基于数据划分的k-近邻分类加速算法机理分析

k-近邻(k NN)分类算法因具有不对数据分布做任何假设、操作简单且泛化性能较强的特点,在人脸识别、文本分类、情感分析等领域被广泛使用.k NN分类算法不需要训练过程,其简单存储训练实例并根据测试实例与存储的训练实例进行相似度比较来预测分类.由于k NN分类算法需要计算测试实例与所有训练实例之间的相似度,故难以高效地处理大规模数据.为此提出将寻找近邻的过程转化为一个优化问题,并给出了原始优化问题与使用数据划分优化问题的最优解下目标函数差异的估计.通过对此估计的理论分析表明,聚类划分可以有效的减小此差异,进而保证基于聚类的k-近邻分类(DC-k NN)算法具有较强的泛化性能.在公开数据集的实验结果显示,DC-k NN分类算法在很大程度上为测试实例提供了与原始k NN分类算法相同的k个近邻进而获得较高的分类精度.     

支持向量机在边坡稳定分析预测的应用

目的提出一种基于改进算法的支持向量机模型(PSO-SVM),利用边坡的参数分析预测边坡稳定性.方法利用支持向量机有效解决小样本、高维数、非线性等问题的优势,建立粒子群算法(PSO)优化的支持向量机模型,粒子群算法优化支持向量机参数,模型中边坡几何参数和强度参数:边坡角β、边坡高度H、岩石容重γ、黏聚力c、内摩擦角φ以及孔隙水压力ru作为输入参数,边坡稳定性系数FS和边坡稳定状态S作为输出参数.结果 PSO-SVM模型与网格搜索算法(GS)、遗传算法(GA)优化SVM模型以及人工神经网络ANN模型相比,具有更高的分类精度和更强的预测能力.结论 PSO-SVM模型能够准确地获得边坡的稳定性系数,评价其稳定性,在边坡稳定分析和预测中具有良好的实际应用价值.     

基于遗传算法的BP神经网络预测石油单井产量

针对传统预测方法建模复杂且预测精度低、传统BP神经网络算法收敛慢和可能陷入局部最优的问题,提出一种基于遗传算法的反向传播神经网络(BPANN-GA)用以预测单井产量,并进行实例分析,获得了较高精度的预测结果.该算法简单实用,预测精度较高,收敛快且避免了陷入局部最优,对石油单井产量预测工作有一定的意义.     

基于CGWO算法的边坡最小安全系数全局寻优方法

针对基本灰狼算法存在初始种群不均匀、早熟收敛等问题,基于混沌理论从三个方面对灰狼优化(grey wolf optimization, GWO)算法进行改进,提出了混沌灰狼优化(chaotic grey wolf optimization,CGWO)算法用于确定边坡的最小安全系数.首先,采用改进Tent混沌映射提高初始种群多样性;其次,通过混沌扰动策略避免算法陷入局部最优;最后,引入参数混沌非线性调节机制均衡算法的全局开发和局部勘探算力.13个基准测试函数的仿真结果表明,改进后的算法与基本GWO,WOA,PSO以及SCA相比具有更强的综合寻优性能.选取ACADS边坡考核题进行计算分析,CGWO算法表现出较高的计算精度和收敛速度,能够有效地搜索到复杂分层边坡的最小安全系数.对比有限元强度折减法,该方法具有操作简易、搜索区域易于设置等优点.     

基于改进麻雀搜索算法和支持向量机的边坡稳定性

边坡失稳是由多种因素共同作用的结果,常规的数学模型难以准确预测。为提高边坡稳定性预测精度,采用多策略融合改进麻雀搜索算法（improved sparrow search algorithm,ISSA）优化支持向量机（support vector machine,SVM）,进而建立边坡稳定性预测模型（ISSA-SVM模型）。将重度、粘聚力、内摩擦角、边坡角、边坡高、孔隙压力比6项因素作为输入特征,将边坡稳定性状态作为输出结果,进而预测边坡稳定性。选取国内外工程实例建立边坡数据库,将ISSA-SVM模型与SSA-SVM模型进行对比分析,通过灰色关联度分析法（grey relation analysis,GRA）进行敏感性分析。结果表明：ISSA-SVM模型预测精度更高、泛化能力更强,粘聚力和内摩擦角是对边坡稳定性最为敏感的因子。所提ISSA-SVM模型不仅能够准确地预测边坡稳定状态,还可以为其他领域相关问题提供参考。     

