Distance and similarity measures of Pythagorean fuzzy sets based on the Hausdorff metric with application to fuzzy TOPSIS

Pythagorean fuzzy sets (PFSs) were proposed by Yager in 2013 to treat imprecise and vague information in daily life more rigorously and efficiently with higher precision than intuitionistic fuzzy sets. In this paper, we construct new distance and similarity measures of PFSs based on the Hausdorff metric. We first develop a method to calculate a distance between PFSs based on the Hasudorff metric, along with proving several properties and theorems. We then consider a generalization of other distance measures, such as the Hamming distance, the Euclidean distance, and their normalized versions. On the basis of the proposed distances for PFSs, we give new similarity measures to compute the similarity degree of PFSs. Some examples related to pattern recognition and linguistic variables are used to validate the proposed distance and similarity measures. Finally, we apply the proposed methods to multicriteria decision-making by constructing a Pythagorean fuzzy Technique for Order Preference by Similarity to an Ideal Solution and then present a practical example to address an important issue related to social sector. Numerical results indicate that the proposed methods are reasonable and applicable and also that they are well suited in pattern recognition, linguistic variables, and multicriteria decision-making with PFSs.     

Chest X-ray enhancement to interpret pneumonia malformation based on fuzzy soft set and Dempster–Shafer theory of evidence

Image enhancement algorithms are commonly used to increase the contrast and visual quality of low-dose x-ray images. This paper proposes an automated enhancement method using soft fuzzy sets with a new decision-making scheme based on Dempster-Shafer theory of evidence for the visual interpretation of pneumonia malformation in low-dose x-ray images, called as XEFSDS. The XEFSDS model first generates an original source x-ray image into a complementary image, then each original and complement image is applied to the characterized image object and background areas of fuzzy space. The S-function is utilized to define fuzzy soft sets for the classification of gray level ambiguity in both images, and hence a decision criterion via Dempster-Shafer approach and fuzzy interval has been adapted to discriminate uncertainties on the pixel intensity and the spatial information. Modified membership grade operations have been performed on each object/background area, and Werner’s AND/OR operator (an aggregation operator) has been utilized to build a new membership function from two modified membership functions. Finally, an enhanced image is obtained from the new membership function via defuzzification. Experiments on different pneumonia X-ray images demonstrate that the XEFSDS scheme produces better results than the existing methods. To show the advantages of the XEFSDS scheme, we have executed a segmentation based examination on enhanced image for the detection of pneumonia malformation as well as abnormal lobe (lobar pneumonia) or bronchopneumonia.     

基于工业机器人的液压支架导向套柔性制造系统

自动化技术在小批量多品种的产品制造中，存在兼容难、换产时间长、人工干预多等问题，导致生产线难以高效率运行。设计的柔性制造系统，在充分分析了导向套不同结构、不同规格的基础上，以一套六关节机器人和行走地轨作为物流搬运设备，同时负责3台数控设备、自动辊道上料设备、自动堆垛下料设备、翻转机构、打标机构及抽检机构的上下料，并通过使用机器人双工位卡爪、机床六爪卡盘、自动刀补等卡具及技术，实现了一键换产，极大提高了制造系统的柔性。     

The validation and further development of a multidimensional cognitive load scale for virtual environments

Measuring cognitive load is important in virtual learning environments (VLE). Thus, valid and reliable measures of cognitive load are important to support instructional design in VLE. Through three studies, we investigated the validity and reliability of Leppink's Cognitive Load Scale (CLS) and developed the extraneous cognitive load (EL) dimension into three sub-scales relevant for VLE: EL instructions, EL interaction, and EL environment. We investigated the validity of the measures using the Partial Credit Model (PCM), Confirmatory Factor Analysis (CFA), and correlations with retention tests. Study 1 (n = 73) investigated the adapted version of the CLS. Study 2 describes the development and validation of the Multidimensional Cognitive Load Scale for Virtual Environments (MCLSVE), with 140 students in higher education. Study 3 tested the generalizability of the results with 121 higher education students in a more complicated VLE. The results provide initial evidence for the validity and reliability of the MCLSVE.     

The development of a single-item Food Choice Questionnaire

Based on the multi-item Food Choice Questionnaire (FCQ) originally developed by Steptoe and colleagues (1995), the current study developed a single-item FCQ that provides an acceptable balance between practical needs and psychometric concerns. Studies 1 (N = 1851) and 2 (2a (N = 3290), 2b (N = 4723), 2c (N = 270)) showed that the single-item FCQ scale has good convergent and discriminant validity. Generally, the results showed the highest correlations with the related multi-item dimensions (>0.40). Study 2 refined the scale. Only the items for convenience (Study 2a), sensory appeal (Study 2b) and mood (Study 2c) needed to be revised (as Study 1 showed a correlation between the multi-item and the single-item below the threshold of 0.60). The results also showed comparable predictive validity. Both methods revealed similar association patterns between food motives and consumption behaviours (Fisher’s z tests revealed agreements of 86.2% for Study 1, 92.9% for Study 2a and 100% for Studies 2b and 2c). Study 3 (N = 6062) showed an example of the added value of a context-specific application for the single-item FCQ. Different motives were shown to be relevant across contexts, and the context-specific motives had additional explained variance beyond the general multi-item FCQ. Studies 2b and 3 also showed the performance of the single-item FCQ in an international context. In sum, the results indicate that the single-item FCQ can be used as a flexible and short substitute for the multi-item FCQ. The study also discusses the conditions that should be considered when using the single-item scale.     

一种基于Spark的不确定数据集频繁模式挖掘算法

如何在海量不确定数据集中提高频繁模式挖掘性能是目前研究的热点.传统算法大多是以期望、概率或者权重等单一指标为数据项集支持度，在大数据背景下，同时考虑概率和权重支持度的算法难以兼顾其执行效率.为此，本文提出一种基于Spark的不确定数据集频繁模式挖掘算法（UWEFP），首先，为了同时兼顾数据项的概率和权重，计算一项集的最大概率权重值并进行剪枝；然后，为了减少对数据集的多次扫描，结合Spark框架的优点，设计了一种具有FP-tree特征的新颖的UWEFP-tree结构进行模式树的构建及挖掘；最后在Spark环境下，以UCI数据集进行实验验证.实验结果表明本文的方法在保证挖掘结果的同时，提高了效率.     

面向文本数据的正则化交叉验证方法

面向文本数据建模时,交叉验证方法是特征选择及模型比较任务中的常用方法。许多研究表明,文本数据模型的性能估计对交叉验证的数据切分方式较为敏感,不合理的切分方式可能会导致不稳定的性能估计值,使得实验结果可复现性差。该文试图论证基于多次重复(m次)的2折交叉验证,通过引入对训练集、验证集分布差异的约束,所构造的正则化m×2交叉验证方法(简记为m×2 BCV)可以改善模型的性能指标的估计,适宜于模型比较。该文首先针对文本数据引入训练集与验证集分布差异的卡方度量,基于该度量构建数据切分的正则化条件,以最大化模型性能指标的信噪比为目标,给出了满足正则化条件的m×2 BCV的数据切分优化算法。最后,以自然语言处理中汉语框架语义角色标注任务为例,验证了基于m×2 BCV方法的有效性。     

新形势下煤矿企业外包工程安全管控模式研究

针对煤矿企业外包工程安全生产问题严重、事故多发的特点，通过对新的煤矿企业外包工程的安全管控模式研究，分析煤矿安全监管、技术和装备水平、安全培训等存在的问题，提出了实施煤矿外包工程队准入制度，创新外包安全管控模式，找到对外包工程安全状况进行管理的行之有效的办法。