基于相似度的快速网页排序算法

将经典的PageRank算法和汉明距离相似度算法结合,提出一种新的网页排序方法。通过结合汉明距离(Hamming distance)相似度算法,计算检索词和网页文本相似度,提高搜索查准率;在增加检索词的同义词的搜索过程中,通过改进汉明距离相似度算法,提高搜索查全率。实验结果表明,该方法与PageRank算法相比,拥有了更好的效果。     

四元数和脉冲耦合神经网络应用于足球检测

提出一种基于四元数傅里叶变换注意力选择和脉冲耦合神经网络在图像中跟踪足球的方法. 首先进行图像预处理以去除球场以外的区域,用四元数注意力选择算法提取感兴趣区域,基于颜色、形状、面积等多种特征检测足球. 若检测失败,则采用卡尔曼滤波器预测足球位置. 仿真结果表明,与基于速度控制的动态卡尔曼滤波和实时足球检测两种方法相比,检测成功率分别提高9.6%和14.9%.     

一种基于距离比值的支持向量机增量训练算法

由于支持向量机具有较好地学习性能和泛化能力,目前已经得到了广泛的应用。如何使支持向量机进行有效的增量学习是目前支持向量机应用中需要解决的问题。深入研究了支持向量分布特点,提出了一种新的支持向量机增量训练淘汰机制——距离比值算法。该算法根据遗忘规则,设定一个合适的参数,按距离比值法中的定义计算各个样本中心距离与其到最优分类面距离的比值,舍弃对后续训练影响不大的样本,即可对训练数据进行有效的淘汰。对标准数据集的实验结果表明,使用该方法进行增量训练在保证分类精度的同时,能有效地提高训练速度。     

西昌市大气重金属苔藓监测种类筛选

为了探究适合西昌市大气重金属苔藓监测的方法,筛选适宜的苔藓监测物种,采集分析了西昌市主要区域冬季苔藓植物的生理生化等特征。通过对苔藓植物的可溶性蛋白、超氧歧化酶、过氧化物酶、过氧化氢酶、叶绿素、花青素、类胡萝卜素和重金属质量分数的比较分析,筛选出富集色素、生长优势突出、重金属富集能力强的物种。结果表明：大灰藓（Hypnum plumaeforme）可溶性蛋白含量最高,牛舌藓（Anomodon minor）抗氧化酶含量最高,美喙藓（Eurhynchium pulchellum （hedw.） jenn.）的重金属富集程度相对较最高,鳞叶藓（Taxiphyllum taxirameum）的重金属富集程度次之,牛舌藓、大灰藓重金属富集能力较低。综合比较表明美喙藓和鳞叶2种苔藓植物是适宜西昌市大气重金属监测的物种。     

基于优化多相编码和天线设计的MIMO STAP研究

多发多收（MIMO,multiple input and multiple output）雷达发射正交波形以获得良好的波形分集增益和更高的自由度。为了提高信号的自相关和互相关性能,采用循环算法（CA,cyclic algorithm）,得到了具有低自相关旁瓣峰值（ASP,auto correlation sidelobe peak）和互相关峰值（CP,cross correlation peak）的多相编码信号。仿真实验基于优化多相编码信号和天线阵列结构实现STAP（space-time adaptive processing）,克服了多信号间相互影响、稀疏天线布阵结构导致的盲速和密布天线布阵影响孔径大小等问题,验证了本文方法有效性。     

基于非对称信息的融资融券业务成本

引入信息不对称,构建证券公司和投资者之间的博弈模型,对融资融券业务过程中的机会成本进行量化分析.同时,针对融资融券业务导致的投资组合风险暴露进行定量分析,得出证券公司开展相关融通业务的条件,认为无条件契约将为证券公司带来更大效用.     

基于BP神经网络的避洪安置区选址研究

为了解决洪水灾害应急管理中避洪安置区的选址问题,在总结前人研究成果的基础上,从自然环境、可行性、生活条件、社会环境、实用性等5个方面出发,通过17个具体指标,构建了避洪安置区选址评价体系;在安置区的选址分析中引入BP神经网络,构建了基于BP神经网络的安置区选址模型,根据选址评价体系及相关经验确定网络模型各层次的神经元节点数量,并采用样本对模型进行训练分析,建立了稳定的网络结构,最终实例验证了该模型在避洪安置区选址问题中的有效性及可行性。     

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.     

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.     

Microhabitat selection in the sand recluse spider (Sicarius thomisoides): the effect of rock size and temperature

In spiders, temperature is considered an important environmental variable for microhabitat selection. In this study, we evaluated the effect of temperature and rock size on the presence of the sand recluse spider Sicarius thomisoides and the degree of selectivity in different locations. This species is a large spider that lives under rocks in desert and semi-desert climates and is particularly active during the summer. In Chile, these spiders can be found at both coastal and inland locations under different thermal conditions, where usually the temperatures are lower near the coast. If large-scale climatic conditions are important for this species, they may be expected to select lower rock temperatures on the coast than at inland locations. In addition, we would expect that the spiders would choose larger rocks in inland compared to coast locations, which reduce the effect of high temperatures. We found that the probability of finding individuals of this species increased according to rock temperature and rock size in the field. Our results suggest that S. thomisoides prefers larger and warmer rocks to shelter under during the day, this selectivity being similar at both coastal and inland locations. Thus, this species tends to select rocks with the same thermal and structural conditions, independent of the climatic conditions.