Understanding dengue fever dynamics: a study of seasonality in vector-borne disease models

Dengue fever dynamics show seasonality, with the disease transmission being higher during the warmer seasons. In this paper, we analyse seasonally forced epidemic models with and without vector dynamics. We assume small seasonal effects and obtain approximations for the real response of each state variable and also for the corresponding amplitude and phase via decomposition of the sinusoidal forcing into imaginary exponential functions. The analysis begins with the simplest susceptible-infected-susceptible (SIS) model, followed by the simplest model with vector dynamics, susceptible-infected-susceptible for hosts and uninfected-vector (SISUV). Finally, we compare the more complex susceptible-infected-recovered (SIR) and susceptible-infected-recovered for hosts and uninfected-vector (SIRUV) models and conclude that the models give basically the same information when we replace, in the SIR model, the human infectivity by a function of both human and mosquito infectivities.     

The impact of false warnings on partial and full lane departure warnings effectiveness and acceptance in car driving

In the past, lane departure warnings (LDWs) were demonstrated to improve driving behaviours during lane departures but little is known about the effects of unreliable warnings. This experiment focused on the influence of false warnings alone or in combination with missed warnings and warning onset on assistance effectiveness and acceptance. Two assistance unreliability levels (33 and 17%) and two warning onsets (partial and full lane departure) were manipulated in order to investigate interaction. Results showed that assistance, regardless unreliability levels and warning onsets, improved driving behaviours during lane departure episodes and outside of these episodes by favouring better lane-keeping performances. Full lane departure and highly unreliable warnings, however, reduced assistance efficiency. Drivers’ assistance acceptance was better for the most reliable warnings and for the subsequent warnings. The data indicate that imperfect LDWs (false warnings or false and missed warnings) further improve driving behaviours compared to no assistance.

Practitioner Summary: This study revealed that imperfect lane departure warnings are able to significantly improve driving performances and that warning onset is a key element for assistance effectiveness and acceptance. The conclusion may be of particular interest for lane departure warning designers.     

Information Ordering with an Event‐Enriched Vector Space Model for Multi‐Document News Summarization

Information ordering is a nontrivial task in multi‐document summarization (MDS), which typically relies on the traditional vector space model (VSM) notorious for semantic deficiency. In this article, we propose a novel event‐enriched VSM to alleviate the problem by building event semantics into sentence representations. The mediation of event information between sentence and term, especially in the news domain, has an intuitive appeal as well as technical advantage in common sentence‐level operations such as sentence similarity computation. Inspired by the block‐style writing by humans, we base the sentence ordering algorithm on sentence clustering. To accommodate the complexity introduced by event information, we adopt a soft‐to‐hard clustering strategy on the event and sentence levels, using expectation–maximization clustering and K‐means, respectively. For the purpose of cluster‐based sentence ordering, the event‐enriched VSM enables us to design an ordering algorithm to enhance event coherence computed between sentence and sentence–context pairs. Drawing on the findings of earlier research, we also incorporate topic continuity measures and time information into the scheme. We evaluate the performance of the model and its variants automatically and manually, with experimental results showing clear advantage of the event‐based model over baseline and non‐event‐based models in information ordering for multi‐document news summarization. We are confident that the event‐enriched VSM has even greater potential in summarization and beyond, which awaits further research. © 2014 Wiley Periodicals, Inc.     

大流量下一种基于活跃熵的DDoS攻击检测方法

为了提高在大流量背景下DDoS攻击检测的实时性。本文提出一种在大流量背景下基于活跃熵的DDoS攻击检测方法。在IP流层面通过分析系统活跃熵值来对整个流量进行初探,剔除正常流量。利用多特征广泛权重最小二乘孪生支持向量机算法（WWLSTSVM）对攻击威胁进行攻击确认。通过实验验证方法的可行性,实验表明在合适场景下本方法可以在保证时效性的同时减少系统误报率。大流量背景下该检测方法比一般的机器学习算法具有更好的检测性能。     

基于支持向量机的无创血糖光谱算法

为了实现无创血糖浓度检测,提出了基于支持向量机回归模型的无创血糖光谱算法. 该算法使用光电容积脉搏波（PPG）设备对志愿者指端红光、红外光交替采样得到PPG信号,然后通过微创血糖仪测得血糖浓度. 对采集到的PPG信号进行处理提取特征组成特征矩阵,分别运用不同机器学习模型对特征矩阵和实时血糖浓度进行回归训练,得到特征矩阵与血糖浓度间的关系,并对训练得到的函数关系进行验证,选取出高斯核支持向量机模型为最佳训练模型. 实验证明,与偏最小二乘回归进行对比,本文提出的运用核函数为高斯核的支持向量机算法的预测准确度能提升10%～15%,预测的高低血糖正确率达到98%.     

