ACTPred： Activity Prediction in Mobile Social Networks

A current trend for online social networks is to turn mobile.Mobile social networks directly reflect our real social life,and therefore are an important source to analyze and understand the underlying dynamics of human behaviors （activities）.In this paper,we study the problem of activity prediction in mobile social networks.We present a series of observations in two real mobile social networks and then propose a method,ACTPred,based on a dynamic factor-graph model for modeling and predicting users＇ activities.An approximate algorithm based on mean fields is presented to efficiently learn the proposed method.We deploy a real system to collect users＇ mobility behaviors and validate the proposed method on two collected mobile datasets.Experimental results show that the proposed ACTPred model can achieve better performance than baseline methods.     

Incremental training for SVM-based classification with keyword adjusting

This paper analyzed the theory of incremental learning of SVM (support vector machinc) and pointed out it is a shortage that the support vector optimization is only considered in present research of SVM incremental learning. According to the significance of keyword in training, a new incremental training method considering keyword adjusting was proposed, which eliminates the differcnce hetween incremental learning and batch learning through the keyword adjusling. The experimental results show that the improved method outperforms the method without the keyword adjusting and achieve the same precision as the hatch method.     

Multi-Axis Projection Based Giant Component Formation in Random Unit-Disk Graphs

This paper proposes a multi-axis projection (MAP) based giant component formation strategy via the Maximal Independent Set (MIS) in a random unit-disk graph.We focus on the problem of virtual back-bone construction in wireless ad hoc and sensor networks,where the coverage areas of the nodes are disks with identical radii.In the simulation,we show that the MAP-based giant component has the ability to connect most nodes and serves as a backbone in the network.The algorithm is localized and may play an important role in efficiently constructing a virtual backbone for ad hoc and sensor networks.     

Two-Level Cross-Talked Admission Control Mechanism for QoS Guarantee in 802.11e EDCA

This paper describes a two-level cross-talked admission control mechanism that guarantees quality of service （QoS） requirements for multimedia applications over wireless local area networks （WLANs）. An enhanced distributed channel access analytical model is used to compute the maximum number of admitted users according to the QoS requirements and the packet arrival characters. Then, some channel resources are reserved for handoff calls based on the maximum number of admitted users and the call-level traffic model. The channel utilization ratio is also measured to reflect the current system traffic load. The maximum number of admitted users and the channel utilization ratio are used for admission control for applications with QoS requirements in the call level and for rate control of best effort applications in the packet level using the p-nonacknowledgement scheme. Thus, the QoS requirements are statistically guaranteed while the system is efficiently utilized. Simulations validate the effectiveness of this mechanism to guarantee the QoS and bandwidth utilization.     

HEAD:A Hybrid Mechanism to Enforce Node Cooperation in Mobile Ad Hoc Networks

Mobile ad hoc networks rely on the cooperation of nodes for routing and forwarding. However, it may not be advantageous for individual nodes to cooperate. In order to make the mobile ad hoc network more robust, we propose a scheme called HEAD (a hybrid mechanism to enforce node cooperation in mobile ad hoc networks) to make the misbehavior unattractive. HEAD is an improvement to OCEAN (observation-based cooperation enforcement in ad hoc networks). It employs only first hand information and works on the top of DSR (dynamic source routing) protocol. By interacting with the DSR, HEAD can detect the misbehavior nodes in the packet forwarding process and isolate them in the route discovery process. In order to detect the misbehavior nodes quickly, HEAD introduces the warning message. In this paper, we also classify the misbehavior nodes into three types:malicious nodes, misleading nodes, and selfish nodes. They all can be detected by HEAD, and isolated from the network.     

Estimation of Cloud Node Acquisition Waseem Ahmed and Yongwei Wu简

Over the past decade, there has been a paradigm shift leading consumers and enterprises to the adoption of cloud computing services. Even though most cases are still in the early stages of transition, there has been a steady increase in the implementation of the pay-as-you-go or pay-as-you-grow models offered by cloud providers. Whether applied as an extension of virtual infrastructure, software, or platform as a service, many users are still challenged by the estimation of adequate resource allocation and the wide variations in pricing. Customers require a simple method of predicting future demand in terms of the number of nodes to be allocated in the cloud environment. In this paper, we review and discuss existing methodologies for estimating the demand for cloud nodes and their corresponding pricing policies. Based on our review, we propose a novel approach using the Hidden Markov Model to estimate the acquisition of cloud nodes.     

Modulated Quasi-plane Tunneling Current

A new tutmeling junction can be formed by an insulator layer inserting into a quantum well, and in the quantum well, a quasi-plane tunneling current can be formed by applying a tunneling voltage. If a P-N junction is grown on the quantum well, the tunneling current can be modulated by a P-N junction-bias voltage. The modulated quasi-plane tunneling current is not only related to the bias voltage, but also to the depth of the quantum well. It is analyzed that the P-N junction-bias voltage how to affect the tunneling current and a method of measuring the depth of the quantum well is presented.     

