Cooperative Strategies for Solving the Bicriteria Sparse Multiple Knapsack Problem

For hard optimization problems, it is difficult to design heuristic algorithms which exhibit uniformly superior performance for all problem instances. As a result it becomes necessary to tailor the algorithms based on the problem instance. In this paper, we introduce the use of a cooperative problem solving team of heuristics that evolves algorithms for a given problem instance. The efficacy of this method is examined by solving six difficult instances of a bicriteria sparse multiple knapsack problem. Results indicate that such tailored algorithms uniformly improve solutions as compared to using predesigned heuristic algorithms.     

空间目标红外辐射特性研究

利用目标的红外光谱特性进行目标探测和识别是一种有效的方法。以空间卫星为目标,研究了其红外辐射特性形成的机理;利用所建立的数学模型,仿真了空间目标在探测面上所形成的红外光谱特性曲线;分析了模型中各参数对目标光谱特性的影响程度和卫星目标的红外光谱曲线。     

多元线性回归应用于近代物理实验

本文介绍非线性方程转化直线性方程和多元线性回归法来分析近代物理实验中塞曼效应分裂干涉圆环多处选点测量的处理过程。     

聚酰胺酸合成工艺研究

 采用超声波在线测量溶液粘度的方法，研究了合成聚酰胺酸过程中实验条件的影响，并对实验结果进行分析，由此确定了合成高分子量聚酰胺酸的最佳实验条件。研究表明：在加料次序为先加二胺后加二酐（二酐与二胺的摩尔比为1.01～1.02：1）、试剂中含水量尽可能少，反应温度0～5℃、反应时间以溶液粘度到达最大值为止的条件下，所合成的聚酰胺酸溶液粘度最大，可满足惯性约束聚变（ICF）充气腔靶端口膜的需要。     

UNIQUENESS PROBLEM WITH TRUNCATED MULTIPLICITIES OF MEROMORPHIC MAPPINGS FOR MOVING TARGETS

In this article,two uniqueness theorems of meromorphic mappings on moving targets with truncated multiplicities are proved.     

Nonlinear Filtering for Diffusions in Random Environments

Suppose that the signal X to be estimated is a diffusion process in a random medium W and the signal is correlated with the observation noise. We study the historical filtering problem concerned with estimating the signal path up until the current time based upon the back observations. Using Dirichlet form theory, we introduce a filtering model for general rough signal X ^W and establish a multiple Wiener integrals representation for the unnormalized pathspace filtering process. Then, we construct a precise nonlinear filtering model for the process X itself and give the corresponding Wiener chaos decomposition.     

Transient free‐surface flow of a viscoelastic fluid in a narrow channel

The interplay between inertia and elasticity is examined for transient free‐surface flow inside a narrow channel. The lubrication theory is extended for the flow of viscoelastic fluids of the Oldroyd‐B type (consisting of a Newtonian solvent and a polymeric solute). While the general formulation accounts for non‐linearities stemming from inertia effects in the momentum conservation equation, and the upper‐convected terms in the constitutive equation, only the front movement contributes to non‐linear coupling for a flow inside a straight channel. In this case, it is possible to implement a spectral representation in the depthwise direction for the velocity and stress. The evolution of the flow field is obtained locally, but the front movement is captured only in the mean sense. The influence of inertia, elasticity and viscosity ratio is examined for pressure‐induced flow. The front appears to progress monotonically with time. However, the velocity and stress exhibit typically a strong overshoot upon inception, accompanied by a plug‐flow behaviour in the channel core. The flow intensity eventually diminishes with time, tending asymptotically to Poiseuille conditions. For highly elastic liquids the front movement becomes oscillatory, experiencing strong deceleration periodically. A multiple‐scale solution is obtained for fluids with no inertia and small elasticity. Comparison with the exact (numerical) solution indicates a wide range of validity for the analytical result. Copyright © 2004 John Wiley & Sons, Ltd.     

多指标区间决策的理想点贴近法

研究了指标的权重不能完全确定但知道其所在区域的条件下的多指标决策问题 ,给出了方案与理想解的贴近度及其算法 ,按贴近度的大小可以对方案进行排序 .它同传统的决策方法相比较 ,具有需要信息量少 ,简单可靠等特点 .最后用该决策方法分析了一个实际问题 .     

红外图像掠海小目标的检测算法研究

针对红外图像掠海小目标信噪比低，且易受到水天线和背景杂波干扰的特点，提出了一种红外掠海小目标的提取与检测算法。该方法的特点是：首先采用中值滤波来减小噪声，并提出了差方和算法，用以抑制背景噪声并对目标增强；再采用了图像行扫描法有效地滤除水天线；最后通过弱化背景边缘和阈值分割等综合算法得到候选小目标。仿真结果表明，该算法达到了较好的效果。     

A Generalized Markov Sampler

A recent development of the Markov chain Monte Carlo (MCMC) technique is the emergence of MCMC samplers that allow transitions between different models. Such samplers make possible a range of computational tasks involving models, including model selection, model evaluation, model averaging and hypothesis testing. An example of this type of sampler is the reversible jump MCMC sampler, which is a generalization of the Metropolis–Hastings algorithm. Here, we present a new MCMC sampler of this type. The new sampler is a generalization of the Gibbs sampler, but somewhat surprisingly, it also turns out to encompass as particular cases all of the well-known MCMC samplers, including those of Metropolis, Barker, and Hastings. Moreover, the new sampler generalizes the reversible jump MCMC. It therefore appears to be a very general framework for MCMC sampling. This paper describes the new sampler and illustrates its use in three applications in Computational Biology, specifically determination of consensus sequences, phylogenetic inference and delineation of isochores via multiple change-point analysis.