Long-release-interval-first real-time scheduling algorithm and its schedulability test

To fulfill the requirements for hybrid real-time system scheduling, a long-release-interval-first （LRIF） real-time scheduling algorithm is proposed. The algorithm adopts both the fixed priority and the dynamic priority to assign priorities for tasks. By assigning higher priorities to the aperiodic soft real-time jobs with longer release intervals, it guarantees the executions for periodic hard real-time tasks and further probabilistically guarantees the executions for aperiodic soft real-time tasks. The schedulability test approach for the LRIF algorithm is presented. The implementation issues of the LRIF algorithm are also discussed. Simulation result shows that LRIF obtains better schedulable performance than the maximum urgency first （MUF） algorithm, the earliest deadline first （EDF） algorithm and EDF for hybrid tasks. LRIF has great capability to schedule both periodic hard real-time and aperiodic soft real-time tasks.

How long is the interval of onsets of bilateral retinoblastoma?

Dear Sir,Iam Dr.Ye Zhang,from Beijing Tongren Eye Center,Beijing Tongren Hospital,Beijing,China.I write to present a case report of bilateral retinoblastoma(Rb)with10-year-interval of onsets.Rb is the most common malignant intraocular tumor of infancy and childhood,with the majority of cases being diagnosed before 5 years of age.It may involve in unilateral or bilateral eye.Studies showed that the interval of the onsets of bilateral Rb is always within three years.So it is very rare

Long-term Traffic Volume Prediction Based on K-means Gaussian Interval Type-2 Fuzzy Sets

This paper uses Gaussian interval type-2 fuzzy se theory on historical traffic volume data processing to obtain a 24-hour prediction of traffic volume with high precision. A K-means clustering method is used in this paper to get 5 minutes traffic volume variation as input data for the Gaussian interval type-2 fuzzy sets which can reflect the distribution of historical traffic volume in one statistical period. Moreover, the cluster with the largest collection of data obtained by K-means clustering method is calculated to get the key parameters of type-2 fuzzy sets, mean and standard deviation of the Gaussian membership function.Using the range of data as the input of Gaussian interval type-2 fuzzy sets leads to the range of traffic volume forecasting output with the ability of describing the possible range of the traffic volume as well as the traffic volume prediction data with high accuracy. The simulation results show that the average relative error is reduced to 8% based on the combined K-means Gaussian interval type-2 fuzzy sets forecasting method. The fluctuation range in terms of an upper and a lower forecasting traffic volume completely envelopes the actual traffic volume and reproduces the fluctuation range of traffic flow.

Traffic Flow Data Forecasting Based on Interval Type-2 Fuzzy Sets Theory

This paper proposes a long-term forecasting scheme and implementation method based on the interval type-2 fuzzy sets theory for traffic flow data. The type-2 fuzzy sets have advantages in modeling uncertainties because their membership functions are fuzzy. The scheme includes traffic flow data preprocessing module, type-2 fuzzification operation module and long-term traffic flow data forecasting output module, in which the Interval Approach acts as the core algorithm. The central limit theorem is adopted to convert point data of mass traffic flow in some time range into interval data of the same time range (also called confidence interval data) which is being used as the input of interval approach. The confidence interval data retain the uncertainty and randomness of traffic flow, meanwhile reduce the influence of noise from the detection data. The proposed scheme gets not only the traffic flow forecasting result but also can show the possible range of traffic flow variation with high precision using upper and lower limit forecasting result. The effectiveness of the proposed scheme is verified using the actual sample application. © 2014 Chinese Association of Automation.

Asymptotic Approximation of the Eigenvalues and the Eigenfunctions for the Orr-Sommerfeld Equation on Infinite Intervals

Asymptotic eigenvalues and eigenfunctions for the Orr-Sommerfeld equation in two-dimensional and three-dimensional incompressible flows on an infinite domain and on a semi-infinite domain are obtained. Two configurations are considered, one in which a short-wave limit approximation is used, and another in which a long-wave limit approximation is used. In the short-wave limit, Wentzel-Kramers-Brillouin (WKB) methods are utilized to estimate the eigenvalues, and the eigenfunctions are approximated in terms of Green’s functions. The procedure consists of transforming the Orr-Sommerfeld equation into a system of two second order ordinary differential equations for which the eigenvalues and the eigenfunctions can be approximated. In the long-wave limit approximation, solutions are expressed in terms of generalized hypergeometric functions. Our procedure works regardless of the values of the Reynolds number.

