Accurate segmentation of leukocyte in blood cell images using Atanassov's intuitionistic fuzzy and interval Type II fuzzy set theory

In this paper automatic leukocyte segmentation in pathological blood cell images is proposed using intuitionistic fuzzy and interval Type II fuzzy set theory. This is done to count different types of leukocytes for disease detection. Also, the segmentation should be accurate so that the shape of the leukocytes is preserved. So, intuitionistic fuzzy set and interval Type II fuzzy set that consider either more number of uncertainties or a different type of uncertainty as compared to fuzzy set theory are used in this work. As the images are considered fuzzy due to imprecise gray levels, advanced fuzzy set theories may be expected to give better result. A modified Cauchy distribution is used to find the membership function. In intuitionistic fuzzy method, non-membership values are obtained using Yager's intuitionistic fuzzy generator. Optimal threshold is obtained by minimizing intuitionistic fuzzy divergence. In interval type II fuzzy set, a new membership function is generated that takes into account the two levels in Type II fuzzy set using probabilistic T co norm. Optimal threshold is selected by minimizing a proposed Type II fuzzy divergence. Though fuzzy techniques were applied earlier but these methods failed to threshold multiple leukocytes in images. Experimental results show that both interval Type II fuzzy and intuitionistic fuzzy methods perform better than the existing non-fuzzy/fuzzy methods but interval Type II fuzzy thresholding method performs little bit better than intuitionistic fuzzy method. Segmented leukocytes in the proposed interval Type II fuzzy method are observed to be distinct and clear.     

New Pythagorean Entropy Measure with Application in Multi-Criteria Decision Analysis

The purpose of this paper is to propose a new Pythagorean fuzzy entropy for Pythagorean fuzzy sets, which is a continuation of the Pythagorean fuzzy entropy of intuitionistic sets. The Pythagorean fuzzy set continues the intuitionistic fuzzy set with the additional advantage that it is well equipped to overcome its imperfections. Its entropy determines the quantity of information in the Pythagorean fuzzy set. Thus, the proposed entropy provides a new flexible tool that is particularly useful in complex multi-criteria problems where uncertain data and inaccurate information are considered. The performance of the introduced method is illustrated in a real-life case study, including a multi-criteria company selection problem. In this example, we provide a numerical illustration to distinguish the entropy measure proposed from some existing entropies used for Pythagorean fuzzy sets and intuitionistic fuzzy sets. Statistical illustrations show that the proposed entropy measures are reliable for demonstrating the degree of fuzziness of both Pythagorean fuzzy set (PFS) and intuitionistic fuzzy sets (IFS). In addition, a multi-criteria decision-making method complex proportional assessment (COPRAS) was also proposed with weights calculated based on the proposed new entropy measure. Finally, to validate the reliability of the results obtained using the proposed entropy, a comparative analysis was performed with a set of carefully selected reference methods containing other generally used entropy measurement methods. The illustrated numerical example proves that the calculation results of the proposed new method are similar to those of several other up-to-date methods.     

On Some Extension of Intuitionistic Fuzzy Synthetic Measures for Two Reference Points and Entropy Weights

In this paper, a novel Double Intuitionistic Fuzzy Synthetic Measure (DIFSM), based on intuitionistic fuzzy values for handling multi-criteria decision-making problems used to rank alternatives, is presented. In the studies, intuitionistic fuzzy sets (IFSs) represented uncertain, imprecise information or human judgment. The intuitionistic fuzzy sets can also reflect the approval, rejection, and hesitation of decision-makers. The degrees of satisfiability and non-satisfiability and uncertainty of each alternative with respect to a set of criteria are described by membership functions, non-membership functions, and hesitancy indexes, respectively. The aggregation algorithm DIFSM is inspired by Hellwig’s method based on two reference points: ideal point (pattern) and anti-ideal point (anti-pattern), measuring distances between the alternative and ideal point and distance between the ideal and anti-ideal point. The proposed methods take into consideration the entropy-based weights of criteria. An illustrative example is given to demonstrate the practicality and effectiveness of the proposed approach. Additionally, the comparative analysis results, using the DIFSM and the Intuitionistic Fuzzy TOPSIS-based framework, are presented.     

