聚偏氟乙烯辐射效应的研究——Ⅰ.高剂量区晶体损伤加速机理的探讨

讨论了聚偏氟乙烯(PVDF)结晶度与电子束辐射效应的关系,利用示差扫描量热分析、X射线衍射等研究了辐射前后PVDF晶体的晶型、结晶度等的变化.发现辐照前后晶体晶型未有明显改变;结晶度在低剂量区(<400kGy),随吸收剂量增大而稍有增加;然后逐渐下降.当吸收剂量高于1000kGy后,结晶度迅速下降.利用二甲基甲酰胺对辐照后样品抽提,研究了其溶胶、凝胶性质.发现在低剂量区,PVDF的辐射效应符合Charlesby-Pinner关系式;高剂量区,当剂量高于约1000kGy时,由于交联网络间分子链段长度已与晶体片晶折叠链长度大致相当,继续辐照,受样品晶体的阻碍作用,样品辐射效应开始偏离Charlesby-Pinner关系.晶体表面及晶体内部缺陷部位发生交联反应几率增加,样品结晶度开始明显降低.     

强磁场中无自旋和自旋粒子的Synchro-Curvature辐射谱

Synchro-Curvature辐射是张家铝和郑广生最近提出的一种新的、更普遍的辐射机制,它全面描述了一个相对论性带电粒子在弯曲磁场中运动时所产生的辐射特征.这种新辐射机制概括了通常同步辐射及曲率辐射的全部经典结果,揭示了其间的有机联系和统一性.并为精确讨论脉冲星研究中的辐射问题给出了普遍而简单的统一公式.不过由于脉冲星磁场已高达10⁸T,量子效应不能不考虑,应用最近Lieu和Axford发展的GFWW方法推广了张家铝和郑广生的结果,对无自旋的K-G粒子及有自旋的Dirac粒子给出了其相应的Synchro-Curvature量子辐射谱公式,并讨论了它们的辐射特性.     

聚偏氟乙烯辐射效应的研究——II.晶区辐射损伤点的研究

研究了聚偏氟乙烯 (PVDF)晶体结构与辐射效应的关系 .利用不同的热处理条件 ,获得了 7种不同规整性、不同结晶度的样品 ;通过对其辐射效应的研究 ,探讨了材料结晶区和晶区内不同部位的辐射效应以及这些不同的结构对材料辐射效应的影响 .使用示差扫描量热分析 (DSC)测量样品零熵产生相变温度、结晶度等随吸收剂量的改变 ,以及不同退火条件样品的辐射效应 .讨论了PVDF样品晶体辐射损伤与样品结构的关系 .利用电子自旋共振谱仪 (ESR)测量不同辐照后样品残余自由基的浓度及这些残余自由基加热条件下的不同命运 .结合DSC分析及X射线衍射分析得到晶体辐射损伤是从晶体表面折叠圈区和晶体茎杆内部缺陷区同时开始的结论     

1997年11月2日太阳射电尖峰辐射对

国家天文台 2.6~3.8 GHz 太阳射电频谱仪首次观测到微波毫秒尖峰(microwave millisecond spike (MMS))辐射对, 并测出MMS的频漂率, 得到 MMS对的分界频率大约在2900MHz附近, 对应辐射层的高度约在光球之上 2×10⁴ km. 它的偏振度为左旋圆偏振(LCP), 其平均值为25%, 随频率的变化是波纹形, 并随频率增加而变小. MMS对与Ⅲ型爆发对之间有明显不同, 在分界频率周围一定频率范围内Ⅲ型爆发对没有射电辐射存在, 但是在MMS对中没有这一特征. 这一现象能使人们更好地了解MMS的机制.     

偶极磁场中的SVC辐射模型及其随磁场变化的辐射——Ⅱ.SVC辐射机制及其辐射随磁场变化

本文计算了太阳射电缓变成分(SVC)的射电辐射,着重研究了由联合辐射机制产生的SVC辐射的主要辐射特性(亮度分布、亮温度谱、流量密度谱、偏振度谱)和SVC辐射源的几何特性(高度和半径),及其随磁场强度的变化。     

微波暗室辐射问题的动态平衡解法

利用动态平衡原理,给出了一种微波暗室辐射问题的求解方法,并进行了计算机迭代仿真,证明了该方法的可行性.     

带电BTZ黑洞一般性辐射的隧穿效应

研究了BTZ黑洞背景下带电、有质量粒子的辐射过程, 此时能量守恒和电荷守恒意味着引力反作用和电磁反作用. 从隧穿过程的观点来看, 辐射谱偏离了纯热谱, 但仍满足幺正理论, 并且有可能对信息佯谬给出解释. 通过计算, 可以得到与不带电、无质量粒子辐射相同的结论.     

