基于DDC楼宇智能控制系统的设计

将楼宇中的电力、照明、空调、给排水、保安、通信和广播等多方面的设备通过现场总线联接进行控制与管理是楼宇智能化的一个主要的发展方向,本设计利用对上位机和下位机的软硬件系统的设计来实现楼宇智能化控制,达到预期的效果。

Abstract：

Building the power, lighting, air conditioning, plumbing, security, communications and broadcasting, and many other devices via field bus connection for intelligent building control and management is a key to the development of the main, this design is th     

DII COE研究与分析

研究和分析了美国国防信息基础设施公共操作环境(DIICOE),详细描述了其产生的背景、发展状况、概念、结构、内容和意义。     

共享数据工程(SHADE)体系结构及实现描述

文章在深入研究DIICOE的基础上,描述了DIICOE中共享数据工程(SHADE)的体系结构及实现方式。     

计算机犯罪侦查途径的探讨

近年来,计算机犯罪案件呈迅速增长趋势,为有效控制和侦破此类案件,急需寻找一套行之有效的侦查办案途径.归纳各种犯罪手段,分别针对单机犯罪、专用网络犯罪和互联网犯罪,提出三种解决方案.     

计算机犯罪侦查途径的探讨

近年来计算机犯罪案件呈迅速增长趋势,为有效控制和侦破此类案件,急需寻找一套行之有效的侦查办案途径.归纳各种犯罪手段,分别针对单机犯罪、专用网络犯罪和互联网犯罪,提出三种解决方案.     

Surface tension calculation of the Ni3S2---FeS---Cu2S mattes

The surface tension of the Ni₃S₂-FeS-Cu₂S ternary mattes have been calculated from those of the Ni₃S₂-FeS·FeS-Cu₂S and Cu₂S-Ni₃S₂ pseudo-binary boundary systems by using geometric models (Kohler and Toop model) and a general solution model (Chou model). Taking account of the experimental accuracy of ±2.5%, the respective calculated results are in agreement with the experimental data. At the same time, surface tensions of molten Ni₃S₂-FeS-Cu₂S mattes and their pseudo-binary boundary systems are predicted based on Butler's original treatment with great attention to the model parameter β. The predicted results also agree with the experimental data. Therefore, the resulting iso-surface tension curves in molten Ni₃S₂-FeS-Cu₂S mattes at 1473K, especially calculated by using Kohler, Toop and Chou models, are generally acceptable.     

CAS-LIBB微束计算机控制系统运行与改进

作为辐射生物学发展的前沿,微束已经成为当今世界上用来研究低剂量辐射生物效应的一个强有力手段.单离子微束装置是一种将离子束斑的直径限制在微米量级范围内,并能将预定数目的离子精确注入到细胞中某一指定位置的辐射装置.结合一套自行开发的控制程序,CAS-LIBB微束装置的计算机控制系统可以实施对生物样品的精确定量和定点辐射.本文首先给出了装置定量和定点辐射的精度测定结果,该精度完全能够胜任细胞-亚细胞尺度的精确辐射.接下来详细探讨了控制系统的改进工作,最后对控制系统进行了展望.     

A new criterion of delay-dependent asymptotic stability for Hopfield neural networks with time delay.

In this brief, the problem of global asymptotic stability for delayed Hopfield neural networks (HNNs) is investigated. A new criterion of asymptotic stability is derived by introducing a new kind of Lyapunov-Krasovskii functional and is formulated in terms of a linear matrix inequality (LMI), which can be readily solved via standard software. This new criterion based on a delay fractioning approach proves to be much less conservative and the conservatism could be notably reduced by thinning the delay fractioning. An example is provided to show the effectiveness and the advantage of the proposed result.     

Equilibria and their bifurcations in a recurrent neural network involving iterates of a transcendental function.

Some practical models contain so complicated mathematical expressions that it is hard to determine the number and distribution of all equilibria, not mentioning the qualitative properties and bifurcations of those equilibria. The three-node recurrent neural network system with two free weight parameters, originally introduced by Ruiz, Owens, and Townley in 1997, is such a system, for which the equation of equilibria involves transcendental function tanh(x) and its iterates. Not computing coordinates of its equilibria, in this paper, we display an effective technique to determine the number and distribution of its equilibria. Without full information about equilibria, our method enables to further study qualitative properties of those equilibria and discuss their saddle node, pitchfork, and Hopf bifurcations by approximating center manifolds.     

A new crossover operator in genetic programming for object classification.

The crossover operator has been considered "the centre of the storm" in genetic programming (GP). However, many existing GP approaches to object recognition suggest that the standard GP crossover is not sufficiently powerful in producing good child programs due to the totally random choice of the crossover points. To deal with this problem, this paper introduces an approach with a new crossover operator in GP for object recognition, particularly object classification. In this approach, a local hill-climbing search is used in constructing good building blocks, a weight called looseness is introduced to identify the good building blocks in individual programs, and the looseness values are used as heuristics in choosing appropriate crossover points to preserve good building blocks. This approach is examined and compared with the standard crossover operator and the headless chicken crossover (HCC) method on a sequence of object classification problems. The results suggest that this approach outperforms the HCC, the standard crossover, and the standard crossover operator with hill climbing on all of these problems in terms of the classification accuracy. Although this approach spends a bit longer time than the standard crossover operator, it significantly improves the system efficiency over the HCC method.