基于邻域彩色变化矢量场的图像边缘检测技术研究*

首先进行了边缘检测系统结构设计,建立了图像邻域彩色变化矢量场的数理模型,提出了用图像邻域彩色变化方向锐度描述图像边缘,进而应用模糊聚类自适应检测边缘.实验表明:与基于梯度的边缘检测技术相比,该方法在噪声抑制以及边缘准确定位上均取得了好的效果,是一种应用广泛的优秀边缘检测算法.     

基于局部密度的最小生成树聚类算法及其在电力大数据的应用

电力大数据主要来源于电力生产和电能使用的发电、输电、变电、配电、用电和调度各个环节,,如何运用这些数据提高电力管理工作的智能化水平已经成为相关电力环节十分重要的研究课题之一。但现有电力大数据中用到的聚类方法却不能发现任意形状的数据集聚类类别(即类簇),这影响了电力大数据在应用中的计算精度与计算时长。因此提出了一种新的算法,即使用局部密度峰值和基于共享邻点的距离,更好地结合了密度与距离的关系,表示出数据之间的差异。使用局部密度峰值并用基于共享邻点的距离来构造最小生成树,然后重复切割最长的边,直到找到给定数量的簇。在电力大数据应用上的实验结果表明,该算法在具有良好的效果。     

基于改进K-means聚类的配电网电压暂降频次估计法

传统考虑保护动作特性的电压暂降频次估计法需要获取详尽的保护配置信息,然而配电网保护配置多样,在不同因素如过渡电阻、运行方式、故障类型等的影响下,阶段式保护各级保护区可能产生较大变化,采用传统方法对电压暂降持续时间进行评估可能会产生较大误差。文中提出一种基于改进K-means聚类的配电网电压暂降频次估计方法,在未知线路保护配置基础上,基于电压暂降历史监测数据与保护动作信息,采用改进K-means聚类算法,对电压暂降幅值-持续时间进行聚类分析,推断线路保护配置情况,计算保护动作时间与保护动作电压。根据计算结果,在考虑不同故障类型、不同运行方式及不同过渡阻抗的情况下进行配电网电压暂降频次估计。在IEEE RBTS-6母线测试系统的母线5配电网中进行仿真,验证了文中方法的有效性和优越性。     

基于能量谱相似度自适应聚类的配电网接地故障区段定位方法

分析谐振接地系统发生单相接地故障时各区段故障暂态零序电流的相似性特征,提出基于故障暂态零序电流Hilbert瞬时能量谱相似性自适应仿射传播聚类(adAP)的故障区段定位方法.所提方法对故障后首半个工频周期的故障线路各区段的故障暂态零序电流进行局部特征尺度分解(LCD),对得到的各频带波形进行Hilbert变换得到Hilbert瞬时能量谱.计算Hilbert瞬时能量谱两两间的动态时间弯曲距离,对动态时间弯曲距离构成的相似度矩阵进行adAP,根据Silhouette指标最高的聚类情况定位故障区段.在电弧故障、存在分布式消弧线圈系统、不同消弧线圈补偿度系统、噪声干扰、两点接地、采样不同步的场景下的仿真结果和现场数据验证结果表明,所提方法能克服工程应用中可能存在的影响,鲁棒性好、准确度高.     

基于可拓距K-均值聚类和正弦微分进化算法的风储联合系统优化配置

以最大化风储联合投资商的收益和风电就地消纳率为目标,考虑源网荷协同优化和需求响应构建了风储联合系统的多目标优化配置模型;采用可拓距K-均值聚类算法对分布式风电出力和负荷需求的不确定性进行多场景分析,以实现更为准确而均衡的场景缩减;通过引入多核并行运行环境与正弦函数的思想,提出基于并行多目标正弦微分进化算法对优化配置模型进行高效求解;以IEEE 33节点配电系统为算例进行风储联合系统的优化配置,仿真结果验证了所建模型的有效性和优越性.     