基于空间信息的直觉模糊C-均值图像分割算法

针对模糊C-均值聚类算法的单一隶属度不能充分描述图像不确定性,且聚类过程中忽略像素空间关系的问题,提出一种基于空间信息的直觉模糊C-均值算法;该算法选取3×3的模板计算邻域像素灰度均值;并引入权重项,来控制灰度信息和空间信息各自所占的比重,同时用犹豫度更新直觉模糊集的隶属度函数;对常用标准图像的仿真结果表明,该算法能更好地保留图像细节信息,得到更加理想的图像分割效果。     

分布式网络异常攻击检测模型仿真分析

针对传统的异常攻击检测方法主要以异常攻击行为规则与网络数据隶属度大小进行判别,只能针对已知异常攻击进行检测,对新型异常攻击,检测算法率低,计算数据量大的问题。提出一种新的分布式网络异常攻击检测方式,通过对分布式网络内数据进行迭代聚类将正常和异常数据进行分类,建立矩阵映射模型进行数据矩阵对比,初步对异常攻击数据进行判断。在矩阵中建立粒子密度函数,通过粒子密度变化计算其异常攻击概率,最后对其数据进行加权和波滤确定数据异常攻击特征,建立攻击检测模型。仿真实验表明,优化的分布式网络异常攻击检测模型提高了异常数据攻击检测的自适应性,在网络信号受到攻击信号干扰情况下,仍然能够准确检测出带有攻击特征的小网络异常数据。有效提高了分布式网络的检测正确率,加快了检测速度和稳定性。     

A New Group Decision-Making Technique under Picture Fuzzy Soft Expert Information

As an extension of intuitionistic fuzzy sets, the theory of picture fuzzy sets not only deals with the degrees of rejection and acceptance but also considers the degree of refusal during a decision-making process; therefore, by incorporating this competency of picture fuzzy sets, the goal of this study is to propose a novel hybrid model called picture fuzzy soft expert sets by combining picture fuzzy sets with soft expert sets for dealing with uncertainties in different real-world group decision-making problems. The proposed hybrid model is a more generalized form of intuitionistic fuzzy soft expert sets. Some novel desirable properties of the proposed model, namely, subset, equality, complement, union and intersection, are investigated together with their corresponding examples. Two well-known operations AND and OR are also studied for the developed model. Further, a decision-making method supporting by an algorithmic format under the proposed approach is presented. Moreover, an illustrative application is provided for its better demonstration, which is subjected to the selection of a suitable company of virtual reality devices. Finally, a comparison of the initiated method is explored with some existing models, including intuitionistic fuzzy soft expert sets.     

Shapley-Based Estimation of Company Value—Concept,Algorithms and Parameters

The aim of the article is to propose a new method of valuation of a company, considering its ownership relations with other companies. For this purpose, the concept of the Shapley value from cooperative game theory is used as the basis for assessing such dependent companies. The paper presents proposals for Shapley value calculation algorithms for our model. We expand our model by discussing personal relations in addition to ownership relations and point out how intuitionistic fuzzy sets may be helpful in this context. As a result, we propose two new expanded models. In the first probabilistic model, we apply Pearson’s correlation coefficient, in the second, we use a correlation coefficient between intuitionistic fuzzy sets to determine the personal relationships. Finally, we present and interpret results for a real-world economic network with 17 companies.     

Network Traffic Anomaly Detection Method Based on a Feature of Catastrophe Theory

For the existing problems of current network traffic anomaly detection, the behavior of the network traffic anomaly will show nonlinearity, non-stationarity and complexity according to the network traffic often driven by the control of multiple factors. Owing to the characteristic that the internal evolution equation will lead to dynamical structure catastrophe, the phase space reconstruction method and the statistical physics method can be used to compute the macro feature values of the network traffic. By choosing some of the feature values which can obviously retlect the unusual change in the network traffic volume as control variables, a network traffic anomaly detection method based on the catastrophe series theory model is developed. Many experimental results show that the proposed network traffic anomaly detection method has a low false alarm rate under the same condition of detection rate.     

