Shape mining: A holistic data mining approach for engineering design

Although the integration of engineering data within the framework of product data management systems has been successful in the recent years, the holistic analysis (from a systems engineering perspective) of multi-disciplinary data or data based on different representations and tools is still not realized in practice. At the same time, the application of advanced data mining techniques to complete designs is very promising and bears a high potential for synergy between different teams in the development process. In this paper, we propose shape mining as a framework to combine and analyze data from engineering design across different tools and disciplines. In the first part of the paper, we introduce unstructured surface meshes as meta-design representations that enable us to apply sensitivity analysis, design concept retrieval and learning as well as methods for interaction analysis to heterogeneous engineering design data. We propose a new measure of relevance to evaluate the utility of a design concept. In the second part of the paper, we apply the formal methods to passenger car design. We combine data from different representations, design tools and methods for a holistic analysis of the resulting shapes. We visualize sensitivities and sensitive cluster centers (after feature reduction) on the car shape. Furthermore, we are able to identify conceptual design rules using tree induction and to create interaction graphs that illustrate the interrelation between spatially decoupled surface areas. Shape data mining in this paper is studied for a multi-criteria aerodynamic problem, i.e. drag force and rear lift, however, the extension to quality criteria from different disciplines is straightforward as long as the meta-design representation is still applicable.     

Product portfolio identification with data mining based on multi-objective GA

In the initial stage of product design, it is essential to define product specifications according to various market niches. An important issue in this process is to provide designers with sufficient design knowledge to find out what customers really want. This paper proposes a data mining method to facilitate this task. The method focuses on mining association rules that reflect the mapping relationship between customer needs and product specifications. Four objectives, support, confidence, interestingness and comprehensibility, are used for evaluating the extracted rules. To solve such a multi-objective problem, a Pareto-based GA is utilized to perform the rule extraction. Through computational experiments on an electrical bicycle case, it is shown that our approach is capable of extracting useful and interesting knowledge from a design database.     

基于AD9978A双通道的CCD相机设计

基于ADI公司的双通道、14 bit串行输出图像预处理芯片(AFE)AD9978A设计了一套CCD黑白数字摄像机电路系统。系统采用Dalsa公司的1 024×1 024帧转移面阵CCD FTT1010M作为图像传感器,采用专用集成芯片DPP2010A作为时序发生器,以FPGA为控制核心,并应用LVDS接口完成图像输出。针对CCD双通道输出时通道间存在的不均匀性问题,使用AD9978A在电路上给出了改进,并系统研究了该芯片复杂的寄存器配置问题。经验证,该系统能在不添加任何均匀性校正算法的情况下,输出均匀性良好的双通道图像。     

面向数字城管的数据分析与挖掘系统的设计与开发

数字城管在现代城市管理中具有重要的意义。介绍了一个面向数字城管的数据分析与挖掘系统的具体设计和实现,该系统基于Java EE平台开发,综合利用了统计分析方法和范例推理技术,能够提供对城市管理的科学决策。     

基于FPGA的CMOS相机实时数据处理设计

针对CMOS图像传感器输出的LVDS串行数据在传输过程中因数据无法对齐引起误码率升高,图像分辨率降低问题,提出一种基于现场可编程门阵列FPGA的CMOS相机实时数据处理研究方案。采用VHDL硬件语言,对数据处理进行模块化设计,确保高速数据的正确采样,减少误码产生。经软件仿真及实测表明:该方案设计合理,系统运行稳定可靠,解决了高分辨率图像在高速传输中的数据校正问题,输出的图像轮廓清晰、无斑点。     

数字摄像能见度仪的能见度算法设计及实现

针对传统能见度仪的不足,在已有的研究结果上,设计了数字摄像能见度仪的能见度算法,其中白天能见度采用双亮度差法,并作出改进使其有更大的适用环境;夜间能见度采用双光源法.整套算法通过CCD摄像机、图像采集卡和计算机平台得到实现.实验结果证明,该算法能够解决在气柱分布不均匀的情况下能见度无法准确观测的问题,提高了现有能见度的观测范围.将实验结果和前向散射仪及大气透射仪测量结果进行相关性对比,得到了较好的一致性,误差范围在国际标准的误差要求之内.     

数字摄像机像素驱动时序的抗光晕技术

当景物中明暗亮度反差较大,而CCD传感器曝光时间较长时,图像中较亮部分将发生光晕现象.夜间行车,汽车的强光灯照明会造成典型的光晕现象,俗称"漏光".光晕问题是CCD图像采集系统中的一大难题,众多厂家、学者均在寻求最佳解决方案.分析了数字摄像机光晕现象产生的原因,通过改变像素驱动时序,消除强光区域的上部光晕带,改善采集图像的质量.     

Feature evaluation for web crawler detection with data mining techniques

Distributed Denial of Service (DDoS) is one of the most damaging attacks on the Internet security today. Recently, malicious web crawlers have been used to execute automated DDoS attacks on web sites across the WWW. In this study we examine the effect of applying seven well-established data mining classification algorithms on static web server access logs in order to: (1) classify user sessions as belonging to either automated web crawlers or human visitors and (2) identify which of the automated web crawlers sessions exhibit ‘malicious’ behavior and are potentially participants in a DDoS attack. The classification performance is evaluated in terms of classification accuracy, recall, precision and F₁ score. Seven out of nine vector (i.e. web-session) features employed in our work are borrowed from earlier studies on classification of user sessions as belonging to web crawlers. However, we also introduce two novel web-session features: the consecutive sequential request ratio and standard deviation of page request depth. The effectiveness of the new features is evaluated in terms of the information gain and gain ratio metrics. The experimental results demonstrate the potential of the new features to improve the accuracy of data mining classifiers in identifying malicious and well-behaved web crawler sessions.     

