临盘油田分层注水管柱有效率影响因素分析

分层注水管柱有效率低一直是困扰临盘油田开发生产的一大难题。针对这一现状，在分析油藏结构、施工工艺和现场资料的基础上，分析了影响分层注水井管柱有效率的因素，阐述了封隔器、工艺操作、管枉自身及温度等制约分层注水管柱有效率提高的原因，并提出了相应的对策和措施。     

不能移动封隔器管柱变形受力分析——封隔器管柱受力分析系统讨论之五

不能移动封隔器管柱是油气井封隔器完井的一种常见类型 ,在井内压力、温度、流体 (井内压力、温度、流体简称井内条件 ,下同 )发生变化时 ,不能移动封隔器管柱受力分析是保证井下安全和完井作业成功的关键 ,同时该管柱受力分析与自由移动封隔器管柱受力分析有很大的差别。文中对不能移动封隔器管柱变形受力的有关问题进行了分析、推导和讨论 ,并给出了算例。     

注气井中油管轴向位移计算比较分析

为了确定油管在正常作业、注入措施作业中封隔器的坐封位置、轴向位移补偿量以及判断封隔器是否解封,根据弹性力学理论得出了不同条件下油管柱的轴向位移量,综合考虑了在不同工况下由活塞效应、螺旋弯曲、径向压力、温度变化、松弛力以及摩阻等因素引起的油管轴向位移;同时考虑了在注气油管内气体的横向压力效应,运用VB语言编制程序,将各自计算结果进行了比较分析。结果表明,在注气油管内考虑横向压力效应时与常规油管长度计算油管轴向位移有明显差异;油管内液体流动引起的油管轴向伸长与注入速度的平方成正比,在注入速度过大时对轴向位移有明显影响。     

High‐error approximate dictionary search using estimate hash comparisons

A method for finding all matches in a pre‐processed dictionary for a query string q and with at most k differences is presented. A very fast constant‐time estimate using hashes is presented. A tree structure is used to minimize the number of estimates made. Practical tests are performed, showing that the estimate can filter out 99% of the full comparisons for 40% error rates and dictionaries of up to four million words. The tree is found to be efficient up to a 50% error rate. Copyright © 2006 John Wiley & Sons, Ltd.     

A novel document similarity measure based on earth mover’s distance

In this paper we propose a novel measure based on the earth mover’s distance (EMD) to evaluate document similarity by allowing many-to-many matching between subtopics. First, each document is decomposed into a set of subtopics, and then the EMD is employed to evaluate the similarity between two sets of subtopics for two documents by solving the transportation problem. The proposed measure is an improvement of the previous OM-based measure, which allows only one-to-one matching between subtopics. Experiments have been performed on the TDT3 dataset to evaluate existing similarity measures and the results show that the EMD-based measure outperforms the optimal matching (OM) based measure and all other measures. In addition to the TextTiling algorithm, the sentence clustering algorithm is adopted for document decomposition, and the experimental results show that the proposed EMD-based measure does not rely on the document decomposition algorithm and thus it is more robust than the OM-based measure.     

Finding approximate matches in large lexicons

Approximate string matching is used for spelling correction and personal name matching. In this paper we show how to use string matching techniques in conjunction with lexicon indexes to find approximate matches in a large lexicon. We test several lexicon indexing techniques, including n-grams and permuted lexicons, and several string matching techniques, including string similarity measures and phonetic coding. We propose methods for combining these techniques, and show experimentally that these combinations yield good retrieval effectiveness while keeping index size and retrieval time low. Our experiments also suggest that, in contrast to previous claims, phonetic codings are markedly inferior to string distance measures, which are demonstrated to be suitable for both spelling correction and personal name matching.     

多特征自适应融合的粒子滤波跟踪算法

针对跟踪过程中目标形态不断变化或部分遮挡导致鲁棒性差的问题,提出一种基于多特征自适应融合的粒子滤波跟踪算法.该算法从视觉特征集中选取了描述能力强的2种特征,并将其按照与目标模型的多尺度相似度进行线性融合;为了减小跟踪漂移,通过计算当前目标模型与初始目标模型的多尺度相似度自适应地更新目标模型.大量仿真实验结果表明,文中算法可以鲁棒地跟踪到部分遮挡和形态变化的运动目标.     

SVM-based ontology matching approach

There are a lot of heterogeneous ontologies in semantic web, and the task of ontology mapping is to find their semantic relationship. There are integrated methods that only simply combine the similarity values which are used in current multi-strategy ontology mapping. The semantic information is not included in them and a lot of manual intervention is also needed, so it leads to that some factual mapping relations are missed. Addressing this issue, the work presented in this paper puts forward an ontology matching approach, which uses multi-strategy mapping technique to carry on similarity iterative computation and explores both linguistic and structural similarity. Our approach takes different similarities into one whole, as a similarity cube. By cutting operation, similarity vectors are obtained, which form the similarity space, and by this way, mapping discovery can be converted into binary classification. Support vector machine (SVM) has good generalization ability and can obtain best compromise between complexity of model and learning capability when solving small samples and the nonlinear problem. Because of the said reason, we employ SVM in our approach. For making full use of the information of ontology, our implementation and experimental results used a common dataset to demonstrate the effectiveness of the mapping approach. It ensures the recall ration while improving the quality of mapping results.     

一种基于知网的句子相似度计算方法

句子相似度是衡量文档相似度的基础,在自然语言处理领域中有着非常重要的作用。目前的句子相似度计算方法忽略了句子的结构对相似度的影响。本文在分析已有研究工作的基础上,提出了一种改进的句子相似度计算方法。依据知网对"实体概念"的描述,构造出义原的语义层次树,由各个义原在树中的相对位置,计算出义原之间的相似度。对三种义原加权求和得到词语之间的语义相似度。综合句子的表层相似度和句子的词语语义以及词语的相对位置关系,得到句子的整体相似度。实验表明,在同等的测试条件下,本文所提出的句子相似度计算方法在相似度比较上更符合人的直观感觉。