基于网络环境的计算机资料室工作

随着信息技术的迅速发展,基于网络的信息资源共享已成为信息化社会的基本特征。数字化图书馆和电子期刊网的出现,正在改变着人们的阅读习惯,丰富的信息资源和跨时空的浏览方式彻底颠覆了传统的图书馆、资料室的服务模式。同时,数字化图书馆也给资料室管理模式创新带来了新的契机。分析了院系资料室面临的挑战与机遇,探讨了新形势下计算机资料室工作,提出了利用信息技术,改革服务方式,拓展服务内容,提高服务水平,提供有特色的专业服务。     

珠钢滤波-无功补偿优化调整方案

本文主要论述了珠钢滤波-无功补偿优化调整方案在生产中的应用。     

生物除臭滤池在污水厂污泥处理段的除臭效能

对采用天然有机滤料和合成无机滤料的生物除臭滤池在加拿大多伦多市Ash-bridge’s Bay污水厂污泥处理站的干化和转运段的应用效果进行了分析。详细介绍了污泥脱水工艺、臭气的产生及其组分，进而对软木树皮滤料和矿物合成永久性滤料的除臭效果进行了比较。还讨论了一些具体的运行经验，并采用动态模型对不同条件下的运行数据进行了分析。     

数字电位器的应用技术

从应用的角度出发，介绍合理设计应用电路和合理选择数字电位器的种类与型号，来克服数字电位器普遍存在的主要缺点的方法。文章具有较强的针对性和可操作性，对数字电位器的应用实践具有指导作用。     

星上多载波解调技术研究

新一代具有星上处理的卫星将急剧降低地球站的复杂度,星上多载波解调(MCD)技术就是在星上分离出各个信道然后再独立解调。研究了星上多路信号即FDMA/QPSK信号的多载波技术,这是一种重要的星上处理技术,它包括频分多路信号的FFT分路算法、频偏估计、多路QPSK信号的全数字化解调算法和解调算法中的位定时恢复方法等。对主要的关键技术进行了计算机仿真。     

Optimal and self‐tuning fusion Kalman filters for discrete‐time stochastic singular systems

Based on the optimal fusion estimation algorithm weighted by scalars in the linear minimum variance sense, a distributed optimal fusion Kalman filter weighted by scalars is presented for discrete‐time stochastic singular systems with multiple sensors and correlated noises. A cross‐covariance matrix of filtering errors between any two sensors is derived. When the noise statistical information is unknown, a distributed identification approach is presented based on correlation functions and the weighted average method. Further, a distributed self‐tuning fusion filter is given, which includes two stage fusions where the first‐stage fusion is used to identify the noise covariance and the second‐stage fusion is used to obtain the fusion state filter. A simulation verifies the effectiveness of the proposed algorithm. Copyright © 2008 John Wiley & Sons, Ltd.     

TFM调制及其改进技术的研究

通过对TFM调制技术的研究,修正了其中预调制滤波器冲击响应波形g(f)在时间轴上的位置及其频谱G(w)的表达式.然后将TFM基带信号进行近似,即对TFM的相位路径进行进一步的控制,得到一个改进的TFM调制技术.改进后的TFM调制技术和原TFM调制技术相比,频带压缩能力提高了一倍,误码性能不变,但会出现3 dB的寄生调幅,需要提高发射机的发射功率.     

A novel distributed approach to robust fault detection and identification

In this paper, we propose a novel distributed robust fault detection and identification (RFDI) scheme for a class of nonlinear systems. Firstly, a detection and identification estimator—robust fault tracking approximator (RFTA) is designed for online health monitoring. A novel feature of the RFTA is that it can simultaneously detect and accurately identify the shape and magnitude of the fault and disturbance. Moreover, it takes less online training time compared with the traditional neural network based fault diagnosis schemes. For some distributed systems, a network of distributed estimators is constructed where the RFTA is embedded into each estimator. Then we use consensus filter to filter the outputs of each estimator. One of the most important merits of the consensus filter is that its outputs can dramatically improve the accuracy of fault detection and identification. Next, the stability of the distributed RFDI scheme is rigorously investigated. Finally, two numerical examples are given to illustrate the feasibility and effectiveness of the proposed approach.     

Kernel Spectral Matched Filter for Hyperspectral Imagery

In this paper a kernel-based nonlinear spectral matched filter is introduced for target detection in hyperspectral imagery, which is implemented by using the ideas in kernel-based learning theory. A spectral matched filter is defined in a feature space of high dimensionality, which is implicitly generated by a nonlinear mapping associated with a kernel function. A kernel version of the matched filter is derived by expressing the spectral matched filter in terms of the vector dot products form and replacing each dot product with a kernel function using the so called kernel trick property of the Mercer kernels. The proposed kernel spectral matched filter is equivalent to a nonlinear matched filter in the original input space, which is capable of generating nonlinear decision boundaries. The kernel version of the linear spectral matched filter is implemented and simulation results on hyperspectral imagery show that the kernel spectral matched filter outperforms the conventional linear matched filter.     

基于可重构FPGA技术的自适应FIR滤波器的实现

有限冲激响应(FIR)滤波器设计遇到的难题是滤波要进行大量乘法运算,即使是在全定制的专用集成电路中也会导致过大的面积与功耗.对于用硬件实现系数是常量的专用滤波器,可以通过分解系数变为应用加、减和移位而实现乘法.FIR滤波器的复杂性主要由用于系数乘法的加法器/减法器的数量决定.而对于自适应FIR滤波器,大多数场合下可用数字信号处理器(DSP)或CPU通过软件编程的方法来实现,但是对于要求高速运算的场合,VLSI实现是很好的选择.基于这一考虑,可以用符号数的正则表示(CSD)码表示系数, 再利用可重构现场可编程门阵列(FPGA)技术实现.可重构结构的应用,能保证系统的其余部分同时处于运行状态时实现FIR滤波器系数的更新.文中利用CSD码和可重构思想,提出了用FPGA实现自适应FIR滤波器的一种方案.