选煤数、质量线的建立

利 用选煤三产品之 间的数、质量 关系，在三 角形坐标 系中绘出 选煤数、质量 线，并对其 作用进行

平稳时间序列模型优选及阶数比选研究——以年最高洪水位预报为例

水位预报因具有数据直观,复核方便,容易确定避险区域等优点而逐渐取代复杂的流量预报.平稳时间序列模型计算简单,容易操作,是水位预报的理想方法.本文对比分析自回归模型、滑动平均模型和自回归滑动平均模型,以简捷原则为原则建立了自回归模型的参数计算方法和水位预报模型.并以丹东一水文站25年水位资料为例进行了两个阶数对比,结果表明3阶预报精度高于5阶,比选结果认为一般情况下可只进行3阶预报就能满足精度要求.

从数学理解的角度探究“二项式定理”教学

数学理解是一个数学知识进入到个体认知结构中与原有认知结构中的旧知识形成内部网络的过程.笔者结合自己的教学经历,从数学理解的角度探究“二项式定理”教学.

基于FPGA和AD8318的预/后选器自检电路设计应用

预/后选器是一种用于短波跳频电台内接收机和发射机的数字调谐跳频滤波器,是大型通信系统中的一个重要组成部分。为了方便测试预/后选器,提出一种基于XC3S200A FPGA和AD8318检波器的自检电路。首先由FPGA PLL模块产生能覆盖所有谐振电路的频率组作为自检信号,然后将通过预/后选器的自检信号送入AD8318检波器检验其增益是否达标。实验结果表明,该自检电路能有效实现对预/后选器各通道性能的快速检验与准确分析,对于及时锁定故障通道具有重要意义。

Censored Composite Conditional Quantile Screening for High-Dimensional Survival Data

In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all useful information across quantiles and can detect nonlinear effects including interactions and heterogeneity,effectively.Furthermore,the proposed screening method based on cCCQC is robust to the existence of outliers and enjoys the sure screening property.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors,particularly when the variables are highly correlated.

中学人口教育课教学方法初探

从1980年9月开始,我们河南省实验中学根据教育部的指示在高中一年级开设人口教育课,每周一课时,讲一个学期。两年来,有三届高中一年级学生共16个班800人已经或正在受到人口教育。 通过教学实践,我们逐步认识到,这一门年轻的课程有以下两个显著的特点:

Curb parking pricing method based on parking choice behavior

In order to improve the use efficiency of curb parking, a reasonable curb parking pricing is evaluated by considering individual parking choice behavior. The parking choice behavior is analyzed from micro-aspects, and the choice behavior utility function is established combining trip time, search time, waiting time, access time and parking fee. By the utility function, a probit-based parking choice behavior model is constructed. On the basis of these, the curb parking pricing model is deduced by considering the constrained conditions, and an incremental assignment algorithm of the model is also designed. Finally, the model is applied to the parking planning of Tongling city. It is pointed out that the average parking time of curb parking decreases 34%, and the average turnover rate increases 67% under the computed parking price system. The results show that the model can optimize the utilization of static traffic facilities.

方便、快捷的“智能ABC”输入法

计算机教学中要求学生熟练使用“五笔字型”输入法输入汉字,但对大多数非专业人员打字来讲,去背字根、拆字似有一定困难,而使用拼音法输入呢?一是要求标准的普通话读音,二是汉字同音字多,要想快捷输入也非易事。笔者认为Windows中自带的“智能ABC”输入法包含的词汇量大,具有较高的智能化,是一种方便。

RBF-based cluster-head selection for wireless sensor networks

The radial basis function （RBF）, a kind of neural networks algorithm, is adopted to select clusterheads. It has many advantages such as simple parallel distributed computation, distributed storage, and fast learning. Four factors related to a node becoming a cluster-head are drawn by analysis, which are energy （ energy available in each node）, number （the number of neighboring nodes）, centrality （ a value to classify the nodes based on the proximity how central the node is to the cluster）, and location （the distance between the base station and the node）. The factors are as input variables of neural networks and the output variable is suitability that is the degree of a node becoming a cluster head. A group of cluster-heads are selected according to the size of network. Then the base station broadcasts a message containing the list of cluster-heads＇ IDs to all nodes. After that, each cluster-head announces its new status to all its neighbors and sets up a new cluster. If a node around it receives the message, it registers itself to be a member of the cluster. After identifying all the members, the cluster-head manages them and carries out data aggregation in each cluster. Thus data flowing in the network decreases and energy consumption of nodes decreases accordingly. Experimental results show that, compared with other algorithms, the proposed algorithm can significantly increase the lifetime of the sensor network.