Improved threshold retracker for satellite altimeter waveform retracking over coastal sea

Data quality is a key factor for the application of satellite altimetry to geodesy and oceanography. Accuracy of altimetry is limited in the coastal area because the altimeter waveforms are seriously contaminated by topography and environmental pollution. So waveform retracking is needed to compute the range correction of geophysical data records (GDRs) for better accuracy. In this paper, a new waveform retracker named the improved threshold retracker (ITR) is put forward. The retracker first builds sub-waveforms based on leading edges detected in a waveform, then determines the middle point of each leading edge to compute the retracking range correction, finally calculates the referenced sea surface heights according to the geoid undulation from a local geopotential model and tide heights from an ocean tide model, and compares it with all retracking ranges to determine the best one. As a test, altimeter waveforms of Geosat/GM are retracked around the Taiwan coastal area. The result shows that accuracy of ITR method is two times better than that of the β-5-parameter function-fitting method and threshold method, and three times better than that of GDRs. ITR can efficiently improve the altimetry accuracy of the coastal sea area.     

Area Query Processing Based on Gray Code in Wireless Sensor Networks

Area query processing is significant for various applications of wireless sensor networks since it can request information of particular areas in the monitored environment. Existing query processing techniques cannot solve area queries. Intuitively, centralized processing on Base Station can accomplish area queries via collecting information from all sensor nodes. However, this method is not suitable for wireless sensor networks with limited energy since a large amount of energy is wasted for reporting useless data. This motivates us to propose an energy-efficient in-network area query processing scheme. In our scheme, the monitored area is partitioned into grids, and a unique gray code number is used to represent a Grid ID (GID), which is also an effective way to describe an area. Furthermore, a reporting tree is constructed to process area merging and data aggregations. Based on the properties of GIDs, subareas can be merged easily and useless data can be discarded as early as possible to reduce energy consumption. For energy-efficiently answering continuous queries, we also design an incremental update method to continuously generate query results. In essence, all of these strategies are pivots to conserve energy consumption. With a thorough simulation study, it is shown that our scheme is effective and energy-efficient.     

Localization in 3D sensor networks using stochastic particle swarm optimization

Localization is one of the key technologies in wireless sensor networks,and the existing PSO-based localization methods are based on standard PSO,which cannot guarantee the global convergence.For the sensor network deployed in a three-dimensional region,this paper proposes a localization method using stochastic particle swarm optimization.After measuring the distances between sensor nodes,the sensor nodes estimate their locations using stochastic particle swarm optimization,which guarantees the global convergence of the results.The simulation results show that the localization error of the proposed method is almost 40% of that of multilateration,and it uses about 120 iterations to reach the optimizing value,which is 80 less than the standard particle swarm optimization.     

Efficient statistical analysis method of power/ground(P/G) network

In this paper, we propose an incremental statistical analysis method with complexity reduction as a pre-process for on-chip power/ground (P/G) networks. The new method exploits locality of P/G network analyses and aims at P/G networks with a large number of strongly connected subcircuits(called strong connects) such as trees and chains. The method consists of three steps. First it compresses P/G circuits by removing strong connects. As a result, current variations (CV) of nodes in strong connects are transferred to some remaining nodes. Then based on the locality of power grid voltage responses to its current inputs, it efficiently calculates the correlative resistor (CR) matrix in a local way to directly compute the voltage variations by using small parts of the remaining circuit. Last it statistically recovers voltage variations of the suppressed nodes inside strong connects. This new method for statistically compressing and expanding strong connects in terms of current or voltage variations in a closed form is very efficient owning to its property of incremental analysis. Experimental results demonstrate that the method can efficiently compute low-bounds of voltage variations for P/G networks and it has two or three orders of magnitudes speedup over the traditional Monte-Carlo-based simulation method, with only 2.0% accuracy loss.     

含电压逆变型分布式电源配电网的短路电流计算

分布式发电接入改变了配电网潮流和短路电流分布,其提供的短路电流将对电网保护和重合闸动作产生影响。文中通过研究电压控制逆变型分布式电源(IIDG)的故障响应特性,分析配电网不对称故障时IIDG三相平均功率与正负序网功率关系,建立计及电压型IIDG对称控制特征的短路计算序分量模型。根据IIDG与配电网正负序网络的交互作用,推导电压型IIDG的故障电流变化规律,提出计算含电压型IIDG配电网短路电流的对称分量迭代算法。在PSCAD/EMTDC仿真软件中建立电压型IIDG的电磁暂态模型,仿真验证了该方法的正确性。     

