Asymptotic Stability of the Dynamic Solution of an N-Unit Series System with Finite Number of Vacations

We investigate an N-unit series system with finite number of vacations. By analyzing the spectral distribution of the system operator and taking into account the irreducibility of the semigroup generated by the system operator we prove that the dynamic solution converges strongly to the steady state solution. Thus we obtain asymptotic stability of the dynamic solution of the system.

Well-Posedness of an N-Unit Series System with Finite Number of Vacations

We investigate the solution of an N-unit series system with finite number of vacations. By using C0-semigroup theory of linear operators, we prove well-posedness and the existence of the unique positive dynamic solution of the system.

美国全国教师资格考试制度探析——以NTE和Praxis Series为例

美国教师资格考试制度经历了从低标准到严要求、从分散独立到统一标准、从重视书本知识到关注教学实践能力的一个发展过程。以美国教育考试服务中心研发的两套全国统一的教师资格考试方案——"全国教师考试"(National Teacher Examinations)和"普瑞克西斯考试系列"(Praxis Series)为例,深入分析美国全国性教师资格考试制度发展过程及特点,对反思和推进当前我国教师资格考试制度改革具有重要的借鉴和参考意义。

I/A Series控制系统在常减压装置中的应用

介绍了FOXBORO公司生产的I/A Series控制系统在炼油厂常减压装置中的应用情况。重点介绍了该系统的软硬件配置、功能,运用其先进技术,实现了常规测量、控制及复杂控制,取得了很好的应用效果。

Testing for Deterministic Components in Vector Seasonal Time Series

Certain locally optimal tests for deterministic components in vector time series have associated sampling distributions determined by a linear combination of Beta variates. Such distributions are nonstandard and must be tabulated by Monte Carlo simulation. In this paper, we provide closed form expressions for the mean and variance of several multivariate test statistics, moments that can be used to approximate unknown distributions. In particular, we find that the two-moment Inverse Gaussian approximation provides a simple and fast method to compute accurate quantiles and p-values in small and asymptotic samples. To illustrate the scope of this approximation we review some standard tests for deterministic trends and/or seasonal patterns in VARIMA and structural time series models.

Volatility in High-Frequency Intensive Care Mortality Time Series: Application of Univariate and Multivariate GARCH Models

Mortality time series display time-varying volatility. The utility of statistical estimators from the financial time-series paradigm, which account for this characteristic, has not been addressed for high-frequency mortality series. Using daily mean-mortality series of an exemplar intensive care unit (ICU) from the Australian and New Zealand Intensive Care Society adult patient database, joint estimation of a mean and conditional variance (volatility) model for a stationary series was undertaken via univariate autoregressive moving average (ARMA, lags (p, q)), GARCH (Generalised Autoregressive Conditional Heteroscedasticity, lags (p, q)). The temporal dynamics of the conditional variance and correlations of multiple provider series, from rural/ regional, metropolitan, tertiary and private ICUs, were estimated utilising multivariate GARCH models. For the stationary first differenced series, an asymmetric power GARCH model (lags (1, 1)) with t distribution (degrees-of- freedom, 11.6) and ARMA (7,0) for the mean-model, was the best-fitting. The four multivariate component series demonstrated varying trend mortality decline and persistent autocorrelation. Within each MGARCH series no model specification dominated. The conditional correlations were surprisingly low (<0.1) between tertiary series and substantial (0.4 - 0.6) between rural-regional and private series. The conditional-variances of both the univariate and multivariate series demonstrated a slow rate of time decline from periods of early volatility and volatility spikes.

I/A Series DCS在柴油加氢装置优化中的应用

运用I/A Series DCS对柴油加氢装置的系统配置和功能进行优化,重点介绍过程控制的优化方法和尚未解决的部分问题。

面向多时间步风功率预测的深度时空网络模型

准确的风功率预测能为风电能源行业提供可靠的指导和决策依据,然而传统的建模方法主要是将风功率预测问题转换为时序预测问题,忽略了机组间的空间信息,因此,提出一种面向多时间步风功率预测的深度时空网络模型。该模型采用编码器-解码器架构设计,首先,编码器根据历史功率信息建图,并使用图注意力网络(GAT)提取融合风场空间信息的机组特征;其次,使用门控循环单元(GRU)提取输入数据中的时间特性,从而得到关于该机组的风能时间特征;最后,在解码器融合编码器输出的时空特征后,使用样本卷积和交互网络(SCINet)融合不同时间尺度分辨率下的时空特征,输出未来多时间步风功率的预测值。在WindFarm1数据集上的实验结果表明,在预测步数为72时,所提模型的绝对平均误差(MAE)低至42.38,相较于双向门控循环单元(Bi-GRU)的MAE下降了4.25%;所提模型的均方根误差(RMSE)低至42.71,相较于Autoformer的RMSE下降了8.70%。而在WindFarm2数据集上的泛化性实验结果表明,所提模型在不同风场中具备适用性,为未来风功率的准确预测提供了一种新的途径。

