REAL OPTIONS WITH COMPETITION AND REGIME SWITCHING

In this paper, we examine irreversible investment decisions in duopoly games with a variable economic climate. Integrating timing flexibility, competition, and changes in the economic environment in the form of a cash flow process with regime switching, the problem is formulated as a stopping‐time game under Stackelberg leader‐follower competition, in which both players determine their respective optimal market entry time. By extending the variational inequality approach, we solve for the free boundaries and obtain optimal investment strategies for each player. Despite the lack of regularity in the leader's obstacle and the cash flow regime uncertainty, the regime‐dependent optimal policies for both the leader and the follower are obtained. In addition, we perform comprehensive numerical experiments to demonstrate the properties of solutions and to gain insights into the implications of regime switching.     

三种电离层射线追踪自导引算法比较

通过建立电离层模型和地磁场模型,采用龙格库塔法求解Haselgrove方程,得到相应的射线路径参数,实现了电离层三维射线追踪。针对射线追踪无法进行自导引计算,分别采用牛顿差分自导引算法、基于变分方程的牛顿法以及单纯形法实现了射线追踪的自导引,并给出了详细的算法分析。重点对变分方程及其二阶微分的求解进行了详细推导。最后对以上三种自导引算法进行性能比较,仿真结果表明:基于变分方程的自导引算法优于其他两种自导引算法。     

唐山市降水变化特征及趋势预测

根据77处雨量站1956-2000年降水量同步系列资料，对唐山市降水变化的周期性、阶段性、不稳定性、降水倾向等特征进行分析，并利用灰色预测模型，预测了降水变化趋势。     

Gibbsian and non-Gibbsian states at Eurandom

We review some of the work on non-Gibbsian states of the last 10 years, emphasizing the developments in which Eurandom played a role.     

Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model

The study of climatic variables that govern the Indian summer monsoon has been widely explored. In this work, we use a non-linear deep learning-based feature reduction scheme for the discovery of skilful predictors for monsoon rainfall with climatic variables from various regions of the globe. We use a stacked autoencoder network along with two advanced machine learning techniques to forecast the Indian summer monsoon. We show that the predictors such as the sea surface temperature and zonal wind can predict the Indian summer monsoon one month ahead, whereas the sea level pressure can predict ten months before the season. Further, we also show that the predictors derived from a combination of climatic variables can outperform the predictors derived from an individual variable. The stacked autoencoder model with combined predictors of sea surface temperature and sea level pressure can predict the monsoon (June-September) two months ahead with a 2.8% error. The accuracy of the identified predictors is found to be superior to the state-of-the-art predictions of the Indian monsoon.     

供应链网络均衡模型

本文试图提出一种供应链网络的均衡模型。在这个模型里将充分考虑决策者的决策与他们各自独立的行为,并推导出供应链网络模型结构均衡的条件。     

随机需求下横向并购供应链网络整合优化策略研究

本文从系统优化视角,针对增设集散中心的横向并购供应链网络整合策略,利用变分不等式建立了随机需求下的整合优化模型。以市场链为主线,以供应链网络均衡理论为基 础,构建了以横向并购供应链网络活动总成本最小为目标的数学模型,通过转换成变分不等式采用改进的欧拉算法进行模型求解。通过数值算例进一步说明了模型的适用性和灵活性,得出了增设集散中心的并购整合策略能够降低供应链网络的平均运营成本的结论,通过对比不同市场紧密程度下产生的供应链网络平均成本协同效应,发现随着市场紧密程度的增加其协同效应更明显,为横向并购供应链网络整合优化提供理论依据与方法。     

一种基于卷积运算与全变分模型的图像去噪方法

梯度模值较易受到外界影响，导致全变分模型在大噪声点处往往不能很好地消除噪声，从而产生阶梯效应。针对该问题，提出了一种基于卷积运算与全变分模型的图像去噪方法。首先，针对以扩散形式获得的图像像素点进行卷积运算，利用滤波去噪降低大噪声点的灰度值；其次，以能量泛函形式构建图像全变分模型，并求解泛函对应的拉格朗日方程极小值来实现图像去噪；最后，将去噪后图像作为双边滤波算法的引导图像进行二次去噪，从而进一步提升图像去噪质量。仿真实验结果表明，与经典方法相比，该模型对去噪过程中的阶梯效应问题具有较好的处理效果。     

改进的乘性噪声去噪算法

文章在AA模型的基础上,提出了一种改进的图像去噪算法。针对AA模型在去噪时边缘信息对噪声敏感且易出现过平滑的缺陷,在新模型中引入自蛇函数,使得新模型根据图像的特征进行平滑,从而更好地保护边缘信息。数值实验表明,新算法无论是在峰值信噪比上还是在视觉效果上都有很大改善。