基于IP Helper的网络连线状态侦测实践

许多程序需要在了解网络连接状态的情况下,才能进行下一步操作。目前公司内部网络结构复杂,与外部网络间设有多重防火墙,造成网络状态检测的困难的。该文首先分析了原有几种检测网络连接状态的方法,然后针对常用查询检测方式的缺点,提出了利用IP Helper提供的消息响应函数,自动响应Windows消息机制检测网络连接的方法。

基于状态检测的服务器热备方案设计与实现

服务器是构成各类信息系统的重要组成部分。在很多应用场合,为了满足业务的可靠性需求,部署两台甚至多台服务器同时运行,以提高系统的可用等级。尝试使用网络状态检测技术,实现多台服务器之间的热备及故障切换,是一种低成本的服务器热备解决方案。

AN INTELLIGENT TOOL CONDITION MONITORING SYSTEM USING FUZZY NEURAL NETWORKS

Reliable on line cutting tool conditioning monitoring is an essential feature of automatic machine tool and flexible manufacturing system (FMS) and computer integrated manufacturing system (CIMS). Recently artificial neural networks (ANNs) are used for this purpose in conjunction with suitable sensory systems. The present work in Norwegian University of Science and Technology (NTNU) uses back propagation neural networks (BP) and fuzzy neural networks (FNN) to process the cutting tool state data measured with force and acoustic emission (AE) sensors, and implements a valuable on line tool condition monitoring system using the ANNs. Different ANN structures are designed and investigated to estimate the tool wear state based on the fusion of acoustic emission and force signals. Finally, four case studies are introduced for the sensing and ANN processing of the tool wear states and the failures of the tool with practical experiment examples. The results indicate that a tool wear identification system can be achieved using the sensors integration with ANNs, and that ANNs provide a very effective method of implementing sensor integration for on line monitoring of tool wear states and abnormalities.

基于时空信息融合的时序动作定位

时序动作定位任务需要识别出一段长视频中的动作类别以及动作的起止时间,候选区域的选择是影响到识别效果和效率的重要因素。提出一种基于时空特征融合的候选区域提取网络,充分利用视频分割段的时间特征和空间特征来判断是否为候选区域。接着将候选区域输入到训练的CDC网络中进行帧级粒度上的动作分类。最后训练动作状态检测网络,对得到的候选区域进行修补,从而可以得到更为精确的动作发生的时间区域。在THUMOS'14数据集上进行实验,结果证明该方法可以有效地进行未剪辑视频的时序动作定位,相对现有方法达到了较高的精度。