自适应属性加权2维FCM分割算法

目的 为了提高2维直方图模糊C均值聚类分割算法的抗噪性和普适性,提出了属性加权2维直方图模糊C均值聚类分割新方法。方法 针对2维直方图模糊C均值聚类分割算法存在阈值参数选取不当导致抗噪性能差的不足,将属性加权引入2维直方图模糊C均值聚类并有效解决了每维属性聚类贡献度的问题。结果 本文算法相比2维直方图模糊C均值聚类分割法抗椒盐和高斯噪声性能平均提高了2～3 dB;同时,相比模糊局部C均值聚类分割法抗椒盐噪声性能平均提高了2～3 dB且抗高斯噪声性能稍差大约1 dB,但本文算法相比模糊局部C均值聚类分割法的速度平均提高了大约40倍。结论 实验结果表明,本文算法相比现有2维直方图模糊C均值聚类算法更适合噪声图像分割;同时,相比模糊局部C均值聚类算法更有利于实时性要求较高场合的目标跟踪和识别等需要。同时从大量图像测试得出,本文算法对于一般人工合成图像、智能交通图像及遥感图像等具有普遍适用性。     

Medical diagnosis of cardiovascular diseases using an interval-valued fuzzy rule-based classification system

ObjectiveTo develop a classifier that tackles the problem of determining the risk of a patient of suffering from a cardiovascular disease within the next 10 years. The system has to provide both a diagnosis and an interpretable model explaining the decision. In this way, doctors are able to analyse the usefulness of the information given by the system.MethodsLinguistic fuzzy rule-based classification systems are used, since they provide a good classification rate and a highly interpretable model. More specifically, a new methodology to combine fuzzy rule-based classification systems with interval-valued fuzzy sets is proposed, which is composed of three steps: (1) the modelling of the linguistic labels of the classifier using interval-valued fuzzy sets; (2) the use of the K_α operator in the inference process and (3) the application of a genetic tuning to find the best ignorance degree that each interval-valued fuzzy set represents as well as the best value for the parameter α of the K_α operator in each rule.ResultsThe suitability of the new proposal to deal with this medical diagnosis classification problem is shown by comparing its performance with respect to the one provided by two classical fuzzy classifiers and a previous interval-valued fuzzy rule-based classification system. The performance of the new method is statistically better than the ones obtained with the methods considered in the comparison. The new proposal enhances both the total number of correctly diagnosed patients, around 3% with respect the classical fuzzy classifiers and around 1% vs. the previous interval-valued fuzzy classifier, and the classifier ability to correctly differentiate patients of the different risk categories.ConclusionThe proposed methodology is a suitable tool to face the medical diagnosis of cardiovascular diseases, since it obtains a good classification rate and it also provides an interpretable model that can be easily understood by the doctors.     

Indistinguishability operators in measurement theory,Part I: Conversions of indistinguishability operators with respect to scales

For two given ordinal scales in a measurement process, the present paper investigates how an indistinguishability operator evaluated according to one of these ordinal scales can be converted to another indistinguishability operator w.r.t. the other ordinal scale, and establishes the mathematical base of these conversions under the framework of measurement theory [Krantz, D.H., Luce, R.D., Suppes, P., Tversky, A. (1971) Foundations of Measurement, Vol. 1 (Academic Press, San Diego)]. Additionally, this work exposes the rudimentary facts behind the studies in [“Fuzzy Numbers and Equality Relations”, Proc. FUZZ'IEEE 93 (1993) 1298–1301; “Fuzzy Sets and Vague Environments”, Fuzzy Sets and Systems 66 (1994) 207–221; “Fuzzy Control on the Basis of Equality Relations-with an Example from Idle Speed Control”, IEEE Transactions on Fuzzy Systems 3 (1995) 336–350; and “T-partitions of the Real Line Generated by Idempotent Shapes”, Fuzzy Sets and Systems 91 (1997) 177–184], and points out the measurement theoretic derivations of the results in these studies.     

