A modified stochastic model for LS+AR hybrid method and its application in polar motion short-term prediction

Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.

Settlement Prediction for Buildings Surrounding Foundation Pits Based on a Stationary Auto-regression Model

To ensure the safety of buildings surrounding foundation pits, a study was made on a settlement monitoring and trend prediction method. A statistical testing method for analyzing the stability of a settlement monitoring datum has been discussed. According to a comprehensive survey, data of 16 stages at operating control point, were verified by a standard t test to determine the stability of the operating control point. A stationary auto-regression model, AR(p), used for the observation point settlement prediction has been investigated. Given the 16 stages of the settlement data at an observation point, the applicability of this model was analyzed. Settlement of last four stages was predicted using the stationary auto-regression model AR (1); the maximum difference between predicted and measured values was 0.6 mm, indicating good prediction results of the model. Hence, this model can be applied to settlement predictions for buildings surrounding foundation pits.

基于自适应AR模型巡航飞行参数预测研究

为更准确实现飞行参数趋势预测,提出一种基于自适应自回归(AR)模型的稳定巡航飞行参数预测方法。根据稳定巡航参数筛选条件,获取建模所需飞行参数。利用卡尔曼滤波原理估计AR模型参数,并与飞行参数构建系统方程,利用无迹卡尔曼滤波实时更新、修正AR模型参数估计值,将自适应AR模型的预测值与曲线拟合模型和灰色模型的预测值进行对比。以波音B777-300ER飞机的快速存取记录器数据样本进行仿真验证,结果表明:自适应AR模型在数据预测和收敛速率方面均更优,可有效降低预报模型随步数增加导致的精度误差,提高参数预测准确性。研究在飞机维修保障、状态监控与预测等方面具有重要作用。

基于D3AR的半球共形阵低空风切变风速估计方法

针对半球共形阵体制下进行低空风切变检测时会受到强地杂波信号的干扰,导致风切变信号难以检测的问题,提出了一种基于空时自回归的直接数据域算法(Space-Time Autoregressive Direct Data Domain,D3AR)的低空风切变风速估计方法。该方法首先将待检测距离单元的数据从空域、时域以及空时域进行信号对消处理;然后将处理后的数据矩阵描述为空时自回归(Autoregression,AR)模型并估计模型参数;再通过构造与杂波子空间正交的空间来实现对杂波的抑制,最后通过提取待检测单元的最大多普勒频率来估计风场速度。根据仿真结果显示,该方法有效地实现了地杂波抑制,并且能够精确估计风速。

基于MS(2)-AR-TVTP模型的I_(BD)波动周期非对称性和持续性分析

国际干散货运输市场源于国际贸易的衍生需求,受世界经济的影响,是一个典型的周期性市场。选取1999年11月~2021年12月的波罗的海干散货运价指数(I_(BD))月度数据,在检验序列平稳性的基础上,确定最优滞后长度,构建两区制的时变转换概率马尔科夫转换自回归模型,分析I_(BD)波动周期的持续时间、转换拐点和非对称性等主要特征。研究结果表明:模型能有效拟合I_(BD)波动周期的主要特征,周期平均持续时间为33.7个月,自2008年9月之后呈缩短态势,上升期和下降期交互更频繁;I_(BD)波动周期具有非对称性,周期内上升期持续时间比下降期长,I_(BD)维持上升期更具有稳定性。周期性特征结果可为干散货航运业造船投资和市场经营提供决策依据。

基于AR-ECM平均差异模型的串联电池组SOC、容量多尺度联合估计方法

考虑电池单体老化差异所致的电池组不一致性,针对串联电池组荷电状态(state of charge,SOC)、容量估计问题,提出一种基于自回归等效电路模型(autoregression equivalent circuit model,AR-ECM)的平均差异模型(mean-difference model,MDM)。基于此模型,提出串联电池组SOC、容量多尺度联合估计算法。该算法由2个部分组成,一是基于AR-ECM的MDM及差异化模型参数辨识策略:条件辨识策略和定频分组辨识策略;二是基于多时间尺度H无穷滤波(multi-timescale H infinity filter,Mts-HIF)的电池组SOC、容量联合估计算法。通过将所提出MDM中的自回归平均模型(autoregression mean model,AR-MM)与传统MDM中的n阶RC平均模型(nRC mean model,nRC-MM)比较,结果表明所提出的AR-MM在复杂运行工况下具有更优的动态跟随性能。依据最小化信息量准则(akaike information criterion,AIC),AR-MM具有更优的复杂度与精度的权衡。通过与基于多时间尺度扩展卡尔曼滤波(multi-timescale extended Kalman filter,Mts-EKF)联合状态估计算法比较,结果表明所提出的Mts-HIF状态估计算法具有更优的鲁棒性、精度和收敛速度。

Optimal zero-crossing group selection method of the absolute gravimeter based on improved auto-regressive moving average model

An absolute gravimeter is a precision instrument for measuring gravitational acceleration, which plays an important role in earthquake monitoring, crustal deformation, national defense construction, etc. The frequency of laser interference fringes of an absolute gravimeter gradually increases with the fall time. Data are sparse in the early stage and dense in the late stage. The fitting accuracy of gravitational acceleration will be affected by least-squares fitting according to the fixed number of zero-crossing groups. In response to this problem, a method based on Fourier series fitting is proposed in this paper to calculate the zero-crossing point. The whole falling process is divided into five frequency bands using the Hilbert transformation. The multiplicative auto-regressive moving average model is then trained according to the number of optimal zero-crossing groups obtained by the honey badger algorithm. Through this model, the number of optimal zero-crossing groups determined in each segment is predicted by the least-squares fitting. The mean value of gravitational acceleration in each segment is then obtained. The method can improve the accuracy of gravitational measurement by more than 25% compared to the fixed zero-crossing groups method. It provides a new way to improve the measuring accuracy of an absolute gravimeter.

