Neuro-fuzzy approach to processing inputs with missing values in pattern recognition problems

An approach to dealing with missing data, both during the design and normal operation of a neuro-fuzzy classifier is presented in this paper. Missing values are processed within a general fuzzy min–max neural network architecture utilising hyperbox fuzzy sets as input data cluster prototypes. An emphasis is put on ways of quantifying the uncertainty which missing data might have caused. This takes a form of classification procedure whose primary objective is the reduction of a number of viable alternatives rather than attempting to produce one winning class without supporting evidence. If required, the ways of selecting the most probable class among the viable alternatives found during the primary classification step, which are based on utilising the data frequency information, are also proposed. The reliability of the classification and the completeness of information is communicated by producing upper and lower classification membership values similar in essence to plausibility and belief measures to be found in the theory of evidence or possibility and necessity values to be found in the fuzzy sets theory. Similarities and differences between the proposed method and various fuzzy, neuro-fuzzy and probabilistic algorithms are also discussed. A number of simulation results for well-known data sets are provided in order to illustrate the properties and performance of the proposed approach.     

运用反例基于识别的手写数字串分割

采用基于识别的分割方法进行手写数字串分割.在识别的过程中,运用反例样本估计分类器参数,实验数据表明,这种运用反例样本训练的分类器与没有经过反例样本训练的分类器相比,将提高拒识率到19%左右,从而保证了较高的识别率,验证了只有经过反例训练的分类器的输出结果才是可信赖的.     

Bounding multi-stage stochastic programs from above

The purpose of this paper is to present general approaches for bounding some multi-stage stochastic programs from above. The results are based on restricting the solution set, such that the remaining multi-stage stochastic program is easy to solve. An example where the methods can be applied is presented.Supported in part by NATO Collaborative Research Grant No. 0785/87.     

The Method of Virtual Experts in Mathematical Diagnostics

Mathematical Diagnostics (MD) deals with identification problems arising in different practical areas. Some of these problems can be described by mathematical models where it is required to identify points belonging to two or more sets of points. Most of the existing tools provide some identification rule (a classifier) by means of which a given point is assigned (attributed) to one of the given sets. Each classifier can be viewed as a virtual expert. If there exist several classifiers (experts), the problem of evaluation of experts’ conclusions arises. In the paper for the case of supervised classification the method of virtual experts (the VE-method) is described. Based on this method, a generalized VE method is proposed where each of the classifiers can be chosen from a given family of classifiers. As a result, a new optimization problem with a discontinuous functional is stated. Examples illustrating the proposed approach are provided. The work of the second author was supported by the Russian Foundation for Fundamental Studies (RFFI) under Grant No 03-01-00668.     

用于空间BIB探测器的0.3 W@4.2 K大冷量轻量化低温系统

大阵列BIB探测器由于其高量子效率和低暗电流而成为广泛的研究对象,特别是在空间应用方向。如2021年发射的詹姆斯韦伯太空望远镜,它已经进行了许多重要的天文观测。一个稳定、高效、轻量化的液氦温区低温系统对BIB探测器的运行至关重要。氦节流制冷机可取代传统的大容积液氦杜瓦,能满足空间液氦温区的制冷要求。为了同时提高4.2 K的制冷量并减轻重量,提出了一种0.3 W@4.2 K的大冷量轻量化的4.2 K低温制冷系统。并且在现有0.1 W@4.1 K制冷量的低温系统上进行实验,验证了该系统的设计方法。在不同的冷却温度区采用不同的冷却方法,以实现冷却的高效性和轻便性。开发了一个新的集成斯特林制冷机,在80 K时提供高效的一级预冷,制冷量为15 W,重量仅为4.5 kg;二级预冷采用主动活塞调相的15 K脉管制冷机,制冷量为0.9 W。此多级低温制冷系统通过耦合氦气JT循环,可以在4.2 K时提供0.3 W的制冷量,输入功低于1.8 kW。此系统将为正在快速发展的红外天文观测所需的空间BIB探测器提供保障。     

