基于季节自回归单整移动平均模型的电梯交通流递归预测方法

针对电梯交通流预测提出了一种基于季节自回归单整移动平均（SARIMA）模型的递归预测方法．通过离线分析,对电梯交通流利用时间序列分析得到初始的SARIMA模型,引入异常值检测对训练数据中的异常值进行修正,利用修正的序列得到电梯交通流SARIMA模型;在线预测时,将离线得到修正的SARIMA模型转化为状态空间形式．通过Kalman滤波实时调整状态向量,实现电梯交通流的实时在线预测仿真表明该方法具有很好的预测性能,且运行时间短,满足实时性的要求．     

基于仿真和流量估计的服务性交通指标预测

提出了基于应用微观交通仿真模型和短期交通量预测的服务性交通参数预测模型.根据实际的交通系统和研究目的建立应用仿真模型并进行模型参数标定和有效性验证.根据实时采集的交通量采用基于小波分析和ARIMA(Autoregressive Integrated Moving Average)模型的混合模型进行下一时刻的交通量预测.将预测的交通量输入到应用仿真模型,根据仿真输出得到下一时刻的服务性交通参数指标.应用实例表明,该模型能够较好地进行服务性交通参数的预测.     

考虑时空相关性的网络交通流短期预测

实时、准确的交通流短期预测是交通诱导、管理的前提.为了提高预测精度,结合交通流数据中的历史时间相关性与网络空间断面相关性,构建了一种基于皮尔森相关系数法(Pearson Cor-relation Coefficient,PCC)与双向长短时记忆(Bidirectional Long Short Term Memory,BLSTM)架构的交通流短时预测模型.该模型可以通过PCC筛选路网中与目标路段空间相关的路段,并将其重构为新数据集,作为BLSTM预测模型的输入,以实现交通流短期预测.通过美国加州交通流数据对模型预测性能进行评价,实验结果表明:该模型可以融合交通流数据中的时空相关性,相对于其他主流预测模型精度平均可提高4.83％.     

基于混沌神经网络的交通流预测算法

为提高 RBF 神经网络的交通流预测精度,提出基于混沌-RBF（Chaos-RBF,C-RBF）神经网络的交通流预测算法,该算法首先计算混沌相空间的嵌入维数和嵌入延迟,构造得到的相空间向量作为 RBF 神经网络的输入,其相空间次邻向量作为期望输出值,滚动训练得到神经网络的权值,然后以实际交通流作为输入,经由网络计算得到预测值。仿真结果表明该算法相比于 RBF 神经网络,预测精度提高 96%,证明了该算法的有效性。     

基于IFA优化RBF神经网络的短时交通流预测模型

针对短时交通流不确定性极强引起的预测结果精度低的问题,提出一种改进萤火虫算法(IFA)优化RBF神经网络的短时交通流预测模型(IFA-RBF).该模型通过引入线性递减惯性权重和混沌机制,来改进FA后期存在的易陷入局部极值和种群多样性匮乏的不足,利用IFA优化RBF神经网络的连接权重和基函数宽度,以提升RBF神经网络的短时交通流预测精度.实验结果表明,与Elman、BP、RBF和FA-RBF模型相比,构建的短时交通流预测模型(IFA-RBF)具有更高的预测精度,预测值与实际值拟合度较高.     

基于RBF神经网络的城市快速路短时交通流预测研究

为检验灰色模型及径向神经网络模型用于短时交通流预测的可行性及适用性,本文分析和比较了灰色模型GM（1,1）和RBF径向神经网络模型对短时交通流的预测效果。仿真实例表明,灰色模型不适合用于短时交通流预测,而径向神经网络能够准确预测短期交通流的未来变化趋势,当径向基函数的分布密度值在0.8~1.0之间时能够取得较高的预测精度。     

基于SARIMA-GA-Elman组合模型的短时交通流预测方法

针对单一模型无法深入挖掘交通流复杂的线性和非线性特征方面的局限性以及神经网络模型在训练时收敛速度缓慢等问题,提出了一种基于SARIMA-GA-Elman的组合预测模型.该组合模型有效地融合了季节性差分自回归滑动平均(seasonal autoregressive integrated moving average,SARIMA)模型良好的线性拟合能力和Elman递归神经网络强大的非线性映射能力;在预测过程中首先基于SARIMA滚动预测时间序列的线性分量,然后使用SARIMA模型的预测误差序列建立Elman-RNN构建非线性误差模型;此外在训练非线性误差模型的过程中使用经过二进制编码的遗传算法(genetic algorithm,GA)优化Elman-RNN,旨在提升Elman-RNN的训练效率,最后把两个模型的预测结果加权组合得到最终的预测值.实验结果表明,该组合模型在预测精度和鲁棒性方面相比单一模型都有较为明显的提升.     

