高光谱遥感图像最优波段选择方法的研究进展与分析

随着成像光谱仪的发展日益成熟,高光谱遥感图像的研究已进入到一个新的阶段--对获取的高光谱数据进行有效处理和利用的阶段。目前的处理方法主要集中在对高光谱图像的数值分析处理上,比如大气校正、降低数据维数、信息提取、分类与目标探测等方面。高光谱图像相邻的波段之间一般具有较大的相关性,并不是所有的波段对于后续处理都有着同等的重要性,通过选择最优波段而组成新的高光谱图像空间,在不损失重要信息的条件下可以代表其他波段的信息。因而需要在更合理的数学模型的指导下,按照一定的准则,来决定最佳波段的选择问题。对目前国内外高光谱图像的各种波段选择方法进行了综合归纳和分析,力图为高光谱图像处理寻找突破点,加强此领域的研究力度。     

"川盐古道"文化线路之研究初探

四川"井盐"数千年的产销历史促成了"川盐古道"的形成,它对川、鄂、渝、湘、黔的地域经济与地域文化产生重要的影响,贯穿了一条极具特色的文化线路.旨在探寻"川盐古道"的成因,拯救文化线路上渐已消亡的珍贵材料,摸清古盐道的分布线路,并为古道的保护提供依据.     

基于模糊粗糙集的层次分类增量特征选择

随着大数据时代的到来,数据的类标签数量急剧增加,对现有的分类任务带来了严峻的挑战。为了解决这个问题,人们通常将标签组织成层次结构,使用结构中所包含的信息来对任务进行学习。考虑样本的不断增加,使用模糊粗糙集信息熵设计了一种面向层次分类的增量特征选择算法。考虑兄弟策略,将现有的λ条件熵推广到了层次分类的情形,设计了一种非增量的层次分类特征选择算法,设计了λ增量条件熵,基于此设计了增量版本的特征选择算法。在实验中,采用了包括非增量版本在内的7种不同的特征选择算法在5个层次数据集上与增量算法进行比较,实验结果验证了2种算法的有效性,并且所设计的增量算法能在不影响性能的情况下加快特征选择的进程。     

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.     

常规公交站台容纳线路能力计算模型

从公交站台停靠线路数和线路特征入手,运用排队理论建立公交站台容纳线路能力的计算模型.该模型充分反映了公交车到达特性对公交站台通行效率的影响,从理论上计算公交站台车位数与站台容纳线路及线路特征的定量关系,为公交站台的车位数以及线路数的设计提供理论设计依据.运用该模型对南京市某公交站台进行了实例分析,对公交站台的线路数进行了优化分析.从实例计算结果可以看出,公交站台的实际流量小于其通行能力,但仍常出现公交车排长队现象,导致站台内拥堵,其原因主要是公交车到达的不均匀性.合理设置站台的线路数和线路发车频率可以有效地控制站台内公交车的排队长度,减少公交车在站的运行延误.     

西部地区产业转移背景下高校毕业生就业路径选择研究——以陕西省为例

在当前西部地区产业转移的背景下,结合西部地区区域特点和"互联网+"发展的时代特征,通过分析近些年陕西省高校毕业生就业的基本情况,产生一个基本判断,即西部地区高校毕业生就业的路径选择应是逐步向非公、中小微型企业流动,特别是东部向西部转移的产业类的小企业中流动.为印证这一判断,本文运用马克思的有机构成理论和凯恩斯的有效需求理论对这一就业途径进行深入分析,并运用实证的方式加以验证和后续预测,明确该途径确实符合当前实际,应是缓解当前高校毕业生就业的重要途径.而在这一路径选择下,本文从承载适合西部发展的东部转移产业、夯实西部新区发展实现承载、以互联网金融等创新融资方式缓解中小微企业资金压力,同时承载地政府应积极履行责任,帮助转移企业的落地升级,并扶持青年创业等角度提出建议.     

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.     

Dynamic Model Averaging and CPI Inflation Forecasts: A Comparison between the Euro Area and the United States

The paper forecasts consumer price inflation in the euro area (EA) and in the USA between 1980:Q1 and 2012:Q4 based on a large set of predictors, with dynamic model averaging (DMA) and dynamic model selection (DMS). DMA/DMS allows not solely for coefficients to change over time, but also for changes in the entire forecasting model over time. DMA/DMS provides on average the best inflation forecasts with regard to alternative approaches (such as the random walk). DMS outperforms DMA. These results are robust for different sample periods and for various forecast horizons. The paper highlights common features between the USA and the EA. First, two groups of predictors forecast inflation: temporary fundamentals that have a frequent impact on inflation but only for short time periods; and persistent fundamentals whose switches are less frequent over time. Second, the importance of some variables (particularly international food commodity prices, house prices and oil prices) as predictors for consumer price index inflation increases when such variables experience large shocks. The paper also shows that significant differences prevail in the forecasting models between the USA and the EA. Such differences can be explained by the structure of these respective economies. Copyright © 2015 John Wiley & Sons, Ltd.     

Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle

Four methods of model selection—equally weighted forecasts, Bayesian model‐averaged forecasts, and two models produced by the machine‐learning algorithm boosting—are applied to the problem of predicting business cycle turning points with a set of common macroeconomic variables. The methods address a fundamental problem faced by forecasters: the most useful model is simple but makes use of all relevant indicators. The results indicate that successful models of recession condition on different economic indicators at different forecast horizons. Predictors that describe real economic activity provide the clearest signal of recession at very short horizons. In contrast, signals from housing and financial markets produce the best forecasts at longer forecast horizons. A real‐time forecast experiment explores the predictability of the 2001 and 2007 recessions. Copyright © 2015 John Wiley & Sons, Ltd.     

Backward‐in‐Time Selection of the Order of Dynamic Regression Prediction Model

We investigate the optimal structure of dynamic regression models used in multivariate time series prediction and propose a scheme to form the lagged variable structure called Backward‐in‐Time Selection (BTS), which takes into account feedback and multicollinearity, often present in multivariate time series. We compare BTS to other known methods, also in conjunction with regularization techniques used for the estimation of model parameters, namely principal components, partial least squares and ridge regression estimation. The predictive efficiency of the different models is assessed by means of Monte Carlo simulations for different settings of feedback and multicollinearity. The results show that BTS has consistently good prediction performance, while other popular methods have varying and often inferior performance. The prediction performance of BTS was also found the best when tested on human electroencephalograms of an epileptic seizure, and for the prediction of returns of indices of world financial markets.Copyright © 2013 John Wiley & Sons, Ltd.