杭州建设“数字社区”服务市民

日前，杭州移动助残服务直通车平台与潮鸣数字社区平台通过呼叫调度系统、短信交互平台、定位系统、管理服务系统、掌上社区系统实现了无缝衔接，今后将精确化满足杭州全市在册13．5万残疾人与潮鸣街道2万居民（重点是老年人、残疾人）在政策咨询管理、     

基于智能手机的老年人跌倒预警系统实现

设计实现了一个Android手机的App,为老年人跌倒提供实时检测,并在检测到跌倒发生时,及时发送报警短信到指定的手机.通过对从手机内置的加速度传感器和陀螺仪获取的数据求向量模,并将求得的向量模与实验测得的跌倒时的标准向量模进行比较,使用阈值算法来判断是否跌倒.判断发生跌倒时,使用百度提供的API进行定位.最终把跌倒的老年人位置信息和求救信号通过短信给指定监护人,以获得及时救助.     

结合位置指纹和行人航迹的室内定位技术

主要对室内定位技术展开研究,首先通过手持智能设备收集指定范围样本点的坐标及wifi热点信息;然后应用位置指纹定位方法进行绝对定位;为了提高行走过程中定位的准确性和实时性,采用行人航迹推算算法,即通过手机传感器采集并经处理的数据进行步频检测、步长估算和方向检测,实现相对位置变化的估算.行人航迹算法克服位置指纹定位的不稳定性,而位置指纹定位算法及时调整行人航迹算法带来的累积误差.实验结果表明两种室内定位技术的结合有效提高了室内定位的准确性,能充分应用到实际生活中.     

基于单片机的无线遥控智能小车设计

文章以STC89C52RC单片机为基础进行一种遥控智能小车的设计。小车的模式主要包括两种,即自动和遥控。在遥控模式下,1公里范围能够将小车遥控至指定的位置,同时其位置坐标还能在在手持设备上显示出来;在自动模式下,将任意坐标输进封闭环境,那么小车就能自行到达这一位置。简单的设计结构,具有较高的可靠性。     

基于智能手机的安全监护系统设计与实现

为了加强对青少年等弱势群体的监护,设计并实现了一种基于手机的安全监护系统;阐述了系统的总体设计,介绍了手机端程序、服务器端程序的架构和程序流程;将OPTICS聚类算法应用于轨迹坐标,提出一种分层的轨迹聚类方法,对被监护人停留点等有意义信息进行挖掘;实验中,被监听端手机每隔10s发送一次坐标数据,数据采用phoneNum,time,lat,lngt四元组的形式存放在服务器中,采用三层的OPTICS聚类算法对轨迹停留点进行发现,实验结果显示分层的OPTICS算法可以有效地发现被监护人的停留位置;通过实际的部署和验证,该系统稳定可靠,算法合理有效,数据收发准确,提高了社会对弱势群体的监护管理效率。     

基于GPS的出租车呼叫与调度系统

GPS手机及安装了车载GPS系统的出租车的应用越来越广泛.以此为背景,首次提出将GPS手机,GSM网络、安装了GPS系统的出租车及GIS应用结合起来的方式进行出租车呼叫与调度.针对嵌入式设备的特点以及系统需求,客户端手机采用经过处理的栅格数据进行电子地图显示,服务器端采用矢量数据进行电子地图显示.该方案具有成本低、速度快以及能有效降低出租车空载率的特点.此外,基于该方案,已经开发出了具有自主知识产权的南京大学基于GPS的出租车呼叫与调度系统软件.     

光测设备引导数据坐标自动转换系统设计

通过对坐标转换方法的认真研究,应用面向对象分析与设计(OOAD)的方法完成了自动坐标转换系统的设计,从而实现了光测设备引导数据的自动坐标转换,实时引导光测设备对导弹或航天器进行平稳跟踪.     

基于SMS和Windows Mobile的手持式地理信息汇报系统①

介绍一种基于SMS和Windows Mobile的手持式地理信息汇报系统,探讨了一种利用目前现成的移动通信设施实现对带有GPS定位功能的手持智能设备持有者监控的系统。本系统利用目前流行的短信系统（SMS）作为数据传输手段,传送手持智能设备持有者的地理信息数据。使用SMS传输数据通信可靠,费用低廉,且覆盖范围广。     

基于FPGA的选择呼叫信号源设计

基于FPGA的选择呼叫信号源主要应用于机载音频管理系统的地面测试设备中,具有飞机地面测定设备的选择呼叫功能.该信号源采用模块化的设计方法将选择呼叫代码的解码、DDS技术以及对选择呼叫信号发送时序的控制集成于一块FPGA芯片中,应用单片机控制USB模块识别/接收虚拟控制面板发送的数据.在机载电子设备自动测试过程中,可以根据需要精确稳定地产生选择呼叫音频信号,提高自动测试系统的可靠性.     

