Models to predict the deformation modulus and the coefficient of subgrade reaction for earth filling structures

Determination of deformation modulus and coefficient of subgrade reaction of soils have major importance, whether the projects are in design, and construction or compaction assessment stage of earth filling structures. Plate load test is one of the frequently used method to directly determine the parameters but the method is both costly and time consuming. For this reason, this paper is concerned with the applications of artificial neural networks (ANN) and simple-multiple regression analysis to predict deformation modulus and coefficient of subgrade reaction of compacted soils from compaction parameters (such as maximum dry density (MDD) and optimum moisture content (OMC), field dry density (FDD), and field moisture content (FMC)). Regression analysis and artificial neural network estimation indicated that there are acceptable correlations between deformation modulus and coefficient of subgrade reaction and these parameters. Artificial neural networks model exhibits higher performance than traditional statistical model for predicting deformation modulus and coefficient of subgrade reaction.     

A biased random‐key genetic algorithm for OSPF and DEFT routing to minimize network congestion

Interior gateway routing protocols like Open Shortest Path First (OSPF) and Distributed Exponentially Weighted Flow Splitting (DEFT) send flow through forward links toward the destination node. OSPF routes only on shortest‐weight paths, whereas DEFT sends flow on all forward links, but with an exponential penalty on longer paths. Finding suitable weights for these protocols is known as the weight setting problem (WSP). In this paper, we present a biased random‐key genetic algorithm for WSP using both protocols. The algorithm uses dynamic flow and dynamic shortest path computations. We report computational experiments that show that DEFT achieves less network congestion when compared with OSPF, while, however, yielding larger delays.     

Line planning on tree networks with applications to the Quito Trolebús system

We discuss an optimization model for the line planning problem in public transport in order to minimize operation costs while guaranteeing a certain level of quality of service, in terms of available transport capacity. We analyze the computational complexity of this problem for tree network topologies as well as several categories of line operations that are important for the Quito Trolebús system. In practice, these instances can be solved quite well, and significant optimization potentials can be demonstrated.     

Adaptive neural dynamic surface control for servo systems with unknown dead-zone

An adaptive neural controller is proposed for nonlinear systems with a nonlinear dead-zone and multiple time-delays. The often used inverse model compensation approach is avoided by representing the dead-zone as a time-varying system. The “explosion of complexity” in the backstepping synthesis is eliminated in terms of the dynamic surface control (DSC) technique. A novel high-order neural network (HONN) with only a scalar weight parameter is developed to account for unknown nonlinearities. The control singularity and some restrictive requirements on the system are circumvented. Simulations and experiments for a turntable servo system with permanent-magnet synchronous motor (PMSM) are provided to verify the reliability and effectiveness.     

有环网络编码关键理论问题研究

同无环网络编码研究相比,有环网络编码的理论存在更多研究空白.在实际网络中,网络编码信息流很可能交错形成环路,造成环路中链路之间逻辑因果关系混乱,时延的引入成为必须.因此,有环网络中网络编码呈现出与无环网络中不一样的特性.由于有环网络编码是网络编码实用化研究的一个关键和难点,而且有环有时延网络更接近实际网络情况,因而有环网络的研究具有较大理论和实用意义.本文从有环网络编码的数学理论框架、有环网络编码容量、码构造算法、多源有环网络编码等几个方面综述了有环网络编码的研究现状和需要进一步研究的方向.     

一种适用于Ad hoc网络的动态多组异步多信道MAC协议

提出一种基于节点物理地址为初始分配信道的动态多组异步多信道媒介访问控制协议(DMAM-MAC).在该协议中,各节点根据自己的物理地址确定初始主信道,并且与同处在一个主信道上的相邻节点构成一个信道组;随着网络运行,各节点根据自身的业务流,动态改变主信道,加入新的信道组,从而有效提高了频率空间复用度.仿真试验证明,与IEEE802.11、AM-CP相比,DMAM-MAC协议在显著提高Ad hoc网络平均吞吐量的同时,还能有效降低数据包平均延迟.     

集群计算环境下基于复杂网络的社会学仿真负载划分优化算法

负载划分是决定集群计算环境下基于复杂网络的并行社会学仿真性能的核心因素之一.由于背景负载等因素的影响,集群系统中往往需要根据实际可用计算资源非均匀分配仿真任务,而现有针对无标度特性拓扑结构的并行仿真负载划分算法无法适应集群环境下计算负载非均匀划分的需求.针对这一问题,提出了一个基于集散节点聚合的负载划分算法,将集群计算...     

基于复杂网络的 Internet 流量分析

基于复杂网络理论对 Internet 网络结构进行分析,模拟 Internet 网络中选择服务器的过程,建立一个基于距离和流量的 Internet 自组织网络结构模型(FDM).通过仿真Internet 网络数据传输过程,分析比较 FDM 与 BBV 模型的丢包率,研究 Interact 网络中心连接节点的缓存器容量、...     

Properties and performance modelling of finite buffer M/G/1/K networks

Finite buffer, single-server queueing systems and networks are difficult to analyze since the length of time a customer spends in the system does not follow the Markovian property. A two-moment approximation schema is developed for the probability distribution of M/G/1/K systems and extended to the analysis of M/G/1/K   queueing networks. The general purpose of this paper is to develop a flexible and practical transform-free approach for computing the probability distribution and performance measures of the system as well as identify the underlying properties of these systems. It is shown that for most performance measures, a sigmoid or S-shaped curve with an inflection point at ρ=1$\rho =1$ appears as K→∞$K\to \infty$. This has direct implications for the analysis and optimization of such systems. The performance modelling of the M/G/1/K queueing networks of general topologies along with extensive numerical results accompany the paper along with the linear concave performance measures for these systems.