A hybrid hub-and-spoke postal logistics network with realistic restrictions: A case study of Korea Post

Postal logistics has a complex transportation network for efficient mail delivery. Therefore, a postal logistics network consists of various functional sites with a hybrid hub-and-spoke structure. More specifically, there are multiple Delivery & Pickup Stations (D&PSs), multiple Mail Processing Centers (MPCs), and one Exchange Center (EC). In this paper, we develop two mathematical models with realistic restrictions for Korea Post for the current postal logistics network by simultaneously considering locations and allocations. We propose an Integer Linear Programming (ILP) model for transportation network organization and vehicle operation and a Mixed Integer Linear Programming (MILP) model that considers potential ECs for decision making while simultaneously regarding the EC location, transportation network organization, and vehicle operation. We use modified real data from Korea Post. Additionally, we consider several scenarios for supporting EC decision makers. The proposed models and scenarios are very useful in decision making for postal logistics network designers and operators.     

An effective node-selection scheme for the energy efficiency of solar-powered WSNs in a stream environment

Previous research on scheduling and solar power issues of wireless sensor networks (WSNs) assumes that the sensors are deployed in a general environment. While monitoring the stream environment, sensors are attached to the stream side to collect the sensed data and transmit the data back to the sink. The stream environment can be scaled in several similar environments. This type of geographic limitation not only exists in a stream environment but also on streets, roads, and trails. This study presents an effective node-selection scheme to enhance the efficiency of saving power and coverage of solar-powered WSNs in a stream environment. Analysis of the sensor deployment in the stream environment permits sensors to be classified into different segments, and then allows the selection of active nodes for building inter-stream connections, inter-segment connections, and intra-segment connections. Based on these connections, the number of active nodes and transmitted packets is minimized. Simulation results show that this scheme can significantly increase the energy efficiency and maintain the monitoring area in solar-powered WSNs.     

SDARP：基于软件定义网络的地址解析协议代理机制

针对以太网中,主机之间存在大量的地址解析协议（ARP）的广播流量,导致以太网规模受限问题的研究,提出基于软件定义网络的地址解析协议代理机制（SDARP）,利用软件定义网络范式,集中处理地址解析协议报文,应答地址解析请求,从而抑制广播流量。SDARP基于RYU控制器实现。基于Mininet仿真器的虚拟试验床实验证明：SDARP对主机透明,能够有效减少92.9%的ARP广播报文,消除广播风暴,并减少35.9%的ICMP传输时间。     

Web挖掘技术及其在网络教学评价中的应用

结合W eb日志挖掘技术和数据立方体技术应用于网络教学评价体系,以网络教学体系的W eb服务端和客户端的日志作为数据源,使用数据挖掘技术采集有用的数据进行分析抽取,建立数据库,再应用数据立方体技术、OLAP进行分析处理,得到所需的评价结果。结果表明,此算法可以客观的评价网络教学结果。     

Development of bond strength model for FRP plates using back‐propagation algorithm

For the flexural reinforcement of bridge and building structure, synthetic materials whose dynamic properties are superior and those containing the merit of corrosion‐proof are widely used as the substitute for a steel plate. Since FRP plate has improved bond strength owing to the fibers externally adhering to the plate, many researches regarding the bond strength improvement have been substantially performed. To search out such bond strength improvement, previous researchers had ever examined the bond strength of FRP plate through their experiment by setting up many variables. However, since the experiment for a research on the bond strength takes much of expenditure for setting up the equipment and is time‐consuming, also is difficult to be carried out, it is limitedly conducted. The purpose of this study was to develop the most suitable artificial neural network model by application of various neural network models and algorithm to the data of the bond strength experiment conducted by previous researchers. Many variables were used as input layers against bond strength: depth, width, modulus of elasticity, tensile strength of FRP plate and the compressive strength, tensile strength, and width of concrete. The developed artificial neural network model has been applied back‐propagation, and its error was learned to be converged within the range of 0.001. Besides, the process for the over‐fitting problem has been dissolved by Bayesian technique. The verification on the developed model was executed by comparison with the test results of bond strength made by other previous researchers, which was never been utilized to the learning as yet. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 5119–5127, 2006     

吉比特里所码和卷积码级联译码器设计

针对60-GHz无线个域网,提出了一种吉比特里所码和卷积码级联译码器架构。在推荐级联架构中,里所码和卷积码分别作为外码和内码,通过交织器级联。采用基于Viterbi算法的8路并行卷积译码结构和基于RiBM算法的里所码译码结构,实现了超高速级联译码器。在TSMC 0.13μm CMOS工艺下,该译码器以0.135 nJ/bit和5.19 mm2的能耗资源开销实现了高达2 Gb/s的吞吐率。     

基于能量优化的LEACH路由协议改进

针对LEACH路由算法中簇头选举随机性和簇头与基站直接通信导致能量消耗过快且不平衡的特点,提出新的改进算法,以达到降低能耗目的。在改进算法中,簇头剩余能量高于网络平均能量。根据簇头节点与基站的相对位置划分不同区域,簇头节点发送数据采用多跳方式,避免簇头节点能量消耗过快,达到平衡网络能量消耗的目的。仿真表明,通过改进簇头选举条件和采用多跳路由的方式,即使在数据通信量增加的情况下,依然能够延长网络通信时间。     

基于马尔可夫链的自适应性神经网络训练算法

提出一种基于马尔可夫链的自适应性神经网络训练方法,对传统的S型激励函数进行了改进,建立了自适应性的神经网络分类器。在假设样本中噪声服从于正态分布的情况下建立最大似然估计,通过后验概率建立马尔科夫链对样本进行训练,提高了神经网络训练速度。在轴承故障诊断中的测试结果表明,该算法可以迅速稳定地训练出神经网络,有效提高诊断的分类结果。     

改进的无线传感网混沌Hopfield盲检测算法

在密集部署的无线传感器网络中,相邻传感器的信号可能高度相关。在无线传感网传输模型分簇的基础上,针对Hopfield神经网络极易陷入局部最优解、收敛速度慢等缺陷,利用混沌序列的遍历性和类随机性,提出一种改进的混沌Hopfield盲检测算法解决无线传感网簇内传感器信号盲检测问题。算法的思想是:利用混沌映射产生初始发送序列,并且在算法出现早熟收敛时进行小幅度的混沌扰动,借此降低算法的误码率。仿真结果表明,改进的混沌Hopfield神经网络算法所需数据量极短,从而成功实现簇内簇首传感器信号盲检测。     

基于浏览器 /服务器的橡胶加工网络测控系统

通过对客户机／服务器模式和浏览器／服务器模式的比较，选择浏览器／服务器模式组建了基于Internt/Intranet的远程橡胶加工网络测控系统，并介绍了系统的开发模式、方法和注意事项。