时间感知的用户长短期兴趣特征分离推荐算法

针对传统推荐算法未充分考虑行为序列间的动态时间间隔、语义不规则以及用户长短期兴趣相互纠缠的问题,提出一种时间感知的用户长短期兴趣特征分离推荐算法。利用用户个性化时间聚合间隔感知和时间位置多头注意力捕获长期兴趣特征,采用动态时间间隔感知和潜在意图注意力的Time-LSTM捕获短期兴趣特征,提出长短期兴趣特征分离获取方法,分别独立捕获两种时间尺度的用户兴趣,通过注意力机制自适应融合长短期兴趣特征,提高用户兴趣特征捕获准确率。实验结果表明,该算法在预测精度指标AUC和GAUC上较对比算法均有提升,消融实验也进一步验证了该算法的必要性。

轻度认知障碍患者的回溯式时距估计

阿尔茨海默病会导致时间知觉障碍,然而尚处于该疾病潜伏期的轻度认知障碍患者是否也存在时间知觉的损伤仍存争议。本研究聚焦轻度认知障碍对回溯式时间知觉的影响,通过回溯式时距估计任务探讨了轻度认知障碍患者和健康老人对15至40分钟的目标时距的回溯式估计。研究发现,相比于控制组,轻度认知障碍患者存在显著低估时距的倾向且时距估计的偏差更大。研究论证了轻度认知障碍对时间感知的影响,对阿尔茨海默病的早期诊断具有重要价值。

基于连续小波变换和高阶统计量的心律失常识别算法

针对可变持续时间心电图(ECG)数据信号的非平稳性和时序性问题,提出一种基于连续小波变换(CWT)和高阶统计量(HOS)的心律失常识别算法。首先,针对可变持续时间ECG数据中每个样本的数据点数量不同,采用RR间期插值法预处理数据,并通过CWT将信号分解为不同的时频分量,从而使网络能够更好地提取心电信号中的时间和频率特征。其次,针对时序信息利用不充分的问题,提出基于HOS和长短期记忆网络的时序挖掘模块,以捕捉和学习ECG信号中的长期依赖关系,从而有助于识别和理解特定的心律失常类别。通过在公开的ECG数据集MIT-BIN上进行的大量实验,验证所提方法的有效性和优越性。

超长电力电缆隧道冬季温度分布特征及通风节能潜力

为了解冬季通风时隧道内的换热情况和节能潜力,对冬季室外气温为4~12℃时超长通风区间特高压电力隧道内的温度分布特征进行数值模拟研究。采用ANSYS Fluent 19.2软件,通过用户自定义程序完成电力隧道全长的模拟。对冬季隧道的传热特征和通风节能潜力进行分析,定义并给出了冬季能实现最大节能效果的临界通风量。结果表明:具有超长通风区间的电力隧道存在通过降低通风量从而实现节能的潜力;冬季电力隧道前段土壤放热、后段土壤吸热,土壤放热量占比受室外气温影响,而土壤吸热量占比受室外空气温度和通风量影响;冬季室外气温越低,临界通风量越小,通风节能潜力越大,当冬季室外气温为4~12℃时,最大可能节能率为91%。

东南亚地区某碾压混凝土坝裂缝成因及防治措施研究

混凝土裂缝是影响水工结构强度和耐久性的主要因素之一,东南亚地区全年高温多雨,月均最大温差较小(8.6℃),年均温差大(20.2℃),混凝土温度容易控制,但裂缝问题时有发生。依托东南亚地区某碾压混凝土坝,采用有限元研究施工期温度应力的变化规律,分析混凝土开裂的敏感因素,并就其温控防裂措施开展详细研究。结果表明,浇筑长间歇及寒潮侵袭显著影响混凝土温度应力的变化,老混凝土的强约束作用及新老混凝土的温差影响叠加寒潮应力,导致混凝土应力超标出现温度裂缝。在上述研究的基础上,进一步探讨了降低新浇混凝土温度应力的温控措施,为东南亚地区大体积混凝土工程的建设提供一定的参考。