基于K均值聚类和粒子群优化的多核SVM图像分割

图像分割是图像理解和计算机视觉的重要内容.针对单核SVM在进行图像分割过程中不能兼顾分割精度高和泛化性能好的问题,提出一种基于K均值聚类和优化多核SVM的图像分割算法.该算法首先运用K均值聚类算法自动选取训练样本,然后提取其颜色特征和纹理特征作为训练样本的特征属性,并使用其对构造的多核SVM分割模型进行训练,最后用粒子群优化算法对多核核参数、惩罚因子以及核权重系数联合寻优,使生成的多核SVM具有更好的分割性能.实验结果表明,本文方法在有效提取图像目标细节的同时,获得了更高的分割精度,与基于单核的SVM分割模型相比,具有更强的泛化能力.     

改进粒子群算法和支持向量机的网络入侵检测

网络入侵检测一直是网络安全领域中的研究热点,针对分类器参数优化难题,为了提高网络入侵检测准确性,提出一种改进粒子群算法和支持向量机相融合的网络入侵检测模型(IPSO-SVM).首先将网络入侵检测率作为目标函数,支持向量机参数作为约束条件建立数学模型,然后采用改进粒子群算法找到支持向量机参数,最后采用支持向量机作为分类器建立入侵检测模型,并在Matlab 2012平台上采用KDD 999数据进行验证性实验.结果表明,IPSO-SVM解决了分类器参数优化难题,获得更优的网络入侵分类器,提高网络入侵检测率,虚警率和漏报率大幅度下降.     

基于综合法的火炮方向机齿轮传动误差分析

方向机对火炮射击精度影响很大,齿轮传动误差是方向机传动误差的主要来源。恰当的分析方法能提高方向机齿轮传动设计精度与效率。本文将齿轮传动误差分为静态误差与动态误差。结合理论分析,用概率分析法计算出静态误差;用有限元分析法计算出动态误差,最后得到总的传动误差。基于分析结果采样拟合出不同载荷下的误差曲线。将这种分析方法应用到火炮方向机齿轮传动误差分析中,得到了该火炮方向机齿轮传动在变载荷下的误差曲线。该分析方法为火炮方向机齿轮传动设计提供参考。为火炮其他部件的设计提供一种思路。     

CS-Chord:基于聚类分离的分布式高维向量索引

M-Chord是一种基于P2P网络的高维向量索引,其聚类边缘的向量容易与搜索圆频繁相交,使得查找的区域增多,降低了M-Chord的效率。提出一种基于聚类分离的分布式高维向量索引(CS-Chord),将边缘区域的高频检索向量从Chord环中分离出来,集中存储在服务器上,中心区域的向量仍存储于Chord环中,节省了大量资源的定位时间,从而提高检索效率。实验结果表明:在查询半径为0.2时,CS-Chord距离计算次数约为2000,比M-Chord减少了约2500次;CS-Chord消息转发次数约降低150次,仅为M-Chord的50%。     

ARM平台的矢量和PID交流电机控制系统

交流电机具有结构简单、制造容易、成本低、容易控制等等特点,在许多场合中逐步取代直流电机,成为电机使用者的首选,但是交流电机的调速系统复杂,在使用上很难达到和直流电机相媲美.文章设计一套基于ARM平台的矢量控制和PID算法的交流电机控制系统,实现对交流电机的精确的控制.系统分为硬件、软件、和上位机界面三部分,硬件电路包括电流采集电流和IGBT电路,软件部分采用C语言开发,使用KEI MDK软件开发工具编程,上位机采用LabVIEW软件编写测试界面,运行PID程序,最终开发出一套用于交流电机调速系统的电机控制.