A Fuzzy Decision Based WSN Localization Algorithm for Wise Healthcare

Wise healthcare is a typical application of wireless sensor network(WSN), which uses sensors to monitor the physiological state of nursing targets and locate their position in case of an emergency situation. The location of targets need to be determined and reported to the control center,and this leads to the localization problem. While localization in healthcare field demands high accuracy and regional adaptability, the information processing mechanism of human thinking has been introduced,which includes knowledge accumulation, knowledge fusion and knowledge expansion. Furthermore, a fuzzy decision based localization approach is proposed. Received signal strength(RSS) at references points are obtained and processed as position relationship indicators, using fuzzy set theory in the knowledge accumulation stage; after that, optimize degree of membership corresponding to each anchor nodes in different environments during knowledge fusion; the matching degree of reference points is further calculated and sorted in decision-making, and the coordinates of several points with the highest matching degree are utilized to estimate the location of unknown nodes while knowledge expansion. Simulation results show that the proposed algorithm get better accuracy performance compared to several traditional algorithms under different typical occasions.

Land Evaluation Method Based on Decision Tree Produced by C4.5 and Fuzzy Decision

[Objective]The aim was to overcome the shortage of being difficult to build land evaluation model when the impact factors had continuous value in the traditional land evaluation process,as well as to improve the intelligibility of the land evaluation knowledge.[Method] The land evaluation method combining classification rule extracted by C4.5 algorithm with fuzzy decision was proposed in this study.[Result] The result of Second General Soil Survey of Guangdong Province had demonstrated that the method was convenient to extract classification rules,and by using only 100 rules,quantity correct rate 86.67% and area correct rate 84.80% of land evaluation could be obtained.[Conclusions] The use of C4.5 algorithm to obtain the rules,combined with fuzzy decision algorithm to build classifiers had got satisfactory results,which provided a practical algorithm for the land evaluation.

A Data and Knowledge Collaboration Strategy for Decision-Making on the Amount of Aluminum Fluoride Addition Based on Augmented Fuzzy Cognitive Maps

In the aluminum reduction process, aluminum uoride (AlF3) is added to lower the liquidus temperature of the electrolyte and increase the electrolytic ef ciency. Making the decision on the amount of AlF3 addi- tion (referred to in this work as MDAAA) is a complex and knowledge-based task that must take into con- sideration a variety of interrelated functions;in practice, this decision-making step is performed manually. Due to technician subjectivity and the complexity of the aluminum reduction cell, it is dif cult to guarantee the accuracy of MDAAA based on knowledge-driven or data-driven methods alone. Existing strategies for MDAAA have dif culty covering these complex causalities. In this work, a data and knowl- edge collaboration strategy for MDAAA based on augmented fuzzy cognitive maps (FCMs) is proposed. In the proposed strategy, the fuzzy rules are extracted by extended fuzzy k-means (EFKM) and fuzzy deci- sion trees, which are used to amend the initial structure provided by experts. The state transition algo- rithm (STA) is introduced to detect weight matrices that lead the FCMs to desired steady states. This study then experimentally compares the proposed strategy with some existing research. The results of the comparison show that the speed of FCMs convergence into a stable region based on the STA using the proposed strategy is faster than when using the differential Hebbian learning (DHL), particle swarm optimization (PSO), or genetic algorithm (GA) strategies. In addition, the accuracy of MDAAA based on the proposed method is better than those based on other methods. Accordingly, this paper provides a feasible and effective strategy for MDAAA.

Vibration Based Tool Insert Health Monitoring Using Decision Tree and Fuzzy Logic

The productivity and quality in the turning process can be improved by utilizing the predicted performance of the cutting tools.This research incorporates condition monitoring of a non-carbide tool insert using vibration analysis along with machine learning and fuzzy logic approach.A non-carbide tool insert is considered for the process of cutting operation in a semi-automatic lathe,where the condition of tool is monitored using vibration characteristics.The vibration signals for conditions such as heathy,damaged,thermal and flank were acquired with the help of piezoelectric transducer and data acquisition system.The descriptive statistical features were extracted from the acquired vibration signal using the feature extraction techniques.The extracted statistical features were selected using a feature selection process through J48 decision tree algorithm.The selected features were classified using J48 decision tree and fuzzy to develop the fault diagnosis model for the improved predictive analysis.The decision tree model produced the classification accuracy as 94.78%with five selected features.The developed fuzzy model produced the classification accuracy as 94.02%with five membership functions.Hence,the decision tree has been proposed as a suitable fault diagnosis model for predicting the tool insert health condition under different fault conditions.