基于模型的子空间聚类与时间段蚁群算法的合同生产批量调度方法

针对目前冷轧薄板厂生产流程复杂、大量的多品种小批量合同并线生产,导致难以制定生产计划的问题,本文提出了混合模型子空间聚类（Subspace clustering mixed model,SCMM）方法,以合同中待加工钢卷的宽度、冷轧机组的入口厚度、 出口厚度以及合同的交货期为约束,对待生产合同进行组批. 依据冷轧厂实际生产过程,将冷轧机组视为核心节点,考虑准时交货、 在制品库存和生产流向产能分配的要求,对组批后的生产合同建立全流程合同计划模型,并且利用提出的时间段蚁群算法（Time-section ant colony optimization,TSA）,制定合同计划.利用生产过程的实际数据测试,本文的方法优于人工排产,可以满足制定冷轧薄板全流程生产计划的要求.     

带观测滞后和不确定噪声方差的多智能体传感网络鲁棒序贯协方差交叉融合Kalman滤波

针对带观测滞后和不确定噪声方差的分簇多智能体传感网络系统,研究鲁棒序贯协方差交叉融合Kalman滤波器的设计问题.应用最邻近法则,传感网络被分成簇.应用极大极小鲁棒估计原理,基于带噪声方差最差保守上界的最差保守传感网络系统,提出了两级序贯协方差交叉(SCI)融合鲁棒稳态Kalman滤波器,可减小通信和计算负担并节省能量,且保证实际滤波误差方差有一个最小保守上界.一种Lyapunov方程方法被提出用于证明局部和融合滤波器的鲁棒性.提出了鲁棒精度的概念且证明了局部和融合鲁棒Kalman滤波器的鲁棒精度关系.证明全局SCI融合器的鲁棒精度高于每簇SCI融合器的精度且两者的鲁棒精度都高于每个局部鲁棒滤波器的精度.一个跟踪系统的仿真例子证明了鲁棒性和鲁棒精度关系.     

Determination of temporal information granules to improve forecasting in fuzzy time series

Partitioning the universe of discourse and determining intervals containing useful temporal information and coming with better interpretability are critical for forecasting in fuzzy time series. In the existing literature, researchers seldom consider the effect of time variable when they partition the universe of discourse. As a result, and there is a lack of interpretability of the resulting temporal intervals. In this paper, we take the temporal information into account to partition the universe of discourse into intervals with unequal length. As a result, the performance improves forecasting quality. First, time variable is involved in partitioning the universe through Gath–Geva clustering-based time series segmentation and obtain the prototypes of data, then determine suitable intervals according to the prototypes by means of information granules. An effective method of partitioning and determining intervals is proposed. We show that these intervals carry well-defined semantics. To verify the effectiveness of the approach, we apply the proposed method to forecast enrollment of students of Alabama University and the Taiwan Stock Exchange Capitalization Weighted Stock Index. The experimental results show that the partitioning with temporal information can greatly improve accuracy of forecasting. Furthermore, the proposed method is not sensitive to its parameters.     

Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems

Recommender systems apply data mining and machine learning techniques for filtering unseen information and can predict whether a user would like a given item. This paper focuses on gray-sheep users problem responsible for the increased error rate in collaborative filtering based recommender systems. This paper makes the following contributions: we show that (1) the presence of gray-sheep users can affect the performance – accuracy and coverage – of the collaborative filtering based algorithms, depending on the data sparsity and distribution; (2) gray-sheep users can be identified using clustering algorithms in offline fashion, where the similarity threshold to isolate these users from the rest of community can be found empirically. We propose various improved centroid selection approaches and distance measures for the K-means clustering algorithm; (3) content-based profile of gray-sheep users can be used for making accurate recommendations. We offer a hybrid recommendation algorithm to make reliable recommendations for gray-sheep users. To the best of our knowledge, this is the first attempt to propose a formal solution for gray-sheep users problem. By extensive experimental results on two different datasets (MovieLens and community of movie fans in the FilmTrust website), we showed that the proposed approach reduces the recommendation error rate for the gray-sheep users while maintaining reasonable computational performance.