基于聚类的离群点检测方法研究综述

离群点检测在数据处理中具有重要研究意义,其检测方法大致可以分为基于统计、基于距离、基于密度和基于聚类的方法。为了及时掌握当前基于聚类技术的离群点检测方法的研究现状,通过归纳与整理,将具有代表性的基于聚类的离群点检测方法进行了介绍和归类,将其主要分为静态数据集中的检测方法、数据流中的检测方法、大规模数据中的检测方法和其他方法等四大类。对每类方法所解决的问题、算法思想、应用场景以及各自的优缺点进行了详细的归纳和分析,指出目前存在的问题以及未来发展方向。     

基于可信执行环境的物联网边缘流处理安全技术综述

万物互联时代,物联网中感知设备持续产生大量的敏感数据。实时且安全的数据流处理是面向物联网关键应用中需要解决的一个挑战。在近年兴起的边缘计算模式下,借助靠近终端的设备执行计算密集型任务与存储大量的终端设备数据,物联网中数据流处理的安全性和实时性可以得到有效的提升。然而,在基于边缘的物联网流处理架构下,数据被暴露在边缘设备易受攻击的软件堆栈中,从而给边缘带来了新的安全威胁。为此,文章对基于可信执行环境的物联网边缘流处理安全技术进行研究。从边缘出发,介绍边缘安全流处理相关背景并探讨边缘安全流处理的具体解决方案,接着分析主流方案的实验结果,最后展望未来研究方向。     

复杂事件处理的自适应制造情景识别方法

制造过程中的任务、自然条件、电力水平等环境因素,制约物体状态及其关系的变化.智能制造单元需要自适应的对不同情境约束下的事件和复杂情形及时理解判断,提出基于复杂事件处理(Complex event processing,CEP)的情境约束情景识别方法,以实时作出合理的优化决策.针对忽视情境约束对事件判别的影响,构建基于情境约束的多层次事件模型,给出同生、情境、协同等事件新算子,提出基于事件聚合的制造情景模型与演算过程.针对情景识别知识库中模式规则生成的不足,通过整合物体数据与环境数据建立映射关联,将感知信息转化为情境事件图谱.通过综合序数、名义变量等距离计算和自适应熵权法,提出改进的混合聚类方法处理事件图谱实例属性的多样性和关联性,构建知识库以为情景实时识别提供服务支持.运用4个真实数据集和1个制造过程仿真数据集进行实验,均验证本文模型和方法的有效性,适用于大规模学习问题,并阐明情境因素能显著提升复杂制造应用中的事件判断、情景识别的准确性.     

事件约束的时间不确定事件流查询处理

提出两种事件约束的时间不确定事件流查询处理方法.基于可能世界的查询处理方法,根据事件约束的不确定事件查询语义,通过对可能世界的遍历获取查询结果.基于事件约束优先的查询处理方法中,根据事件约束计算其所涉及事件发生时间的联合分布律,进而获得复合查询所涉及事件的边缘分布律,最后利用时间不确定事件查询处理技术进行后续的查询处理.实验证明了2种方法对时间不确定约束事件查询有效.基于事件约束优先查询处理方法的可能世界集规模更小,比基于可能世界的查询处理方法更高效.     

苏州河水环境治理20年回顾与展望

简要阐述了苏州河历史演变、水文特性和水环境变化过程,回顾了自1998年开始的苏州河环境综合整治一期、二期、三期工程及开展的相关科研工作,并利用干流历年水质变化过程分析了历次治理取得的成效。结果表明:一期整治工程对水质指标的改善效果较好,而二、三期工程影响不明显,但当郊区河道进一步治理后,干流水质得到进一步好转。最后,针对目前正开展的苏州河综合整治四期工程总体方案,指出更大范围、更加彻底的标本兼治苏州河支流水环境会提升苏州河干流水环境,但要恢复苏州河的"水清岸绿、鱼翔浅底"的生态功能,还需有漫长的过程,且需要上游的江苏省内河道同步治理。     

In‐stream construction‐induced suspended sediment in riverine ecosystems

Construction activities within the wetted‐perimeter of rivers, referred to as in‐stream construction, are prevalent economically and environmentally motivated activities having direct interactions with sensitive lotic environments. Currently, there is a paucity of research related to in‐stream construction activities and their effects on aquatic ecosystems. In‐stream construction‐induced suspended sediment may result in harmful effects to aquatic flora and fauna. Regulatory frameworks worldwide focus primarily on concentration, with limited consideration for duration and no consideration for spatial extents of suspended sediment exposures. This research develops theoretical concentration, duration, and spatial extent exposure risk relationships for riverine ecosystems to demonstrate the influence of each mechanism during typical in‐stream construction activities. To reduce exposure risk, concentration and duration may be considered pragmatically, based on anticipated activity characteristics and site conditions. Spatial exposure characteristics are important to consider, as illustrated by our finding that activities located near the channel centerline may result in greater exposure risk than similar activities conducted near the streambank. Current regulatory frameworks worldwide do not sufficiently consider all exposure risk mechanisms present during in‐stream construction‐induced suspended sediment releases, possibly inhibiting efforts to reduce adverse environmental effects. This research improves our understanding of suspended sediment in lotic environments and may help environmental managers better evaluate and manage in‐stream construction activities.     

