室内几何条件对土壤高光谱数据波动性的影响

利用方差分析方法,研究了光源照射角度、探头距离、光源距离等3个测试几何条件对室内土壤高光谱数据波动性的影响,并探讨了其物理机理.结果表明,探头距离对室内高光谱样本曲线的波动性影响不明显,而光源照射角度、光源距离对室内高光谱样本曲线波动性影响在所有的研究波段都达到了极显著水平;两个因素之间的交互作用的显著性与因素和波段有关.3个因素间的交互作用不显著.在本试验研究条件下,15°的光源照射角度、15cm的探头距离和30cm的光源照射距离是最理想的室内测试几何条件组合.

基于趋势性度量的有序聚类方法探讨

在有序聚类分析方法的研究中,针对原有方法中对时序的趋势性特征体现不够和一些趋势性特征方法计算复杂度较高的问题,对类的直径度量进行了改进,在类的直径度量中,提出了基于斜率滑动均值的有序聚类方法:首先对有序样本中的邻近点计算相互之间的斜率,对新的斜率序列进行滑动平均,将之结果定义为类的直径,最终实现趋势性度量的有序聚类。模拟与实证结果显示,考虑了趋势性度量的有序聚类方法,对波动性序列的趋势性具有更好的分辨能力,且降低了计算的复杂程度,能够提高有序样本聚类的准确性。

结合去趋势的AR模型变形数据预测

本文针对累计变形数据呈现波动的特性,提出了一种将累计变形数据变换生成新序列并采用AR模型建模预测的方法。该方法以时间为横轴、累计变形量为纵轴构建平面坐标系,采用相邻三期数据点,计算中间点至两端点连线垂距,并以中间点与两端点连线的相对位置定义垂距正负,从而得到在零附近上下波动的新数据序列。以新数据序列建立AR模型进行预测,并将预测值还原为最终累计沉降预测值。采用实测隧道围岩收敛数据及高层建筑沉降数据对该方法进行验证,均得到较好的预测效果,说明该方法具有一定实践应用价值。

基于VMD⁃HS⁃LSSVM的风电功率短期预测

风功率时间序列易受到多种因素影响,呈现出高度的随机性和波动性,对电网的安全运行构成了潜在的威胁,因此准确的风功率预测至关重要。针对风电时间序列低预测性问题,提出一种组合变分模态分解(VMD)、和声搜索(HS)算法和最小二乘支持向量机(LSSVM)的短期风功率预测方法。首先采用VMD将风功率时间序列分解为不同模态,减少数据的波动性;然后对各模态分别建立HS⁃LSSVM预测模型,并运用HS优化最小二乘支持向量机相关参数,从而建立VMD⁃HS⁃LSSVM风功率预测模型,提高模型在短尺度时序的预测能力;最后将各模型预测结果进行求和重构。实验结果表明,相比传统预测模型中的仿真结果,文中方法运用在风功率时序预测中具有优越性,能有效提高短期风功率时间序列预测的准确性。

基于波动性分析的调频辅助服务需求计算与应用

调频辅助服务需求是电力系统调度运行中调频辅助服务调用的基础数据,更是调频辅助服务市场的交易标的,准确计算该需求是组织调频辅助服务市场交易的前提。提出了基于波动分布图的数据波动性分析方法和基于"二八原则"的数据波动性评价指标计算方法;根据调频辅助服务需求的物理概念,设计了基于波动性分析的调频辅助服务需求计算方法,实现了风电、光伏等新能源参与下电力市场调频辅助服务需求的规范化计算框架。以该计算方法在某省电网调频辅助服务管理系统中实际应用的效益为例,验证了该计算方法能够满足电网实际运行要求,为新能源参与消纳提供了良好条件,提升了调频辅助服务资源利用率。

Volatility forecasting in Chinese nonferrous metals futures market

This paper seeks to model and forecast the Chinese nonferrous metals futures market volatility and allows new insights into the time-varying volatility of realized volatility and leverage effects using high-frequency data.The LHAR-CJ model is extended and the empirical research on copper and aluminum futures in Shanghai Futures Exchange suggests the dynamic dependencies and time-varying volatility of realized volatility,which are captured by long memory HAR-GARCH model.Besides,the findings also show the significant weekly leverage effects in Chinese nonferrous metals futures market volatility.Finally,in-sample and out-of-sample forecasts are investigated,and the results show that the LHAR-CJ-G model,considering time-varyingvolatility of realized volatility and leverage effects,effectively improves the explanatory power as well as out-of sample predictive performance.

