Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.     

动态污水处理过程的多模型动态矩阵控制

针对活性污泥法污水处理系统的强耦合和动态变化特性,提出一种用于前置反硝化脱氮工艺的多模型动态矩阵(DMC)解耦控制方法;通过K-均值聚类分析法对外界入水中氨氮浓度进行聚类,获得以聚类中心为入水氨氮浓度的静态模型集合,分别设计线性DMC解耦控制器;再以当前时刻外界进水氨氮浓度和聚类中心为基本元素,构造切换函数,实现多模型切换。将该方法应用于活性污泥1号模型(ASM1)中,获得了较好的动态仿真效果。     

公路隧道火灾发生位置与人群疏散通道仿真研究

利用EXODUS建立隧道的仿真场景,确定疏散人群仿真参数,并将出口工效OPS作为评价疏散效率的指标,分析不同火灾发生位置对疏散时间的影响。采用K-means算法分别对火灾发生位置和人群疏散通道位置进行聚类,并建立基于两者影响下的出口工效模型,通过对该模型求偏导,得出隧道火灾发生位置和人群疏散通道选择之间的关系。研究结果表明:火灾发生位置越接近人行横通道,疏散时间波动越大,人行横通道和隧道入口的疏散时间随人群疏散区域分界线变化,当火灾发生距人行横通道20~110 m时,变化明显,当距140~350 m时,变化平稳;出口工效OPS总体呈现不稳定的阶梯状变化趋势;火灾发生位置和人群疏散通道的最优位置呈线性递减关系。研究结果可应用于隧道应急指挥中心管理人员制定有效疏散路径和采取诱导分流人群措施。     

上海市突发环境污染事故风险区划

环境风险区划是区域布局型环境风险管理及环境风险分区管理的重要手段.本研究在环境风险系统理论的指导下,借鉴“自上而下”和“自下而上”传统区划方对上海市突发环境风险进行区划研究.上海市突发环境风险区划中“自上而下”环境风险一级区的划分是依据上海市1990~2008年突发污染事故历史时空格局获得;而“自下而上”是通过构建上海市风险区划指标体系,在对指标进行概念模型量化的基础上,运用基于遗传算法的K均值聚类在最小区划单元进行聚类区划,并依据上海市政府宏观规划对聚类后的图斑碎块进行科学性和实用性调整,获得上海市突发环境污染事故风险亚区和小区;将上海市突发环境风险一级区及亚区和小区集成分析,实现上海市突发环境污染事故风险综合区划.结果表明:上海市突发环境污染事故风险区划包含2个风险一级区,5个风险亚区和21个风险小区,客观揭示了上海市突发环境污染事故风险的空间分布规律.针对上海市布局型环境风险和不同风险区提出相应的管理措施,为上海市综合减灾降险和风险管理决策提供科学依据.     

基于改进K-means和CNN的储罐罐底点蚀诊断模型*

为解决储罐罐底点蚀问题,提出基于改进K-means和CNN的储罐罐底点蚀诊断模型,在传统聚类基础上引入肘部法则,保证k值选取3的准确性,将原始声发射信号特征参数和聚类后的类别信息输入模型进行训练,系统预测准确率高达99%。研究结果表明:该模型能够及时发现点蚀现象,指导管理者确定储罐开罐检查时间顺序,避免点蚀穿孔造成的人力、物力损失,降低储罐运行风险,保障储罐运行安全,研究结果可为罐底点蚀诊断提供技术支撑。     

湖南攸县稻米镉（Cd）富集特征及原因解析

攸县大米富镉(Cd)问题受到社会的广泛关注.通过实地调查和采样分析,应用路径分析模型(PA),多元回归分析和K-means聚类方法分析Cd在土壤-稻米系统中的富集特征,潜在风险和主要影响因子.结果显示攸县稻米Cd平均含量为0.47 mg·kg~(-1),约为国家粮食安全质量标准的2.5倍.稻米Cd富集因子(PUF)服从自然对数分布,变化范围较大.回归分析和PA分析显示土壤pH、土壤Mn和土壤Zn含量为影响PUF变化的主要环境因子,其中土壤Mn对PUF的影响主要体现在对稻米Cd的直接作用,土壤pH和土壤Zn对PUF的影响主要是通过影响土壤Cd含量而间接影响稻米Cd含量.K-means聚类分析显示土壤pH=5.6和土壤Mn含量=333 mg·kg-1可作为研究区对生产严重超标Cd米的土壤进行治理的初步调控阈值.     

