Style synthesis and analysis of car designs for style quantification based on product appearance similarities

Understanding how similar design appears is a key element to understanding companies’ design strategies. However, it is difficult to evaluate companies’ design strategies with conventional style measurement methods since they only taxonomically measure whether a specific characteristic is included in a specific style. This study numerically measured car design similarities to synthesize and analyze car brand styles, thereupon discovering the design trends among car brands for strategic design positioning. This paper aims to find methods for quantifying style differences and identifying unique design elements of car designs among 23 automobile manufacturers based on design similarities of a large quantity of car designs (N = 119). To achieve this goal, a hybrid style quantification methodology – a mixture of Fourier decomposition, eye tracker, and shape grammar – was created to evaluate similarities, visual significance, and combinations of 19 car design elements. Fourier decomposition was incorporated to find the quantifiable values of design similarities of car design elements. Visual significance analysis was also conducted for each car design element through eye tracker to measure the importance of certain design elements for weighting factors. Then, each combination of design elements was compared with car design elements of other cars for similarity calculations. Finally, car design alternatives were synthesized, and transitions of design positioning were analyzed based on the similarity values weighed by the visual significance results. Using the suggested methods, alternate designs can be synthesized while preserving brands’ design styles, and design trends can be analyzed for strategic evaluation.     

数据挖掘技术在教育学中的应用

随着信息化时代的不断发展,数据挖掘技术日趋成熟,满足了人们对于大量信息的处理要求。目前,数据挖掘技术已经越来越多的应用于金融、通讯和交通等各行各业,但是在教育领域的应用相对较少。针对这一现状,在传统的分析方法上采用了关联规则挖掘,聚类挖掘等多种算法,对青少年同伴关系、人际关系和网络成瘾等各方面进行研究,得到不同性别的青少年同伴亲疏程度不同等一系列结论,提出了数据挖掘技术在教育学领域的应用的新前景。     

校园网安全防护的研究

目的:探讨校园网组建起来后,所面临的各种问题,并提供有效的解决方案。方法:从网络的结构性,网络的可靠性,网络的安全性三个方面进行分析,并提出相应的优化策略。结果:提高了校园网运行的可靠性、安全性和效率。结论:采用路径分析、关联规则待方法对网络进行优化,可以提高校园网的运行效率以及安全性。     

Pedestrian Detection and Tracking Using Deformable Part Models and Kalman Filtering

Abstract： Pedestrian detection techniques are important and challenging especially for complex real world scenes. They can be used for ensuring pedestrian safety, ADASs （advance driver assistance systems） and safety surveillance systems. In this paper, we propose a novel approach for multi-person tracking-by-detection using deformable part models in Kalman filtering framework. The Kalman filter is used to keep track of each person and a unique label is assigned to each tracked individual. Based on this approach, people can enter and leave the scene randomly. We test and demonstrate our results on Caltech Pedestrian benchmark, which is two orders of magnitude larger than any other existing datasets and consists of pedestrians varying widely in appearance, pose and scale. Complex situations such as people occluded by each other are handled gracefully and individual persons can be tracked correctly after a group of people split. Experiments confirm the real-time performance and robustness of our system, working in complex scenes. Our tracking model gives a tracking accuracy of 72.8% and a tracking precision of 82.3%. We can further reduce false positives by 2.8%, using Kalman filtering.     

小型水电站水轮机导叶关闭规律的优化——以猴场水电站为例

以猴场水电站为例,探讨了小型水电站水轮机导叶关闭规律的优化。在水轮机组的GD2值因制造难度等原因不能加大时,采用导叶关闭的方法,通过优化导叶关闭规律,以满足水电站水力过渡过程计算的要求,从而可以有效地解决机组甩负荷后转速上升和蜗壳进口压力上升的问题。     

一种利用关联规则的改进朴素贝叶斯分类算法

朴素贝叶斯分类是一种简单而高效的分类模型,然而条件独立性假设在现实中很少出现,致使其性能有所下降。通过引入关联规则,从两方面来改善朴素贝叶斯分类的性能。一方面,通过对关联规则的挖掘,发现条件属性之间的关联关系,并且利用这种关联关系弱化朴素贝叶斯的独立性假设;另一方面,通过关联规则的置信度,给朴素贝叶斯加权。     

基于RIA的企业规则管理系统的设计与实现

目前国内的企业中,规则制度大多数存在于企业业务人员的大脑中,或者规则手册、管理规程、操作手册等非结构化的文档中。结合现在现状,开发企业规则管理系统,实现当业务和需求发生变化时,能做出相应的响应和变化,并就系统的实现方法、总体设计、系统详细设计和功能模块进行详细的阐述。     

试析C语言表达式中自加、自减运算

自加、自减运算是C语言中的一个语法难点部分,对于初学者来说一直都是个难题,甚至很多编程高手也会产生困惑。通过实例分析总结了自加、自减运算符的运算规则过程,解析困惑,简洁明了。     

关联规则挖掘在教学评价中的应用研究

教学评价是院校最重要的工作之一,而科学的评教方法又显得尤为重要。本文介绍一种更适合于关系型数据库关联规则挖掘的、基于SQL的频繁项关联规则挖掘算法,并将其应用于教学评价。通过对大量的样本数据进行试验,得出了应用以往传统的教学评价方法所无法得到重要的教学参考信息。     

Efficient mining of frequent episodes from complex sequences

Discovering patterns with great significance is an important problem in data mining discipline. An episode is defined to be a partially ordered set of events for consecutive and fixed-time intervals in a sequence. Most of previous studies on episodes consider only frequent episodes in a sequence of events (called simple sequence). In real world, we may find a set of events at each time slot in terms of various intervals (hours, days, weeks, etc.). We refer to such sequences as complex sequences. Mining frequent episodes in complex sequences has more extensive applications than that in simple sequences. In this paper, we discuss the problem on mining frequent episodes in a complex sequence. We extend previous algorithm MINEPI to MINEPI+$\mathrm{MINEPI}+$ for episode mining from complex sequences. Furthermore, a memory-anchored algorithm called EMMA is introduced for the mining task. Experimental evaluation on both real-world and synthetic data sets shows that EMMA is more efficient than MINEPI+$\mathrm{MINEPI}+$.