基于SWLSTM-Stacking集成学习的源荷区间预测方法

针对光伏出力和电动汽车充电特性的随机特性对电力系统的冲击不断增强,准确及时的源荷预测是实现增强电力系统适应性和稳定性的重要课题。因此,提出一种基于共享权重长短期记忆网络(shared weight long short-term networks,SWLSTM)与Stacking集成模型相结合的源荷区间预测方法。首先,光伏出力存在时序性特征,采用局部线性嵌入改进k-means算法聚类提取特征日,在实现数据降维同时,减少了网络训练难度;其次,在Stacking集成模型的框架下,将SWLSTM作为元学习器,并通过Q统计量筛选合适的基学习器模型,从而实现多模型融合的多异学习器Stacking集成学习的源荷预测;紧接着,为了得到预测的不确定信息,引入置信度区间预测;最后,采用实测数据对本文所提方法进行验证。结果表明改进k-means算法能够降低其求解难度,加快求解速度,可以快速获取聚类特征;所引入集成学习模型和置信度区间,有效表征源荷预测的不确定性,提升区间预测模型的泛化能力。

基于长短期记忆网络和梯度提升的高血压患者RR间期时间序列预测方法

目的 对高血压患者的RR间期进行预测,帮助临床医生对患者心脏状况进行分析和预警。方法 以8位患者数据为样本,通过长短期记忆网络(LSTM)和梯度提升树(XGBoost)分别对患者的RR间期进行预测,将2个模型的预测结果通过方差倒数法进行组合,克服单一模型预测的劣势。结果 提出的组合模型相较于单一模型在8位患者RR间期的预测上具有不同程度的改善效果。结论 LSTM-XGBoost模型为高血压患者RR间期预测提供了方法,具有一定的临床价值。

Comparison of QT Correction Methods in the Pediatric Population of a Community Hospital: A Retrospective Study

Objective:Accurate measurement of QT interval,the ventricular action potential from depolarization to repolarization,is important for the early detection of Long QT syndrome.The most effective QT correction(QTc)formula has yet to be determined in the pediatric population,although it has intrinsically greater extremes in heart rate(HR)and is more susceptible to errors in measurement.The authors of this study compare six dif-ferent QTc methods(Bazett,Fridericia,Framingham,Hodges,Rautaharju,and a computer algorithm utilizing the Bazett formula)for consistency against variations in HR and RR interval.Methods:Descriptive Retrospective Study.We included participants from a pediatric cardiology practice of a community hospital who had an ECG performed in 2017.All participants were healthy patients with no past medical history and no regular med-ications.Results:ECGs from 95 participants from one month to 21 years of age(mean 9.7 years)were included with a mean HR of 91 beats per minute(bpm).The two-sample paired t-test or Wilcoxon signed-rank test assessed for any difference between QTc methods.A statistically significant difference was observed between every combination of two QTc formulae.The Spearman’s rank correlation analysis explored the QTc/HR and QTc/RR relationships for each formula.Fridericia method was most independent of HR and RR with the lowest absolute value of correlation coefficients.Bazett and Computer had moderate correlations,while Framingham and Rautaharju exhibited strong correlations.Correlations were positive for Bazett and Computer,reflecting results from prior studies demonstrating an over-correction of Bazett at higher HRs.In the linear QTc/HR regression analysis,Bazett had the slope closest to zero,although Computer,Hodges,and Fridericia had comparable values.Alternatively,Fridericia had the linear QTc/RR regression coefficient closest to zero.The Bland-Altman method assessed for bias and the limits of agreement between correction formulae.Bazett and Computer exhibited good agreement with minimal bias along with Framingham and Rautaharju.To account for a possible skewed distri-bution of QT,all the above analyses were also performed excluding the top and bottom 2%of data as sorted by heart rate ranges(N=90).Results from this data set were consistent with those derived from all participants(N=95).Conclusions:Overall,the Fridericia correction method provided the best rate correction in our pedia-tric study cohort.