Additive consistency-based priority-generating method of q-rung orthopair fuzzy preference relation

The q-rung orthopair fuzzy set, whose membership function and nonmembership function belong to the interval [0,1], is more powerful than both intuitionistic fuzzy set and Pythagorean fuzzy set in expressing imprecise information of decision-makers. The aim of this paper is to investigate a method to determine the priority weights from individual or group q-rung orthopair fuzzy preference relations (q-ROFPRs). To do so, firstly, a new definition of additively consistent q-ROFPR is presented based on the preference relation of alternatives given by decision-makers. Afterward, according to individual and group q-ROFPRs, two kinds of goal programming models are proposed, respectively, to generate the q-rung orthopair fuzzy priority weight vector of the given q-ROFPR(s). Finally, two numerical examples are given to illustrate the effectiveness and superiority of the method proposed in this paper.     

Tension Spline for the Solution of Self-adjoint Singular Perturbation Problems

A numerical method based on spline in tension is given for the self-adjoint singularly perturbed two point boundary value problems. The schemes derived in this method are second order accurate. One numerical example is given to support the predicted theory.     

鲁棒空间约束的模糊聚类图像分割

目的 为进一步提高分割精度,在模糊聚类的基础上引入统计信息,提出一种鲁棒型空间约束的模糊聚类分割算法。方法 基于局部空间信息的先验概率与后验概率,提出一种新型空间约束项,并通过卷积操作提高运行效率;进而引入负对数联合概率作为测度函数,进一步提高算法对于各像素点所属类别的甄别能力;同时将测度函数与空间约束项整合至目标函数中,通过迭代更新各参数达到最小化目标函数的目的。结果 对于合成图像的实验结果表明,本文算法对于噪声类型和噪声强度具有较强的鲁棒性;对于彩色图像的实验结果表明,在适当的特征描述符的辅助下,本文算法也能够获得令人满意的分割结果和较高的分割精度。结论 本文算法克服了现有算法的缺陷,进一步提升了图像的分割精度。其适用于分割带噪声图像,且在适当纹理特征的辅助下分割彩色图像,与同类算法的比较实验结果验证了本文算法的有效性。     

模糊熵的区域合并和图割的快速矿岩分割

目的 针对现有区域合并和图割的结合算法没有考虑矿岩图像模糊特性,导致分割精度和运行效率较低,模糊边缘无法有效分割的问题,利用快速递推计算的最大模糊2-划熵信息设置以区域为顶点的图割模型似然能来解决。方法 首先利用双边滤波器和分水岭算法对矿岩图像进行预处理,并将其划分为若干一致性较好的区域;然后利用图像在计算最大模糊2-划分熵时,目标和背景的模糊隶属度函数来设计图割能量函数似然能,使得能量函数更接近模糊图像的真实情况,期间为了提高最大模糊2-划分熵值的搜索效率,提出了时间复杂度为O（n²）的递推算法将模糊熵的计算转化为递推过程,并保留不重复的递推结果用于后续的穷举搜索;最后利用设计的图割算法对区域进行标号,以完成分割。结果 本文算法的分割精度较其他区域合并和图割结合算法提高了约23%,分割后矿岩颗粒个数的统计结果相对于人工统计结果,其误差率约为2%,运行时间较其他算法缩短了约60%。结论 本文算法确保精度同时,有效提高矿岩图像的分割效率,为自动化矿岩图像高效分割的工程实践提供重要指导依据。     