基于遗传算法径向基神经网络的交通流预测

为提高径向基（RBF）神经网络预测模型对交通流预测的准确性,提出了一种基于遗传算法优化径向基神经网络的交通流预测方法。利用遗传算法优化径向基神经网络的权值和阈值,然后训练RBF神经网络预测模型以求得最优解,并将该预测方法与RBF神经网络和BP神经网络的预测结果进行对比。仿真结果表明,该方法对交通流具有较好的非线性拟合能力,预测精度高于径向基神经网络和BP神经网络。     

基于图Transformer网络的城市路网短时交通流预测模型

针对城市路网短时交通流预测问题,在考虑路网交通状态时空相关性基础上,提出一种基于图Transformer（graph transformer,Graformer）的预测方法。该方法将多条路段的交通状态预测问题转化为图节点状态预测问题,针对区分相同结构的空间路网结构图,本文将带有边的图同构网络（graph isomorphism network with edges,GINE）和Transformer网络相结合,对交通状态在路网层面的时空相关性进行建模,从而实现城市路网短时交通流预测。具体来说,Graformer模型首先利用长短期记忆网络（long short-term memory,LSTM）对交通数据的时序信息进行预处理,接着采用基于GINE与Transformer的全局注意力机制提取交通数据的空间特征,最后实现路网各路段交通流的同步预测。通过使用PeMS数据集进行实验验证,结果表明提出的Graformer模型在各项性能指标上均优于对比模型,证明了其作为一种可靠且高效的路网短时交通流预测方法的有效性。     

基于IOWA算子的短时交通流预测方法研究

本文首次将诱导有序加权平均（IOWA）算子应用到短时交通流预测中,建立了以整体预测误差平方和最小为目标的组合预测模型。在分析短时交通流预测模型的基础上,本文选取了指数平滑法、季节自回归求和移动平均模型（SARIMA）、BP神经网络模型对短时交通流进行预测,再用IOWA算子将这三种模型进行组合预测。最后进行实例验证,通过MAE、MSE和MAPE三项指标比较分析四种模型的预测效果。结果证明,IOWA算子组合预测模型明显优于其他的预测模型,有效地提高了短时交通流的预测精度。     

Markedness and its Interpretation in Language Use

Language markedness is a common phenomenon in languages, and is reflected from hearing, vision and sense, i.e. the variation in the three aspects such as phonology, morphology and semantics. This paper focuses on the interpretation of markedness in language use following the three perspectives, i.e. pragmatic interpretation, psychological interpretation and cognitive interpretation, with an aim to define the function of markedness.     

Characterization of Ordovician carbonate reservoirs, southeastern Saskatchewan, Canada

The discovery of the prolific Ordovician Red River reservoirs in 1995 in southeastern Saskatchewan was the catalyst for extensive exploration activity which resulted in the discovery of more than 15 new Red River pools. The best yields of Red River production to date have been from dolomite reservoirs. Understanding the processes of dolomitization is, therefore, crucial for the prediction of the connectivity, spatial distribution and heterogeneity of dolomite reservoirs.The Red River reservoirs in the Midale area consist of 3～4 thin dolomitized zones, with a total thickness of about 20 m, which occur at the top of the Yeoman Formation. Two types of replacement dolomite were recognized in the Red River reservoir: dolomitized burrow infills and dolomitized host matrix. The spatial distribution of dolomite suggests that burrowing organisms played an important role in facilitating the fluid flow in the backfilled sediments. This resulted in penecontemporaneous dolomitization of burrow infills by normal seawater. The dolomite in the host matrix is interpreted as having occurred at shallow burial by evaporitic seawater during precipitation of Lake Almar anhydrite that immediately overlies the Yeoman Formation. However, the low δ18O values of dolomited burrow infills (-5.9‰～ -7.8‰, PDB) and matrix dolomites (-6.6‰～ -8.1‰, avg. -7.4‰ PDB) compared to the estimated values for the late Ordovician marine dolomite could be attributed to modification and alteration of dolomite at higher temperatures during deeper burial, which could also be responsible for its 87Sr/86Sr ratios (0.7084～0.7088) that are higher than suggested for the late Ordovician seawaters (0.7078～0.7080). The trace amounts of saddle dolomite cement in the Red River carbonates are probably related to "cannibalization" of earlier replacement dolomite during the chemical compaction.     

Features of Women's language

Language is a means of verbal communication. People use language to communicate with each other. In the society, no two speakers are exactly alike in the way of speaking. Some differences are due to age, gender, statue and personality. Above all, gender is one of the obvious reasons. The writer of this paper tries to describe the features of women＇s language from these perspectives： pronunciation, intonation, diction, subjects, grammar and discourse. From the discussion of the features of women＇s language, more attention should be paid to language use in social context. What＇s more, the linguistic phenomena in a speaking community can be understood more thoroughly.     

