A new clustering method and its application in social networks

In a graph theory model, clustering is the process of division of vertices into groups, with a higher density of edges within groups than between them. In this paper, we introduce a new clustering method for detecting such groups and use it to analyse some classic social networks. The new method has two distinguished features: non-binary hierarchical tree and the feature of overlapping clustering. A non-binary hierarchical tree is much smaller than the binary-trees constructed by most traditional methods and, therefore, it clearly highlights meaningful clusters which significantly reduces further manual efforts for cluster selections. The present method is tested by several bench mark data sets for which the community structure was known beforehand and the results indicate that it is a sensitive and accurate method for extracting community structure from social networks.     

Admission control for a responsive distributed middleware using decision trees to model run-time parameters

The software in modern systems has become too complex to make accurate predictions about their performance under different configurations. Real-time or even responsiveness requirements cannot be met because it is not possible to perform admission control for new or changing tasks if we cannot tell how their execution affects the other tasks already running. Previously, we proposed a resource-allocation middleware that manages the execution of tasks in a complex distributed system with real-time requirements. The middleware behavior can be modeled depending on the configuration of the tasks running, so that the performance of any given configuration can be calculated. This makes it possible to have admission control in such a system, but the model requires knowledge of run-time parameters. We propose the utilization of machine-learning algorithms to obtain the model parameters, and be able to predict the system performance under any configuration, so that we can provide a full admission control mechanism for complex software systems. In this paper, we present such an admission control mechanism, we measure its accuracy in estimating the parameters of the model, and we evaluate its performance to determine its suitability for a real-time or responsive system.     

BP神经网络在剩余油分布预测中的应用研究

我国多数油田经过一次、二次采油后,仅能采出地下总储量的30%左右,这意味着有60%～70%的剩余石油仍然残留在地下成为剩余油。加强剩余油分布规律研究、提高石油采收率不仅有着可观的经济效应,而且关系到国家石油战略的安全。本研究应用神经网络的原理,基于BP网络使用MATLAB语言建立一个剩余油分布的预测系统。该系统通过学习在地理坐标和孔隙度之间建立一个非线性函数关系,以此来预测任何区域的孔隙度,再通过孔隙度与剩余油饱和度之间的关系达到剩余油分布预测的目的。     

A global localization approach based on Line-segment Relation Matching technique

This paper presents a novel global localization approach for mobile robots by exploring line-segment features in any structured environment. The main contribution of this paper is an effective data association approach, the Line-segment Relation Matching (LRM) technique, which is based on a generation and exploration of an Interpretation Tree (IT). A new representation of geometric patterns of line-segments is proposed for the first time, which is called as Relation Table. It contains relative geometric positions of every line-segment respect to the others (or itself) in a coordinate-frame independent sense. Based on that, a Relation-Table-constraint is applied to minimize the searching space of IT therefore greatly reducing the processing time of LRM. The Least Square algorithm is further applied to estimate the robot pose using matched line-segment pairs. Then a global localization system can be realized based on our LRM technique integrated with a hypothesis tracking framework which is able to handle pose ambiguity. Sufficient simulations were specially designed and carried out indicating both pluses and minuses of our system compared with former methods. We also presented the practical experiments illustrating that our approach has a high robustness against uncertainties from sensor occlusions and extraneous observation in a highly dynamic environment. Additionally our system was demonstrated to easily deal with initialization and have the ability of quick recovery from a localization failure.     

鄂尔多斯盆地东南部长7油层组沉积相研究

为了明确鄂尔多斯盆地东南部长7油层组沉积体系类型和沉积相展布特征,总结其沉积演化规律,以沉积学的基本原理和方法为指导,综合运用地球化学、盆地分析等理论和方法,通过野外露头、钻井、测井等资料的综合分析,认为鄂尔多斯盆地东南部长7油层组主要发育三角洲和湖泊沉积体系,可进一步划分为3类亚相,其中以三角洲前缘和深湖2种亚相为主．     

减震油弹性模量及疲劳特性试验台设计与实现

油液的体积弹性模量以及疲劳特性参数是油液非常重要的物理参数,在液压系统中,直接影响系统的稳定性和动态品质.研究设计了弹性模量及疲劳特性参数测试试验台,基于弹性模量的定义对弹性模量进行测量计算,同时以专用车辆油气悬挂装置为对象,对悬挂内液压油疲劳特性参数进行测试,如:温度、粘度等.完成控制系统以及数据采集系统的设计,与上位机实时通讯,实现测试过程的自动化.     

金鸡滩水电厂2号机组异常运行分析及处理

金鸡滩2号机组在运行中出现了油压装置压油泵频繁打油,受油器下游侧端盖与挡油环、端盖与小轴相互碰刮的异常运行情况,文章对此异常情况进行了分析并介绍了故障的排查处理过程,对类似机组的相同故障处理有借鉴作用。     

基于单片机的凝析油撬控制系统设计

提出了一种基于单片机的凝析油撬控制系统的设计方案,该系统能有效地控制凝析油撬塔顶的气体压力、稳定塔的液位,换热器的温度及凝析油输出的流量。保证凝析油撬的稳定可靠的工作。该系统具备安全保护、工艺自动报警及管理功能。     

锅炉等离子点火技术的应用及燃油系统选择

适用于贫煤、烟煤、褐煤,可连续运行的多根碳棒等离子点火技术,较前苏联采用银棒等离子点火技术具有造价低、使用寿命长等优点。以包头第二热电厂为例,对采用等离子点火技术前后的运行情况分析比较,经济效益可观。针对丰镇电厂三期2×600MW机组,设计采用等离子无油点火技术,对燃油系统进行了优化选择,简化了油罐设置。工程具有显著的经济效益和环境效益。