Deducing Complete Selection Rule Set for Driver Nodes to Guarantee Network’s Structural Controllability

Structural controllability is critical for operating and controlling large-scale complex networks. In real applications, for a given network, it is always desirable to have more selections for driver nodes which make the network structurally controllable. Different from the works in complex network field where structural controllability is often used to explore the emergence properties of complex networks at a macro level,in this paper, we investigate it for control design purpose at the application level and focus on describing and obtaining the solution space for all selections of driver nodes to guarantee structural controllability. In accord with practical applications,we define the complete selection rule set as the solution space which is composed of a series of selection rules expressed by intuitive algebraic forms. It explicitly indicates which nodes must be controlled and how many nodes need to be controlled in a node set and thus is particularly helpful for freely selecting driver nodes. Based on two algebraic criteria of structural controllability, we separately develop an input-connectivity algorithm and a relevancy algorithm to deduce selection rules for driver nodes. In order to reduce the computational complexity,we propose a pretreatment algorithm to reduce the scale of network's structural matrix efficiently, and a rearrangement algorithm to partition the matrix into several smaller ones. A general procedure is proposed to get the complete selection rule set for driver nodes which guarantee network's structural controllability. Simulation tests with efficiency analysis of the proposed algorithms are given and the result of applying the proposed procedure to some real networks is also shown, and these all indicate the validity of the proposed procedure.

The information content of rules and rule sets and its application

The information content of rules is categorized into inner mutual information content and outer impartation information content. Actually, the conventional objective interestingness measures based on information theory are all inner mutual information, which represent the confidence of rules and the mutual information between the antecedent and consequent. Moreover, almost all of these measures lose sight of the outer impartation information, which is conveyed to the user and help the user to make decisions. We put forward the viewpoint that the outer impartation information content of rules and rule sets can be represented by the relations from input universe to output universe. By binary relations, the interaction of rules in a rule set can be easily represented by operators： union and intersection. Based on the entropy of relations, the outer impartation information content of rules and rule sets are well measured. Then, the conditional information content of rules and rule sets, the independence of rules and rule sets and the inconsistent knowledge of rule sets are defined and measured. The properties of these new measures are discussed and some interesting results are proven, such as the information content of a rule set may be bigger than the sum of the information content of rules in the rule set, and the conditional information content of rules may be negative. At last, the applications of these new measures are discussed. The new method for the appraisement of rule mining algorithm, and two rule pruning algorithms, λ-choice and RPClC, are put forward. These new methods and algorithms have predominance in satisfying the need of more efficient decision information.

Rough Set Theory Based Approach for Fault Diagnosis Rule Extraction of Distribution System

As the first step of service restoration of distribution system,rapid fault diagnosis is a significant task for reducing power outage time,decreasing outage loss,and subsequently improving service reliability and safety.This paper analyzes a fault diagnosis approach by using rough set theory in which how to reduce decision table of data set is a main calculation intensive task.Aiming at this reduction problem,a heuristic reduction algorithm based on attribution length and frequency is proposed.At the same time,the corresponding value reduction method is proposed in order to fulfill the reduction and diagnosis rules extraction.Meanwhile,a Euclid matching method is introduced to solve confliction problems among the extracted rules when some information is lacking.Principal of the whole algorithm is clear and diagnostic rules distilled from the reduction are concise.Moreover,it needs less calculation towards specific discernibility matrix,and thus avoids the corresponding NP hard problem.The whole process is realized by MATLAB programming.A simulation example shows that the method has a fast calculation speed,and the extracted rules can reflect the characteristic of fault with a concise form.The rule database,formed by different reduction of decision table,can diagnose single fault and multi-faults efficiently,and give satisfied results even when the existed information is incomplete.The proposed method has good error-tolerate capability and the potential for on-line fault diagnosis.