复杂环境下矩型顶管设计全过程关键技术总结

太原地铁2号线长风街站矩型顶管过街通道工程具有以下特点,顶进距离较长、地层敏感且富水、下穿城市高架桥同时近距离并行上跨既有盾构区间隧道。依托该工程,该文对复杂环境下矩型顶管通道全过程设计要点、关键技术措施、工程影响评价进行分析和总结。工程实践证明在做好这些设计要点、关键技术措施的前提下,该种复杂环境条件下设计采用顶管工法是安全可靠的,具有很大的社会经济效益。

基于CEEMDAN-GSA-LSTM和SVR的光伏功率短期区间预测

针对光伏输出功率存在间歇性和波动性的问题,提出一种光伏功率短期区间预测模型.首先,该模型采用自适应噪声完备集合经验模态分解将历史光伏出力数据分解为不同的分量并按照其与赤纬角、时角等时序特征量的相关性定义为时序分量和随机分量.其次,分别使用经过引力搜索算法优化的长短期记忆神经网络和支持向量回归模型对时序分量和随机分量进行预测.再次,叠加时序分量和随机分量的预测结果得到点预测结果.然后,对误差进行Johnson变换及正态分布建模后得到光伏功率区间预测结果.最后,利用算例验证该模型的有效性.结果表明:在不同天气情况下,上述模型比现有预测模型精度更高,具有较好的鲁棒性,能够基于预测值提供较为精准的置信区间.

商用车超长效齿轮油应用研究

本文致力于对商用车桥箱通用型超长效齿轮油的应用性能进行研究。通过开展油品的理化性能分析、专项台架试验和道路耐久试验,监测油品关键指标衰变情况,对变速箱及驱动桥进行拆解评分,全面考察和评估油品与硬件的配套性和耐久性。结果显示,该产品展现出卓越的氧化稳定性、高效的油泥控制能力、出色的黏度保持性以及优异的齿轴保护能力,满足至少60万km换油的技术要求,且具备进一步延长的潜力。

基于改进EVM的雷达PRI调制类型开集识别

雷达脉冲重复间隔(PRI)的调制类型是分析雷达工作状态和任务的重要手段。针对常见PRI调制类型识别算法无法识别未知调制类型的问题,文中提出一种基于改进极值机(EVM)的雷达PRI调制类型开集识别方法。首先,采用残差-双向长短时记忆网络进行PRI序列的特征提取;其次,结合原型学习,利用基于距离的交叉熵损失和原型损失对特征提取网络进行训练;最后,在特征空间中引入已知类特征的线性组合以模仿未知类的行为,提出了改进的EVM模型。实验结果表明,与EVM相比,文中所提方法能够提升雷达PRI调制类型的识别准确率,且在开放的电磁环境下具有良好的开集适应性。

基于主成分分析和长短期记忆神经网络的光伏功率区间预测

针对光伏发电功率的随机变化,提出一种基于主成分分析和长短期记忆神经网络的光伏功率区间预测方法,有效实现了光伏功率的区间预测。首先,将用于训练模型的输入数据进行主成分分析法降维,在提取数据特征的同时降低数据维度;然后,将降维后的数据与真实光伏功率一同输入基于分位数的长短期记忆神经网络预测模型中迭代训练,得到训练完毕的预测模型;最后,在对比仿真中验证了所提方法的有效性。

基于竞争学习机制的LSTM风电多目标区间预测

为了进一步提升区间预测的综合效果,提出一种基于竞争学习机制的LSTM风电多目标区间预测方法。提出基于LSTM的上下界估计模型来构建风电区间多目标预测模型,研究多目标系统中的估计误差与预测区间平均宽度的关系;进一步考虑预测误差,引入一种新的偏最小二乘评价指标;通过引入竞争学习机制,提出一种改进的非支配快速排序遗传算法,有效实现了多目标优化。最后通过两个实际风电数据集进行实验验证,结果表明提出方法具有较高的预测精度。

基于优化变分模态分解的光伏功率超短期区间预测方法

针对城市分布式光伏电站在进行超短期功率预测时所需气象资料难以获取,在转折天气下光伏出力不确定性增加的问题,提出一种光伏功率超短期区间预测模型。首先该模型采用麻雀算法优化变分模态分解(VMD),在不同天气下将历史光伏出力分解成多个时序特征强的子模态;其次,通过长短期记忆神经网络LSTM对各子模态分别预测;再次,将各子模态的点预测结果叠加;算例验证结果表明:在各类天气条件下,所提模型相比于单纯使用气象因子的预测方法,具有更高的预测准确度和更强的适应性,同时也能在点预测的基础上提供较为准确的置信区间。