Customer Requirements Mapping Method Based on Association Rule Mining for Mass Customization

Customer requirements analysis is the key step for product variety design of mass customiza-tion(MC). Quality function deployment (QFD) is a widely used management technique for understanding the voice of the customer (VOC), however, QFD depends heavily on human subject judgment during extracting customer requirements and determination of the importance weights of customer requirements. QFD pro-cess and related problems are so complicated that it is not easily used. In this paper, based on a general data structure of product family, generic bill of material (GBOM), association rules analysis was introduced to construct the classification mechanism between customer requirements and product architecture. The new method can map customer requirements to the items of product family architecture respectively, accomplish the mapping process from customer domain to physical domain directly, and decrease mutual process between customer and designer, improve the product design quality, and thus furthest satisfy customer needs. Finally, an example of customer requirements mapping of the elevator cabin was used to illustrate the proposed method.

Optimization of Mapping Rule of Bit-Interleaved Turbo Coded Modulation with 16QAM

Optimization of mapping rule of bit-interleaved Turbo coded modulation with 16 quadrature amplitude modulation (QAM) is investigated based on different impacts of various encoded bits sequence on Turbo decoding performance. Furthermore, bit-interleaved in-phase and quadrature phase (I-Q) Turbo coded modulation scheme are designed similarly with I-Q trellis coded modulation (TCM). Through performance evaluation and analysis, it can be seen that the novel mapping rule outperforms traditional one and the I-Q Turbo coded modulation can not achieve good performance as expected. Therefore, there is not obvious advantage in using I-Q method in bit-interleaved Turbo coded modulation.

Automated soil resources mapping based on decision tree and Bayesian predictive modeling

This article presents two approaches for automated building of knowledge bases of soil resources mapping. These methods used decision tree and Bayesian predictive modeling, respectively to generate knowledge from training data. With these methods, building a knowledge base for automated soil mapping is easier than using the conventional knowledge acquisition approach. The knowledge bases built by these two methods were used by the knowledge classifier for soil type classification of the Longyou area, Zhejiang Province, China using TM bi-temporal imageries and GIS data. To evaluate the performance of the resultant knowledge bases, the classification results were compared to existing soil map based on field survey. The accuracy assessment and analysis of the resultant soil maps suggested that the knowledge bases built by these two methods were of good quality for mapping distribution model of soil classes over the study area.

Knowledge Fusion and Synchronization over Ubiquitous Ontology Mapping

With the rapid development and popularization of web services, the available information types and structure are becoming more and more complex and challenging. Actually web services involve the need for dynamic integration and transparent knowledge integration, in light of the urgent information changing track. Under this situation, the traditional search engine and information integration cannot finish this challenge, thereby bringing the opportunity for knowledge fusion and synchronization. This paper proposes a multi-matching strategy ontology mapping method for web information, i.e., ubiquitous ontology mapping method (U-Mapping), which can be viewed as a base collection of information on multiple ontologies made to appear anytime and everywhere. This approach is usually built independently by different information providers, avoiding the grammatical and semantic conflict. Finally, the ontology case information can be utilized under the consolidation of the U-Mapping, concerning language technology and machine learning methods.

Combinations of Right Half-Plane Mappings and Vertical Strip Mappings Convex in the Vertical Direction

In this paper, we explore the linear combinations of right half-plane mappings and vertical strip mappings. We demonstrate that the combinations of these harmonic mappings are convex in the vertical direction provided they are locally univalent and sense-preserving. Furthermore, we extend this analysis to a more general case by setting specific conditions. Additionally, we take some common parameters such as as the dilatation of these harmonic mappings, and prove the sufficient conditions that their combinations are locally univalent and convex in the vertical direction. Several examples are constructed by the Mathematica software to demonstrate our main results.

基于关联规则的网络信息内容安全事件发现及其Map-Reduce实现

针对网络中信息内容安全事件的发现问题,该文提出一种基于关联规则的多维度用户行为特征关联分析法;对于存在的虚警问题,提出了基于邦弗朗尼校正的检验准则;为满足在海量数据中的应用需求,提出了一种Map-Reduce框架下的分布式幂集Apriori算法。实验结果表明,该文提出的方法及相应算法,并行运算能力强,在低虚警率和漏检率的情况下,具有较好的检测率,且运行时间短,收敛速度快。

一种基于MapReduce的关联规则挖掘算法

本文从减少I/O时间的角度出发,结合云计算Hadoop平台的Map Reduce模型,提出了一种基于Map Reduce的关联规则挖掘算法.算法采用幂集计算候选项集,采用Map Reduce模型在多个节点上并行找出所有频繁项集,只需要扫描事务数据库1次.实验结果表明:在事务的平均项长较小的情况下,算法具有很好的加速比和数据规模增长性.

