DCKPDP：改进k-prototype聚类的差分隐私混合属性数据发布方法

当前混合属性数据发布中隐私保护方法大多存在隐私保护效果不佳或数据效用较差的问题,采用差分隐私与优化的k-prototype聚类方法相结合,提出改进k-prototype聚类的差分隐私混合属性数据发布方法(DCKPDP)。为解决传统k-prototype聚类算法没有考虑不同数值型属性对聚类结果有较大影响的问题,利用信息熵为每个数值型属性添加属性权重;为解决聚类初始中心点人为规定或者由随机算法随机确定,导致聚类结果精确度不高的问题,结合数据对象的局部密度和高密度对聚类过程中初始中心点进行自适应选择;为解决数据信息泄露风险较高的问题,对聚类中心值进行差分隐私保护。实验结果表明,DCKPDP算法满足差分隐私保护所需的噪声量更小,数据的可用性更好。     

Seasonal variation in light penetration and subsurface chlorophyll-α in southern Lake Michigan observed by a glider

The Cooperative Institute for Great Lakes Research (CIGLR) in collaboration with the Great Lakes Observing System and National Oceanic and Atmospheric Administration, Great Lakes Environmental Research Laboratory (NOAA GLERL) deployed an autonomous underwater glider in southern Lake Michigan several times per year between 2012 and 2019 to collect offshore (>30 m depth) limnological measurements, including temperature, photosynthetically active radiation (beginning during 2015), and chlorophyll-α fluorescence. From these data, we calculated mixed layer depth, several measures of light penetration (diffuse attenuation coefficient, first optical depth, euphotic zone depth), and depth of the subsurface chlorophyll-α maxima. During summer, mean offshore mixed layer depth was typically 10–15 m, K_d for PAR was 0.1–0.17 m^?1, first optical depth was 6–9 m, euphotic zone depth was 35–40 m, and depth of subsurface chlorophyll-α maxima was 30–35 m. We also observed substantial spatial and temporal variation in these values across the basin and within and among seasons. Glider-based observations provide a wider horizontal and vertical perspective than other methods (e.g., ship- and satellite-based observations, buoys, and fixed moorings), and are therefore a valuable, complementary tool for Great Lakes limnology. The set of observations reported here provide seasonal and basin-scale information that may help to identify anomalies useful for future glider-assisted investigation into the role of biophysical processes in Great Lakes limnology and ecology.     

基于生态位理论的黄河流域用水结构分析

为分析和探究黄河流域用水结构的变化特征,科学支撑流域水资源合理配置与调度,保障流域可持续发展,以黄河流域为研究对象,基于生态位理论构建了用水结构生态位及其熵值模型,分析了流域及流域内主要省区用水结构演变趋势和特征,并与发达国家用水特征作比较。在此基础上提出了相关建议。结果表明:黄河流域的农业用水生态位(0.7～0.78)最大但呈现波动减少趋势,工业用水生态位(0.12～0.15)次之且变化趋势为波动且有所下降,生活用水生态位(0.06～0.08)与城镇公共及生态用水生态位(0.03～0.09)相对较少但皆有不同程度的增长趋势。各省区的生态位熵值差异较大,表明流域及各省区间用水结构差异较大。统计年间宁夏、内蒙古及山东的农业用水占有率高于全流域,陕西、山西、河南及甘肃的工业用水占有率高于全流域,各省区城镇公共及生态用水的占有率变化剧烈,陕西、山西及河南的生活用水占有率高于全流域。相较于黄河流域,美国的工业用水(0.42～0.52)在用水结构中的优势更加突出,农业用水(0.31～0.44)相对处于劣势地位;两个区域生活用水在用水结构中的地位相对其他的用水类型更为接近。     

海绵城市低影响开发措施综合效益评价体系及应用

为对低影响开发(LID)措施综合效益进行量化评价,综合考虑生态、经济及社会等因素,选取径流量削减率、污染物削减率、成本投资、运行效益、雨水利用、生态价值等16项LID措施评价指标,采用层次分析法和熵权法确定指标权重,基于云模型构建了LID措施综合效益评价体系。将评价体系应用于南京一住宅小区的4个LID措施初步设计方案量化评价,结果显示,功效最大型方案截污减排效益最优,社会效益良好,对于“较优”的隶属度最大,为最优方案。     

