Likelihood‐based dynamic factor analysis for measurement and forecasting

We present new results for the likelihood‐based analysis of the dynamic factor model. The latent factors are modelled by linear dynamic stochastic processes. The idiosyncratic disturbance series are specified as autoregressive processes with mutually correlated innovations. The new results lead to computationally efficient procedures for the estimation of the factors and for the parameter estimation by maximum likelihood methods. We also present the implications of our results for models with regression effects, for Bayesian analysis, for signal extraction, and for forecasting. An empirical illustration is provided for the analysis of a large panel of macroeconomic time series.     

基于FCM聚类算法的MRI脑组织图像分割方法比较研究

目的磁共振成像(magnetic resonance imaging,MRI)对脑组织有较好的成像效果,但噪声、偏移场和部分容积效应(partial volume effect,PVE)的存在,使得全自动分割MRI图像面临一定的困难。模糊C均值(fuzzy C-means,FCM)聚类算法在脑组织分割中得到较广泛研究。本文以存在噪声和偏移场影响的脑MRI图像分割为应用背景,研究了大量相关方法,探讨FCM算法分割脑部图像的改进思想。方法本文主要研究了9种FCM算法的理论基础,并通过脑组织分割实验对各种算法进行了分析。结果比较了不同算法的优劣,给出各类算法直观及定量评价结果。结论偏移场和噪声对脑磁共振图像组织分类质量有明显影响。其中几种方法可以减弱这些不利影响,但由于难以选择合适的参数,其分类效果并不理想。如何合理利用空间信息在未来仍有较大研究价值。     

遗传算法与变邻域搜索混合模型在护士排班中的应用

目的在探讨基于层级管理的护士APN排班模型的基础上,引入具有精确的全局搜索能力的遗传算法与变邻域搜索混合模型来解决护士排班过程中人员层级复杂、不同班次人员需求量不同等问题。方法首先,初始化排班表,然后利用遗传算法对初始化的排班表进行搜索,得到一个初步的排班表,最后利用变邻域搜索优化排班表,得到满足要求的排班表。结果使用该混合模型得到的排班表,能满足每日护士的人数需求和每名护士的工作量要求。同时,基于层级管理,各层级护士分开排班,能降低排班难度,在增加护士人员时,效率仍然较高。结论遗传算法与变邻域搜索的混合模型能解决护士排班中的各种复杂问题,是一种符合实际需求的模型。基于此模型来编制护士排班软件,能提升护理工作效率和满意度,便于对护理人员进行量化考核统计分析。     

A random pattern mixture model for ordinal outcomes with informative dropouts

We extend a random pattern mixture joint model for longitudinal ordinal outcomes and informative dropouts. The patients are generalized to ‘pattern’ groups based on known covariates that are potentially surrogated for the severity of the underlying condition. The random pattern effects are defined as the latent effects linking the dropout process and the ordinal longitudinal outcome. Conditional on the random pattern effects, the longitudinal outcome and the dropout times are assumed independent. Estimates are obtained via the Expectation–maximization algorithm. We applied the model to the end‐stage renal disease data. Anemia was found to be significantly affected by the baseline iron treatment when the dropout information was adjusted via the study model; as opposed to an independent or shared parameter model. Simulations were performed to evaluate the performance of the random pattern mixture model under various assumptions. Copyright © 2015 John Wiley & Sons, Ltd.     

Analysis of transtheoretical model of health behavioral changes in a nutrition intervention study—a continuous time Markov chain model with Bayesian approach

Continuous time Markov chain (CTMC) models are often used to study the progression of chronic diseases in medical research but rarely applied to studies of the process of behavioral change. In studies of interventions to modify behaviors, a widely used psychosocial model is based on the transtheoretical model that often has more than three states (representing stages of change) and conceptually permits all possible instantaneous transitions. Very little attention is given to the study of the relationships between a CTMC model and associated covariates under the framework of transtheoretical model. We developed a Bayesian approach to evaluate the covariate effects on a CTMC model through a log‐linear regression link. A simulation study of this approach showed that model parameters were accurately and precisely estimated. We analyzed an existing data set on stages of change in dietary intake from the Next Step Trial using the proposed method and the generalized multinomial logit model. We found that the generalized multinomial logit model was not suitable for these data because it ignores the unbalanced data structure and temporal correlation between successive measurements. Our analysis not only confirms that the nutrition intervention was effective but also provides information on how the intervention affected the transitions among the stages of change. We found that, compared with the control group, subjects in the intervention group, on average, spent substantively less time in the precontemplation stage and were more/less likely to move from an unhealthy/healthy state to a healthy/unhealthy state. Copyright © 2015 John Wiley & Sons, Ltd.     

