气象信息系统中海量数据访问的优化方法

在气象信息系统中,海量气象数据的存储,操作逐渐成为影响系统应用的关键问题。结合实际气象信息系统项目开发中所使用的方法,以SQLSERVER为例,从数据库设计,应用索引和存储过程等几个方面讨论海量数据访问优化方法,为基于海量数据的应用系统实现提供有益的探索。     

高职学院学生信息管理系统性能优化问题分析

本文分析了影响学生信息管理系统性能的若干因素,介绍了系统性能优化的方法。通过禁用视图状态、用HTML元素代替WEB控件、减少图片和Flash的使用达到对Web性能优化的目的;通过对存储过程、索引、查询语句等的优化达到对数据库性能优化的目的。     

基于Solr的海量日志信息查询性能优化的研究

ApacheSolr是一个基于ApacheLucene的开源企业搜索服务器。Apache SolrCloud是基于Solr和Zookeeper的分布式搜索方案,它的主要思想是使用Zookeeper作为集群的配置信息中心。文章在海量日志索引信息存储和查询方面进行了探索,证明了在相关优化策略下Solr Cloud具备了优秀的查询性能。     

基于关系数据库的栅格数据存储和访问技术研究

提出了基于关系模型和OLE／COM的栅格数据库解决方案。栅格数据及其元数据存储在关系数据库中,使用OLE DB Profider进行客户端和数据库服务器之间的连接;数据访问模型参考OpenGIS的相关规范和其他相关产品的实现方法设计,并使用COM作为实现技术。此外,通过在数据存储方面采取分块存储、建立金字塔结构、进行数据压缩等策略,在数据访问模型方面建立数据缓冲区及智能更新机制,以改善系统的运行效率。以上方案和优化措施在基础GIS组件系统ComGIS中做了相关的实现;实践证明,这种解决方案基本上解决了基于关系数据库的栅格数据存储和访问问题。     

船体结构钢及焊接材料数据库应用系统设计开发

为了解决结构钢及焊接材料数据的有效存储及高效利用问题,使用SQL Server作为后台数据仓库,VB作系统开发,ODBC数据源作为连接技术,设计开发了船体结构钢及焊接材料数据库应用系统。系统具有结构钢及焊接材料信息查询、标准及报告查询等四大功能模块,并以结构钢配套焊条为例,通过性能预测智慧框架和神经网络模型构建、神经网络矩阵模型转换为VB代码等工作,实现了其力学性能预测,对结构钢及焊接材料设计有一定的参考和实用价值。      

基于J2EE连锁医药经销系统的设计与实现

本文分析了原有医药经销系统在运作管理上的平台异构和信息不能共享等问题,设计并完成了异地连锁医药经销系统。针对管理系统中分布异构数据的集成问题,着重论述了异地信息协作、数据存储转化及性能优化等关键问题的解决方法,实现了灵活的销售管理和内部调拨管理机制,较好地改善了应用开发中的信息共享和数据库性能优化等问题,提高了系统的扩展性和复用性。     

基于Web的应用程序数据库安全分析与设计

随着计算机和网络技术的迅速发展,基于Web的数据库应用越来越广,同时给数据库的维护和管理带来了很多安全问题.为提高Web应用程序中的数据库安全性,从构建系统体系模型,建立登入机制,进行存储访问控制,审计追踪,数据库备份与恢复,使用视图机制和数据加密技术等方面分析了基于Web的数据库应用系统的设计.     

基于分布式异构数据库的查询优化算法

对于分布式异构数据库,查询优化既是非常复杂的问题,又是影响系统性能的关键因素。结合遗传算法和模拟退火算法的优点,提出基于遗传模拟退火算法的分布式异构数据库查询优化方法。仿真实验表明,该方法有效地提高分布式异构数据库的查询优化效率。     

基于C/S与B/S混合模式的计量信息管理系统

为了优化计量工作流程,提高工作效率,规范工作程序,从计量技术和业务管理的性质特征出发,开发了基于C/S与B/S混合结构的计量管理系统.介绍了系统的结构组成、工作原理以及设计思路,重点说明了系统软件与系统数据库的设计和应用程序的创建.该系统实现了计量信息的网上统计、维护、查询和发布等功能.     

