基于属性识别理论的大坝结构性态综合评价

大坝结构性态分析评价问题是一个多层次多项目的系统整体分析评价问题,应利用系统分析的观点,将多个观测项目结合起来进行综合分析和评价．在分析了已有综合评价方法的基础上,提出了基于属性识别理论的大坝结构性态综合评价方法．示例表明,这一方法是合理、可行的．     

Sensor control for multi-object state-space estimation using random finite sets

The problem addressed in this paper is information theoretic sensor control for recursive Bayesian multi-object state-space estimation using random finite sets. The proposed algorithm is formulated in the framework of partially observed Markov decision processes where the reward function associated with different sensor actions is computed via the Rényi or alpha divergence between the multi-object prior and the multi-object posterior densities. The proposed algorithm in implemented via the sequential Monte Carlo method. The paper then presents a case study where the problem is to localise an unknown number of sources using a controllable moving sensor which provides range-only detections. Four sensor control reward functions are compared in the study and the proposed scheme is found to perform the best.     

On finding fundamental cut sets

In this paper a linear time algorithm is proposed for preprocessing the edges of a graph. After preprocessing (in linear time), the fundamental cut set of any tree edge can be determined in time proportional to the size of that cut set.     

Attribute selection with fuzzy decision reducts

Rough set theory provides a methodology for data analysis based on the approximation of concepts in information systems. It revolves around the notion of discernibility: the ability to distinguish between objects, based on their attribute values. It allows to infer data dependencies that are useful in the fields of feature selection and decision model construction. In many cases, however, it is more natural, and more effective, to consider a gradual notion of discernibility. Therefore, within the context of fuzzy rough set theory, we present a generalization of the classical rough set framework for data-based attribute selection and reduction using fuzzy tolerance relations. The paper unifies existing work in this direction, and introduces the concept of fuzzy decision reducts, dependent on an increasing attribute subset measure. Experimental results demonstrate the potential of fuzzy decision reducts to discover shorter attribute subsets, leading to decision models with a better coverage and with comparable, or even higher accuracy.     

Consequences and conjectures in preordered sets

In a preordered set, or preset, consequence operators in the sense of Tarski, defined on families of subsets, are introduced. From them, the corresponding sets of conjectures, hypotheses, speculations and refutations are considered, studying the relationships between these sets and those previously defined on ortholattices. All the concepts introduced are illustrated with three particular consequence operators, whose behavior is studied in detail. The results obtained are applied to the case of fuzzy sets endowed with the usual pointwise ordering.     

A comparative evaluation of interactive segmentation algorithms

In this paper we present a comparative evaluation of four popular interactive segmentation algorithms. The evaluation was carried out as a series of user-experiments, in which participants were tasked with extracting 100 objects from a common dataset: 25 with each algorithm, constrained within a time limit of 2 min for each object. To facilitate the experiments, a “scribble-driven” segmentation tool was developed to enable interactive image segmentation by simply marking areas of foreground and background with the mouse. As the participants refined and improved their respective segmentations, the corresponding updated segmentation mask was stored along with the elapsed time. We then collected and evaluated each recorded mask against a manually segmented ground truth, thus allowing us to gauge segmentation accuracy over time. Two benchmarks were used for the evaluation: the well-known Jaccard index for measuring object accuracy, and a new fuzzy metric, proposed in this paper, designed for measuring boundary accuracy. Analysis of the experimental results demonstrates the effectiveness of the suggested measures and provides valuable insights into the performance and characteristics of the evaluated algorithms.     

A class of aggregation functions encompassing two-dimensional OWA operators

In this paper we prove that, under suitable conditions, Atanassov’s K_α operators, which act on intervals, provide the same numerical results as OWA operators of dimension two. On one hand, this allows us to recover OWA operators from K_α operators. On the other hand, by analyzing the properties of Atanassov’s operators, we can generalize them. In this way, we introduce a class of aggregation functions - the generalized Atanassov operators - that, in particular, include two-dimensional OWA operators. We investigate under which conditions these generalized Atanassov operators satisfy some properties usually required for aggregation functions, such as bisymmetry, strictness, monotonicity, etc. We also show that if we apply these aggregation functions to interval-valued fuzzy sets, we obtain an ordered family of fuzzy sets.     

依赖于真子集的频繁邻近类别集挖掘

针对现有频繁邻近类别集挖掘算法存在重复计算和冗余邻近类别集的问题,提出一种依赖于真子集的频繁邻近类别集挖掘算法,适合在海量数据中挖掘空间对象的频繁邻近类别集。该算法用析构法建立邻近类别集数据库,用产生邻近类别集真子集的方法计算支持数,实现一次扫描数据库提取频繁邻近类别集。算法无需产生候选频繁邻近类别集,且计算支持数时无需重复扫描,从而达到提高挖掘效率的目的。实验结果表明,在海量空间数据中挖掘频繁邻近类别集时,该算法比现有算法更快速有效。     

基于粒极值的最简规则提取算法

应用粗糙集的分辨关系,分别从表的行、列2个方面求出每个属性值的分辨、组合能力,以此确定出粒极值,将含粒极值的粒定义为极值粒。应用粒计算理论,以极值粒集为主要运算对象进行粒逻辑运算,使最终的组合粒最简,即所提取的规则最简。实验验证了算法的有效性。