Extracting Decision Rules for Cooperative Team Air Combat Based on Rough Set Theory

In order to reduce redundant features in air combat information and to meet the requirements of real-time decision in combat, rough set theory is introduced to the tactical decision analysis in cooperative team air combat. An algorithm of attribute reduction for extracting key combat information and generating tactical rules from given air combat databases is presented. Then, considering the practical requirements of team combat, a method for reduction of attribute-values under single decision attribute is extended to the reduction under multi-decision attributes. Finally, the algorithm is verified with an example for tactical choices in team air combat. The results show that, the redundant attributes in air combat information can be reduced, and that the main combat attributes, i.e., the information about radar command and medium-range guided missile, can be obtained with the algorithm mentioned above, moreover, the minimal reduced strategy for tactical decision can be generated without losing the result of key information classification. The decision rules extracted agree with the real situation of team air combat.

Mining Frequent Sets Using Fuzzy Multiple-Level Association Rules

At present, most of the association rules algorithms are based on the Boolean attribute and single-level association rules mining. But data of the real world has various types, the multi-level and quantitative attributes are got more and more attention. And the most important step is to mine frequent sets. In this paper, we propose an algorithm that is called fuzzy multiple-level association （FMA） rules to mine frequent sets. It is based on the improved Eclat algorithm that is different to many researchers’ proposed algorithms thatused the Apriori algorithm. We analyze quantitative data’s frequent sets by using the fuzzy theory, dividing the hierarchy of concept and softening the boundary of attributes’ values and frequency. In this paper, we use the vertical-style data and the improved Eclat algorithm to describe the proposed method, we use this algorithm to analyze the data of Beijing logistics route. Experiments show that the algorithm has a good performance, it has better effectiveness and high efficiency.

Rough Set Based K-Exception Approach to Approximate Rule Reduction

There are rules refering to infrequent instances after the procession of attribute reductionand value reduction with traditional methods.A rough set RS based k-exception approach (RSKEA) torule reduction is presented.Its main idea lies in a two-phase RS based rule reduction.An ordinarydecision table is attained through general method of RS knowledge reduction in the first phase.Then a k-exception candidate set is nominated according to the decision table.RS rule reduction is employed forthe reformed source data set,which remove all the instances included in the k-exception set.We apply theapproach to the automobile database.Results show that it can reduce the number and complexity of ruleswith adjustable conflict rate,which contributes to approximate rule reduction.

Research on knowledge acquisition method about the IF/THEN rules based on rough set theory

The basic principles of IF/THEN rules in rough set theory are analyzed first, and then the automatic process of knowledge acquisition is given. The numerical data is qualitatively processed by the classification of membership functions and membership degrees to get the normative decision table. The regular method of relations and the reduction algorithm of attributes are studied. The reduced relations are presented by the multi-representvalue method and its algorithm is offered. The whole knowledge acquisition process has high degree of automation and the extracted knowledge is true and reliable.

抗肿瘤药审方规则的设置优化及实施效果评价

目的探讨抗肿瘤药审方规则设置及优化流程,提高处方合理率,降低不合理处方退费率。方法统计抗肿瘤药规则设置优化前(2021年12月~2022年3月)规则维护后(2022年4月~2022年7月)的门诊处方。比较规则设置优化前后处方合理率及退费率评价实施效果。结果设置了抗肿瘤药规则,建立了规则优化模式,形成了超说明书用药规则库。门诊处方合理率由96.53%提高到99.19%(P<0.001),处方退费率由1.11%降至0.23%(P<0.001),差异有统计学意义。结论我院维护的抗肿瘤药审方规则库提高了处方合理率,保障临床用药的同时提高了患者用药安全性及合理性。

基于词模式规则的轻量级日志模板提取方法

传统基于规则的日志解析方法针对每类日志需单独编写规则,且随着系统更新,出现新的日志模式时,需人工再次干预;基于深度学习的日志解析方法虽准确率高,但计算复杂度高。为解决日志解析方法人力成本和计算复杂度高的问题,文中提出一种基于词模式规则的轻量级日志模板提取方法,该方法由初始规则集生成、词模式规则应用、潜在错误样本发掘三个部分构成。首先,原始日志基于自适应随机抽样获取彼此间相似度较低的代表性日志;然后,基于专家反馈提取初始词模式规则集,在词模式规则应用模块对原始日志进行处理并提取日志模板;最后,在潜在错误样本发掘模块检查生成的日志模板聚类,发现潜在的错误分类样本并对其进行规则集更新。经过实验验证,在16个公开日志数据集上,文中方法的平均准确度达到97.8%,与基于深度学习的日志解析算法准确度基本持平;在计算效率方面,文中方法的单线程解析速度达到每秒20000条,且随着可用内核数量的增加,性能持续提升,满足系统日志的故障诊断和安全分析需求。

