails and/or outliers and provides robust estimation of means and regression coefficients.In this paper,the varying dispersion Student-t regression model is discussed,in which both the mean and the dispersion depend upon explanatory variables.The problem of interest is simultaneously select significant variables both in mean and dispersion model.A unified procedure which can simultaneously select significant variable is given.With appropriate selection of the tuning parameters,the consistency and the oracle property of the regularized estimators are established.Both the simulation study and two real data examples are used to illustrate the proposed methodologies.