Probabilistic proximity search: Fighting the curse of dimensionality in metric spaces

Proximity searches become very difficult on “high dimensional” metric spaces, that is, those whose histogram of distances has a large mean and/or a small variance. This so-called “curse of dimensionality”, well known in vector spaces, is also observed in metric spaces. The search complexity grows sharply with the dimension and with the search radius. We present a general probabilistic framework applicable to any search algorithm and whose net effect is to reduce the search radius. The higher the dimension, the more effective the technique. We illustrate empirically its practical performance on a particular class of algorithms, where large improvements in the search time are obtained at the cost of a very small error probability.     

Orthogonal queries in segments

We consider theorthgonal clipping problem in a set of segments: Given a set ofn segments ind-dimensional space, we preprocess them into a data structure such that given an orthogonal query window, the segments intersecting it can be counted/reported efficiently. We show that the efficiency of the data structure significantly depends on a geometric discrete parameterK named theProjected-image complexity, which becomes Θ(n ²) in the worst case but practically much smaller. If we useO(m) space, whereK log^4d−7 n≥m≥n log^4d−7 n, the query time isO((K/m)^1/2 log^{max{4, 4d−5}} n). This is near to an Ω((K/m)^1/2) lower bound.     

Application of design parameters space search for belt conveyor design

Belt conveyor design is examined as an application of a proposed Design Parameters Space Search technique. First, the main characteristics of the belt-conveyor design process are presented as they appear in the current literature. Furthermore, a proposed general knowledge-representation platform is described, and its ability to house the relevant conveyor design knowledge is also shown.

Next, the extended search technique of the design space is discussed, and an integrated example of a belt-conveyor design is presented, based on the proposed representation platform and the extended search technique. Finally, it is shown that the design of belt conveyors according to the proposed approach presents the following significant advantages:

• • Due to the knowledge-representation scheme adopted, both qualitative and quantitative knowledge can be used within the same platform.

• • Multiple solutions can be easily produced through user-defined design criteria. These solutions can be further modified and/or evaluated to produce more-specific designs.

• • The required for user-input data is kept to a minimum. Due to the applied extended search method, semiautomatic design can be achieved. As a consequence, the design process is completed in less time than that required by the conventional methods.

     

一种求解W(s)=0的有效算法

复变函数的幅角原理[1]给出了方程W（s）=0的根存在的充分和必要条件，但如何有效地确定这些根，还需要进一步探索。本文给出一种在复域解决这一问题的数值方法─—正交搜索法。该方法利用解析函数保角映射的特性，通过沿复变量s的实部和虚部交替搜索，逐步逼近和达到所求方程的根．该方法不需计算任何导数，直观、简便，且能在计算机上自动完成。应用该方法解决某工程问题，获得了满意的结果．     

Monitoring of multivariable dynamic processes and sensor auditing

Industrial continuous processes are usually operated under closed-loop control, yielding process measurements that are autocorrelated, cross correlated, and collinear. A statistical process monitoring (SPM) method based on state variables is introduced to monitor such processes. The statistical model that describes the in-control variability is based on a canonical variate (CV) state space model. The CV state variables are linear combinations of the past process measurements which explain the variability of the future measurements the most, and they are regarded as the principal dynamic dimensions. A T² statistic based on the CV state variables is utilized for developing the SPM procedure. The CV state variables are also used for monitoring sensor reliability. An experimental application to a high temperature short time (HTST) pasteurization process illustrates the proposed methodology.     

无网格线探索布线算法

提出的一种高效快速的无网格线探索面线算法适用于焊盘外形尺寸不一，线宽及线间距离可变的印制电路板及ＭＣＭ电路布线，精心设计的数据结构及灵活的绕障探索方式可大大地提高搜索效率；多级多扁优化策略为改善布线结果提供了可靠的保证，该算法已成功地应用于通用印制电路板设计系统ＭＰＣＢ，取得了令人满意的效果。     

具有子串检索功能的可扩展分布式数据结构

EH*S是可扩展分布式数据结构EH*的一个改进，增加了子串检索功能。通过对子串和关键字计算描述符向量，为EH*文件中的每个桶添加一个桶描述符向量，然后把子串描述符向量分别与关键字和桶的描述符向量进行比较，得到包含子串的关键字集。     

铅锌烧结过程智能集成优化控制技术

针对复杂的铅锌烧结过程。提出了基于神经网络和模糊专家规则模型的自学习模糊专家控制方法进行状态参数优化，基于神经网络模型的模糊C均值聚类搜索混沌遗传算法进行综合指标参数优化．智能集成优化控制技术具有高效性和实用性，有效地抑制了工况波动，提高了烧结矿产量和质量，取得了较好的工业控制效果．     

自然语言理解与智能检索

智能信息处理是中文信息处理中的重要课题,尤以智能检索和信息获取为重要.而自然语言处理和理解技术是智能信息处理的核心.本文讨论了自然语言理解应用于智能检索和信息获取的若干问题,阐述了标注!完善基于词典的知识库的处理手段.     

基于递进制编码的遗传算法研究

首先分析了编码的进制数对遗传算法收敛速度的影响．分析结果表明，当种群不稳定时，高进制编码较低进制编码具有更快地搜索至最优区域的能力，当种群较为稳定时，低进制编码较高进制编码具有更快地逼近最优点的能力．然后提出了基于递进制的遗传算法，该算法能提高优化问题的收敛速度，在优化参数较多时，与单一进制编码的遗传算法相比具有明显的优势．仿真实例也验证了这一结论．