Characterizing the Urban Temperature Trend Using Seasonal Unit Root Analysis:Hong Kong from 1970 to 2015

This paper explores urban temperature in Hong Kong using long-term time series. In particular, the characterization of the urban temperature trend was investigated using the seasonal unit root analysis of monthly mean air temperature data over the period January 1970 to December 2013. The seasonal unit root test makes it possible to determine the stochastic trend of monthly temperatures using an autoregressive model. The test results showed that mean air temperature has increased by 0.169~ C （10 yr） - 1 over the past four decades. The model of monthly temperature obtained from the seasonal unit root analysis was able to explain 95.9% of the variance in the measured monthly data -- much higher than the variance explained by the ordinary least-squares model using annual mean air temperature data and other studies alike. The model accurately predicted monthly mean air temperatures between January 2014 and December 2015 with a root-mean-square percentage error of 4.2%. The correlation between the predicted and the measured monthly mean air temperatures was 0.989. By analyzing the monthly air temperatures recorded at an urban site and a rural site, it was found that the urban heat island effect led to the urban site being on average 0.865~C warmer than the rural site over the past two decades. Besides, the results of correlation analysis showed that the increase in annual mean air temperature was significantly associated with the increase in population, gross domestic product, urban land use, and energy use, with the R2 values ranging from 0.37 to 0.43.

基于VMD-TCN-GRU模型的水质预测研究

为充分挖掘水质数据在短时震荡中的变化特征,提升预测模型的精度,提出一种基于VMD(变分模态分解)、TCN(卷积时间神经网络)及GRU(门控循环单元)组成的混合水质预测模型,采用VMD-TCN-GRU模型对汾河水库出水口高锰酸盐指数进行预测,并与此类研究中常见的SVR(支持向量回归)、LSTM(长短期记忆神经网络)、TCN和CNN-LSTM(卷积神经网络-长短期记忆神经网络)这4种模型预测结果对比表明:VMD-TCN-GRU模型能更好挖掘水质数据在短时震荡过程中的特征信息,提升水质预测精度;VMD-TCN-GRU模型的MAE(平均绝对误差)、RMSE(均方根误差)下降,R^(2)(确定系数)提高,其MAE、RMSE、R^(2)分别为0.0553、0.0717、0.9351;其预测性能优越,预测精度更高且拥有更强的泛化能力,可以应用于汾河水质预测。

基于门控循环单元网络的钻井井漏智能监测方法

井漏是钻井过程中常见的钻井风险,若对该风险发现、处理不及时,极易导致井塌事故,轻则延长施工周期,重则危害现场人员人身安全。为了提高油气井钻井过程中井漏风险识别的准确性,降低风险识别对人为经验的依赖,结合钻井参数的非线性以及长时依赖特征,提出了一种基于门控循环单元(Gated Recurrent Unit,GRU)网络的井漏风险智能识别方法。该模型以池体积、出口流量和立管压力作为监测参数构建GRU网络,能够提取监测参数的时间序列特征,以实现对井漏风险的准确识别。利用现场实测钻井数据对模型进行了实验测试,结果表明,该方法对井漏风险的识别准确率达到了90.1%,优于长短期记忆网络的识别结果。

