Solving Multi-dimensional Evolution Problems with Localized Structures using Second Generation Wavelets

A dynamically adaptive numerical method for solving multi-dimensional evolution problems with localized structures is developed. The method is based on the general class of multi-dimensional second-generation wavelets and is an extension of the second-generation wavelet collocation method of Vasilyev and Bowman to two and higher dimensions and irregular sampling intervals. Wavelet decomposition is used for grid adaptation and interpolation, while O ( N ) hierarchical finite difference scheme, which takes advantage of wavelet multilevel decomposition, is used for derivative calculations. The prowess and computational efficiency of the method are demonstrated for the solution of a number of two-dimensional test problems.     

Adaptive discretization of convex multistage stochastic programs

We propose a new scenario tree reduction algorithm for multistage stochastic programs, which integrates the reduction of a scenario tree into the solution process of the stochastic program. This allows to construct a scenario tree that is highly adapted on the optimization problem. The algorithm starts with a rough approximation of the original tree and locally refines this approximation as long as necessary. Promising numerical results for scenario tree reductions in the settings of portfolio management and power management with uncertain load are presented.     

Stopping criteria for inner iterations in inexact potential reduction methods: a computational study

We focus on the use of adaptive stopping criteria in iterative methods for KKT systems that arise in Potential Reduction methods for quadratic programming. The aim of these criteria is to relate the accuracy in the solution of the KKT system to the quality of the current iterate, to get computational efficiency. We analyze a stopping criterion deriving from the convergence theory of inexact Potential Reduction methods and investigate the possibility of relaxing it in order to reduce as much as possible the overall computational cost. We also devise computational strategies to face a possible slowdown of convergence when an insufficient accuracy is required.     

一类非仿射不确定非线性系统的自适应模糊输出反馈控制

针对一类非仿射的不确定非线性系统,利用H∞控制技术和模糊系统,提出一种基于观测器的混合的直接自适应模糊控制方法,该方法不需要系统的状态变量完全可测,同时取消了最优逼近误差平方可积的假设条件,而且跟踪误差渐近收敛到零。仿真结果表明所提设计方法的有效性。     

Searching for a counterfeit coin with two unreliable weighings

We provide a worst-case optimal procedure to identify one unknown heavy coin among N identically looking coins when a balance scale is used and at most two weighing results can be erroneous. The exact minimal number of weighings is determined. This solves completely the weighing problem of the case of two unreliable weighings.     

Y分支器单元的有限差分光束传输法分析

Y分支器是集成光学中一种重要的器件单元。本文使用有限差分光束传输法(FD-BPM)系统地分析了Y分支器的工作机理,包括对称与非对称两种结构,其中着重对非对称Y分支器单元进行了分析,指出其欲完成模式分离所需满足的条件。通过对Y分支器的深入研究,我们也说明了FD-BPM法在对Y分支器及类似器件和单元的模拟分析中所具有的方便性和优越性,这为实际的设计制作工作提供了极好的基础。     

用于纳米生物膜层厚度测试的光纤生物传感器

研制出光纤感应生物传感器,用以测试nm级的生物膜层的厚度.给出了测试的实现过程、实验结果和提高信噪比的方法,并与表面等离子体共振(SPR)技术进行了比较.     

Adaptive detection technique for optical wireless communication over strong turbulence channels

Optical wireless communication (OWC) systems use the atmosphere as a propagation medium, so the atmospheric turbulence effects lead to fading related with signal intensity. The received signal of OWC over strong turbulence channels is assumed to be a mixture of K-distributed fading and Gaussian distributed thermal noise. Second-order spectral analysis is unable to separately estimate the mixed signal. In order to mitigate the fading induced by turbulence, the decision threshold-updating algorithm based on second and higher order cumulants is proposed and is able to operate in an unknown turbulence environment. The performance of the adaptive processing scheme has been evaluated by means of Monte Carlo simulations. Simulation results show the improvement of the bit error rate (BER) performance.     

Effective learning in the presence of adaptive counterparts

Adaptive learning algorithms (ALAs) is an important class of agents that learn the utilities of their strategies jointly with the maintenance of the beliefs about their counterparts' future actions. In this paper, we propose an approach of learning in the presence of adaptive counterparts. Our Q-learning based algorithm, called Adaptive Dynamics Learner (ADL), assigns Q-values to the fixed-length interaction histories. This makes it capable of exploiting the strategy update dynamics of the adaptive learners. By so doing, ADL usually obtains higher utilities than those of equilibrium solutions. We tested our algorithm on a substantial representative set of the most known and demonstrative matrix games. We observed that ADL is highly effective in the presence of such ALAs as Adaptive Play Q-learning, Infinitesimal Gradient Ascent, Policy Hill-Climbing and Fictitious Play Q-learning. Further, in self-play ADL usually converges to a Pareto efficient average utility.     

Unscented fuzzy-controlled current statistic model and adaptive filtering for tracking maneuvering targets

A novel adaptive algorithm for tracking maneuvering targets is proposed. The algorithm is implemented with fuzzy-controlled current statistic model adaptive filtering and unscented transformation. A fuzzy system allows the filter to tune the magnitude of maximum accelerations to adapt to different target maneuvers, and unscented transformation can effectively handle nonlinear system. A bearing-only tracking scenario simulation results show the proposed algorithm has a robust advantage over a wide range of maneuvers and overcomes the shortcoming of the traditional current statistic model and adaptive filtering algorithm.