单摆非线性运动方程的微扰近似解法

本文研究具有阻尼因素的单摆非线性运动规律。根据相应的线性运动方程精确解,通过求解修正项,给出逐步逼近的近似解方法,并讨论了解的误差问题。     

Discrete–Time Approximations of the Holmström–Milgrom Brownian–Motion Model of Intertemporal Incentive Provision

This paper studies the relation between discrete–time and continuous–time principal–agent models. We derive the continuous–time model as a limit of discrete–time models with ever shorter periods and show that optimal incentive schemes in the discrete–time models approximate the optimal incentive scheme in the continuous model, which is linear in accounts. Under the additional assumption that the principal observes only cumulative total profits at the end and the agent can destroy profits unnoticed, an incentive scheme that is linear in total profits is shown to be approximately optimal in the discrete–time model when the length of the period is small.     

次分数布朗运动下带交易费用的备兑权证定价

为了体现金融资产的长记忆性,采用次分数布朗运动刻画备兑权证标的资产价格变化的行为模式。利用随机分析理论和偏微分方程方法,建立了次分数布朗运动下带交易费用的备兑权证定价模型,进一步研究了定价模型的参数估计问题。最后,采用我国权证市场实际数据进行了实证分析,通过比较不同定价模型的结果说明了长记忆性和交易费用对定价结果有着显著的影响。     

画有法画无定法--浅议写实摄影构图的六个基本法则

在摄影过程中应遵循和运用黄金分割、三角形、对角线、天头留白、运动空白、均衡稳定等构图法则,方能使写实摄影作品获得较为理想的艺术效果.     

浅谈中国古典园林建筑的美学思想

中国古典园林中的建筑是园林中的重要组成部分,亭、廊、桥、榭、舫等主要构筑物无不具备各自的美学特征,形成了丰富的美的景观,其美学价值、哲理内涵极具研究价值.中国古典园林中的建筑主要表现了飞动之美、空间之美和诗情画意之美等主要美学特征.     

一种基于UMHexagonS的运动估计优化算法

UMHexagonS算法是H.264/AVC所采纳的整像素的快速运动估计算法之一,该算法在保持良好的率失真性能的前提下,相比全搜索算法(FS)可节约70%的运算量。根据UMHexagonS算法的特点,可从三个方面对该算法进行优化:加入零运动块判决;改进的5*5螺旋搜索;优化的多层次六边形搜索。仿真实验结果表明,在保证PSNR和码率几乎不变的情况下,优化算法可有效降低运动估计时间,从而提高H.264/AVC编码器的实时性。     

分数布朗运动下带违约风险的可转换债券定价

应用均衡定价方法,考虑股票价格和公司资产价值服从分数布朗运动以及存在公司违约风险情况下的可转债定价问题;建立相应的可转债定价模型,采用拟鞅定价方法,得出了欧式看涨期权的显式解,进而得出了可转债定价公式;最后利用数值算例进行分析,结论表明公司违约风险、股票价格和公司资产价格的长程关联性是可转债定价时不可忽略的因素。     

A Non‐Parametric Estimator of the Spectral Density of a Continuous‐Time Gaussian Process Observed at Random Times

Abstract. In numerous applications data are observed at random times and an estimated graph of the spectral density may be relevant for characterizing and explaining phenomena. By using a wavelet analysis, one derives a non‐parametric estimator of the spectral density of a Gaussian process with stationary increments (or a stationary Gaussian process) from the observation of one path at random discrete times. For every positive frequency, this estimator is proved to satisfy a central limit theorem with a convergence rate depending on the roughness of the process and the moment of random durations between successive observations. In the case of stationary Gaussian processes, one can compare this estimator with estimators based on the empirical periodogram. Both estimators reach the same optimal rate of convergence, but the estimator based on wavelet analysis converges for a different class of random times. Simulation examples and an application to biological data are also provided.