A New Kind of Generalized Pythagorean Fuzzy Soft Set and Its Application in Decision-Making

The aim of this paper is to introduce the concept of a generalized Pythagorean fuzzy soft set(GPFSS),which is a combination of the generalized fuzzy soft sets and Pythagorean fuzzy sets.Several of important operations of GPFSS including complement,restricted union,and extended intersection are discussed.The basic properties of GPFSS are presented.Further,an algorithm of GPFSSs is given to solve the fuzzy soft decision-making.Finally,a comparative analysis between the GPFSS approach and some existing approaches is provided to show their reliability over them.

High-speed corner detection based on fuzzy ID3 decision tree

A high-speed comer detection algorithm based on fuzzy ID3 decision tree was proposed. In the algorithm, the Bresenham circle with 3-pixel radius was used as the test mask, overlapping the candidate comers with the nucleus. Connected pixels on the circle were applied to compare the intensity value with the nucleus, with the membership function used to give the fuzzy result. The pixel with maximum information gain was chosen as the parent node to build a binary decision tree. Thus, the comer detector was derived. The pictures taken in Fengtai Railway Station in Beijing were used to test the method. The experimental results show that when the number of pixels on the test mask is chosen to be 9, best result can be obtained. The comer detector significantly outperforms existing detector in computational efficiency without sacrificing the quality and the method also provides high performance against Poisson noise and Gaussian blur.

Selecting Best Software Vulnerability Scanner Using Intuitionistic Fuzzy Set TOPSIS

Software developers endeavor to build their products with the least number of bugs.Despite this,many vulnerabilities are detected in software that threatens its integrity.Various automated software i.e.,vulnerability scanners,are available in the market which helps detect and manage vulnerabilities in a computer,application,or a network.Hence,the choice of an appropriate vulnerability scanner is crucial to ensure efficient vulnerability management.The current work serves a dual purpose,first,to identify the key factors which affect the vulnerability discovery process in a network.The second,is to rank the popular vulnerability scanners based on the identified attributes.This will aid the firm in determining the best scanner for them considering multiple aspects.The multi-criterion decision making based ranking approach has been discussed using the Intuitionistic Fuzzy set(IFS)and Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS)to rank the various scanners.Using IFS TOPSIS,the opinion of a whole group could be simultaneously considered in the vulnerability scanner selection.In this study,five popular vulnerability scanners,namely,Nessus,Fsecure Radar,Greenbone,Qualys,and Nexpose have been considered.The inputs of industry specialists i.e.,people who deal in software security and vulnerability management process have been taken for the ranking process.Using the proposed methodology,a hierarchical classification of the various vulnerability scanners could be achieved.The clear enumeration of the steps allows for easy adaptability of the model to varied situations.This study will help product developers become aware of the needs of the market and design better scanners.And from the user’s point of view,it will help the system administrators in deciding which scanner to deploy depending on the company’s needs and preferences.The current work is the first to use a Multi Criterion Group Decision Making technique in vulnerability scanner selection.

Generalized Algorithms of Discrete Optimization and Their Power Engineering Applications

Generalized algorithms for solving problems of discrete, integer, and Boolean programming are discussed. These algorithms are associated with the method of normalized functions and are based on a combination of formal and heuristic procedures. This allows one to obtain quasi-optimal solutions after a small number of steps, overcoming the NP-completeness of discrete optimization problems. Questions of constructing so-called “duplicate” algorithms are considered to improve the quality of discrete problem solutions. An approach to solving discrete problems with fuzzy coefficients in objective functions and constraints on the basis of modifying the generalized algorithms is considered. Questions of applying the generalized algorithms to solve multicriteria discrete problems are also discussed. The results of the paper are of a universal character and can be applied to the design, planning, operation, and control of systems and processes of different purposes. The results of the paper are already being used to solve power engineering problems.

