基于等幅度充电时间的锂离子电池健康状态估计

目的提出一种基于健康因子的SOH估计方法,以准确估计锂离子电池的健康状态。方法选取锂离子电池恒流充电过程中两恒定压差下的时间间隔作为健康因子,基于健康因子估计电池的健康状态。采用高斯过程回归模型进行电池健康状态估计,通过共轭参数法优化超参数。健康因子作为模型输入,输出相应的电池健康状态,并选取NASA不同实验条件下6个电池的实验数据验证该算法。结果所选6个电池估计结果的MAPE与RMSE值均低于0.02。结论选取的健康因子可以较好地表征电池的健康状态,验证了基于健康因子的SOH估计方法的可行性。该方法可以对不同温度、放电倍率、放电深度下的电池进行准确的SOH估计,具有较强的适用性。     

Thresholds in Songbird Occurrence in Relation to Landscape Structure

Abstract:  Theory predicts the occurrence of threshold levels of habitat in landscapes, below which ecological processes change abruptly. Simulation models indicate that below critical thresholds, fragmentation of habitat influences patch occupancy by decreasing colonization rates and increasing rates of local extinction. Uncovering such putative relationships is important for understanding the demography of species and in developing sound conservation strategies. Using segmented logistic regression, we tested for thresholds in occurrence of 15 bird species as a function of the amount of suitable habitat at multiple scales (150–2000-m radii). Suitable habitat was defined quantitatively based on previously derived, spatially explicit distribution models for each species. The occurrence of 10 out of 15 species was influenced by the amount of habitat at a landscape scale (≥500-m radius). Of these species all but one were best predicted by threshold models. Six out of nine species exhibited asymptotic thresholds; the effects of habitat loss intensified at low amounts of habitat in a landscape. Landscape thresholds ranged from 8.6% habitat to 28.7% (     = 18.5 ± 2.6%[95% CI]). For two species landscape thresholds coincided with sensitivity to fragmentation; both species were more likely to occur in large patches, but only when the amount of habitat in a landscape was low. This supports the fragmentation threshold hypothesis. Nevertheless, the occurrence of most species appeared to be unaffected by fragmentation, regardless of the amount of habitat present at landscape extents. The thresholds we identified may be useful to managers in establishing conservation targets. Our results indicate that findings of landscape-scale studies conducted in regions with relatively high proportions of habitat and low fragmentation may not be applicable in regions with low habitat proportions and high fragmentation.     

Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions

Two statistical modelling techniques, generalized additive models (GAM) and multivariate adaptive regression splines (MARS), were used to analyse relationships between the distributions of 15 freshwater fish species and their environment. GAM and MARS models were fitted individually for each species, and a MARS multiresponse model was fitted in which the distributions of all species were analysed simultaneously. Model performance was evaluated using changes in deviance in the fitted models and the area under the receiver operating characteristic curve (ROC), calculated using a bootstrap assessment procedure that simulates predictive performance for independent data. Results indicate little difference between the performance of GAM and MARS models, even when MARS models included interaction terms between predictor variables. Results from MARS models are much more easily incorporated into other analyses than those from GAM models. The strong performance of a MARS multiresponse model, particularly for species of low prevalence, suggests that it may have distinct advantages for the analysis of large datasets. Its identification of a parsimonious set of environmental correlates of community composition, coupled with its ability to robustly model species distributions in relation to those variables, can be seen as converging strongly with the purposes of traditional ordination techniques.     

煤中痕量元素与三态硫关系的模型

将青山烟煤按不同比重分级后，分别测定了各级煤粉中痕量元素Ｂｅ、Ｃｄ、Ｐｂ、Ｃｏ，Ｃｒ，Ｃｕ，Ｎｉ，Ａｓ以及三态硫的含量，发现痕量元素与三态硫之间有一定的相关关系，本文用回归分析法建立了痕量元素与三态硫关系的模型，并用该模型对青山烟煤某一比重范围内煤粉中的痕量元素含量进行预报，结果令人满意。     

The importance of spatial autocorrelation,extent and resolution in predicting forest bird occurrence

Concerns about declines in forest biodiversity underscore the need for accurate estimates of the distribution and abundance of organisms at large scales and at resolutions that are fine enough to be appropriate for management. This paper addresses three major objectives: (i) to determine whether the resolution of typical air photo-derived forest inventory is sufficient for the accurate prediction of site occupancy by forest birds. We compared prediction success of habitat models using air photo variables to models with variables derived from finer resolution, ground-sampled vegetation plots. (ii) To test whether incorporating spatial autocorrelation into habitat models via autologistic regression increases prediction success. (iii) To determine whether landscape structure is an important factor in predicting bird distribution in forest-dominated landscapes. Models were tested locally (Greater Fundy Ecosystem [GFE]) using cross-validation, and regionally using an independent data set from an area located ca. 250 km to the northwest (Riley Brook [RB]). We found significant positive spatial autocorrelation in the residuals of at least one habitat model for 76% (16/21) of species examined. In these cases, the logistic regression assumption of spatially independent errors was violated. Logistic models that ignored spatial autocorrelation tended to overestimate habitat effects. Though overall prediction success was higher for autologistic models than logistic models in the GFE, the difference was only significantly improved for one species. Further, the inclusion of spatial covariates did little to improve model performance in the geographically discrete study area. For 62% (13/21) of species examined, landscape variables were significant predictors of forest bird occurrence even after statistically controlling for stand-level variability. However, broad spatial extents explained less variation than local factors. In the GFE, 76% (16/21) of air photo and 81% (17/21) of ground plot models were accurate enough to be of practical utility (AUC > 0.7). When applied to RB, both model types performed effectively for 55% (11/20) of the species examined. We did not detect an overall difference in prediction success between air photo and ground plot models in either study area. We conclude that air photo data are as effective as fine resolution vegetation data for predicting site occupancy for the majority of species in this study. These models will be of use to forest managers who are interested in mapping species distributions under various timber harvest scenarios, and to protected areas planners attempting to optimize reserve function.     