基于以太网的电能计量采集装置的设计

传统的电能计量计费装置通过本地232中继、484远传、GPRS通信或者电台通信使主站用户得到实时数据,往往这些主站只和电力营销部门相关,作为电力消费结算的依据,而并没有接入到电网的调度自动化系统,这使得大用户的信息不能共享给全局调度系统.本装置通过采用以太网技术实现远动设备及系统的102规约,使其能够接入调度自动化系统,为电网调度的统筹安排提供一线大客户数据.     

保证Java精确异常的指令调度技术

Java语言的精确异常要求和Java程序中频繁出现的异常检测严重阻碍或限制了指令调度在Java本地代码编译中的应用,从而减少了代码的指令级并行度。提出的算法可以使指令调度打破Java精确异常要求,能最大程度地发挥作用,并在有效提高代码执行效率的同时确保精确异常要求在异常发生时不被破坏。实验结果证明该算法的有效性和正确性。     

基于多任务强化学习的堆垛机调度与库位推荐

堆垛机调度是物流仓储自动化中的重要任务,任务中的出入库效率、货物存放等情况影响仓储系统的整体效益。传统调度方法在面对较大规模调度问题时,因处理大状态空间从而导致性能受限和收益降低。与此同时,库位优化与调度运行联系密切,但现有多数工作在处理调度问题时未能考虑到库位优化问题。为解决仓储中堆垛机调度问题,提出一种基于深度强化学习算法的近端策略优化调度方法。将调度问题视为序列决策问题,通过智能体与环境的持续交互进行自我学习,以在不断变化的环境中优化调度。针对调度中伴生的库位优化问题,提出一种基于多任务学习的调度、库位推荐联合算法,并基于调度网络构建适用于库位推荐的Actor网络,通过与Critic网络进行交互反馈,促进整体的联动和训练,从而提升整体效益。实验结果表明,与原算法模型相比,该调度方法的累计回报值指标平均提升了33.6%,所提的多任务学习的联合算法能有效地应对堆垛机调度和库位优化的应用场景,可为该类多任务问题提供可行的解决方案。     

Adaptive location policies for global scheduling

Two important components of a global scheduling algorithm are its transfer policy and its location policy. While the transfer policy determines whether a task should be transferred, the location policy determines where it should be transferred. Based on their location policies, global scheduling algorithms can be broadly classified as receiver-initiated, sender-initiated, or symmetrically-initiated. The relative performance of these classes of algorithms has been shown to depend on the system workload. We present two adaptive location policies for global scheduling in distributed systems. These location policies are general, and can be used in conjunction with many existing transfer policies. By adapting to the system workload, the proposed policies capture the advantages of both sender-initiated and receiver-initiated policies. In addition, by adaptively directing their search activities toward the nodes that are most likely to be suitable counterparts in task transfers, the proposed policies provide short transfer latency and low overhead, and more important, high probability of finding a suitable counterpart if one exists. These properties allow these policies to deliver good performance over a very wide range of system operating conditions. The proposed policies are compared with nonadaptive policies, and are shown to considerably improve performance and to avoid causing system instability     

Simultaneous scheduling and location (ScheLoc): the planar ScheLoc makespan problem

While in classical scheduling theory the locations of machines are assumed to be fixed we will show how to tackle location and scheduling problems simultaneously. Obviously, this integrated approach enhances the modeling power of scheduling for various real-life problems. In this paper, we introduce in an exemplary way theory and three polynomial solution algorithms for the planar ScheLoc makespan problem, which includes a specific type of a scheduling and a rather general, planar location problem, respectively. Finally, a report on numerical tests as well as a generalization of this specific ScheLoc problem is presented.     