大气-海洋系统辐射传递方程的一种求解方法及海水辐射度分布的渐近态

在把大气和海水分别看作均匀介质以及将其分界面视为平面的条件下,本文给出了一个求解大气-海洋系统辐射传递方程的完整方法。根据所求得的辐射传递方程的通解可自然导出海水中辐射度分布的渐近态及其光学特性。本文给出了一个计算实例,其结果与理论符合得很好。本文还讨论了此方法在实际应用中的一些问题。     

一类一维的辐射流体力学方程组激波的存在性

该文主要讨论一维空间中一类辐射流体力学方程组的激波. 由Rankine-Hugoniot条件及熵条件得此问题可表述为关于辐射流体力学方程组带自由边界的初边值问题. 首先通过变量代换, 将其自由边界转换为固定边界, 然后研究关于此非线性方程组的一个初边值问题解的存在唯一性. 为此先构造了此问题的一个近似解, 然后分别通过Picard迭代与Newton迭代对此非线性问题构造近似解序列. 通过一系列估计与紧性理论得到此近似解序列的收敛性, 其极限即为原辐射热力学方程组的一个激波.     

辐照下晶体缺陷浓度的自振频域

采用线性化处理、拓扑映射及数值计算相结合的方法，求解了离子辐照下各向同性晶体薄片中缺陷浓度和温度满足的非线性微分方程组，证明了当可控参量，即缺陷产生速度和恒温箱温度处于某些范围时.会出现缺陷浓度和温度的周期性振动这种时间耗散结构.研究了自振频率与辐射条件和晶体性质的依赖关系,并以硅晶体薄片为例．求出了自振频域及特定的几个自振频率和自振振幅．     

Unsupervised quadratic surface support vector machine with application to credit risk assessment

Unsupervised classification is a highly important task of machine learning methods. Although achieving great success in supervised classification, support vector machine (SVM) is much less utilized to classify unlabeled data points, which also induces many drawbacks including sensitive to nonlinear kernels and random initializations, high computational cost, unsuitable for imbalanced datasets. In this paper, to utilize the advantages of SVM and overcome the drawbacks of SVM-based clustering methods, we propose a completely new two-stage unsupervised classification method with no initialization: a new unsupervised kernel-free quadratic surface SVM (QSSVM) model is proposed to avoid selecting kernels and related kernel parameters, then a golden-section algorithm is designed to generate the appropriate classifier for balanced and imbalanced data. By studying certain properties of proposed model, a convergent decomposition algorithm is developed to implement this non-covex QSSVM model effectively and efficiently (in terms of computational cost). Numerical tests on artificial and public benchmark data indicate that the proposed unsupervised QSSVM method outperforms well-known clustering methods (including SVM-based and other state-of-the-art methods), particularly in terms of classification accuracy. Moreover, we extend and apply the proposed method to credit risk assessment by incorporating the T-test based feature weights. The promising numerical results on benchmark personal credit data and real-world corporate credit data strongly demonstrate the effectiveness, efficiency and interpretability of proposed method, as well as indicate its significant potential in certain real-world applications.     

A LASSO-penalized BIC for mixture model selection

The efficacy of family-based approaches to mixture model-based clustering and classification depends on the selection of parsimonious models. Current wisdom suggests the Bayesian information criterion (BIC) for mixture model selection. However, the BIC has well-known limitations, including a tendency to overestimate the number of components as well as a proclivity for underestimating, often drastically, the number of components in higher dimensions. While the former problem might be soluble by merging components, the latter is impossible to mitigate in clustering and classification applications. In this paper, a LASSO-penalized BIC (LPBIC) is introduced to overcome this problem. This approach is illustrated based on applications of extensions of mixtures of factor analyzers, where the LPBIC is used to select both the number of components and the number of latent factors. The LPBIC is shown to match or outperform the BIC in several situations.     

Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study

In this study, we present a comprehensive comparative analysis of kernel-based fuzzy clustering and fuzzy clustering. Kernel based clustering has emerged as an interesting and quite visible alternative in fuzzy clustering, however, the effectiveness of this extension vis-à-vis some generic methods of fuzzy clustering has neither been discussed in a complete manner nor the performance of clustering quantified through a convincing comparative analysis. Our focal objective is to understand the performance gains and the importance of parameter selection for kernelized fuzzy clustering. Generic Fuzzy C-Means (FCM) and Gustafson–Kessel (GK) FCM are compared with two typical generalizations of kernel-based fuzzy clustering: one with prototypes located in the feature space (KFCM-F) and the other where the prototypes are distributed in the kernel space (KFCM-K). Both generalizations are studied when dealing with the Gaussian kernel while KFCM-K is also studied with the polynomial kernel. Two criteria are used in evaluating the performance of the clustering method and the resulting clusters, namely classification rate and reconstruction error. Through carefully selected experiments involving synthetic and Machine Learning repository (http://archive.ics.uci.edu/beta/) data sets, we demonstrate that the kernel-based FCM algorithms produce a marginal improvement over standard FCM and GK for most of the analyzed data sets. It has been observed that the kernel-based FCM algorithms are in a number of cases highly sensitive to the selection of specific values of the kernel parameters.     

