二维浮体大幅运动水动力物面非线性时域解

基于时域自由面格林函数，建立了线性自由面条件下计及物体大幅运动瞬时湿表面条件的非线性水动力理论计算模型，重点研究了时域分布源在水线上的特性，指出了传统分布源法在处理浮体与自由面外张斜交的计算失败的原因，并提出了新的时域分布源数值计算模型，对实船计算表明，这种新模型简便有效。     

基于模型变换的系统参数快速辨识方法

研究了系统参数辨识的一种间接方法，该方法将要估计的系统模型转换为一种容易估值的高阶系统模型，然后再将高阶模型降阶得到所要的模型参数。证明了该方法与预报误差法具有相同的渐近和统计性能。仿真结果表明该方法比预报误差法具有更高的计算效率。     

野外大口径管道焊缝X射线检测机器人

利用管内移动载体拖动周向Ｘ射线探伤机，借助ＣＣＤ摄像系统确定焊缝并对正拍片，实现单壁成像，该机器人结构紧凑，工作效率高，稳定可靠，是野外管首宛损检测的理想设备。     

智能结构的构造识别有限元分析

研究了二维结构的构造识别问题,采用有限元方法与正则化最小化过程相结合的方法。并给出了二个算例,表明该方法具有较好的收敛性与稳定性。     

实用半导体激光电源的研制

分析了半导体激光器的特性及损坏的各种原因，介绍了一种实用的半导体激光电源，该电源已成功地应用于我们研制的光纤测温仪中。     

语音合成库的建立与汉语数码语音识别途径

利用语音合成芯片 TC8830 AF的 CPU控制方式下的 DMA功能 ,主控微机可共享语音数据存储器 SRAM资源 ,对语音数据直接操作 ,实现语音数据的快速处理 ,对语音合成库的建设机理进行了深入探讨 ,并提出了一种实用的汉语数码语音识别方法 .实践结果表明本系统适用于语音合成产品的开发     

脉冲多弧离子源引弧效率的定量检测与过程控制

介绍了一种应用于脉冲多弧离子源镀膜过程控制的电子监控装置．用该装置定量检测脉冲多弧离子源的引弧效率，克服了因停弧带来的膜厚误差的缺点．本文主要叙述了该装置的工作原理及有关电路．     

A Spurious Equilibria-free Learning Algorithm for the Blind Separation of Non-zoer Skewness Signals

We present a new learning algorithm for the blind separation of independent source signals having non-zero skewness (the 3rd-order cumulant) (the source signals have non-symmetric probability distribution.), from their linear mixtures. It is shown that for a class of source signals whose probability distribution functions is not symmetric, a simple adaptive learning algorithm using quadratic function (f(x)=x²) is very efficient for blind source separation task. It is proved that all stable equilibria of the proposed learning algorithm are desirable solutions. Extensive computer simulation experiments confirmed the validity of the proposed algorithm.     

On Pattern Frequency Occurrences in a Markovian Sequence

Consider a given pattern H and a random text T generated by a Markovian source. We study the frequency of pattern occurrences in a random text when overlapping copies of the pattern are counted separately. We present exact and asymptotic formulae for moments (including the variance), and probability of r pattern occurrences for three different regions of r , namely: (i) r=O(1) , (ii) central limit regime, and (iii) large deviations regime. In order to derive these results, we first construct certain language expressions that characterize pattern occurrences which are later translated into generating functions. We then use analytical methods to extract asymptotic behaviors of the pattern frequency from the generating functions. These findings are of particular interest to molecular biology problems (e.g., finding patterns with unexpectedly high or low frequencies, and gene recognition), information theory (e.g., second-order properties of the relative frequency), and pattern matching algorithms (e.g., q -gram algorithms).