Distance-Based Knowledge Measure for Intuitionistic Fuzzy Sets with Its Application in Decision Making

Much attention has been paid to construct an applicable knowledge measure or uncertainty measure for Atanassov’s intuitionistic fuzzy set (AIFS). However, many of these measures were developed from intuitionistic fuzzy entropy, which cannot really reflect the knowledge amount associated with an AIFS well. Some knowledge measures were constructed based on the distinction between an AIFS and its complementary set, which may lead to information loss in decision making. In this paper, knowledge amount of an AIFS is quantified by calculating the distance from an AIFS to the AIFS with maximum uncertainty. Axiomatic properties for the definition of knowledge measure are extended to a more general level. Then the new knowledge measure is developed based on an intuitionistic fuzzy distance measure. The properties of the proposed distance-based knowledge measure are investigated based on mathematical analysis and numerical examples. The proposed knowledge measure is finally applied to solve the multi-attribute group decision-making (MAGDM) problem with intuitionistic fuzzy information. The new MAGDM method is used to evaluate the threat level of malicious code. Experimental results in malicious code threat evaluation demonstrate the effectiveness and validity of proposed method.     

晚期HEMP作用下铁路牵引供电系统GIC算法研究

高空核爆电磁脉冲晚期效应（E₃）会引起地磁场剧烈变化并形成地面感应电场。感应电场等效为激励源与地面长距离导体和大地构成回路,产生地磁感应电流 （GIC）。GIC可引起牵引供电系统中变压器直流偏磁,从而严重威胁牵引供电系统的安全运行。本文基于平面波理论、分层大地电导率模型并结合牵引供电系统的电路模型,提出E₃作用下的牵引供电系统GIC算法,并以带回流线的直接供电方式的铁路牵引供电系统为例,首次计算了系统GIC情况。结果表明,该供电方式下牵引供电系统中的GIC远大于系统中变压器等设备的耐受值,为进一步研究E₃作用下牵引供电系统效应及我国铁路设备选型、灾害防治等提供支撑。     

电气线路互联系统线缆抗高空核电磁脉冲耦合效应

高空核电磁脉冲（HEMP）对电子设备的耦合途径主要有两方面：一方面是通过装备（产品）上的天线耦合通道进入到电子系统内的“前门耦合”方式;另一方面则是“后门耦合”,即通过装备（产品）上的壳体、电源线、电缆、机箱的缝隙、孔洞等途径进行耦合。主要研究电气线路互联系统(EWIS)线缆抗高空核电磁脉冲耦合效应,通过研究HEMP干扰的特征、能量分布,搭建HEMP数学模型,采用控制变量法,改变EWIS线缆类型、离地高度等要素,通过在CST上建立仿真模型以及开展试验,分析HEMP对电子设备造成的影响程度,得到HEMP耦合效应的一般性结论与规律。     

Dynamical analysis,circuit realization,and application in pseudorandom number generators of a fractional-order laser chaotic system

A new five-dimensional fractional-order laser chaotic system (FOLCS) is constructed by incorporating complex variables and fractional calculus into a Lorentz-Haken-type laser system. Dynamical behavior of the system, circuit realization and application in pseudorandom number generators are studied. Many types of multi-stable states are discovered in the system. Interestingly, there are two types of state transition phenomena in the system, one is the chaotic state degenerates to a periodical state, and the other is the intermittent chaotic oscillation. In addition, the complexity of the system when two parameters change simultaneously is measured by the spectral entropy algorithm. Moreover, a digital circuit is design and the chaotic oscillation behaviors of the system are verified on this circuit. Finally, a pseudo-random sequence generator is designed using the FOLCS, and the statistical characteristics of the generated pseudo-random sequence are tested with the NIST-800-22. This study enriches the research on the dynamics and applications of FOLCS.     

基于逆卷积神经网络的等离子体辐射分布断层重建方法

发展了一种基于逆卷积神经网络的图像级重建方法用于聚变等离子体辐射分布的断层反演.通过引入结构相似度(SSIM)作为损失函数,该方法在模拟数据实验中表现出了较好的重建效果.模拟实验结果表明,在弦积分信号噪声强度为10％、15％及20％时,该方法的重建结果依然具有良好的精确度和鲁棒性.     

Intelligent Fault Diagnosis of Rolling-Element Bearings Using a Self-Adaptive Hierarchical Multiscale Fuzzy Entropy

The fuzzy-entropy-based complexity metric approach has achieved fruitful results in bearing fault diagnosis. However, traditional hierarchical fuzzy entropy (HFE) and multiscale fuzzy entropy (MFE) only excavate bearing fault information on different levels or scales, but do not consider bearing fault information on both multiple layers and multiple scales at the same time, thus easily resulting in incomplete fault information extraction and low-rise identification accuracy. Besides, the key parameters of most existing entropy-based complexity metric methods are selected based on specialist experience, which indicates that they lack self-adaptation. To address these problems, this paper proposes a new intelligent bearing fault diagnosis method based on self-adaptive hierarchical multiscale fuzzy entropy. On the one hand, by integrating the merits of HFE and MFE, a novel complexity metric method, named hierarchical multiscale fuzzy entropy (HMFE), is presented to extract a multidimensional feature matrix of the original bearing vibration signal, where the important parameters of HMFE are automatically determined by using the bird swarm algorithm (BSA). On the other hand, a nonlinear feature matrix classifier with strong robustness, known as support matrix machine (SMM), is introduced for learning the discriminant fault information directly from the extracted multidimensional feature matrix and automatically identifying different bearing health conditions. Two experimental results on bearing fault diagnosis show that the proposed method can obtain average identification accuracies of 99.92% and 99.83%, respectively, which are higher those of several representative entropies reported by this paper. Moreover, in the two experiments, the standard deviations of identification accuracy of the proposed method were, respectively, 0.1687 and 0.2705, which are also greater than those of the comparison methods mentioned in this paper. The effectiveness and superiority of the proposed method are verified by the experimental results.     