基于改进模糊层次分析法的病险水库除险加固效果评价

为选择可行的加固方案,最大程度地改善病险水库的工作性能,综合考虑除险加固方案的效果可靠性、经济合理性、技术可行性和施工便利性等指标,构建水库除险加固方案多层次优选模型,采用经加速遗传算法改进的模糊层次分析法确定各优选子系统及各指标权重,提出水库加固效果多层次优选评价方法,并选择典型水库进行了验证。结果表明:对于优选子系统,技术可行性和效果可靠性子系统权重较大,分别为0.309和0.298;对于优选指标,综合权重较大的是施工单位水平的高低、稳定性要求满足程度、加固方案与引起大坝加固原因的适应性、加固方案与大坝所处地域适应性等指标。典型水库应用实例中塑性混凝土防渗墙方案综合评价值(0.797)相对较优。

加工时间模糊车间多目标调度与奖惩灰靶决策

为了实现加工时间模糊条件下柔性车间多目标优化调度,提出了基于邻域动态选择NSGA-II算法的优化方法和基于奖惩灰靶理论的决策方法。针对加工时间模糊条件下的车间调度问题,采用模糊集理论建立了多目标优化调度模型。在调度优化方面,对NSGA-II算法选择策略进行改进,构造了邻域动态选择NSGA-II的车间调度多目标优化方法。在决策方面,在灰靶决策理论中引入了奖惩算子,该方法可以决策出信息熵意义下的最优结果。经生产案例验证,与标准NSGA-II算法、混沌映射NSGA-II算法、双层遗传算法等相比,邻域动态选择NSGA-II算法的Pareto解集处于支配地位,表明该方法优化能力最强;经加权灰靶理论决策的最优调度方案满足时间约束和逻辑约束,是一种可行调度方案。实验结果表明,优化和决策方法是可行的,且具有一定优越性。

改进决策树算法的大数据分类优化方法

针对当前海量数据的结构和特征较为复杂,对其分类时很难确保较高的精准度与效率的问题,提出了改进决策树算法的大数据分类优化方法。构建模糊决策函数检测大数据的序列特征,并将其输入决策树模型中挖掘和训练规则;利用灰狼优化算法改进决策树模型,使用改进后模型对大数据简化、粗略分类,再建立分类器准确度目标函数,实现对大数据的精准分类。实验结果表明,所提方法取得分类结果准确度最高、假正例率最低,保证了算法整体具有较高的吞吐量,提高了算法分类效率。

Application Comparison of Association Rules and C4.5 Rules in Land Evaluation

Association rules and C4.5 rules can overcome the shortage of the traditional land evaluation methods and improve the intelligibility and efficiency of the land evaluation knowledge.In order to compare these two kinds of classification rules in the application,two fuzzy classifiers were established by combining with fuzzy decision algorithm especially based on Second General Soil Survey of Guangdong Province.The results of experiments demonstrated that the fuzzy classifier based on association rules obtain a higher accuracy rate,but with more complex calculation process and more computational overhead;the fuzzy classifier based on C4.5 rules obtain a slightly lower accuracy,but with fast computation and simpler calculation.

结合改进PSO和模糊决策树的医院信息系统数据分类研究

医疗系统的使用量与日俱增导致医疗系统中的相关医疗数据也在不断地增加。为此优化模糊ID3算法和改进粒子群算法,并将改进粒子群算法进行智能寻优从而改善模糊决策树性能。实验结果表明,提出的IPSO-FDT算法在皮马印第安人糖尿病、威斯康星乳腺癌和帕金森氏病数据集中的测试准确率分别为97.9%、89.5%和81.8%,在生成树的概括能力数值分别为1.003、1.034、1.026,显著高于比较模型。实验数据证明,在医院信息系统进行数据分类的多种算法中,此设计的结合改进PSO与模糊决策树算法具有一定适用性。

基于模糊多目标决策的物联网大数据聚类算法

现有的物联网大数据聚类算法容易受到相似性攻击,聚类效果较差。为了提升自适应能力,提出了一种基于模糊多目标决策的物联网大数据聚类算法。选取梯度下降法进行重复迭代,得到物联网事件的模糊置信度和支持度阈值,利用模糊C均值聚类算法获取最优模糊划分矩阵;建立目标决策矩阵,确定目标权重,明确理想决策目标和负理想决策目标,获取最终决策结果,从而实现物联网大数据的有效聚类。选取某电力企业的物联网大数据平台进行聚类实验,结果表明,该算法可有效聚类物联网平台中的海量数据,聚类结果的簇间区分度、簇间关联性和聚类敏捷性高。

