Average cluster sizes and cluster size distributions of superfine nickel particles in light media

Effects of shear rates on average cluster sizes (ACSs) and cluster size distributions (CSDs) in uni- and bi-systems of partly charged superfine nickel particles were investigated by Brownian dynamics, and clustering properties in these systems were compared with those in non-polar systems. The results show that the ACSs in bi-polar systems are larger than those in the non-polar systems. In uni-polar systems the behavior of clustering property differs: at the lower ionic concentration (10%), repulsive force is not strong enough to break clusters, but may greatly weaken them. The clusters are eventually cracked into smaller ones only when concentration of uni-polar charged particles is large enough. In this work, the ionic concentration is 20%. The relationship between ACS and shear rates follows power law in a exponent range of 0.176-0.276. This range is in a good agreement with the range of experimental data, but it is biased towards the lower limit slightly.

Design of Clustering Techniques in Cognitive Radio Sensor Networks

In recent decades,several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during trans-mission to a shorter distance while restricting the Primary Users(PUs)interfer-ence.The Cognitive Radio(CR)system is based on the Adaptive Swarm Distributed Intelligent based Clustering algorithm(ASDIC)that shows better spectrum sensing among group of multiusers in terms of sensing error,power sav-ing,and convergence time.In this research paper,the proposed ASDIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their connectivity.In this research,multiple random Sec-ondary Users(SUs),and PUs are considered for implementation.Hence,the pro-posed ASDIC algorithm improved the convergence speed by combining the multi-users clustered communication compared to the existing optimization algo-rithms.Experimental results showed that the proposed ASDIC algorithm reduced the node power of 9.646%compared to the existing algorithms.Similarly,ASDIC algorithm reduced 24.23%of SUs average node power compared to the existing algorithms.Probability of detection is higher by reducing the Signal-to-Noise Ratio(SNR)to 2 dB values.The proposed ASDIC delivers low false alarm rate compared to other existing optimization algorithms in the primary detection.Simulation results showed that the proposed ASDIC algorithm effectively solves the multimodal optimization problems and maximizes the performance of net-work capacity.

Subspace Distribution Clustering HMM for Chinese Digit Speech Recognition

As a kind of statistical method, the technique of Hidden Markov Model （HMM） is widely used for speech recognition. In order to train the HMM to be more effective with much less amount of data, the Subspace Distribution Clustering Hidden Markov Model （SDCHMM）, derived from the Continuous Density Hidden Markov Model （CDHMM）, is introduced. With parameter tying, a new method to train SDCHMMs is described. Compared with the conventional training method, an SDCHMM recognizer trained by means of the new method achieves higher accuracy and speed. Experiment results show that the SDCHMM recognizer outperforms the CDHMM recognizer on speech recognition of Chinese digits.

A Cooperative Security Monitoring Mechanism Aided by Optimal Multiple Slave Cluster Heads for UASNs

As each cluster head(CH)sensor node is used to aggregate,fuse,and forward data from different sensor nodes in an underwater acoustic sensor network(UASN),guaranteeing the data security in a CH is very critical.In this paper,a cooperative security monitoring mechanism aided by multiple slave cluster heads(SCHs)is proposed to keep track of the data security of a CH.By designing a low complexity“equilateral triangle algorithm(ETA)”,the optimal SCHs(named as ETA-based multiple SCHs)are selected from the candidate SCHs so as to improve the dispersion and coverage of SCHs and achieve largescale data security monitoring.In addition,by analyzing the entire monitoring process,the close form expression of the probability of the failure attack identification for the SCHs with respect to the probability of attack launched by ordinary nodes is deduced.The simulation results show that the proposed optimal ETA-based multiple SCH cooperation scheme has lower probability of the failure attack identification than that of the existing schemes.In addition,the numerical simulation results are consistent with the theoretical analysis results,thus verifying the effectiveness of the proposed scheme.

DCAD:a Dual Clustering Algorithm for Distributed Spatial Databases

Spatial objects have two types of attributes： geometrical attributes and non-geometrical attributes, which belong to two different attribute domains （geometrical and non-geometrical domains）. Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clustering： local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted clustering is obtained based on those features fective and efficient. Second, local features from each site are sent to a central site where global Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient.

Clustering properties of partly charged superfine particles in a light medium

This paper investigated average cluster sizes （ACS） and cluster size distributions （CSD） at different shear rates by Brownian dynamics in non-, bi-, and uni-polar systems with partly charged superfine particles, The investigation indicates that clusters in non- polar systems are the weakest and easiest to be damaged by increasing shear stresses; charged particles play important and different roles： in bi-polar system, it intends to strengthen clusters to some extent provided that the sign-like ions homogeneously arranged; in uni-polar system charged particles cracked the clusters into smaller ones, but the small clusters are strong to stand with larger shear stress. The relationship between ACS and shear rates follows power law with exponents in a range 0.18-0.28, these values are in a good agreement with experiment range but at the lower limit compared with other systems of non-metallic cluster particles.