露天煤矿边坡稳定性的随机森林预测模型

边坡工程是露天煤矿中的重点工程,边坡的稳定性关系着煤矿的安全生产。边坡稳定性预测是边坡防治工作的前提,针对煤矿边坡工程稳定性预测的复杂性,为了快速、有效地判别煤矿边坡稳定性,利用随机森林算法建立煤矿边坡稳定性预测模型。通过选取与煤矿边坡工程密切相关的岩石重度、黏聚力、内摩擦角、边坡角、边坡高度、孔隙水压力6个指标作为边坡稳定性的影响因素,即为随机森林预测模型的输入,边坡稳定性状态作为随机森林预测模型的输出,通过随机森林算法建立边坡稳定性影响因素与边坡稳定状态之间的非线性关系。利用煤矿实测30组边坡稳定性数据作为随机森林预测模型的训练数据集,进行模型的学习训练;另用12组边坡稳定性数据作为预测模型的测试数据,通过训练好的边坡稳定性预测模型进行测试;为了验证随机森林预测模型的准确率,同时与SVM和BP神经网络的测试数据进行比较。结果说明,选取煤矿边坡稳定性的6个指标建立的随机森林预测模型,人工控制参数较少、结构简单、容易实现,且具有较高的准确度,边坡稳定状态预测结果与煤矿边坡工程实际状态相吻合,能有效预测边坡稳定性状态,指导煤矿边坡防治工作的开展。     

露天煤矿边坡稳定性的随机森林预测模型

 边坡工程是露天煤矿中的重点工程,边坡的稳定性关系着煤矿的安全生产。边坡稳定性预测是边坡防治工作的前提,针对煤矿边坡工程稳定性预测的复杂性,为了快速、有效地判别煤矿边坡稳定性,利用随机森林算法建立煤矿边坡稳定性预测模型。通过选取与煤矿边坡工程密切相关的岩石重度、黏聚力、内摩擦角、边坡角、边坡高度、孔隙水压力6 个指标作为边坡稳定性的影响因素,即为随机森林预测模型的输入,边坡稳定性状态作为随机森林预测模型的输出,通过随机森林算法建立边坡稳定性影响因素与边坡稳定状态之间的非线性关系。利用煤矿实测30 组边坡稳定性数据作为随机森林预测模型的训练数据集,进行模型的学习训练;另用12 组边坡稳定性数据作为预测模型的测试数据,通过训练好的边坡稳定性预测模型进行测试;为了验证随机森林预测模型的准确率,同时与SVM 和BP 神经网络的测试数据进行比较。结果说明,选取煤矿边坡稳定性的6 个指标建立的随机森林预测模型,人工控制参数较少、结构简单、容易实现,且具有较高的准确度,边坡稳定状态预测结果与煤矿边坡工程实际状态相吻合,能有效预测边坡稳定性状态,指导煤矿边坡防治工作的开展。     

基于PSO-SVM的岩石边坡稳定性预测

边坡稳定性与其影响因素之间存在着复杂的非线性关系.结合粒子群优化算法和支持向量机,提出了边坡稳定评价的粒子群优化支持向量机模型.模型采用支持向量机建立边坡稳定性和影响因素之间的非线性关系;同时,利用粒子群算法对支持向量机参数进行全局寻优,从而确保了模型参数的准确性.模型的测试结果显示了良好的精度.将该模型应用到某岩石高边坡中,预测结果与实际情况符合较好,表明该模型在岩石边坡稳定性预测中的可行性和有效性.     