灰度级信息的目标边界精确周长估算

目的 针对图像目标边界不连续或具有模糊性导致的目标周长无法精确估算这一问题,结合边界跟踪,提出一种基于灰度级信息的目标边界精确周长估算方法。方法 该方法利用目标边界的灰度级信息,同时结合边界跟踪得到的内外边界来估计目标图像的边界周长,从而提高边界周长估计的精确性和鲁棒性。为了获得目标物体真实周长,实验采用人工合成图像。结果 实验应用所提方法和3种传统周长估算方法分别计算合成目标对象的周长,并与真实周长比较。为了验证所提方法的有效性和鲁棒性,实验中对目标对象的边界进行不同程度的加厚模糊化;并在边界加入噪声,使边界不连续。当边界变得复杂时,本文所提方法的优势得到极大体现。结论 实验结果表明,在边界模糊和边界不连续的情况下,本文所提的算法具有更好的适应性和稳定性。     

Implementation of the Generalized WZ Factorization on a Wavefront Array Processor

In this work, the potential of a block generalized WZ factorization procedure for direct hardware implementation on a special-purpose network of Wavefront Array Processor Modules (WAPm s ) is investigated. The definition of a wavefront module is given and the development of an Eigenvalue Evaluator Engine ( E 3 ) based on this module is discussed.     

结合结构相似度的自适应多尺度SAR图像变化检测

目的 结合高斯核函数特有的性质,提出一种基于结构相似度的自适应多尺度SAR图像变化检测算法。方法 本文提出的算法包括差异图像获取、高斯多尺度分解、基于结构相似性的最优尺度选择、特征矢量构造以及模糊C均值分类。首先,通过对多时相SAR图像进行对数比运算获取差异图像,然后,利用基于图像的结构相似度估计高斯多尺度变换的最优尺度,继而在该最优尺度参数下逐像素构建变化检测特征矢量,最后通过模糊C均值聚类方法实现变化像素与未变化像素的分离,生成最终的变化检测结果图。结果 在两组真实的SAR图像数据上测试本文算法,正确检测率分别达到0.9952和0.9623,Kappa系数分别为0.8200和0.8540,相比传统算法有了较大的提高。结论 本文算法充分利用了尺度信息,对噪声的鲁棒性有所提高。实测SAR数据的实验结果表明,本文算法可以智能获取最优分解尺度,显著提高了SAR图像变化检测性能。     

On the representations of L-equivalence relations on L-fuzzy sets with applications to locally vague environments

ABSTRACT

L-equivalence relations on L-fuzzy sets, equivalent to L-valued equalities on ordinary sets, are modelling fuzzy similarity of two objects that are partial members of a fuzzily defined set. This paper deals with construction and representation properties of L-equivalence relations on L-fuzzy sets. To this end, we set up a method for constructing them by means of quasi-pseudo-metrics, and then point out how L-equivalence relations on L-fuzzy sets are represented by families of L-fuzzy subsets of L-fuzzy sets. The present results are applied to locally vague environments to elucidate conceptual structures of locally vague environments, and to show how the points in a locally vague environment are described.     

STRUCTURING THE ORGANIZATION OF PARTIAL MODELS†

Abstract

An organization of partial models is a system in which no one model can answer all questions about a real large-scale system, but where there is a means of determining whether there is a model appropriate to a given question and if so indicating which it is. The structuring of such an organization based on the theory of modelling and simulation is discussed.     

Formal methods for security in the Xenon hypervisor

This paper reports on the Xenon project’s use of formal methods. Xenon is a higher-assurance secure hypervisor based on re-engineering the Xen open-source hypervisor. The Xenon project used formal specifications both for assurance and as guides for security re-engineering. We formally modelled the fundamental definition of security, the hypercall interface behaviour, and the internal modular design. We used three formalisms: CSP, Z, and Circus for this work. Circus is a combination of Standard Z, CSP with its semantics given in Hoare and He’s unifying theories of programming. Circus is suited for both event-based and state-based modelling. Here, we report our experiences to date with using these formalisms for assurance.     

