基于改进四元数阻尼误差模型的SINS初始对准算法

针对传统捷联惯导系统(strapdown inertial navigation system, SINS)四元数非线性误差模型存在坐标系不一致的问题, 对姿态误差模型和速度误差模型进行改进, 将误差矢量统一投影至计算导航坐标系下。此外, 引入全球定位系统阻尼信息, 在阻尼SINS解算基础上, 结合四元数无迹估计器提出了一种改进四元数阻尼误差模型对准算法, 可应用于系泊状态下的SINS初始对准。仿真和车载试验结果表明, 在不同的大失准角下, 该改进算法相比传统四元数阻尼误差模型对准算法和欧拉角阻尼误差模型对准算法, 具有更好的对准精度、收敛速度以及稳定性。     

The futility of decision making research

We have each spent more than 50 years doing research that has had little impact. Even more lamentable is that our field, judgment and decision making (JDM), has on the whole had little impact during that span. We attribute that failure to the use of methodologies that emphasize testing models rather than looking for differences in behavior. The “cognitive revolution” led the field astray, toward the goal of studying model fit rather than comparing observable results. With modeling as the goal, experimentation was stultified. Simple tasks became dominant. Although a poor metaphor for real decision making, the gambling paradigm has lasted forever because the inputs to the decision are known to the researcher and thus easily modeled.     

An ensemble of LSTM neural networks for high‐frequency stock market classification

We propose an ensemble of long–short‐term memory (LSTM) neural networks for intraday stock predictions, using a large variety of technical analysis indicators as network inputs. The proposed ensemble operates in an online way, weighting the individual models proportionally to their recent performance, which allows us to deal with possible nonstationarities in an innovative way. The performance of the models is measured by area under the curve of the receiver operating characteristic. We evaluate the predictive power of our model on several US large‐cap stocks and benchmark it against lasso and ridge logistic classifiers. The proposed model is found to perform better than the benchmark models or equally weighted ensembles.     

时速80 km/h地铁地下线梯形轨枕轨道现场测试分析

为评价时速80 km/h地铁作用下梯形轨枕的工作性能,对北京地铁某线梯形轨枕道床进行现场动位移和加速度测试,从时域、频域和Z振级角度对加速度指标进行分析,从时域角度对动位移数据进行分析,评价梯形轨枕轨道工作性能。结果表明:地铁列车作用下普通道床钢轨、道床和隧道壁振动有效值为14.1、0.48、0.069 m/s~2,梯形轨枕断面对应测点振动有效值为18.1、0.62、0.016 m/s~2,隧道壁处振动加速度在1～1 000 Hz内均有一定减振效果,最大Z振级差值为11.9 dB,梯形轨枕道床钢轨垂向、钢轨横向、梯轨垂向、梯轨横向最大动位移分别为0.34、0.13、1.21、0.081 mm。     

现代西欧对术语学传统思想的质疑和新的思想突破

维斯特创立的普通术语学思想在20世纪70年代基本形成后,在30年中稳步前进。自21世纪以来,欧洲对术语学的讨论忽然变得热烈,产生了一些"颠覆性"的质疑。文章介绍了欧洲术语学传统思想的承继者们如何应对这些质疑,以及"现代术语学革新派"突破性的理论思想贡献,并浅析未来的术语学理论发展的趋势和远景。     

三阶非线性中立型阻尼泛函微分方程的振动性

研究一类三阶非线性中立型阻尼泛函微分方程,利用广义Riccati变换和积分平均技巧,建立了该类方程的所有解振动或收敛于零的若干新的充分条件,推广和改进最近文献的结果.     

废车、减震沟及沙袋缓冲对塌落振动减振效果对比研究

针对城市爆破拆除工程中的塌落振动问题,结合落锤冲击数值模拟与塌落冲击试验研究了减震沟、废弃机动车及沙袋缓冲层等减振措施对塌落振动的减振效果。结果表明,落锤冲击数值模拟与塌落冲击试验动力响应规律较一致,减震沟、废车及沙袋缓冲等均能在一定程度上降低地面质点振动响应峰值;塌落冲击试验中所有振动分量频率均介于10～40 Hz之间,废车及沙袋缓冲对部分振动分量频率影响不明显;在减震沟、废车及沙袋缓冲等3种减振措施中,废车尤其微型面包车缓冲减振效果最好。     

非经典阻尼隔震结构近似实空间解耦分析

提出了可应用于中低层和多高层隔震结构的近似实空间解耦方法.利用强迫解耦法、Kelly法和近似实空间解耦法对具有非经典阻尼特性的隔震结构进行了对比分析,并将结构位移响应用具有明确物理与工程意义的系列二阶标准振子位移响应与速度响应线性组合来表示.通过理论分析和算例求解可知:近似实空间解耦法具有很强的优越性和很高的计算精度,结构隔震层和顶层的位移响应最大误差仅为0.361%和1.444%,可推广应用于隔震结构的动力响应分析.     

结合时间序列分解和神经网络的河流溶解氧预测

为克服小流域数据资料少,河流溶解氧的非平稳特性及动态变化造成的预测困难,提出结合具有自适应噪声的完整集成经验模态分解(CEEMDAN)和Elman动态神经网络的预测方法.使用CEEMDAN方法对原始溶解氧时序数据进行平稳化处理及降噪,提取溶解氧随时间变化的波动特征、周期特征,以及长期趋势,通过计算样本熵(SE)值,将相似的特征序列合并,以减小误差累积,对合并后的新序列分别采用布谷鸟搜索(CS)算法优化的Elman模型进行预测,将各预测值叠加,得到最终预测结果.实验结果表明:CEEMDAN-SE-CS-Elman方法平均绝对误差(E_MA)为0.14;平均绝对百分误差(E_MPA)为2.07%;均方根误差(E_RMS)为0.24;可决系数(R²)达到0.951 6,精度较其他时间序列预测模型有所提高.     

Spatial dependence model with feature difference

This paper presents a new spatial dependence model with an adjustment of feature difference. The model accounts for the spatial autocorrelation in both the outcome variables and residuals. The feature difference adjustment in the model helps to emphasize feature changes across neighboring units, while suppressing unobserved covariates that are present in the same neighborhood. The prediction at a given unit incorporates components that depend on the differences between the values of its main features and those of its neighboring units. In contrast to conventional spatial regression models, our model does not require a comprehensive list of global covariates necessary to estimate the outcome variable at the unit, as common macro-level covariates are differenced away in the regression analysis. Using the real estate market data in Hong Kong, we applied Gibbs sampling to determine the posterior distribution of each model parameter. The result of our empirical analysis confirms that the adjustment of feature difference with an inclusion of the spatial error autocorrelation produces better out-of-sample prediction performance than other conventional spatial dependence models. In addition, our empirical analysis can identify components with more significant contributions.