基于高斯模糊的摔倒自检测算法设计与应用

随着智能设备的普及,如何快速准确地检测、识别人体摔倒已逐渐成为研究的热点。然而现阶段对摔倒动作识别与检测仍然存在很多问题。为此,以智能设备的传感器系统采集的三轴加速度与角加速度为基础,结合经过高斯过滤后形成人体活动的信号幅度向量和陀螺仪信号幅度向量特征曲线与摔倒检测的模糊隶属函数特征模型,提出一种基于模糊的摔倒自检测算法。算法重点针对急速跑动、上下楼梯、手机平抛和自由落体等摔倒检测中的干扰动作进行了分析与区分,经过实验测试表明该算法有较快的反馈速度、较好的区分度以及较低的误判率。     

A new infrared small and dim target detection algorithm based on multi-directional composite window

This work presents a new method based on gray characteristic analysis for infrared dim small target detection under complex backgrounds. Firstly, an improved detection window with eight directions and three layers is introduced to investigate the gray distribution characteristic of different structure in an infrared image. Secondly, we adopt a pretreatment process based on morphology filter and mean filter to reduce the running time and propose a detection rule on characteristic analysis for infrared targets. Meanwhile a new parameter optimization algorithm based on fuzzy control theory is employed so that the detection rule could be independent of the initial parameters. Finally, experimental results indicate that the proposed method can effectively detect the dim small targets and has better tracking performance.     

Fuzzy neural network based on a Sigmoid chaotic neuron

The theories of intelligent information processing are urgently needed for the rapid development of modem science. In this paper, a novel fuzzy chaotic neural network, which is the combination of fuzzy logic system, artificial neural network system, and chaotic system, is proposed. We design its model structure which is based on the Sigmoid map, derive its mathematical model, and analyse its chaotic characteristics. Finally the relationship between the accuracy of map and the membership function is illustrated by simulation.     

模糊理论改进算法的CT图像弱边缘检测

为改进传统工业CT图像弱边缘检测效果及速度不佳问题,研究了基于分步式模糊推理法与改进解模糊算法的CT图像弱边缘检测方法。选取了相关度、一致性测度、梯度作为模糊化特征,推理过程中相对于整体推理法,采用了Mandani推理法依据简化的推理规则表进行分步模糊推理,在解模糊过程中依据隶属度函数图像提出改进解模糊方法。通过实验验证得出分步推理法对CT图像弱边缘的检测效果更好。在保证解模糊精度的前提下,采用重心法改进的解模糊法,相对传统方法计算速度有了很大提高。     

物联网环境下的差异网络数据库异常数据检测

当前的物联网环境下,各个智能网络的数据库的使用没有统一标准,不同生产商的数据库中的异常数据标准也不同,这就使得传统的以模式识别为基础的网络数据库异常检测方法在进行异常阀值设置时,无法形成统一标准,数据库数据量庞大且存在无序性,无法保证检测的准确性和检测效率。提出基于混沌特征分析算法的物联网环境下的差异网络数据库异常数据检测方法。依据混沌特征分析相关理论构建物联网环境下的差异网络数据库模型,构建一种异常数据的偏差函数,对不同数据库下的异常数据进行偏差统计,通过对偏差函数的统计结果进行最小值求解,根据求解描述最小化的阀值请求,实现物联网环境下的差异网络数据库异常数据的检测。实验结果表明,利用改进算法进行异常数据检测,能够提高检测的有效性与准确性。     

Robust adaptive fuzzy neural tracking control for a class of unknown chaotic systems

In this paper, an adaptive fuzzy neural controller (AFNC) for a class of unknown chaotic systems is proposed. The proposed AFNC is comprised of a fuzzy neural controller and a robust controller. The fuzzy neural controller including a fuzzy neural network identifier (FNNI) is the principal controller. The FNNI is used for online estimation of the controlled system dynamics by tuning the parameters of fuzzy neural network (FNN). The Gaussian function, a specific example of radial basis function, is adopted here as a membership function. So, the tuning parameters include the weighting factors in the consequent part and the means and variances of the Gaussian membership functions in the antecedent part of fuzzy implications. To tune the parameters online, the back-propagation (BP) algorithm is developed. The robust controller is used to guarantee the stability and to control the performance of the closed-loop adaptive system, which is achieved always. Finally, simulation results show that the AFNC can achieve favourable tracking performances.     