Estimating primaries by sparse inversion of the 3D Curvelet transform and the L1-norm constraint

In this paper, we built upon the estimating primaries by sparse inversion （EPSI） method. We use the 3D curvelet transform and modify the EPSI method to the sparse inversion of the biconvex optimization and Ll-norm regularization, and use alternating optimization to directly estimate the primary reflection coefficients and source wavelet. The 3D curvelet transform is used as a sparseness constraint when inverting the primary reflection coefficients, which results in avoiding the prediction subtraction process in the surface-related multiples elimination （SRME） method. The proposed method not only reduces the damage to the effective waves but also improves the elimination of multiples. It is also a wave equation- based method for elimination of surface multiple reflections, which effectively removes surface multiples under complex submarine conditions.

Semantic role labeling based on conditional random fields

Due to the fact that semantic role labeling （SRL） is very necessary for deep natural language processing, a method based on conditional random fields （CRFs） is proposed for the SRL task. This method takes shallow syntactic parsing as the foundation, phrases or named entities as the labeled units, and the CRFs model is trained to label the predicates＇ semantic roles in a sentence. The key of the method is parameter estimation and feature selection for the CRFs model. The L-BFGS algorithm was employed for parameter estimation, and three category features： features based on sentence constituents, features based on predicate, and predicate-constituent features as a set of features for the model were selected. Evaluation on the datasets of CoNLL-2005 SRL shared task shows that the method can obtain better performance than the maximum entropy model, and can achieve 80. 43 % precision and 63. 55 % recall for semantic role labeling.

CFD numerical simulation of flow velocity characteristics of hydrocyclone

A CFD based numerical simulation of flow velocity of hydrocyclone was conducted with different structural and operational parameters to investigate its distribution characteristics and influencing mechanism. The results show there exist several unsymmetrical envelopes of equal vertical velocities in both upward inner flows and downward outer flows in the hydrocyclone, and the cone angle and apex diameter have remarkable influence on the vertical location of the cone bottom of the envelope of zero vertical velocity. It is also found that the tangential velocity isolines exist in the horizontal planes located in the effective separation region of hydrocyclone. The increase of feed pressure has almost no effect on the distribution characteristics of both vertical velocity and tangential velocity in hydrocyclone, but the magnitude and gradient of tangential velocity are increased obviously to make the motion velocity of high density particles to the wall increased and to make the cyclonic separation effect improved.

Combined Selection Index for New Yorkshire Dam Line with High Prolificacy

The combined selection index used in the breeding of new Yorkshire dam line with high prolificacy according to breeding objects was formulated as /：2.272E- BVNB-0.056EBVDAYS. After 5 generations breeding, the two main selected traits such as total number of born and age at 100 kg weight was 12.17 piglets/litter and 165.18 d, respectively. The genetic improvements per generates was 0.156 and -2.198, respec- tively. The breeding objects of the new Yorkshire dam line with high prolificacy were basically reached. It indicated that the methods and index could be used in pig breeding.

Extrapolated Tikhonov method and inversion of 3D density images of gravity data

Tikhonov regularization（TR） method has played a very important role in the gravity data and magnetic data process. In this paper, the Tikhonov regularization method with respect to the inversion of gravity data is discussed. and the extrapolated TR method（EXTR） is introduced to improve the fitting error. Furthermore, the effect of the parameters in the EXTR method on the fitting error, number of iterations, and inversion results are discussed in details. The computation results using a synthetic model with the same and different densities indicated that. compared with the TR method, the EXTR method not only achieves the a priori fitting error level set by the interpreter but also increases the fitting precision, although it increases the computation time and number of iterations. And the EXTR inversion results are more compact than the TR inversion results, which are more divergent. The range of the inversion data is closer to the default range of the model parameters, and the model features and default model density distribution agree well.

