级联三能级介质中非线性光学信号的增强

揭示非最大相干可能比最大相干产生更有效的非线性频率转换. 为此以三能级级联系统(没有最大相干)为例计算其非线性光学信号的强度, 并与三能级Λ型系统(具有最大相干)进行比较. 利用非微扰方法计算表明, 在双光子共振和相同参数的条件下, 级联系统产生的非线性光学信号的最大强度约是Λ系统的两倍. 利用缀饰态表象分析表明, 两个系统中耦合跃迁的自发辐射及其介入的不可逆三光子过程具有相反的作用. 它们在级联系统中加强非线性转换, 而在Λ系统中削弱非线性转换. 关键词： 电磁感应透明 非线性光学过程 双光子共振     

Toward Fuzzy Optimization without Mathematical Ambiguity

Fuzzy programming has been discussed widely in literature and applied in such various disciplines as operations research, economic management, business administration, and engineering. The main purpose of this paper is to present a brief review on fuzzy programming models, and classify them into three broad classes: expected value model, chance-constrained programming and dependent-chance programming. In order to solve general fuzzy programming models, a hybrid intelligent algorithm is also documented. Finally, some related topics are discussed.     

电子回旋共振放电的电离特性PIC/MCC模拟(Ⅰ)——物理模型与理论方法

采用粒子模拟与蒙特卡罗相结合(PIC/MCC)的方法对电子回旋共振(ECR)放电中的电离过程进行了模拟，其中带电粒子与微波的相互作用由PIC方法的电磁模型描述，粒子间的碰撞过程由MCC方法描述.考虑的碰撞类型有电子与中性粒子的弹性、激发、电离碰撞，离子与中性粒子的弹性、电荷交换碰撞，碰撞截面均依赖于能量而变化.阐述了理论分析的过程，为数值模拟ECR放电奠定了基础. 关键词： 电子回旋共振放电 粒子模拟 蒙特卡罗 电离     

Lower Bounds from State Space Relaxations for Concave Cost Network Flow Problems

In this paper we obtain Lower Bounds (LBs) to concave cost network flow problems. The LBs are derived from state space relaxations of a dynamic programming formulation, which involve the use of non-injective mapping functions guaranteing a reduction on the cardinality of the state space. The general state space relaxation procedure is extended to address problems involving transitions that go across several stages, as is the case of network flow problems. Applications for these LBs include: estimation of the quality of heuristic solutions; local search methods that use information of the LB solution structure to find initial solutions to restart the search (Fontes et al., 2003, Networks, 41, 221–228); and branch-and-bound (BB) methods having as a bounding procedure a modified version of the LB algorithm developed here, (see Fontes et al., 2005a). These LBs are iteratively improved by penalizing, in a Lagrangian fashion, customers not exactly satisfied or by performing state space modifications. Both the penalties and the state space are updated by using the subgradient method. Additional constraints are developed to improve further the LBs by reducing the searchable space. The computational results provided show that very good bounds can be obtained for concave cost network flow problems, particularly for fixed-charge problems.     

Active long pulse shaping technique of pulsed CO2 laser using multi-pulse discharge control

We describe the pulse forming of pulsed CO₂ laser using multi-pulse superposition technique. Various pulse shapes, high duty cycle pulse forming network (PFN) are constructed by time sequence. This study shows a technology that makes it possible to make various long pulse shapes by activating SCRs of three PFN modules consecutively at a desirable delay time with the aid of a PIC one-chip microprocessor. The power supply for this experiment consists of three PFN modules. Each PFN module uses a capacitor, a pulse forming inductor, a SCR, a high voltage pulse transformer, and a bridge rectifier on each transformer secondary. The PFN modules operate at low voltage by driving the primary of HV pulse transformer. The secondary of the transformer has a full-wave rectifier, which passes the pulse energy to the load in a continuous sequence.We investigated various long pulse shapes as different trigger time intervals of SCRs among three PFN modules. As a result, we could obtain laser beam with various pulse shapes and durations from about 250 to 1000 μs.     

量子算法的NMR实现方法

量子计算机是一种以量子耦合方式进行信息处理的装置[1 ] 。原则上 ,它能利用量子相干干涉方法以比传统计算机更快的速度进行诸如大数的因式分解、未排序数据库中的数据搜索等工作[2 ] 。建造大型量子计算机的主要困难是噪音、去耦和制造工艺。一方面 ,虽然离子陷阱和光学腔实验方法大有希望 ,但这些方法都还没有成功实现过量子计算。另一方面 ,因为隔离于自然环境 ,核自旋可以成为很好的“量子比特” ,可能以非传统方式使用核磁共振 (NMR)技术实现量子计算。本文介绍一种用NMR方法实现量子计算的方法 ,该方法能够用比传统方法少的步骤解决一个纯数学问题。基于该方法的简单量子计算机使用比传统计算机使用更少的函数“调用”判断一未知函数的类别。     

Experimental analysis, modeling, and theoretical design of McMaster pore-filled nanofiltration membranes

A side-by-side comparison of the performance of McMaster pore-filled (MacPF) and commercial nanofiltration (NF) membranes is presented here. The single-salt and multi-component performance of these membranes is studied using experimental data and using a mathematical model. The pseudo two-dimensional model is based on the extended Nernst–Planck equation, a modified Poisson–Boltzmann equation, and hydrodynamic calculations. The model includes four structural properties of the membrane: pore radius, pure water permeability, surface charge density and the ratio of effective membrane thickness to water content. The analysis demonstrates that the rejection and transport mechanisms are the same in the commercial and MacPF membranes with different contributions from each type of mechanism (convection, diffusion and electromigration). Solute rejection in NF membranes is determined primarily by a combination of steric and electrostatic effects. The selectivity of MacPF membranes is primarily determined by electrostatic effects with a significantly smaller contribution of steric effects compared to commercial membranes. Hence, these membranes have the ability to reject ions while remaining highly permeable to low molecular weight organics. Additionally, a new theoretical membrane design approach is presented. This design procedure potentially offers the optimization of NF membrane performance by tailoring the membrane structure and operating variables to the specific process, simultaneously. The procedure is validated at the laboratory scale.     

相对论重离子碰撞实验中混合事件方法的研究

把RQMD(Relativistic Quantum Molecular Dynamics)产生器产生的数据输入作为原始事例取样,讨论了在比较复杂的背景情况下,一种新的混合事件方法,用以证实在相对论重离子碰撞实验中高能量激发共振态的存在.并以共振态重子Δ⁺⁺为例演示了这一方法的应用.     

Time Series Prediction Based on Chaotic Attractor

A new prediction technique is proposed for chaotic time series. The usefulness of the technique is thatit can kick off some false neighbor points which are not suitable for the local estimation of the dynamics systems. Atime-delayed embedding is used to reconstruct the underlying attractor, and the prediction model is based on the timeevolution of the topological neighboring in the phase space. We use a feedforward neural network to approximate thelocal dominant Lyapunov exponent, and choose the spatial neighbors by the Lyapunov exponent. The model is testedfor the Mackey-Glass equation and the convection amplitude of lorenz systems. The results indicate that this predictiontechnique can improve the prediction of chaotic time series.