平面波超声成像中的波束合成方法研究进展

平面波成像通过单次全孔径发射-接收即可获取整幅图像,将成像帧频显著地提升至1000帧/秒以上.然而,平面波成像过程中发射的非聚焦波束将导致回波信号信噪比降低,进而使图像的分辨率和对比度变差.通过多角度相干复合成像技术可以改善平面波成像的图像质量,但是会以牺牲帧频为代价.因此研究人员们开始将新型波束合成技术引入平面波成像...     

Application of a Near-Optimal Reinforcement Learning Controller to a Robotics Problem in Manufacturing: A Hybrid Approach

Optimization theory provides a framework for determining the best decisions or actions with respect to some mathematical model of a process. This paper focuses on learning to act in a near-optimal manner through reinforcement learning for problems that either have no model or the model is too complex. One approach to solving this class of problems is via approximate dynamic programming. The application of these methods are established primarily for the case of discrete state and action spaces. In this paper we develop efficient methods of learning which act in complex systems with continuous state and action spaces. Monte-Carlo approaches are employed to estimate function values in an iterative, incremental procedure. Derivative-free line search methods are used to obtain a near-optimal action in the continuous action space for a discrete subset of the state space. This near-optimal control policy is then extended to the entire continuous state space via a fuzzy additive model. To compensate for approximation errors, a modified procedure for perturbing the generated control policy is developed. Convergence results under moderate assumptions and stopping criteria are established.     

Classes of analytic functions subordinate to convex functions and extreme points

In this paper we prove two sufficient conditions for an analytic function f to be an extreme point of the set of functions subordinate to a given convex mapping F when the image of the unit disk under F is a convex domain other than a half-plane, a strip or an infinite wedge.     

剖析博彩弹子机的赢钱原理

介绍了博彩弹子机 ,运用数学建模法剖析了它的赢钱原理 ,强调了一般博彩业主永远是赢家的道理     

高光谱图像降维的判别流形学习方法

本文提出了一种高光谱图像降维的判别流形学习方法.针对获取的大量遥感对地观测数据存在大量冗余信息的特点,引入改进的流形学习方法对高光谱遥感数据进行降维处理,以提高遥感图像自动分类的总体准确度.该方法充分利用遥感图像自动分类中训练样本的判别信息,将输入样本的类别信息加入到常规流形学习方法的框架中,从本质上提高输出的特征在低维空间中的判别力.同时,引入线性化模型以解决流形学习方法中常见的小样本问题.对高光谱遥感图像自动分类的实验表明,基于判别流形学习的高光谱遥感图像自动分类方法能够显著地提高图像分类准确度.     

激光等离子体极紫外光刻光源

研究并讨论了下一代光刻的核心技术之一—激光等离子体极紫外光刻光源。简要介绍了欧美和日本等国极紫外光刻技术的发展概况,分析了新兴的下一代13.5 nm极紫外光刻光源的现状,特别讨论了国内外激光等离子体极紫外光刻光源的现状,指出目前其存在的主要问题是如何提高光源的转化效率和减少光源的碎屑。文中同时概述了6.x nm（6.5～6.7 nm）极紫外光刻光源的最新研究工作。最后,介绍了作者所在研究小组近年来在极紫外光源和极紫外光刻掩模缺陷检测方面开展的研究工作。     

CAEP闪光X射线机的发展

闪光X射线机技术在中国工程物理研究院(下称CAEP)已有三十年的发展历史在爆轰物理学和y射线辐照效应研究中发挥了重要作用。本文总结了CAEP几种类型闪光X射线机的研制概况,阐述了高压脉冲技术、场发射技术、强流束聚焦等的研究进展,并介绍几种装置的主要技术参数。闪光X射线机是研究爆轰物理过程及其它高速瞬变过程的重要工具。国防科学技术研究中,大型闪光X射线机的研制具有很大的意义。文中对开拓强流电了束新的应用领域提出了展望。     

Koopman analysis of nonlinear systems with a neural network representation

The observation and study of nonlinear dynamical systems has been gaining popularity over years in different fields. The intrinsic complexity of their dynamics defies many existing tools based on individual orbits, while the Koopman operator governs evolution of functions defined in phase space and is thus focused on ensembles of orbits, which provides an alternative approach to investigate global features of system dynamics prescribed by spectral properties of the operator. However, it is difficult to identify and represent the most relevant eigenfunctions in practice. Here, combined with the Koopman analysis, a neural network is designed to achieve the reconstruction and evolution of complex dynamical systems. By invoking the error minimization, a fundamental set of Koopman eigenfunctions are derived, which may reproduce the input dynamics through a nonlinear transformation provided by the neural network. The corresponding eigenvalues are also directly extracted by the specific evolutionary structure built in.     

Realizing number recognition with simulated quantum semi-restricted Boltzmann machine

Quantum machine learning based on quantum algorithms may achieve an exponential speedup over classical algorithms in dealing with some problems such as clustering. In this paper, we use the method of training the lower bound of the average log likelihood function on the quantum Boltzmann machine (QBM) to recognize the handwritten number datasets and compare the training results with classical models. We find that, when the QBM is semi-restricted, the training results get better with fewer computing resources. This shows that it is necessary to design a targeted algorithm to speed up computation and save resources.