基于电压稳定指标的分布式电源位置与容量优化配置

 分布式发电(Distributed Generation,DG)的电源位置和注入容量对配电网有重要影响。以配电网网损最小及电压稳定指标为目标,将DG位置和容量优化问题转化为一个多目标的非线性规划问题。构造模糊性的多目标优化算法,将2个优化子目标转化为单一目标,同时将节点电压越限和DG有功出力越限以罚函数的方式进行处理。采用惯性因子自适应的粒子群算法进行求解,为了加快求解速度,先计算节点有功网损微增率并进行排序,选出绝对值较大的节点作为DG安装候选节点。仿真结果表明,此算法收敛效率高,具有较强的搜索能力和自适应能力。     

氯化锂溶液为工质的溶液浓差发电实验研究

利用配制的氯化锂溶液作为工作介质,通过实验研究稀溶液和浓溶液浓度变化对逆向电渗析电池组(REDCs)开路电压、内阻以及功率密度等电池特性参数的影响.研究结果表明,由10个电池单元构成的REDCs在所研究的浓度范围内,最大开路电压为1.88V,最大功率密度为1.67 W/m~2.电池的开路电压随稀溶液浓度增大而降低,而随浓溶液浓度增大出现先增后降的趋势.电池内阻随浓、稀溶液的浓度增大而降低.电池的端电压与功率密度受电流影响.随电流增大,端电压呈线性下降的趋势,而功率密度变化却呈上凸的二次曲线.当电路总电阻为电池内阻两倍时,电池功率密度达到最大值.     

基于SVPWM的变参数三相并联APF的控制策略

为了抑制三相并联有源电力滤波器(APF)运行过程中参数变化对补偿效果的影响,提出了基于递推最小二乘算法的参数实时在线辨识控制策略。三相并联有源电力滤波器的参考电流则依据瞬时无功功率理论检测获得。该控制策略基于在线辨识参数将参考电流转换为相应的参考电压,通过空间矢量脉宽调制技术控制电压源逆变器的输出,使它产生对应补偿电流。该控制策略原理简单,能改善补偿效果并易于实际应用。仿真实验验证了该控制策略的可行性、可靠性及鲁棒性。     

配电网潮流的混合模型降规模计算

为了减少大规模配电网潮流分析的计算量,在分析了等效电压降落模型法(VDM)和等效线损模型法(LLM)的基础上,提出了二者相混合的配电网潮流算法.该方法的计算精度比较高,可大大减少参与迭代计算的节点数,而且各负荷点的电压以及配电网的线损不需迭代就可以直接计算得到,从而精确得到整个配电网的线损.经过对IEEE中30节点的实际配电网的计算,验证了该方法的有效性.     

基于图像处理技术的无线传感器网络覆盖与连通

无线传感网中覆盖与连通问题对网络通信的性能有很大影响,并且连通与覆盖问题也是无线传感网研究的重点内容之一.首先采用图像灰度处理技术来计算节点对监测区域的覆盖率;然后采用洪泛路由协议判断网络是否连通并建立不同通信半径下,节点数与网络连通概率的关系;最后综合分析实验结果获得保证网络覆盖与连通的参数.与图像分割方法相比,采用图像灰度处理技术计算覆盖率,减少了计算量和对计算的要求,同时也提高了计算效率.通过理论分析与实验仿真,本文提出的方法可以实现网络的覆盖和连通.     

求线性有源电路灵敏度的新方法

本文提出利用双图法(电压图与电流图)表示网络的拓扑结构,然后在增量网络模型基础上导出了网络元件的一阶灵敏度计算公式。这种方法具有比伴随网络法优越的特点。     

基于综合灵敏度的两阶段电压控制分区方法

针对传统电压分方法中发电机节点转换会影响负荷区域灵敏度的问题,提出一种基于综合灵敏度的两阶段电压控制分区方法.第一阶段采用综合灵敏度实现负荷节点的聚合分区,并在该阶段完成主导节点的识别.第二阶段首先考虑电压稳定性归并PV节点,然后采用灵敏度法逐次归并剩余发电机节点.综合灵敏度模型能全面反映负荷区域功率与电压的耦合性,主...     

基于Simplorer的三相开关磁阻起动/发电系统建模研究

在分析开关磁阻起动／发电系统（SRS/G）运行机理的基础上，提出了一种基于Ansoft/Simplorer的SRS/G系统建模方法．该方法利用Simplorer的电路元件库搭建SRS／G的功率变换器及其外围电路，根据SRM小信号分析模型进行比例积分（PI）参数整定和参数优化，电机起动时采用转速外环PI调节加内环电流斩波、发电时采用电压外环PI调节加内环变开通角的控制策略对SRS/G系统进行了仿真分析．仿真结果与样机试验结果的比较验证了模型的正确性．该模型修改容易、直观性强，便于对不同的控制策略进行特性分析，为研究和设计SRS/G控制系统提供了一种有效的工具．