A computer generator for randomly layered structures

AcomputergeneratorforrandomlylayeredstructuresYUJia shun1,2,HEZhen hua2(1.TheInstituteofGeologicalandNuclearSciences,NewZealand;2.StateKeyLaboratoryofOilandGasReservoirGeologyandExploitation,ChengduUniversityofTechnology,China)Abstract:Analgorithmisintrod…     

低渗透非均质砂岩油藏启动压力梯度研究

理论推导与室内实验相结合,建立了低渗透非均质砂岩油藏启动压力梯度确定方法。首先借助油藏流场与电场相似的原理,推导了非均质砂岩油藏启动压力梯度计算公式。其次基于稳定流实验方法,建立了非均质砂岩油藏启动压力梯度测试方法。结果表明:低渗透非均质砂岩油藏的启动压力梯度确定遵循两个等效原则。平面非均质油藏的启动压力梯度等于各级渗透率段的启动压力梯度关于长度的加权平均;纵向非均质油藏的启动压力梯度等于各渗透率层的启动压力梯度关于渗透率与渗流面积乘积的加权平均。研究成果可用于有效指导低渗透非均质砂岩油藏的合理井距确定,促进该类油藏的高效开发。     

On Tragic Consciousness In The Works By Ernest Hemingway

As an American modern novelist who were famous in the literary world, Hemingway was not a person who always followed the trend but a sharp observer. At the same time, he was a tragedy maestro, he paid great attention on existence, fate and end-result. The dramatis personae's tragedy of his works was an extreme limit by all means tragedy on the meaning of fearless challenge that failed. The beauty of tragedy was not produced on the destruction of life, but now this kind of value was in the impact activity. They performed for the reader about the tragedy on challenging for the limit and the death.     

海南岛地体及其毗邻陆缘晚中生代—新生代古地磁研究和构造演化

本文叙述了对海南岛及其毗邻大陆边缘白垩纪到第四纪地层岩石进行古地磁研究的全部工作过程。通过分析岩石中剩余磁矢量的磁偏角及磁倾角的变化,提出海南岛白垩纪以来经历的构造演化模式如下:早期伴随顺时针旋转而向南迁移,后期伴随逆时针转动并向北运移。联系该地区及邻区的地质、地球物理资料,对海南岛上述的构造地体运动提出以下认识:北部湾内早期有一拉张作用,主要是该作用使湾内地壳显著伸长减薄,形成北部湾盆地。从而导致了海南岛的早期构造运动,而海南岛后期的构造运动则主要是受南海海底扩张的影响。海南地体运动规律的阐明对于了解北部湾油气盆地的形成演化有重要的理论和实际意义。     

An Index mechanism for multi-scale view in spatial databases

There are numerous geometric objects stored in the spatial databases. An importance function in a spatial database is that users can browse the geometric objects as a map efficiently. Thus the spatial database should display the geometric objects users concern about swiftly onto the display window. This process includes two operations:retrieve data from database and then draw them onto screen. Accordingly, to improve the efficiency, we should try to reduce time of both retrieving object and displaying them. The former can be achieved with the aid of spatial index such as R-tree, the latter require to simplify the objects. Simplification means that objects are shown with sufficient but not with unnecessary detail which depend on the scale of browse. So the major problem is how to retrieve data at different detail level efficiently. This paper introduces the implementation of a multi-scale index in the spatial database SISP (Spatial Information Shared Platform) which is generalized from R-tree. The difference between the generalization and the R-tree lies on two facets: One is that every node and geometric object in the generalization is assigned with a importance value which denote the importance of them, and every vertex in the objects are assigned with a importance value,too. The importance value can be use to decide which data should be retrieve from disk in a query. The other difference is that geometric objects in the generalization are divided into one or more sub-blocks, and vertexes are total ordered by their importance value. With the help of the generalized R-tree, one can easily retrieve data at different detail levels.Some experiments are performed on real-life data to evaluate the performance of solutions that separately use normal spatial index and multi-scale spatial index. The results show that the solution using multi-scale index in SISP is satisfying.     

《Chinese Science Bulletin》SUBJECT INDEX TO VOLUME 59(2014)

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Achieving Unusual States of Matter Under High Pressure

The elongation method,originally proposed by Imamura was further developed for many years in our group.As a method towards O(N)with high efficiency and high accuracy for any dimensional systems.This treatment designed for one-dimensional(ID)polymers is now available for three-dimensional(3D)systems,but geometry optimization is now possible only for 1D-systems.As an approach toward post-Hartree-Fock,it was also extended to