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.     

一种用于解调失真QAM信号的改进K-means聚类算法

在短距离无线通信中,无线节点或移动终端通常有低成本、小体积、低功耗的要求,因此无法使用复杂的预失真或补偿电路克服功放的非线性影响,这是无线节点或移动终端在上行链路中难以使用高阶QAM调制的重要原因之一。基于QAM矩形星座的特点,提出了一种K-means聚类的改进算法作为中央基站节点的高阶QAM解调算法。在发送信号受到较严重的功放非线性失真时,所提改进算法解调性能更优,算法复杂度更低。     

Impact of class clustering in a multiclass FCFS queue with order-dependent service times

In multi-class queueing systems, customers of different classes can enter the system. When studying such systems, it is traditionally assumed that the different classes of customers occur randomly and independently in the arrival stream of customers in the system. This is often in contrast to the actual situation. Therefore, we study a multi-class system with so-called class clustering in the customer arrival stream, i.e., (Markovian) correlation occurs in the classes of consecutive customers. The system under investigation consists of one server that is able to serve two classes of customers. In addition, the service-time distribution of a customer depends on the equality or non-equality of its class with the class of the previous customer. This latter feature occurs frequently in practice. For instance, execution of the same task again can lead to both faster or slower processing times. The first case can occur when the execution of a different task entails resetting a machine, or loading new data, et cetera. The opposite situation appears, for instance, when execution of the same task requires postprocessing (such as cooling down or reinitialization of a machine). We deduce the probability generating function (pgf) of the system content, from which we can extract various performance measures, among which the mean values of the system content and the customer delay. We demonstrate that class clustering has a tremendous impact on the system performance, which highlights the necessity to include it in the performance assessment of any system in which it occurs.     

Detecting similarities in virtual machine behavior for cloud monitoring using smoothed histograms

The growing size and complexity of cloud systems determine scalability issues for resource monitoring and management. While most existing solutions consider each Virtual Machine (VM) as a black box with independent characteristics, we embrace a new perspective where VMs with similar behaviors in terms of resource usage are clustered together. We argue that this new approach has the potential to address scalability issues in cloud monitoring and management. In this paper, we propose a technique to cluster VMs starting from the usage of multiple resources, assuming no knowledge of the services executed on them. This innovative technique models VMs behavior exploiting the probability histogram of their resources usage, and performs smoothing-based noise reduction and selection of the most relevant information to consider for the clustering process. Through extensive evaluation, we show that our proposal achieves high and stable performance in terms of automatic VM clustering, and can reduce the monitoring requirements of cloud systems.     

A Framework for Interactive t-SNE Clustering

In this paper, we describe our progress in creating the framework for an interactive application that allows humans to actively participate in a t-SNE clustering process. t-SNE (t-Distributed Stochastic Neighbor Embedding) is a dimensionality reduction technique that maps high dimensional data sets to lower dimensions that can then be visualized for human interpretation. By prompting users to monitor outlying points during the t-SNE clustering process, we hypothesize that users may be able to make clustering faster and more accurate than purely algorithmic methods. Further research would test these hypotheses directly. We would also attempt to decrease the lag time between the various components of our application and develop an intuitive approach for humans to aid in clustering unlabeled data. Research into human assisted clustering can combine the strengths of both humans and computer programs to improve the results of data analysis.