湖泊水质参数空间分析中异常值的识别与处理

异常值的存在对湖泊水质参数局部估计精度产生重要影响 ,制约了地统计学在湖泊水质研究中的应用。文章以太湖为例 ,介绍了在湖泊水质参数研究中的三种异常值识别和处理的方法。应用这些方法可以有效地降低异常值的影响水平     

Outlier identification and visualization for Pb concentrations in urban soils and its implications for identification of potential contaminated land

Outliers in urban soil geochemical databases may imply potential contaminated land. Different methodologies which can be easily implemented for the identification of global and spatial outliers were applied for Pb concentrations in urban soils of Galway City in Ireland. Due to its strongly skewed probability feature, a Box–Cox transformation was performed prior to further analyses. The graphic methods of histogram and box-and-whisker plot were effective in identification of global outliers at the original scale of the dataset. Spatial outliers could be identified by a local indicator of spatial association of local Moran's I, cross-validation of kriging, and a geographically weighted regression. The spatial locations of outliers were visualised using a geographical information system. Different methods showed generally consistent results, but differences existed. It is suggested that outliers identified by statistical methods should be confirmed and justified using scientific knowledge before they are properly dealt with.     

Deriving heart rate variability indices from cardiac monitoring—An indicator of driver sleepiness

Objective: Driver fatigue is considered to be a major contributor to road traffic crashes. Cardiac monitoring and heart rate variability (HRV) analysis is a candidate method for early and accurate detection of driver sleepiness. This study has 2 objectives: to evaluate the (1) suitability of different preprocessing strategies for detecting and removing outlier heartbeats and spectral transformation of HRV signals and their impact of driver sleepiness assessment and (2) relation between common HRV indices and subjective sleepiness reported by a large number of drivers in real driving situations, for the first time.

Methods: The study analyzed >3,500 5-min driving epochs from 76 drivers on a public motorway in Sweden. The electrocardiograph (ECG) data were recorded in 3 studies designed to evaluate the physiological differences between awake and sleepy drivers. The drivers reported their perceived level of sleepiness according to the Karolinska Sleepiness Scale (KSS) every 5?min. Two standard methods were used for identifying outlier heartbeats: (1) percentage change (PC), where outliers were defined as interbeat intervals deviating >30% from the mean of the four previous intervals and (2) standard deviation (SD), where outliers were defined as interbeat interval deviating >4 SD from the mean interval duration in the current epoch. Three standard methods were used for spectral transformation, which is needed for deriving HRV indices in the frequency domain: (1) Fourier transform; (2) autoregressive model; and (3) Lomb-Scargle periodogram. Different preprocessing strategies were compared regarding their impact on derivation of common HRV indices and their relation to KSS data distribution, using box plots and statistical tests such as analysis of variance (ANOVA) and Student’s t test.

Results: The ability of HRV indices to discriminate between alert and sleepy drivers does not differ significantly depending on which outlier detection and spectral transformation methods are used. As expected, with increasing sleepiness, the heart rate decreased, whereas heart rate variability overall increased. Furthermore, HRV parameters representing the parasympathetic branch of the autonomous nervous system increased. An unexpected finding was that parameters representing the sympathetic branch of the autonomous nervous system also increased with increasing KSS level. We hypothesize that this increment was due to stress induced by trying to avoid an incident, because the drivers were in real driving situations.

Conclusions: The association of HRV indices to KSS did not depend on the preprocessing strategy. No preprocessing method showed superiority for HRV association to driver sleepiness. This was also true for combinations of methods for frequency domain HRV indices. The results prove clear relationships between HRV indices and perceived sleepiness. Thus, HRV analysis shows promise for driver sleepiness detection.     