基于北斗/GSM技术的掌上智能防丢防盗系统

基于北斗二代定位技术和GSM通信技术,对物品进行追踪、监控以及防丢防盗等远程智能化管理。系统包括Android智能手机管理端和北斗设备监控端。北斗设备端采用μC/OS-II轻巧型操作系统实现快速任务切换。手机端软件实现手机端用户位置服务功能,如用户定位、用户移动轨迹记录和查询等;实现监控端定位、警报、监控、搜索等功能,用于贵重物体、小孩、老人或智障人等的防丢、防盗远程智能监控。     

Data Intensive and Network Aware (DIANA) Grid Scheduling

In Grids scheduling decisions are often made on the basis of jobs being either data or computation intensive: in data intensive situations jobs may be pushed to the data and in computation intensive situations data may be pulled to the jobs. This kind of scheduling, in which there is no consideration of network characteristics, can lead to performance degradation in a Grid environment and may result in large processing queues and job execution delays due to site overloads. In this paper we describe a Data Intensive and Network Aware (DIANA) meta-scheduling approach, which takes into account data, processing power and network characteristics when making scheduling decisions across multiple sites. Through a practical implementation on a Grid testbed, we demonstrate that queue and execution times of data-intensive jobs can be significantly improved when we introduce our proposed DIANA scheduler. The basic scheduling decisions are dictated by a weighting factor for each potential target location which is a calculated function of network characteristics, processing cycles and data location and size. The job scheduler provides a global ranking of the computing resources and then selects an optimal one on the basis of this overall access and execution cost. The DIANA approach considers the Grid as a combination of active network elements and takes network characteristics as a first class criterion in the scheduling decision matrix along with computations and data. The scheduler can then make informed decisions by taking into account the changing state of the network, locality and size of the data and the pool of available processing cycles.     

A hybrid simulated annealing algorithm for location and routing scheduling problems with cross-docking in the supply chain

The location and routing scheduling problems with cross-docking can be regarded as new research directions for distribution networks in the supply chain. The aims of these problems are to concurrently design a cross-docking center location and a vehicle routing scheduling model, known as NP-hard problems. This paper presents a two-stage mixed-integer programming (MIP) model for the location of cross-docking centers and vehicle routing scheduling problems with cross-docking due to potential applications in the distribution networks. Then, a new algorithm based on a two-stage hybrid simulated annealing (HSA) with a tabu list taken from tabu search (TS) is proposed to solve the presented model. This proposed HSA not only prevents revisiting the solution but also maintains the stochastic nature. Finally, small and large-scale test problems are randomly generated and solved by the HSA algorithm. The computational results for different problems show that the proposed HSA performs well and converges fast to reasonable solutions.     

基于聚类的城市共同配送海量订单调度问题研究

针对云物流环境下城市共同配送海量订单调度难的问题,本文提出基于订单聚类的调度算法.首先针对单中心多车辆调度问题,提出基于单亲遗传的优化调度算法;在此基础上综合考虑城市配送中心的地理位置、车辆及配送点的地理位置、货物的种类、需求量,提出采用蚁群算法构建基于配送中心的海量订单聚类、优化调度算法.     

Random coverage with guaranteed connectivity: joint scheduling for wireless sensor networks

Sensor scheduling plays a critical role for energy efficiency of wireless sensor networks. Traditional methods for sensor scheduling use either sensing coverage or network connectivity, but rarely both. In this paper, we deal with a challenging task: without accurate location information, how do we schedule sensor nodes to save energy and meet both constraints of sensing coverage and network connectivity? Our approach utilizes an integrated method that provides statistical sensing coverage and guaranteed network connectivity. We use random scheduling for sensing coverage and then turn on extra sensor nodes, if necessary, for network connectivity. Our method is totally distributed, is able to dynamically adjust sensing coverage with guaranteed network connectivity, and is resilient to time asynchrony. We present analytical results to disclose the relationship among node density, scheduling parameters, coverage quality, detection probability, and detection delay. Analytical and simulation results demonstrate the effectiveness of our joint scheduling method.     

基于模糊优化的物流配送路径(MLRP)问题研究

研究采用嵌入模糊决策规则的遗传算法（即模糊优化方法）求解物流配送多目标定位-运输路线安排问题（MLRP）,重点考虑了时间和运输成本两个目标的MLRP的求解方法.该算法分成3个阶段,首先利用遗传算法对初始种群搜索选择优化配送路径;然后应用配送网络调度算法综合评价来确定配送路径中的关键路径和非关键路径;最后根据模糊决策规则计算其各个调度相应的指标,并对已挑选出来的染色体中的某些位基因进行调整,以提高算法的收敛性.计算机仿真结果证明了将此混合算法用于求解中、小规模物流配送问题的有效性.