汽轮机故障诊断的因果网络群智能算法模型

在利用概率因果网络模型进行汽轮机故障诊断过程中,传统的基于简约覆盖理论的求算方法不能直接得到诊断问题的解,而且当故障节点较多或网络层次较多时,存在着"组合爆炸"、计算量呈指数速度增加等问题;对此,基于群智能理论,建立汽轮发电机组故障诊断系统概率因果网络群智能算法模型,给出基本的群运算法则及改进方法,将焦点放在有限空间的诊断推理上,并实现并行处理,很好地解决了传统算法中的问题;最后一个应用实例验证了此方法的优越性及工程实用性。     

基于TMS320F2812的液晶显示模块接口设计

为适应工业应用现场以及智能化仪器仪表前端显示的需要,阐述了TMS320F2812DSP和QH2001液晶显示模块的原理及特性,提出了一种基于高速DSP处理器控制低速液晶显示模块的接口设计方案,并对该方案的硬件接口电路和软件驱动程序进行了设计,同时给出了软、硬件实现的相应电路图、程序流程图以及具体程序段;实验调试表明,设计方案结构简单、成本低、抗干扰能力强、显示性能稳定可靠,达到设计要求,能够有效解决高速DSP对低速LCD的驱动及显示问题,并取得了良好的显示效果,对其它便携式系统显示终端的设计也具有一定参考价值。     

基于形态学多结构元多尺度的自适应边缘检测

数学形态学广泛应用于图像处理和模式识别领域;针对形态学单结构元在边缘检测中边缘信息丢失的问题,提出了用不同方向的结构元素对图像进行多尺度检测的自适应边缘检测方法;首先利用形态学高低帽运算对原始图像进行平滑处理,采用差分最大值确定结构元素的方向,利用形态学运算调整结构元素尺度,改进了数学形态学边缘检测算法;实验结果表明,与传统边缘检测算法相比,该算法在保持图像边缘清晰的同时.有很强的去除噪声能力.     

气象雷达通用测试台的设计与实现

采用自动测试和综合仿真技术,解决了对雷达的多路被测信号的选择和陀螺平台的模拟等问题,适用于部分气象雷达的故障检测和性能测试,实现对雷达天线、收发机和显示器的自动测试;采用高灵敏度温度传感器、过压过流检测等多种保护措施,满足被测雷达对电源的特殊要求;其智能化程度高,操作简便,实用,可以减少维修检测的人为误差,对于提高飞行安全具有及其重要的意义,因此推广应用前景好,经济效益明显,填补了国内气象雷达检测设备的空白。     

基于WINCE的数据采集与监控系统设计与实现

为了更有效地对电力系统进行监控,提出了一种适合于电力系统的嵌入式WINCE的数据采集与监控系统的设计和实现方案;它是对现场数据进行采集,分析并存储,并将监测到的数据返回给监控界面,实现对远程设备的监控;分析了系统的内部体系结构,介绍了各个模块的功能,并完成了数据采集与监控系统的各个部分的软件设计与实现,并通过了各项性能测试;系统的人性化的监控界面和双机热备的加入,提高了系统的实用性、稳定性和安全性,有很好的应用前景。     

基于小波分解和RBF网络的三极管电路故障诊断

在分析现有三极管放大电路故障诊断算法不足的基础上,提出了一种基于模型的小波-RBF网络故障诊断算法。在PSPICE环境下建立三极管常见的故障模型,利用多层小波分解优异的时频特性提取故障特征参数,利用RBF强大的非线性分类能力和快速的收敛特性进行了典型共基极放大电路中三极管的软、硬故障诊断仿真。计算及仿真结果显示,这种故障诊断算法具有诊断速度快、诊断正确率高的特点。     

Learning in an Ambient Intelligent World: Enabling Technologies and Practices

The rapid evolution of information and communication technology opens a wide spectrum of opportunities to change our surroundings into an Ambient Intelligent (AmI) world. AmI is a vision of future information society, where people are surrounded by a digital environment that is sensitive to their needs, personalized to their requirements, anticipatory of their behavior, and responsive to their presence. It emphasizes on greater user friendliness, user empowerment, and more effective service support, with an aim to bring information and communication technology to everyone, every home, every business, and every school, thus improving the quality of human life. AmI unprecedentedly enhances learning experiences by endowing the users with the opportunities of learning in context, a breakthrough from the traditional education settings. In this survey paper, we examine some major characteristics of an AmI learning environment. To deliver a feasible and effective solution to ambient learning, we overview a few latest developed enabling technologies in context awareness and interactive learning. Associated practices are meanwhile reported. We also describe our experience in designing and implementing a smart class prototype, which allows teachers to simultaneously instruct both local and remote students in a context-aware and natural way.     

A DR algorithm based on artificial potential field method

Considering player entity’s motion regularity into DR (Dead Reckoning) algorithm can improve its prediction accuracy in MMOG (Massively Multiplayer Online Games), a novel DR algorithm was proposed to solve this problem in this paper. First the artificial potential field model of player entities is created, and then the acceleration of player entities is weighted with the acceleration produced by the potential field force and the acceleration reckoned by the traditional DR algorithm. In order to calculate the weight, Q-Learning algorithm is used. The experiments show that the method can improve prediction accuracy and reduce the network traffic.     

The asymptotic optimization of pre-edited ANN classifier

The generalization problem of an artificial neural network (ANN) classifier with unlimited size of training sample, namely asymptotic optimization in probability, is discussed in this paper. As an improved ANN network model, the pre-edited ANN classifier shows better practical performance than the standard one. However, it has not been widely applied due to the absence of the related theoretical support. To further promote its application in practice, the asymptotic optimization of the pre-edited ANN classifier is studied in this paper. To help study ANN asymptotic optimization in probability, we gives a review of the previous research works on asymptotic optimization in probability of non-parametric classifier, and grouped the main methods into four classes: two-step method, one-step method, generalization method and hypothesis method. In this paper, we adopt generalization/hypothesis mixed method to prove that pre-edited ANN is asymptotically optimal in probability. Furthermore, a simulation is presented to provide an experimental support for our theoretical work.     

Image Retargeting Using Mesh Parametrization

Image retargeting aims to adapt images to displays of small sizes and different aspect ratios. Effective retargeting requires emphasizing the important content while retaining surrounding context with minimal visual distortion. In this paper, we present such an effective image retargeting method using saliency-based mesh parametrization. Our method first constructs a mesh image representation that is consistent with the underlying image structures. Such a mesh representation enables easy preservation of image structures during retargeting since it captures underlying image structures. Based on this mesh representation, we formulate the problem of retargeting an image to a desired size as a constrained image mesh parametrization problem that aims at finding a homomorphous target mesh with desired size. Specifically, to emphasize salient objects and minimize visual distortion, we associate image saliency into the image mesh and regard image structure as constraints for mesh parametrization. Through a stretch-based mesh parametrization process we obtain the homomorphous target mesh, which is then used to render the target image by texture mapping. The effectiveness of our algorithm is demonstrated by experiments.