高阶FD-WENO格式用于Richtmyer-Meshkov不稳定性数值模拟

用双曲守恒律组的高阶加权本质上无振荡有限差分格式(FD-WENO)求解在众多领域有着重要应用的高马赫数二维流体界面Richtmyer-Meshkov不稳定性问题及激波与气泡相互作用问题,获得了较为理想的数值结果。表明高阶FD-WENO格式的确特别适合于求解这类既具有激波有具有复杂流动结构的流体动力学问题,它比通常的二阶Godunov格式(如PPM等)更为优越。     

浅谈高校实验室文档资料的管理

该文从实验室各类文档资料的管理角度，并结合自己的实验室工作情况，阐述了高等院校实验室文档资料管理的现状，同时提出改进的方案和具体措施，对实验室管理水平的提高有一定的促进作用。     

基于神经网络技术自动筛选Monastrol抑制剂的算法研究

带自动荧光显微镜的HCS（high content screening）系统是一种新兴的显微摄影、筛选和处理系统.HCS系统在摄影过程中会产生大量数据,人工筛选和识别费时费力.本文为了进行Monastrol抑制剂的筛选,基于前馈式神经网络技术研究了一种对HCS系统摄影的大量细胞同时进行特征提取和细胞显型识别的自动算法.在得到各个通道分离的图像后,对不同通道图像进行并行预处理,并采用神经网络和逻辑运算相结合的算法进行处理.我们将该方法运用于Monastrol抑制剂的筛选中,并将结果与人工识别结果进行分析比较.相比较前人提出的multi-phenotypic mitotic analysis（MMA）算法自动识别的正确率得以提高,可更好评估抗癌药剂效用.     

高阶复域微分方程解取小函数的收敛指数

研究了亚纯函数系数的高阶复域微分方程的解取小函数的点的收敛指数,得到了方程的解取小函数的点的收敛指数的精确估计.     

关于不锈钢压力容器防污染及酸洗、钝化的问题

不锈钢材料在加工过程中会出现黑色、黄色的氧化皮,为了提高不锈钢压力容器的外观和耐蚀性,加工过程中的不锈钢压力容器零件等需进行防污染处理,完工后不锈钢压力容器需进行酸洗、钝化。     

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.     

建材取样与管理不规范对工程质量的影响

建筑材料检验是保证工程质量的重要措施.本文针对目前施工现场建筑材料在取样方面存在的问题,就如何规范建材取样,确保工程质量提出了建议.     

ETFE双层气枕计算分析及比较

基于U.L.格式的非线性有限单元法,编制了ETFE双层气枕找形分析、受荷分析的计算程序,并选取圆形、正六边形、正三角形、菱形及正方形5种工程中常用的气枕作为计算模型进行对比分析.在找形分析时,将矢高、内压和初始张力作为控制参数进行计算并依次给出了三者之间的相互关系.在受荷分析时,考虑6种给定的荷载工况,计算了ETFE气枕体积、内压的变动以及膜面的主应力.数值计算结果表明,形状与圆形相差较大的双层气枕其力学性能变化明显.在气枕的体积和内压变化不大时,可直接根据气枕上层膜面的主应力判定气枕的工作状态.     

Testing Interval Forecasts: A GMM‐Based Approach

This paper proposes a new evaluation framework for interval forecasts. Our model‐free test can be used to evaluate interval forecasts and high‐density regions, potentially discontinuous and/or asymmetric. Using a simple J‐statistic, based on the moments defined by the orthonormal polynomials associated with the binomial distribution, this new approach presents many advantages. First, its implementation is extremely easy. Second, it allows for a separate test for unconditional coverage, independence and conditional coverage hypotheses. Third, Monte Carlo simulations show that for realistic sample sizes our GMM test has good small‐sample properties. These results are corroborated by an empirical application on SP500 and Nikkei stock market indexes. It confirms that using this GMM test leads to major consequences for the ex post evaluation of interval forecasts produced by linear versus nonlinear models. Copyright © 2011 John Wiley & Sons, Ltd.     

A Neuro‐wavelet Model for the Short‐Term Forecasting of High‐Frequency Time Series of Stock Returns

We propose a wavelet neural network (neuro‐wavelet) model for the short‐term forecast of stock returns from high‐frequency financial data. The proposed hybrid model combines the capability of wavelets and neural networks to capture non‐stationary nonlinear attributes embedded in financial time series. A comparison study was performed on the predictive power of two econometric models and four recurrent neural network topologies. Several statistical measures were applied to the predictions and standard errors to evaluate the performance of all models. A Jordan net that used as input the coefficients resulting from a non‐decimated wavelet‐based multi‐resolution decomposition of an exogenous signal showed a consistent superior forecasting performance. Reasonable forecasting accuracy for the one‐, three‐ and five step‐ahead horizons was achieved by the proposed model. The procedure used to build the neuro‐wavelet model is reusable and can be applied to any high‐frequency financial series to specify the model characteristics associated with that particular series. Copyright © 2013 John Wiley & Sons, Ltd.