抽水蓄能电站施工控制网的设计与施测

本文通过对桐柏、宜兴、泰安等抽水蓄能电站施工控制网的技术设计、网形优化及施测方案等方面的论述,总结了在特殊困难地区采用LeicaTCA2003全站仪和ZeissDiNi12电子水准仪建立高精度的施工控制网的经验,并通过对施测成果的精度评定,对施测方案提出合理的建议。     

北京核电站模拟器硬件的功能和特点

本文介绍北京核电站模拟器硬件构成并分析其功能和特点。     

乌江流域大型复杂水电站群联合优化调控关键技术及其应用研究

根据乌江流域总体规划、长江三峡以上防洪要求、南方电网运行特点、乌江流域水文特性等,贵州乌江水电开发有限责任公司组织开展了乌江梯级水库优化调度和发电优化控制的基本理论研究,并根据该研究建立了乌江流域水电站群联合优化调控系统,经过一系列优化调度规则、方法、措施在系统中的应用,实现了节水增发电的目的,取得了良好的社会经济效益。     

探地雷达在莲花电站二坝坝顶路面裂缝探测中的应用

介绍探地雷达探测方法,探讨使用高频探地雷达探测粘土心墙堆石坝坝顶混凝土路面裂缝,达到快速、无损查明坝顶混凝土裂缝的性质与成因的目的.     

试论点检定修制在水电厂的应用

依据点检定修制的原理和程序,讨论了其在水电厂应用的基本要点和管理要求,论述了在水电厂实施点检定修制的主要职能部门:设备管理部门、点检部门、检修部门、设备使用部门的基本职能,探索了点检定修制在水电厂应用的程序,初步建立了水电厂点检定修制的管理模武。     

Adherence clustering: an efficient method for mining market-basket clusters

We explore in this paper the efficient clustering of market-basket data. Different from those of the traditional data, the features of market-basket data are known to be of high dimensionality and sparsity. Without explicitly considering the presence of the taxonomy, most prior efforts on clustering market-basket data can be viewed as dealing with items in the leaf level of the taxonomy tree. Clustering transactions across different levels of the taxonomy is of great importance for marketing strategies as well as for the result representation of the clustering techniques for market-basket data. In view of the features of market-basket data, we devise in this paper a novel measurement, called the category-based adherence, and utilize this measurement to perform the clustering. With this category-based adherence measurement, we develop an efficient clustering algorithm, called algorithm k-todes, for market-basket data with the objective to minimize the category-based adherence. The distance of an item to a given cluster is defined as the number of links between this item and its nearest tode. The category-based adherence of a transaction to a cluster is then defined as the average distance of the items in this transaction to that cluster. A validation model based on information gain is also devised to assess the quality of clustering for market-basket data. As validated by both real and synthetic datasets, it is shown by our experimental results, with the taxonomy information, algorithm k-todes devised in this paper significantly outperforms the prior works in both the execution efficiency and the clustering quality as measured by information gain, indicating the usefulness of category-based adherence in market-basket data clustering.     

A new algorithm for fast mining frequent itemsets using N-lists

Mining frequent itemsets has emerged as a fundamental problem in data mining and plays an essential role in many important data mining tasks.In this paper,we propose a novel vertical data representation called N-list,which originates from an FP-tree-like coding prefix tree called PPC-tree that stores crucial information about frequent itemsets.Based on the N-list data structure,we develop an efficient mining algorithm,PrePost,for mining all frequent itemsets.Efficiency of PrePost is achieved by the following three reasons.First,N-list is compact since transactions with common prefixes share the same nodes of the PPC-tree.Second,the counting of itemsets’ supports is transformed into the intersection of N-lists and the complexity of intersecting two N-lists can be reduced to O(m + n) by an efficient strategy,where m and n are the cardinalities of the two N-lists respectively.Third,PrePost can directly find frequent itemsets without generating candidate itemsets in some cases by making use of the single path property of N-list.We have experimentally evaluated PrePost against four state-of-the-art algorithms for mining frequent itemsets on a variety of real and synthetic datasets.The experimental results show that the PrePost algorithm is the fastest in most cases.Even though the algorithm consumes more memory when the datasets are sparse,it is still the fastest one.     

传感器技术在智能变电站中的应用

介绍传感器技术在智能变电站中的应用,以及使用中的注意事项。     

矿井提升机常见故障分析

介绍矿井提升机的常见故障及分类。     

重症监护病房紧急状况预警算法

重症监护病房中的病人身体状况通常很不稳定,常出现各种需要医护人员介入治疗的紧急状况。由于医疗资源有限,医护人员可能无法及时发现并处理这些紧急状况,给病人的存活率带来严重的负面影响。如果可以预测这些紧急状况的发生,并及时通知相关医护人员,将大大提高病人的存活率。常见重症监护病房紧急状况包括突然死亡、败血症、肺部感染、急性低血压、以及器官衰竭等。紧急状况预警建模主要采用病人的长时间生命体征监测数据,预测在一定时间之后发生某种紧急状况的可能性。预警模型所采用的监测数据分为静态数据、事件数据和时间序列数据等三类。静态数据具有容易采集、但预测准确性偏低的特点。事件数据或时间序列数据、以及多种类型数据的混合数据对于紧急状况预警模型的预测性能的提高有重要作用,将会获得更广泛的应用。