Applicability of the Group Method for Analyzing Decay Curves

A formerly proposed method for analyzing experimental decay curves was tested to estimate its applicability depending on the following conditions: ratio of half-lives, time elapsing from measuring the first point to measuring the last one, number of points measured, portions of individual activities, and way of performing preliminary analysis for chosing the groups of experimental points.     

荧光衰减曲线的一种多指数拟合方法

在荧光衰减测量中，总要遇到荧光衰减解卷积，进行非线性参数拟合问题。目前已有的非线性求极小值方法，都对参数估计值非常敏感，而且计算程序运行时间较长。我们建立了一种基于经典的步长加速法实现非线性最小二乘拟合，进行荧光衰减解卷积的方法，按程序给出参数初值，即能解卷积。     

A New Method for Calculating the Double Beta Decay Probability

A new method of calculating 2υ-2β decay probability ia derived. It is shown that with the present knowledge of nucleon-nucleon force and reasonable nuclear wave functions for the parent and daughter nuclei this method can provide a reliable result without using closure approximation.     

扩宽OMA-Ⅵ同时谱宽的新方法

OMA－Ⅵ存在同时谱宽窄的缺点，本文用一种新方法大幅度地扩宽了它的同时谱宽，且不降低其系统分辨率，应用效果甚佳。     

一种鲁棒的目标跟踪方法

针对传统特征光流场跟踪方法中由于误差积累和错误匹配而导致的特征点丢失问题,基于一种新的Harris-SIFT特征点表示方法,提出基于预测帧与关键帧的算法框架,实现了光流场运动估计与局部特征识别相结合的目标跟踪方法.预测帧利用塔式分解和递归算法计算特征点的光流场运动矢量,使用运动矢量直方图获取目标的运动矢量,并剔除误匹配点;当特征点数量小于5个时,关键帧使用Harris-SIFT特征点进行局部特征匹配,利用仿射模型对目标精确定位及姿态修正.实验结果表明,本方法对视频序列中的纹理特征目标跟踪的鲁棒性较好,在背景复杂、目标遮挡或暂时丢失情况下,仍可以继续完成目标的可靠跟踪.     

一种鲁棒的目标跟踪方法

针对传统特征光流场跟踪方法中由于误差积累和错误匹配而导致的特征点丢失问题,基于一种新的Harris-SIFT特征点表示方法,提出基于预测帧与关键帧的算法框架,实现了光流场运动估计与局部特征识别相结合的目标跟踪方法.预测帧利用塔式分解和递归算法计算特征点的光流场运动矢量,使用运动矢量直方图获取目标的运动矢量,并剔除误匹配点;当特征点数量小于5个时,关键帧使用Harris-SIFT特征点进行局部特征匹配,利用仿射模型对目标精确定位及姿态修正.实验结果表明,本方法对视频序列中的纹理特征目标跟踪的鲁棒性较好,在背景复杂、目标遮挡或暂时丢失情况下,仍可以继续完成目标的可靠跟踪.     

一种稳健的道路主方向提取算法

基于高分辨遥感影像下直线型道路段的形状特征，提出了一种稳健的道路主方向提取算法．该算法基于结构张量计算的方向直方图探测初始主方向，并根据矢量化的边缘计算精确的方向角，使得探测过程稳健、结果准确．在此基础上可以发展交互式的直线道路段或道路网提取算法．实验结果表明，该算法的结果为进一步的道路边线的准确提取提供了保障．     

一种同时测量液体折射率和温度的方法

由长周期光纤光栅的相位匹配条件与光纤纤芯基模LP01和光纤包层模LP0P的模式方程,利用长周期光纤光栅透射谱的谐振峰随温度与外围介质折射率变化而移动的特性,提出了同时测量水的折射率和温度的方法,并分析了其工作原理.对温度与谐振波长的关系进行了计算机仿真.用该方法进行了实验研究,实验结果与理论研究结果基本吻合.     

