基于概率图的作战任务智能规划方法

针对人工调配作战资源及规划方案效率低下的问题，本文提出一种基于概率图的作战任务智能规划方法，通过统计分析判定任务间因果关系，采用GNN抽取任务中的关键事件构建概率图并计算任务规划方案成功的概率，进而基于时间序列方法预测战场态势变化，实现辅助指挥员智能决策。最后，本文在某联合登岛案例中开展了方法验证，结果表明，所提出的方法可成功实现任务规划并具有可解释性，可实现对战场态势变化的预测和快速响应，在战场上为军队提供强有力的支持。     

基于深度学习的节假日高速公路交通流预测方法

准确的预测节假日期间高速公路交通流量，能够为节假日高速公路应急管理提供重要的数据基础。利用深度学习的理论框架建立了LSTM-SVR 预测模型，利用BP 神经网络对样本数据进行处理，再将LSTM 捕获的数据特征输入SVR 回归层中实现交通流预测。选取“ 十一” 黄金周前后时段，利用位于丽江市的交调站流量监测数据对LSTM-SVR 模型进行验证，并将LSTM-SVR 模型与其它模型预测效果进行对比。发现LSTM-SVR 模型在节假日不同时段、天气、流量状态下的高速公路交通流预测中有较好的适用性。     

Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods

Financial distress prediction (FDP) has been widely considered as a promising approach to reducing financial losses. While financial information comprises the traditional factors involved in FDP, nonfinancial factors have also been examined in recent studies. In light of this, the purpose of this study is to explore the integrated factors and multiple models that can improve the predictive performance of FDP models. This study proposes an FDP framework to reveal the financial distress features of listed Chinese companies, incorporating financial, management, and textual factors, and evaluating the prediction performance of multiple models in different time spans. To develop this framework, this study employs the wrapper-based feature selection method to extract valuable features, and then constructs multiple single classifiers, ensemble classifiers, and deep learning models in order to predict financial distress. The experiment results indicate that management and textual factors can supplement traditional financial factors in FDP, especially textual ones. This study also discovers that integrated factors collected 4 years prior to the predicted benchmark year enable a more accurate prediction, and the ensemble classifiers and deep learning models developed can achieve satisfactory FDP performance. This study makes a novel contribution as it expands the predictive factors of financial distress and provides new findings that can have important implications for providing early warning signals of financial risk.     

The industrial asymmetry of the stock price prediction with investor sentiment: Based on the comparison of predictive effects with SVR

As a representative emerging financial market, the Chinese stock market is more prone to volatility because of investor sentiment. It is reasonable to use efficient predictive methods to analyze the influence of investor sentiment on stock price forecasting. This paper conducts a comparative study about the predictive performance of artificial neural network, support vector regression (SVR) and autoregressive integrated moving average and selects SVR to study the asymmetry effect of investor sentiment on different industry index predictions. After studying the relevant financial indicators, the results divide the Shenwan first-class industries into two types and show that the industries affected by investor sentiment are composed of young companies with high growth and high operative pressure and there are a great number of investment bubbles in those companies.     

非自治随机Kuramoto-Sivashinsky方程的Wong-Zakai逼近

在Wong-Zakai逼近下证明了非自治Kuramoto-Sivashinsky方程吸引子的存在性.     

Curcin和curcin C与腺嘌呤互作位点的比较分析

麻疯树核糖体失活蛋白curcin和curcin C均具有抗肿瘤活性，但后者的活性水平显著高于前者，为了探索造成这一差异的结构基础，本文采用在线的同源建模预测软件SWISSMODEL，对两种麻疯树核糖体失活蛋白curcin、curcin C的三维结构模型进行预测，采用SYBYL对预测模型进行能量最小化优化，采用PROCHECK、VERIFY 3D和ERRAT软件对优化前后的模型进行质量评估，随后采用AutoDock软件将预测的模型与腺嘌呤进行分子对接分析.结果显示，两种蛋白采用与Ricin A类似的方式与腺嘌呤相互作用，但在相互作用的氨基酸残基种类、数量以及形成的氢键和疏水相互作用上还是存在差异，其中，curcin C与腺嘌呤具有最强的结合能力，curcin则最低.     

The Predictive Performance Evaluation of Biased Regression Predictors With Correlated Errors

When the interdependence of disturbances is present in a regression model, the pattern of sample residuals contains information which is useful in the prediction of post‐sample drawings and when multicollinearity among regressors is also present, it is useful to use biased regression estimators. This information is exploited in the biased predictors derived here. Also, the predictive performance of various biased predictors with correlated errors is discussed and all pair‐wise comparisons are made among these predictors. The theoretical results are illustrated by a numerical example. Copyright © 2015 John Wiley & Sons, Ltd.     

粒子的散射方程、孤子解和高能统计模型

基于粒子散射的实验结果和已知的理论,首先,讨论了散射的各种方程,包括Lipkin方程、Pearson方程和Laguerre方程等,并且应用于各种散射;其次,重点研究了非线性方程,如Kd V方程、非线性统一方程等及其孤子解;然后,探讨了共振态和弹性、非弹性碰撞;进而,讨论高能散射和相应的统计模型;最后,提出不同能量的散射可能对应粒子低能具有对称性和高能具有统计性的新二重性.     

一种面向阴雨天气大规模水域交互模拟方法

针对计算机图形学中大规模水域雨场景绘制的难题,提出了一种适合大规模水域雨场景实时绘制的方法.将改进的波动方程引入雨天湖面波动以及雨滴与湖面的交互效果模拟中,结合快速傅里叶变换的风力趋向性以及周期性,建-了雨天水面交互波动的数学模型,根据数学模型构建了雨天水面交互高度图,通过光照渲染,实现了湖面反射效果和雨线绘制.为了提高模拟效率,整个过程在GPU中实现,并采用基于四叉树的多细节层次技术进行加速.在1 440×900的图像分辨率下,帧速达到了98 fps,满足了实时交互模拟的需求.     

周期的二阶Camassa-Holm方程弱整体解

考虑了二阶Camassa-Holm方程在周期条件下的柯西问题.利用奇异扰动的方法构造了二阶Camassa-Holm方程的黏性方程.通过压缩映射原理以及先验估计讨论了方程黏性解的存在性,然后根据黏性解的紧致性得到了周期的二阶Camassa-Holm方程在有限能量空间上弱整体解的存在性.