针对星载FPGA配置数据的高可靠存储设计

为了提高星载SRAM型FPGA在轨应用的可靠性,星载SRAM型FPGA在轨重构系统需要提高FPGA配置数据的存储可靠性。对配置数据存储方法进行了研究,提出了一种“三模+巡检”的复合式存储设计方法及相关系统实施方案。采用Markov模型,结合星上设备在轨运行时出现的永久性错误、暂时性错误以及修复率的影响,对“三模+巡检”的复合式存储设计进行可靠度分析,给出了系统可靠度微分方程组。通过Matlab仿真分析了永久性错误率、修复率等因素对可靠度的影响。经过仿真分析,“三模+巡检”的复合式存储系统在轨可靠度大于0.99, 进一步证明其可靠性明显优于传统三模冗余的存储方式,满足在轨可靠性要求。     

A comparison study of support vector machines and hidden Markov models in machinery condition monitoring

Condition classification is an important step in machinery fault detection, which is a problem of pattern recognition. Currently, there are a lot of techniques in this area and the purpose of this paper is to investigate two popular recognition techniques, namely hidden Markov model and support vector machine. At the beginning, we briefly introduced the procedure of feature extraction and the theoretical background of this paper. The comparison experiment was conducted for gearbox fault detection and the analysis results from this work showed that support vector machine has better classification performance in this area.     

IEEE802.11e无线局域网中的接入延迟分析模型

IEEE802.11e标准为各个服务类别制定了不同的信道竞争参数数值,使得无线局域网具备了业务区分能力。标准中所提出的增强型分布式接入方式主要包括虚拟碰撞、节点回退、最小竞争窗口以及仲裁帧间隔等机制。本文建立三维离散马尔可夫模型分析队列在接入过程中的竞争行为,进而准确估计各个队列的接入延迟。仿真结果表明,该模型能够准确地描述资源竞争情况,接入延迟计算法方法可根据不同系统参数精确地计算接入延迟性能。     

基于灰色Markov链配送中心物流量的预测

准确有效地预测配送中心的物流量 ,成为配送中心管理与规划的重要部分。作者根据配送中心物流量的观测数据 ,基于GM( 1,1)模型和Markov链原理 ,提出用于配送中心物流量变化趋势预测的灰色离散随机过程模型     

Common causes and the direction of causation

Is the common cause principle merely one of a set of useful heuristics for discovering causal relations, or is it rather a piece of heavy duty metaphysics, capable of grounding the direction of causation itself? Since the principle was introduced in Reichenbach’s groundbreaking work The Direction of Time (1956), there have been a series of attempts to pursue the latter program—to take the probabilistic relationships constitutive of the principle of the common cause and use them to ground the direction of causation. These attempts have not all explicitly appealed to the principle as originally formulated; it has also appeared in the guise of independence conditions, counterfactual overdetermination, and, in the causal modelling literature, as the causal markov condition. In this paper, I identify a set of difficulties for grounding the asymmetry of causation on the principle and its descendents. The first difficulty, concerning what I call the vertical placement of causation, consists of a tension between considerations that drive towards the macroscopic scale, and considerations that drive towards the microscopic scale—the worry is that these considerations cannot both be comfortably accommodated. The second difficulty consists of a novel potential counterexample to the principle based on the familiar Einstein Podolsky Rosen (EPR) correlations in quantum mechanics.     

基于小波包与隐马尔可夫的矿井提升机主轴故障诊断

为解决当前矿井提升机主轴故障数据提取困难且诊断方法存在易受干扰、误差大、准确度低等缺点,设计了基于小波包与隐马尔可夫(HMM)的矿井提升机主轴故障诊断模型。该模型预先把主轴振动信号用小波包分解来获取小波包能量,再把高能量频带CEEMD分解,选取相关系数满足条件的IMF分量完成信号重构,通过重构信号来获得特征参数并构建特征向量,然后对每种故障完成HMM训练,构建HMM故障识别库,并把测试样本送入库中完成测试,从而测试模型的准确度。测试数据表明了基于小波包与HMM的故障诊断模型,准确度高、误差小、抗干扰能力强,比较适用于故障诊断。     

关于齐次树指标可列马氏链的若干极限性质

研究了齐次树指标可列马氏链的一类强极限定理。作为推论。得到了齐次树指标可列马氏链关于状态和状态序偶出现频率的强大数定律。     

基于CHMM的齿轮箱状态识别研究

针对离散隐Markov模型(HMM)在状态识别中的不足,结合齿轮箱全寿命实验数据,研究了基于连续隐Markov模型(CHMM)的状态识别方法。建立了基于齿轮箱原始振动信号的CHMM状态识别框架,提出了基于K均值算法和交叉验证相结合的状态数优化方法,通过计算待确定观测数据的极大似然概率值来确定齿轮箱当前状态。结果表明,用原始振动信号作为CHMM的输入可以实现状态识别,验证了模型的有效性,为齿轮箱基于状态的维修提供了科学依据。     

基于Markov链的锅炉热负荷预测方法

基于Markov链的锅炉热负荷预测方法,提出多参数预测调控模型。根据室外温度,对日均供水温度进行Markov链预测,结合日均燃煤量、鼓风机频率、引风机频率等参数的状态空间,进行复合概率预测。具体应用时,将调控量的大概率最低值作为起点,根据监测量变化,优化调节。试验结果表明:在居民室温达标范围内,日均煤耗量较模型应用前降低约8%;日均供水温度降低约7%,燃煤投放准确率及调控精确率有效提升实现了锅炉热负荷的精细调控。     

基于二维HMM的中文离线签名认证系统

介绍了一种基于隐马尔可夫模型的中文离线签名认证系统。在分析中文离线签名特点的基础上,提出了一种简单有效的特征抽取方法;使用二维HMM对每个签名者的签名建模,并对实际应用中训练样本不足、字体随时间变化等因素带来的系统识别率下降的问题给出了解决方法。实验结果显示该系统取得了令人满意的效果,可以用来辅助人类专家进行签名鉴定。