Multi-sink Deployment Strategy for Wireless Sensor Networks Based on Improved Particle Swarm Clustering Optimization Algorithm

In wireless sensor networks(WSNs) with single sink,the nodes close to the sink consume their energy too fast due to transferring a large number of data packages,resulting in the "energy hole" problem.Deploying multiple sink nodes in WSNs is an effective strategy to solve this problem.A multi-sink deployment strategy based on improved particle swarm clustering optimization(IPSCO) algorithm for WSNs is proposed in this paper.The IPSCO algorithm is a combination of the improved particle swarm optimization(PSO) algorithm and K-means clustering algorithm.According to the sink nodes number K,the IPSCO algorithm divides the sensor nodes in the whole network area into K clusters based on the distance between them,making the total within-class scatter to minimum,and outputs the center of each cluster.Then,multiple sink nodes in the center of each cluster can be deployed,to achieve the effects of partition network reasonably and deploy multi-sink nodes optimally.The simulation results show that the deployment strategy can prolong the network lifetime.

虚拟化技术在基于自律计算的高可用性系统中的应用

针对服务器集群的管理复杂度高的问题,在对自律计算和大型可分区服务器硬件特征研究的基础上,提出了一种虚拟化技术,通过对硬件资源的虚拟化,实现了物理资源向逻辑资源的映射,从而统一管理所有物理资源。此外,在资源统一管理的基础上,对服务器集群的资源配置提出了完整的解决方案,实现了系统的自我管理。

Cluster voltage control method for “Whole County” distributed photovoltaics based on improved differential evolution algorithm

China is vigorously promoting the “whole county promotion” of distributed photovoltaics (DPVs). However, the high penetration rate of DPVs has brought problems such as voltage violation and power quality degradation to the distribution network, seriously affecting the safety and reliability of the power system. The traditional centralized control method of the distribution network has the problem of low efficiency, which is not practical enough in engineering practice. To address the problems, this paper proposes a cluster voltage control method for distributed photovoltaic grid-connected distribution network. First, it partitions the distribution network into clusters, and different clusters exchange terminal voltage information through a “virtual slack bus.” Then, in each cluster, based on the control strategy of “reactive power compensation first, active power curtailment later,” it employs an improved differential evolution (IDE) algorithm based on Cauchy disturbance to control the voltage. Simulation results in two different distribution systems show that the proposed method not only greatly improves the operational efficiency of the algorithm but also effectively controls the voltage of the distribution network, and maximizes the consumption capacity of DPVs based on qualified voltage.

一种基于多级聚类的VLSI电路划分算法

超大规模集成电路(VLSI)划分问题,属于NP-难问题。结合了贪心随机自适应搜索过程(GRASP)和多级聚类方法的思想,提出了一种基于多级聚类的电路划分算法。算法采用贪心随机自适应的思想改进了多级划分方法中重边粗化聚类(HEM)方法。通过对ISPD98的18个标准测试样例的测试结果表明,该方法与著名的划分工具h Metis相比,划分质量有一定的提高,最多可以改进3%左右。

Identifying representative days of solar irradiance and wind speed in Brazil using machine learning techniques

The investment levels in electricity production capacity from variable Renewable Energy Sources have substantially grown in Brazil over the last decades,following the worldwide-seeking-goal of a carbon-neutral economy and the country’s incentives in diversifying its generation mix.From a long-term perspective,the current non-storable capability of renewable energy sources requires an adequate uncertainty characterization over the years.In this context,the main objective of this work is to provide a thorough descriptive analytics of the time-linked hourly-based daily dynamics of wind speed and solar irradiance in the main resourceful regions of Brazil.Leveraging on unsupervised Machine Learning methods,we focus on identifying similar days over the years,Representative Days,that can depict the fundamental underlying behaviour of each source.The analysis is based on a historical dataset of different sites with the highest potential and installed capacity of each source spread over the country:three in the Northeast and one in the South Regions,for wind speed;and three in the Northeast and one in the Southeast Regions,for solar irradiance.We use two Partitioning Methods(𝐾-Means and𝐾-Medoids),the Hierarchical Ward’s Method,and a Model-Based Method(Self-Organizing Maps).We identified that wind speed and solar irradiance can be effectively represented,respectively,by only two representative days,and two or three days,depending on the region and method(segments data with respect to the intensity of each source).Analysis with higher Representative Days highlighted important hidden patterns such as different wind speed modulations and solar irradiance peak-hours along the days.