硼酸铝钕(NAB)晶体光学均匀性的测量

NAB晶体是一种综合性能较好的新型激光工作物质,有希望做成高效的小型固体激光器。本文介绍一种测量这种晶体光学均匀性的方法。 一般都采用在干涉仪上观测干涉图样来判定激光棒的光学均匀性。NAB晶体的吸收光谱曲线显示出其在可见光光谱区内有多个吸收峰。我们注意到:通常用做激光干涉仪光源的He-Ne激光器发射的632.8nm谱线正位于NAB晶体的一个吸收峰,并用输出约为45mW     

千兆比以太网技术

千兆比以太网是一种新型高速局域网，它一方面兼容传统以太网的主要标准结构，保护了用户先前的投资和系统配置，并使系统升级方便，以较实际的价格赢得了支持的发展。另一方面，它又采纳了光纤通道的低层协议以及其他一些在行兆比速率下应作的改进，使它获得了高速宽带性能。本文还列举了从快速以太网和ＦＤＤＩ向千兆比以太网迁移的网络配置策略。     

聚合铝生产工艺研究

以铝矿为原料，在常压下，采用两段溶出法，对聚合铝的合成工艺进行了系统研究。     

纸张背衬不同衬底时的光反射率的研究

从理论上研究了纸张背衬不同衬底的光反射率，得出下列表达式：Ｒｂ＝Ｒｏ＋（Ｒ∞－Ｒｏ）×（１／Ｒ∞－Ｒｏ）／（１／Ｂ－Ｒｏ）（Ｂ为衬底的光反射率），该式简单实用且物理意义明确。利用上述公式，可将Ｔａｐｐｉ标准列出的Ｔａｐｐｉ不透明度Ｃ０．８９的换算公式简化，并可从理论上定性地讨论纸张不透明度与印刷透印性的关系。     

Full Or-Parallemism and Restricted And-Parallelism in BTM

BTM is a new And/Or parallel execution model for logic programs which exploits both full Or-parallelism and restricted And-parallelism.The advantages of high parallelism and low run time cost make BTJ,an experimental execution system of BTM implemented on a nonshared-memory multiprocessor system,achieve significant speedup for both And-paralled and Or-parallel logic programs.     

用二甲基二氯硅烷水解物代替混合环体生产107甲基室温硫化硅橡胶

分析了１０７甲基室温硫化硅橡胶生产中造成粘度波动和收率较低的因素，通过改变原料，用二甲基二氯硅烷水解物代替甲基混合环体，提高了产品收率、降低了成本，粘度也易于控制     

新的钢铁工业结构与铁矿资源条件的能源优化模型研究

发展我国钢铁工业，资源和能源已经成为两个重要的制约因素。到本世纪末，我国钢产量达1．2亿t，所需资源必须有部分进口。本文论述了新的钢铁企业模型结构及采用部分进口废钢和铁矿石的能源优化问题。     

松软矿体有底柱高分段崩落法的改进方案

锦屏磷矿对其东山采区松软矿体所使用的分段崩落法做了集中快速采准施工、增加分段高度、实行水平或上向扇形深孔连续崩矿等改进,简化了采场结构,矿石损失率由原来的63％下降到改进后的20.34％,贫化率由23.92％下降到15.10％。     

Application of the recurrent multilayer perceptron in modelingcomplex process dynamics

A nonlinear dynamic model is developed for a process system, namely a heat exchanger, using the recurrent multilayer perceptron network as the underlying model structure. The perceptron is a dynamic neural network, which appears effective in the input-output modeling of complex process systems. Dynamic gradient descent learning is used to train the recurrent multilayer perceptron, resulting in an order of magnitude improvement in convergence speed over a static learning algorithm used to train the same network. In developing the empirical process model the effects of actuator, process, and sensor noise on the training and testing sets are investigated. Learning and prediction both appear very effective, despite the presence of training and testing set noise, respectively. The recurrent multilayer perceptron appears to learn the deterministic part of a stochastic training set, and it predicts approximately a moving average response of various testing sets. Extensive model validation studies with signals that are encountered in the operation of the process system modeled, that is steps and ramps, indicate that the empirical model can substantially generalize operational transients, including accurate prediction of instabilities not in the training set. However, the accuracy of the model beyond these operational transients has not been investigated. Furthermore, online learning is necessary during some transients and for tracking slowly varying process dynamics. Neural networks based empirical models in some cases appear to provide a serious alternative to first principles models.