基于TimeGAN-GRU的镍镉蓄电池RUL预测

镍镉蓄电池广泛用作我国高速列车的辅助电源,其性能可靠性直接关系到高速列车行车安全。蓄电池剩余使用寿命是指其性能从当前状态退化至失效的时长或可充放电次数,是表征电池性能的重要指标。目前,高速列车镉镍蓄电池寿命模型受限于小样本数据而存在精确性和泛化性差的问题。因此,从全新镍镉蓄电池寿命实验数据中提取电池退化特征,采取时序对抗生成网络对其进行增强从而提高数据规模和质量,并依据分类分数、预测分数、主成分分析、t-分布随机邻域嵌入分析方法对增强效果进行评价。其次,使用增强数据建立门控循环单元方法的高速列车镍镉蓄电池剩余使用寿命预测模型。最终,通过四级修镉镍蓄电池循环寿命实验数据进行不同预测起点验证,并对比时序对抗生成网络-门控循环单元融合模型、门控循环单元模型、长短期记忆模型的预测效果。研究结果表明:对于时序对抗生成网络数据增强效果,真实数据与模拟数据分布相近,平均绝对误差小,模拟数据质量较高;经四级修镍镉蓄电池数据验证的时序对抗生成网络-门控循环单元融合模型相比门控循环单元模型、长短期记忆模型,具有更高的泛化性能和预测精度。针对高速列车镍镉蓄电池在小样本数据限制下建立了具有较好精确性和泛化性的剩余寿命预测模型,为保障高速列车行车安全和优化制定检修方案提供了参考。

基于模态分解与SRU网络的时间序列预测

时间序列预测在工业、农业、金融及军事等领域中具有重要的应用价值。为了进一步提高预测的可靠性和准确性,构建一种基于模态分解与SRU网络的杂交预测模型。首先,针对模态个数难以确定的问题,构建基于平均样本熵来确定模态个数的自适应变分模态分解(AVMD)模型,以减少不同频率上的混叠及降低随机噪声的干扰。通过在Adam算法中引入了随机调整参数,来提高SRU网络的训练速度及增强网络跳出局部最优解的能力。最后,发展一种基于AVMD与SRU网络的杂交模型。为评估提出的预测模型的可靠性和准确性,将之与一些最新预测方法做比较。电力负荷序列的实验结果表明,所提出的杂交预测模型具有较高的准确性和可靠性。

一种轴承预紧用施力检测装置的设计与应用

当机床主轴单元配置NN系列圆柱滚子轴承时,为增强轴承刚性、提高主轴回转精度等性能指标,需对所配置的主轴轴承施加适当的预紧力以调整其径向游隙。目前,常用的冲击轴承锁紧螺母的方式是向轴承直接或间接地施加轴向预紧力,此施力方式不仅会使轴承内环端面相对主轴轴肩产生一定的倾斜,从而影响后序相关尺寸的测量精度,还难以快速得到需要的轴承游隙量,以至于影响装配效率。基于上述原因,设计了一种能够向NN系列主轴轴承提供平稳轴向力的施力检测装置,并在某知名品牌的TS-A0620和TS-A0823两种型号主轴单元的装配过程中试用。统计数据显示,该装置对提高主轴轴承的装配精度和提升主轴单元装配效率起到了积极作用。因此,对该施力检测装置的结构进行推广和使用具有一定的实际意义。

针对光伏发电功率预测的LSTformer模型

为了提高光伏发电功率预测精度,提出了一种基于长短期时序数据融合的Transformer生成式预测模型:LSTformer,能准确有效地预测光伏发电功率。LSTformer创新性地提出了时序分析模块(time series analysis,TSA)、时序特征融合模块(time series feature fusion,TSFF)和多周期嵌入模块(cycleEmbed),利用数据融合解决难以提取多时间尺度时序特征问题。设计时间卷积前馈(time convolution feedforward,TCNforward)单元,在编解码的过程中进一步提取时序特征。利用某光伏电站实际历史发电数据,通过实验验证LSTformer模型在光伏发电功率预测领域得到最低的均方误差(mean squared error,MSE)、平均绝对误差(mean absolute error,MAE),并通过消融实验验证了各模块的有效性。

基于时间序列与CNN-GRU的滑坡位移预测模型研究

滑坡位移预测是滑坡预警的重要依据之一。针对以往预测模型在预测精度上存在的不足,提出了一种基于时间序列与卷积门控循环单元(CNN-GRU)的滑坡位移动态预测模型。首先,利用小波分析确定存在趋势项位移后,利用指数平滑法对累计位移分解得到趋势项与周期项位移,将趋势项采用五次多项式拟合;之后,采用自相关函数检验位移的周期特征,利用灰色关联法判断各因子与周期项之间的关联度,并将周期项与影响因子一起输入CNN-GRU模型进行预测;最终,叠加得到累计位移预测值。以三峡库区白水河滑坡为例,选取2004年1月至2012年12月数据进行研究,最终预测结果平均绝对误差百分比仅为0.525%,RMSE为9.614、R^(2)为0.993。试验结果表明,CNN-GRU具有更高的预测精度。