基于TL矩法的洪水频率分布参数估计方法优选

为分析TL矩法在洪水频率分析中的普适性,以陕北地区神木、绥德、杏河、刘家河、黄陵和志丹6个水文站的年最大洪峰流量序列为例,选取广义Pareto分布、广义极值分布和广义Logistic分布,对线性矩法、极大似然法与TL矩法3种参数估计方法进行拟合优度评估。结果表明:除杏河站用广义极值分布外,其他5个水文站的年最大洪峰流量序列用广义Pareto分布拟合效果更佳;与线性矩法和极大似然法相比,TL矩法在各水文站均表现出更佳的拟合效果,是一种洪水频率分布参数估计的较好方法。     

基于级差最大化组合赋权法的地下水功能评价

针对地下水功能评价中单独采用主观赋权法或客观赋权法存在的问题,结合二者的优点,采用基于主观赋权的层次分析法与客观赋权的熵权法相结合的级差最大化组合赋权法评价地下水功能,并通过计算不同赋权方法权重结果与平均值的相对偏差来衡量级差最大化组合赋权法的准确性。将该方法应用于伊犁河谷西北部平原区浅层地下水功能评价,评价结果基本符合评价区实际情况,验证了该评价方法在地下水功能评价中的可行性。     

基于AHP和熵权法组合权重的京津冀地区水循环健康模糊综合评价

在阐明健康水循环内涵的基础上,构建涵盖水资源、水环境、水生态、水效用和水灾害等5个准则层,包括降水量、人均水资源量、亩均水资源量、地下水供水占比等18个指标的水循环健康评价指标体系,采用层次分析法与熵权法组合赋权,运用模糊综合评价法评价了京津冀地区2009-2018年水循环健康状况。评价结果显示:近10年京津冀地区水循环健康状况总体呈逐步向好趋势,北京、天津和河北水循环健康等级分别为亚健康、一般和亚病态;中水供水率、平原地下水埋深下降量、河湖调蓄能力、万元工业增加值用水量等是影响京津冀地区健康水循环的主要指标,但各指标间发展不平衡,仍有较大提升空间。     

DETECTOR: A knowledge-based system for injection molding diagnostics

A knowledge-based system (KBS) for diagnosis of multiple defects in injection molding is presented. The general scheme for knowledge representation based on fuzzy set theory has been shown useful in representing inexact and incomplete information for developing the KBS. An optimality criterion is created for selecting a simple and best cover to explain the given problem. An efficient search algorithm for finding such cover is also discussed.     

The Bivariate Transmuted Family of Distributions: Theory and Applications

The bivariate distributions are useful in simultaneous modeling of two random variables. These distributions provide a way to model models. The bivariate families of distributions are not much widely explored and in this article a new family of bivariate distributions is proposed. The new family will extend the univariate transmuted family of distributions and will be helpful in modeling complex joint phenomenon. Statistical properties of the new family of distributions are explored which include marginal and conditional distributions, conditional moments, product and ratio moments, bivariate reliability and bivariate hazard rate functions. The maximum likelihood estimation (MLE) for parameters of the family is also carried out. The proposed bivariate family of distributions is studied for the Weibull baseline distributions giving rise to bivariate transmuted Weibull (BTW) distribution. The new bivariate transmuted Weibull distribution is explored in detail. Statistical properties of the new BTW distribution are studied which include the marginal and conditional distributions, product, ratio and conditional momenst. The hazard rate function of the BTW distribution is obtained. Parameter estimation of the BTW distribution is also done. Finally, real data application of the BTW distribution is given. It is observed that the proposed BTW distribution is a suitable fit for the data used.     

基于改进LMD方法的风电机组齿轮箱故障诊断研究

局部均值函数的求取是局部均值分解(LMD)的关键环节。针对局部均值函数求取存在偏差进而造成模态混叠的问题,提出了一种基于局部积分均值的LMD风电机组齿轮箱故障诊断方法。该方法改变了对相邻两极值点求平均值的思路,采用求取相邻两极值点的局部积分均值,再通过滑动平均法进行平滑处理,最终得到局部均值函数。为实现风电机组齿轮箱故障诊断,首先采用改进LMD方法对信号进行降噪处理,然后采用多尺度熵提取降噪处理后信号的特征向量,最后采用极限学习机进行故障诊断。通过仿真分析,证明了该方法能有效解决模态混叠现象,提高了LMD的分解精度。试验验证分析表明,该方法的故障诊断准确率为100%,通过对比分析表明,该方法优于其他故障诊断方法,具有工程应用价值。