Distributed Document Clustering Analysis Based on a Hybrid Method

Clustering is one of the recently challenging tasks since there is an ever.growing amount of data in scientific research and commercial applications. High quality and fast document clustering algorithms are in great demand to deal with large volume of data. The computational requirements for bringing such growing amount data to a central site for clustering are complex. The proposed algorithm uses optimal centroids for K.Means clustering based on Particle Swarm Optimization(PSO).PSO is used to take advantage of its global search ability to provide optimal centroids which aids in generating more compact clusters with improved accuracy. This proposed methodology utilizes Hadoop and Map Reduce framework which provides distributed storage and analysis to support data intensive distributed applications. Experiments were performed on Reuter's and RCV1 document dataset which shows an improvement in accuracy with reduced execution time.

Performance Analysis of Spectrum Sensing Based on Distributed Satellite Clusters under Perturbation

In this paper,we investigate the spectrum sensing performance of a distributed satellite clusters(DSC)under perturbation,aiming to enhance the sensing ability of weak signals in the coexistence of strong and weak signals.Specifically,we propose a cooperative beamforming(BF)algorithm though random antenna array theory to fit the location characteristic of DSC and derive the average far-field beam pattern under perturbation.Then,a constrained optimization problem with maximizing the signal to interference plus noise ratio(SINR)is modeled to obtain the BF weight vectors,and an approximate expression of SINR is presented in the presence of the mismatch of signal steering vector.Finally,we derive the closedform expression of the detection probability for the considered DSC over Shadowed-Rician fading channels.Simulation results are provided to validate our theoretical analysis and to characterize the impact of various parameters on the system performance.

考虑频率动态安全的孤岛微网群分布鲁棒调度方法

针对孤岛微网群互联运行下的频率安全问题,提出了一种考虑微网频率时空差异的分布式日前调度方法。首先,提出一种微网群联络线功率振荡及摇摆方程等值模型,基于此推导计及频率差异的频率动态安全约束集;其次,建立孤岛微网群两阶段分布鲁棒日前优化调度模型,利用Copula函数及混合范数建模源荷出力不确定性;然后,通过对偶分解法生成孤岛微网的分布式调度模型,利用嵌套的次梯度与列与约束生成法求解。最后,基于改进的IEEE-15节点算例进行仿真,验证模型日前调度策略在提高孤岛微网群频率安全性上的优势。

计及源荷时序特性的低压台区分布式光伏接入分布鲁棒优化方法

为提高低压配电台区中分布式光伏(DPV)的接入容量,促进光伏消纳,本文提出一种计及源荷时序特性的低压台区DPV接入分布鲁棒优化方法.首先,针对低压台区中分布式光伏出力和负荷需求的不确定性,提出一种基于优化聚类的源-荷联合时序场景生成方法;其次,计及电压约束、线路容量约束、逆变器无功补偿约束及光伏消纳约束等,构建低压台区分布式光伏接入分布鲁棒优化模型,在保证各典型场景最恶劣概率分布下的弃光率期望值符合要求的情况下,最大化低压台区中分布式光伏接入容量;然后,建立低压台区分布式储能接入的数学模型,以研究储能接入及其充放电机制对低压台区分布式光伏接入的影响;最后,以实际配电台区为例进行仿真计算,验证了本文模型的有效性.

基于场景聚类的主动配电网分布鲁棒综合优化

为较好地考虑分布式电源(DG)和负荷的不确定性对配电网的影响,基于风电场景预测,通过调整网络拓扑结构和风电、光伏发电、微型燃气轮机、储能、无功补偿等的出力,提出了实现动态重构与无功优化相结合的主动配电网综合优化模型。在考虑DG间相关性的基础上,基于原始数据采用拉丁超立方采样得到初始场景,利用手肘法确定K-means算法的聚类数,从而得到典型场景,场景分布的概率置信区间由1-范数和∞-范数约束。另外,为了消除储能与机组组合的时间相关性约束,重点介绍了基于盒式分解算法的两阶段分解模型。第1阶段以机组启停成本和运行成本为目标函数对机组与储能的各时段运行域进行约束。将第2阶段转化为单时间尺度优化问题,通过求解得到经济性最优的结果。模型采用列与约束生成算法进行求解,通过对IEEE 33节点系统与Taipower 84节点系统的仿真验证了模型的鲁棒性与可行性。

