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本文主要介绍Flash多媒体课件交互功能的实现方法。具体设计思路是:将授课教学内容分成一个主目录课件主控模块和几个子目录内容课件功能模块。利用Flash技术分别设计主课件模块和各个课件功能模块,最后把它们组织起来形成合理的课件交互结构。设计多媒体课件交互功能是制作多媒体课件的一个关键环节。 相似文献
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本文介绍了基于B／S体系结构的网上书店系统功能模型的架构设计,给出了系统的流程设计与总体架构设计思路,实现了主要功能模块和数据库模块的功能。 相似文献
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分析图书馆网站的需求,介绍Joomla的基本框架,设计图书馆网站的模板及功能,利用Joomla及其扩展（包括组件、模块等）创建图书馆网站的具体功能模块,为Joomla在图书馆领域的应用拓展思路. 相似文献
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针对我国农产品管理中信息发布不及时、物流成本过高的问题,构建农产品云管理模式,并确定其构建目标。根据农产品云管理模式的构建目标,研究农产品云管理模式的层次结构,对云资源构成、农产品信息交互方式、云管理模式分层逻辑结构进行详细地探讨;将农产品云管理模式分为信息发布模块、在线交易模块、物流优化模块和功能维护模块四大功能模块,并对各模块功能进行详细阐述,为农产品的信息化管理提供参考。 相似文献
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有一些研究已经表明生物系统是由功能模块间的相互作用组成的。因此,鉴别这些功能模块对于理解生物系统的模块性和组织结构是十分必要的。本文提出了一个框架来鉴别功能模块通过整合蛋白质互作网络和基因表达谱数据。应用算法于双重加权酵母蛋白质互作网络中,得到了120个功能模块。这些模块显著的富集在蛋白质相关的GO的生物学过程上。因此,本文提出的算法可以很好的挖掘蛋白质互作网络中功能模块。 相似文献
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针对专利的相关特性,根据TRIZ的功能分析及理想化水平理论,提出了一种高价值专利判断方法,从技术角度阐释专利价值及分析局部技术优劣。首先通过客观定量指标对专利库进行样本选取和数据清理;其次对目标领域的技术系统的功能进行总结,划分功能模块;然后针对单一功能模块,提取其相关属性特征并分别计算功能、性能、成本和有害功能四个指标的评价值;采用最大离差和熵的方法调节成本及有害功能评价值,并通过理想化水平公式得出各功能模块的评价值及专利的最终评价值。最后,通过对管道机器人的价值评价,验证了其可行性。 相似文献
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介绍了Pro/REPORT模块相关功能,着重阐述了利用Pro/REPORT功能模块自动生成产品设计明细表的方法,达到了提高明细表制作效率的目的。 相似文献
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基于我国电力行业标准《架空送电线路杆塔结构设计技术规定》(DL/T5154-2002),利用ObjectARX技术开发了输电塔架结构计算机辅助设计系统。介绍了系统的结构和主要功能,对系统的编制原理及各个功能模块进行了分析和说明。探讨了如何应用ObjectARX二次开发工具对AutoCAD数据库进行操作并实现各模块的不同功能。 相似文献
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本文介绍了基于FFT的伪随机码快速捕获的原理,并且对快速捕获模块的核心单元,即采样率转换单元和FFT/IFFT计算单元的FPGA实现进行了详细论述。仿真和实现结果表明,此方法能快速地捕获扩频信号中的伪随机码相位。 相似文献
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在IC设计中,HDL以其可移植性好、使用方便等优点几乎完全取代了传统的原理图输入方法,但使用HDL设计芯片也有编码效率低、开发周期长、调试困难等问题,为克服这些困难,提高IC设计的速度和准确率,结合实际项目经历提出一个适用于协议类型芯片的高效通用的自动化设计模型,借助HDL的描述和综合工具来实现,并以SerialATA芯片的链路层为例,说明设计过程。 相似文献
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本文根据安全工程专业学生实践能力结构的要求,构建了包含五个模块完整的实践能力培养体系,包括课程实验模块、课程设计实践模块、现场实践模块、创新能力培养模块和社会实践模块,培养体系注重课内与课外、校内与校外教育相结合,为学生创设多维实践环境,能够保障系统地对学生的实践能力进行培养。 相似文献
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从企业办公自动化系统常用的功能需求出发,提出OA总体设计方案,规划设计了OA的整体框架及其主要功能模块。选择Eclipse作为主要开发工具,运用该可视化编程工具实现OA系统的界面交互,对 Tomcat服务器和数据库进行优化设置,确保企业OA的信息发布、工作流应用、知识共享和公文管理等功能完整呈现。 相似文献
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本图书管理系统采用Java做前台界面,后台支持则采用数据库Access,本系统提供6个功能模块,分别是图书类别管理模块、图书信息管理模块、读者信息管理模块、新书订购管理模块、图书借阅模块以及系统模块。这6个模块里又有许多子模块,通过这些模块之间的相互连接与配合,完成操作员发出的各种指令。 相似文献
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《Information processing & management》2020,57(1):102141
Detecting sentiments in natural language is tricky even for humans, making its automated detection more complicated. This research proffers a hybrid deep learning model for fine-grained sentiment prediction in real-time multimodal data. It reinforces the strengths of deep learning nets in combination to machine learning to deal with two specific semiotic systems, namely the textual (written text) and visual (still images) and their combination within the online content using decision level multimodal fusion. The proposed contextual ConvNet-SVMBoVW model, has four modules, namely, the discretization, text analytics, image analytics, and decision module. The input to the model is multimodal text, m ε {text, image, info-graphic}. The discretization module uses Google Lens to separate the text from the image, which is then processed as discrete entities and sent to the respective text analytics and image analytics modules. Text analytics module determines the sentiment using a hybrid of a convolution neural network (ConvNet) enriched with the contextual semantics of SentiCircle. An aggregation scheme is introduced to compute the hybrid polarity. A support vector machine (SVM) classifier trained using bag-of-visual-words (BoVW) for predicting the visual content sentiment. A Boolean decision module with a logical OR operation is augmented to the architecture which validates and categorizes the output on the basis of five fine-grained sentiment categories (truth values), namely ‘highly positive,’ ‘positive,’ ‘neutral,’ ‘negative’ and ‘highly negative.’ The accuracy achieved by the proposed model is nearly 91% which is an improvement over the accuracy obtained by the text and image modules individually. 相似文献
