基于HMM-SVR的船舶动力设备故障模式识别与状态预测研究

船舶动力设备因故障监测信号样本少、变化缓慢且数据特征呈非线性,使得设备故障模式的准确识别和状态预测比较难。鉴于此,文章研究了基于隐马尔科夫模型的故障模式识别方法,利用该模型将微弱变化的信号特征转换为变化较大的对数似然概率对故障模式实现有效识别。在此基础上进一步提出基于HMM-SVR的设备状态预测模型,将遗传算法用于支持向量回归模型参数寻优,并结合隐马尔科夫模型,实现对设备状态的预测。对船用柴油机进行仿真,结果表明上述模型具有较高的识别率,能准确预测船舶动力设备的当前状态。     

Spatiotemporal traffic forecasting: review and proposed directions

This paper systematically reviews studies that forecast short-term traffic conditions using spatial dependence between links. We extract and synthesise 130 research papers, considering two perspectives: (1) methodological framework and (2) methods for capturing spatial information. Spatial information boosts the accuracy of prediction, particularly in congested traffic regimes and for longer horizons. Machine learning methods, which have attracted more attention in recent years, outperform the naïve statistical methods such as historical average and exponential smoothing. However, there is no guarantee of superiority when machine learning methods are compared with advanced statistical methods such as spatiotemporal autoregressive integrated moving average. As for the spatial dependency detection, a large gulf exists between the realistic spatial dependence of traffic links on a real network and the studied networks as follows: (1) studies capture spatial dependency of either adjacent or distant upstream and downstream links with the study link, (2) the spatially relevant links are selected either by prejudgment or by correlation-coefficient analysis, and (3) studies develop forecasting methods in a corridor test sample, where all links are connected sequentially together, assume a similarity between the behaviour of both parallel and adjacent links, and overlook the competitive nature of traffic links.     

A copula-based approach for estimating the travel time reliability of urban arterial

Estimating the travel time reliability (TTR) of urban arterial is critical for real-time and reliable route guidance and provides theoretical bases and technical support for sophisticated traffic management and control. The state-of-art procedures for arterial TTR estimation usually assume that path travel time follows a certain distribution, with less consideration about segment correlations. However, the conventional approach is usually unrealistic because an important feature of urban arterial is the dependent structure of travel times on continuous segments. In this study, a copula-based approach that incorporates the stochastic characteristics of segments travel time is proposed to model arterial travel time distribution (TTD), which serves as a basis for TTR quantification. First, segments correlation is empirically analyzed and different types of copula models are examined. Then, fitting marginal distributions for segment TTD is conducted by parametric and non-parametric regression analysis, respectively. Based on the estimated parameters of the models, the best-fitting copula is determined in terms of the goodness-of-fit tests. Last, the model is examined at two study sites with AVI data and NGSIM trajectory data, respectively. The results of path TTD estimation demonstrate the advantage of the proposed copula-based approach, compared with the convolution model without capturing segments correlation and the empirical distribution fitting methods. Furthermore, when considering the segments correlation effect, it was found that the estimated path TTR is more accurate than that by the convolution model.     

大丰港海域挟沙力关系式分析及应用

基于大丰港附近海域实测水文泥沙资料,对目前国内应用较为广泛的5种挟沙力关系式在大丰港附近海域进行应用计算,在此基础上,采用集中系数法和偏离系数法对计算结果进行对比分析。结果表明:河口水流挟沙力公式能较好地反映大丰港海域的水流挟沙情况,并拟合得到适用于大丰港海域的挟沙力关系式,将该公式应用于大丰港实际围垦工程中,通过建立二维水动力泥沙数学模型,计算得到的围垦后垦区海域年冲淤变化趋势,与数值模拟结果基本相同,表明该公式在大丰港海域具有良好的适用性。     

基于小波和仿射不变矩特征融合的舰船型号识别

针对不变矩对仿射形变目标描述的不足,为提高舰船型号的识别精度,提出一种基于小波和仿射不变矩特征融合的舰船型号识别方法.首先对二值舰船图像进行归一化处理,并分别提取归一化舰船图像的小波矩特征值和仿射不变矩特征值;然后通过计算样本特征均值与标准差的比值,选择出鲁棒性好、稳定性高的特征,通过归一化方法进行融合;最后构造五类舰船的样本集,采用支持向量机(SVM)作为分类器识别测试样本的型号,分析不同矩特征、样本集大小、SVM参数、本文方法对识别精度、稳定性的影响.实验结果表明,文中给出的算法提高了识别精度,并且在训练样本集较小时仍能获得88%以上的识别率.     