基于BP神经网络和遗传算法优化茺蔚子水苏碱提取工艺的研究

【目的】优化茺蔚子中水苏碱的提取工艺。【方法】采用正交设计,应用误差反向后传（BP）人工神经网络建立茺蔚子水苏碱提取时间、乙醇浓度、料液比与盐酸水苏碱含量之间的关系模型,再结合遗传算法优化提取工艺参数。【结果】提取工艺优化参数为：提取时间62 min、乙醇浓度体积分数69%、料液比11倍,网络模型优化结果稍优于正交试验结果。【结论】 BP神经网络结合遗传算法寻优,能较好地实现茺蔚子水苏碱提取工艺参数的优化,可为解决多维非线性系统的工艺参数优化问题提供崭新而有效的途径。     

基于论文相似网络拓扑结构的聚类方法比较

以R语言中的复杂网络处理包igraph为工具,基于语义相似性算法构建论文相似网络,然后采用四种代表性网络聚类算法（随机游走法、标签传播法、最大模块度法、边介数法）对构建出的网络进行聚类分析。最后结合金标准和网络社团划分评价指标D函数比较四种算法的准确性和稳定性,发现随机游走算法最为卓越,同时明确了复杂网络的预处理也是一个影响聚类效果的重要因素。     

基于肌音信号的步态动作模式识别研究

通过采集腿部肌肉5个通道的肌音信号,利用3层决策树对跑步、上楼、下楼、走路、静止5种步态动作进行模式识别研究。在决策树的第1层和第2层,应用双阈值门限法识别静止和跑步两种步态模式,在第3层,提出基于步态信号的自适应不等长分割算法以及改进的模糊熵算法,利用线性分类器对走路、上楼、下楼进行分类识别。结果表明:双门限阈值法可有效地对静止和跑步进行识别,当采用改进的模糊熵特征时,对走路、上楼、下楼3种步态模式的分类准确率达到了94.87%;而当综合利用近似熵、样本熵和改进的模糊熵3种特征时,其分类准确率达到了98.76%。     

基于混合离散人工蜂群算法的阻塞Flow Shop调度

针对带阻塞的Flow Shop调度问题,以最小化总流水时间为调度目标,提出了一种混合离散人工蜂群（Hybrid Discrete Artificial Bee Colony, HDABC）算法。HDABC算法采用基于NEH和NEH变体初始化,保证种群的质量和多样性。在雇佣蜂阶段采用差分进化策略产生邻域个体;在跟随蜂阶段采用锦标赛选择方法选择个体跟随,并对选择的个体采用优化插入操作产生新的邻域个体。此外,在侦查蜂阶段再一次采用锦标赛选择方法选择个体,并对较好的个体执行破坏重建操作,用产生的新个体代替原来较差的个体。用正交设计方法调节了该算法的参数。通过与其他两个算法的仿真实验结果比较,验证了本文算法的优越性。     

基于水流作用机制的人工鱼群算法

针对人工鱼群算法（AFSA）在函数优化问题中易陷入局部极值和求解精度较低的缺点,提出了一种在基本人工鱼群算法中引入水流作用机制的改进方案。通过水流作用机制中的持续性水流和周期性水流对鱼群施加的有益影响来改进原有算法。持续性水流影响鱼群的体力变化从而控制视野和步长参数的自适应调整以提高求解精度;周期性水流冲击鱼群并改变部分鱼的位置,从而保持鱼群的种群多样性以利于全局收敛。仿真实验结果表明：本文的改进算法具有更高的求解精度和更好的全局搜索性能,并验证了算法的有效性。