智能支持的LC-QDPDS集成模型关键技术应用研究

质量驱动产品设计(QDPD)是一种产品设计新思想，在分析QDPD国内外研究现状的基础上，建立了智能支持的基于全生命周期质量驱动产品设计系统(LC-QDPDS)的集成模型，详细论述了LC-QDPDS集成模型的五大重要组成模块及其实现的关键技术，并将其应用于仪表新产品开发决策支持系统中。     

塔里木油田水平井优化设计方法及跟踪效果评价

塔里木油田坚持“稀井高产”的开发原则,不仅利用水平井技术实现了新油田整体高效开发,而且在老油田综合调整中也取得了巨大成效,还成功应用于气藏及凝析气藏的开发,以水平井技术为主实现了气田开发高水平和高效益,逐渐形成了一套比较成熟的适合塔里木气区实际的水平井优化设计技术。 \r\n     

甲醇气相氧化羰化合成碳酸二甲酯 Ⅲ.复合载体铜基催化剂的表征

采用X射线衍射、表面物理吸附、CO原位吸附红外光谱、X射线光电子能谱以及热重分析方法对甲醇直接气相氧化羰化合成碳酸二甲酯的复合载体铜基催化剂(Cu(Ⅱ)/Naβ-AC)的物化性能进行了表征。实验结果表明,微波辐射条件下制备的Cu(Ⅱ)/Naβ-AC催化剂中,活性组分CuC l2一方面与分子筛载体间发生了固相离子交换反应,并形成了铜分子筛物种,另一方面,可直接高度分散在复合载体表面。反应后Cu(Ⅱ)/Naβ-AC催化剂中部分Cu2+被还原为Cu+或Cu0,Cu2+的结合能峰向低结合能方向迁移,但微波辐射处理并未破坏分子筛载体的骨架结构。采用复合载体增加了催化剂的比表面积,并产生一定量的微孔,从而提高了催化剂的氧化羰化反应活性。热重分析表明,当温度低于350℃时,Cu(Ⅱ)/Naβ-AC催化剂稳定性较好。     

超高、大跨度混凝土结构模板施工

本文通过工程实例,详细介绍了超高、大跨度混凝土结构模板及支撑施工工艺。     

军事地理信息系统的现状与发展

在信息作战条件下，军事地理信息系统（MGIS）作为作战指挥自动化、数字化战场和现代武器系统的战场地理环境平台备受世界各国的重视。美国投入了大量人力、物力和财力先后研制并使用了地形分析系统等一系列应用系统，目前正着手研制地理影像情报综合管理系统（TPED）和地理影像与地理空间信息系统。我国的MGIS起步较晚，但发展很快，已成功地研制出了第一个具有自主版权的MGIS工具软件。该软件采用层次化体系结构和面向对象的设计方法，具有功能齐全、结构紧密、安全性好等特点。文章在分析国内外MGIS的基础上，着重介绍了军事地理信息系统软件的研制特点，并对军事地理信息系统进一步发展要解决的若干问题进行了探讨。     

Excavation of Inlet Tunnels at Geheyan Power Station

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开滦集团公司煤层间配采模型的建立

运用线性规划建立了数学模型，解决了开栾集团公司煤层间的配采问题，提高了煤炭企业的经济效益，有利于企业实现可持续发展。     

Negotiation support systems: roots, progress and needs

Abstract. This paper identifies game theories and social behavior science as important roots for negotiation support systems (NSS) research. As these are not typically cited in the NSS literature, summary reviews of them are provided with indications of their relevance to NSS study. On the other hand, neither offers a sufficiently general-purpose formal model of negotiation that could serve as a backbone for NSS research. A survey of that research is provided, indicating that prugress to date has been somewhat eclectic and devoid of such a model. Our central contention is that a theoretical foundation for NSS study is much needed. Desirable characteristics of a suitable negotiation model are identified as guidance for future research that will aim to devise (and subsequently apply and test) such a model.     

低位放顶煤液压支架的改造

对ZFSB4 0 0 0 / 18.5 / 2 8、ZFG4 4 0 0 / 18.5 / 2 9型液压支架在新集一矿使用中存在的问题进行了分析 ,提出了改进措施。通过改造 ,解决了支架在顶板支护、后溜拉移中存在的问题 ,消除安全隐患 ,为设备检修创造条件。     

Tree-based partitioning of date for association rule mining

The most computationally demanding aspect of Association Rule Mining is the identification and counting of support of the frequent sets of items that occur together sufficiently often to be the basis of potentially interesting rules. The task increases in difficulty with the scale of the data and also with its density. The greatest challenge is posed by data that is too large to be contained in primary memory, especially when high data density and/or low support thresholds give rise to very large numbers of candidates that must be counted. In this paper, we consider strategies for partitioning the data to deal effectively with such cases. We describe a partitioning approach which organises the data into tree structures that can be processed independently. We present experimental results that show the method scales well for increasing dimensions of data and performs significantly better than alternatives, especially when dealing with dense data and low support thresholds. Shakil Ahmed received a first class BSc (Hons) degree from Dhaka University, Bangladesh, in 1990; and an MSc (first class), also Dhaka University, in 1992. He received his PhD from The University of Liverpool, UK, in 2005. From 2000 onwards he is a member of the Data Mining Group at the Department of Computer Science of the University of Liverpool, UK. His research interests include data mining, Association Rule Mining and pattern recognition. Frans Coenen has been working in the field of Data Mining for many years and has written widely on the subject. He received his PhD from Liverpool Polytechnic in 1989, after which he took up a post as a RA within the Department of Computer Science at the University of Liverpool. In 1997, he took up a lecturing post within the same department. His current Data Mining research interests include Association rule Mining, Classification algorithms and text mining. He is on the programme committee for ICDM'05 and was the chair for the UK KDD symposium (UKKDD'05). Paul Leng is professor of e-Learning at the University of Liverpool and director of the e-Learning Unit, which is responsible for overseeing the University's online degree programmes, leading to degrees of MSc in IT and MBA. Along with e-Learning, his main research interests are in Data Mining, especially in methods of discovering Association Rules. In collaboration with Frans Coenen, he has developed efficient new algorithms for finding frequent sets and is exploring applications in text mining and classification.