网络安全隔离技术浅析

介绍建设行政主管部门信息网络安全现状及其解决方案     

水库最优调度规则的神经网络模型

利用人工神经网络的非线性决策特点，提出了水库（电站）最优调度规则的人工神经网络模型，以四川宝珠寺水电站为例，进行其最优调度规则的模拟，研究结果表明了模型的合理性，可行性及实用性。     

低潮时期宽带网建设需要注意的问题

宽带建设低潮期实际上是一次优胜劣汰的过程,宽带运营竞争的手段将由低价竞争转向服务质量和特色的竞争。应在重视宽带接入建设的同时,也要重视业务运营管理平台的建设。应从长远考虑,不应仅为了节省投资而降低对设备的要求。由于降低成本的主要方向是在线缆部分,因此EVDSL技术应用的速度将大大超过人们的预期。在网络建设上,IPDSLAM虽有优势但也有不足,还需要进一步的成熟、稳定和实践的检验。     

Quantitative analysis of Ni effect on CCT diagrams of novel air-cooled bainite steels using artificial neural network models

The quantitative effect of Ni content on continuous cooling transformation （CCT） diagrams of novel air-cooled bainite steels was analyzed using artificial neural network models. The results showed that Ni may retard the high- and medium-temperature transformation and martensite transformation. The results conform to the materials science theories.     

采用BP神经网络记忆模糊规则的控制

本文提供了一种比模糊推理更为自然的方式使用人们的经验知识,通过一组神经元不同 程度的兴奋表达一个抽象的概念值,由此将抽象的经验规则转化成多层神经网络的输入一输 出样本.通过Back-Propagation学习算法使得网络记忆这些样本.控制器以"联想记忆"方 式使用这些经验.本文介绍了控制器的构造方法,给出了控制仿真结果,并讨论了这种控制器 的特点和发展前途.     

VLSI Implementation of Forward Error Control Technique for ATM Networks

In asynchronous transfer mode (ATM) networks, fixed length cells of 53 bytes are transmitted. A cell may be discarded during transmission due to buffer overflow or a detection of errors. Cell discarding seriously degrades transmission quality. The quality degradation can be reduced by employing efficient forward error control (FEC) to recover discarded cells. In this paper, we present the design and implementation of decoding equipment for FEC in ATM networks based on a single parity check (SPC) product code using very‐large‐scale integration (VLSI) technology. FEC allows the destination to reconstruct missing data cells by using redundant parity cells that the source adds to each block of data cells. The functionality of the design has been tested using the Model Sim 5.7cXE Simulation Package. The design has been implemented for a 5 ° 5 matrix of data cells in a Virtex‐E XCV 3200E FG1156 device. The simulation and synthesis results show that the decoding function can be completed in 81 clock cycles with an optimum clock of 56.8 MHz. A test bench was written to study the performance of the decoder, and the results are presented.     

化工软测量技术研究进展

详细说明了软测量技术的含义以及软测量系统的组成。在此基础上，介绍了软测量技术中基于机理分析建模、应用状态观测器建模、应用统计分析建模、神经元网络、模糊技术、以及支持向量机的智能建模等软测量建模理论方法和最新研究成果，之后介绍了小波分析、推断控制算法在软测量数据处理中的应用，最后介绍了基于虚拟仪器开发平台的软测量系统实现技术。     

Revising deductive knowledge and stereotypical knowledge in a student model

A user/student model must be revised when new information about the user/student is obtained. But a sophisticated user/student model is a complex structure that contains different types of knowledge. Different techniques may be needed for revising different types of knowledge. This paper presents a student model maintenance system (SMMS) which deals with revision of two important types of knowledge in student models: deductive knowledge and stereotypical knowledge. In the SMMS, deductive knowledge is represented by justified beliefs. Its revision is accomplished by a combination of techniques involving reason maintenance and formal diagnosis. Stereotypical knowledge is represented in the Default Package Network (DPN). The DPN is a knowledge partitioning hierarchy in which each node contains concepts in a sub-domain. Revision of stereotypical knowledge is realized by propagating new information through the DPN to change default packages (stereotypes) of the nodes in the DPN. A revision of deductive knowledge may trigger a revision of stereotypical knowledge, which results in a desirable student model in which the two types of knowledge exist harmoniously.     

基于双因子认证技术的网络身份识别

本文提出了一种基于动态双因子认证技术的网络身份识别方法,用户每次登录的口令是利用系统时间和用户ID通过MD5加密算法计算得到,其通过MD5加密后生成的登录口令是随机的,这将进一步提高开放网络环境下身份识别的可靠性和安全性。同时,本文还提出网络身份识别技术可以应用到各类网站的服务器上,用来完成对用户身份的识别,以提高网络系统的安全性。     

应用遗传神经网络研究低渗透储层成岩储集相—以胜利渤南油田三区沙河街组为例

以渤南油田三区沙河街组为例,应用遗传人工神经网络模式识别方法,开展了低渗透储层成岩储集相的研究。该工作是在储集层沉积相、成岩作用研究的基础上,选用流动层带指标、孔隙度、渗透率、粒度中值、泥质含量、孔喉半径均值和变异系数等7项参数,采用神经网络模式识别方法,通过建立遗传神经网络的学习及预测模型,对渤南油田三区沙河街组进行了成岩储集相识别,识别出4类成岩储集相:不稳定组分强溶解次生孔隙成岩储集相、碳酸盐胶结物溶解次生孔隙成岩储集相、强压实强胶结残余粒间孔成岩储集相和极强压实强胶结致密成岩储集相。Ⅰ类储集相的储集性能最好,Ⅳ类最差,为非储层或差储层。