SQL Server数据库性能优化研究

随着计算机技术的突飞猛进,大多数企业都在大量使用SQL Server数据库,其具有存储容量大、方便查询的特点,因此,本文首先对SQL Server数据库的特点进行分析,针对SQL Server数据库的常见问题提出相应的优化数据库性能优化策略。     

A bilevel game theoretic approach to optimum design of flywheels

Multiobjective optimization problems arise frequently in mechanical design. One approach to solving these types of problems is to use a game theoretic formulation. This article illustrates the application of a bilevel, leader–follower model for solving an optimum design problem. In particular, the optimization problem is modelled as a Stackelberg game. The partitioning of variables between the leader and follower problem is discussed and a variable partitioning metric is introduced to compare various variable partitions. A computational procedure based on variable updating using sensitivity information is developed for exchanging information between the follower and leader problems. The proposed approach is illustrated through the design of a flywheel. The two objective functions used for the design problem include maximizing the kinetic energy stored in the flywheel while simultaneously minimizing the manufacturing cost.     

DHT系统的安全性优化方法研究

对分布式哈希表(DHT)系统的安全脆弱性问题进行了研究,提出了多种安全性优化策略,并给出了一个原型系统。进行了真实网络实验,实验数据表明,现有DHT网络易受索引毒害和路由污染攻击,产生的错误查询结果甚至会引发更大规模的网络安全事件。通过改进一个个DHT系统的节点ID生成机制、路由表更新机制和搜索路径选择机制,从系统运行的各个阶段提升其安全场,抵御攻击者共谋。基于上述方法设计的原型系统在保证平均查询跳数增加不到1跳的情况下,在共谋攻击节点占比60%的网络中,将系统查询成功率保持在65%以上,其方法适用于各种分布式哈希表结构,具有重要的实际应用前景。     

基于信息熵改进的粒子群算法在桁架结构

提出了信息熵改进的粒子群优化算法用于解决有应力约束、位移约束的桁架结构杆件截面尺寸优化设计问题.首先介绍了信息熵基本理论和基本粒子群优化算法理论,然后对粒子群优化算法作了合理的参数设置,并将信息熵引入粒子群优化算法的适应函数和停机判别准则中.最后对2个经典的优化问题进行求解并与其他算法进行了比较.数据结果表明信息熵改进后的粒子群优化算法在桁架结构优化设计中优于其他同类算法.     

Second Order Mutual Information based Grey Wolf Optimization for effective storage and de-duplication

This paper intends to perform de-duplication for enhancing the storage optimization by utilizing the similarity in mutual information. Hence, this paper contributes by proposing a hybrid fingerprint extracting using SH and HC algorithms. Secondly, the data is clustered using the latest technique called as SOMI-GO to extract the metadata. The extracted metadata is stored in metadata server which provides better storage optimization and de-duplication. SOMI-GO is adopted as it provides maximum second-order mutual information based on the similarity index. The proposed SOMI-GO technique is compared with the existing methods such as K-means, K-mode, ED-PSO, ED-GA and ED-GWO in terms of accuracy, TPR, TNR and performance time and the significance of the SOMI-GO method is described.     

WPERF+:一种有效的分布式查询处理优化算法

分布式查询是一种网络通信成本较大的查询过程,设计和开发出有效的查询策略和技术是分布式查询面临的挑战。本文展示了一种新的分布式查询的算法。即WPERF 连接算法,它结合了W算法和PERF算法的特点。进一步强化了W算法和PERF的性能。文章从理论上证明了WPERF 算法具有有效降低分布式查询处理成本的效能。     

Ontology-based Tamil–English cross-lingual information retrieval system

Cross-lingual information retrieval (CLIR) systems facilitate users to query for information in one language and retrieve relevant documents in another language. In general, CLIR systems translate query in source language to target language and retrieve documents in target language based on the keywords present in the translated query. However, the presence of ambiguity in source and translated queries reduces the performance of the system. Ontology can be used to address this problem. The current approaches to ontology-based CLIR systems use manually constructed multilingual ontology, which is expensive. However, many methods exist to automatically construct ontology for any domain in English but not in other languages like Tamil. We propose a methodology for Tamil–English CLIR system by translating the Tamil query to English and retrieve pages in English to address these issues. Our approach uses a word sense disambiguation module to resolve the ambiguity in Tamil query. An automatically constructed ontology in English is used to address the ambiguity of English query. We have developed a morphological analyser for Tamil language, Tamil–English bilingual dictionary and named entity database to translate a Tamil query to English. The translated query is reformulated using ontology and the reformulated queries are given to a search engine to retrieve English documents from the Internet. We have evaluated our methodology for agriculture domain and the evaluation results show that our approach outperforms other approaches in terms of precision.     