Dynamic vaccine distribution model based on epidemic diffusion rule and clustering approach

Due to the fact that the emergency medicine distribution is vital to the quick response to urgent demand when an epidemic occurs, the optimal vaccine distribution approach is explored according to the epidemic diffusion rule and different urgency degrees of affected areas with the background of the epidemic outbreak in a given region. First, the SIQR （susceptible, infected, quarantined,recovered） epidemic model with pulse vaccination is introduced to describe the epidemic diffusion rule and obtain the demanded vaccine in each pulse. Based on the SIQR model, the affected areas are clustered by using the self-organizing map （SOM） neutral network to qualify the results. Then, a dynamic vaccine distribution model is formulated, incorporating the results of clustering the affected areas with the goals of both reducing the transportation cost and decreasing the unsatisfied demand for the emergency logistics network. Numerical study with twenty affected areas and four distribution centers is carried out. The corresponding numerical results indicate that the proposed approach can make an outstanding contribution to controlling the affected areas with a relatively high degree of urgency, and the comparison results prove that the performance of the clustering method is superior to that of the non-clustering method on controlling epidemic diffusion.

基于Map Reduce的大数据并行关联数据挖掘算法

关联规则是数据挖掘常用的算法之一,能够根据数据之间的关联程度进行数据的分析和挖掘。关联规则,特别是Apriori算法在关系型数据的处理上得心应手,但是对大数据的处理存在着不能够自动负载均衡及容错能力差等诸多弊端。文章从Map Reduce算法着手,介绍了大数据下关联规则的相关概念及Map Reduce分布式计算模型,在此基础上介绍了Map Reduce模式下的关联规则算法及其优点,Map Reduce模式下Apriori算法有效实现关联规则的数据挖掘,并且能够在Hadoop下实现负载均衡及容错能力。

基于Map/Reduce的电力监控系统规则挖掘技术研究

鉴于电力监控系统的多异类信源和动态融合特性,对系统海量实时监控信息进行故障规则挖掘和预测性监测。针对传统决策树法效率难提升问题,提出一种基于并行框架Map/Reduce和包含度测量相融合的规则挖掘算法(MRDT)。通过构建云计算Hadoop平台,在其分布式并行计算框架Map/Reduce基础上实现基于包含度的决策树规则挖掘算法的并行处理,高效地提取信任度较高的故障规则。以某水电站实时监控系统的电气信息为例,对MRDT算法进行实验测试,结果表明:MRDT算法在保证传统DT算法规则信任度较高的同时,提高了挖掘效率。

SYSTEM FOR AUTOMATIC GENERALIZATION OF TOPOGRAPHIC MAPS

With the construction of spatial data infi＇astructure, automated topographic map generalization becomes an indispensable component in the community of cartography and geographic information science. This paper describes a topographic map generalization system recently developed by the authors. The system has the following characteristics： 1） taking advantage of three levels of automation, i.e. fully automated generalization, batch generalization, and interactive generalization, to undertake two types of processes, i.e. intelligent inference process and repetitive operation process in generalization; 2） making use of two kinds of sources for generalizing rule library, i.e. written specifications and cartographers＇ experiences, to define a six-element structure to describe the rules; 3） employing a hierarchical structure for map databases, logically and physically; 4） employing a grid indexing technique and undo/redo operation to improve database retrieval and object generalization efficiency. Two examples of topographic map generalization are given to demonstrate the system. It reveals that the system works well. In fact, this system has been used for a number of projects and it has been found that a great improvement in efficiency compared with traditional map general- ization process can be achieved.

Dynamic Event-Triggered Fixed-Time Consensus Control and Its Applications to Magnetic Map Construction

This article deals with the consensus problem of multi-agent systems by developing a fixed-time consensus control approach with a dynamic event-triggered rule. First, a new fixedtime stability condition is obtained where the less conservative settling time is given such that the theoretical settling time can well reflect the real consensus time. Second, a dynamic event-triggered rule is designed to decrease the use of chip and network resources where Zeno behaviors can be avoided after consensus is achieved, especially for finite/fixed-time consensus control approaches. Third, in terms of the developed dynamic event-triggered rule, a fixed-time consensus control approach by introducing a new item is proposed to coordinate the multi-agent system to reach consensus. The corresponding stability of the multi-agent system with the proposed control approach and dynamic eventtriggered rule is analyzed based on Lyapunov theory and the fixed-time stability theorem. At last, the effectiveness of the dynamic event-triggered fixed-time consensus control approach is verified by simulations and experiments for the problem of magnetic map construction based on multiple mobile robots.

Alternative Error Bounds for Some Numerical Quadrature Rules

Similar to having done for the mid-point and trapezoid quadrature rules,we obtain alternative estimations of error bounds for the Simpson＇s quadrature rule involving n-time（1 ≤ n ≤ 4） differentiable mappings and then to the estimations of error bounds for the adaptive Simpson＇s quadrature rule.

An Intelligent Medical Expert System Using Temporal Fuzzy Rules and Neural Classifier

As per World Health Organization report which was released in the year of 2019,Diabetes claimed the lives of approximately 1.5 million individuals globally in 2019 and around 450 million people are affected by diabetes all over the world.Hence it is inferred that diabetes is rampant across the world with the majority of the world population being affected by it.Among the diabetics,it can be observed that a large number of people had failed to identify their disease in the initial stage itself and hence the disease level moved from Type-1 to Type-2.To avoid this situation,we propose a new fuzzy logic based neural classifier for early detection of diabetes.A set of new neuro-fuzzy rules is introduced with time constraints that are applied for thefirst level classification.These levels are further refined by using the Fuzzy Cognitive Maps(FCM)with time intervals for making thefinal decision over the classification process.The main objective of this proposed model is to detect the diabetes level based on the time.Also,the set of neuro-fuzzy rules are used for selecting the most contributing values over the decision-making process in diabetes prediction.The proposed model proved its efficiency in performance after experiments conducted not only from the repository but also by using the standard diabetic detection models that are available in the market.

