A function-to-task process model for adaptive automation system design

Adaptive automation systems allow the user to complete a task seamlessly with a computer performing tasks at which the human operator struggles. Unlike traditional systems that allocate functions to either the human or the machine, adaptive automation varies the allocation of functions during system operation. Creating these systems requires designers to consider issues not present during static system development. To assist in adaptive automation system design, this paper presents the concept of inherent tasks and takes advantage of this concept to create the function-to-task design process model. This process model helps the designer determine how to allocate functions to the human, machine, or dynamically between the two. An illustration of the process demonstrates the potential complexity within adaptive automation systems and how the process model aids in understanding this complexity during early stage design.     

Challenges in real-time virtualization and predictable cloud computing

Cloud computing and virtualization technology have revolutionized general-purpose computing applications in the past decade. The cloud paradigm offers advantages through reduction of operation costs, server consolidation, flexible system configuration and elastic resource provisioning. However, despite the success of cloud computing for general-purpose computing, existing cloud computing and virtualization technology face tremendous challenges in supporting emerging soft real-time applications such as online video streaming, cloud-based gaming, and telecommunication management. These applications demand real-time performance in open, shared and virtualized computing environments. This paper identifies the technical challenges in supporting real-time applications in the cloud, surveys recent advancement in real-time virtualization and cloud computing technology, and offers research directions to enable cloud-based real-time applications in the future.     

A Class of Cross‐Layer Optimization Design for Congestion and Energy Efficiency with Compressed Sensing in Wireless Sensing Networks

In wireless sensor networks (WSNs), the congestion problem not only causes packet loss, but also leads to an increase in delays and energy consumption. The actual performance of wireless sensor networks (WSNs) can be severely influenced by the quality of the communication channel and the bit in transmission. In this paper, the distributed protocols, which attain global optimum control for signals by the compressed sensing technique and achieve fair channel allocation by the scheduling algorithm, are proposed for WSNs. We take into account the congestion problem by robust optimization with congestion ratio for two classic aspects in energy limited WSNs: minimum transmission rate and maximum transmitted information. To achieve the goal, three protocols are developed. In the first protocol, the desired control input is designed based on the compressed sensing technique. A minimal bit of signal is provided to reduce the transmission flow for the congestion model. The second protocol is resource allocation. The resources can be allocated increasingly to the channel in order to avoid more severe congestion. This can also avoid conservative reduction of resource allocation for eliminating congestion. Channel selection abides by the fair resource allocation principle. The above protocols separately are implemented through a congestion ratio at network layer, transport layer, and MAC layer. Simulation results demonstrate that the proposed algorithm effectively relieves congestion, and achieves higher throughput and lower energy consumption.     

多编组协同任务分配模型及DLS-QGA 算法求解

为解决多智能体编组协同任务分配问题, 定义任务、智能体编组和相关的分配过程变量, 建立以最高任务执行效率为目标的数学模型. 在问题模型中设计考虑资源损耗的编组资源能力更新机制, 提出用于求解该模型的动态列表规划和量子遗传算法的混合任务分配算法, 使用动态列表规划选择处理的任务, 利用量子遗传算法为选定任务分配最合适编组. 最后通过算例表明, 所提出的方法在解决时序逻辑任务分配时能够得到更优更稳定的方案.

     

基于发射功率最小化的MIMO-OFDM系统资源分配算法

研究了一种针对多用户MIMO-OFDM系统的资源分配算法. 该算法是在保证用户公平性的前提下,基于发射功率最小化对系统进行子载波和比特分配. 首先按照用户信道增益状况由最差到最好的顺序进行子载波的初次分配,之后再比较其功率大小找到最终适合用户的子载波,最后利用贪婪注水法对子载波进行比特分配. 仿真结果表明,算法降低了系统的发射功率,并提高了系统的性能.     

一种权重融合模型的物联网频谱分配方案及其免疫实现

采用认知无线电技术, 设计了一种空闲频谱资源选择排序方法。在进行频谱选择时, 提出了一种无偏好信息的主客观权重融合方法, 提高权重设置的合理性, 并将求解权重转换为一个最优化问题, 通过免疫优化算法进行求解。实验结果表明, 该算法可以有效选择满足物联网节点传输需求的频谱, 并具有较高的网络吞吐量。     

一种基于RAN架构无线接入网系统容量最大化的功率分配算法

针对新型RAN架构无线接入网中功率分配问题进行研究, 建立系统容量最大化的最优化数学模型, 提出了一种基于RAN架构无线接入网的系统容量最大化的功率分配算法, 并且应用人工鱼群算法求解了算法中目标函数的全局最优解, 即一组使系统容量最大化的发射功率。仿真结果表明, 与现有的分布式网络架构中非合作的功率控制博弈算法相比, 新算法可以显著提高系统容量, 即证明了该算法的有效性。     

B/S架构的宿舍管理信息系统研究与开发

论述了B/S架构的宿舍管理模式的特点以及在该领域的优势,分析了系统建模的方案和数据库结构体系的设计.提出了宿舍的自动化分配和与财务系统进行集成的方案,解决了高校学生宿舍分配的难题.基于以上方案和技术,开发了B/S架构的宿舍管理信息系统,并且在安徽省芜湖市某高校中已成功应用.     

移动终端动态加载机制研究

随着移动终端系统深入发展,应用程序扩展能力已成为一个基本需求,中低端移动终端系统迫切需要一种机制能使系统在运行状态时动态添加模块。该文提出一种内存分配策略及基于虚拟函数表结构动态加载机制,给出了应用程序加载实现方法。实验结果表明:基于该机制所开发的加载器具有体积小,运行速度快,性能稳定等特点。     

基于企业生命周期的人力资源经理角色分析

文章从企业生命周期的角度,探讨了人力资源经理在不同阶段主要扮演的角色,为企业选拔和培训人力资源经理提供新的视角。将企业生命周期分为创业期、成长期、成熟期和衰退期四个阶段,依据企业生命周期不同阶段的特点及人力资源管理的策略,得出人力资源经理在不同阶段分别主要扮演战略合作伙伴、HRM专家、员工激励者、变革推动者的角色。