基于预测模式蚁群优化的传感网节能路由机制

面向传感器网络中时间序列数据的时域关联性特征,针对网内数据流量不均衡分布模式所导致的传输能耗漏斗效应问题,提出了基于预测模式的能量感知数据路由机制。机制的设计结合了蚁群优化机理自适应网络状况动态性的优势和预测模型揭示数据流量变化规律的优势,通过将节点负载因子引入蚁群优化算法中启发式因子的构造和局部信息素更新规则的设计,赋予蚂蚁代理在路由解空间探索中预知网络局域能量状况的能力,提高了数据路由构建的自适应性和能量均衡性。实验表明,论文提出的面向预测模式的路由机制,通过引入蚁群优化机理和挖掘数据内涵的时域关联性,有效降低和均衡了数据路由能耗。     

一种提高模糊人脸图片识别率的方法

介绍了一个通过人脸检测程序对模糊人脸图片进行识别的方法：先对图片修改其灰度阈值,以使Haar能够对模糊人脸信息进行快速分辨,再使用0pencv人脸检测程序对模糊人脸图片进行检测。实验结果表明,该方法能够提高对模糊人脸的识别率。     

H．264帧内预测快速算法的研究

H-264视频编解码标准中,帧内预测是很重要的组成部分,它显著提高了压缩率,但增加了计算复杂性。文章研究了采用率失真优化（RDO）技术进行帧内模式选择以及目前常用的快速帧内模式选择方法。     

基于自适应灰色模型的路面使用性能预测

在灰色理论建模的基础上,通过采用预测的最新数据替代最老数据这种方式,建立了自适应灰色模型,并将其应用于路面使用性能预测。能够克服缺少历史数据的不足,同时,与以往的预测模型相比,更加准确,更加符合路面使用性能的变化规律。通过MATLAB编程实现,使得预测更加简便易行。     

利用虚拟仪器构建的注水机组故障预测系统

大型注水机组故障预测系统由于系统复杂,采用传统的开发方式,开发周期长并且硬件系统庞大。虚拟仪器可以通过软件把计算机强大的计算处理能力和仪器硬件的测量、控制能力有机的结合起来。利用PC-DAQ与LabWindows/CVI构建大型旋转机组故障分析系统,减少了系统所需的硬件资源,加快了系统软件的开发速度,实现集成运行环境,使操作人员操作起来更加方便。     

Prediction of vehicle reliability performance using artificial neural networks

Product development is an important but also dynamic, lengthy and risky phase in the life of a new product. The optimisation of the product development phase through extensive knowledge of the involved procedures is believed to reduce the risks and improve the final product quality. Artificial intelligence and expert systems have been used successfully in optimising the development phase of some new products as it will be demonstrated by the first sections of this publication. This paper presents the first module of an expert system, a neural network architecture that could predict the reliability performance of a vehicle at later stages of its life by using only information from a first inspection after the vehicle’s prototype production. The paper demonstrates how a tool like neural networks can be designed and optimised for use in reliability performance predictions. Also, this paper presents an optimisation methodology that enabled the neural network to deal with the limited amount of available training data, common during new product development, and to finally achieve acceptable prediction performance with small error. A case example is presented to demonstrate the methodology.     

A neural network-based approach for dynamic quality prediction in a plastic injection molding process

This paper presents an innovative neural network-based quality prediction system for a plastic injection molding process. A self-organizing map plus a back-propagation neural network (SOM-BPNN) model is proposed for creating a dynamic quality predictor. Three SOM-based dynamic extraction parameters with six manufacturing process parameters and one level of product quality were dedicated to training and testing the proposed system. In addition, Taguchi’s parameter design method was also applied to enhance the neural network performance. For comparison, an additional back-propagation neural network (BPNN) model was constructed for which six process parameters were used for training and testing. The training and testing data for the two models respectively consisted of 120 and 40 samples. Experimental results showed that such a SOM-BPNN-based model can accurately predict the product quality (weight) and can likely be used for various practical applications.     

网格应用程序执行时间预测系统

为了合理利用网格资源和改进应用程序的执行性能,需要对应用程序的执行时间进行实时预测,为任务调度系统和网格用户提供调度依据.本文工作建立了执行时间预测系统,周期性的产生预测信息并转换为统一格式注册到网格信息服务中.本文使用资源映射方法预测应用程序执行时间,并且设计一组实验测试系统性能.实验结果表明,本系统能够低开销,灵敏地预测执行时间,并且预测误差较小.     

暂态混沌神经网络在蛋白质关联图预测中应用研究

蛋白质结构预测是生物信息学的一个主要研究方向,而蛋白质关联图预测是其中的一个重要内容.针对蛋白质关联图预测问题,提出一种暂态混沌神经网络实现方法,所提出的方法具有暂态混沌特性和平稳收敛特性,能有效避免传统Hopfield 神经网络极易陷入局部极值的缺陷.它通过短暂的倒分叉过程,能很快进入稳定收敛状态.仿真结果表明:暂态混沌神经网络解决蛋白质关联图预测问题时,总能收敛到全局最优或几乎接近全局最优,同时具有更高的搜索效率.这种方法预测精度达到0.27,比随机预测器高9倍.