爆破振动对矿山边坡稳定性影响的预测

针对矿山爆破过程中爆破点附近的边坡稳定性问题,应用因子分析和免疫最小二乘支持向量机方法,选取爆破振幅、主频率、主频率持续时间、岩石重度、粘聚力、内摩擦角、边坡角、边坡高度、孔隙水压力等为影响因素.对相关性较高的因素进行因子分析,提取公共因子,建立爆破振动对矿山边坡稳定性影响的免疫最小二乘支持向量机预测模型.利用24组爆破数据作为学习样本进行训练,并采用回代估计法进行检验,误判率为1/12.用另外10组数据作为测试样本进行预测,同时通过与其他预测模型预测结果进行对比,验证本文所得模型在边坡稳定性预测中具有更高的准确度.研究结果表明:所得预测模型泛化误差满足精度要求,预测结果的误判率仅为1/10,优于其他预测模型.     

爆破振动对边坡稳定性影响的FA-IGA-LSSVM模型

为对矿山开采爆破过程中边坡的稳定性进行预测,将因子分析、免疫算法及最小二乘支持向量机相结合,共提取爆破振幅、主频率、主频率持续时间、岩石重度、粘聚力、边坡角、边坡高度7个影响指标.通过因子分析对样本数据进行降维,提取出一个公共因子.利用实际测量的29组样本数据对模型进行训练,构建基于因子分析和IGA-LSSVM的边坡稳定性预测模型;采用回代估计法对模型进行检验,误判率为3/29.使用其他5组样本检验模型的泛化能力,同时与基本最小二乘支持向量机进行对比,结果表明:所得模型的预测精度高于基本最小二乘支持向量机,预测结果的误判率为0.     

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.     

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.     

A computer generator for randomly layered structures

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

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。     

Exciton Seebeck in Molecular Systems

Various applications relevant to the exciton dynamics,such as the organic solar cell,the large-area organic light-emitting diodes and the thermoelectricity,are operating under temperature gradient.The potential abnormal behavior of the exicton dynamics driven by the temperature difference may affect the efficiency and performance of the corresponding devices.In the above situations,the exciton dynamics under temperature difference is mixed with     

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     

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

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An on-line scaling method for improving scalability of a database cluster

The explosive growth of the Internet and database applications has driven database to be more scalable and available, and able to support on-line scaling without interrupting service. To support more client's queries without downtime and degrading the response time, more nodes have to be scaled up while the database is running. This paper presents the overview of scalable and available database that satisfies the above characteristics. And we propose a novel on-line scaling method. Our method improves the existing on-line scaling method for fast response time and higher throughputs. Our proposed method reduces unnecessary network use, i.e. , we decrease the number of data copy by reusing the backup data. Also, our on-line scaling operation can be processed parallel by selecting adequate nodes as new node. Our performance study shows that our method results in significant reduction in data copy time.     

An improved R-tree based on childnode''s probability

R-Tree is a good structure for spatial searching. But in this indexing structure,either the sequence of nodes in the same level or sequence of traveling these nodes when queries are made is random. Since the possibility that the object appears in different MBR which have the same parents node is different, if we make the subnode who has the most possibility be traveled first, the time cost will be decreased in most of the cases. In some case, the possibility of a point belong to a rectangle will shows direct proportion with the size of the rectangle. But this conclusion is based on an assumption that the objects are symmetrically distributing in the area and this assumption is not always coming into existence. Now we found a more direct parameter to scale the possibility and made a little change on the structure of R-tree, to increase the possibility of founding the satisfying answer in the front sub trees. We names this structure probability based arranged R-tree (PBAR-tree).     

Integrating GIS Web services based on mediating architecture

The geographic information service is enabled by the advancements in general Web service technology and the focused efforts of the OGC in defining XML-based Web GIS service. Based on these models, this paper addresses the issue of services chaining,the process of combining or pipelining results from several interoperable GIS Web Services to create a customized solution. This paper presents a mediated chaining architecture in which a specific service takes responsibility for performing the process that describes a service chain. We designed the Spatial Information Process Language (SIPL) for dynamic modeling and describing the service chain, also a prototype of the Spatial Information Process Execution Engine (SIPEE) is implemented for executing processes written in SIPL. Discussion of measures to improve the functionality and performance of such system will be included.