A Study on Consumers’ Visual Image Evaluation of Wrist Wearables

This study aimed to investigate consumers’ visual image evaluation of wrist wearables based on Kansei engineering. A total of 8 representative samples were screened from 99 samples using the multidimensional scaling (MDS) method. Five groups of adjectives were identified to allow participants to express their visual impressions of wrist wearable devices through a questionnaire survey and factor analysis. The evaluation of eight samples using the five groups of adjectives was analyzed utilizing the triangle fuzzy theory. The results showed a relatively different evaluation of the eight samples in the groups of “fashionable and individual” and “rational and decent”, but little distinction in the groups of “practical and durable”, “modern and smart” and “convenient and multiple”. Furthermore, wrist wearables with a shape close to a traditional watch dial (round), with a bezel and mechanical buttons (moderate complexity) and asymmetric forms received a higher evaluation. The acceptance of square- and elliptical-shaped wrist wearables was relatively low. Among the square- and rectangular-shaped wrist wearables, the greater the curvature of the chamfer, the higher the acceptance. Apparent contrast between the color of the screen and the casing had good acceptance. The influence of display size on consumer evaluations was relatively small. Similar results were obtained in the evaluation of preferences and willingness to purchase. The results of this study objectively and effectively reflect consumers’ evaluation and potential demand for the visual images of wrist wearables and provide a reference for designers and industry professionals.     

Parameter Analysis of Multiscale Two-Dimensional Fuzzy and Dispersion Entropy Measures Using Machine Learning Classification

Two-dimensional fuzzy entropy, dispersion entropy, and their multiscale extensions (MFuzzyEn2D and MDispEn2D, respectively) have shown promising results for image classifications. However, these results rely on the selection of key parameters that may largely influence the entropy values obtained. Yet, the optimal choice for these parameters has not been studied thoroughly. We propose a study on the impact of these parameters in image classification. For this purpose, the entropy-based algorithms are applied to a variety of images from different datasets, each containing multiple image classes. Several parameter combinations are used to obtain the entropy values. These entropy values are then applied to a range of machine learning classifiers and the algorithm parameters are analyzed based on the classification results. By using specific parameters, we show that both MFuzzyEn2D and MDispEn2D approach state-of-the-art in terms of image classification for multiple image types. They lead to an average maximum accuracy of more than 95% for all the datasets tested. Moreover, MFuzzyEn2D results in a better classification performance than that extracted by MDispEn2D as a majority. Furthermore, the choice of classifier does not have a significant impact on the classification of the extracted features by both entropy algorithms. The results open new perspectives for these entropy-based measures in textural analysis.     

Personal Interest Attention Graph Neural Networks for Session-Based Recommendation

Session-based recommendations aim to predict a user’s next click based on the user’s current and historical sessions, which can be applied to shopping websites and APPs. Existing session-based recommendation methods cannot accurately capture the complex transitions between items. In addition, some approaches compress sessions into a fixed representation vector without taking into account the user’s interest preferences at the current moment, thus limiting the accuracy of recommendations. Considering the diversity of items and users’ interests, a personalized interest attention graph neural network (PIA-GNN) is proposed for session-based recommendation. This approach utilizes personalized graph convolutional networks (PGNN) to capture complex transitions between items, invoking an interest-aware mechanism to activate users’ interest in different items adaptively. In addition, a self-attention layer is used to capture long-term dependencies between items when capturing users’ long-term preferences. In this paper, the cross-entropy loss is used as the objective function to train our model. We conduct rich experiments on two real datasets, and the results show that PIA-GNN outperforms existing personalized session-aware recommendation methods.     

Equality and Freedom as Uncertainty in Groups

In this paper, I investigate a connection between a common characterisation of freedom and how uncertainty is managed in a Bayesian hierarchical model. To do this, I consider a distributed factorization of a group’s optimization of free energy, in which each agent is attempting to align with the group and with its own model. I show how this can lead to equilibria for groups, defined by the capacity of the model being used, essentially how many different datasets it can handle. In particular, I show that there is a “sweet spot” in the capacity of a normal model in each agent’s decentralized optimization, and that this “sweet spot” corresponds to minimal free energy for the group. At the sweet spot, an agent can predict what the group will do and the group is not surprised by the agent. However, there is an asymmetry. A higher capacity model for an agent makes it harder for the individual to learn, as there are more parameters. Simultaneously, a higher capacity model for the group, implemented as a higher capacity model for each member agent, makes it easier for a group to integrate a new member. To optimize for a group of agents then requires one to make a trade-off in capacity, as each individual agent seeks to decrease capacity, but there is pressure from the group to increase capacity of all members. This pressure exists because as individual agent’s capacities are reduced, so too are their abilities to model other agents, and thereby to establish pro-social behavioural patterns. I then consider a basic two-level (dual process) Bayesian model of social reasoning and a set of three parameters of capacity that are required to implement such a model. Considering these three capacities as dependent elements in a free energy minimization for a group leads to a “sweet surface” in a three-dimensional space defining the triplet of parameters that each agent must use should they hope to minimize free energy as a group. Finally, I relate these three parameters to three notions of freedom and equality in human social organization, and postulate a correspondence between freedom and model capacity. That is, models with higher capacity, have more freedom as they can interact with more datasets.