非理想状态下的塔板理论模型

 在过程中 ,将流动相看成是由许多连续的塔板组成 ,每一塔板的高度与固定相塔板的高度相同。初始浓度的溶质被认为全部集中在流动相的第一塔板中 ;溶质在流动相和固定相之间动态分布。由于动力学因素的影响 ,当流动相流过一个塔板距离时 ,溶质不能够迅速地从固定相释放到流动相中 ,因此溶质在流动相和固定相中的分布浓度受到两个因素即反映系统热力学性质的分配系数P和反映系统动力学性质的释放概率因子α的影响。这一过程被认为是非理想状态下的过程。     

贝叶斯融合在SAR图像分类中的应用

本文研究将数据融合理论用于SAR图像分类.通过贝叶斯理论进行多通道SAR图像测量级数据融合,充分利用像素的从属信息并获得单通道分类无法获取的分类结果,有效保留各通道有用信息并抑制图像中的斑点噪声;针对贝叶斯融合涉及到的先验概率的问题采用两种方式进行先验概率估计,对估计引起的马赛克现象提出了三种解决方法;并提出三种先验概率融合方法,比较得出相对最优的SAR图像多通道分类方案.     

IP网端到端性能测量技术研究的进展

本文介绍了Internet端到端测量技术的最新进展,包括性能拓扑推测、时延测量、丢包率测量.最后讨论了未来Internet端到端测量的研究趋势和应用前景.     

关于两两NQD列的若干极限性质

本文讨论了两两ＮＱＤ列的Ｍａｒｃｉｎｋｉｅｗｉｃｚ型弱大数律及Ｊａｍｉｓｏｎ型加权和的强稳定性，指出了两两ＮＱＤ列与独立列的一个本质性区别，从而提出了解决两两ＮＱＤ旬几乎处处收敛和完全收敛的一种方式。     

On a class of dependent Sparre Andersen risk models and a bailout application

In this paper a one-dimensional surplus process is considered with a certain Sparre Andersen type dependence structure under general interclaim times distribution and correlated phase-type claim sizes. The Laplace transform of the time to ruin under such a model is obtained as the solution of a fixed-point problem, under both the zero-delayed and the delayed cases. An efficient algorithm for solving the fixed-point problem is derived together with bounds that illustrate the quality of the approximation. A two-dimensional risk model is analyzed under a bailout type strategy with both fixed and variable costs and a dependence structure of the proposed type. Numerical examples and ideas for future research are presented at the end of the paper.     

一种高炮集火射击快速命中检测方法

高炮作为重要的近程地面防空武器,命中精度是衡量其作战能力的重要指标。为了快速准确地计算出命中概率,尽量避免炮弹在相邻的离散时间点上出现检测遗漏和检测缓慢的情况,本文提出忽略射击间隔对命中概率的影响,根据目标大小确定合理的检测间隔,并提高发射弹数的碰撞检测方法,在检测遗漏和检测时间上找到一个平衡。结果表明,该方法能快速准确地检测炮弹和目标在高速运动下的碰撞问题。     

Content-aware image resizing based on random-carving with probability

To improve the running speed of image resizing,a fast content-aware image resizing algorithm was proposed based on the threshold learning and random-carving with probability.Firstly the important map was calculated by combining the graph-based visual saliency map and gradient map.Then the image threshold value was obtained by radial basis function (RBF) neural network learning.And by the threshold,the original image was separated into the protected part and the unprotected part which was corresponding to the important part and the unimportant part of the original image individually.Finally,the two parts were allocated different resizing scales and the random-carving with probability was applied to them respectively.Experiments results show that the proposed algorithm has lower time cost comparing to the state-of-arts algorithms in MSRA image database,and has a better visual perception on image resizing.     

Perfect controlled joint remote state preparation independent of entanglement degree of the quantum channel

We construct a quantum circuit to produce a task-oriented partially entangled state and use it as the quantum channel for controlled joint remote state preparation. Unlike most previous works, where the parameters of the quantum channel are given to the receiver who can accomplish the task only probabilistically by consuming auxiliary resource, operation and measurement, here we give them to the supervisor. Thanks to the knowledge of the task-oriented quantum channel parameters, the supervisor can carry out proper complete projective measurement, which, combined with the feed-forward technique adapted by the preparers, not only much economizes (simplifies) the receiver's resource (operation) but also yields unit total success probability. Notably, such apparent perfection does not depend on the entanglement degree of the shared quantum channel. Our protocol is within the reach of current quantum technologies.     

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In this paper, for a kind of risk models with heavy-tailed and delayed claims, we derive the asymptotics of the infinite-time ruin probability and the uniform asymptotics of the finite-time ruin probability. The numerical simulation results are also presented. The results of theoretical analysis and numerical simulation show that the influence of the delay for the claim payment is nearly negligible to the ruin probability when the initial capital and running-time are all large.     

Adaptive visual target detection and tracking using weakly supervised incremental appearance learning and RGM-PHD tracker

Multiple visual target tracking is a challenging problem due to various uncertainties including occlusion, miss-detection and noisy measurement. Most tracking approaches utilize an object-specific detector, pre-trained on many labeled images, to provide suitable measurements for their tracking system. In this paper, we use a simple background subtraction detector which only needs the background image to localize targets independent of their shape or type. In order to cope with the uncertainties resulted by the detector, we propose an adaptive appearance model and develop an incremental appearance learning algorithm to learn the target appearances in time. The proposed method employs the background information and our defined keypoints’ miss-matched history to adapt the target appearances within different frames. Furthermore, we combine Refined Gaussian Mixture Probability Hypothesis Density (RGM-PHD) tracker with the detectors to keep target trajectories and handle uncertainties. The experiments conducted on several video datasets show the effectiveness of our proposed method.