潮流场作用下的航标漂移计算方法研究

针对目前航标遥控遥测系统中误报警过多的问题,对当前航标漂移计算方法做出优化。首先采集了航标遥测数据并通过拉依达数学准则对采集数据进行预处理;结合Kmeans算法和ISODATA算法,对预处理数据进行聚类,对比分析计算出的聚类中心,选取精确度更高的聚类中心作为计算航标漂移量的基准点并计算航标漂移距离;采用Person相关性分析方法和回归分析方法,构建潮流场作用下的航标漂移模型。结合航标和潮流场实际数据进行回归分析,确定模型参数,计算均方根误差,对模型进行验证。结果表明,该模型能较好地反映潮流场作用下的航标漂移运动,能有效减少航标漂移误报警,提高航标的管理效率和智能化水平。     

基于时间的进场航班效率评估方法研究

终端区作为空中交通的起讫点,是拥堵、延误的多发区,其运行效率对航班整体的运行效率至关重要。欧洲航行安全组织(EUROCONTROL)采用畅通进场时间(Unimpeded AMSA Time)作为量化进场效率的标杆。我国目前还没有明确的进场效率量化指标,因此可以借鉴畅通进场时间这个指标来评估进场效率。以首都机场为研究对象,首先对进港航班ADS-B数据进行预处理来计算进场时间,然后利用K-means聚类算法对航班进行分组,运用回归分析法找寻与终端区拥挤程度关联性最强的解释变量,提出关于畅通航班拥挤阈值的方法来计算畅通进场时间,最后用时间效率指标对进场效率进行评估,得出5月首都机场的进场效率为83. 39%,可为进场效能评估提供参考。     

基于K-means聚类的沙尘天气快速识别技术研究

依据沙尘天气判定条件分析2018年春季（2—4月）陕西省西安市环境空气质量小时监测数据，共识别9 d受沙尘影响。研究基于K-means聚类沙尘天气分析方法，分析沙尘天气监测数据特征，并对沙尘识别聚类模型进行优化研究，识别的沙尘影响天气与传统方法一致。分析表明，聚类方法可用于沙尘天气监测数据的识别，与传统方法相比较，基于K-means聚类方法能够快速、准确识别沙尘影响天气。     

Future prediction & estimation of faults occurrences in oil pipelines by using data clustering with time series forecasting

The world of oil pipelines is subjected to serious issues due to occurrences of toxic spills, explosions and deformations like particle deposition, corrosions and cracks due to the contact of oil particles with the pipeline surface. Hence, the structural integrity of these pipelines is of great interest due to the probable environmental, infrastructural and financial losses in case of structural failure. Based on the existing technology, it is difficult to analyze the risks at the initial stage, since traditional methods are only appropriate for static accident analyses. Nevertheless, most of these models have used corrosion features alone to assess the condition of pipelines. To sort out the above problem in the oil pipelines, fault identification and prediction methods based on K-means clustering and Time-series forecasting incorporated with linear regression algorithm using multiple pressure data are proposed in this paper. The real-time validation of the proposed technique is validated using a scaled-down experimental hardware lab setup resembling characteristics exhibited by onshore unburied pipeline in India. In the proposed work, crack and blockages are identified by taking pressure rise and pressure drop inferred from two cluster assignment. The obtained numerical results from K-means clustering unveils that maximum datasets accumulated range of multiple pressures are within 16.147–10.638 kg/cm², 14.922–12.1674 kg/cm², 2.7645–1.2063 kg/cm² correspondingly. Hence by this final cluster center data, inspection engineers able to estimate the normal and abnormal performance of oil transportation in a simple-robust manner. The developed forecast model successfully predicts future fault occurrences rate followed by dissimilarity rate from clustering results holds the validity of 91.9% when applied to the historical pressure datasets. The models are expected to help pipeline operators without complex computation processing to assess and predict the condition of existing oil pipelines and hence prioritize the planning of their inspection and rehabilitation.