增强现实场景下移动边缘计算资源分配优化方法

针对高速数据传输及计算所带来时延和终端设备能耗问题，提出了一种在上行链路采用等功率分配的传输方案。首先，依据增强现实（AR）业务的协作属性建立了针对AR特性的系统模型；其次，详细分析了系统帧结构，建立以最小化系统消耗总能量为优化目标的约束条件；最后，在保障延迟和功耗满足约束的条件下，建立了基于凸优化的移动边缘计算（MEC）资源优化求解数学模型，从而获得最优的通信和计算资源分配方案。与独立传输相比，该方案在最大延迟时间分别为0.1 s和0.15 s时的总能耗降幅均为14.6%。仿真结果表明，在相同条件下，与基于用户独立传输的优化方案相比，考虑用户间协作传输的等功率MEC优化方案能显著减少系统消耗的总能量。     

建筑能耗分析用逐时降雨模型(2):月降水量随机模拟

在计算与分析广州地区33年实测月降水量序列统计参数的基础上,将月降水量序列进行对数变换,采用一阶AR(P)模型建立了月降水量随机模拟模型。通过与实测结果的比较检验了该模型的适用性。     

基于实时数据的网状城市快速路行驶时间预测方法研究

基于城市快速路实时管理和控制的思路,利用观测断面的数据,给出动态预测网状城市快速路上的交通状态和任意两点间行驶时间的方法。具体做法是,依据时序列数据、状态空间模型和自回归模型进行模型参数自拟合,并且预测出将来几个时段的交通状态,然后计算出行驶时间。最后,给出应用于城市快速路网的实例计算结果。结果表明,提出的方法可以用于预测路网上的交通状态和行驶时间,并为城市快速路的信息服务和交通诱导提供一种方法。     

声光Q开关用1.064μm融石英低损耗增透膜实验研究

根据声光腔内调Q对光学薄膜的要求,对1.064 μm声光Q开关用融石英低损耗增透膜进行了初步实验研究,通过对工艺因素的比较分析,使用基本配置型国产设备得到了较好的结果.讨论了为降低薄膜损耗,提高薄膜激光损伤阈值应做的几项工作.     

基于Hilbert边际谱和IPSO-SVDD的滚动轴承故障诊断

滚动轴承是旋转机械状态监控及故障诊断的重要研究内容。为了更加高效的对轴承故障位置及故障程度进行诊断,提出了一种基于Hilbert边际谱和改进粒子群算法(IPSO)优化支持向量数据描述(SVDD)相结合的滚动轴承故障诊断方法。该方法首先求取轴承振动信号的本征模态函数,在此基础上得到信号的边际谱以及信号的AR模型参数,积分求取边际谱的能量特征函数和AR模型参数相结合构成系统特征向量。然后针对传统网格搜索法或凭经验确定SVDD核心参数的缺点,提出利用基于动态因子的粒子群算法对SVDD的核心参数惩罚常数C及核函数宽度σ进行优化,利用优化后的SVDD模型对滚动轴承各状态信号进行智能诊断。人工数据集及真实数据集实验结果表明,该方法可以有效识别各故障状态信号,并且优化后模型的诊断效率及诊断精度高于传统网格搜索法确定的模型。     

基于unity3D AR 体感游戏的设计与实现

针对传统游戏方式存在影响人身心健康的弊端,设计基于kinect2.0 体感设备和 unity3D 2017.4.3 开发AR 体感游戏。介绍游戏总体设计,给出开发流程图,详细阐述游戏开发过程中的关键技术、方法和插件,程序首次大 胆地将人物体感过程中的自拍照片按照Sisley 油画风格和多尺度弧形笔刷风格进行处理。实验结果表明：该游戏运 行流畅,功能和创新达到设计要求,能让玩家在娱乐中保留美好油画回忆。     

ASYMPTOTIC INFERENCES FOR AN AR(1) MODEL WITH A CHANGE POINT: STATIONARY AND NEARLY NON‐STATIONARY CASES

This article examines the asymptotic inference for AR(1) models with a possible structural break in the AR parameter β near the unity at an unknown time k₀. Consider the model y_t = β₁y_{t − 1}I{t ≤ k₀} + β₂y_{t − 1}I{t > k₀} + ϵ_t, t = 1,2, … ,T, where I{ ⋅ } denotes the indicator function. We examine two cases: case I | β₁ | < 1,β₂ = β_2T = 1 − c ∕ T; and case II β₁ = β_1T = 1 − c ∕ T, | β₂ | < 1, where c is a fixed constant, and {ϵ_t,t ≥ 1} is a sequence of i.i.d. random variables, which are in the domain of attraction of the normal law with zero means and possibly infinite variances. We derive the limiting distributions of the least squares estimators of β₁ and β₂ and that of the break‐point estimator for shrinking break for the aforementioned cases. Monte Carlo simulations are conducted to demonstrate the finite‐sample properties of the estimators. Our theoretical results are supported by Monte Carlo simulations.     

Discrete-time convolution feedback systems †

This paper considers multi-input multi-output discrete-time feedback systems characterized by y = G?e and e = u — y. Theorem I shows that if the closed-loop impulse response H is stable in the sense that H ? ? ¹ _n×n (p), then [Gtilde] (z) =Ñ(z) [ [Dtilde](z)] ^? where Ñ(z), ;[Dtilde](z) are in ? ¹ _n×n (p) Theorem III gives necessary and sufficient conditions for H ? ? ¹ _n×n (p) Finally Theorem III gives necessary and sufficient conditions for stability when [Gtilde] (z) has a finite number of multiple poles in ∣z∣ > p : the case where the leading term of the Laurent expansion at each of these poles is singular is treated in detail.     

Performance prediction of hard rock Tunnel Boring Machines (TBMs) in difficult ground

Performance prediction of TBMs is an essential part of project scheduling and cost estimation. This process involves a good understanding of the complexities in the site geology, machine specification, and site management. Various approaches have been used over the years to estimate TBM performance in a given ground condition, many of them were successful and within an acceptable range, while some missing the actual machine performance by a notable margin. Experience shows that the best approach for TBM performance prediction is to use various models to examine the range of estimated machine penetration and advance rates and choose a rate that best represents the working conditions that is closest to the setting of the model used for the estimation. This allows the engineers to avoid surprises and to identify the parameters that could dominate machine performance in each case. This paper reviews the existing models for performance prediction of TBMs and some of the ongoing research on developing better models for improved accuracy of performance estimate and increasing TBM utilization.