基于改进SPRINT分类算法的数据挖掘模型

为解决目前数据挖掘模型分类时间长、挖掘准确率不高的问题,提出基于改进决策树分类算法(SPRINT)的数据挖掘模型。先采用最大-最小规范化公式完成原始数据线性变换,利用改进后的SPRINT分类算法按照输入数据特性进行分类,使用协同过滤技术生成与数据相近的属性集,计算数据属性相似度,生成语义规则集,为用户提供更优的数据服务。选取某公司营销数据集作为样本进行对比实验,结果表明,与对比模型相比,所提出的数据挖掘模型分类时间更短,挖掘准确率更高,能为用户提供更优质的数据服务。

基于关联规则的群组共享密钥协议研究

静态的密钥管理策略难以适应网络动态变化.针对这一问题提出一种基于关联规则的群组密钥管理方案,使用关联规则分析移动分布式网络中密钥之间的关系,通过路由中密钥使用频率挖掘群组密钥之间的频繁关系,根据可信度建立合理的密钥共享方案.在效率上,该方案适应网络动态变化,降低对先验知识的需求,发现频繁密钥集规模与路由延时之间的折中关系,网络实体可根据密钥之间的频繁关系动态调整群组密钥管理方式.在安全性上,节点根据密钥规则选择密钥策略,在频繁密钥集上实施共享密钥协议,提高了安全性.

某型复杂工程机械变速箱体复合工艺约束机加工线平衡研究

为解决机加工生产线平衡问题所包含的加工任务刀具需求、机床类型需求、加工方位约束、“紧密”型和“或”型约束等复杂条件,建立了机加工生产线平衡数学模型,并采用含多重筛选机制的粒子群算法进行了求解。首先,建立了满足此复杂实际约束条件的机加工生产线平衡问题数学模型;然后,采用粒子的位置坐标作为粒子群搜索的权重信息,进行了加工任务、集中任务的选取,并设计了多重筛选机制构造启发式任务集生成规则;采用此规则对待分配加工任务进行了多重筛选,以得到可供直接分配的加工任务集合,粒子群算法(PSO)从此集合中依次选取了加工任务,构成了完整的解,并形成了具体的任务分配方案;最后,为提高程序的实用性和可视性,设计了加工任务的甘特图生成模块,通过对某复杂工程机械变速箱体零件的实际案例研究,将简化后的任务信息代入算法进行了求解。研究结果表明:该方法实现了多组平衡率高于90%的优化结果,在节拍时间为1120 s时,得到94.66%的较高平衡率,排产方案表格内容与甘特图显示一致;算法推演结果满足设定的多种复合约束条件,通过与人工排产对比说明了该算法的有效性并具有较好的经济性、实用性;对柔性生产案例进行探讨,证明该算法运算结果具备一定的生产柔性。

基于粗糙集理论的均匀设计阿魏酸合成工艺因素规律的方法研究

目的利用粗糙集理论研究药物合成生产过程中生产因素之间的内在规律,探求最优合成工艺方案的方法。方法采用粗糙集理论方法,以阿魏酸合成工艺为案例,利用均匀设计试验预处理,形成基于粗糙集的决策表,建立粗糙集规则模型。通过决策规则分析工艺因素对试验指标的影响,优选最佳合成工艺。结果阿魏酸合成工艺的反应时间水平与阿魏酸收率水平呈线性增加的趋势(P<0.01),原料配比、吡啶量因素水平与阿魏酸收率水平并没有线性增加的趋势(P>0.05)。当试验三因素都取7水平时,试验结果最好,为最佳组合。结论粗糙集理论方法能够全面地分析药物合成工艺因素与试验结果之间的内在动态规律,为提高药物合成工艺质量,提出了一种独特的优选方法和思路以供参考。

