知识驱动的UG中的产品开发

重点论述了UG中的KBE即KF模块的知识驱动产品开发设计过程及其关键技术，介绍了知识驱动自动化过程，实例剖析了UG中的KF在产品开发设计过程中的应用。     

中国网上银行成长迅速、潜力巨大

2006年12月15日,“中国网上银行年会暨2006中国网上银行调查结果公布和颁奖”活动在北京民族饭店举行。会议上,中国金融认证中心(简称CFCA)联合金融时报社共同发布了《2006中国网上银行调查报告》结果,介绍了中国网上银行评测体系,并为在测评体系中表现优异的银行颁发了相关奖项。国务院信息化办公室相关领导、中国人民银行支付结算司谢众副司长、中国银监会业务创新监管协作部李伏安主任、金融时报社社长汪洋以及二十多家中资、外资商业银行电子银行部负责人及相关代表出席了当天的活动。目前,我国互联网的用户已经超过了1.2亿之多,但是…     

Reticulated Porous Multiphase Ceramics with Improved Compressive Strength and Fracture Toughness

A multiphase reticulated porous ceramic (RPC) as Si₃N₄–Al₂O₃–SiO₂ was fabricated by replication techniques. Proper volumes of additives and twice sinter- twice immerse process endow the RPC an excellent crack healing and submerging property. The compressive strength and fracture toughness improved owing to the crack bridging behavior. The existence of pores in struts in RPC blunt the crack tip and increased the external force needed to propagate the crack. The mechanisms play a beneficial role in enhancing the compressive strength and fracture strength. Si₃N₄ RPC with additives of 5%Al and 5% Al₂O₃ yielded the compressive strength of 9.8 MPa and fracture toughness of 0.3 MPa m^1/2.     

锂离子电池正极材料锂锰氧化物的固相合成研究进展

阐述了固相合成反应的原理，综述了锂锰氧化物的几种固相合成方法，并着重介绍了熔融渍法，多步加热法，机械化学法和微波化学法等在锂锰氧化物合成中的研究进展。     

虚拟专用网的体系结构及实现

提出VPN接入设备的体系结构 ,包括系统的应用模型、层次划分和内部详细结构 ,阐述实现VPN的关键技术。     

复射线展开法分析圆柱面对平面波散射的精度问题

讨论了用复射线展开法计算圆柱面对入射平面波散射时的计算误差特性，提出了控制其计算精度的复射线展开参数选择方法，证实了通过恰当选择展开参数可以有效地提升计算精度、实现误差控制。     

防汛应用系统整合与完善的探讨

随着黄委“数字黄河”工程建设的开展以及计算机广域网络的扩大、开发技术运行方式的变化和用户的迅速增长，需要将防汛系统资源进行综合利用，满足业务生产需求。“数字黄河”工程规划完成后．我们根据规划首先选择防汛应用系统为突破口，以防汛会商综合信息支持为目标，将单个的防汛应用系统进行规范、完善，最终建成防汛信息综合平台．在平台的基础上实现现有应用系统和新开发系统的集成。     

热流量计的原理

根据工业及家庭供暖的要求，设计并研制成一种新型的热流量计，它采用方便而新颖的采集信息方式，并使用CPU精确实时的进行控制和运算，保证了热流量计的精确、实时和“分户计量”。     

存储媒体的媒体内同步反馈控制算法

本文提出了一种存储媒体的媒体内同步反馈控制算法，该方案是建立在缓冲区占用率控制基础上，通过周期性地检查缓冲区的占用情况来检测失步，并将其反馈给发送方，由发送方对发送帧速率进行调整。文章给出了接收方所需缓冲区的设计准则及发送帧速率的调整方法，并分析了算法中各参数对同步性能的影响。实验表明该方案能够处理由于网络传输时延特性变化引起的失步，与其它反馈控制方案相比具有更低的数据丢弃率，从而使接收端播放平滑连续，具有良好的视频播放质量。     

A hierarchical model for structure learning based on the physiological characteristics of neurons

Almost all applications of Artificial Neural Networks (ANNs) depend mainly on their memory ability. The characteristics of typical ANN models are fixed connections, with evolved weights, globalized representations, and globalized optimizations, all based on a mathematical approach. This makes those models to be deficient in robustness, efficiency of learning, capacity, anti-jamming between training sets, and correlativity of samples, etc. In this paper, we attempt to address these problems by adopting the characteristics of biological neurons in morphology and signal processing. A hierarchical neural network was designed and realized to implement structure learning and representations based on connected structures. The basic characteristics of this model are localized and random connections, field limitations of neuron fan-in and fan-out, dynamic behavior of neurons, and samples represented through different sub-circuits of neurons specialized into different response patterns. At the end of this paper, some important aspects of error correction, capacity, learning efficiency, and soundness of structural representation are analyzed theoretically. This paper has demonstrated the feasibility and advantages of structure learning and representation. This model can serve as a fundamental element of cognitive systems such as perception and associative memory.