A novel intelligent verification platform based on a structured analysis model

An intelligent verification platform based on a structured analysis model is presented.Using an abstract model mechanism with specific signal interfaces for user callback,the unified structured analysis data,shared by the electronic system level design,functional verification,and performance evaluation,enables efficient management review,auto-generation of code,and modeling in the transaction level.We introduce the class tree,flow parameter diagram,structured flow chart,and event-driven finite state machine as structured analysis models.As a sand table to carry maps from different perspectives and levels via an engine,this highly reusable platform provides the mapping topology to search for unintended consequences and the graph theory for comprehensive coverage and smart test cases.Experimental results show that the engine generates efficient test sequences,with a sharp increase in coverage for the same vector count compared with a random test.     

饲料与动物营养配方专家系统中配方程序设计与实现

本文概述了我们研制成功的饲料与动物营养配方专家系统（ＦＡＮＰＥＳ），着重叙述了ＦＡＮＰＥＳ中配方程序设计与实现的方法．     

基于IDSS架构的缉私警务系统的分析与设计

传统的警务管理系统存在着对知识和经验信息处理不足的缺陷，不能很好地支持对预测性信息和决策性信息的处理活动。文中在分析警务管理工作特点的基础上，给出了系统的主要功能。根据系统的功能特点，利用案例学习和模糊查询等人工智能技术，提出了一个IDSS架构的系统解决方案，并对知识库的管理过程进行了研究。     

Recovering unknown topology in a two-layer multiplex network: One layer infers the other layer

There have been increasing interests in studying multiplex dynamical networks recently. This paper focuses on topology identification of two-layer multiplex networks with peer-to-peer interlayer couplings. For a two-layer network model in which different layers have different coupling patterns, we propose novel methods to recover unknown topological structure of one layer, using the information of the other layer known as a prior. The proposed methods make full use of the measured evolutional states of the multiplex network itself, and treat the layer with a known structure as an auxiliary layer which is designed to identify the unknown topological layer. Compared with the traditional synchronization-based identification method, the proposed methods are in no need of constructing an additional auxiliary network to identify the unknown topological layer, and thus greatly reduce the cost of topology identification. Finally, numerical simulations validate the effectiveness of the proposed methods.