A customised approach to determining the geomorphic effectiveness of small flood events in a regulated river

The construction of dams significantly alters flow and sediment regimes with subsequent deleterious effects on the morphological and ecological character of rivers. Effective experimental floods can ameliorate the downstream geomorphic impacts of dams. The traditional view is that large floods are required to perform effective geomorphic work, and the geomorphic outcomes of small floods are often overlooked. Many river restoration frameworks do not consider small floods. Yet, there is evidence that the hydrological characteristics that ameliorate specific geomorphic impacts in a river are unique to each river, and a customised approach to setting the right mix of floods (including small experimental floods) is needed. In this study, we modify an existing flood effectiveness model developed for large floods, for determining the geomorphic effectiveness of small floods in a highly regulated Australian river. Two flood classes were added to the model (medium peak stream power and moderate total energy expenditure), and the flood power characteristics were rescaled to reflect the relative difference in the magnitude of the small floods and the magnitude of the geomorphic work performed. Using a step‐wise approach, this customised model determined the geomorphic effectiveness of small floods. The best flood for ameliorating the geomorphic impacts of flow regulation had medium to long duration (10 to 51 days), high peak unit stream power (77 to 123 Wm^?2) and moderate to large total energy expenditure (78,600 to 342,320 × 10³ J). This approach to determining flood effectiveness for small floods is applicable to other geomorphically impacted river channels downstream of dams and can be used to inform experimental flood releases for geomorphic outcomes.     

基于Landsat的淮河干流水质监测的可行性分析

流域水体污染已成为影响岸边带经济社会发展的热点问题,明确流域水质变化趋势是实施水体污染控制和综合治理的前提。针对流域水质状况及其发展趋势的监测问题,以淮河干流为研究对象,在现有的流域水质监测手段的基础上,结合实测水质数据和同步Landsat数据,采用多元统计分析方法,建立了TM、OLI水质遥感监测模型,开展了2006-2017年淮河干流水体污染趋势分析。结果表明：TM、OLI水质综合污染指数遥感监测模型计算值与实测值之间绝对误差不超过 0.17,相对误差不超过7.11%;模型计算的2006-2017年淮河干流各类污染水体面积占比变化情况与实际监测结果一致,模型可用于淮河干流水质动态监测中;将遥感影像数据与典型断面监测数据相结合,建立流域水质监测模型的思路是可行的。研究结果为大尺度流域水质动态监测和生态环境保护提供了一种新的思路。     

流计算模式下概率粗糙集三支决策的快速计算

概率粗糙集三支决策是不确定问题求解的一种重要理论,流计算模式是一种新型的动态内存计算形式,实施流计算模式下三支决策的快速动态计算是一项具有挑战性的新议题。本研究以流计算模式中的两个核心计算步骤即动态增量与动态减量作为研究对象,提出了一种流计算模式下概率粗糙集三支决策域的快速动态学习方法。首先对流计算模式中三支决策动态增量和动态减量的不同变化情况进行了数据建模。然后基于不同数据变化情况分别讨论了数据增量与数据减量时三支决策域的变化推理,并且基于上述理论给出了流计算模式下的三支决策动态增减学习算法。该算法能够以更低的时间复杂度获得与经典三支决策算法相同决策效果。最后通过八种UCI数据集的实验证明了流计算模式下三支决策动态增减学习算法在时间消耗上明显优于经典概率粗糙集三支决策算法,并且在不同阈值下具有稳定的决策效率。本研究表明了流计算模式下三支决策快速计算是可行的。     

2n周期优秀二元序列生成及其特性分析简

The 2ⁿ-periodic binary sequence with high linear complexity and high k-error linear complexity is defined as an excellent sequence. We design a genetic algorithm for generating excellent sequences and studying their features. Choosing the N-periodic binary sequences, where N=8, 16, 32, k=N/4, we search the resulted sequences by the genetic algorithm with various parameters, and compute the linear complexity profiles of results sequences by using the Lauder-Paterson algorithm, to confirm that the obtained sequences are the real excellent sequences. By numerous experiments, we speculate that the k-error linear complexity of the N-periodic binary excellent sequence meets the formula LC_k(S)≤N-2k+1, when k=N/4、N/8 (we also do experiments on sequences with periods 64, 128 and 256). By the brute-force method we obtain that the proportion of the excellent sequence in all binary sequences of the same period is 1/4.