SAMPLING QUALITATIVE AND FUZZY VARIABLES AS A TOOL FOR THE GENERAL SYSTEM PROBLEM SOLVER

Abstract

An important phase in the application of the General System Problem Solving framework (GSPS∥ is the choice of the sampling frequency of relevant variables. Optimal sampling frequency is generally a compromise between our endeavour to catch all dynamical changes of a signal without having redundant samples. This problem is not completely solved for crisp metric variables and very little is known about sampling qualitative and fuzzy variables. In this paper, one possible approach based on the data compression is suggested for determining the optimal sampling frequency of ordinal and nominal variables. The submitted approach can be on the specific conditions extended for the fuzzy variables also     

A dynamic fuzzy genetic algorithm for natural image segmentation using adaptive mean shift

Abstract

In this paper, a colour image segmentation approach based on hybridisation of adaptive mean shift (AMS), fuzzy c-mean and genetic algorithms (GAs) is presented. Image segmentation is the perceptual faction of pixels based on some likeness measure. GA with fuzzy behaviour is adapted to maximise the fuzzy separation and minimise the global compactness among the clusters or segments in spatial fuzzy c-mean (sFCM). It adds diversity to the search process to find the global optima. A simple fusion method has been used to combine the clusters to overcome the problem of over segmentation. The results show that our technique outperforms state-of-the-art methods.     

An integrated PC environment for assessment of drought

Abstract

The design of a versatile software environment to support routine activities of a satellite based operational drought monitoring system is presented in this article. In addition, software provides the assistance to analyse and interpret drought conditions based on satellite derived normalised difference vegetation index (NDVI) statistics and ground information pertaining to a given area. The environment currently under development deals with district-wise drought assessment for nine states, taluk-wise for Karnataka State and operates on a standard PC with EGA graphics.     

基于能量泛函和视觉特性的全变分图像降噪模型

目的 基于能量泛函的全变分图像复原模型(ROF)为偏微分方程在图像处理上的应用开辟了一个新的研究领域。针对ROF模型存在的缺陷,很多学者提出了改进的模型和算法,并取得了一定的效果。基于能量泛函和视觉特性提出一种全变分图像降噪模型。方法 首先利用偏微分方程比较原理证明了该模型解的整体存在性,并利用变分原理给出了该模型的Euler-Lagrange方程;在数值计算时,选用人工时间演算法和有限差分方法,对数值近似解的离散形式进行了图像降噪matlab实验;最后利用峰值信噪比和平均结构相似度两个指标进行了降噪质量评价。结果 从实验数据上来分析,本文的模型在峰值信噪比上都有0.5~1 dB的提高,结构相似度有0.05~0.3的改进。结论 从降噪效果上分析,基于能量泛函和视觉特性的全变分图像降噪模型能够在降噪的同时,保持良好的边缘和纹理特征,优于其他改进的全变分降噪模型。     

BAYESIAN INFERENCE OF FUZZY PROBABILITIES

Abstract

In this paper, we propose a new method to generalize Bayesian methods both for fuzzy likelihood and fuzzy prior probabilities. Based on interval Bayesian inference developed by Pan and Klir [1996], the proposed method overcomes the difficulty in developing a normalized fuzzy Bayesian inference recognized in the literature [Friihwirth-Schnatter, 1993].     

肺实质CT图像细化分割

目的 由于肺部CT图像中各组织结构复杂、灰度分布不均匀,造成肺实质部分难以准确分割和提取。为了提高肺实质分割的准确率,本文提出了一种基于超像素的细化分割与模糊C均值聚类相结合的自动分割算法。方法 该算法充分利用肺部CT图像的灰度、纹理特征,同时为了正确标记超像素的分类,引入一种空间邻域信息来增强空间约束进而有效地解决灰度不均匀的问题,它能够对肺实质进行分割并除去其周围的主血管,然后利用形态学知识去除肺部的分支血管。结果 在临床患有四类疾病的患者CT图像数据集上采用改进的图像特征,使得肺实质分割的准确率提高了0.8%。同时,算法准确率提高到99.46%。结论 实验结果表明,本文算法能够实现肺部CT图像肺实质的自动细化分割,结果准确适用。该算法鲁棒性好、速度快,是一种精确有效的自动肺实质分割方法。