化学事故危害评估

根据化学事故危害的主要影响因素对化学事故进行危害评估,考查了危害纵深、人员杀伤率、危害地域估算、爆炸燃烧事故危害等内容。     

Average consensus in networks of dynamic agents with switching topologies and multiple time-varying delays

In this paper, we discuss average consensus problem in undirected networks of dynamic agents with fixed and switching topologies as well as multiple time-varying communication delays. By employing a linear matrix inequality method, we prove that all the nodes in the network achieve average consensus asymptotically for appropriate communication delays if the network topology is connected. Particularly, several feasible linear matrix inequalities are established to determine the maximal allowable upper bound of time-varying communication delays. Numerical examples are given to demonstrate the effectiveness and the sharpness of the theoretical results.     

One-to-Many and Many-to-One Optical Chaos Communications Using Semiconductor Lasers

A theoretical construction of one-to-many (OTM) and many-to-one (MTO) chaos synchronization communications using semiconductor lasers is presented. One center laser provides strong injections (strong link) to other side lasers, and the side lasers also provide weak injections (weak link) to the center one. Simulation results show that the side lasers synchronize with the center laser through injection-locking. In addition, messages transmitted via the strong links or the weak links experience strong chaos pass filtering, enabling us to realize OTM and MTO communications.     

Tri-Gate Bulk MOSFET Design for CMOS Scaling to the End of the Roadmap

A tri-gate bulk MOSFET design utilizing a low-aspect-ratio channel is proposed to provide an evolutionary pathway for CMOS scaling to the end of the roadmap. 3-D device simulations indicate that this design offers the advantages of a multi-gate FET (reduced variability in performance and improved scalability) together with the advantages of a conventional planar MOSFET (low substrate cost and capability for dynamic threshold-voltage control).     

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.     

Evolving artificial neural network ensembles

Using a coordinated group of simple solvers to tackle a complex problem is not an entirely new idea. Its root could be traced back hundreds of years ago when ancient Chinese suggested a team approach to problem solving. For a long time, engineers have used the divide-and-conquer strategy to decompose a complex problem into simpler sub-problems and then solve them by a group of solvers. However, knowing the best way to divide a complex problem into simpler ones relies heavily on the available domain knowledge. It is often a manual process by an experienced engineer. There have been few automatic divide-and-conquer methods reported in the literature. Fortunately, evolutionary computation provides some of the interesting avenues to automatic divide-and-conquer methods. An in-depth study of such methods reveals that there is a deep underlying connection between evolutionary computation and ANN ensembles. Ideas in one area can be usefully transferred into another in producing effective algorithms. For example, using speciation to create and maintain diversity had inspired the development of negative correlation learning for ANN ensembles, and an in-depth study of diversity in ensembles. This paper will review some of the recent work in evolutionary approaches to designing ANN ensembles.     

基于DSP的电力数据采集平台的设计与研究

介绍了一种用于电力系统的数据采集系统.该系统采用数字信号处理器(DSP)和复杂可编程逻辑器件(CPLD)作为核心芯片,给出了主要部分的电路及其原理、功能,系统中使用CPLD实现基本逻辑,使系统有足够的冗余和灵活性,具有可靠性高、可升级等优点.     

裂缝性油气藏采收率：100个裂缝性油气田实例的经验总结

通过对世界上100个裂缝性油气藏的综合评价，研究储集层及流体本身的性质(包括孔隙度、渗透率、黏度、可动油比例、含水饱和度、润湿性及裂缝分布特征等)和驱动机制及油藏管理战略(优化日产量和采用不同类型的提高采收率技术)对其最终采收率的影响。将裂缝性油气藏分为4类：I类的基质几乎没有孔隙度和渗透率，裂缝是储存空间和流体流动的通道；Ⅱ类的基质有较低的孔隙度和渗透率，基质提供储存空间，裂缝提供流动通道；Ⅲ类(微孔隙)的基质具有高孔隙度和低渗透率，基质提供储存空间，裂缝提供流动通道；Ⅳ类(大孔隙)的基质具有高的孔隙度和渗透率，基质提供储存空间和流动通道，裂缝仅增加渗透率。对26个Ⅱ类油气藏和20个Ⅲ类油气藏的开采历史的研究表明：Ⅱ类油气藏的采收率受水驱强度和最优日产量控制，日产量过高会很容易破坏Ⅱ类油气藏，一些Ⅱ类油气藏如果管理得当，采收率可以很高，不需要二次或三次采油；Ⅲ类油气藏的采收率主要受岩石和流体本身性质的影响，特别是基质渗透率、流体重度、润湿性以及裂缝强度等，不进行二次或三次采油不可能完全开采，往往需要采用一些提高采收率的专门技术。以往将Ⅱ类和Ⅲ类裂缝性油气藏归为一类，认识它们的区别将有助于选择更好的开发策略。     

Perturbation analysis and control of two-class stochastic fluid models for communication networks

This paper uses stochastic fluid models (SFMs) for the control and optimization (rather than performance analysis) of communication network nodes processing two classes of traffic: one is uncontrolled and the other is subject to threshold-based buffer control. We derive gradient estimators for packet loss and workload related performance metrics with respect to threshold parameters. These estimators are shown to be unbiased and directly observable from a sample path without any knowledge of underlying stochastic characteristics of the traffic processes. This renders them computable in online environments and easily implementable for network management and control. We further demonstrate their use in buffer control problems where our SFM-based estimators are evaluated based on data from an actual system.