Machine tool selection based on fuzzy evaluation and optimization of cutting parameters

The paper analyzes the factors influencing machine tool selection. By using fuzzy mathematics theory, we establish a theorietical model for optimal machine tool selection considering geometric features, clamping size, machining range, machining precision and surface roughness. By means of fuzzy comprehensive evaluation method, the membership degree of machine tool selection and the largest comprehensive evaluation index are determined. Then the reasonably automatic selection of machine tool is realized in the generative computer aided process planning （CAPP） system. Finally, the finite element model based on ABAQUS is established and the cutting process of machine tool is simulated. According to the theoretical and empirical cutting parameters and the curve of surface residual stress, the optimal cutting parameters can be determined.

Integrity of Four Theories on Natural Selection Unit

[Objective] The aim was to study the internal relationship of the four theories on natural selection unit. [Method] The value field of fitness of heterozygote was investigated by constructing mathematical models, to clarify the internal relations of the four theories on natural selection unit. [Result] According to mathematical modes constructed in the study, only the mutated genes meet the requirements of natural selection on heterozygous and homozygous aspects, as well as show high fitness in heterozygous condition, could the mutated genes be kept, giving consideration to both individual and population adaptation. Thus, this methodology theoretically inte- grates the theories of individual selection, collective selection, and genetic selection as well as Kimura＇s neutral theory of health information. [Conclusion] The result of this study suggested that the four theories on natural selection unit can co-exist, and share common premises.

五代十国时期的任官资格制度

五代十国时期的任官资格制度曾小华五代十国时期，是自秦始皇统一中国以后的第二次大分裂时期，与魏晋南北朝的分裂阶段相比，五代十国的年代要短得多，只有短短的５３年历史，再加上长期以来人们对这个阶段的忽视，使其影响要比魏晋南北朝小得多。事实上，五代十国的短暂...

台湾儿童书插图有明显进步

意大利波伦那国际儿童图书博览会于4月上旬在意大利的波伦那举办。今年博览会期间举办的插图原作展中,参选作品1488件,入选作品88件。台湾出版社这次取得了好成绩,《快乐的死刑犯》、《美女还是老虎》、《旅馆的那一夜》、《威尼斯商人》、《中国传家宝故事实录》等11部作品入选。这次入选数打破了历年记录,并居参展国家和地区之冠。这说明台湾儿童书的出版已打破过去主要依靠翻译外国作品的局面,走向自编自绘。目前台湾儿童书有的已打入国际出版市场。

CoMP-transmission-based energy-efficient scheme selection algorithm for LTE-A systems

In order to achieve higher system energy efficiency （EE）,a new coordinated multipoint （CoMP）-transmission-based scheme selection energy saving （CTSES）algorithm is proposed for downlink homogeneous cellular networks.The problem is formulated as an optimization of maximizing system EE,under the constraints of the data rate requirement and the maximum transmit power.The problem is decomposed into power allocation and alternative scheme selection problems.Optimal power allocation is calculated for CoMP-JT （joint transmission）and CoMP-CS （coordinated scheduling） transmissions,and the scheme with higher EE is chosen. Since the optimal problem is a nonlinear fractional optimization problem for both CoMP transmission schemes, the problem is transformed into an equivalent problem using the parametric method. The optimal transmit power and optimal EE are obtained by an iteration algorithm in CoMP-JT and CoMP-CS schemes.Simulation results show that the proposed algorithm offers obvious energy-saving potential and outperforms the fixed CoMP transmission scheme.Under the condition of the same maximum transmit power limit,the empirical regularity of user distribution for scheme choice is presented, and using this regularity, the computational complexity can be reduced.

Application of BP neural network model with fuzzy optimization in retrieval of biomass parameters

The retrieval of the biomass parameters from active/passive microwave remote sensing data （10.2 GHz） is performed based on an iterative inversion of BP neural network model with fuzzy optimization. The BP neural network is trained by a set of the measurements of active and passive remote sensing and the ground truth data versus Day of Year during growth. Once the network training is complete, the model can be used to retrieve the temporal variations of the biomass parameters from another set of observation data. The model was used in weights and microware observation data of wheat growth in 1989 to retrieve biomass parameters change of wheat growth this year. The retrieved biomass parameters correspond well with the real data of the growth, which shows that the BP model is scientific and sound.