一种基于类不平衡学习的情感分析方法

针对网络评论中普遍存在的负面评论较少而影响力却较大的类不平衡问题, 提出一种基于类不平衡学习的情感分析方法. 该方法利用深度学习训练过程中的概率输出, 以计算样例的信息熵作为影响因子构建交叉信息熵损失函数. 在IMDB公开数据集上进行实验验证的结果表明, 基于集成信息熵损失函数的双向长短期记忆网络能处理类不平衡问题; 对数据的统计分析结果表明, 该策略能提升基于双向长短期记忆网络的评论情感极性分类性能. 针对AUC(area under curve)指标, 使用集成信息熵损失函数的双向长短期记忆网络模型比未考虑类不平衡的深度学习模型在中位数上最多提升15.3%.     

海、陆两用风力发电实验平台设计

在风电场通过现场测试分析研究风力机性能由于受到现场环境的限制,能够获得的研究数据有限,室内模拟实验平台能够有效弥补研究不足.为此提出了一种海、陆两用风力发电实验平台方案.该实验平台由来风装置、风轮、发电机、两段式塔架、漂浮台、波浪产生装置、传感检测和数据采集系统等构成.在实验平台整体方案基础上,进行了结构参数设计,建立了漂浮台受力分析模型并获得了合理的结构参数,以Lab VIEW为开发环境设计了上位机运行状态监控系统.最后,基于构建的实验平台,开展了部分风力机动态特性研究工作,对比分析了模拟陆地风力机和海上漂浮式风力机时塔架振动特性,为进一步深入开展风力机动态特性研究提供了基础.     

Forecasting in long horizons using smoothed direct forecast

This paper constructs a forecast method that obtains long‐horizon forecasts with improved performance through modification of the direct forecast approach. Direct forecasts are more robust to model misspecification compared to iterated forecasts, which makes them preferable in long horizons. However, direct forecast estimates tend to have jagged shapes across horizons. Our forecast method aims to “smooth out” erratic estimates across horizons while maintaining the robust aspect of direct forecasts through ridge regression, which is a restricted regression on the first differences of regression coefficients. The forecasts are compared to the conventional iterated and direct forecasts in two empirical applications: real oil prices and US macroeconomic series. In both applications, our method shows improvement over direct forecasts.     

Dynamic Model Averaging and CPI Inflation Forecasts: A Comparison between the Euro Area and the United States

The paper forecasts consumer price inflation in the euro area (EA) and in the USA between 1980:Q1 and 2012:Q4 based on a large set of predictors, with dynamic model averaging (DMA) and dynamic model selection (DMS). DMA/DMS allows not solely for coefficients to change over time, but also for changes in the entire forecasting model over time. DMA/DMS provides on average the best inflation forecasts with regard to alternative approaches (such as the random walk). DMS outperforms DMA. These results are robust for different sample periods and for various forecast horizons. The paper highlights common features between the USA and the EA. First, two groups of predictors forecast inflation: temporary fundamentals that have a frequent impact on inflation but only for short time periods; and persistent fundamentals whose switches are less frequent over time. Second, the importance of some variables (particularly international food commodity prices, house prices and oil prices) as predictors for consumer price index inflation increases when such variables experience large shocks. The paper also shows that significant differences prevail in the forecasting models between the USA and the EA. Such differences can be explained by the structure of these respective economies. Copyright © 2015 John Wiley & Sons, Ltd.     

基于变量选择-神经网络模型的复杂路网短时交通流预测

摘要： 针对传统交通流预测模型正在由单断面历史数据处理向多断面、多时刻历史数据处理转变,但在考虑各断面间的影响时,多变的交通状况往往会使预测模型复杂化的问题,引入一种多元线性回归最小绝对收缩和选择算子方法(Lasso),并利用其优秀的变量选择能力,在复杂路网多断面中选出相关性较高的断面；结合神经网络(NN)的非线性特性,提出了Lasso NN组合模型.结果表明：Lasso NN模型在路网交叉口对未来15 min交通流数据预测的误差率低于9.2%；在非交叉口的误差率低于6.7%,总体优于各自单独使用得出的结果.     

PXI在测控系统中的应用

随着有关PXI总线的规范和产品的推出，由于优良的性能指标和适宜的价格，使其受到各相关行业的广泛关注。对风洞测控系统的结构和系统配置作了具体的阐述，并对PXI总线在具体使用中的效果和特点作了较详细的介绍。     

用带时序子模块的系统动力学模型预测Brent原油价格

提出在系统动力学模型中加入时序自回归AR(p)子模块，并且自动确定每个AR(p)子模块的阶数p。带时序子模块的系统动力学模型既体现了因素问的横向因果关系，又体现了每个因素的纵向关系。建立欧佩克(OPEC)石油产量、世界GDP、煤炭产量与价格、天然气、需求量、供求差额、消费系数、非欧佩克石油产量、非欧佩克供求差额，石油库存和OPEC组织的期望油价共十一个因素影响下的Brent原油价格预测模型，模型的预测结果表明：在系统动力学模型中加入时序自回归AR(p)子模块的方法是可行的、有效的，并且能提高模型的预测精度。     

风能地板辐射采暖系统

随着经济快速发展,化石燃料已被大量开发和使用,一方面人类面临能源日渐匮乏的危机,另一方面能源大量浪费也引起全球环境问题.本文以环保和节能为目标,提出一种综合利用风力发电技术、热泵技术和低温地板辐射采暖技术的新型采暖模式,对使用可再生风能进行采暖,进行了可行性探讨.     

异步轧机轧辊稳定性的探讨

本文通过对异步轧制特点的分析,求出中性角及力能参数间的关系,从而导出了不同工作制度下的异步轧机工作辊偏移值的计算公式。本文结论将对改造现有轧机和设计新轧机提供理论依据。     

浅谈智能模糊控制恒压供水系统

文章介绍在变频恒压供水系统的基础上采用智能模糊控制等特性来完成恒压供水。