基于遗传算法的通风网络两步法风流调节优化算法

概括混合型通风网络优化模型,归纳混合型通风网络风量最优分配和风流最优调控两步法优化基本框架。首先,在满足矿井通风风量需求的基础上,实现风量分配优化。然后,结合矿井通风网络实际调风需求,引入遗传算法随机产生动态网络的邻接矩阵,使用附有条件的最小支撑树算法求解部分余树弦(调风地点)约束下的最小支撑树和独立回路矩阵,根据回路矩阵计算通风网络余树弦风阻调节值,在通风总功率和约束条件基础上构建广义最小化目标函数,依此对调节方案进行评价,使用遗传算法中的进化算子对调节方案编码实施进化操作,最后通过迭代得到满意解。研究结果表明:两步法思想简化混合型通风网络优化问题,解决调风地点约束的通风网络优化问题,执行效率高,能够解决较大规模的通风网络优化问题。     

无线传感器/执行器网络SA协作分簇算法

针对现有分簇算法大多没有考虑监控区域内事件发生频率和执行任务能耗的因素,导致无法适应无线传感器/执行器网络的特点,提出一种基于SA协作模型的分簇算法（CASA）.算法从全网的能耗和时延影响的角度,建立基于SA协作的能耗模型,综合考虑时延和连通度等约束条件,以网络能量优化为目标,构造非线性优化函数,利用KKT条件求解网络理想执行器节点个数和传感器节点传输半径等网络分簇所需参数,并在此基础上完成节点的部署和成簇.该算法通信开销较小,网络平均时延和能耗等仿真结果表明：相比典型算法,能够在满足一定连通度的前提下,优化网络部署,增强网络实时性和能量均衡性.     

神经网络在广义预测控制中的应用

利用ＢＰ神经网络技术把多变量系统解耦为单变量系统,并推导出基于ＡＲＭＡＸ模型的隐式ＧＰＣ算法。用此算法控制解耦后的系统。     

基于Elman神经网络的汽油机过渡工况空燃比多步预测模型

为了减小车用汽油机空燃比传输延迟对空燃比控制精度的影响,提出一种基于Elman神经网络的空燃比多步预测模型.通过对空燃比数学模型的分析,确定神经网络空燃比多步预测模型的输入向量,同时,为了提高过渡工况空燃比预测精度,在神经网络输入向量中增加反映空燃比变化趋势的导数信息.对HL495发动机过渡工况实验数据进行学习,采用梯度算法对Elman神经网络的权值进行调整.研究结果表明：采用该方法能精确预测过渡工况空燃比,预测模型的最大误差小于1%,平均误差小于0.5%.该预测模型可用于实现车用汽油机过渡工况空燃比的精确控制,提高车用汽油机过渡工况排放性能.     

Reliable and Energy Efficient Target Coverage for Wireless Sensor Networks

A critical aspect of applications with Wireless Sensor Networks (WSNs) is network lifetime.Power-constrained WSNs are usable as long as they can communicate sense data to a processing node.Poor communication links and hazardous environments make the WSNs unreliable.Existing schemes assume that the state of a sensor covering targets is binary:success (covers the targets) or failure (cannot cover the targets).However,in real WSNs,a sensor covers targets with a certain probability.To improve WSNs’ reliability,we should consider that a sensor covers targets with users’ satisfied probability.To solve this problem,this paper first introduces a failure probability into the target coverage problem to improve and control the system reliability.Furthermore,we model the solution as the α-Reliable Maximum Sensor Covers (α-RMSC) problem and design a heuristic greedy algorithm that efficiently computes the maximal number of α-Reliable sensor covers.To efficiently extend the WSNs lifetime with users’ pre-defined failure probability requirements,only the sensors from the current active sensor cover are responsible for monitoring all targets,while all other sensors are in a low-energy sleep mode.Simulation results validate the performance of this algorithm,in which users can precisely control the system reliability without sacrificing much energy consumption.     