Evolutionary-based feature selection approaches with new criteria for data mining: A case study of credit approval data

In this paper, the feature selection problem was formulated as a multi-objective optimization problem, and new criteria were proposed to fulfill the goal. Foremost, data were pre-processed with missing value replacement scheme, re-sampling procedure, data type transformation procedure, and min-max normalization procedure. After that a wide variety of classifiers and feature selection methods were conducted and evaluated. Finally, the paper presented comprehensive experiments to show the relative performance of the classification tasks. The experimental results revealed the success of proposed methods in credit approval data. In addition, the numeric results also provide guides in selection of feature selection methods and classifiers in the knowledge discovery process.     

Visual data mining modeling techniques for the visualization of mining outcomes

The visual senses for humans have a unique status, offering a very broadband channel for information flow. Visual approaches to analysis and mining attempt to take advantage of our abilities to perceive pattern and structure in visual form and to make sense of, or interpret, what we see. Visual Data Mining techniques have proven to be of high value in exploratory data analysis and they also have a high potential for mining large databases. In this work, we try to investigate and expand the area of visual data mining by proposing new visual data mining techniques for the visualization of mining outcomes.     

A review of design techniques for programs and data

The proliferation of papers on programming methodology focus on the program development process but only hint at the form of the final program. This paper distinguishes between the development process and the program product and presents a catalogue of possible program organizations and data structures with examples drawn from the published literature. The methods for sharing data among modules and a classification scheme for programs and data structures is presented.     

数据挖掘技术在CRM中的应用研究

本文介绍了CRM以及数据挖掘的基本概念,讲述了数据挖掘的常用算法以及客户关系管理中数据挖掘的流程,并用实例讲解了数据挖掘算法在CRM中的应用。     

Application of data mining techniques in customer relationship management: A literature review and classification

Despite the importance of data mining techniques to customer relationship management (CRM), there is a lack of a comprehensive literature review and a classification scheme for it. This is the first identifiable academic literature review of the application of data mining techniques to CRM. It provides an academic database of literature between the period of 2000–2006 covering 24 journals and proposes a classification scheme to classify the articles. Nine hundred articles were identified and reviewed for their direct relevance to applying data mining techniques to CRM. Eighty-seven articles were subsequently selected, reviewed and classified. Each of the 87 selected papers was categorized on four CRM dimensions (Customer Identification, Customer Attraction, Customer Retention and Customer Development) and seven data mining functions (Association, Classification, Clustering, Forecasting, Regression, Sequence Discovery and Visualization). Papers were further classified into nine sub-categories of CRM elements under different data mining techniques based on the major focus of each paper. The review and classification process was independently verified. Findings of this paper indicate that the research area of customer retention received most research attention. Of these, most are related to one-to-one marketing and loyalty programs respectively. On the other hand, classification and association models are the two commonly used models for data mining in CRM. Our analysis provides a roadmap to guide future research and facilitate knowledge accumulation and creation concerning the application of data mining techniques in CRM.     

A case study in iterative keyboard design using participatory design techniques

Replacing a keyboard can be stressful for operators who take calls all day, particularly when special functions must be sacrificed. This study applied several participatory design techniques to involve over 3,000 operators in the redistribution of function keys on their replacement keyboard. Video analysis and manual data logging methods were surprisingly accurate when measured against software logs. User acceptance was excellent. In the initial cutover program, operator performance levels returned to normal very quickly, resulting in over-confidence and unrealistic expectations of post-implementation performance for the remaining call centres because the focus was on the technology rather than on the impact of technology on business benefits. As a consequence, business plan requirements for future projects have now been changed to accommodate predictions of post-implementation performance and taking into account other changes happening at the same time.     

基于数据挖掘的桥梁抗震分析结果可视化系统设计

结合数据挖掘技术与分析结果可视化系统的开发,主要研究了基于知识的数据挖掘技术和桥梁抗震分析结果可视化系统模型;最终开发了一套实用的桥梁抗震分析结果可视化系统。     

基于实例模板的产品杂交配置设计

产品杂交配置设计是产品智能与创新设计方法的研究热点。基于产品模板配置设计概念,结合产品杂交配置设计需求,定义了面向杂交配置设计的知识库模型,提出了基于实例模板的机械产品杂交配置总体框架,给出了需求驱动、知识制导的产品杂交配置设计算法。该方法提供了统一的模型和框架,可有效支持各类机械产品杂交配置设计系统的研究和开发。     

Assessing loan risks: a data mining case study

The USDA's (US Department of Agriculture) Rural Housing Service administers a loan program that lends or guarantees mortgage loans to people living in rural areas. To administer these nearly 600000 loans, the department maintains extensive information about each one in its data warehouse. As with most lending programs, some USDA loans perform better than others. Basic data mining techniques helped the USDA's Rural Housing Service better understand and classify problem loans     

基于数据挖掘和因子试验设计的聚丙烯装置生产优化

在将工艺参数优化技术用于优化某聚丙烯装置的温度、压力和反应时间等过程参数,使装置生产运行趋于平稳的基础上,采用23全因子试验设计对装置生产中的催化剂TiCl4、活化剂(C2H5)3Al和助催化剂CMMS的用量开展设计实验,通过对实验结果分析得出优化的催化剂、活化剂及助催化剂配料方案为TiCl4:(C2H5)3Al:CMMS=95g:0.9L:200g。以此配料方案进行生产试验,试验结果聚丙烯釜产量均值为3119.50kg,较试验对照期釜产量均值3033.75kg增加85.75kg,增幅达2.83%。