基于改进多属性决策的铁路车站改建方案评价研究

为进一步提升铁路车站改建项目方案决策的科学性和有效性,首先在对铁路车站改建方案的主要影响因素进行全面梳理的基础上,构建改建方案评价体系,涵盖地区枢纽适应性与协调性、运输组织及能力、既有营运线干扰、工程可实施性、经济与社会效益5个指标层;然后,提出基于改进多属性决策的铁路车站改建方案评价方法,即区间直觉模糊环境下基于累积前景理论的改进MABAC算法,并考虑在属性权重未知的情况下采用熵权法确定属性权重,以更准确地刻画决策者的心理偏好;随后,给出铁路车站改建方案评价的具体步骤;最后,以某枢纽车站改建为例,采用所提方法对其改建方案进行评价,并将评价结果与实际采用方案以及其他决策方案评价结果对比,对比结果的一致性证明该方法具有实用性和有效性,而且在逻辑上更加贴近现实决策因素,可为铁路车站改建方案的优劣评判提供依据。

模糊粗糙集理论在空间电力负荷预测中的应用

基于遗传算法对连续数据进行区间数的最优量化和区间的分点值计算,同时将各量化区间模糊化,对预处理后的数据利用粗糙集理论建立小区用地属性决策表,并进行属性约简,得出决定小区用地类型的决策规则。同时,分析计算了各条件属性对各用地类型的不同权重,克服了传统方法确定权重系数的主观性。

一种FCM聚类算法的改进与优化

针对一种FCM聚类算法的一些不足 ,提出了具体的改进与提高的方法 ,并引入模糊决策理论 ,进行算法参数m的优化选择。改进后的算法 ,一方面有效地弥补了原算法中存在的不足 ,更好地解决了聚类数目选择等初值问题 ;另一方面 ,通过对参数m的优化选择 ,取得了较理想的聚类效果。最后给出了几种聚类算法对某数据样本集的聚类对比结果。

基于模糊聚类的决策树算法在教学质量评价中的应用

以目前的高等学校教育为平台,就如何将数据挖掘技术与教学质量评价相结合的问题进行了研究.通过教学质量评价指标体系的有效挖掘,运用模糊聚类的决策树技术来解决目前教学质量评价中的不合理性,提出基于模糊聚类的决策树法的教学质量评价方法,使教学质量评价公平、公正、合理、高效.

基于粒子群优化的直觉模糊核匹配追踪算法

针对现有直觉模糊核匹配追踪算法采用贪婪算法搜索最优基函数而导致学习时间过长的问题,汲取了粒子群优化算法全局搜索能力强、收敛速度快的优势对最优基函数的搜索过程进行优化,提出了一种基于粒子群优化的直觉模糊核匹配追踪算法,并将该算法应用于时效性要求更高的空天目标识别领域.实验结果表明,与传统方法相比,本文方法在识别率相当的情况下有效缩短一次匹配追踪时间,计算效率明显提高,且所得模型具有稀疏性好,泛化能力高等优点,特别适用于兼顾识别率和实时性的应用领域.

基于三角模糊数犹豫直觉模糊集的多属性智能决策

通过剖析现实生活中数据对象复杂性以及决策人思考的犹豫模糊性,提出了基于三角模糊数的犹豫直觉模糊集决策方法。首先,给出了三角模糊数犹豫直觉模糊集的定义,构建并证明了三角犹豫直觉模糊元及模糊数的基本运算法则和集成算子。其次,通过对三角犹豫直觉模糊元的得分函数和精确函数的定义,实现了三角犹豫直觉模糊数下的对象间的取值比较,针对三角犹豫直觉模糊数下多属性决策分析中的不确定性权重求解难题,提出了一种基于得分函数和最大熵理论的最优权重求解模型,并构建遗传算法模型实施最优化求解。最后,给出了三角犹豫直觉模糊数下的多属性智能决策算法,并以算例证明了所提方法的可行性和有效性。