基于三电平逆变器的永磁同步电动机直接转矩控制新方法

在永磁同步电动机数学模型和三电平空间矢量调制法的基础上,提出一种基于三电平逆变器拓扑结构的永磁同步电机直接转矩控制新方法。该方法采用bang-bang控制器对电机进行控制,通过新型合成矢量开关表对固定合成矢量的选取,有效地抑制了三电平逆变器直流侧支撑电容中点电位波动、限制了输出电压变化率dv/dt并降低了开关损耗,同时系统还具有良好的稳态响应特性及动态响应特性。通过理论分析与仿真结果证明了该控制方法的可行性。     

基于AIGA-WLSSVM的埋地管道腐蚀速率预测方法

为了降低埋地管道腐蚀影响因素之间的复杂相关性,提高腐蚀预测精度,文中提出一种基于自适应免疫遗传算法-加权最小二乘支持向量机(AIGA-WLSSVM)的埋地管道腐蚀速率预测建模方法,并采用AIGA优化模型参数,进一步提高模型的学习能力和稳定性。最后通过实例分析验证了AIGA-WLSSVM建模方法在埋地管道腐蚀速率预测中的可行性和有效性,为埋地管道的检修与更换提供参考。     

基于滑模变结构的船用永磁同步电机直接转矩控制

电力推进在船舶动力系统中应用得越来越多,本文对船用永磁同步电机直接转矩控制进行仿真研究.针对传统永磁同步电机直接转矩控制中存在的磁链和转矩脉动较大,逆变器开关频率不稳定等问题,将滑模变结构控制策略引入.设计转矩和磁链2个滑模控制器,取代传统直接转矩控制中的2个滞环比较器,再运用空间矢量脉宽调制方法,控制电机运行.仿真结果表明这种控制策略能有效减小传统直接转矩控制存在的磁链和转矩脉动,实现逆变器开关频率恒定,并且对系统参数变化及外界扰动具有鲁棒性强的优点.     

Apoptin原核表达载体的构建及在大肠杆菌中的表达

目的构建Apoptin的原核表达载体,并制备抗原物质Apoptin融合蛋白。方法在获得Apoptin融合基因的基础上,成功构建了Apoptin的高效原核表达载体pET-28a( )-Apoptin,将该质粒转化至大肠杆菌E.coliBL21(DE3)受体菌中,以IPTG对其进行诱导表达,聚丙烯酰胺凝胶电泳分析目的蛋白。结果转化有Apoptin的原核表达载体pET-28a( )-Apoptin的大肠杆菌E.coliBL21(DE3)经IPTG诱导后,经SDS-PAGE分析,在相对分子质量约17 000的位置出现目的蛋白条带,大小与Apoptin融合蛋白一致。结论Apoptin原核表达载体pET-28a( )-Apoptin能够表达出Apoptin融合蛋白,为进一步的Apoptin研究和制备Apoptin抗体奠定了基础。     

NT4-Ant-Shepherdin[79-87]融合基因重组载体的构建

目的设计构建携带NT4-Ant-Shepherdin[79-87]的cDNA融合基因的重组载体,为针对Survivin的靶向治疗奠定基础。方法应用非对称引物/模板法、PCR技术制备NT4-Ant-Shepherdin[79-87]cDNA片断,连接于pGEM-T-Easy载体,经克隆测序、酶切后与PBV220/NT4质粒连接;转化感受态细胞E.coliDH5α,亚克隆获得NT4-Ant-Shepherdin[79-87]融合基因。结果克隆出Ant-Shepherdin[79-87]基因,经酶切及测序证实结果正确;连接PBV220/NT4,经克隆、酶切,琼脂糖凝胶电泳证实获得321bp的NT4-Ant-Shepherdin[79-87]目的基因片断。结论通过分子生物学技术成功构建了携带NT4-Ant-Shepherdin[79-87]融合基因的重组载体,为进一步研究针对Survivin的靶向抗肿瘤作用奠定了基础。