A Robust and Efficient Method for Steady State Patterns in Reaction-Diffusion Systems

An inhomogeneous steady state pattern of nonlinear reaction-diffusion equations with no-flux boundary conditions is usually computed by solving the corresponding time-dependent reaction-diffusion equations using temporal schemes. Nonlinear solvers (e.g., Newton's method) take less CPU time in direct computation for the steady state; however, their convergence is sensitive to the initial guess, often leading to divergence or convergence to spatially homogeneous solution. Systematically numerical exploration of spatial patterns of reaction-diffusion equations under different parameter regimes requires that the numerical method be efficient and robust to initial condition or initial guess, with better likelihood of convergence to an inhomogeneous pattern. Here, a new approach that combines the advantages of temporal schemes in robustness and Newton's method in fast convergence in solving steady states of reaction-diffusion equations is proposed. In particular, an adaptive implicit Euler with inexact solver (AIIE) method is found to be much more efficient than temporal schemes and more robust in convergence than typical nonlinear solvers (e.g., Newton's method) in finding the inhomogeneous pattern. Application of this new approach to two reaction-diffusion equations in one, two, and three spatial dimensions, along with direct comparisons to several other existing methods, demonstrates that AIIE is a more desirable method for searching inhomogeneous spatial patterns of reaction-diffusion equations in a large parameter space.     

158A GeV的相对论重离子碰撞的强子快度分布及温度拟合

针对最近NA49在158A GeV的实验(C+C,Si+Si,Pb+Pb)数据,应用LUCIAE3.0模型进行模拟,得到了强子的快度分布和横向动量分布,并与实验数据进行比较,发现基本与实验数据相吻合。并对横向动量分布进行温度拟合,给出了和实验数据吻合很好的结果,但是对于重系统或重强子LUCIAE3.0给出的结果与实验符合相对较差.     

提高双参量光纤传感器精度的一种方法

分析了光纤传感器进行双参量测量时，系数矩阵的态性对待测量精度的影响，提出采用对系数矩阵进行预处理的方法，使得处理后的矩阵由病态变成良态。用方法对文献「４」和文献「５」给出的系数矩阵分别进行了处理。处理后，文献「４」系数矩阵的条件数从３４２降低到４，文献「５」系数矩阵的条件数从１６１降低到３，传感器输出不确定主降低了两个数量级。     

A new model for ideal gases. Decay to the Maxwellian distribution

In this work, a new model in kinetic gas theory for deriving the Maxwellian velocity distribution (MVD) is proposed. We construct an operator that governs the discrete time evolution of the velocity distribution. This operator, which conserves the momentum and the energy of the ideal gas, has the MVD as a fixed point. Moreover, for any initial out-of-equilibrium velocity distribution, it is shown that the gas decays to the equilibrium distribution, that is, to the MVD.     

一种基于无人机高光谱数据的植被盖度估算新方法

从分析对植被覆盖度(FVC)敏感的光谱特性入手,使用Avafield-3光谱仪(测量范围300～2 500 nm),利用人工草坪控制植被覆盖度的方式研究混合光谱与植被覆盖度的关系,通过实验发现红边区间(680～760 nm)对植被覆盖度最为敏感,而红边区间光谱的一阶导数与植被覆盖度的相关性最高(>0.98),且有较强的稳定性,因此选择红边斜率k作为估算植被盖度的参数。参考混合光谱分解法反演植被覆盖度的经典模型--即以NDVI(normalized difference vegetation index)为参数的植被覆盖度反演模型,以红边斜率代替NDVI构建了2个反演植被覆盖度FVC的新的红边斜率模型,该模型是对经典模型的进一步改进。为验证模型精度,以研究区内无人机(UVA)的高光谱数据和研究区实际测量的植被覆盖度数据进行验证：对高光谱数据计算每个像元680～760 nm之间的斜率,利用PPI(pixel purity index)提取纯像元,计算纯植被像元光谱斜率的最大值和纯土壤像元光谱斜率最小值,利用新的红边斜率FVC模型求取植被覆盖度;实测数据采用照相方法,经过几何校正、监督分类后统计植被覆盖度,结果表明：通过实测数据与无人机高光谱数据获取的植被覆盖数据进行验证,新构建的基于红边斜率的两个植被覆盖度模型的精度(R2分别达0.893 3和0.892 7)都略高于以NDVI为参数的模型(R2分别达0.839 9和0.829 9)。提出使用红边斜率计算植被覆盖度的模型,具有较明确的生物物理意义,具有较高的应用潜力和推广价值。     

A Method for Estimating the Entropy of Time Series Using Artificial Neural Networks

Measuring the predictability and complexity of time series using entropy is essential tool designing and controlling a nonlinear system. However, the existing methods have some drawbacks related to the strong dependence of entropy on the parameters of the methods. To overcome these difficulties, this study proposes a new method for estimating the entropy of a time series using the LogNNet neural network model. The LogNNet reservoir matrix is filled with time series elements according to our algorithm. The accuracy of the classification of images from the MNIST-10 database is considered as the entropy measure and denoted by NNetEn. The novelty of entropy calculation is that the time series is involved in mixing the input information in the reservoir. Greater complexity in the time series leads to a higher classification accuracy and higher NNetEn values. We introduce a new time series characteristic called time series learning inertia that determines the learning rate of the neural network. The robustness and efficiency of the method is verified on chaotic, periodic, random, binary, and constant time series. The comparison of NNetEn with other methods of entropy estimation demonstrates that our method is more robust and accurate and can be widely used in practice.     

