A Survey of Various Schedulers Used for Fair Bandwidth Allocation in WiMAX: IEEE 802.16

ABSTRACT

Wireless Interoperability for Multiple Access (WiMAX) is one of the emerging fields of high-speed wireless communication that has enormous capabilities due to its range and the connection speed. Like wireless local area networks (LANs), WiMAX networks implement multiple quality of service (QoS) frameworks at the Media Access Control (MAC) level for assured data, voice, and video services. The question of ensuring QoS is basically how to distribute available resources to users in order to satisfy QoS parameters such as latency, jitter and throughput requirements. IEEE 802.16 standard does not have any particular guidelines on scheduling of incoming and outgoing data. This has caught the attention of researchers working on WiMAX. This article discusses the various issues in WiMAX along with a classification of various scheduling approaches based upon the type of scheduler for the sake of better understanding the scheduling problem and analyzing various available theories.     

基于深度学习的传感器优化布置方法

针对传感器优化布置(optimal sensor placement,简称OSP)问题,提出了一种新的使用深度神经网络的解决方案,并以简化的桥梁形状的桁架结构中的振动测试传感器优化为例进行了验证。首先,选择一种传统的传感器优化布置方法,对自动化生成的大量不同的桁架结构分别进行传感器优化布置计算,将所得优化布置结果在进行数据预处理后构建出深度学习方法所需要的训练集与验证集;其次,使用Python语言和深度学习框架TensorFlow设计实现与本研究问题适配的深度神经网络模型并训练;然后,随机生成了新的桁架结构参数;最后,将深度神经网络输出的传感器布置结果和传统方法的计算结果进行了比较,验证了本研究方法的有效性以及在速度上、可移植性与可扩展性方面的性能优势。     

Test sample allocation method for testability verification test

An extended failure mode effect and criticality analysis (FMECA)-based sample allocation method for testability verification is presented in this study to deal with the poor representativeness of test sample sets and the randomness of the testability evaluation results caused by unreasonable selection of failure samples. First, the fault propagation intensity is introduced as part of the extended information of FMECA, and the sample allocation impact factors of component units and failure modes are determined under this framework. Then, the failure mode similarity and impact factor support are defined, and the game decision method for weighing the relationship between similarity and support is proposed to obtain the weight of failure mode impact factor. Finally, a two-step allocation framework of test samples is formulated to realize the sample allocation of component units and failure modes. This method is applied to the testability verification test of a launch control system. Results show that this method can obtain more representative test samples compared with the traditional sample allocation method while effectively reducing randomness of single testability evaluation result.     

枞树型核电轮槽精铣刀制造技术优化设计

以枞树型叶根精铣刀为例,阐述了可换硬质合金型线刀体和刀片精铣刀的优化设计、制造工艺、建模及优化设计,进行切削过程加工策略优化和加工过程模拟,设计制造了锥柄专用工装及检测环规、磨削刀片后角专用工装,采用NUMROTO软件编制刀片加工工步。该项目研制的可换刀片精铣刀型线精度完全能达到进口铣刀水平,能满足大部分轮槽与叶根加工对型线精度的要求,可以大幅度节约刀具费用。     

干湿联合冷却塔冷却节水分析软件开发及应用

为实现对干湿联合冷却塔冷却特性、节水特性的综合分析，编制干湿联合冷却塔校核计算流程，基于Visual Studio开发平台，本文开发了干湿联合冷却塔冷却节水特性分析优化软件。通过对某在运干湿联合冷却塔进行冷却节水特性计算，验证了所开发软件计算结果的准确性；在消雾节水计算结果基础上，对标干湿联合消雾冷却塔验收测试规程，生成其成雾频率曲线、耗水量曲线、塔雾指数、冷却特性曲线，综合分析其消雾特性、节水特性及冷却特性，从而确定了干湿联合冷却塔的最佳运行工况。同时运用所开发软件对比分析了某冷却塔设计干段散热面积对其消雾特性、节水特性及冷却特性的耦合影响，并分析了百叶窗开度对干湿联合冷却塔性能的影响，为干湿联合冷却塔的运行优化和设计优化提供了工具性软件支持。     

论三峡水库“蓄清排浑”运用方式及其优化

泥沙问题是三峡工程的关键技术问题之一,在三峡工程论证和初步设计阶段提出水库采取"蓄清排浑"的运用方式,可以解决泥沙问题。2003年水库蓄水运用以来的实践表明,水库基本遵循了"蓄清排浑"调度运用原则,并根据上游来水来沙减少等新情况对水库运用方式进行了优化调整。本文系统分析了三峡水库"蓄清排浑"运行方式及其优化调整的利弊,包括提前5年实施175 m试验性蓄水、汛期水位动态变化、汛末提前蓄水等对水库淤积和坝下游河道演变的影响,提出了进一步优化水库调度方式,形成"蓄清排浑"运用的新模式,保持水库长期使用的建议,试图为三峡水库科学高效安全运用提供科技支撑。     

建筑材料供应中断对承包商成本的冲击研究——基于鲁棒模型分析

为了揭示供应商与承包商为主体的二级供应链中，两者间耦合作用条件下发生的供应中断对供应商最优决策及对承包商成本的影响机制，且剖析供应中断对承包商成本的冲击效应，以供应商存在高退出风险为研究对象，在仅知产量均值与方差条件下，运用Scarf提出的“极大极小值”鲁棒决策方法，通过设定不同决策情况下的供应链系统反馈，分析了不同退出标准情况下二级供应链的博弈过程。基于理论支撑下的鲁棒模型数值仿真结果得出：在退出标准α为100时，中间变量在290~350范围内，供应商存在最优决策，但承包商最优决策无法确定；与α为0时相比，在订货量达到一定状态，即α为140时，承包商成本增加了1.5%，表明承包商成本与供应商退出标准呈正相关，退出标准越高，对承包商成本造成的冲击效应越大。此研究结论为供应链初期不稳定且信息不完整条件下发生的供应中断影响机理提供了新的视角。     

Common due date assignment scheduling for a no-wait flowshop with convex resource allocation and learning effect

This article addresses the no-wait flowshop scheduling problem with simultaneous consideration of common due date assignment, convex resource allocation and learning effect in a two machine setting. The processing time of each job can be controlled by its position in a sequence and also by allocating extra resource, which is a convex function of the amount of a common continuously divisible resource allocated to the job. The objective is to determine the optimal common due date, the resource allocation and the schedule of jobs such that the total earliness, tardiness and common due date cost (the total resource consumption cost) are minimized under the constraint condition that the total resource consumption cost (the total earliness, tardiness and common due date cost) is limited. Polynomial time algorithms are developed for two versions of the problem.     

基于需求预测的PaaS平台资源分配方法

针对缺乏PaaS平台下资源需求的有效预测与优化分配的问题，提出一种资源需求预测模型和分配方法。首先，根据PaaS平台中应用对资源需求的周期性来对资源序列进行切分，并在短期预测的基础上结合应用的多周期性特征，利用多元回归算法建立综合的预测模型。然后，基于MapReduce架构设计实现了一个Master-Slave模式的PaaS平台资源分配系统。最后，结合当前任务请求和资源需求预测结果进行资源分配。实验结果表明，采用该资源需求预测模型和分配方法后，相比于自回归模型和指数平滑算法，平均绝对百分比误差分别下降8.71个百分点和2.07个百分点，均方根误差分别下降2.01个百分点和0.46个百分点。所提预测模型的预测结果不仅误差小，与真实值的拟合程度也较高，而且利用较小的时间开销就可以获得较高的准确度。此外，使用该预测模型的PaaS平台的资源请求的平均等待时间有了明显的下降。