带式输送机传动滚筒材料结构损伤检测方法的研究

滚筒的损坏会导致皮带倾斜,严重时会导致皮带撕裂,准确检测滚筒的损伤程度能够避免事故发生,并能够提高经济效益。选择带式输送机的滚筒材料作为研究对象,模拟滚筒所处的温度变化环境,对不同损坏程度的滚筒材料进行环境模拟并测试,通过传感器采集频率响应信号,通过主成分分析、异常值分析和微量组分分析方法,只改变温度数据滚筒材料马氏平方距离值相对平缓,控制在0.06×104,真正损坏的马氏平方距离值远远大于临界值,成功去除温度变化对材料频率响应信号的影响,最终找到了能够准确检测滚筒材料的损伤程度的方法。     

Exceptional responders in conservation

Conservation operates within complex systems with incomplete knowledge of the system and the interventions utilized. This frequently results in the inability to find generally applicable methods to alleviate threats to Earth's vanishing wildlife. One approach used in medicine and the social sciences has been to develop a deeper understanding of positive outliers. Where such outliers share similar characteristics, they may be considered exceptional responders. We devised a 4‐step framework for identifying exceptional responders in conservation: identification of the study system, identification of the response structure, identification of the threshold for exceptionalism, and identification of commonalities among outliers. Evaluation of exceptional responders provides additional information that is often ignored in randomized controlled trials and before–after control‐intervention experiments. Interrogating the contextual factors that contribute to an exceptional outcome allow exceptional responders to become valuable pieces of information leading to unexpected discoveries and novel hypotheses.     

现场监测数据审核方法及异常数据的判别研究

现场监测数据审核是整个环境监测质量管理中的重要一环,关系到监测数据的真实、公正、有效.现场监测原始记录准确完整的填写是数据审核的前提条件,文章论述了原始记录的填写要求及填写方法.针对审核过程中遇到异常数据的具体问题加以判别,分析原始记录的完整性、计算公式的准确性在现场监测数据审核工作中的作用.结合实际工作,通过对环境监测数据相关性、逻辑性进行研究,分析了现场监测数据审核过程中应注意的问题及异常数据的产生原因,并提出了相应的解决方法.     

水环境模拟中变异函数的稳健性分析

文章将稳健统计学（RS）引入地质统计学（GS）,对廊坊市地下水现场采样数据中Cl^-运用影响系数法和估计邻域法识别并处理特异值,取得了较好的稳健效果。在此基础上,用Cressie-Hawkins法、中位调节法两种稳健统计学方法系统的对进行特异值处理前后的数据作了处理和分析。结果表明,变量Cl^-离子经ICM法处理特异值后再进行稳健计算方法中以MA法有较大的优势,而且获得稳健变异函数γR（h）及其估计量对于改善变异函数的结构性和理论模型的主要参数有明显效果。因此,稳健地质统计学的方法可以移植到水环境科学中应用,这对加强水环境空间变异性以及稳健变异函数的基础研究有着重要的应用价值和学术价值。     

OUTLIER DETECTION IN TOTAL PHOSPHORUS CONCENTRATION DATA FROM SOUTH FLORIDA RAINFALL1

ABSTRACT: Atmospheric deposition can be a significant source of phosphorus to South Florida's aquatic system. Deposition samples are often contaminated to varying degrees by bird droppings or other foreign materials. This study attempted to use statistical and other methods to detect and remove the outliers in the rain-borne total phosphorus concentration data. Some outliers in the data were identified using field notes derived from visual inspection of the samples. Outlier detection statistics based on a simple linear regression were then used for additional data screening. As a result of these analyses, about 35 percent of the observed values were identified as outlying data which needed to be removed prior to further data analyses. Based on detected outliers in the data from 15 monitoring sites, a lumped cutoff value of 130 4mUg/L was determined. This lumped cutoff value may be useful for further quality control and analyses of the data from the region.     

基于LOF的K-means聚类方法及其在微震监测中的应用

矿山微震事件集群是分析矿震的重要参考之一，其准确的划分对矿山微震分布特征和微震活动分析具有重要作用。提出了1种基于局部离群因子(Local Outlier Factor, LOF)的K-means聚类算法并构建了综合SSE评价指标和模型，通过LOF算法检测异常微震事件和选取初始聚类中心，利用Krzanowski-Lai指数确定最佳聚类分组数；采用模拟计算比较了不同数据集大小的聚类效果。结果表明:基于LOF的K-means聚类方法评分最高，聚类结果最好；并利用该聚类方法分析用沙坝矿1 649个微震事件的分布特征与微震活动性。实例表明，K=7为最佳聚类分组数，聚类簇的划分受断层滑移和矿山生产活动的影响。