A General Method for Constrained Analysis of Fluorescence Anisotropy Decay: Application of the Steady-State Anisotropy

Time-resolved fluorescence anisotropy is an invaluable method for investigating the internal and rotational dynamics of biomolecules. The range of rotational motions detectable by anisotropy decay is limited by the fluorescence lifetime; typically, a depolarizing motion may be resolved if the associated correlation time is between 0.1 and 10 times the intensity decay lifetime. To extend that range and to improve the recovery of anisotropy decay parameters, a general analytical method has been developed. This procedure utilizes a modification of Lagrange multiplier methods to constrain the values of the iterated kinetic parameters during nonlinear least-squares analysis of anisotropy decay data. The form of the constraint equation is derived from the classic relationship between the decay parameters and the steady-state anisotropy, which can be simply and accurately measured. Application of the constraint to analyses of synthetic data sets increased the accuracy of recovery by decreasing the uncertainty in the iterated parameters. The constraint also enabled the accurate recovery of correlation times that were a factor of 30 greater than the fluorescence lifetime, although it did not improve recovery of correlation times that were much shorter than the lifetime. Using this technique, it should now be possible to characterize the dynamics of larger macromolecules and assemblies than those that can currently be studied by fluorescence anisotropy decay.     

一种基于荧光信号的海面厚油膜评估方法

油膜厚度是海面溢油污染评估分析的一个重要指标,激光诱导荧光(LIF)技术是目前最有效的海面溢油探测技术之一,基于LIF探测技术的油膜厚度反演算法当下仅有适用于薄油膜(≤10~20 μm)的评估方法,而对于较厚油膜(>20 μm)的评估目前尚无有效的反演算法。鉴于此,提出一种基于LIF技术适用于较厚油膜的反演算法,该算法采用油膜荧光信号反演油膜厚度,推导了油膜厚度反演公式,并给出了基于该反演算法的油膜厚度评估方法。首先采用最大类间方差算法(Otsu)选取合适的荧光光谱波段,然后根据选取波段内每个波长的光谱数据反演油膜厚度,最后采用反演油膜厚度的平均值作为油膜厚度评估结果。研究了该算法的适用范围,给出了该算法有效评估范围最大值与测量相对误差的关系,并结合消光系数给出了在多种测量误差条件下不同消光系数油品有效评估范围的最大值。通过实验对本文方法进行了验证,选用原油和白油的混合油(1∶50)作为实验油品,以波长为405 nm的激光作为激发光源,采集波长范围为420～750 nm,采集了海水背景荧光和拉曼散射光光谱、实验油品的荧光特征光谱和多种不同厚度的较厚油膜的荧光光谱。采用Otsu算法选取420～476 nm波段评估油膜厚度,在实验油品油膜厚度≤800 μm时,该算法对油膜厚度的评估具有较高的精度,平均误差为10.5%;在油膜厚度>800 μm时,平均误差为28.8%,评估误差较大且随油膜厚度的增加快速变大,该实验结果与利用测量相对误差和消光系数的分析结果一致。实验结果表明,该方法可以实现对海面较厚油膜厚度的有效评估,并可以根据测量相对误差和消光系数判断评估结果的有效性。     

一种强健的守恒型间断跟踪法在一维Euler方程组上的实现

文[8,9]研究设计了一种守恒型的间断跟踪法,其主要特点是以解的守恒性作为跟踪的机制而不是像传统的间断跟踪法利用Rankine-Hugoniot条件,将该跟踪法对一维Euler方程组进行实现.对一维单个守恒律凸流的实现见文[7].由于Euler方程组有3类特征线,信息沿每类特征线传播并相互穿透.如果仍然象单个守恒律那样间断两侧的数值解的计算只用到来自间断同侧的信息,则间断一侧的信息就不能传到间断的另一侧,而会堆积在间断附近.设计了一种几乎是二阶的按特征方式分离信息的方法,将堆积在间断附近的信息分离.而且按此思想可做成高阶方法.该方法中不仅用到了精确Riemann分解,还用到了Roe^[12]的近似Riemann分解及各种插值和重构技巧.数值实验表明这样的处理是有效的.