Relations Between Regularization and Diffusion Filtering

Regularization may be regarded as diffusion filtering with an implicit time discretization where one single step is used. Thus, iterated regularization with small regularization parameters approximates a diffusion process. The goal of this paper is to analyse relations between noniterated and iterated regularization and diffusion filtering in image processing. In the linear regularization framework, we show that with iterated Tikhonov regularization noise can be better handled than with noniterated. In the nonlinear framework, two filtering strategies are considered: the total variation regularization technique and the diffusion filter technique of Perona and Malik. It is shown that the Perona-Malik equation decreases the total variation during its evolution. While noniterated and iterated total variation regularization is well-posed, one cannot expect to find a minimizing sequence which converges to a minimizer of the corresponding energy functional for the Perona–Malik filter. To overcome this shortcoming, a novel regularization technique of the Perona–Malik process is presented that allows to construct a weakly lower semi-continuous energy functional. In analogy to recently derived results for a well-posed class of regularized Perona–Malik filters, we introduce Lyapunov functionals and convergence results for regularization methods. Experiments on real-world images illustrate that iterated linear regularization performs better than noniterated, while no significant differences between noniterated and iterated total variation regularization have been observed.     

Robot arm reaching through neural inversions and reinforcement learning

We present a neural method that computes the inverse kinematics of any kind of robot manipulators, both redundant and non-redundant. Inverse kinematics solutions are obtained through the inversion of a neural network that has been previously trained to approximate the manipulator forward kinematics. The inversion provides difference vectors in the joint space from difference vectors in the workspace. Our differential inverse kinematics (DIV) approach can be viewed as a neural network implementation of the Jacobian transpose method for arm kinematic control that does not require previous knowledge of the arm forward kinematics. Redundancy can be exploited to obtain a special inverse kinematic solution that meets a particular constraint (e.g. joint limit avoidance) by inverting an additional neural network The usefulness of our DIV approach is further illustrated with sensor-based multilink manipulators that learn collision-free reaching motions in unknown environments. For this task, the neural controller has two modules: a reinforcement-based action generator (AG) and a DIV module that computes goal vectors in the joint space. The actions given by the AG are interpreted with regard to those goal vectors.     

计算对称带状矩阵特征值问题的并行二分／多分法

文中提出了在分布式环境下并行求解对称带状矩阵特征值问题的并行二分．多分法及其改进,该算法利用变形高斯消去法计算对称带状矩阵的Sturm序列,并利用Rayleigh商迭代对二分／多分法加以改进,在算法的并行执行过程中,各处理机间不需通信,特别适用在分布式环境下的并行计算,最后给出了数值实验结果。     

Reinforcement Learning Based Algorithms for Average Cost Markov Decision Processes

This article proposes several two-timescale simulation-based actor-critic algorithms for solution of infinite horizon Markov Decision Processes with finite state-space under the average cost criterion. Two of the algorithms are for the compact (non-discrete) action setting while the rest are for finite-action spaces. On the slower timescale, all the algorithms perform a gradient search over corresponding policy spaces using two different Simultaneous Perturbation Stochastic Approximation (SPSA) gradient estimates. On the faster timescale, the differential cost function corresponding to a given stationary policy is updated and an additional averaging is performed for enhanced performance. A proof of convergence to a locally optimal policy is presented. Next, we discuss a memory efficient implementation that uses a feature-based representation of the state-space and performs TD(0) learning along the faster timescale. The TD(0) algorithm does not follow an on-line sampling of states but is observed to do well on our setting. Numerical experiments on a problem of rate based flow control are presented using the proposed algorithms. We consider here the model of a single bottleneck node in the continuous time queueing framework. We show performance comparisons of our algorithms with the two-timescale actor-critic algorithms of Konda and Borkar (1999) and Bhatnagar and Kumar (2004). Our algorithms exhibit more than an order of magnitude better performance over those of Konda and Borkar (1999).

一类指数型变量均值的Bayes 递进修正估计

基于统计决策理论,给出一种借助先验分布求解一类只有不完全样本的指数型变量均值的Bayes递进修正估计方法,并证明了该方法是最佳的。这是一种动态离散数据融合方法,能够根据观测样本的不断增加而修正估计结果。该方法在预估服役中的导弹武器性能方面具有重要的实用价值。     

A FORMULA FOR THE INVERSE AUTOCORRELATION FUNCTION OF AN AUTOREGRESSIVE PROCESS

Abstract. The determination of the inverse autocorrelation function of a weakly stationary autoregressive process using the autocorrelation function is considered. Usually this is carried out either by using frequency domain methods or by solving first the parameters of the process and then using them. In this paper we give a simple formula by which the inverse autocorrelation function can be determined directly from the autocorrelation function.     

基于循环迭代算法的便携式烟气分析仪

为了在紫外光谱烟气分析时从连续的混合气体吸收光谱信号中分离并解算出各气体的浓度,在实验数据的基础上,基于朗伯-比尔定律提出了一种新的循环迭代算法,该算法利用吸光度的叠加性,将数据进行非线性拟合,并利用波长优选和循环迭代,将混合气体的不同成分区分开来.实验结果表明:该方法能够一次同时解算出多种有害气体浓度且精度达±3%,抗干扰能力强,适合工程实际应用.     

混合关键度驱动的实时调度研究

针对当前运行于不可预测开放环境下的嵌入式多使命复杂关键型系统,需要减少运行成本和处理不可预测工作负载情况的问题,文章提出一种混合关键度驱动的非对称式过载保护最小空闲调度策略。系统过载时,为共享同一处理器的不同关键度任务提供非对称式保护,禁止低关键度任务干扰高关键度任务,完全避免了传统的"关键度反转"问题。在恢复暂时阻塞的不同关键度任务时,在速率单调调度的基础上,引入关键度主导的截止期驱动动态调度策略,可使潜在处理器利用率达到100%。实验结果表明,这种新算法的综合性能优于当前已有的混合关键度任务调度算法。     

Multiplicative noise removal combining a total variation regularizer and a nonconvex regularizer

A novel variational model for removing multiplicative noise is proposed in this paper. In the model, a novel regularization term is elaborately designed which is inherently equivalent to a combination of the classical total variation regularizer and a nonconvex regularizer. The proposed regularization term, on the one hand, can better remove the noise in homogeneous regions of a noisy image and, on the other hand, can preserve edge details of the image during the denoising process. In order to solve the model efficiently, we design an alternating iteration process in which two coupling minimization problems are solved. For each of the two minimization problems, the existence and uniqueness of their solutions are proved under some necessary assumptions. Numerical results are reported to demonstrate the effectiveness of the proposed regularization term for multiplicative noise removal.