一种转基因猪血中重组人血清白蛋白分离纯化新方法

基因工程技术已经成为研究和生产重组人血清白蛋白(rHSA)替代人血清白蛋白(HSA)的重点技术,而白蛋白的纯化则是该技术的关键。本文主要介绍了从转基因猪血中纯化rHSA的一种新方法,即热乙醇沉淀与多级色谱分离相结合的rHSA纯化方法。热乙醇沉淀法可从猪血浆中获得rHSA粗提取液,此时rHSA的纯度可达69.5%,回收率达51.3%。进一步采用多级色谱分离法,即阴离子交换色谱和反相色谱法进一步纯化,得到rHSA的最终纯度约为100.0%,总回收率为41.1%。该方法为从转基因猪血浆中大规模纯化用于临床和生化研究的高纯度rHSA提供可能,同时也为rHSA替代HSA奠定了基础。     

误差合成空间固定系数模型的多阶段广义最小二乘估计

本文对时变系数的空间面板数据模型进行了研究,所研究的模型利用扰动项中的空间个体成分将不同时期的方程联系起来,同时,自变量系数和空间自回归系数是时变的,但不会随着观测个体的变动而变动。本文利用基于可行的广义最小二乘估计的多阶段方法对模型参数进行了估计,研究了估计量的大样本性质,并利用Monte Carlo方法模拟了其小样本性质。模拟结果表明,估计量的渐近性质随着样本容量的增加而改善。对中国省级地区间财税策略互动行为的实证案例也体现了本文理论模型的应用价值。     

一种基于权重融合的JPEG隐写分析方法

针对JPEG图像通用隐写检测中检测效率低、训练时间长的问题,提出一种基于集成分类器的新检测方法。算法以CC-PEV为特征对图像进行描述并作为隐写分析特征;然后,随机构造若干个特征子空间,用bootstrap方法构造图像训练子集,分别进行训练得到数个基分类器;根据基分类器的分类结果赋予基分类器不同的权重,将基分类器的结果按照其权重进行融合得到最终的结果。本文对该算法进行了测试,对它的集成性、检测准确率和训练时间进行分析。实验结果表明,相对于传统的集成方法,本文方法用自举方法构造训练集、随机方法构造子特征空间、赋予基分类器不同权重进行融合能够显著地提高算法准确率。本文方法相对于SVM和传统的集成分类方法,具有更高的检测率,对于特征维数更大的图像检测,具有更好的拓展性和一般适用性。     

TargetATPsite: A template‐free method for ATP‐binding sites prediction with residue evolution image sparse representation and classifier ensemble

Understanding the interactions between proteins and ligands is critical for protein function annotations and drug discovery. We report a new sequence‐based template‐free predictor (TargetATPsite) to identify the Adenosine‐5′‐triphosphate (ATP) binding sites with machine‐learning approaches. Two steps are implemented in TargetATPsite: binding residues and pockets predictions, respectively. To predict the binding residues, a novel image sparse representation technique is proposed to encode residue evolution information treated as the input features. An ensemble classifier constructed based on support vector machines (SVM) from multiple random under‐samplings is used as the prediction model, which is effective for dealing with imbalance phenomenon between the positive and negative training samples. Compared with the existing ATP‐specific sequence‐based predictors, TargetATPsite is featured by the second step of possessing the capability of further identifying the binding pockets from the predicted binding residues through a spatial clustering algorithm. Experimental results on three benchmark datasets demonstrate the efficacy of TargetATPsite. © 2013 Wiley Periodicals, Inc.     

基于高斯混合模型的车辆字符识别算法

在基于统计方法的车牌字符识别算法中,为了更有效地描述车牌字符特征的类条件概率密度,提出了一种基于高斯混合模型(GMM)的识别方法.首先采用Gabor变换来提取车牌字符的特征参数,然后通过GMM来逼近字符特征的类条件概率密度分布,最后根据得到的类条件概率密度分布函数构造贝叶斯分类器.其中,GMM的参数估计采用EM算法.实验表明,这种方法具有良好的字符识别效果、较强的鲁棒性以及较大的应用价值.