A Study of Wind Statistics Through Auto-Regressive and Moving-Average (ARMA) Modeling

Statistical properties of winds near the Taichung Harbour are investigated. The 26 years'incomplete data of wind speeds, measured on an hourly basis, are used as reference. The possibility of imputation using simulated results of the Auto-Regressive (AR), Moving-Average (MA), and/ or Auto-Regressive and Moving-Average (ARMA) models is studied. Predictions of the 25-year extreme wind speeds based upon the augmented data are compared with the original series. Based upon the results, predictions of the 50- and 100-year extreme wind speeds are then made.

基于AR-LSTM-BP的CPI组合预测模型

针对居民消费价格指数(CPI)预测准确性的问题,提出一种AR-LSTM-BP组合预测模型.首先分别用回归(AR)、长短时记忆网络(LSTM)和BP神经网络这三种模型对CPI预测,并对预测结果进行比较分析;随后引入诱导有序加权调和平均算子(IOWHA)的概念,构建AR-LSTM-BP组合预测模型.结果表明,IOWHA组合预测模型的误差均小于单项预测模型,预测结果准确性较高,能够更好地反映CPI的波动走势.

基于时间序列AR(P)模型的边坡变形预测与应用

获取边坡的监测数据进行分析,并预测其接下来的变化趋势,具有重要的意义。本文以贵州省福泉市高坪矿区英坪矿段内边坡工程项目为研究对象,对监测数据采用时间序列AR(P)模型方法进行了分析与预测。研究结果表明,模型拟合的结果和预测精度较好地反映了监测点的变化趋势,可为矿区边坡模型建立和监测数据的预测提供一定的参考。

BIM Improved with RV and AR Technologies

The study addresses the integration of the Building Information Modelling (BIM) methodology with Virtual Reality (VR) and Augmented Reality (AR) technologies in the context of the development of a multidisciplinary project, involving architecture, structures, water network and electrical system components. In order to cover in detail the various design features, the case study was limited to a specific area of a house, the sanitary rooms, as it presents sufficient complexity in modeling and the application of VR and AR software. The VR/AR functionalities applied over the BIM model increase the potential of BIM in the construction sector, contributing to the achievement of a high level of collaboration and control of the project based on an immersive and interactive environment. The elaboration of the different phases of a BIM design requires the transfer of models between BIM and VR/AR systems, allowing us to analyze the main advantages that BIM/VR/AR integration can introduce in the construction industry. The study contributes positively to achieving new knowledge in BIM, being disseminated in an academic research work and illustrated in a practical context.

Parametric SNR Estimation Based on Auto-Regressive Model in AWGN Channels

Signal-to-noise ratio（SNR）estimation for signal which can be modeled by Auto-regressive（AR）process is studied in this paper.First,the conventional frequency domain method is introduced to estimate the SNR for the received signal in additive white Gauss noise（AWGN）channel.Then a parametric SNR estimation algorithm is proposed by taking advantage of the AR model information of the received signal.The simulation results show that the proposed parametric method has better performance than the conventional frequency doma in method in case of AWGN channel.

基于AR感官体验的模型玩具盲盒包装设计

传统模型玩具盲盒的包装拆开即弃,造成一系列可玩性操作的不足与包装盒资源的浪费。本文采取基于AR感官体验的模型玩具盲盒包装设计方式,制作了作品所需的各个部分,包括角色设计、包装盒外观设计、3D模型制作、AR程序化交互设计等部分,通过与角色互动程序相链接,实现玩家与所抽中角色的虚拟现实互动功能。本文旨在增强盲盒包装的智能化,丰富包装设计的功能性,增加盲盒产品的可玩性,让玩家获得更好的互动交流体验。

Application of Auto-regressive Linear Model in Understanding the Effect of Climate on Malaria Vectors Dynamics in the Three Gorges Reservoir

It is important to understand the dynamics of malaria vectors in implementing malaria control strategies. Six villages were selected from different sections in the Three Gorges Reservoir fc,r exploring the relationship between the climatic ｜：actors and its malaria vector density from 1997 to 2007 using the auto-regressive linear model regressi^n method. The result indicated that both temperature and precipitation were better modeled as quadratic rather than linearly related to the density of Anopheles sinensis.