基于方向多尺度变换和统计建模的纹理分类方法

纹理是图像分析和识别中经常使用的关键特征, 而小波变换则是图像纹理表示和分类中的常用工具. 然而, 基于小波变换的纹理分类方法常常忽略了小波低频子带信息, 并且无法提取图像纹理的块状奇异信息. 本文提出小波子带系数的局部能量直方图建模方法、轮廓波特征的Poisson 混合模型建模方法和基于轮廓波子带系数聚类的特征提取方法, 并将其应用于图像纹理分类上. 基于局部能量直方图的纹理分类方法解决了小波低频子带的建模难题, 基于Poisson 混合模型的纹理分类方法则首次将Poisson 混合模型用于轮廓子带特征的建模, 而基于轮廓波域聚类的纹理分类方法是一种快速的分类方法. 实验结果显示, 本文所提出的三类方法都超过了当前典型的纹理分类方法.     

A clustering ensemble framework based on elite selection of weighted clusters

Each clustering algorithm usually optimizes a qualification metric during its progress. The qualification metric in conventional clustering algorithms considers all the features equally important; in other words each feature participates in the clustering process equivalently. It is obvious that some features have more information than others in a dataset. So it is highly likely that some features should have lower importance degrees during a clustering or a classification algorithm; due to their lower information or their higher variances and etc. So it is always a desire for all artificial intelligence communities to enforce the weighting mechanism in any task that identically uses a number of features to make a decision. But there is always a certain problem of how the features can be participated in the clustering process (in any algorithm, but especially in clustering algorithm) in a weighted manner. Recently, this problem is dealt with by locally adaptive clustering (LAC). However, like its traditional competitors the LAC suffers from inefficiency in data with imbalanced clusters. This paper solves the problem by proposing a weighted locally adaptive clustering (WLAC) algorithm that is based on the LAC algorithm. However, WLAC algorithm suffers from sensitivity to its two parameters that should be tuned manually. The performance of WLAC algorithm is affected by well-tuning of its parameters. Paper proposes two solutions. The first is based on a simple clustering ensemble framework to examine the sensitivity of the WLAC algorithm to its manual well-tuning. The second is based on cluster selection method.     

基于模糊的投影寻踪算法在中医证候分类中的应用

本文提出一种新的聚类算法-基于模糊的投影寻踪算法,可以有效的处理医学中常常遇到的高维混合数据的模糊聚类问题.并将其应用在慢性肾衰的辩证分析问题中,为已有的慢性肾衰证候的分型标准提供科学支持.本文的研究方法为中医辩证的现代化研究开拓了新的思路,值得进一步深入探讨。     

Clustering bivariate mixed-type data via the cluster-weighted model

The cluster-weighted model (CWM) is a mixture model with random covariates that allows for flexible clustering/classification and distribution estimation of a random vector composed of a response variable and a set of covariates. Within this class of models, the generalized linear exponential CWM is here introduced especially for modeling bivariate data of mixed-type. Its natural counterpart in the family of latent class models is also defined. Maximum likelihood parameter estimates are derived using the expectation-maximization algorithm and some computational issues are detailed. Through Monte Carlo experiments, the classification performance of the proposed model is compared with other mixture-based approaches, consistency of the estimators of the regression coefficients is evaluated, and several likelihood-based information criteria are compared for selecting the number of mixture components. An application to real data is also finally considered.     

An new initialization method for fuzzy c-means algorithm

In this paper an initialization method for fuzzy c-means (FCM) algorithm is proposed in order to solve the two problems of clustering performance affected by initial cluster centers and lower computation speed for FCM. Grid and density are needed to extract approximate clustering center from sample space. Then, an initialization method for fuzzy c-means algorithm is proposed by using amount of approximate clustering centers to initialize classification number, and using approximate clustering centers to initialize initial clustering centers. Experiment shows that this method can improve clustering result and shorten clustering time validly.     

小麦植株节间长度分析

在小麦高产育种中,除根据产量及其构成因素进行选育外,对与产量有关的植株形态性状及生理特性进行选择也是育种者要考虑的问题.多年来,对于株高性状的选育引起了普遍重视,并且小麦植株高度的差异主要决定于各节间长度的差异,对此有关专家作了大量研究.通过对斐波那契数列的研究,提出了广义斐波那契数列出概念并用统计分析的方法对采集的小麦茎秆的数据进行数据分析,得到了小麦的茎秆结构符合广义斐波那契数列的结论.研究为简化小麦茎秆研究提出了数学上的理论支持.     

小麦茎秆与单穗重的相关性分析

主要是分析茎秆与单穗重和产量的相关性.以6个小麦品种为材料,分别测取了穗下茎长,茎粗,穗长,株高,节壁厚,节重及单穗重,对茎质与穗重进行了相关分析,结果表明:茎秆性状与穗重存在不同程度的相关性.其中,茎粗与单穗重呈正相关;节壁厚与单穗重呈正相关;节壁重与单穗重呈正相关;而株高和茎长与单穗重的关系不明显且较为复杂,应分情况讨论.