基于傅里叶红外光谱的SiO_2基相变储湿复合材料结构分析及性能优选预测

以SiO_2为载体材料、脂肪酸为相变材料制备具有相变调温性能与储湿调湿性能的SiO_2基相变储湿复合材料。采用等温吸放湿法和步冷曲线法测试不同脂肪酸用量的SiO_2基相变储湿复合材料相变调温性能与储湿调湿性能。利用傅里叶红外光谱测试SiO_2基相变储湿复合材料的结构组成,分析SiO_2与脂肪酸的嵌合机理。以SiO_2基相变储湿复合材料的傅里叶红外光谱特征吸收峰作为输入层,以SiO_2基相变储湿复合材料的脂肪酸用量、相对湿度52.89%下SiO_2基相变储湿复合材料吸湿平衡含湿量与放湿平衡含湿量的平均值、SiO_2基相变储湿复合材料从30~15℃降温所需的时间作为输出层,以S型激活函数作为隐含层,利用BE神经网络建立结构参数与综合相变储湿性能的SiO_2基相变储湿复合材料性能优选预测模型。结果表明,SiO_2基相变储湿复合材料中SiO_2与脂肪酸仅为物理嵌合,未发生化学作用;当脂肪酸用量0.079 mol时,所制备的SiO_2基相变储湿复合材料具有最优的综合相变储湿性能,即在相对湿度52.89%下的吸湿平衡含湿量为0.132 3 g·g~(-1)、放湿平衡含湿量0.147 5 g·g~(-1)、平衡含湿量的平均值为0.139 9 g·g~(-1),从30~15℃降温所需的时间为1 305 s;SiO_2基相变储湿复合材料的性能优选预测模型吻合性较好,具有较高的精确度,其预测值与实测值的相对误差为-2.07%和2.45%,可以用于优选预测SiO_2基相变储湿复合材料的储湿调湿性能和相变调温性能。     

Net demand prediction for power systems by a new neural network‐based forecasting engine

Integration of renewable generations, such as wind and photovoltaic, into electrical power systems is rapidly growing throughout the world. Stochastic and variable nature of these resources makes some operational challenges to power systems. The most effective way to tackle these challenges is short‐term prediction of their available powers. Despite various developed methods to forecast generation of renewable resources, still they have large errors, which may lead to under/over‐commitment of conventional generators in power systems. Prediction of net demand (ND), defined as electrical load minus renewable generations, can provide useful information for accurate scheduling of conventional generators. In this article, characteristics of the time series of electric load, renewable generations and ND are analyzed, and a new hybrid prediction strategy is presented for direct prediction of ND. The training mechanism of the proposed forecasting engine is composed of a new stochastic search method and Levenberg–Marquardt learning algorithm based on an iterative procedure and greedy search. The suggested prediction strategy is tested on different real‐world power systems and its obtained results are compared with the results of several other forecast methods and published literature figures. These comparisons confirm the validity of the developed forecasting strategy. © 2016 Wiley Periodicals, Inc. Complexity 21: 296–308, 2016     

季节调整的PSO-SVR模型及其在旅游客流量预测中的应用——以海南省三亚市为例

准确的旅游客流量预测对旅游目的地做好事前准备工作至关重要.然而旅游客流量具有明显的非线性和季节性特征,采取季节调整方法对样本数据进行预处理,消除季节性的影响,可以提高客流量预测的准确性.同时SVR(支持向量回归机)是一种良好的机器学习方法,非常适合预测研究,辅以PSO(粒子群算法)选取合适的回归参数可以获得更加精确的预测结果.提出了一种考虑季节影响并通过PSO优化SVR模型的旅游客流量预测模型,并以海南省三亚市为例进行了实证研究.研究结果表明,季节调整的PSO-SVR模型预测精度明显高于SVR、季节调整的SVR和PSO-SVR模型,是进行旅游客流量预测的有效工具.     

Methods of humidity determination Part I: Hygrometry

Part I of the short survey covers definitions of air humidity and the respective measuring methods such as hygrometry, psychrometry, dew point measurement, LIDAR hygrometry and humidity sensors. Techniques based on property changes of matter with adsorbed moisture from air are reviewed.     

热带气旋路径客观预报方法的评估

本文对西北太平洋热带气旋路径的客观预报方法进行了评估,所得结果可供业务应用参考。     

Two Issues in Setting Call Centre Staffing Levels

Motivated by a problem facing the Police Communication Centre in Auckland, New Zealand, we consider the setting of staffing levels in a call centre with priority customers. The choice of staffing level over any particular time period (e.g., Monday from 8 am–9 am) relies on accurate arrival rate information. The usual method for identifying the arrival rate based on historical data can, in some cases, lead to considerable errors in performance estimates for a given staffing level. We explain why, identify three potential causes of the difficulty, and describe a method for detecting and addressing such a problem.     

ARIMA模型在深圳GDP预测中的应用

对1979至2006年深圳国内生产总值进行了分析,建立了ARIMA(1,2,2)模型,检验结果表明该模型具有较好的预测效果,可为深圳制定经济发展目标提供决策参考.     

基于神经网络的期货市场预测及模型实现

通过对期货市场的研究,尝试用人工神经网络预测期货行情走势.介绍了如何将期货市场与改进的BP网络有机结合起来构造适合期价预测的模型,并应用Matlab工具,设计一个具有较强通用性的人工神经网络模型,在降低重复开发的同时,为更多潜在的用户提供一个适合各自需求的人工神经网络.通过实例证实运用神经网络进行期货价格预测相对于传统的经济预测方法具有更好的精确性.     

一种基于在线训练的交通信息实时预测快速算法

对于实时交通信息预测,预测精度与预测时间效率始终是一对难以解决的矛盾.重点研究如何提高预测时间效率问题.以精确在线支持向量回归算法(AOSVR)为基础,提出了基于云模型的sigmoid核函数简化计算方法,建立了改进的AOSVR交通信息实时预测模型.该模型应用于实际的交通流实时预测,预测结果表明,由于简化了计算,以损失较小回归精度的代价,显著提高AOSVR模型预测效率.     

基于成本粘性的盈利预测及其精度检验

本文以2007-2009年沪深2652家A股上市公司为样本,验证了成本粘性的存在性,并以2008年的相关财务数据为基础构建盈利预测模型,对2009年的公司盈利进行预测,通过与当年各公司的实际利润相比较,发现基于成本粘性的盈利预测模型具有更高的预测能力,其预测精度显著高于其他常用预测模型,表明将诸如成本习性等管理会计的工具引入财务会计研究领域具有重大意义,可以提高会计信息的决策相关性。