Overlapping Community Detection Based on Membership Degree Propagation

A community in a complex network refers to a group of nodes that are densely connected internally but with only sparse connections to the outside. Overlapping community structures are ubiquitous in real-world networks, where each node belongs to at least one community. Therefore, overlapping community detection is an important topic in complex network research. This paper proposes an overlapping community detection algorithm based on membership degree propagation that is driven by both global and local information of the node community. In the method, we introduce a concept of membership degree, which not only stores the label information, but also the degrees of the node belonging to the labels. Then the conventional label propagation process could be extended to membership degree propagation, with the results mapped directly to the overlapping community division. Therefore, it obtains the partition result and overlapping node identification simultaneously and greatly reduces the computational time. The proposed algorithm was applied to a synthetic Lancichinetti–Fortunato–Radicchi (LFR) dataset and nine real-world datasets and compared with other up-to-date algorithms. The experimental results show that our proposed algorithm is effective and outperforms the comparison methods on most datasets. Our proposed method significantly improved the accuracy and speed of the overlapping node prediction. It can also substantially alleviate the computational complexity of community structure detection in general.     

Multicriteria Group Decision Making Based on Intuitionistic Normal Cloud and Cloud Distance Entropy

The uncertainty of information is an important issue that must be faced when dealing with decision-making problems. Randomness and fuzziness are the two most common types of uncertainty. In this paper, we propose a multicriteria group decision-making method based on intuitionistic normal cloud and cloud distance entropy. First, the backward cloud generation algorithm for intuitionistic normal clouds is designed to transform the intuitionistic fuzzy decision information given by all experts into an intuitionistic normal cloud matrix to avoid the loss and distortion of information. Second, the distance measurement of the cloud model is introduced into the information entropy theory, and the concept of cloud distance entropy is proposed. Then, the distance measurement for intuitionistic normal clouds based on numerical features is defined and its properties are discussed, based on which the criterion weight determination method under intuitionistic normal cloud information is proposed. In addition, the VIKOR method, which integrates group utility and individual regret, is extended to the intuitionistic normal cloud environment, and thus the ranking results of the alternatives are obtained. Finally, the effectiveness and practicality of the proposed method are demonstrated by two numerical examples.     

基于模糊边界模块化神经网络的混沌时间序列预测

提出一种模糊边界模块化神经网络（FBMNN）的混沌时间序列预测方法,该方法先对混沌时间序列观测点重构的相空间进行模块化划分,划分点的选取由遗传算法自动寻优.然后定义一个模糊隶属度函数,在划分边界一侧按照一定的模糊隶属度设定模糊边界带,通过模糊化处理,解决了各模块划分点附近预测结果的跳跃问题.最后每一模块,及其模糊边界的样本点都对应一个递归神经网络进行训练,通过预测合成模块输出结果.该方法对三个混沌时间序列基准数据集Mackey-Glass,Lorenz,Henon进行实验,结果表明该方法有效地提高了混沌时间序列预测效果. 关键词： 模糊边界 模块化神经网络 混沌时间序列 预测     

Threat Assessment Method of Low Altitude Slow Small (LSS) Targets Based on Information Entropy and AHP

In order to deal with the new threat of low altitude slow small (LSS) targets in air defense operations and provide support for LSS target interception decision, we propose a simple and reliable LSS target threat assessment method. Based on the detection capability of LSS targets and their threat characteristics, this paper proposes a threat evaluation factor and threat degree quantization function in line with the characteristics of LSS targets. LSS targets not only have the same threat characteristics as traditional air targets but also have the unique characteristics of flexible mobility and dynamic mission planning. Therefore, we use analytic hierarchy process (AHP) and information entropy to determine the subjective and objective threat factor weights of LSS targets and use the optimization model to combine them to obtain more reliable evaluation weights. Finally, the effectiveness and credibility of the proposed method are verified by experimental simulation.     

基于三维磁成像的近场磁性体探测

提出一种近场条件下未知磁源的三维磁成像方法.考虑到大多数磁性体不仅受背景磁场磁化,本身也带有较强剩磁,将观测面上的磁场转换为磁场矢量异常模量,并建立目标函数进行最优化求解,以得到符合观测磁场特征的磁性体磁化模型.仿真和实验表明：此方法可有效消除剩磁对反演结果的影响,能够实现对近场多个磁源磁化率分布的成像,验证了所提方法用于探测隐含磁体位置和形状的可行性.