一种基于P系统的图像阈值分割方法

将P系统引入到图像阈值分割中,提出了一种基于P系统的图像阈值分割方法。采用一个两层膜的组织型P系统作为其计算框架,每个膜作为独立的并行计算单元,速度一位置模型作为进化规则,同时采用同向／反向转运规则交换和共享膜之间的对象。实验结果表明,这个基于P系统框架的阈值分割方法具有可用性和有效性,能为图像寻找最优的分割阈值。     

一类非线性m点边值问题正解的存在性与多解性

考察了一类非线性常微分方程的m点边值问题,通过考察非线性项在有界集上的性质,运用Krasnosel’skii锥拉伸与锥压缩型不动点定理及格林函数的性质,获得了其正解的存在性与多解性,推广和改进了以前文献的相关结果.     

侧向引水及进水泵站进水流态CFD 模拟分析与优化

应用realizable k - e紊流模型与SIMPLEC算法,对某侧向引水及进水泵站进水侧水流流场进行数值模拟和流动分析,分析表明,该泵站的进水侧水流具有弯道水流特点,数值模拟结果与理论分析结论相吻合。根据该类泵站进水侧水流流场特点,提出采用导流栅作为水力优化途径,并以泵站进水断面处动量分布和进水池断面平均流速均匀度为评判依据,对不同的导流栅整流方案的水力优化效果进行数值模拟研究。成果表明,设置导流栅对泵站引水段的动量分布具有明显的调整作用,泵站进水断面的流速分布与引水段动量分配均匀度有关。适宜尺寸的导流栅可以有效减弱引水段的进水环流作用,使水流在转向后纵向流速较快地恢复正常分布,从而进一步提高泵站进水池流量分配的均匀度。     

地下洞室衬砌混凝土裂缝分析及预防措施

1引言新疆下坂地水利枢纽工程地下洞室衬砌混凝土浇筑一般采用泵送入仓,其坍落度多在14～16 cm,局部范围由于仓位狭窄,无法入仓振捣,坍落度可达18 cm左右,同时对混凝土的和易性、流动性要求较高,一些过水建筑物还要求水胶比不大于0.5,     

我国中压动态无功补偿装置的发展及展望

从无功补偿装置应用的必要性出发,介绍了无功补偿装置的原理及组成,分析了常见无功补偿装置的优缺点,论述了国内外无功补偿装置的应用现状。着重介绍了新技术在SVC装置中应用,对比分析了无功补偿装置中的电力电容器、控制方案和投切元件,最后展望了智能型SVC的广泛应用是中压无功补偿装置的发展方向。     

Aggregate keyword search on large relational databases

Keyword search has been recently extended to relational databases to retrieve information from text-rich attributes. However, all the existing methods focus on finding individual tuples matching a set of query keywords from one table or the join of multiple tables. In this paper, we motivate a novel problem of aggregate keyword search: finding minimal group-bys covering a set of query keywords well, which is useful in many applications. We develop two interesting approaches to tackle the problem. We further extend our methods to allow partial matches and matches using a keyword ontology. An extensive empirical evaluation using both real data sets and synthetic data sets is reported to verify the effectiveness of aggregate keyword search and the efficiency of our methods.     

Optimization of oval–round pass design using genetic algorithm

The primary purpose of this paper is to propose a computer aided optimal design system to support a generalized oval–round pass design, which is widely used as both intermediate and final passes in the process of rod rolling. This system, which is based on a hybrid model and the genetic algorithm, is developed to improve the efficiency, to reduce the manufacturing errors, as well as to extend the useful life of rolls through uniform wear design. Generalized parametric equations are established for geometrical modeling, graphic plotting of oval–round passes, as well as calculation of the cross section area, contact area and the lengths of contact arcs along the cross section of round groove in the MATLAB programming environment. Moreover, these equations can also realize the parametric transformation between roll profile and mathematical models for the oval–round pass design and optimization. The genetic algorithm is employed for the optimal design of oval–round passes in this paper. The objective functions are formulated for minimization of power consumption in the rolling process, variances between ideal dimensions and design dimensions, as well as variances between the lengths of contact arcs. To reduce the complexity and computational burden of the system, some reliable empirical formulas for the calculations of contact area and contact arc length are applied. Finally, the proposed approach is applied to an oval–round pass design. Through simulation and comparison of results against experimental data acquired from literature, it is found that this system is reliable, effective and easier to use.     

Contextual correlation based thread detection in short text message streams

Short text message streams are produced by Instant Messaging and Short Message Service which are wildly used nowadays. Each stream contains more than one thread usually. Detecting threads in the streams is helpful to various applications, such as business intelligence, investigation of crime and public opinion analysis. Existing works which are mainly based on text similarity encounter many challenges including the sparse eigenvector and anomaly of short text message. This paper introduces a novel concept of contextual correlation instead of the traditional text similarity into single-pass clustering algorithm to cover the challenges of thread detection. We firstly analyze the contextually correlative nature of conversations in short text message streams, and then propose an unsupervised method to compute the correlative degree. As a reference, a single-pass algorithm employing the contextual correlation is developed to detect threads in massive short text stream. Experiments on large real-life online chat logs show that our approach improves the performance by 11% when compared with the best similarity-based algorithm in terms of F1 measure.     

Probabilistic skylines on uncertain data: model and bounding-pruning-refining methods

Uncertain data are inherent in some important applications. Although a considerable amount of research has been dedicated to modeling uncertain data and answering some types of queries on uncertain data, how to conduct advanced analysis on uncertain data remains an open problem at large. In this paper, we tackle the problem of skyline analysis on uncertain data. We propose a novel probabilistic skyline model where an uncertain object may take a probability to be in the skyline, and a p-skyline contains all objects whose skyline probabilities are at least p (0 < p ≤ 1). Computing probabilistic skylines on large uncertain data sets is challenging. We develop a bounding-pruning-refining framework and three algorithms systematically. The bottom-up algorithm computes the skyline probabilities of some selected instances of uncertain objects, and uses those instances to prune other instances and uncertain objects effectively. The top-down algorithm recursively partitions the instances of uncertain objects into subsets, and prunes subsets and objects aggressively. Combining the advantages of the bottom-up algorithm and the top-down algorithm, we develop a hybrid algorithm to further improve the performance. Our experimental results on both the real NBA player data set and the benchmark synthetic data sets show that probabilistic skylines are interesting and useful, and our algorithms are efficient on large data sets.     

View angle effects on MODIS snow mapping in forests

Binary snow maps and fractional snow cover data are provided routinely from MODIS (Moderate Resolution Imaging Spectroradiometer). This paper investigates how the wide observation angles of MODIS influence the current snow mapping algorithm in forested areas. Theoretical modeling results indicate that large view zenith angles (VZA) can lead to underestimation of fractional snow cover (FSC) by reducing the amount of the ground surface that is viewable through forest canopies, and by increasing uncertainties during the gridding of MODIS data. At the end of the MODIS scan line, the total modeled error can be as much as 50% for FSC. Empirical analysis of MODIS/Terra snow products in four forest sites shows high fluctuation in FSC estimates on consecutive days. In addition, the normalized difference snow index (NDSI) values, which are the primary input to the MODIS snow mapping algorithms, decrease as VZA increases at the site level. At the pixel level, NDSI values have higher variances, and are correlated with the normalized difference vegetation index (NDVI) in snow covered forests. These findings are consistent with our modeled results, and imply that consideration of view angle effects could improve MODIS snow monitoring in forested areas.