Accounting for large-scale factors in the study of understory vegetation using a conditional logistic model

Local-scale and large-scale factors can affect the presence of a species of understory vegetation in the forest. Local-scale factors may be the influence of surrounding trees, while climate and latitude are typically considered large-scale factors. A model for the presence of a species needs to take into account both scales. A conditional logistic model is proposed for those studies where only the local-scale factors are of interest and that avoids estimating the large-scale parameters. Conditioning is carried out by the number of quadrats in the plot where the vegetation is found. As the latter is a sufficient statistic for the large-scale factors, a model free from these parameters is obtained. Data gathered in the permanent sample plots of the 1985–1986 National Forest Inventory of Finland is used for illustration, where the local-scale factor of interest is the influence of the trees, quantified by an index based on the size and location of the trees. The model fitted to Vaccinium vitis-idaea showed a significant and positive influence of Scots pine on the presence of this species, while for Calamagrostis arundinacea, a decrease in the odds ratio was observed due to the influence of Norway spruce.     

A stratified model for the assessment of meteorologically adjusted trends of surface ozone

We consider the problem of assessing long-term trends of ozone concentrations measured on a single site located in an urban area. Among the many methods proposed in the literature to eliminate the confounding effect of changing weather conditions, we employ a stratification of daily maxima based on regression trees. Within each stratum conditional independence and Weilbull distribution are assumed for maxima. Long-term trend is defined non-parametrically by the sequence of yearly medians. Models are estimated following the Bayesian approach. The alternative assumptions of common and stratum specific trends are compared and a model with common trend for all strata is selected for the analyzed real dataset. The conditional independence assumption is checked by the comparison with a model including an autoregressive component.     

Multivariate analysis of spatial patterns: a unified approach to local and global structures

We propose a new approach to the multivariate analysis of data sets with known sampling site spatial positions. A between-sites neighbouring relationship must be derived from site positions and this relationship is introduced into the multivariate analyses through neighbouring weights (number of neighbours at each site) and through the matrix of the neighbouring graph. Eigenvector analysis methods (e.g. principal component analysis, correspondence analysis) can then be used to detect total, local and global structures. The introduction of the D-centring (centring with respect to the neighbouring weights) allows us to write a total variance decomposition into local and global components, and to propose a unified view of several methods. After a brief review of the matrix approach to this problem, we present the results obtained on both simulated and real data sets, showing how spatial structure can be detected and analysed. Freely available computer programs to perform computations and graphical displays are proposed.     

广州市空气污染物和气象要素的主成分与典型相关分析

用主成分和典型相关分析方法分析广州市近4a(2001—2004年)的空气污染物与气象要素之间的关系,按夏半年、冬半年和全年3个时间尺度分别进行。结果表明:污染物数据所得到的主成分分别代表机动车污染源(汽油燃烧和扬尘)和工业污染源(工业燃煤和燃油),气象数据的主成分分析表明空气的温度、湿度及对流速度对空气污染作用明显,而污染物和气象要素的主成分分析表明气温高低和空气干湿程度对大气污染的影响较大。污染物与气象要素两组数据之间的典型相关分析表明污染物与气象要素之间存在显著的相关关系,其中温度和风速对气态污染物有显著影响。     

大麦秆控藻研究进展

大量实验研究证明：大麦秆是一种很具前景的抑藻物。它能有效控制引起水体富营养化的各种藻类生长，优化水生生物的组成结构。例如水体中放入大麦秆可以增加无脊椎动物以及鱼类的数量，从而达到改善水生生态系统的目的。大麦秆的抑藻机理非常复杂，研究已经排除了营养物质以及大麦秆上遗留杀虫剂对藻的抑制作用。现在除了传统所认为物理和生物机理外，最为认可是化学机理，即大麦秆在有氧降解过程中能产生的诸如酚类、酯类、蒽醌类活性物质，它们能够很好的抑制藻类生长，但是确切的有效化学物质的提取还需要通过GC/MS等方法进一步研究。在小型水体，尤其是鱼类养殖水体中可以直接施用大麦秆以改善水体。总之大麦秆是一种环境友好、生态安全、经济适用的有效控藻物质，应用前景广阔。