基于出错认知模型的矿山救援AR头盔界面交互设计研究

目的为更好地减少矿山救援过程中的出错行为,提高矿山救援的成效。方法从出错认知理论出发,分析救援场景下对任务中相关信息的视觉认知行为,从认知加工的四个过程分析得出矿山救援场景下的出错因子,以矿山救援场景AR交互信息为研究对象进行出错因子认知模拟实验,获得矿山救援场景相较于其他场景具有特殊性的AR头盔交互界面信息设计影响要素。结论依据实验结论,对矿山救援AR头盔交互界面开展信息设计,以提高救援效率,最终输出初步的交互设计方案,通过降低交互界面的认知难度,提高救援人员的反应速度和准确度,保证该系统可以有效降低任务失败率。

BIM+AR技术在贵南高铁河池站建设施工管理中的应用

鉴于传统BIM技术很难将三维信息模型融入施工现场真实环境中,技术人员无法将BIM与现场深度结合应用的问题,依托贵南高铁河池站建设项目,积极探索“互联网+”、BIM技术、物联网和大数据技术应用。将AR技术与BIM技术相结合,研发基于BIM+AR的辅助施工管理平台。将BIM模型及相关信息加载到移动终端中,利用二维码进行模型与现场匹配的精确定位,通过移动终端平板电脑即可查看模型。在项目现场以真实的比例对建筑的结构、空间、管道设计等进行检查,实现更精准的进度控制和资源管理,取得了一定的经济效益、管理效益和社会效益。

基于声发射AR模型的滚动轴承故障诊断

滚动轴承是旋转机械中应用最广泛的零部件之一,滚动轴承故障诊断方法及其状态监测技术是保障机器安全平稳运行的关键技术之一。采用功率谱分析、经典谱估计等方法,从中提取故障特征信息,完成基于振动加速度信号的轴承状态分析;但由于感知受本体振动及高噪声环境影响,很难完成轴承早期故障损伤表征特征的识别。论文提出具有高频高灵敏度的感知方法与振动检测技术,具有较高信噪比特性,并能够感知小幅的轴承早期故障损伤冲击响应信息,同时采用AR模型方法与功率谱估计方法相结合,可实现轴承早期故障特征识别。此外,以天然气压缩机的高速轴承为分析实例,采用具有高频高灵敏度的声发射感知技术,实现故障特征状态识别与分析,结果表明优于经典谱估计的方法,为轴承故障损伤产生、演变至故障的状态监测及其修正提出可行的理论支持,进一步保障设备运行安全。

一种基于AR预测模型的波浪测量系统

本文提出一种基于AR预测模型的波浪测量系统,通过雷达传感器和AR预测模型,可在实时测量波浪数据的基础上,分析波浪走势,预测出未来15s的波浪数据,预测适合母船收放小艇的时机,帮助船员安全收放小艇操作有效减少海上小艇收放的事故发生率,提高海上作业安全。

基于JT-AR转换模型的非高斯风荷载特性分析

为了研究大跨度屋盖结构的非高斯风荷载特性,提出一种采用JT-AR转换模型模拟大跨度球面屋盖结构非高斯脉动风压的方法。基于JT变换和AR模型理论进行耦合,提出并构建JT-AR转换模型,模拟生成非高斯脉动风压时程样本数据,与目标功率谱及高阶统计量对比验证;通过已有风洞试验结果与作用在大跨度球面屋盖结构表面的非高斯分布特性作对比验证。结果表明:JT-AR转换模型的模拟结果与风洞试验作用在建筑上的非高斯脉动风具有同等作用效应,其模拟仿真结果具备可靠性及普适性。研究结论为大跨度结构抗风设计提供一种新的模拟方法,可代替复杂的风洞试验。

β_(2)-AR减敏哮喘小鼠模型的建立及验证

目的:建立β_(2)-AR减敏哮喘小鼠模型并对其进行验证。方法:SPF级雄性BALB/c30只小鼠随机分为空白组、普通哮喘模型组、β_(2)-AR减敏哮喘模型组。建立普通哮喘模型,并在此基础上采用雾化吸入同时腹腔注射沙丁胺醇的方法进行β_(2)-AR减敏哮喘模型的制备,造模21 d末次激发后,测定小鼠气道阻力、ELISA法检测小鼠血清IgE含量,HE染色观察肺组织炎细胞浸润程度,Western blot法检测肺组织中β_(2)-AR含量,RT-PCR检测肺组织中β_(2)-ARmRNA的表达。结果:与空白组相比,随着乙酰甲胆碱(Mch)浓度升高,OVA诱导的各组气道阻力升高,β_(2)-AR减敏哮喘模型组气道阻力增加更加显著(P<0.05);与空白组相比,普通哮喘组及β_(2)-AR减敏哮喘模型组IgE水平上升(P<0.01);病理组织学观察发现β_(2)-AR减敏哮喘小鼠气道炎症浸润,黏液过度分泌及胶原明显沉积,且均较普通哮喘模型组的病理表现显著加重;β_(2)-AR减敏哮喘小鼠模型肺组织中β_(2)-AR含量及β_(2)-ARmRNA的表达水平较空白组及普通哮喘模型组均明显下降(P<0.05)。结论:β_(2)-AR减敏哮喘小鼠模型构建成功,且造模周期短。