基于高维特征选择的跳频电台细微特征识别

将高维特征用于跳频电台细微特征个体识别具有很大优势,为了增强对跳频电台的分类识别能力,需要增加特征类型和维数,提高特征集的表征能力,但同时会引入大量冗余特征,导致分类器计算时间过长,分类正确率降低。为了降低高维特征集维数,首先采用相关性快速过滤特征选择算法,删除高维特征集中的不相关冗余特征,得到最优特征集。然后利用经过参数优化的支持向量机(support vector machine, SVM)分类器进行训练分类。实验表明,所提算法能够对高维特征集进行合理的降维,提高了SVM的分类器的分类性能,在保证分类正确率的基础上,降低了运算量,提高了跳频电台细微特征识别的时效性。     

基于中继选择的星地协作传输系统性能分析

由于星地混合协作网络（hybrid satellite-terrestrial cooperative network,HSTCN）能显著提升卫星通信的服务质量,近年来受到了国内外学者的广泛关注,分析了基于中继选择的星地协作传输系统的性能。首先,在卫星和地面用户之间存在直达链路,并且所有中继节点在采用门限判断译码转发协议辅助卫星通信的情况下,得到用户端经过最大比合并后的输出信噪比（signal-to-noise ratio,SNR）表达式。其次,针对卫星信道服从阴影莱斯分布,而地面信道服从Nakagami-m分布,推导出随机选择和最佳选择两种中继选择策略下系统的中断概率闭合表达式。接着,进一步得到高信噪比条件下中断概率的近似表达式,为分析各种参数对系统性能的影响提供了更加便捷的方法。最后,计算机仿真不仅验证了理论推导的正确性,而且定量分析了协作传输带来的好处,从而为HSTCN技术的实际应用提供理论依据。     

典型陆军部队装备维修作业能力评估系统仿真建模研究

针对陆军部队维修作业仿真评估需求,提出了一套仿真模型设计方案。分析了部队维修作业能力仿真评估原理,进而明确了评估模型需求;给出了维修任务生成、维修保障系统建模方案;结合备件调度和保障设备使用过程,设计了维修保障资源模型。为陆军部队装备维修作业能力评估提供建模基础,用于支撑部队维修作业能力仿真评估系统的设计和应用案例研究。     

Forecasting under model uncertainty: Non-homogeneous hidden Markov models with Pòlya-Gamma data augmentation

We consider finite state-space non-homogeneous hidden Markov models for forecasting univariate time series. Given a set of predictors, the time series are modeled via predictive regressions with state-dependent coefficients and time-varying transition probabilities that depend on the predictors via a logistic/multinomial function. In a hidden Markov setting, inference for logistic regression coefficients becomes complicated and in some cases impossible due to convergence issues. In this paper, we aim to address this problem utilizing the recently proposed Pólya-Gamma latent variable scheme. Also, we allow for model uncertainty regarding the predictors that affect the series both linearly — in the mean — and non-linearly — in the transition matrix. Predictor selection and inference on the model parameters are based on an automatic Markov chain Monte Carlo scheme with reversible jump steps. Hence the proposed methodology can be used as a black box for predicting time series. Using simulation experiments, we illustrate the performance of our algorithm in various setups, in terms of mixing properties, model selection and predictive ability. An empirical study on realized volatility data shows that our methodology gives improved forecasts compared to benchmark models.     

马尾松种子园优良家系选择研究

对广西南宁地区林科所种子园,藤县大芒界种子园和贵港市覃塘种子园的440个马尾松(P inusm asson ianaL am b.)自由授粉子代进行年度和地点重复的半胞子代测定试验,以本地马尾松等11个对照来分析马尾松自由授粉子代的遗传变异规律,并根据遗传稳定性和材积生长快慢,采用性状表现水平分析法从中选择出优良家系。结果表明,440个参试家系中有20.0%~98.0%的家系树高比对照大8.3%~67.7%,有5.5%~99.0%的家系胸径比对照大11.4%~106.9%、有9.1%~99.0%的家系材积比对照大29.2%~484.6%。评选出适合在广西普遍推广的广普性优良家系64个,局部推广的局部性优良家系48个,需淘汰的家系83个;选择出的优良家系在11年生子代林中,材积生长量比对照大64.71%~94.51%。用子代林数据估算马尾松各性状的家系遗传力,树高遗传力为0.306~0.724,胸径为0.174~0.686,单株材积为0.286~0.677。     