A multiple surrogate assisted evolutionary algorithm for optimization involving iterative solvers

In many real-world optimization problems, the underlying objective and constraint function(s) are evaluated using computationally expensive iterative simulations such as the solvers for computational electro-magnetics, computational fluid dynamics, the finite element method, etc. The default practice is to run such simulations until convergence using termination criteria, such as maximum number of iterations, residual error thresholds or limits on computational time, to estimate the performance of a given design. This information is used to build computationally cheap approximations/surrogates which are subsequently used during the course of optimization in lieu of the actual simulations. However, it is possible to exploit information on pre-converged solutions if one has the control to abort simulations at various stages of convergence. This would mean access to various performance estimates in lower fidelities. Surrogate assisted optimization methods have rarely been used to deal with such classes of problem, where estimates at various levels of fidelity are available. In this article, a multiple surrogate assisted optimization approach is presented, where solutions are evaluated at various levels of fidelity during the course of the search. For any solution under consideration, the choice to evaluate it at an appropriate fidelity level is derived from neighbourhood information, i.e. rank correlations between performance at different fidelity levels and the highest fidelity level of the neighbouring solutions. Moreover, multiple types of surrogates are used to gain a competitive edge. The performance of the approach is illustrated using a simple 1D unconstrained analytical test function. Thereafter, the performance is further assessed using three 10D and three 20D test problems, and finally a practical design problem involving drag minimization of an unmanned underwater vehicle. The numerical experiments clearly demonstrate the benefits of the proposed approach for such classes of problem.     

Studies of multi‐start clustering for global optimization

Global/multi‐modal optimization problems arise in many engineering applications. Owing to the existence of multiple minima, it is a challenge to solve the multi‐modal optimization problem and to identify the global minimum especially if efficiency is a concern. In this paper, variants of the multi‐start with clustering strategy are developed and studied for identifying multiple local minima in nonlinear global optimization problems. The study considers the sampling procedure, the use of Hessian information in forming clusters, the technique for cluster analysis and the local search procedure. Variations of multi‐start with clustering are applied to 15 multi‐modal problems. A comparative study focuses on the overall search effectiveness in terms of the number of local searches performed, local minima found and required function evaluations. The performance of these multi‐start clustering algorithms ranges from very efficient to very robust. Copyright © 2002 John Wiley & Sons, Ltd.     

纸箱结构参数化设计中的数据处理

介绍了利用AutoCAD二次开发平台对瓦楞纸箱结构图进行参数化设计的过程,解决一些参数化设计中的数据处理的问题,包括表格的数值化和程序化的过程。在数值化时,首先将各种查询表格转成Microsoft Access关系数据库;程序化是将具有一定逻辑关系的数据以程序的方式进行存储。以上数据通过VBA建立的窗体,结合客户输入的已知产品的信息,检索出相应的数据,自动计算出纸箱结构最优化尺寸等参数,并绘出纸箱的展开图。     

Experiments with hybrid Bernstein global optimization algorithm for the OPF problem in power systems

This article presents an algorithm based on the Bernstein form of polynomials for solving the optimal power flow (OPF) problem in electrical power networks. The proposed algorithm combines local and global optimization methods and is therefore referred to as a ‘hybrid’ Bernstein algorithm in the context of this work. The proposed algorithm is a branch-and-bound procedure wherein a local search method is used to obtain a good upper bound on the global minimum at each branching node. Subsequently, the Bernstein form of polynomials is used to obtain a lower bound on the global minimum. The performance of the proposed algorithm is compared with the previously reported Bernstein algorithm to demonstrate its efficacy in terms of the chosen performance metrics. Furthermore, the proposed algorithm is tested on the OPF problem for several benchmark IEEE power system examples and its performance is compared with generic global optimization solvers such as BARON and COUENNE. The test results demonstrate that the hybrid Bernstein global optimization algorithm delivers satisfactory performance in terms of solution optimality.