水文序列相依变异识别的RIC定阶准则——以自回归模型为例

水文过程相依性是水文变异的主要表现形式之一,应用自回归模型对其进行拟合时合理确定模型阶数是一个难点问题。本文在分析AIC和BIC准则的基础上,提出了一种以原序列与其相依成分的相关系数作为拟合度指标,同时借用信息熵形式的函数式,作为模型不确定性度量指标的自回归模型定阶准则(简称RIC准则)。以AR(1)、AR(2)、AR(3)和AR(4)模型为例进行统计试验,将不同序列长度下该准则的定阶准确率与其他定阶准则进行比较,试验结果表明,RIC准则对于上述模型均具有较好的适应性,且定阶准确率远高于AIC准则,其中对于前三阶模型RIC准则优于BIC准则,但四阶模型略低于BIC准则。RIC准则的优势是可以同时满足模型定阶、相依程度分级与模型检验的需求,将其应用于实测水文序列分析,结果显示,该准则能较准确地识别自回归模型的阶数,且符合提出的"相依有变异而残差无变异的最小阶数"的检验标准。     

基于自回归谱外推的全聚焦成像分辨力提升

针对相邻缺陷全聚焦超声成像混叠问题，结合低阶、宽有效频带自回归谱外推方法，压缩超声波时域脉冲宽度，实现亚波长级全聚焦（Total focusing method，TFM）成像分辨力。建立碳钢试块模型，设置两个中心间距1.8 mm，直径1.3 mm圆孔，选用中心频率2.25 MHz，32阵元相控阵探头采集全矩阵数据。针对全矩阵数据，选择自回归阶数为2，信号频谱最大幅值下降14 dB为有效频带，建立自回归模型并外推有效频带外的高频与低频成分，随后对全矩阵数据进行延迟叠加处理和TFM成像。仿真结果表明，低阶、宽有效频带自回归谱外推处理方法具有较高的鲁棒性和准确性，TFM成像后可有效分离中心间距0.7λ（λ为超声波长）圆孔，保留缺陷横向位置信息的同时，定位误差不超过0.73%。对碳钢试块中相同位置及尺寸的圆孔进行试验验证，定位误差不超过1.06%，有效地提高TFM成像分辨力。     

An improved damage diagnostic technique based on Singular Spectrum Analysis and time series models

An approach to enhance the sensitivity of the damage index using the prediction errors of autoregressive – autoregressive exogenous models by augmenting Singular Spectrum Analysis (SSA) to detect and locate minor damage is presented. Numerical simulation studies are carried out by considering a simply supported beam with single and multiple cracks. An experimental study on a reinforced cement concrete beam has been carried out to validate the technique using different levels of damage. The studies emphasise that the SSA improves the sensitivity of the damage index for detection and also localisation, handling environmental/operational variability and measurement noise.     

On monitoring the process mean of autocorrelated observations with measurement errors using the w-of-w runs-rules scheme

Runs-rules have been widely used since the 1950s in industrial and nonindustrial process monitoring applications to improve the performance of basic and other traditional monitoring schemes. However, none of the studies on runs-rules have accounted for a process with a combined effect of measurement errors and autocorrelation. Hence, in this paper, the use of the w-of-w runs-rules to improve the performance of the Shewhart $\overline{X}$ scheme using an additive model with a constant variance and a first-order autoregressive model is proposed. To reduce the combined negative effect of measurement errors and autocorrelation, we implement a sampling strategy based on rational subgroups in which (a) multiple measurements per item are taken (instead of a standard single measurement) and (b) non-neighboring observations are gathered. Moreover, the latter sampling strategy is incorporated into the values of probability elements of a Markov chain matrix which is used to derive some closed-form expressions for the zero- and steady-state run-length distribution. The main finding of this study is that, with respect to some overall performance measures, the proposed scheme outperforms the existing Shewhart $\overline{X}$ scheme by a significant margin. A real-life example is used to illustrate the practical implementation of the proposed scheme.     

Filling the gap between Continuous and Discrete Time Dynamics of Autoregressive Processes

Discretization of continuous time autoregressive (AR) processes driven by a Brownian motion and embedding of discrete time AR sequences driven by a Gaussian white noise are classical issues. The article aims at establishing and using such discretization and embedding formulae between extended AR continuous time processes and discrete time sequences. The continuous-time processes are driven by either Brownian or jump processes, and may have random coefficients depending on time; Lévy-driven processes are also considered. The innovation of the discrete time processes may be of many types – including Gaussian. In one way, observing the continuous time AR process at discrete times leads the AR dynamics of the discretized process to be characterized. The other way round, AR sequences can be embedded, in the almost sure sense, into continuous time AR processes with the same dynamics. Illustration is provided through many examples and simulation.     

基于SARIMA-GRNN-SVM的短期商业电力负荷组合预测方法

针对短期商业电力负荷预测准确性与周期难以满足现有电力现货市场的问题,提出了一种基于SARIMAGRNN-SVM(seasonal autoregressive integrated moving average-generalized regression neural network-support vector machine)的商业电力负荷组合预测模型。首先,对商业电力负荷变化的周期规律与随机因素的复杂影响进行了分析;然后,结合以上分析,选用SARIMA和GRNN为单一预测模型对商业电力负荷进行预测,并利用SVM进行组合,实现日前商业电力负荷预测;最后,通过某商业综合体的电力负荷数据进行验证。所提组合预测模型较单一预测模型拥有更优的预测精度与鲁棒性,可以为短期商业电力负荷预测提供借鉴。     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

建筑能耗分析用室外气象数学模型的建立

选用5个气象参数作为模拟量建立了逐日气象参数的模拟模型，通过平稳化变换将模拟量变换成平稳时间序列，采用混合回归模型进行了逐日量的模拟，根据模拟得出的逐日参数建立了逐时气象参数的模拟模型，用逐日、逐时模型对徐州地区的气象参数进行了模拟。     

Periodic autoregressive conditional duration

We propose an autoregressive conditional duration (ACD) model with periodic time-varying parameters and multiplicative error form. We name this model periodic autoregressive conditional duration (PACD). First, we study the stability properties and the moment structures of it. Second, we estimate the model parameters, using (profile and two-stage) Gamma quasi-maximum likelihood estimates (QMLEs), the asymptotic properties of which are examined under general regularity conditions. Our estimation method encompasses the exponential QMLE, as a particular case. The proposed methodology is illustrated with simulated data and two empirical applications on forecasting Bitcoin trading volume and realized volatility. We found that the PACD produces better in-sample and out-of-sample forecasts than the standard ACD.     

Continuous monitoring of the dynamic behavior of a high‐rise telecommunications tower

This article describes the experimental evaluation of the dynamic effects induced by wind on a high‐rise telecommunications tower based on a permanent monitoring system. Monte da Virgem telecommunications tower is located near the city of Porto (Portugal), and its structure consists in a reinforced concrete shaft and a steel mast, with a total height of 177 m. The monitoring system includes accelerometers, anemometers, and a meteorological station, allowing the characterization of the maximum accelerations of the structure and wind regimes during a period of 6 months. The analysis of the results enabled identifying specific events, denominated as critical events, for which the dynamic response of the tower under wind actions appears significantly amplified due to wind aeroelastic instability phenomena in the steel mast. The automatic identification of the critical events was based on the application to the acceleration's records of an autoregressive model and estimation of its optimal order number based on a singular value decomposition. The results proved the robustness and efficiency of the proposed technique in identifying the number, duration, and maximum amplitude of accelerations associated to the critical events, envisaging its potential integration in structural health monitoring systems.