云数据中心心跳检测与故障评估

为提高云数据中心的抗毁能力,针对软硬件日趋复杂化带来的诸多故障问题,以传统的服务器心跳检测机制为基础,结合云计算的特点,提出一种保证云数据中心有效运行的心跳检测与故障评估方案。在实验室搭建仿真平台进行实验,实验结果表明,该方案能够及时检测到节点故障,依据预先制定的故障等级评估标准,采用模糊综合法进行评估,为云数据中心的管理与运作提供参考依据与保障。     

基于模拟分析技术和随机搜索算法的化工过程能量集成方法研究

夹点和模拟分析技术已在化工能量集成中广泛应用.今以夹点技术为基础,对化工过程系统用能进行诊断,根据"过程用能一致性"原则提出能量集成改造方案,通过改变塔压实现塔顶冷凝器与塔底再沸器的热集成多效蒸馏.夹点技术实现的优化目标仅是热回收最大或公用工程能耗最小,不能实现包括设备整体费用最小的过程全局最优化.研究基于模拟技术,通过灵敏度分析,得到精馏塔各操作参数的样本映射数据,通过多项式拟合得到参数关联方程,并在遗传算法及模式搜索中设计过程费用函数,提出了将模拟分析技术与随机搜索算法相结合的,以过程费用函数最小化为优化目标的能量集成方法.工业实例的成功设计应用证明了其有效性和可行性.     

施工项目管理与贯标工作相融合的探索与实践

由于建筑工程施工的单件性、复杂性，在工程项目实施过程中贯标工作与工程项目管理工作通常不太融合，有时甚至形成“两层皮”现象。因此，针对这一现象，在承建410t／h锅炉工程中，借贯彻2000版ISO9001标准的有力时机，进行了工程项目管理与贯标工作融合性的探索与实践，取得了较好的效果。     

产业与部门用水关联及尺度效应分析

以河南省为例,应用基于纵向集成的改进假设抽取法,对该区域三次产业和八大部门的水资源消耗及转移状况进行量化分析,由分析结果可知:农业和基础工业是河南省的主要水资源净输出部门,轻工业、高科技工业和建筑业是主要的净输入部门。河南省用水关联性较强产业部门包括农业-轻工业、农业-高科技工业、基础工业-高科技工业以及农业-基础工业。对不同尺度的用水关联成果进行了分析,分析结果表明:不同分析尺度的纵向集成消耗总量是相等的,且与其直接消耗总量相同;用水关联具有非线性的尺度效应,不能通过线性运算实现不同尺度关联效应的转换;分析尺度越小,分类越细化,内部效应越低,系统中流动转移的水量就越多,反之则相反。研究成果可为区域产业结构调整和水资源管理政策的制定提供参考。     

炼油信息集成技术

论述利用美国RIS公司的集成软件,解决信息应用中的"信息孤岛"问题.重点阐述数据集成技术,简要介绍数据库技术,及不同数据库之间的接口技术.     

企业信息系统多领域协同优化研究

企业信息化的前沿性问题是企业信息集成和优化。本文依据设计理论中的多学科协同优化的思想,将协同优化的概念引入到企业信息集成中。文中介绍了三种用于高层优化的算法,基于信息总线结构的EA I平台,提出了优化集成平台的构造方法。     

Preconditioning the solution of the time-dependent neutron diffusion equation by recycling Krylov subspaces

Spectral preconditioners are based on the fact that the convergence rate of the Krylov subspace methods is improved if the eigenvalues of the smallest magnitude of the system matrix are ‘removed’. In this paper, two preconditioning strategies are studied to solve a set of linear systems associated with the numerical integration of the time-dependent neutron diffusion equation. Both strategies can be implemented using the matrix–vector product as the main operation and succeed at reducing the total number of iterations needed to solve the set of systems.     

The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the minority samples and generated high total accuracy meanwhile. The proposed approach makes CBR useful in imbalanced forecasting.     

UNIT ROOTS IN PERIODIC AUTOREGRESSIONS

Abstract. This paper analyzes the presence and consequences of a unit root in periodic autoregressive models for univariate quarterly time series. First, we consider various representations of such models, including a new parametrization which facilitates imposing a unit root restriction. Next, we propose a class of likelihood ratio tests for a unit root, and we derive their asymptotic null distributions. Likelihood ratio tests for periodic parameter variation are also proposed. Finally, we analyze the impact on unit root inference of misspecifying a periodic process by a constant-parameter model.     

整合资源调整职能促进发展--关于陕煤技校改革发展的思考

经过60年的发展,陕西煤炭技工院校形成了以服务煤业为特色的专业结构和办学模式,为祖国煤炭发展做出了不可替代的贡献。近年来,由于煤炭市场遇冷,行业技校吸引力持续减弱,如何走出困境,重铸辉煌,作者从整合职教资源,调整管理职能,转换办学模式的角度进行了分析思考,或许能给陕煤技校改革发展提供一些有益参考。