多目标投资组合模型的模糊两阶段解法

对多目标证券组合投资模型进行了研究,模型以绝对偏差和代替方差作为投资组合的风险度量.该模型是一个多目标线性优化问题,采用两阶段模糊算法对模型进行求解,最后给出了该模型的一个实例的最优解.     

一种基于不同粒度语言判断矩阵的群决策方法

针对基于不同粒度语言判断矩阵的群决策问题,提出了一种新的群决策分析方法.对有序语言短语集中的语言短语进行“量化”,通过变量转换关系将语言判断矩阵转换为互补判断矩阵,并进行了判断信息转换的一致性分析;采用OWA算子将每个决策者给出语言判断矩阵所对应的互补判断矩阵集结为群的偏好矩阵,并通过计算每个方案的优势度来确定最优方案;最后给出一个算例说明了所提出的方法的有效性与实用性.     

改进的模糊一致矩阵决策方法及其应用

对基于模糊一致矩阵的决策方法目前尚存在的不足之处,如模糊优先关系矩阵的元素不能体现相应两个对象之间优劣的差异程度、满足条件a的最小值不容易确定、计算优度值进行单指标排序的方法有待改进等进行了分析,从模糊优先关系矩阵的构造、模糊一致矩阵的转化、排序方法等方面对基于模糊一致矩阵的决策方法进行了改进.将改进后的决策方法用于方案优选的实际问题,进行实证分析.分析结果表明,改进后的决策方法能更好地反映参评对象的实际情况.     

基于组合策略的印刷公式符号切分方法

对公式符号切分这一公式识将符号分离与符号合并策略相结合,提取公式符号的方法,对具有包含关系的符号进行分离操作,而对由多个连通部件构成的符号进行合并处理.实验表明,该方法能够较好地适应公式特点,具有较高的准确性.     

Space use by two arboreal rodent species in a Neotropical cloud forest

Studying animal space use patterns can help increase our understating of ecological processes such as competition and community dynamics. To quantify space and habitat use in an isolated and patchy cloud forest community in Mexico, we evaluate the vertical stratification, home range and habitat selection of two arboreal rodents: Habromys schmidlyi and Reithrodontomys microdon. Using live-traps at ground level and different forest strata, we radio-equipped nine individuals of H. schmidlyi and seven of R. microdon, and evaluated fine-scale space use and broad-scale habitat selection between cloud forest and oak forest. We found an average home range of 0.24 ha for R. microdon males and 0.72 ha for females, with a preference for higher canopy in the cloud forest. For H. schmidlyi the home range was 0.83 ha for males and 0.29 ha for females, with a preference for the understory level in the cloud forest. Home range is three-dimensional for these rodents, so we estimate that on average, individuals of both species used eight trees in the time they were tracked. We characterised the vegetation at the trap sites, and used recursive partitioning to relate the presence of different plants with the probability of finding these two species and Peromyscus aztecus, a third rodent species also present in the area and considered in our analysis of habitat use. The highest probability of finding R. microdon (96%) was related to the presence of Brachythecium occidentale and Renauldia mexicana, while H. schmidlyi (95%) was found in close proximity to Fabronia ciliaris and Everniastrum. We highlight the importance of arboreal trapping in biodiversity assessments, and the role of arboreal rodents in maintaining tropical forest ecosystems. We suggest that these rodent species could avoid or reduce competition by using the vertical strata differentially, and that H. schmidlyi and R. microdon can be biological indicators for cloud forest management and conservation.