AT89C2051芯片在车载产品中的应用

本文论述了采用单片机技术研制成功的泊车用超声波测距仪的基本原理,用AT89C2051作为控制器的实现方案,以及采用软件校正的方法,提高了测量精度和整机的可靠性.     

IDS告警融合与关联模型的研究

入侵检测是保障网络安全的重要手段,针对现有入侵检测系统中告警数量多、协调性差等问题,论文提出了一种具有告警融合与关联功能的告警处理系统模型,该模型冗余告警量少、整体检测能力强,并能进行攻击企图的预测,能有效提高入侵检测的效率,有助于进一步增强网络的健壮性。     

DISPERSION COMPENSATION CFBG IN100km 10Gbit／s RECIRCULATING SYSTEMS

A 10Gbit/s recirculating system is configured with Chirped Fiber Bragg Grating (CFBG) for the dispersion compensation. For the first time, the transmission distance in the loop reaches 1000km with bit error rate of 10-9. The effect of the group delay ripple of the fiber grating is also investigated in the recirculating systems, and it is shown that the transmission distance is limited to 4 cycles (4×167.1km ) in the loop with the power penalty fluctuation below 1.0dB. Thus the group delay ripple should be reduced to allow for the wavelength drift of±5GHz. At the end of this letter, the principles are given for designing long haul recirculating systems with dispersion compensation CFBG.     

基于压电陶瓷动态信息的结构裂纹识别方法

利用压电陶瓷的动态特性和压电系统的动态信息,对铝梁的裂纹损伤进行了分析研究。随着梁裂纹尺寸的增加,压电陶瓷片的导钠幅值下降,且系统固有频率减小。利用导钠幅值的变化和系统的动态信息,可以对裂纹的位置和尺寸大小进行识别。实验证明该方法的有效性。     

差分跳频通信系统的特点及性能

文章介绍了一种新型的短波跳频通信技术——差分跳频,分析了差分跳频技术区别于常规跳频技术的主要特点。针对按序列检测的信号接收方法,对差分跳频通信系统在AWGN信道下的性能进行了理论分析,同时做出相应的计算机仿真,证实了差分跳频通信技术和按序列检测方法的结合,使通信系统在AWGN信道下的性能得到了比较显著的提升。     

含氮金刚石结构中Jahn-Teller畸变效应的计算

研究了由J.Bourgoin和M.Lannoo提出的半导体内Jahn-Teller畸变效应的计算理论。应用Bourgoin和Lannoo的方法研究了含中心氮杂质金刚石结构的Jahn-Teller畸变问题,计算了34C+N簇团的T2模耦合沿%111&晶向的Jahn-Teller畸变效应,结果与实验相符。     

基于积分原理的低功耗超声波流量检测方法

文章通过对时差式超声波流量检测方法的分析，提出了基于过零检测技术及积分法的信号处理方法，避免了繁琐的信号处理，降低了整机功耗。结合工作时序的设计，提出了实现低功耗超声波液体流量检测的措施。     

光纤光栅感温火灾探测报警系统在原油罐区的应用

通过对现有罐区火灾自动报警技术的认真考察、对比,在石西集中处理站原油罐区安装了光纤光栅感温火灾探测报警系统.该报警系统的传感信号是光的波长变化,传感器及其探测系统是靠光缆连接而进入被测现场,它具有高安全性、高可靠性、高绝缘性、高抗电磁干扰性、防潮耐蚀、寿命长等其他传统火灾探测报警系统无法比拟的优越性能.阐述了其系统构成和运行效果.该系统在石西集中处理站运用效果良好.     

相关条件下基于N-P准则的分布式检测理论研究

本文针对并行网络、二元局部判决的情况,研究了在相关条件下基于N-P准则的分布式检测融合算法,给出了在联合概率密度已知和未知两种情况下的最优融合规则的理论推导及相应的解决方案,并在实验仿真的基础上,得到了部分有益的结论。     

A possibilistic clustering approach to novel fault detection and isolation

In this paper, a new approach for fault detection and isolation that is based on the possibilistic clustering algorithm is proposed. Fault detection and isolation (FDI) is shown here to be a pattern classification problem, which can be solved using clustering and classification techniques. A possibilistic clustering based approach is proposed here to address some of the shortcomings of the fuzzy c-means (FCM) algorithm. The probabilistic constraint imposed on the membership value in the FCM algorithm is relaxed in the possibilistic clustering algorithm. Because of this relaxation, the possibilistic approach is shown in this paper to give more consistent results in the context of the FDI tasks. The possibilistic clustering approach has also been used to detect novel fault scenarios, for which the data was not available while training. Fault signatures that change as a function of the fault intensities are represented as fault lines, which have been shown to be useful to classify faults that can manifest with different intensities. The proposed approach has been validated here through simulations involving a benchmark quadruple tank process and also through experimental case studies on the same setup. For large scale systems, it is proposed to use the possibilistic clustering based approach in the lower dimensional approximations generated by algorithms such as PCA. Towards this end, finally, we also demonstrate the key merits of the algorithm for plant wide monitoring study using a simulation of the benchmark Tennessee Eastman problem.