基于粗糙集理论和正交设计的冠心苏合滴丸制药生产工艺参数优选的方法研究

目的利用粗糙集理论和正交设计研究制药生产工艺参数之间的规律,探求优选生产工艺参数优选的数学方法。方法采用粗糙集理论方法,以冠心苏合滴丸生产工艺参数优选为案例,利用正交设计试验预处理数据,建立决策表,通过粗糙集规则模型动态分析工艺参数对结果的影响,优选最佳工艺参数。结果当冠心苏合滴丸生产工艺的参数药物与基质的配比分别与药液温度和冷却剂温度的水平逐渐增高时,溶散时限的平均水平呈现低-高-低的动态变化趋势;但当药液温度和冷却剂温度水平逐渐增高时,溶散时限的平均水平呈现动态的递减变化趋势。药物与基质的配比为1∶2,药液温度为70℃,冷却剂温度为12~14℃时,为冠心苏合滴丸生产工艺的参数的最佳组合。结论粗糙集理论能够动态、全面地分析制药生产工艺参数与试验指标之间的内在关系,提供了一种研究提高制药生产工艺质量的数学方法。

基于集合可视化分析名老中医王秀霞教授治疗卵巢巧克力囊肿患者的用药规律

目的:利用集合可视化分析系统探讨名老中医王秀霞治疗卵巢巧克力囊肿的学术思想及用药规律。方法:收集2017年9月1日至2018年12月31日黑龙江中医药大学附属医院王秀霞教授治疗卵巢巧克力囊肿的有效方剂,建立处方数据库,利用集合可视化分析王秀霞教授治疗卵巢巧克力囊肿的用药规律。结果:共纳入有效处方145首,涉及中药115味,分析处方中的常用药对及药物组合,演化得到新处方1首。结论:王教授治疗卵巢巧克力囊肿谨守“正虚邪实”的本质,在益肾疏肝的基础上,辅以化瘀、消癥之品,充分体现了“益肾疏肝为主兼顾化瘀消癥”的学术思想。

基于Rough set理论的增量式规则获取算法

从 Rough set理论出发 ,讨论在新增数据时 ,新数据与已有规则集的关系、属性约简以及值约简的变化规律 .并在此基础上提出一个新的基于 Rough Set理论的增量式算法 .从理论上和实验上对新算法和传统算法在算法复杂度上做了分析与比较 .

基于Rough Set的规则自动抽取设计方案

知识获取是专家系统的重要研究领域,而理论以理论的独特之处成为这一领域的有效工具。文章针对一具体专家系统Rough Set--专家系统在知识获取方面能力的不足,简要介绍其知识表示和知识获取的方法后,提出了一种基于理论的规则自动抽取OTCA-ES--Rough Set的设计方案。

基于Rough Set的缺省加权规则挖掘算法

本文在引入规则加权支持度概念后 ,提出了一种基于RoughSet的缺省加权规则挖掘算法———MDWRBR算法 .实验结果表明 ,该算法能有效地过滤躁声、提高规则的挖掘效率 .

数字贸易规则开放水平特征及其影响因素的实证分析

随着全球数字贸易增长迅速,数字贸易规则成为全球治理中心议题之一。然而,各国主导构建的数字贸易规则存在显著差异和碎片化趋势。本文基于TAPED数据库测算各国数字贸易规则开放水平,采用fsQCA方法分析构成数字贸易规则影响因素的组态。研究发现,数字主权维护能力是一国进行数字贸易规则谈判的前提,数据要素数量与质量是达成高水平数字贸易规则开放的必要条件,数字产业竞争力为一国开展数字贸易规则领域开放的基础。中国虽在数据要素数量与质量、数字产业具有较强竞争力,但数字主权维护能力待提升,故应选择审慎的数字贸易规则开放态度;未来应从数据要素培育、数字技术发展、数字主权保障体系入手,强化中国在数字经贸领域的话语权。