RLL码的计算机辅助设计

本文着重讨论计算机辅助设计RLL码的具体实现问题.该过程首先根据RLL码两个参数d,k确定一个编码率p/q,然后构造出RLL码的高阶状态转换-输出符号矩阵并求相应的近似特征向量.最后通过状态分裂和状态归并过程,即可得到一个满足d,k约束的编码输出表.其中状态转换-输出符号矩阵为实现计算机辅助设计RLL码提供了有效的数据结构,而且要优化RLL码编码器,就必须有选择地进行状态分裂.     

制作矢量轮廓字库

本文介绍了一种如何从常见的点阵字库制作矢量轮廓字库的算法。算法采用了预处理、轮廓跟踪和轮廓逼近这三个主要步骤来制作每一个矢量文字，然后采用一种２字节的索引结构加以存储。     

用线性状态观测器重构交流矢量控制系统中的负载扰动

在异步电动机磁场定向数学模型的基础上,给出了恒磁条件下异步电动机的解耦模型及传动系统的线性状态方程,并提出了重构负载扰动转矩的线性状态观测器。利用重构的负载转矩在矢量控制系统中进行扰动前馈,明显地提高了系统的抗负载扰动能力,从而为进一步提高矢量控制系统的动态性能开辟了一条有效的途径。     

宜昌地区下寒武统沉积环境分析

宜昌地区早寒武世除天柱山期(南部和西部称岩家河期)岩性变化较大外,其他依次沉积了以黑色炭质页岩夹灰岩、细碎屑岩、石灰岩、白云岩等为主的岩性组合,反映了中扬子区岩性四分的特点。晚震旦世灯影末期区内是一个东高西低、东北高西南低的浅海沉积区。在此背景上,早寒武世早期(即天柱山期)遭受了短暂而快速海进之后,依次经历了浅海盆地边缘、斜坡带、浅海陆棚及潮坪等沉积环境的缓慢演化,显示了有规律的海退序列。在区内天柱山组、岩家河组及水井沱组等均首次发现了重力流沉积;在水井沱组下部炭质页岩所夹的薄层粉晶云岩中,首次发现了沉积型重晶石富集层。根据区内天柱山期为浅海斜坡带(岩家河期为浅海盆地边缘)、水井沱期为浅海盆地边缘至斜坡带等沉积环境判断。天柱山组(岩家河组)与水井沱组之问应为连续沉积、整合接触。图3表1参7     

一种新型微处理器功能验证

文中介绍一种新型微处理器的验证方法，并对相关的一些验证策略进行深入的讨论。     

变频驱动在长运距带式输送机系统的应用

介绍了赵庄煤矿主斜井长运距带式输送机系统的构成,通过变频器在输送系统上的应用实践,表明该变频驱动技术具有先进性。     

黄河下游平滩流量对来水来沙变化的响应

本文探讨了平滩流量对来水来沙的滞后响应规律，发现平滩流量不仅与当年的水沙条件有关，而且与前期水沙条件有关，是一定时期内的水沙条件累积作用的结果。当采用滑动平均值来反应前期水沙条件的累积影响时，得出平滩流量与滑动平均汛期流量和滑动平均来沙系数的相关程度分别在4年和5年时达到最大值。据此建立了能够反映来水流量和来沙系数前期综合影响的平滩流量计算方法，可以用来估算黄河下游主要测站平滩流量随水沙条件的变化。     

Achievement of specificational information usage with true and false feedback in learning a visual relative-mass discrimination task.

Participants' usage of informational variables in learning visual relative-mass discrimination in collisions was tracked by means of PROBIT correlations. Four groups received feedback that was true or accorded with either of three nonspecificational cue variables. A majority in each group adopted the feedback, but several participants defied the false feedback. Unlike in previous research, the fit to data of the relative-mass invariant could not be bettered by post hoc linear combinations of the cues. Discriminability was lower in the use of the invariant. Analytic complexity was rejected as an explanation for discriminability differences. A "smart mechanism" for pickup of the relative-mass invariant was developed as an extension of G. Johansson's (1950/1994) vector model. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Two-tier architecture for unconstrained handwritten character recognition

In this paper, we propose an approach that combines the unsupervised and supervised learning techniques for unconstrained handwritten numeral recognition. This approach uses the Kohonen self-organizing neural network for data classification in the first stage and the learning vector quantization (LVQ) model in the second stage to improve classification accuracy. The combined architecture performs better than the Kohonen self-organizing map alone. In the proposed approach, the collection of centroids at different phases of training plays a vital role in the performance of the recognition system. Four experiments have been conducted and experimental results show that the collection of centroids in the middle of the training gives high performance in terms of speed and accuracy. The systems developed also resolve the confusion between handwritten numerals.