摘要：Corrosion in complex coupling environments is an important issue in corrosion field, because it is difficult to take into account a large number of environment factors and their interactions. Design of Experiment(DOE) can present a methodology to deal with this difficulty, although DOE is not commonly spread in corrosion field. Thus, modeling corrosion of Ni-Cr-Mo-V steel in deep sea environment was performed in order to provide example demonstrating the advantage of DOE. In addition, an artificial neural network mapping using back-propagation method was developed for Ni-Cr-Mo-V steel such that the ANN model can be used to predict polarization curves under different complex sea environments without experimentation. Furthermore, roles of environment factors on corrosion of Ni-Cr-Mo-V steel in deep sea environment were discussed.

摘要：针对目前deep Web数据集成在数据获取方面存在代价大和查询选择效率低等问题,提出了一种基于循环策略和动态知识的deep Web数据获取方法,该方法根据同领域数据源之间的关联关系,提出使用循环策略分多次完成数据源的数据获取,同时利用集成系统已获取的数据动态构建知识,并设计了基于集成系统动态知识的查询选择方法。与现有方法比较该方法能降低数据获取的代价,提高查询选择的准确性。实验结果表明,该方法有效地提高了deep Web数据集成的数据获取效率。

摘要：针对deep Web特点,采用基于本体的信息检索方法,设计和实现了一种deep Web语义搜索系统,方便用户获取需要的高价值信息。

摘要：Convolutional neural networks(CNNs)have shown tremendous progress and performance in recent *** emergence,CNNs have exhibited excellent performance in most of classification and segmentation ***,the CNN family includes various architectures that dominate major vision-based recognition ***,building a neural network(NN)by simply stacking convolution blocks inevitably limits its optimization ability and introduces overfitting and vanishing gradient *** of the key reasons for the aforementioned issues is network singularities,which have lately caused degenerating manifolds in the loss *** situation leads to a slow learning process and lower *** this scenario,the skip connections turned out to be an essential unit of the CNN design to mitigate network *** proposed idea of this research is to introduce skip connections in NN architecture to augment the information flow,mitigate singularities and improve *** research experimented with different levels of skip connections and proposed the placement strategy of these links for any *** prove the proposed hypothesis,we designed an experimental CNN architecture,named as Shallow Wide ResNet or SRNet,as it uses wide residual network as a base network *** have performed numerous experiments to assess the validity of the proposed ***-10 and CIFAR-100,two well-known datasets are used for training and testing *** final empirical results have shown a great many of promising outcomes in terms of performance,efficiency and reduction in network singularities issues.

摘要：如今Web上越来越多的信息可以通过查询接口来获得,为了获取某deepWeb站点的页面用户不得不键入一系列的关键词集。由于没有直接指向deepWeb页面的静态链接,当前大多搜索引擎不能发现和索引这些页面。然而,近来研究表明deepWeb站点提供的高质量的信息对许多用户来说是非常有价值。这里研究了怎样建立起一个有效的deepWeb爬虫,它可以自动发现和下载deepWeb页面。由于deepWeb惟一“入口点”是查询接口,deepWeb爬虫设计面对的主要挑战是怎样对查询接口自动产生有意义的查询。这里提出一种针对查询接口查询自动产生问题的理论框架。通过在实际deepWeb站点上的实验证明了此方法是非常有效的。

摘要：针对《基础教育教学资源元数据应用规范》中资源建设标准的缺陷,提出一种基于deep Web集成技术的教育资源跨库检索系统设计方案。该系统包含知识发现、集成查询、知识库和系统监控等模块,加入了环境变化监控构件,并在多个环节中采用自动化方式。

摘要：deep Web信息量大,主题专一,信息质量好。然而deep Web信息存在着不确定问题,因此难以对其进行知识表示和推理。基于动态模糊逻辑理论,提出了一种新的描述逻辑,即动态模糊描述逻辑(DFDLs)。给出了DFDLs形式化定义以及DFDLs的语法和语义,设计了DFDLs的tableau的推理算法和策略。采用动态模糊描述逻辑对面向deep Web的不确定知识进行表示并实现合理的推理和利用,能更好地表达deep Web的动态和模糊信息。

摘要：在现有相关研究的基础上,设计一种基于数据库分类的deep web爬行器。该爬行器首先从抓取的网页中识别出deep web数据库的入口表单,然后采用查询探测方法对数据库进行自动分类,并根据分类结果来选取一组合适的关键词作为查询词,自动填写入口表单中的文本框并向数据库提出查询请求。实验结果表明,基于数据库分类的deep web爬行器的爬行效果要优于基于指定查询词的deep web爬行器的爬行效果。

摘要：deep Web查询结果的语义标注,是deep Web数据集成的关键问题之一。提出基于多标注源的deep Web查询结果自动标注框架,根据不同的特征设计多个标注器。基于搜索引擎的标注器,扩展AI领域常用的问答技术,构造验证查询并提交到搜索引擎,利用返回结果选择最合适的词汇用于标注,有效提高了标注的查准率和查全率。多个领域Web数据库的测试证明了该方法的有效性。

摘要：对deep Web中的海量信息进行集成是Web信息检索领域的一个新兴研究热点。本文提出了一个基于移动Agent的deep Web数据集成框架,设计了数据集成中间件DWDIM(deep Web Data Integration Middleware),采用Java语言和Aglet平台对集成系统进行实现。并将控制学中的负反馈技术引入到系统设计当中,提高了集成系统的性能。