基于分布式鲁棒的城市群救灾网络与灾民疏散联合规划研究

城市群的出现极大地提高了城市间的分工协作功能,大规模灾害发生时,其对应急资源的高效配置和应急救援活动的快速展开至关重要。为了提高城市群的应急救援效率,基于城市群区域协同机制,进行了应急设施的选址、灾民疏散和物资配送联合规划研究。大规模灾害发生之前,通过构造一个包含多个指标因素的综合评价体系,对各应急设施面对灾害的应急响应时间进行分区处理,确定设施选址及其差异化的应急响应能力;灾害发生之后,依托避难所和配送中心完成各灾区灾民的安置和物资的需求满足。由于地震灾难发生具有不确定性的特点,引入联合机会约束的方法来确保各灾区灾民疏散的可靠性,以此建立了总成本最小化的分布式鲁棒优化模型,并将其转化为易于求解的二阶锥规划模型。结果表明,联合机会约束的分布式鲁棒模型在选址数量上少于随机模型,并以较少的成本疏散了更多的灾民,相较于传统鲁棒模型,降低了其保守性。同时分析了城市群的区域异质性,表明考虑区域异质性使得整个救援过程更具针对性、有效性和经济性。

Performance Analysis of DEBT Routing Protocols for Pocket Switch Networks

Pocket Switched Networks(PSN)represent a particular remittent network for direct communication between the handheld mobile devices.Compared to traditional networks,there is no stable topology structure for PSN where the nodes observe the mobility model of human society.It is a kind of Delay Tolerant Networks(DTNs)that gives a description to circulate information among the network nodes by the way of taking the benefit of transferring nodes from one area to another.Considering its inception,there are several schemes for message routing in the infrastructure-less environment in which human mobility is only the best manner to exchange information.For routing messages,PSN uses different techniques such asDistributed Expectation-Based Spatio-Temporal(DEBT)Epidemic(DEBTE),DEBT Cluster(DEBTC),and DEBT Tree(DEBTT).Understanding on how the network environment is affected for these routing strategies are the main motivation of this research.In this paper,we have investigated the impact of network nodes,the message copies per transmission,and the overall carrying out of these routing protocols.ONE simulator was used to analyze those techniques on the basis of delivery,overhead,and latency.The result of this task demonstrates that for a particular simulation setting,DEBTE is the best PSN routing technique among all,against DEBTC and DEBTT.

Distributed Clustering Algorithm for Energy Efficiency and Load-Balance in Large-Scale Multi-Agent Systems

To improve the energy efficiency and load-balance in large-scale multi-agent systems, a large-scale distributed cluster algorithm is proposed. At first, a parameter describing the spatial distribution of agents is designed to assess the information spreading capability of an agent. Besides, a competition resolution mechanism is proposed to tackle the competition problem in large-scale multiagent systems. Thus, the proposed algorithm can balance the load, adjust the system network locally and dynamically, reduce system energy consumption. Finally, simulations are presented to demonstrate the superiority of the proposed algorithm.

Distributed Consensus-Based K-Means Algorithm in Switching Multi-Agent Networks

This paper discusses a distributed design for clustering based on the K-means algorithm in a switching multi-agent network, for the case when data are decentralized stored and unavailable to all agents. The authors propose a consensus-based algorithm in distributed case, that is, the double- clock consensus-based K-means algorithm （DCKA）. With mild connectivity conditions, the authors show convergence of DCKA to guarantee a distributed solution to the clustering problem, even though the network topology is time-varying. Moreover, the authors provide experimental results on vari- ous clustering datasets to illustrate the effectiveness of the fully distributed algorithm DCKA, whose performance may be better than that of the centralized K-means algorithm.

Architecture and critical technologies of space information networks

Focusing on its main requirements and challenges and by analyzing the characteristics of different space platforms,an overall architecture for space information networks is proposed based on national strategic planning and the present development status of associated technologies.Furthermore,the core scientific problems that need to be solved are expounded.In addition,the primary considerations and a preliminary integrated demonstration environment for verification of key technologies are presented.

Distributed Algorithms for Event Reporting in Mobile-Sink WSNs for Internet of Things

Wireless Sensor Networks（WSNs） have many applications, such as climate monitoring systems, fire detection, smart homes, and smart cities. It is expected that WSNs will be integrated into the Internet of Things（IoT）and participate in various tasks. WSNs play an important role monitoring and reporting environment information and collecting surrounding context. In this paper we consider a WSN deployed for an application such as environment monitoring, and a mobile sink which acts as the gateway between the Internet and the WSN. Data gathering is a challenging problem in WSNs and in the IoT because the information has to be available quickly and effectively without delays and redundancies. In this paper we propose several distributed algorithms for composite event detection and reporting to a mobile sink. Once data is collected by the sink, it can be shared using the IoT infrastructure. We analyze the performance of our algorithms using WSNet simulator, which is specially designed for event-based WSNs. We measure various metrics such as average residual energy, percentage of composite events processed successfully at the sink, and the average number of hops to reach the sink.