Document Clustering Using Graph Based Fuzzy Association Rule Generation

With the wider growth of web-based documents,the necessity of automatic document clustering and text summarization is increased.Here,document summarization that is extracting the essential task with appropriate information,removal of unnecessary data and providing the data in a cohesive and coherent manner is determined to be a most confronting task.In this research,a novel intelligent model for document clustering is designed with graph model and Fuzzy based association rule generation(gFAR).Initially,the graph model is used to map the relationship among the data(multi-source)followed by the establishment of document clustering with the generation of association rule using the fuzzy concept.This method shows benefit in redundancy elimination by mapping the relevant document using graph model and reduces the time consumption and improves the accuracy using the association rule generation with fuzzy.This framework is provided in an interpretable way for document clustering.It iteratively reduces the error rate during relationship mapping among the data(clusters)with the assistance of weighted document content.Also,this model represents the significance of data features with class discrimination.It is also helpful in measuring the significance of the features during the data clustering process.The simulation is done with MATLAB 2016b environment and evaluated with the empirical standards like Relative Risk Patterns(RRP),ROUGE score,and Discrimination Information Measure(DMI)respectively.Here,DailyMail and DUC 2004 dataset is used to extract the empirical results.The proposed gFAR model gives better trade-off while compared with various prevailing approaches.

企业智能会计信息系统研究与设计——基于改进的Apriori算法和属性归纳学习算法

随着人工智能技术的发展,智能化会计核算系统成为会计发展的趋势。本文研究了会计核算智能化,重点在于实现学习和推理功能。采用机器学习算法,基于改进的关联规则挖掘算法(Apriori)和属性归纳学习算法,探索会计核算智能解决方案,促进会计人员的角色转变。本文结合会计实务流程,设计了智能会计核算系统,并实现了学习、推理和智能判断功能,为会计智能化研究与实践开辟了新途径。

非扩张映射的广义粘性三步隐式双中点法则

不动点的迭代算法是非线性泛函分析研究的热点问题,本文在实一致光滑Banach空间中构造新的非扩张映射不动点的广义三步隐式双中点法则的粘性算法,在适当条件下,用对偶映射和Banach极限的定义与技巧,证明由该算法生成的迭代序列强收敛于3个非扩张映射的公共不动点集的公共元,给出其特殊情况下的推论。本文结果改进和推广了近期文献的相关结果。

省级基础测绘DLG增量更新设计与实现

传统的省级基础测绘DLG更新采用版本数据全量更新模式,从作业效率、数据可追溯性、时空数据关联性等多方面考虑,增量更新机制更加切实有效,也符合新型基础测绘的发展方向。针对增量更新的设计与实现,文章研究了基于地理要素对象标识与数据状态追踪的方法,基于该方法设计面向省级基础测绘DLG的增量更新模型与基本规则,以及增量更新应用于省级基础测绘DLG年度更新与建库的整体技术流程,最后通过实验对本文方法的可行性与效率进行了验证。文章方法可为基础地理信息数据更新提供借鉴与参考。

Weak and Strong Convergence of Self Adaptive Inertial Subgradient Extragradient Algorithms for Solving Variational Inequality Problems

Many solutions of variational inequalities have been proposed,among which the subgradient extragradient method has obvious advantages.Two different algorithms are given for solving variational inequality problem in this paper.The problem we study is defined in a real Hilbert space and has L-Lipschitz and pseudomonotone condition.Two new algorithms adopt inertial technology and non-monotonic step size rule,and their convergence can still be proved when the value of L is not given in advance.Finally,some numerical results are designed to demonstrate the computational efficiency of our two new algorithms.

基于需求驱动的轨道交通车辆设计方法

[目的]针对不同的产品设计多样化需求,为了基于客户需求快速设计、制造出满足要求的高速列车,有必要对基于需求驱动的轨道交通车辆设计方法进行研究。[方法]通过收集、归类和总结轨道交通车辆的各种需求,并对需求进行梳理、匹配、参数化等操作,建立了需求数据库。针对轨道交通车辆的个性化特征,将个性化需求逐渐分解到轨道交通车辆各层级及组份中,形成需求模型。采用直接映射、函数映射、知识映射等规则构建需求与指标之间的映射规则。[结果及结论]本文所提基于需求驱动的轨道交通车辆设计方法,能够将所收集到的轨道交通车辆需求进行结构化、规范化、属性化的定义和归类,形成不同层级、不同属性及不同车型的需求数据,基于客户指定需求提出相应的轨道交通车辆设计需求,再映射到轨道交通车辆技术指标,最终获得一组轨道交通车辆快速设计数据。