均匀经验过程几乎处处中心极限定理的一个注记

设{ξ1,ξ2,…,ξn}为来自［0,1］上服从
均匀分布的独立同分布样本, 产生的经验过程为Fn(t)=n-1/2∑〖DD(〗n〖〗i=1
〖DD)〗(I{ξi≤t}-t), 0≤t≤1; ‖·‖表示一致模, 即‖Fn‖=sup〖D
D(〗〖〗0≤t≤1〖DD)〗〖JB(|〗Fn(t)〖JB)|〗; U为D［0,1］上的Brown桥, ‖U‖
=sup〖DD(〗〖〗0≤t≤1〖DD)〗〖JB(|〗U(t)〖JB)|〗. 利用概率强收敛工具,
得到了关于‖Fn‖及sup〖DD(〗〖〗0≤t≤1〖DD)〗Fn(t)的形如l
im〖DD(〗〖〗n→∞〖DD)〗〖SX(〗1〖〗log
n〖SX)〗∑〖DD(〗n〖〗k=1〖DD)〗〖SX(〗1〖〗k〖SX)〗I{‖Fk‖≤x}=P{‖U‖≤x}=1
+2∑〖DD(〗∞〖〗k=1〖DD)〗(-1)ke-2k2x2 a.s.
的几乎处处中心极限定理.     

人工神经网络及其在岩土建模中的应用

探讨了应用人工神经网络技术模拟岩土本构特征的可行性,认为神经网络为岩土材料本构特性的研究提供了一种新方法,并结合重庆红粘土本构特性的社会网络模型,介绍了该方法的具体应用,计算结果与试验结果均说明了该方法的有效性。     

一类批处理排队网络在批FIFO排队原则下稳定性

在标准排队网络稳定性研究的基础上，定义了批FIFO排队原则，通过引进批长度随机变量，将批处理排队网络模型转化为对应标准排队网络模型，证明了这两个网络模型具有相同的流体模型；在批FIFO排队原则下，给出了这个批处理排队网络稳定的一个充分条件.     

基于残差网络的海洋温跃层分析方法

首先, 以世界海洋地图集2013（WOA13）海洋数据为实验数据, 提出将不等距微分法、 垂直梯度法应用于海洋数据预处理、 海洋区域划分和跃层分析中, 并通过对多种神经网络在基于WOA13海洋三维数据二分类实验的性能分析, 选取残差网络作为二分类实验的网络模型, 在三层残差网络模型基础上增加了Dropout保留层以防止过拟合. 其次, 将残差网络模型用于温跃层分析判定, 并针对改进模型进行超参数优化、 残差单元改进、 保留率调整等对比实验. 实验结果表明, 改进的ResNet 26网络对WOA13海洋区域数据的温跃层数据分类有效, 分类准确率超过94%.     

On Peer-Assisted Data Dissemination in Data Center Networks: Analysis and Implementation简

Data Center Networks （DCNs） are the fundamental infrastructure for cloud computing. Driven by the massive parallel computing tasks in cloud computing, one-to-many data dissemination becomes one of the most important traffic patterns in DCNs. Many architectures and protocols are proposed to meet this demand. However, these proposals either require complicated configurations on switches and servers, or cannot deliver an optimal performance. In this paper, we propose the peer-assisted data dissemination for DCNs. This approach utilizes the rich physical connections with high bandwidths and mutli-path connections, to facilitate efficient one-to-many data dissemination. We prove that an optimal P2P data dissemination schedule exists for FatTree, a specially- designed DCN architecture. We then present a theoretical analysis of this algorithm in the general multi-rooted tree topology, a widely-used DCN architecture. Additionally, we explore the performance of an intuitive line structure for data dissemination. Our analysis and experimental results prove that this simple structure is able to produce a comparable performance to the optimal algorithm. Since DCN applications heavily rely on virtualization to achieve optimal resource sharing, we present a general implementation method for the proposed algorithms, which aims to mitigate the impact of the potentially-high churn rate of the virtual machines.