局部自相似过程的小波估计

首先, 针对时变自相似参数提出了一种基于最大重叠离散小波变换的估计方法; 对此进行了蒙特卡洛模拟研究, 发现提高了估计的精度; 最后, 将研究结果应用于海洋垂直切变序列.     

高度场格点距平的长期统计预报试验

采用多通道奇异谱分析和相似预报方法，建立了长期统计预报模型．以７００ｈＰａ前期位势高度场显著时空主分量为预报因子，直接预报后期高度场格点距平的类别，由此获得了一定的预报技巧     

长跑运动员过度训练问题分析及其对策

旨在为过度训练提供训练量制定和训练效果评价的理论依据 .通过中外中长跑运动员经常出现的过度训练表象及生理机能变化的分析 ,提出了防止过度训练的原则与措施     

长距离跑径赛项目2组弧形起跑线画法例析

根据田径规则对有些长距离跑径赛项目起跑的有关规定，讨论了这些径赛项目分2组同时起跑时，其2组弧形起跑线的渐开线画法和放射点连线画法．     

Tourism in the Canary Islands: forecasting using several seasonal time series models

This paper deals with the analysis of the number of tourists travelling to the Canary Islands by means of using different seasonal statistical models. Deterministic and stochastic seasonality is considered. For the latter case, we employ seasonal unit roots and seasonally fractionally integrated models. As a final approach, we also employ a model with possibly different orders of integration at zero and the seasonal frequencies. All these models are compared in terms of their forecasting ability in an out‐of‐sample experiment. The results in the paper show that a simple deterministic model with seasonal dummy variables and AR(1) disturbances produce better results than other approaches based on seasonal fractional and integer differentiation over short horizons. However, increasing the time horizon, the results cannot distinguish between the model based on seasonal dummies and another using fractional integration at zero and the seasonal frequencies. Copyright © 2008 John Wiley & Sons, Ltd.     

LTE系统基于移动用户分组的切换优化

针对长期演进(long term evolution,LTE)网络中由于用户移动速度不同导致系统切换失败率高的问题,提出了基于移动用户分组设置切换参数的优化方案.建立了用户移动模型,利用用户参考信号强度值来估算用户移动速度;利用遗传算法的编码、选择以及交叉变异3个步骤对移动特性相似的用户进行分组,得到不同的分组速度,对不同分组的用户设置不同切换参数.仿真结果表明,该优化方案可以获得良好的鲁棒性,减少了无线链路失败率和切换失败率,降低了用户掉话率.     

深井长距离大断面回采巷道支护方案优化

为解决文家坡煤矿4101首采工作面长距离大断面回采巷道顶板下沉量大、钢带变形严重以及帮部破碎扩容显著等围岩失稳带来的支护质量差甚至支护失效现象,采用钻孔窥视仪对巷道进行布孔观测和工程类比法对原支护方案进行优化。结果表明:采用优化方案后,顶板下沉量从600 mm降低到100 mm,并且钢带受剪切变形显著减少;巷道尺寸收敛现象为50~100 mm,低于优化前的300 mm.为避免巷道变形严重,在监测监控基础上增加支护强度是合理可行的。     

超短期风电功率预测的混合深度学习模型

针对风电功率预测(WPF)问题,提出一种基于离散小波变换(DWT)、时间卷积网络(TCN)和长短期记忆(LSTM)神经网络的混合深度学习模型(DWT-TCN-LSTM),对超短期风电功率进行预测.将DWT-TCN-LSTM模型分别与差分整合移动平均自回归(ARIMA)模型,支持向量回归(SVR)模型,长短期记忆神经网络模型和卷积长短期记忆(TCN-LSTM)混合模型进行对比实验,通过对称平均绝对百分比误差(SMAPE),均方根误差(RMSE)和平均绝对误差(MAE)3种评价指标值对各个模型进行评价.实验结果表明:DWT-TCN-LSTM模型具有较好的预测性能.     

苦丁茶水提物的毒理学研究

研究苦丁茶（Ilet latifolia Thunb)水提物对小鼠的急性毒性作用和对大鼠长期毒性作用，结果表明小鼠对苦丁茶水提物的最大耐受量为168g/kg，属于无毒性级，服用安全，在大鼠90天长期毒性试验中，苦丁茶对大鼠的生长发育，造血功能，血液生化指标等均无不良影响。     

Forecasting high‐frequency financial data with the ARFIMA–ARCH model

Financial data series are often described as exhibiting two non‐standard time series features. First, variance often changes over time, with alternating phases of high and low volatility. Such behaviour is well captured by ARCH models. Second, long memory may cause a slower decay of the autocorrelation function than would be implied by ARMA models. Fractionally integrated models have been offered as explanations. Recently, the ARFIMA–ARCH model class has been suggested as a way of coping with both phenomena simultaneously. For estimation we implement the bias correction of Cox and Reid ( 1987 ). For daily data on the Swiss 1‐month Euromarket interest rate during the period 1986–1989, the ARFIMA–ARCH (5,d,2/4) model with non‐integer d is selected by AIC. Model‐based out‐of‐sample forecasts for the mean are better than predictions based on conditionally homoscedastic white noise only for longer horizons (τ > 40). Regarding volatility forecasts, however, the selected ARFIMA–ARCH models dominate. Copyright © 2001 John Wiley & Sons, Ltd.