光纤微缆对SARV操纵性的影响分析

以新概念小型自治遥控水下机器人SARV及其光纤微缆系统为研究对象,在对光纤微缆进行受力分析的基础上,采用集中质量法建立了光纤微缆的动力学模型。结合作业中SARV载体的实际运动,利用直接计算法分析了不同运动状态下光纤微缆对SARV载体产生的附加力和附加力矩及其对SARV载体操纵性的影响,并提出了合理的操纵建议。为SARV载体端设计光纤微缆释放装置张力控制提供了一定的理论依据,在一定程度上有助于完善SARV控制系统,提高SARV航行的稳定性。

SARV光纤收放装置的控制系统设计与实现

根据小型自治遥控水下机器人SARV的运动特性,研制了光纤微缆收放的控制系统。设计使用了嵌入式QNX软件开发技术,系统稳定可靠。采用系统辨识的方法,获得被控对象的等效数学模型。采用单神经元自适应PID控制器对控制参数进行在线自调节,实现了SARV在水中运动时光纤收放的恒张力控制,满足光纤收放装置的设计要求。

SARV光纤微缆收放系统的研究与设计

通过对光纤微缆的受力分析,结合光纤微缆的特点设计并实现了光纤微缆收放系统.该系统采用恒张力控制收放光纤微缆,使光纤微缆随着水下机器人的运动释放和回收,减小了光纤微缆对水下机器人运动的影响,避免了光纤微缆的缠绕和损伤.

Mind in Dispute: The Section on Mind in Harivarman’s *Tattvasiddhi

Thesis (Ph.D.)--University of Washington, 2015

Aggregations and Marginalization of GARCH and Stochastic Volatility Models

The GARCH and Stochastic Volatility paradigms are often brought into conflict as two competitive views of the appropriate conditional variance concept : conditional variance given past values of the same series or conditional variance given a larger past information (including possibly unobservable state variables). The main thesis of this paper is that, since in general the econometrician has no idea about something like a structural level of disaggregation, a well-written volatility model should be specified in such a way that one is always allowed to reduce the information set without invalidating the model. To this respect, the debate between observable past information (in the GARCH spirit) versus unobservable conditioning information (in the state-space spirit) is irrelevant. In this paper, we stress a square-root autoregressive stochastic volatility (SR-SARV) model which remains true to the GARCH paradigm of ARMA dynamics for squared innovations but weakens the GARCH structure in order to obtain required robustness properties with respect to various kinds of aggregation. It is shown that the lack of robustness of the usual GARCH setting is due to two very restrictive assumptions : perfect linear correlation between squared innovations and conditional variance on the one hand and linear relationship between the conditional variance of the future conditional variance and the squared conditional variance on the other hand. By relaxing these assumptions, thanks to a state-space setting, we obtain aggregation results without renouncing to the conditional variance concept (and related leverage effects), as it is the case for the recently suggested weak GARCH model which gets aggregation results by replacing conditional expectations by linear projections on symmetric past innovations. Moreover, unlike the weak GARCH literature, we are able to define multivariate models, including higher order dynamics and risk premiums (in the spirit of GARCH (p,p) and GARCH in mean) and to derive conditional moment restrictions well suited for statistical inference. Finally, we are able to characterize the exact relationships between our SR-SARV models (including higher order dynamics, leverage effect and in-mean effect), usual GARCH models and continuous time stochastic volatility models, so that previous results about aggregation of weak GARCH and continuous time GARCH modeling can be recovered in our framework.

ARMA Representation of Integrated and Realized Variances

This paper derives the ARMA representation of integrated and realized variances when the spot variance depends linearly on two autoregressive factors, i.e., SR SARV(2) models. This class of processes includes affine, GARCH diffusion, CEV models, as well as the eigenfunction stochastic volatility and the positive Ornstein-Uhlenbeck models. We also study the leverage effect case, the relationship between weak GARCH representation of returns and the ARMA representation of realized variances. Finally, various empirical implications of these ARMA representations are considered. We find that it is possible that some parameters of the ARMA representation are negative. Hence, the positiveness of the expected values of integrated or realized variances is not guaranteed. We also find that for some frequencies of observations, the continuous time model parameters may be weakly or not identified through the ARMA representation of realized variances.