基于Web的法律咨询系统的设计与实现

针对法律案例多样化,且法律条文中通常都含有大量专业性很强的法律专用语,使得法律按照条文来分析自身面临了很大难题,本文研究了基于Web的法律信息服务平台的设计与实现,将冗杂无序的网上法律信息进行统一搜集和分类,并提供了大量案例供用户阅读,为用户提供更多有价值的法律信息.系统主要功能有:商务实务查询,相关信息查询,用户权限管理.案例及其类别管理,法律法规及其类别管理,简易论坛,案例搜索,法律法规查询,相似案例查找等.本系统的核心功能是相似案例查找和关键字搜索功能,常要利用Lucene进行切词处理.相似案例查找功能逆向运用了KNN算法的思想,在切词处理后,形成空间向量,利用空间向量计算来分析两案例的相似度,从而实现了相似案例查找.为了实现关键字搜索功能,利用Lucene实现了对数据库中的案例的全文动态索引处理,从而提高了搜索速度和搜索精度.     

一种基于双向2DMSD的人脸识别方法

提出一种基于双向二维最大散度差线性判别分析(Bidirectional 2DMSD)的人脸识别方法.该方法通过在水平和垂直2个方向上顺序执行2次二维最大散度差线性判别分析(2DMSD)运算,将判别特征信息压缩到图像的左上角,大大减少了图像特征的维数;选用二维最小近邻分类法进行分类,计算识别率.在ORL和Yale人脸数据库上的实验结果表明,该方法不仅在识别率上优于最大散度差线性判别分析(MSD),而且在与2DMSD具有相同识别率的情况下,特征维数比2DMSD大大减小,降低了计算复杂度,减少了识别时间,提高了人脸识别效率.     

基于ObjectARX的轻型木桁架节点模拟软件

为克服轻型木桁架设计过程中计算工作量大、手算难以实现的问题,利用ObjectARX编写豪式桁架和芬克式桁架的任意外形建模及计算模型自动生成程序.该程序采用规范推荐的节点模拟法体现齿板连接而产生的节点半刚性特征：用多根模拟弦杆、腹杆和若干分节点构成模拟节点;所有杆件数据通过Xdata扩展数据形式集成在杆件单元中,便于后续程序进行内力计算、简化轻型木桁架的工程设计计算过程.经豪式桁架试算实例,验证该程序生成的模型满足规范要求,易于调整,数据结构良好,且能体现齿板连接引起的刚度修正结果.     

基于数字网络的校园网络安全对策与分析

随着计算机网络的普及,网络安全日益受到人们的关注,但由于网络本身的复杂性,其管理难度也越来越大,任何一个小的问题都可能影响到整个网络的正常运行.     

基于B+树索引的实时数据库缓冲区管理算法

考虑到工控位号历史数据时间戳递增的特点,针对已有实时数据库产品历史数据存储索引查询效率不高,磁盘空间利用率低的不足,提出了一种以B+树为索引方式,以状态链表和页式存储为管理方式的历史数据缓冲区管理方式.经过实际测试表明,基于该缓冲区管理算法的历史数据存储模块存取效率高,并发性能好,磁盘空间的利用率高,完全可以替代关系数据库作为实时数据库的历史数据存储模块.     

Development of an operational fitness test for the Royal Air Force

Since 2002, the Royal Air Force (RAF) has been working towards developing role-related physical tests for use as an operational fitness test (OFT). The purpose of this study was to establish reliability of the OFT (comprising four tests), investigate gym-based tests as predictors of performance and establish performance standards. Fifty-eight RAF personnel performed the OFT on three occasions. A separate cohort carried out fitness and anthropometric tests before performing the OFT, by way of establishing performance predictors. Documented evidence and views of an expert panel were used to determine OFT standards. Reliability ranged from moderate to good for three tests, with one test (Dig) showing poor reliability. The 95% limits of agreement for the prediction models ranged from good to poor (6.7-34.2%). The prediction models were not sufficiently accurate to estimate confidently OFT performance, but could be used as a guide to quantify likely outcome and training needs.     

Multi-strategy ensemble particle swarm optimization for dynamic optimization

Optimization in dynamic environments is important in real-world applications, which requires the optimization algorithms to be able to find and track the changing optimum efficiently over time. Among various algorithms for dynamic optimization, particle swarm optimization algorithms (PSOs) are attracting more and more attentions in recent years, due to their ability of keeping good balance between convergence and diversity maintenance. To tackle the challenges of dynamic optimization, several strategies have been proposed to enhance the performance of PSO, and have gained success on various dynamic optimization problems. But there still exist some issues in dynamic optimization which need to be studied carefully, i.e. the robustness of the algorithm to problems of various dynamic features. In this paper, a new multi-strategy ensemble particle swarm optimization (MEPSO) for dynamic optimization is proposed. In MEPSO, all particles are divided into two parts, denoted as part I and part II, respectively. Two new strategies, Gaussian local search and differential mutation, are introduced into these two parts, respectively. Experimental analyses reveal that the mechanisms used in part I can enhance the convergence ability of the algorithm, while mechanisms used in part II can extend the searching area of the particle population to avoid being trapped into the local optimum, and can enhance the ability of catching up with the changing optimum in dynamic environments. The whole algorithm has few parameters that need to be tuned, and all of them are not sensitive to problems. We compared MEPSO with other PSOs, including MQSO, PHPSO and Standard PSO with re-initialization, on moving peaks Benchmark and dynamic Rastrigin function. The experimental results show that MEPSO has pretty good performance on almost all testing problems adopted in this paper, and outperforms other algorithms when the dynamic environment is unimodal and changes severely, or has a great number of local optima as dynamic Rastrigin function does.