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面向个性化服务的信息组织本体模式 总被引：1，自引：0，他引：1
本文在总结目前个性化服务信息组织的特点和模式的基础上,结合智能化本体方法,提出一种基于用户本体和领域本体的面向个性化服务的信息组织模式,并详述了其中的资源描述、用户建模和信息重组3个模块的详细流程、描述语言和具体算法. 相似文献
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《Information processing & management》2020,57(6):102344
Relation classification is one of the most fundamental tasks in the area of cross-media, which is essential for many practical applications such as information extraction, question&answer system, and knowledge base construction. In the cross-media semantic retrieval task, in order to meet the needs of cross-media uniform representation and semantic analysis, it is necessary to analyze the semantic potential relationship and construct semantic-related cross-media knowledge graph. The relationship classification technology is an important part of solving semantic correlation classification. Most of existing methods regard relation classification as a multi-classification task, without considering the correlation between different relationships. However, two relationships in the opposite directions are usually not independent of each other. Hence, this kind of relationships are easily confused in the traditional way. In order to solve the problem of confusing the relationships of the same semantic with different entity directions, this paper proposes a neural network fusing discrimination information for relation classification. In the proposed model, discrimination information is used to distinguish the relationship of the same semantic with different entity directions, the direction of entity in space is transformed into the direction of vector in mathematics by the method of entity vector subtraction, and the result of entity vector subtraction is used as discrimination information. The model consists of three modules: sentence representation module, relation discrimination module and discrimination fusion module. Moreover, two fusion methods are used for feature fusion. One is a Cascade-based feature fusion method, and another is a feature fusion method based on convolution neural network. In addition, this paper uses the new function added by cross-entropy function and deformed Max-Margin function as the loss function of the model. The experimental results show that the proposed discriminant feature is effective in distinguishing confusing relationships, and the proposed loss function can improve the performance of the model to a certain extent. Finally, the proposed model achieves 84.8% of the F1 value without any additional features or NLP analysis tools. Hence, the proposed method has a promising prospect of being incorporated in various cross-media systems. 相似文献
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Gang Li Yahui Luo Qiang Chen Lingying Liao Jianlong Zhao 《Biomicrofluidics》2012,6(1):014118-014118-16
This paper presents an easy-to-use, power-free, and modular pump for portable microfluidic applications. The pump module is a degassed particle desorption polydimethylsiloxane (PDMS) slab with an integrated mesh-shaped chamber, which can be attached on the outlet port of microfluidic device to absorb the air in the microfluidic system and then to create a negative pressure for driving fluid. Different from the existing monolithic degassed PDMS pumps that are generally restricted to limited pumping capacity and are only compatible with PDMS-based microfluidic devices, this pump can offer various possible configures of pumping power by varying the geometries of the pump or by combining different pump modules and can also be employed in any material microfluidic devices. The key advantage of this pump is that its operation only requires the user to place the degassed PDMS slab on the outlet ports of microfluidic devices. To help design pumps with a suitable pumping performance, the effect of pump module geometry on its pumping capacity is also investigated. The results indicate that the performance of the degassed PDMS pump is strongly dependent on the surface area of the pump chamber, the exposure area and the volume of the PDMS pump slab. In addition, the initial volume of air in the closed microfluidic system and the cross-linking degree of PDMS also affect the performance of the degassed PDMS pump. Finally, we demonstrated the utility of this modular pumping method by applying it to a glass-based microfluidic device and a PDMS-based protein crystallization microfluidic device. 相似文献