基于GWO-GRU的光伏发电功率预测

针对长短期记忆网络(LSTM)应用于光伏发电功率预测时存在的耗时长或精准度低的问题,提出基于灰狼算法(GWO)优化门控循环单元(GRU)的光伏发电功率预测模型。通过GWO算法优化GRU模型的超参数,以近似最优参数建立光伏发电功率预测模型。结果表明,长时功率预测时,GWO-GRU模型的均方根误差更低、拟合系数更高、耗时更少,比传统LSTM模型的平均绝对误差降低10.20%;短时功率预测时,GWO-GRU模型在3种典型天气条件下不仅预测的平均误差最低、稳定性最强,而且比GWO-LSTM模型的平均用时节省17.24%。不同时长的功率预测表明,GWO-GRU相对于LSTM光伏功率预测效果更佳。

柴达木盆地盐类及地下水矿床成矿系列与找矿方向

新生代以来,柴达木盆地形成了复杂的构造体系及一系列相对独立的构造、成矿单元。柴达木盆地不同的构造单元因沉积环境与成矿条件的差异,在第四系浅层盐湖区、更新统冲洪积扇区、古近系—新近系圈闭构造区等特定的时空范围形成了赋存状态、矿物组合等相对独特的矿产。对柴达木盆地盐类及地下水资源时空分布特征、成矿作用及成矿地质背景进行了归纳研究,划分了盆地盐类及地下水矿床成矿系列。以成矿系列理论为指导,结合柴达木盆地最新的勘查研究成果,对盆地演化、盐类成矿单元、矿产时空分布及成矿类型等进行梳理,将柴达木盆地盐类及地下水矿床划分为3个成矿系列、6个成矿亚系列、15个矿床式。根据矿床自然分类及矿床成矿系列“全位成矿、缺位找矿”的预测思维,提出了柴达木盆地盐类及地下水矿产的找矿方向。

Performance Degradation Prediction of Proton Exchange Membrane Fuel Cell Based on CEEMDAN-KPCA and DA-GRU Networks

In order to improve the performance degradation prediction accuracy of proton exchange membrane fuel cell(PEMFC),a fusion prediction method(CKDG)based on adaptive noise complete ensemble empirical mode decomposition(CEEMDAN),kernel principal component analysis(KPCA)and dual attention mechanism gated recurrent unit neural network(DA-GRU)was proposed.CEEMDAN and KPCA were used to extract the input feature data sequence,reduce the influence of random factors,and capture essential feature components to reduce the model complexity.The DA-GRU network helps to learn the feature mapping relationship of data in long time series and predict the changing trend of performance degradation data more accurately.The actual aging experimental data verify the performance of the CKDG method.The results show that under the steady-state condition of 20%training data prediction,the CKDA method can reduce the root mean square error(RMSE)by 52.7%and 34.6%,respectively,compared with the traditional LSTM and GRU neural networks.Compared with the simple DA-GRU network,RMSE is reduced by 15%,and the degree of over-fitting is reduced,which has higher accuracy.It also shows excellent prediction performance under the dynamic condition data set and has good universality.

基于翻转网络的低相关性序列数据预测研究

在某些实际应用中,通常不存在与被预测时间变量具有高相关性的其他维度变量,或者这些维度变量难以采集。而具有较低相关性的时间序列数据普遍存在,其对于数据预测具有更重要的意义。提出一种基于注意力翻转网络的低相关性多维时间序列数据预测模型。针对低相关性时序数据具有相关性随时间而变化的特点,引入批处理滑动窗口以摆脱时间变化带来的干扰,更好地捕获维度相关性。针对传统门控循环单元(GRU)网络大量丢弃低相关性样本的问题,建立翻转GRU网络对低相关性多维数据进行初次过滤,控制多维数据在网络中的传递数量,避免维度变量因相关性较低而被丢弃,提升相关性较低的多维数据在模型中的存活时间。同时,利用基于维度的注意力机制自适应调整不同维度序列在相关性提取过程中的重要性。建立平方长短期记忆(LSTM)网络对分配权重后的数据进行拟合,更细致地确定相关性对被预测参数的影响。实验结果表明,该模型的决定系数可达0.95,预测性能优于GRU、LSTM等传统神经网络模型。