Wind Wave Characteristics and Engineering Environment of the South China Sea

Wave simulation was conducted for the period 1976 to 2005 in the South China Sea （SCS） using the wave model, WAVEWATCH-III. Wave characteristics and engineering environment were studied in the region. The wind input data are from the objective reanalysis wind datasets, which assimilate meteorological data from several sources. Comparisons of significant wave heights between simulation and TOPEX/Poseidon altimeter and buoy data show a good agreement in general. By statistical analysis, the wave characteristics, such as significant wave heights, dominant wave directions, and their seasonal variations, were discussed. The largest significant wave heights are found in winter and the smallest in spring. The annual mean dominant wave direction is northeast （NE） along the southwest （SW）-NE axis, east northeast in the northwest （NW） part of SCS, and north northeast in the southeast （SE） part of SCS. The joint distributions of wave heights and wave periods （directions） were studied. The results show a single peak pattern for joint significant wave heights and periods, and a double peak pattern for joint significant wave heights and mean directions. Furthermore, the main wave extreme parameters and directional extreme values, particularly for the 100-year return period, were also investigated. The main extreme values of significant wave heights are larger in the northern part of SCS than in the south- ern part, with the maximum value occurring to the southeast of Hainan Island. The direction of large directional extreme Hs values is focus in E in the northem and middle sea areas of SCS, while the direction of those is focus in N in the southeast sea areas of SCS.

灰色自记忆模型的高层建筑物沉降分析

针对GM(1,1)模型对非线性数据的沉降趋势及其波动特征无法进行准确地预测,而灰色残差模型和灰色马尔科夫模型又无法解决这个问题,提出了灰色自记忆预测模型。该模型利用了自记忆原理考虑过去和现在对未来的影响的记忆性特点,克服了GM(1,1)模型对初值比较敏感、预测精度低等局限性,提高了对波动性数据的预测能力。通过实例验证表明了灰色自记忆模型的可靠性和可行性。

ＯＮ ＴＨＥ ＣＬＡＳＳＩＦＩＣＡＴＩＯＮ ＯＦ ＩＮＩＴＩＡＬ ＤＡＴＡＦＯＲ ＮＯＮＬＩＮＥＡＲ ＷＡＶＥ ＥＱＵＡＴＩＯＮＳ

The purpose of the present paper is to call for attention to the following question: Which of the initial data (nonsmall) admit global smooth solutions to the Cauchy problem for nonlinear wave equations. A few cases and examples are sketched, showing that the general answer of this question may be quite complicated.

Unified deep learning model for El Niño/Southern Oscillation forecasts by incorporating seasonality in climate data

Although deep learning has achieved a milestone in forecasting the El Niño-Southern Oscillation(ENSO),the current models are insufficient to simulate diverse characteristics of the ENSO,which depends on the calendar season.Consequently,a model was generated for specific seasons which indicates these models did not consider physical constraints between different target seasons and forecast lead times,thereby leading to arbitrary fluctuations in the predicted time series.To overcome this problem and account for ENSO seasonality,we developed an all-season convolutional neural network(A_CNN)model.The correlation skill of the ENSO index was particularly improved for forecasts of the boreal spring,which is the most challenging season to predict.Moreover,activation map values indicated a clear time evolution with increasing forecast lead time.The study findings reveal the comprehensive role of various climate precursors of ENSO events that act differently over time,thus indicating the potential of the A_CNN model as a diagnostic tool.